Hypospadiology: Current Challenges and Future Perspectives 9811976651, 9789811976650

Citation preview

Hypospadiology Current Challenges and Future Perspectives Tariq Abbas Editor

123

Hypospadiology

Tariq Abbas Editor

Hypospadiology Current Challenges and Future Perspectives

Editor Tariq Abbas Sidra Medicine Doha, Qatar

ISBN 978-981-19-7665-0    ISBN 978-981-19-7666-7 (eBook) https://doi.org/10.1007/978-981-19-7666-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Acknowledgment

I would like to thank my wife, Eynas, who has granted her life to our family. She is kind, loving, and helpful. When things were hard, I really appreciated and took note of your words of support. I’m very grateful. To my children, Aseel, Akram, Ahmed, and Aram: thank you for allowing me time away from you to do research and write. People who teach and help others make the world a better place. Those who give their time to train surgeons make it even better. I really want to thank my main mentor, JL Pippi Salle. Making a book out of an idea is just as hard as it sounds. The experience is both challenging and rewarding on a personal level. I want to thank the authors who made this possible. Thanks to everyone who worked on and facilitated the publishing of this book.

v

About the Editor

Tariq Abbas  is a consultant pediatric urologist and reconstructive surgeon with extensive clinical, preclinical, and essential science experience. He spans the spectrum of pediatric surgery, urology in general, and hypospadias in particular. He regularly operates on large numbers of hypospadias patients and produces cuttingedge research outcomes. He completed a Ph.D. from Aalborg University, Denmark, in Tissue Engineering and Regenerative Medicine of the urethra as a future management tool for hypospadias cases in children and adults. He got all the critical appraisal tools to critically evaluate the current literature on hypospadias, present the hot research topics, and discuss future directions. Dr. Abbas is a member of several editorial boards and published more than 80 international peer-reviewed articles, about 40 in hypospadias territory.

vii

Contents

 1 Toward  an Ecosystem Model of Hypospadiology ����������������������������������   1 Tariq Abbas and Santiago Vallasciani  2 Urethral  Anatomy, Physiology, and Regeneration����������������������������������  25 Gina T. Baaklini, Tariq Abbas, and Matthias D. Hofer  3 The  Rabbit Model in Preclinical Hypospadias Research: Strengths and Limitations ������������������������������������������������������������������������  37 Tariq Abbas, Petra De Graaf, and Cristian Pablo Pennisi  4 General Perioperative Considerations ����������������������������������������������������  53 Tariq Abbas, Muthana AlSalihi, Yasir El-Hout, Mansour Ali, and Eynas AbdAlla  5 Management  of Distal Hypospadias: New Insights and Stepwise Management Algorithm������������������������������������������������������������  67 Tariq Abbas  6 Management  of Proximal Hypospadias: Current Challenges and Future Directions ��������������������������������������������  81 Milan Gopal, Tariq Abbas, and J. L. Pippi Salle  7 Penile  Curvature Assessment in Hypospadias����������������������������������������  93 Carlos Villanueva and Tariq Abbas  8 Evaluating  the Results of Hypospadias Repair: What? Why? When? And How?�������������������������������������������������������������� 103 Anil Takvani and Mahakshit Bhat  9 Tissue  Engineering and Regenerative Medicine in Hypospadias Management������������������������������������������������������������������������ 127 G. Tsachouridis, Tariq Abbas, L. M. O. de Kort, and Petra de Graaf 10 Artificial  Intelligence in Hypospadiology: Role, Applications, and Benefits �������������������������������������������������������������� 137 Mohamed AbdulMoniem, Tariq Abbas, Amith Khandakar, Md Nazmul Islam Shuzan, Moajjem Hossain Chowdhury, and Muhammad E. H. Chowdhury ix

1

Toward an Ecosystem Model of Hypospadiology Tariq Abbas

and Santiago Vallasciani

Abstract

Hypospadias displays a variety of phenotypic presentations and is rising in incidence globally, but to date no definitive etiological cause has been identified. Genetic, environmental, and parental factors appear to be involved, and emerging evidence suggests that interaction between these factors likely contributes to the majority of cases (Hypospadias ecosystem). In part due to this complexity, current classification methods lack sensitivity, objectivity, and reproducibility. New hypospadias classification systems are therefore being introduced to overcome these limitations. Among these are the Glans-Urethral Meatus-Shaft (GMS) scoring system, which generates a more elaborate picture of the anatomical defects associated with hypospadias, and the urethral defect ration

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/978-­981-­19-­7666-­7_1. T. Abbas (*) · S. Vallasciani Urology Division, Surgery Department, Sidra Medicine, Doha, Qatar e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 T. Abbas (ed.), Hypospadiology, https://doi.org/10.1007/978-981-19-7666-7_1

1

2

T. Abbas and S. Vallasciani

(UDR) - Abbas 2022 system which identifies the level of bifurcation in the corpus spongiosum to quantify urethral hypoplasia. While several patientrelated variables either alone or in combination can affect hypospadias grading, these newly developed methods represent significant advances over traditional assessments of predictive risk factors. Active efforts are being made to further refine these tools for quantifying hypospadias risk factors in order to improve patient outcomes. (See Video 1.1).

Key Messages • Genetic testing for hypospadias can be focused on known and emerging risk gene loci. • Major challenges include the identification of substances that interfere with normal development but also the timing of these exposures. • While investigating the root cause of hypospadias, a growing number of molecular biology-based research have considered the severity of the disorder. • Unraveling complex interactions between multiple risk factors will require collaboration of different specialties, including genetics, physiology, biochemistry, endocrinology, and epidemiology. • Several classification systems have been proposed to describe hypospadias severity and ease communication between surgeons and researchers. Most prior hypospadias classification systems lack precision and do not consider the true site of spongiosal bifurcation.

1  Toward an Ecosystem Model of Hypospadiology

3

• New hypospadias classification systems are now being introduced, including the Glans-Urethral Meatus-Shaft (GMS) which gives a more elaborate picture of anatomical defects, and the Abbas system which uses the level of bifurcation in the corpus spongiosum to quantify urethral defect. • Several patient-related variables either alone or in combination can impact surgical outcome, but current predictive risk factors have significant limitations. • Molecular genetics screening before a urethroplasty could help parents have more honest conversations about what to expect from the treatment in the future. • Active/ongoing efforts are being made to overcome current limitations in hypospadias stratification tools, such that predictive risk factors can be more accurately defined.

1.1 Background Hypospadias ecosystem is meant to reflect on how key components of the etiological and phenotypic patterns of hypospadias cases and processes of disease affect each other. Increasingly intimate associations between the genetic background and the anatomical elements are driving the emergence of a wide spectrum of hypospadias phenotypes and with significant consequences on postoperative outcomes. One reflection of the significant variability and subsequent difficulty in risk stratification in the field of hypospadiology is the subjectivity of most descriptive and analytical terms used and the existence of more than 300 different surgical procedures which cumulatively represents a lack of adequate understanding of this pathology. Predictive ecology system models are powerful tools for the reconstruction of ecosystem function and understanding of complex pathologies but have yet to be considered for hypospadias. Here, we explore how developing and applying such ecology models at a landscape scale would greatly enhance our understanding of the reciprocal interactions between genetic, phenotypic, and clinical outcomes post hypospadias repair, placing hypospadiology in an ecological context while identifying key variables and simplifying assumptions that underly its risk stratification. There is a need to develop an objective assessment of the hypospadias phenotypes and identify novel associations to the genotype that will facilitate genotype–phenotype correlations and may improve patient care. As well as transforming our understanding of hypospadias anomaly, this would also allow us to better direct resources in preparation for future studies (Fig. 1.1).

4

T. Abbas and S. Vallasciani

ral Ureth sia la p Hypo

Mea t Pos al ition

ns la e z Si

P Le e n

G

le ni th g

Phenotypes

Ureth ra Plate l

ature Curv

Proce

ty

iq u

no

e

Ge

l Racia nd grou Back

hn

p

e

e nc

G

c Te

rie

l S urgic a

en

dure Type

Hypospadias Ecosystem

pe

et

Ex

ic Ureth ral Stent

g

ssin

Dre

Outcomes Fig. 1.1  Hypospadias ecosystem; factors affecting hypospadias etiology and patient outcomes

1.1.1 Etiology The Online Mendelian Inheritance in Man® (OMIM) database (www.omim.org) has linked 250 different genes with hypospadias in the context of syndromic conditions (https://www.omim.org/search?index=entry&search=hypospadias&start=1&limi t=10&retrieve=geneMap&genemap_exists=true). However, these loci are implicated in only 7% of total cases and are mostly associated with proximal (perineal) variants. The major genes involved include Homebox A13 (HOXA13), mediators of

1  Toward an Ecosystem Model of Hypospadiology

5

androgen metabolism (5 alfa reductase or SRD5A2, 17 beta dehydrogenase or HSD17B3), and Wilms tumor 1 (WT1), which are primarily involved in the early phases of genital development (hence most genetic cases are proximal in nature) [1]. Beyond mutation of key genes, understanding of the underlying mechanisms is limited, but likely involves DNA methylation of developmental loci under the influence of environmental exposures [2]. Genetic testing for hypospadias can be directed toward key genes known to be involved in the development of the genital tubercle, e.g., via targeted Sanger sequencing or as part of the New “Next-Generation Sequencing” (NGS). A recent systematic review showed that sequencing and genotyping have been the most popular methodological approaches, while single nucleotide polymorphisms (SNPs) have been the most frequently reported finding [3] (Fig.  1.2). Because androgen signaling is necessary for the development of the male external genitalia, genes involved in these pathways have long been a focus of hypospadias etiology research. Androgens, estrogens, growth factors, and transcription factors were shown to be the most common gene pathways. A recent prospective study of ~300 isolated hypospadias patients found that Sanger sequencing detected anomalies in 3% of cases, whereas combined with NGS

a Profiling (Arrays) Chromatography Binding assays Immunoassays Sequencing & Genotyping 0

Main findings (%)

b

20

40 60 Percent (%) of studies

80

100

Genome-wide linkage Copy number variants Gene expression changes DNA modifications mRNA and/or protein expression levels Mutations Polymorphisms Single nucleotide polymorphism 0

10

20

30

40

50

Fig. 1.2  Genetics and molecular biology of hypospadias. Over 68 studies have used sequencing and genotyping as the principal methodological tool (a), and single nucleotide polymorphisms (SNPs) have been the most commonly reported findings in the search for the etiology of hypospadias (b). (With permission from [3])

6

T. Abbas and S. Vallasciani

the anomaly detection rate increased to 5%. This result challenges the principle of limiting genetic assessment only to severe hypospadias [4]. Most patients with isolated hypospadias have Variants of Uncertain Significance (VUS), indicating mutation of genes involved in genital development but with unknown mechanism/impact on hypospadias phenotype. Over 90% of VUS are later reclassified as benign changes with no functional impact. Currently, no medical recommendations are made based on VUS [5]. In the 1990s, a rising incidence of hypospadias was observed among certain populations in specific geographical areas and professions. This raised awareness that exogenous factors can influence hypospadias onset, e.g., medication use during pregnancy, placental insufficiency, and mother-fetus pollutant exposures. It has previously been demonstrated that certain environmental pollutants can act as endocrine disruptors that modify sex hormone concentrations or function during organogenesis. Environmental factors can be further classified according to the consistency of their association with hypospadias, the most frequently implicated being low birth weight, small size for gestational age, placental insufficiency, maternal hypertension, pre-eclampsia, and maternal exposure to DES (diethylstilbestrol). Other influences have also been reported but less consistently (assisted fertility techniques, use of antiepileptics, preexisting maternal diabetes, high maternal body mass index, etc.). As is also the case with genetic factors, environmental influences can act at different levels and various time points, with early effects often resulting in more severe forms of hypospadias [6, 7].

1.1.2 Classification A critical early step in management is to classify hypospadias severity, which directs the surgeon to select a particular surgical approach or repair method [8]. The precise anatomical phenotype can also shed light on the underlying mechanism of defective embryogenesis, including the timeframe in which urethral development was disrupted. Smith [9] was the first to classify hypospadias according to the location of the urethral meatus, while Sheldon and Duckett [10] consider meatal position after chordee has been released in order to grade severity.

1.1.3 Severity Stratification Despite many advances in surgical procedures, complication rates after hypospadias repair remain unacceptably high. Several patient-related variables can affect the surgical outcome hence knowing the extent of these variables and how they interact can guide surgeons toward appropriate repair methods (as well as the likelihood of intraoperative complications). Precise determination of key risk factors in addition to meatal location will therefore be vital to improving the accuracy of current prognostic tools. Better stratification of patient risk will enhance multiple aspects of hypospadias management, from patient counseling to surgical decision-making and

1  Toward an Ecosystem Model of Hypospadiology

7

analysis of postoperative outcomes. Identifying key features that predict complications after hypospadias repair also remains a priority in hypospadiology. For patients found to be at higher risk, surgeons might elect to use different techniques and/or take further precautions. Risk-adjusted analysis of postoperative outcomes is also important in the context of outcomes-based evaluation and reimbursement [11].

1.2 Current Challenges 1.2.1 Etiology Many centers are now conducting Next-Generation Sequencing (NGS) studies of hypospadias patients, which could in the future lead to a reduced number of cases with unknown genetic basis. The benefit of diagnosis is to identify conditions needing specific medical care (including future adult gender and fertility), as well as determination of gonadal tumor risk and the likelihood of recurrence for family planning purposes [5]. Whether or not cases of isolated hypospadias warrant extensive genetic assessment is currently debated. It is evident that centers with strong genetic departments are already carrying out more tests than those with less well-established services, although it is generally recommended to always involve the genetic team in cases of severe hypospadias (which may indicate the presence of other congenital malformations). In these cohorts, abnormal findings on NGS analysis can increase up to one-­ third of cases [1, 12]. For environmental exposures, the evidence to date implicates a wide range of different substances and times of exposure. Current hypotheses suggest that pollutants can trigger hypospadias onset in a subset of genetically susceptible individuals, which may explain the variable incidence rate reported by different studies [7]. A major challenge is to not only to identify the substances that interfere with normal development, but also the time window in which they exert detrimental effects. Current studies on environmental epidemiology aim to identify periods of vulnerability, but these efforts are limited to retrospective review of medical records or patient surveys. This type of information is usually insufficient to characterize dose–response relationships [13].

1.2.2 Classification of Hypospadias Several classification systems have been used to stratify hypospadias severity based on meatal position, which does not consider the true site of spongiosal bifurcation and therefore lack precision. Indeed, some variations of distal hypospadias are associated with proximal spongiosal hypoplasia and penile curvature, which may necessitate a more complex surgical repair. Other apparently severe cases of proximal hypospadias are in fact less of a surgical challenge when favorable anatomy is existing [8, 14, 15]. It has long been appreciated by hypospadias surgeons that the

8

T. Abbas and S. Vallasciani

external meatal position does not consistently reflect hypospadias severity and the complexity of surgical repair needed [16, 17]. Additionally, Arlen et al. reported that meatal location did not impact postoperative complications in a multivariate analysis of anatomical factors. This study and others highlight the importance of considering the entire hypospadias complex when determining severity, rather than just evaluating the position of the meatus [18]. Therefore, an objective method of assessing hypospadias phenotype and severity is still clearly needed [19].

1.2.3 Severity Stratification In this section, our aim is to highlight the factors currently perceived as exerting the greatest impact on hypospadias repair outcomes (Fig. 1.3). Rather than seeking to establish a consensus, we suggest that undertaking more robust studies in the future will overcome the stated limitations in current methods. Refining stratification methods will allow surgeons to draw stronger conclusions about individual variables and help to construct personalized management tools (Table 1.1).

Urethral Plate Glans Size Meatal Position

Spongiosal Bifurcation Hypospadiac Meatus

Spongiosal bifurcation

S

Curvature Degree

Fig. 1.3  Illustration of key anatomical variables currently perceived to be affecting the outcome of hypospadias repair

9

1  Toward an Ecosystem Model of Hypospadiology

Table 1.1  Factors influencing the hypospadias complex with corresponding supportive evidence from the literature and associated limitations Factor Meatal position

Urethral plate (UP)

Penile curvature

Glans size

Evidence of effect  – Proximal meatal position is linked with high rates of post-repair complication in primary hypospadias and represents an independent risk factor [20]  – Meatal location at the time of urethroplasty is most likely the best means of consistent, reproducible, classification [8]  – Increased complication risk was seen in patients with severe hypospadias and proximal meatus or chordee [21]  – UP quality appears to represent an independent factor affecting postoperative results of hypospadias repair [22]

 – Men with probable untreated ventral penile curvature conveyed more dissatisfaction with the shape of their penis, increased difficulty with intercourse, and more unhealthy mental days [23]  – Persistent or recurrent ventral penile curvature was strongly associated with complications after urethroplasty [24]  – Ventral corporal lengthening with grafts has been linked with urethroplasty complications after second-stage hypospadias surgery [25]  – Small glans size, defined as width 50% of the total periurethral/spongiosal area during active inflammation (which also extended to the ventral dermis of the penile shaft), and this later increased to >80% during the

2  Urethral Anatomy, Physiology, and Regeneration

31

maturation and remodeling phase. The healing process is therefore not limited to the exact site of injury, but is instead defined by cellular infiltration of almost all periurethral tissue and the majority of corpus spongiosum. Indeed, given the marked vascularity of the corpus spongiosum, this can rapidly distribute pro-healing cell types and mediators to adjacent tissues. In contrast, when wound healing after urethral injury is dysregulated, adverse consequences can include fistula formation (where closure of the urethral/spongiosal defect is incomplete) or stricture development (caused by fibroblast differentiation into myofibroblasts that contract the healing tissue) [22]. Successful urethral surgeries are therefore dependent on efficient wound healing processes. Urethral wound healing features the same major events as dermal healing, but the individual phases are extended relative to skin regeneration. This may be related to the effects of urine extravasation into periurethral tissue [20]. While a tight closure of the urethra might be noted during surgery, this cannot exclude the possibility that some urine is still able to extravasate, potentially leading to a prolonged inflammatory phase. Urethral catheters may decrease urine exposure to some degree but are unlikely to completely prevent dissemination into periurethral tissue. As discussed previously, subcutaneous tissue serves as a firm base for cellular proliferation, whereas the anterior urethra is surrounded by corpus spongiosum (which may impair inflammatory cell migration and adhesion to the injury site). In addition, regenerating urethral tissue is encased within the dense tunica albuginea, which may generate localized pressure and edema that will be transduced directly onto the cells involved in healing [23]. This additional stress can also decrease perfusion to further impair wound healing, potentially resulting in urethral fistula and stricture formation. Current models suggest that fistula formation is stimulated by prolonged inflammation under increased tissue pressure. Inadequate angiogenesis is also associated with fistula formation since decreased perfusion in conjunction with excessive expression of pro-inflammatory cytokines can promote tissue breakdown [23]. Anti-inflammatory treatments may therefore be capable of accelerating urethral healing and decreasing the involvement of periurethral tissue, potentially resulting in reduced scar formation.

2.4 Effect of Androgens and Estrogen on Urethral Wound Healing There is ongoing controversy surrounding the use of testosterone supplementation in boys about to undergo hypospadias repair. Antagonizing 5alpha-reductase significantly accelerated the healing process, pointing to this enzyme as a potential therapeutic target for enhancing the healing of urethral wounds [24] (Fig. 2.3). This approach was initially proposed to render surgery easier by increasing tissue vascularity and penile length [25]. In a previous study of androgen effects on urethral healing in a Sprague-Dawley rat model, animals were castrated before puberty and then either supplemented or not via intramuscular injection of testosterone cypionate [26]. Both control and test groups underwent urethroplasty, and pretreatment with testosterone was found to increase penile length and tissue vascularity while

32

G. T. Baaklini et al.

Androgens

Wound healing

Smads

Testosterone

Inflammation

S3

Activation

5α-R

IL-6 S3 P

Delayed repair

DHT S7

Macrophages MAPK PI3K

AR

DHT

IL-6 TNF-α TGF-β1

AR

PI3K

Fibroblasts

Fig. 2.3  Mechanism proposed for androgenic control of wound healing. Once bound, DHT activates the AR, creating an effector complex whose functions may be controlled by Smad3. AR activation increases pro-inflammatory cytokine production by macrophages and decreases cytokine expression and secretion by fibroblasts. Enhanced inflammation and subsequent slow healing are outcomes of the MAPK and PI 3-kinase signaling-dependent upregulation of cytokine release from macrophages. (With permission from [24])

reducing surgery time. In parallel, testosterone supplementation was also found to increase the magnitude and duration of the inflammatory response, which was also followed by a prolonged proliferative phase and delayed overall healing of the urethra [26]. The presence or absence of androgens therefore exerts a significant influence on the regeneration of urethral tissues. Estrogen supplementation has also been proposed to restore periurethral vascularity in the context of androgen deprivation. Interestingly, it was recently shown that as hypospadias gets worse (more severe forms), the expression of androgen receptors in the penile tissue goes down, while the expression of estrogen receptors goes up [27]. The role of estrogen in urethral healing was investigated in a study of control and castrated rats that were supplemented or not with either testosterone or estrogen. As previously described, castrated rats exhibited a decrease in periurethral vascularity that could be restored by supplementation with estrogen, although not to the same extent as induced by testosterone administration [26]. Of note, androgen expression was only identified in testosterone-supplemented rats and in control animals that were not castrated thus confirming that testosterone is required for the expression of the androgen receptor. Testosterone was also necessary for the expression of endothelium-specific receptor tyrosine kinase 2 (Tie-2) and resultant increases in angiogenesis. Taken together, these data suggest that testosterone supplementation restores physiologic androgen receptor and TIE-2 expression with subsequent improvements in tissue perfusion. This further supports the notion that hypogonadism limits regenerative potential of the urethra and highlights the

2  Urethral Anatomy, Physiology, and Regeneration

33

importance of preoperative hormone replacement therapy. (For further discussion regarding the clinical use of hormonal supplementation in hypospadias, please see “Chap. 4” General Perioperative Considerations).

2.5 Future Perspectives Accounting for the complex anatomy of the penis is critically important during surgical planning and reconstructive procedures such as urethroplasty. A better understanding of the urethral healing process could aid the design of new surgical techniques or algorithms for selecting the optimal surgical approach [28]. Furthermore, detailed knowledge of both macro- and microscopic components of the healthy penis will be key to developing suitable tissue-engineered replacements for future applications [10, 29]. To date, tissue engineering studies have only succeeded in constructing single- or occasionally double-layer scaffolds. Typically these scaffolds are seeded with cells capable of forming only epithelial and muscular layers thus disregarding the roles of submucosa and surrounding corpus spongiosum in urethral regeneration [30–32]. Since the corpus spongiosum is deficient in hypospadias [19, 33], reconstitution of this tissue warrants further investigation in future studies. (Please “Chap. 9” about Tissue engineering and regenerative medicine in hypospadias management). Moreover, precise categorization of hypospadias anatomical variables using artificial intelligence tools and machine learning approaches may further improve classification accuracy. Eliminating inter- and intra-observer disparities while at the same time integrating new pre- and postoperative factors should result in more robust methods of determining hypospadias severity [34, 35]. (Please “Chap. 10” about Artificial Intelligence). The human urethra exhibits nonlinear cross-sectional pressure, meaning that the tissue is significantly deformable at low pressure (to facilitate voiding), but less deformable at higher stress levels (which protects against over-distention) [36]. However, this structure–function relationship has not been sufficiently investigated in preclinical studies of urethral tissue engineering. Uroflowmetry is considered a sensitive and noninvasive measure of lower urinary tract dynamics, and could therefore be used to better assess functional outcomes following surgical intervention [37]. Therefore, optimization of the design and reporting of animal urethral experiments could potentially improve the translatability of preclinical findings into patient use [38, 39]. (Please “Chap. 10” about The rabbit model in preclinical hypospadias research: strengths and limitations).

2.6 Conclusion Understanding urethral healing is crucial to optimizing tissue regeneration and reducing rates of postoperative complications. Low testosterone levels can lead to poor outcomes of reconstructive surgery, while testosterone and estrogen supplementation can promote angiogenesis in native urethral tissue and postsurgical settings.

34

G. T. Baaklini et al.

References 1. Verla W, Oosterlinck W, Spinoit A-F, Waterloos M.  A comprehensive review emphasizing anatomy, etiology, diagnosis, and treatment of male urethral stricture disease. Biomed Res Int. 2019;2019:9046430. https://doi.org/10.1155/2019/9046430. 2. Özbey H, Etker Ş. Hypospadias repair with the glanular-frenular collar (GFC) technique. J Pediatr Urol. 2017;13:34.e1–6. https://doi.org/10.1016/j.jpurol.2016.09.016. 3. Carroll PR, Dixon CM. Surgical anatomy of the male and female urethra. Urol Clin N Am. 1992;19:339–46. 4. da Silva EA, Sampaio FJB, Ortiz V, Cardoso LEM. Regional differences in the extracellular matrix of the human spongy urethra as evidenced by the composition of glycosaminoglycans. J Urol. 2002;167:2183–7. 5. de Graaf P, Ramadan R, Linssen EC, Staller NA, Hendrickx APA, Pigot GLS, et al. The multilayered structure of the human corpus spongiosum. Histol Histopathol. 2018;33:1335–45. https://doi.org/10.14670/HH-­18-­022. 6. Shirozu H, Koyanagi T, Takashima T, Horimoto N, Akazawa K, Nakano H. Penile tumescence in the human fetus at term—a preliminary report. Early Hum Dev. 1995;41:159–66. 7. Young B, O’Dowd G, Woodford P.  Wheater’s functional histology: a text and colour atlas. Amsterdam: Elsevier Health Sciences; 2013. 8. Bhat A, Bhat M, Kumar V, Kumar R, Mittal R, Saksena G. Comparison of variables affecting the surgical outcomes of tubularized incised plate urethroplasty in adult and pediatric hypospadias. J Pediatr Urol. 2016;12:108.e1–7. https://doi.org/10.1016/j.jpurol.2015.09.005. 9. Yiee JH, Baskin LS. Penile embryology and anatomy. Sci World J. 2010;10:1174–9. https:// doi.org/10.1100/tsw.2010.112. 10. Abbas TO, Yalcin HC, Pennisi CP.  From acellular matrices to smart polymers: degradable scaffolds that are transforming the shape of urethral tissue engineering. Int J Mol Sci. 2019;20:1763. https://doi.org/10.3390/ijms20071763. 11. Abbas TO, Ali M. Urethral meatus and glanular closure line: normal biometrics and clinical significance. Urol J. 2018;15:277–9. https://doi.org/10.22037/uj.v0i0.4402. 12. Abbas TO, Vallasciani S, Elawad A, Elifranji M, Leslie B, Elkadhi A, et al. Plate Objective Scoring Tool (POST); an objective methodology for the assessment of urethral plate in distal hypospadias. J Pediatr Urol. 2020;16:675–82. https://doi.org/10.1016/j.jpurol.2020.07.043. 13. Abbas TO, Hatem M, Chandra P. Plate Objective Scoring Tool: a new preoperative indicator of penile curvature degree in children with distal hypospadias. Int J Urol. 2022;29(6):511–5. https://doi.org/10.1111/iju.14822. 14. Abbas TO. The Plate Objective Scoring Tool (POST): further reflections and extended applications. Res Rep Urol. 2021;13:783–91. https://doi.org/10.2147/RRU.S321188. 15. Bush NC, Villanueva C, Snodgrass W. Glans size is an independent risk factor for urethroplasty complications after hypospadias repair. J Pediatr Urol. 2015;11:355.e1–5. https://doi. org/10.1016/j.jpurol.2015.05.029. 16. Orkiszewski M. A standardized classification of hypospadias. J Pediatr Urol. 2012;8:410–4. https://doi.org/10.1016/j.jpurol.2011.08.011. 17. Abbas TO. Ultrasonographic evaluation of the hypospadiac penis in children. Front Pediatr. 2022;10:932201. https://doi.org/10.3389/fped.2022.932201. 18. Abbas TO, Braga LH, Spinoit AF, Salle JP. Urethral plate quality assessment and its impact on hypospadias repair outcomes: a systematic review and quality assessment. J Pediatr Urol. 2021;17(3):316–25. https://doi.org/10.1016/j.jpurol.2021.02.017. 19. Abbas TO, Charles A, Ali M, Salle JLP.  Long-term fate of the incised urethral plate in Snodgrass procedure; a real concern does exist. Urol Case Rep. 2020;32:101216. https://doi. org/10.1016/J.EUCR.2020.101216. 20. Abbas TO, Ali TA, Uddin S.  Urine as a main effector in urological tissue engineering—a double-edged sword. Cell. 2020;9:538. https://doi.org/10.3390/cells9030538. 21. Jacobs ME, de Kemp VF, Albersen M, de Kort LMO, de Graaf P. The use of local therapy in preventing urethral strictures: a systematic review. PLoS One. 2021;16:e0258256. https://doi. org/10.1371/journal.pone.0258256.

2  Urethral Anatomy, Physiology, and Regeneration

35

22. Ninan N, Thomas S, Grohens Y.  Wound healing in urology. Adv Drug Deliv Rev. 2015;82:93–105. https://doi.org/10.1016/j.addr.2014.12.002. 23. Hofer MD, Cheng EY, Bury MI, Park E, Xu W, Hong SJ, et  al. Analysis of primary urethral wound healing in the rat. Urology. 2014;84:246.e1–7. https://doi.org/10.1016/j. urology.2014.04.012. 24. Gilliver SC, Ashworth JJ, Mills SJ, Hardman MJ, Ashcroft GS.  Androgens modulate the inflammatory response during acute wound healing. J Cell Sci. 2006;119:722–32. https://doi. org/10.1242/jcs.02786. 25. Kaya C, Radmayr C. The role of pre-operative androgen stimulation in hypospadias surgery. Transl Androl Urol. 2014;3:340–6. https://doi.org/10.3978/j.issn.2223-­4683.2014.12.01. 26. Morey AF.  Discovering the role of androgens in urethral homeostasis and regeneration. In: Scientific advances in reconstructive urology and tissue engineering. New  York: Academic Press; 2022. p. 105–24. https://doi.org/10.1016/B978-­0-­323-­91199-­3.00007-­4. 27. Khanna S, Shankar Raman V, Badwal S, Vinu Balraam KV. Quantification of the androgen and estrogen receptors in the penile tissues of hypospadias in comparison with normal children. Fetal Pediatr Pathol. 2022;42(2):175–86. https://doi.org/10.1080/15513815.2022.2104496. 28. Abbas TO, Pippi Salle JL.  When to graft the incised plate during TIP repair? A suggested algorithm that may help in the decision-making process. Front Pediatr. 2018;6:326. https://doi. org/10.3389/fped.2018.00326. 29. Abbas TO, Mahdi E, Hasan A, AlAnsari A, Pennisi CP. Current status of tissue engineering in the management of severe hypospadias. Front Pediatr. 2018;5:283. https://doi.org/10.3389/ fped.2017.00283. 30. Versteegden LRM, de Jonge PKJD, IntHout J, van Kuppevelt TH, Oosterwijk E, Feitz WFJ, et al. Tissue engineering of the urethra: a systematic review and meta-analysis of preclinical and clinical studies. Eur Urol. 2017;72:1–13. https://doi.org/10.1016/j.eururo.2017.03.026. 31. Orabi H, Bouhout S, Morissette A, Rousseau A, Chabaud S, Bolduc S. Tissue engineering of urinary bladder and urethra: advances from bench to patients. Sci World J. 2013;2013:154564. https://doi.org/10.1155/2013/154564. 32. De Kemp V, De Graaf P, Fledderus JO, Bosch JLHR, De Kort LMO. Tissue engineering for human urethral reconstruction: systematic review of recent literature. PLoS One. 2015;10:1–14. https://doi.org/10.1371/journal.pone.0118653. 33. Djakovic N, Nyarangi-Dix J, Ozturk A, Hohenfellner M.  Hypospadias. Adv Urol. 2008;2008:650135. https://doi.org/10.1155/2008/650135. 34. Fernandez N, Lorenzo AJ, Rickard M, Chua M, Pippi-Salle JL, Perez J, et  al. Digital pattern recognition for the identification and classification of hypospadias using artificial intelligence vs experienced pediatric urologist. Urology. 2021;147:264–9. https://doi.org/10.1016/j. urology.2020.09.019. 35. Abbas TO, AbdelMoniem M, Chowdhury M.  Automated quantification of penile curvature using artificial intelligence. Front Artif Intell. 2022;5:188. https://doi.org/10.3389/ FRAI.2022.954497. 36. Lalla M, Gregersen H, Olsen LH, Jørgensen TM. In vivo biomechanical assessment of anterior rabbit urethra after repair of surgically created hypospadias. J Urol. 2010;184:675–82. https:// doi.org/10.1016/j.juro.2010.03.055. 37. Hammouda HM, El-Ghoneimi A, Bagli DJ, McLorie GA, Khoury AE. Tubularized incised plate repair: functional outcome after intermediate followup. J Urol. 2003;169:331–3. https:// doi.org/10.1016/S0022-­5347(05)64120-­1. 38. Abbas TO, Elawad A, Kareem A, Pullattayil SAK, Ali M, Alnaimi A.  Preclinical experiments for hypospadias surgery: systematic review and quality assessment. Front Pediatr. 2021;9:718647. https://doi.org/10.3389/fped.2021.718647. 39. Abbas TO, Elawad A, Pullattayil SAK, Pennisi CP. Quality of reporting in preclinical urethral tissue engineering studies: a systematic review to assess adherence to the ARRIVE guidelines. Animals. 2021;11(8):2456. https://doi.org/10.3390/ani11082456.

3

The Rabbit Model in Preclinical Hypospadias Research: Strengths and Limitations Tariq Abbas , Petra De Graaf, and Cristian Pablo Pennisi

Abstract

The rabbit model is widely used to evaluate routine urethroplasty procedures and also to test new tissue engineering approaches for the treatment of hypospadias. The overall goal is to evaluate long-term structural and functional effects prior to clinical application, but advances in this area must be balanced against the need to replace, reduce, and refine the use of animals in research. To ensure translatability and reproducibility, some of the most critical issues to define when designing preclinical studies of hypospadias are animal age, anatomic location, and injury size. Importantly, future research strategies should consider new methods for assessing functional performance and models that approximate human pathology. (See Video 3.1).

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/978-­981-­19-­7666-­7_3. T. Abbas (*) Urology Division, Surgery Department, Sidra Medicine, Doha, Qatar e-mail: [email protected] P. De Graaf Regenerative Medicine Utrecht, Utrecht, The Netherlands Department of Urology, University Medical Center Utrecht, Utrecht, The Netherlands C. P. Pennisi Regenerative Medicine Group, Department for Health Science and Technology, Aalborg University, Aalborg, Denmark

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 T. Abbas (ed.), Hypospadiology, https://doi.org/10.1007/978-981-19-7666-7_3

37

38

T. Abbas et al.

Key Messages • Rabbits are the most commonly used animal model for preclinical hypospadias research because the rabbit’s urethra has histological similarity to human urethra and the overall dimensions of the urethra are comparable to those of a 1-year-­old boy. • In preclinical studies, rabbits are typically 10 weeks of age (postpubertal), which is not the age at which hypospadias surgery is performed in humans (prepubertal). • To ensure adequate reproducibility and translatability of preclinical urethroplasty experiments, several critical issues were identified, including sample size calculation, adequate follow-up duration, and selection of outcome assessment parameters. • In future, models with structural defects that approximate human pathology will be essential to gain more accurate insights from animal studies.

3.1 Animal Models for Urethral Repair Research Urethral anomalies warranting replacement may occur as a result of both congenital and acquired pathologies. In particular, hypospadias is a common congenital anomaly in male children in which urethral shortage presents a major surgical challenge [1]. However, the current treatment of hypospadias relies on surgical techniques that have a high complication rate and require special skills to be performed effectively [1–5]. Consequently, there is a clear need for preclinical models that can be used to refine current urethral surgical techniques and develop new approaches for affected patients [6].

3  The Rabbit Model in Preclinical Hypospadias Research: Strengths and Limitations

39

Biomedical research in animal models has provided considerable insight into various methods of urethroplasty and hypospadias repair and, in particular, serves as an important means to study urethral healing [7, 8], approaches to tissue replacement [9, 10], and to evaluate the feasibility of novel surgical procedures [11–13]. However, progress in this field must be weighed against the need to replace, reduce, and refine the use of animals in research [14]. Although various animal models have been used in previous studies, none of them is currently considered to be definitively better than the others [9, 15]. According to four recent systematic reviews, rabbits are the most commonly used model for urethral experiments [9, 10, 12, 16]. Regardless of the species used, it is important to ensure that animal models accurately reflect human urethral pathologies in order to test potential therapeutic applications. Although some experimental models of hypospadias have been described [8, 17], the human male urethra and extracellular matrix (ECM) are structurally unique [18, 19] hence translating animal data to healing in humans may lead to errors [20]. Small rodents such as mice and rats have penile anatomy that differs from humans. For example, the penis is completely covered by hairy skin on the lower abdominal wall called the “prepuce.” The penis consists of two elements: the shaft or body attached to the pubic bones, and a long glans containing the os penis (an ossified bone) and the corpus cavernosum urethrae (CCU), similar to the corpus spongiosum in humans. As a distal extension of the os penis, a fibrocartilaginous part, the male urogenital mating protuberance (MUMP), is found [21]. The ossified structure in the penis is not unique for rodents, as dogs and even some monkeys have an os penis. It is common to believe that primates have virtually the same anatomy and share all features with humans because they are phylogenetically close. However, this is not entirely true for the anatomy of the penis. The human penis is longer and wider than that of all other primates, including the great apes (gorillas, chimpanzees, and orangutans). Primate penises differ in length, complexity, and specialization (e.g., lumps, flanges, and ridges) specific to each species. These characteristics are related to their mating behavior [22, 23]. Nevertheless, the main reason why it is not possible to conduct large-scale urethroplasty experiments on primates are ethical reasons and high experimental costs.

3.2 The Rabbit Urethroplasty Model After primates, lagomorphs have the closest phylogenetic relationship to humans, and within this taxonomic order, rabbits have the closest resemblance to human physiology [24, 25]. Rabbits have corporal tissue and lack the os penis. For biomedical research, the strain most commonly used is the New Zealand White (NZW) rabbit (Oryctolagus cuniculus) [26]. Some of the main

40

T. Abbas et al.

characteristics of rabbits include cost-effectiveness, ease of husbandry, wide availability, and frequent breeding resulting in large litters. These advantageous characteristics are arguably similar to those of the more versatile mouse and rat models, with the added advantage that rabbits are more genetically similar to humans than rodents [25]. Indeed, because of their larger size, rabbits also have a high blood volume and greater hemodynamic/histological similarity to humans, which is not the case in mice and rats [24]. The short life cycles of rabbits further increase the usefulness of these model animals, allowing research studies to be completed in a shorter time. For this reason, various implantation research methods have already been tested in this mammal [27]. Kajbafzadeh et al. not only tested different surgical techniques for hypospadias repair but also analyzed cellular and ultrastructural alterations in the urethral wall following the application of different hemostasis techniques in a rabbit model for hypospadias surgery [28]. Interestingly, Xie et al. [11] also investigated the outcomes of specific urethroplasty techniques in a rabbit congenital hypospadias model induced by maternal ingestion of finasteride (a 5α-reductase inhibitor drug that possess antiandrogenic effects).

3.2.1 Rabbit Penile Anatomy Rabbits provide a good preclinical model because the urethra is very similar to human male histology, which displays a thin epithelium supported by spongy tissue rich in blood vessels. Furthermore, the overall dimensions of the urethra are comparable to those of a 1-year-old boy [29, 30]. Thus, this allows the use of transurethral instruments that are easily handled with a pediatric urethroscope. However, there are some notable anatomic differences between the rabbit and human urethra. The rabbit urethra has excellent healing ability, possibly due to the excellent blood supply to the rabbit corpus spongiosum [31, 32]. It is very interesting that the corpus spongiosum surrounding the anterior urethra in rabbits has a similar “crescent” configuration as in humans [33] (see Fig. 3.1). This makes it a valuable model for evaluating novel urethroplasty procedures, since several techniques rely on incision of the dorsal urethra, and a model with similar relative thickness of the corpus spongiosum provides a suitable model for simulating the potential healing process, which depends to some extent on the adjacent thickness of the spongiosum [34].

3  The Rabbit Model in Preclinical Hypospadias Research: Strengths and Limitations

a

41

b

c

Fig. 3.1 (a) H&E image of the penile urethra showing overall thickness of corpus spongiosum enveloping the urethra in a 5-week-old male NZW rabbit (×10) (adapted with permission from [35]). (b) H&E image of penile urethra showing overall thickness of corpus spongiosum enveloping adult human urethra (×4) (adapted from [36]). (c) Schematic diagram showing the “crescent” configuration of the corpus spongiosum

3.2.2 Relationship Between Rabbit and Human Age The effects of natural aging on human tissue biology are of great scientific interest, and hold potential for translating new knowledge into clinical practice. To optimize the accuracy and specificity of preclinical models, animal ages should be matched to human ages as closely as possible, both in terms of total lifespan and individual life stages. Because the most common age for hypospadias repair in humans is between 9  months and 2  years, it is critical to determine the extent to which a selected animal model has the appropriate physiological developmental and maturational stage. The study of animal models at early life stages (juvenile) is becoming increasingly common, particularly in the pharmaceutical industry, which wishes to account for potentially unusual drug kinetics in this age group [37]. This has also been implemented in surgery, where the small size of implantable devices and different growth patterns of various body organs may affect clinical outcomes in younger patients [38, 39]. The life expectancy of rabbits depends on breed, living conditions, and health care [40]. Rabbits enter puberty at 3–5 months of age and are sexually mature at ~5–6 months of age [41]. During their lifespan, most organisms undergo various

42

T. Abbas et al.

physiological changes due to development, growth, reproduction, and aging. As a result, cells, tissues, and organs are exposed to a variety of external and internal stimuli, which can induce either transient or permanent structural and/or functional changes. It is worth noting that structural changes in the extracellular matrix of the penis result in decreased distensibility with the natural aging process [42, 43], which also involves a decrease in urethral distensibility [44]. Previous studies have also shown that multiparity leads to disorganization of various tissue components in the urethral and vaginal walls of female rabbits, particularly altered thickness of the middle and outer muscle layers and altered distribution of collagen and blood vessels [45]. During their lifespan, most organisms exhibit several physiological changes due to development, growth, reproduction, and aging. As a consequence, cells, tissues, and organs are exposed to external and internal stimuli, which can induce transient or permanent structural and/or functional modifications. For preclinical urethroplasty studies in rabbits, 10–12-week-old animals are preferred because the urethra is large enough to perform the necessary surgical procedures [9, 15]. Although at this age the rabbit penis is similar in size to that of a 1-year-old human, rabbits this age are considered postpubertal, so there are numerous potential hormonal and biophysical differences compared with human patients. Rabbits at 3–5 weeks of age are thought to correspond more closely to the prepubertal age at which most hypospadias correction surgery is performed (Fig.  3.2). In addition, there is evidence that there are marked mechanical differences in the anterior urethra of male rabbits between young, postpubertal, and older animals, which underscores the importance of conducting experiments in appropriate age groups [46].

Currently most utilized age for hypospadias rabbits experiments

Most common age for hypospadias surgeries in humans

Human days

P180

Birth

Rabbit days

P4198

P7300

P18,615

10,585 Days

1 year

1 year

1 year

1 year

1 year

Weaning

Puberty

Adulthood

Reproductive Senescence

Post Senescence

56.77 days

13.04 days

18.25 days

42.94 days

50.34 days

P28

P150

P365

P2190

1460 DAYS

Rabbit

Fig. 3.2  Rabbits at 3–5 weeks of age correspond to human infancy when most operations to correct hypospadias are performed. Most urethroplasty preclinical experiments have been performed in rabbits at 10–12  weeks of age when the urethra is large enough to simulate the anatomy of 1-year-old males. However, rabbits at this age are considered postpubertal, which can lead to significant hormonal and biophysical discrepancies. (With permission from [40])

3  The Rabbit Model in Preclinical Hypospadias Research: Strengths and Limitations

43

3.2.3 Mechanical Properties of the Rabbit Urethra As the rabbit ages, the tensile strength of the urethra increases, while the extensibility of both the penis and urethra gradually decreases, which has important consequences for the surgical repair of urethral defects [47, 48]. Elastic fibers play a critical role in tissue compliance, especially in organs that change shape under physiologic conditions (e.g., the spongy human urethra) [46, 49].

3.3 Limitations of Rabbit Urethra Models 3.3.1 General Inherent Limitations Despite many advantages, experiments in rabbits also present researchers with a number of significant challenges, some of which are specific to the rabbit model and some others which are common to other animal models. These include the lack of well-equipped animal facilities and experienced animal caretakers, the lack of postand intraoperative medications suitable for rabbits, and the scarcity of relevant literature on animal care and experimental procedures.

3.3.2 Structural and Biomechanical Differences Between Rabbit and Human Urethra Animal studies, of course, cannot and should not be extrapolated directly to the human clinical situation because of significant differences in species anatomy, molecular tissue composition, and other potentially confounding effects, such as the effects caused by anesthesia [34]. The conclusions presented here should also be considered in light of numerous other experimental issues, particularly with regard to the study of urethral biomechanics. Some of the specific limitations of studies involving reconstructive urethral surgery in rabbits include: • Hairy skin covers the urethra and the presence of glands in the subcutaneous tissue, so techniques involving the use of skin or subcutaneous tissue for repair may not be translatable to humans. The extreme fragility of the rabbit dorsal urethral plate means that some surgical urethroplasty techniques are technically difficult [50]. • Hypospadias-related anatomic defects are rarely present in current animal models. Baskin et al. [51] and Erol et al. [52] noted that the hypospadic penis has a distinct vascularity of the abortive spongiosum urethral and glans that is markedly different from the healthy penis. This ultrastructure may support healing of the hypospadic penis without scarring after urethroplasty with tubularized incised urethral plate (TIPU) [17]. • The most common hypospadias rabbit model involves creation of an acute urethral injury and followed by immediate repair of the damage [53]. Other authors

44

T. Abbas et al.

have instead induced urethral injury and then delayed repair to mimic chronic rather than acute injury, which more closely replicates real clinical scenarios [54]. Developmental delay models may theoretically offer even greater potential for translation into patients, such as the approach of Xie et  al. [11], in which congenital hypospadias was induced by maternal ingestion of a 5α-reductase inhibitor. Pharmacologically induced hypospadias could lead to improved animal models with penile defects more similar to the human condition. Investigation of the different biomechanical profiles resulting from the alteration of normal hormonal and embryological developmental pathways could provide valuable information on different patterns of tissue healing and regrowth.

3.3.3 Lack of Sample Size Calculation A review of 271 randomly selected papers involving rats, mice, and nonhuman primates [55] found that none of these papers provided a rationale for the choice of sample size, and a substantial number did not even report the sex, age, or weight of the experimental animals [56]. Most investigators choose sample sizes based on previous experiments that appear to have produced satisfactory results. However, funding agencies and ethics committees usually require researchers to determine their sample size based on power calculations. Unfortunately, no single method for determining sample size is entirely satisfactory. The resource equation method provides a useful rule of thumb for avoiding experiments that are likely to be too small to yield significant results, although this approach lacks the mathematical justification for power analysis [57]. The alternative approach, KISS [58], involves investigators making a preliminary estimate of sample size based on “common sense” and/or the resource equation, whereupon it is possible to use a simple table/simple arithmetic to determine the effect size (ES) that the planned experiments are likely to yield at a given power. Optionally, ES can be displayed as a percent change. If, in retrospect, researchers wish to demonstrate a lower ES, they can increase the preliminary sample size and recalculate if necessary to justify their actions in terms of power analysis.

3.3.4 Short Follow-Up Durations in Rabbit Urethroplasty Models The response to a particular urethral repair technique or a grafted implant depends on the health of the surrounding tissue and its vascular supply [59]. Another consideration is that implantation almost always elicits a significant local tissue response as a result of surgical trauma. Therefore, it is essential to include sham operations and/or control grafts to distinguish responses induced by specific implants from those primarily due to surgery. It is also important to consider relevant time frames for studying the healing process, as urethroplasty injuries may require a recovery period longer than 12  weeks to allow for epithelial, elastic, and smooth muscle regeneration, remodeling, and evidence of implant absorption/degradation.

3  The Rabbit Model in Preclinical Hypospadias Research: Strengths and Limitations

45

3.3.5 Lack of Optimized Assessment Methods for the Structural and Functional Outcomes Histologic processing of urethral implant specimens can be challenging, and variations in implants are too numerous to address here. The integration of objective assessment tools in the evaluation of urethroplasty outcomes is critical to maximize transparency and reproducibility and to facilitate the comparison of techniques. For example, fibrosis can be divided into subcategories based on features such as epithelial integrity and structure, edema, and inflammatory cell infiltration [60]. This type of histologic examination should be performed in a blinded fashion, with investigators evaluating specimens without knowing in advance to which group each specimen belongs. Software-assisted histomorphometric analysis can also be performed to assess the degree of smooth muscle and epithelial tissue regeneration in both the control and implant groups [61]. Additional analytical techniques (macroscopic evaluation, radiographs, functional studies) can provide important information about 3D structure and graft–tissue interactions, as these are difficult to determine by histology alone. Anatomic studies relevant to urethroplasty include retrograde urethrogram (RUG) contrast studies, which are commonly used to diagnose various urologic conditions, including urethral strictures. Test results provide information about mechanical diameters at the surgical site, as well as possible obstructive mechanisms due to proximal dilatation. Current protocols for performing and interpreting retrograde urethrograms in animal models are based on investigator preference. Independently reported RUGs in humans are not as accurate as physician-reported RUGs, and caution should be exercised when using them for preoperative planning [62]. To perform this test in rabbits, ~1–1.5 mL of a water-soluble iodine-based contrast agent is injected into the urethra under direct fluoroscopic or radiological vision before several images are acquired. This is called a dynamic retrograde urethrogram, which allows live assessment of the urethra during contrast administration. There are three main features of a stricture that RUG must identify, including the location of the stricture, the length of the stricture, and the associated fistula. Because retrograde urethrogram relies on intraluminal opacification of the urethra, this technique provides little direct information about periurethral pathology. The retrograde urethrogram is also limited in assessing spongiofibrosis, which can instead be interpolated by histologic examination. The degree of urethral stenosis in the experimental groups should be determined by calculating the percentage of luminal urethral diameter at the injured/ repaired sites relative to an uninjured internal control region distal to the surgical site. Because this is a dynamic study in a live animal, stenosis should be defined as a 50% reduction in urethral diameter at the same site detected on three repeated examinations. The investigators evaluating the retrograde urethrogram data should be blinded to the rabbit groups. A typical limitation in urethral preclinical urethroplasty studies is the lack of postoperative evaluation of pressure-flow studies [63]. Urethra lumen size and biomechanical parameters (stress–strain relationships) can be determined by impedance planimetry [54]. These parameters can explain the structure–function

46

T. Abbas et al.

relationship of the urethra (lumen size and elastic properties of the normal anterior urethra) and the altered force-deformation properties associated with changes in the cross-sectional area (CSA) of the operated urethra. Previous studies have reported that the urethra has a nonlinear CSA that is somewhat deformable at low pressures to facilitate voiding, whereas the urethral CSA is much less deformable at higher pressures. Consequently, uroflowmetry can be used noninvasively for sensitive functional assessment of lower urinary tract dynamics in rabbits. It has already been shown that abnormal narrowing or obstruction of the urethra as well as postoperative complications and recurrence of disease can be reliably detected by uroflowmetry measurements [64, 65] (Fig. 3.3).

Flow Rate (ml/sec)

a Maximum Flow Rate

Voided Volume

Flow Rate (ml/sec)

b

Flow Time

Time

Maximum Flow Rate

Voided Volume Flow Time

Time

c Flow Rate (ml/sec)

Fig. 3.3 Uroflowmeter curve patterns as depicted from [66]. (a) General illustration of the uroflowmetry trace. (b) Typical pattern: “Bell-­ shape” = short flow time/ high maximum flow rate. (c) Obstructive pattern: “Plateau shape” = longer time to complete voiding/ low maximum flow rate

Maximum Flow Rate

Voided Volume Flow Time

Time

3  The Rabbit Model in Preclinical Hypospadias Research: Strengths and Limitations

47

It is important to test the functional elasticity of scaffolds in a way that replicates physiological compliance during human micturition. Using a modified version of previously described methodology [67], passive flow of normal saline over a gravity-­based pressure system can be evaluated using a uroflowmetry device. As illustrated in Fig. 3.4, a fluid column of 50 cm normal saline was created using a 60  cm syringe fixed to an intravenous fluid stand, which was then connected to plastic tubing via a controllable 2-way valve. Excised penis can then be connected to the end of the plastic tubing and directed towards a plastic jug fluid collector positioned on top of the uroflowmeter sensor (e.g., Flowtaker® fra Laborie).

Syringe

50 cm

Control Lock

Plastic I.V. Tube Explanted Urethra I.V. Stand Collector

Uroflowmeter Sensor

Fig. 3.4  Passive flow rate device. (Illustrated based on figure from [67])

Floor

48

T. Abbas et al.

3.3.6 Poor Reporting of Rabbits’ Animal Experiments for Hypospadias Surgery Several important principles of experimental design are poorly described in the literature, making it difficult to rigorously evaluate the scientific quality and reproducibility of experiments. Comprehensive implementation of ARRIVE guidelines in animal studies of urethral repair is necessary to enable effective translation of preclinical research into new clinical therapies [12]. Table  3.1 summarizes the key points to consider and highlight in the design and reporting of preclinical hypospadias experiments to achieve better reproducibility between researchers and transferability to humans. This can be considered as an important adjunct beside the PREPARE guidelines: Planning Research and Experimental Procedures on Animals: Recommendations for Excellence [68]. Formulation, communication between researchers and the animal facility, and quality control over the study’s numerous components are the three main pillars of PREPARE, which together determine the quality of the preparation for animal studies. Table 3.1  Critical points in the design of urethroplasty preclinical experiments Variables specific to hypospadias animal experiments Specific variables to be considered Significance Preoperative  Pathogen-free/induction period The risk of infection is minimized  Age and weight Translation to human is more accurate  Sex Sex influences the anatomical and hormonal milieu  Status of the urethra prior to the Animal models of the disease (e.g., intervention pharmacologically induced hypospadias) may better represent human disease Intraoperative To better describe the severity of the hypospadias  Urethrotomy description: model    – Length, with or without excision    –  To provide the length of suture line    – Proportion to circumference of    –  The risk of complications is minimized the urethra    – Distance from the glans meatus    – Status of the penis (stretched or not)    – Mentioned stretched penile length  Urethrotomy ratio    – To provide a quantitative means for severity (UI) = urethrotomy/penile length assessment (distal)    –  A measure to improve reproducibility  Urethral defect ratio (UDR) = urethral defect length/ penile length (severe) (continued)

3  The Rabbit Model in Preclinical Hypospadias Research: Strengths and Limitations

49

Table 3.1 (continued) Variables specific to hypospadias animal experiments Specific variables to be considered Significance  Magnification/microscopy Part of the quality assessment  Tourniquet (specify duration) Part of the quality assessment  Intervention following acute vs. To improve translation to humans chronic injury  Suture (size and types) Part of the quality assessment A measure to improve reproducibility  Presence of a stent (specify type/ To provide for urine diversion, and to minimize the manufacturer/duration) risk of complications  Antibiotics The risk of infection is minimized Postoperative  Long-term vs. short-term outcomes To improve translation to humans  Postoperative dressing Part of the quality assessment  Restrainment To minimize stress and improve reproducibility  Antibiotics The risk of infection is minimized  Anatomical assessment, e.g., Provides an objective measurement histology, cystoscopy, contrast study  Functional assessment, e.g., Provides an objective measurement uroflowmetry (passive/active)

3.4 Concluding Remarks For now, the rabbit seems to be the best preclinical model for urethroplasty in pediatric patients. This is due to various factors, including the ease of rabbit husbandry, their wide availability, their hemodynamic/histological similarities to humans, and their short life cycles. Despite the numerous advantages, rabbit experiments present researchers with a number of significant challenges, including a lack of optimized assessment methods for structural and functional outcomes, a lack of sample size calculation, and the fact that the age most commonly used in rabbit urethral experiments differs from the age at which hypospadias repair is performed in humans. To improve future studies, the use of power calculations and a standardized readout method after surgery are recommended. By using a functional readout method, such as urodynamics, on the live animal using minimally invasive techniques, the same animal can be followed for a longer period of time rather than sacrificing animals at each time point. This will reduce the number of animals used in the experiments and is expected to make the preclinical model more translatable to humans.

References 1. Keays MA, Dave S. Current hypospadias management: diagnosis, surgical management, and long-term patient-centred outcomes. Can Urol Assoc J. 2017;11(1–2 Suppl 1):S48–53.

50

T. Abbas et al.

2. Chapple C, Andrich D, Atala A, Barbagli G, Cavalcanti A, Kulkarni S, et al. SIU/ICUD consultation on urethral strictures: the management of anterior urethral stricture disease using substitution urethroplasty. Urology. 2014;83(3):S31–47. 3. Snodgrass W, Bush N. Primary hypospadias repair techniques: a review of the evidence. Urol Ann. 2016;8(4):403–8. 4. Barbagli G, Sansalone S, Djinovic R, Romano G, Lazzeri M. Current controversies in reconstructive surgery of the anterior urethra: a clinical overview. Int Braz J Urol. 2012;38(3):307–16. 5. Mundy AR, Andrich DE. Urethral strictures. BJU Int. 2011;107(1):6–26. 6. Abbas TO, Mahdi E, Hasan A, AlAnsari A, Pennisi CP. Current status of tissue engineering in the management of severe hypospadias. Front Pediatr. 2018;5:283. 7. Hofer MD, Cheng EY, Bury MI, Park E, Xu W, Hong SJ, et al. Analysis of primary urethral wound healing in the rat. Urology. 2014;84(1):246.e1–7. 8. Lopes JF, Schned A, Ellsworth PI, Cendron M. Histological analysis of urethral healing after tubularized incised plate urethroplasty. J Urol. 2001;166(3):1014–7. 9. Versteegden LRM, de Jonge PKJD, IntHout J, van Kuppevelt TH, Oosterwijk E, Feitz WFJ, et al. Tissue engineering of the urethra: a systematic review and meta-analysis of preclinical and clinical studies. Eur Urol. 2017;72(4):1–13. 10. Žiaran S, Galambošová M, Danišovič L. Tissue engineering of urethra: systematic review of recent literature. Exp Biol Med. 2017;242(18):1772–85. 11. Xie L, Xi Y, Zhang X, Ding H, Li S. Effects of spongioplasty on neourethral function following hypospadias repair: an experimental study in rabbits. Int Braz J Urol. 2020;46(3):436–43. 12. Abbas TO, Elawad A, Kareem A, Pullattayil SAK, Ali M, Alnaimi A.  Preclinical experiments for hypospadias surgery: systematic review and quality assessment. Front Pediatr. 2021;9:718647. 13. Tan X-H, Liu X, Long C-L, Zhang D-Y, Lin T, He D-W, et al. Histological and biochemical evaluation of urethral scar following three different hypospadias repairs: an experimental study in rabbits. Eur J Pediatr Surg. 2017;28(05):420–5. 14. Fenwick N, Griffin G, Gauthier C. The welfare of animals used in science: how the “Three Rs” ethic guides improvements. Can Vet J = La Rev Vet Can. 2009;50(5):523–30. 15. Qi N, Li W, Tian H. A systematic review of animal and clinical studies on the use of scaffolds for urethral repair. J Huazhong Univ Sci Technol [Med Sci]. 2016;36(1):111–7. 16. De Kemp V, De Graaf P, Fledderus JO, Bosch JLHR, De Kort LMO.  Tissue engineering for human urethral reconstruction: systematic review of recent literature. PLoS One. 2015;10(2):1–14. 17. Kurzrock EA, Jegatheesan P, Cunha GR, Baskin LS. Urethral development in the fetal rabbit and induction of hypospadias: a model for human development. J Urol. 2000;164(5):1786–92. 18. Simmons MN, Jones JS. Male genital morphology and function: an evolutionary perspective. J Urol. 2007;177(5):1625–31. 19. da Silva EA, Sampaio FJB, Ortiz V, Cardoso LEM. Regional differences in the extracellular matrix of the human spongy urethra as evidenced by the composition of glycosaminoglycans. J Urol. 2002;167(5):2183–7. 20. Liebschner MAK. Biomechanical considerations of animal models used in tissue engineering of bone. Biomaterials. 2004;25(9):1697–714. 21. Blaschko SD, Mahawong P, Ferretti M, Cunha TJ, Sinclair A, Wang H, et  al. Analysis of the effect of estrogen/androgen perturbation on penile development in transgenic and diethylstilbestrol-­treated mice. Anat Rec. 2013;296(7):1127–41. 22. Dixson AF. Observations on the evolution of the genitalia and copulatory behaviour in male primates. J Zool. 1987;213(3):423–43. 23. Dixson A. Sexual selection by cryptic female choice and the evolution of primate sexuality. Evol Anthropol Issues News Rev. 2003;11(S1):195–9. 24. Pritchett-Corning KR. Handbook of clinical signs in rodents and rabbits. Wilmington: Charles River Laboratories; 2011. p. 136. 25. Graur D, Duret L, Gouy M. Phylogenetic position of the order Lagomorpha (rabbits, hares and allies). Nature. 1996;379(6563):333–5.

3  The Rabbit Model in Preclinical Hypospadias Research: Strengths and Limitations

51

26. Weisbroth SH, Flatt RE, Kraus AL. The biology of the laboratory rabbit. New York: Academic Press; 2013. p. 513. 27. Foote RH, Carney EW.  The rabbit as a model for reproductive and developmental toxicity studies. Reprod Toxicol. 2000;14(6):477–93. 28. Kajbafzadeh A-M, Payabvash S, Tavangar SM, Salmasi AH, Sadeghi Z, Elmi A, et  al. Comparison of different techniques for hemostasis in a rabbit model of hypospadias repair. J Urol. 2007;178(6):2555–60. 29. Theodorescu D, Balcom A, Smith CR, McLorie GA, Churchill BM, Khoury AE.  Urethral replacement with vascularized tunica vaginalis: defining the optimal form of use. J Urol. 1998;159(5):1708–11. 30. Kemppainen E, Talja M, Riihelä M, Pohjonen T, Törmälä P, Alfthan O. A bioresorbable urethral stent. An experimental study. Urol Res. 1993;21(3):235–8. 31. Choma TJ, Poppas DP, Presberg HJ, Cundiff M, Schlossberg SM. CO2 laser urethroplasty in the rabbit: a preclinical model. Lasers Surg Med. 1992;12(6):639–44. 32. Italiano G, Abatangelo G, Calabrò A, Abatangelo G, Zanoni R, O’Regan M, et  al. Reconstructive surgery of the urethra: a pilot study in the rabbit on the use of hyaluronan benzyl ester (Hyaff-11) biodegradable grafts. Urol Res. 1997;25(2):137–42. 33. Ottenhof SR, de Graaf P, Soeterik TFW, Neeter LMFH, Zilverschoon M, Spinder M, et al. Architecture of the corpus spongiosum: an anatomical study. J Urol. 2016;196(3):919–25. 34. Abbas TO, Ali TA, Uddin S.  Urine as a main effector in urological tissue engineering—a double-edged sword. Cells. 2020;9(3):538. 35. Skonieczna J, Madej JP, Kaczmarek-Pawelska A, Będziński R. Histological and morphometric evaluation of the urethra and penis in male New Zealand white rabbits. Anat Histol Embryol. 2021;50(1):136–43. 36. Michigan Histology and Virtual Microscopy Learning Resources. Male Reproductive System | histology. 37. Duarte DM, Silva-Lima B. Juvenile animal studies in the development of pediatric medicines: experience from European medicines and pediatric investigation plans. Birth Defects Res Part B Dev Reprod Toxicol. 2011;92(4):353–8. 38. Soellner L, Olejniczak K.  The need for juvenile animal studies—a critical review. Regul Toxicol Pharmacol. 2013;65(1):87–99. 39. Weinberg AC, Xie HW, Hardy BE, Skinner DG. A juvenile animal model to study the growth potential of bowel segments in the urinary tract. J Urol. 1990;143(2):377–80. 40. Dutta S, Sengupta P. Rabbits and men: relating their ages. J Basic Clin Physiol Pharmacol. 2018;29(5):427–35. 41. Adams CE.  Reproductive performance of rabbits on a low protein diet. Lab Anim. 1983;17(4):340–5. 42. Moreira de Goes P, Wespes E, Schulman C. Penile extensibility: to what is it related? J Urol. 1992;148(5):1432–4. 43. Bondil P, Costa P, Daures JP, Louis JF, Navratil H. Clinical study of the longitudinal deformation of the flaccid penis and of its variations with aging. Eur Urol. 1992;21(4):284–6. 44. Da Silva EA, Sampaio FJB. Urethral extensibility applied to reconstructive surgery. J Urol. 2002;167(5):2042–5. 45. Xelhuantzi N, Rodríguez-Antolín J, Nicolás L, Castelán F, Cuevas E, Martínez-Gómez M. Tissue alterations in urethral and vaginal walls related to multiparity in rabbits. Anat Rec. 2014;297(10):1963–70. 46. Abidu-Figueiredo M, Costa WS, Chagas MA, Sampaio FJB, de Cardoso LEM. Age-related changes in the concentration of elastic fibers in different regions of the rabbit penis. Acta Cir Bras. 2013;28(5):378–84. 47. Bondli P, Costa P, Daures JP, Louis JF, Navratil H. Clinical study of the longitudinal deformation of the flaccid penis and of its variations with aging. Eur Urol. 1992;21(4):284–6. 48. da Silva EA, de Marins RL, Rondon A, Damião R. Age-related structural changes of the urethral plate in hypospadias. J Pediatr Urol. 2013;9(6):1155–60.

52

T. Abbas et al.

49. Murakumo M, Ushiki T, Abe K, Matsumura K, Shinno Y, Koyanagi T.  Three-dimensional arrangement of collagen and elastin fibers in the human urinary bladder: a scanning electron microscopic study. J Urol. 1995;154(1):251–6. 50. Lalla M, Danielsen CC, Austevoll H, Olsen LH, Jørgensen TM. Biomechanical and biochemical assessment of properties of the anterior urethra after hypospadias repair in a rabbit model. J Urol. 2007;177(6):2375–80. 51. Baskin LS, Erol A, Li YW, Cunha GR. Anatomical studies of hypospadias. J Urol. 1998;160(3 Pt 2):1108–15. 52. Erol A, Baskin LS, Li YW, Liu WH. Anatomical studies of the urethral plate: why preservation of the urethral plate is important in hypospadias repair. BJU Int. 2000;85(6):728–34. 53. Shirazi M, Rahimi M, Noorafshan A. Single vs. double layer suturing method repair of the urethral plate in the rabbit model of hypospadias. Cent Eur J Urol. 2016;69(4):425–30. 54. Lalla M, Gregersen H, Olsen LH, Jørgensen TM. In vivo biomechanical assessment of anterior rabbit urethra after repair of surgically created hypospadias. J Urol. 2010;184(2):675–82. 55. Kilkenny C, Parsons N, Kadyszewski E, Festing MFW, Cuthill IC, Fry D, et al. Survey of the quality of experimental design, statistical analysis and reporting of research using animals. PLoS One. 2009;4(11):e7824. 56. Bebarta V, Luyten D, Heard K. Emergency medicine animal research: does use of randomization and blinding affect the results? Acad Emerg Med. 2003;10(6):684–7. 57. Arifin WN, Zahiruddin WM. Sample size calculation in animal studies using resource equation approach. Malays J Med Sci. 2017;24(5):101–5. 58. Festing MF.  On determining sample size in experiments involving laboratory animals. Lab Anim. 2018;52(4):341–50. 59. Simsek A, Aldamanhori R, Chapple CR, MacNeil S. Overcoming scarring in the urethra: challenges for tissue engineering. Asian J Urol. 2018;5(2):69–77. 60. Sartoneva R, Nordback PH, Haimi S, Grijpma DW, Lehto K, Rooney N, et al. Comparison of poly(l-lactide-co-ɛ-caprolactone) and poly(trimethylene carbonate) membranes for urethral regeneration: an in vitro and in vivo study. Tissue Eng Part A. 2018;24(1–2):117–27. 61. Chung YG, Algarrahi K, Franck D, Tu DD, Adam RM, Kaplan DL, et al. The use of bi-layer silk fibroin scaffolds and small intestinal submucosa matrices to support bladder tissue regeneration in a rat model of spinal cord injury. Biomaterials. 2014;35(26):7452–9. 62. Bach P, Rourke K.  Independently interpreted retrograde urethrography does not accurately diagnose and stage anterior urethral stricture: the importance of urologist-performed urethrography. Urology. 2014;83(5):1190–4. 63. Nuininga JE, van Moerkerk H, Hanssen A, Hulsbergen CA, Oosterwijk-Wakka J, Oosterwijk E, et al. Rabbit urethra replacement with a defined biomatrix or small intestinal submucosa. Eur Urol. 2003;44(2):266–71. 64. Erickson BA, Breyer BN, McAninch JW.  Changes in uroflowmetry maximum flow rates after urethral reconstructive surgery as a means to predict for stricture recurrence. J Urol. 2011;186(5):1934–7. 65. Erickson BA, Breyer BN, McAninch JW. The use of uroflowmetry to diagnose recurrent stricture after urethral reconstructive surgery. J Urol. 2010;184(4):1386–90. 66. Chun K, Kim SJ, Cho ST. Noninvasive medical tools for evaluating voiding pattern in real life. Int Neurourol J. 2017;21(Suppl 1):S10–6. 67. Leslie B, Jesus LE, El-Hout Y, Moore K, Farhat WA, Bägli DJ, et al. Comparative histological and functional controlled analysis of tubularized incised plate urethroplasty with and without dorsal inlay graft: a preliminary experimental study in rabbits. J Urol. 2011;186(4):1631–7. 68. Smith AJ, Clutton RE, Lilley E, Hansen KEA, Brattelid T. PREPARE: guidelines for planning animal research and testing. Lab Anim. 2018;52(2):135–41.

4

General Perioperative Considerations Tariq Abbas , Muthana AlSalihi, Yasir El-Hout, Mansour Ali, and Eynas AbdAlla

Abstract

Hypospadias care is a rapidly developing field which includes several controversies that complicate each step of the management process. These controversial aspects include selection of the best age for intervention, the role of perioperative hormone supplements, efficacy of transurethral stenting, expedient use of perioperative antibiotics, applications of hyperbaric oxygen therapy, and appropriate use of analgesic blocks. Here, we summarize the recent literature addressing these major points and assess the quality of related evidence, as well as outline key areas to be addressed by future studies. (See Video 4.1).

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/978-­981-­19-­7666-­7_4.

T. Abbas (*) · M. AlSalihi · Y. El-Hout Urology Division, Surgery Department, Sidra Medicine, Doha, Qatar e-mail: [email protected] M. Ali Surgery Department, Sidra Medicine, Doha, Qatar E. AbdAlla Anesthesia Department, Hamad Medical Corporation, Doha, Qatar © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 T. Abbas (ed.), Hypospadiology, https://doi.org/10.1007/978-981-19-7666-7_4

53

54

T. Abbas et al.

Controversial Topics in rative Managem Periope ent

Hormones

Stent

Caudal Block

Antibiotics

Age

HBOT

Key Messages • The ideal patient age at which to perform hypospadias repair remains controversial, but current data suggest surgery between 6 months and 2 years is associated with the lowest rates of postoperative complications. • Studies of hormonal supplement outcomes have generated conflicting data that require further investigation.

4  General Perioperative Considerations

55

• Hyperbaric Oxygenation Therapy is safe and may decrease complications of hypospadias repair, but more studies are needed to identify the patient subgroups most likely to benefit. • Current evidence suggests that stented urethroplasty results in fewer complications after hypospadias repair, but most previous studies have been conducted in distal cases with relatively short follow-up duration. • Investigations of the relationship between caudal block and rates of post-repair complications have generated contradictory results that warrant further study.

4.1 Ongoing and Emerging Perioperative Dilemmas 4.1.1 What is the Ideal Age to Perform Hypospadias Repair? The optimal time for repairing hypospadias remains a subject of ongoing debate [1]. Most forms of hypospadias are discovered during a routine physical examination at birth, while subtypes with complete foreskin might only be diagnosed at the time of circumcision. Early family counseling is always recommended to fully explain the condition and address all queries in a timely fashion. According to recommendations of the American Academy of Pediatrics, the ideal age for hypospadias repair and genital surgery is between 6 and 12 months [2]. Most children tolerate anesthesia well by the age of 6 months, while by 18 months they start to exhibit more genital awareness and begin toilet training. Consequently, the majority of surgeons perform repair within this early window. Many types of hypospadias surgery have been implemented over the years and modifications have been added to suit a variety of hypospadias phenotypes. The choice of surgical technique depends on many factors including glans size, position of the meatus, length of penile shaft, and urethral plate quality [3–6]. Other associated anomalies and risks of anesthesia might contribute to selecting alternative timing and types of surgical repair. The majority of complications reported in the literature occur in older children and adults (Fig.  4.1) the most common of which are fistula, stricture, and total breakdown [7–9]. In a retrospective study of 307 patients by Yildiz et al. the authors noted a significant complication rate in children older than 10  years compared to those under 1.5 years (23% vs 3.3%, respectively), with the most prevalent complication being urethral fistula [10]. Another retrospective study by Huang et al. again showed that the urethrocutaneous fistula rate is increased in patients older than 2 years relative to those younger than 2 (38.9% vs 3.9%, respectively) [11]. Similarly, in a series of 693 patients Marrocco et al. observed that complication risk was more prominent in those older than 1 year compared with those younger than 1 (18.7% vs 3.4%) [12]. Contrasting with these data, other authors have reported that age does not influence hypospadias repair outcome. In a series of 669 patients, Bush et al. found no significant difference in complication rates between those older or younger than 1 year [13]. In an extensive series of 823 patients, Kim et al. were also unable to detect distinct outcomes in patients older or younger than 1.5  years [14]. More

56

T. Abbas et al.

Fig. 4.1  Summary of studies that investigated complication rates after hypospadias repair in different age groups

recently, Tack et al. analyzed the long-term need for redo operations which identified an increased rate of reintervention in children who underwent initial repair at younger than 12 months (reoperation rates 52.9% versus 39.2%) [15]. The key factors influencing complication rates after hypospadias repair in older children and adults are still unknown. Several theories suggest that wound healing is more efficient in younger children, while susceptibility to infection is increased in older children, and differences in vascular supply and erection frequency modify outcomes in the adult population [16, 17]. In summary, most prior studies recommend that surgeons perform hypospadias repair between 6 months and 2 years since higher complication rates are likely outside of this window. Children aged 6 months to 2 years are also easier to nurse and thus recovery is likely to be more straightforward.

4.1.2 What is the Current Status of Preoperative Hormonal Supplementation? The use of hormone therapy in hypospadias cases was first reported in 1971 in a failed attempt to increase the penile size and facilitate repair [18]. The rationale for androgen therapy is to induce growth of the shaft and glans to achieve tissue robustness, with the aim of easing subsequent technical steps of the repair procedure and thus improving the patient outcome [19, 20]. Preoperative androgen is also believed to increase vascularization and oxygenation of the penile skin and prepuce while reducing local inflammation and tissue fibrosis. As a result, this intervention is thought to promote healing and decrease rates of scarring and other postoperative complications [21]. While the literature agrees on the positive effect of androgens

4  General Perioperative Considerations

57

on penile growth (albeit via potentially short-lived effects), current data are inconsistent with respect to whether this promotes a favorable outcome, leading to disparity in adoption by hypospadias surgeons. Use of hormonal therapy is also not without side effects, the short-term profile of which is not concerning clinically, but the long-term consequences remain unknown. The use of hormone supplements remains at the surgeon’s discretion without solid evidence for optimal dosing (2 mg/kg), duration of treatment (once per month, up to 3 times pre-op), or mode of application (topical versus intramuscular). In a previous study, patients were selected for treatment based on an arbitrary glans width of 12–14 mm or less, whereas glans width of 15 mm or wider was used as an endpoint to conclude therapy [22]. The most common types of hormone used preoperatively are human chorionic gonadotropin (HCG), dihydrotestosterone (DHT), and testosterone (T) [23]. Different modes of application seem to be comparably effective for inducing growth [24]. Many researchers disagree with hormonal therapy because of the potential side effects including pubic hair growth, agitation, bone aging, and frequent erections [25]. Several pediatric urologists follow Koff’s regimen, which employs systemic testosterone of two injections per week for 5 weeks, while others prefer local 5% testosterone cream twice daily for 5 weeks. Asgari et al. and Babu et al. have also advocated a protocol of three doses of intramuscular testosterone enanthate 2 mg/kg every 3–4 weeks and showed a decrease in postoperative complication rates [26, 27]. While androgens positively influence penile growth in hypospadias, their impact on urethroplasty outcomes remains controversial. Most recently, a systematic review and meta-analysis of preoperative testosterone in hypospadias repair revealed a significant decrease in complications including glans dehiscence, but no difference in rates of meatal stenosis, urethral fistula, or need for reoperation [28]. Snodgrass and colleagues have also reported a significant rate of postoperative complications in patients who were pretreated with testosterone, especially with respect to urethrocutaneous fistula [29]. The short-term side effects of androgen supplementation appear minimal and self-limited. Some pubertal changes including pubic hair growth have been variably reported in different studies: 2/25 (8%) [24], 5/21 (19%) [30], and 0/25 (0%) [31]. Another report assessed bone age 1 year after testosterone injection and noted no detrimental effects [32]. However, to date, no study has investigated the long-term effects of androgen stimulation. Further studies and randomized control trials including large numbers of patients will therefore be required to resolve this issue. Finally, it has been hypothesized that using estrogen as an alternative hormone stimulant may offer the required growth with a safer outcome profile. A French study conducted a prospective, randomized study comparing topical estrogen to placebo in 244 boys with midshaft or milder hypospadias, but rates of healing complications were comparable between groups [33]. Preoperative estrogen topical application could be more common in the future and warrants further evaluation. Estrogen has the advantage of promoting cutaneous wound healing in contrast to androgens, [34– 36] and it has been demonstrated that estrogen receptors increase in the penile skin as the degree of hypospadias worsens, [37] which raises its efficacy potential.

58

T. Abbas et al.

4.1.3 What is the Effect of Transurethral Stenting on Postoperative Outcomes? While not universally adopted, transurethral stenting is becoming increasingly common in hypospadias repair. Practice patterns are often inherited through unquestioned dogma rather than being based on high-level evidence in the literature [38]. Stenting is used almost as routine in proximal urethroplasties, but stenting in distal repairs remains controversial. Supporters of stenting argue that this prevents immediate postoperative urinary retention thus allowing splinting for plate incision during TIP repairs and potentially improving outcome (as well as reducing the risk of fistula and neo-meatal stenosis). On the other hand, supporters of non-stented repairs do so to prevent bladder spasms and argue that outcomes are not inferior, with additional benefits of avoiding UTIs/need for additional medications, minimizing parent anxiety, and avoiding short-term follow-up to remove the stent [39–41]. There is no gold standard stent material or number of days these should remain in situ. However, most surgeons prefer a period of 5–7 days in distal hypospadias and longer duration for proximal cases [42]. In a retrospective multicenter study, Hakim et al. compared stented versus non-stented Mathieu’s repair and found no difference in rates of postoperative urethrocutaneous fistula [40]. In another retrospective analysis of infants undergoing distal hypospadias repair, Chalmers et  al. observed that complication rates were low and comparable between patients with or without a postoperative stent [43]. Waterman et al. also reported a modified TIP urethroplasty repair method without the use of stents [44]. Contrasting with these findings, Manzoni et al. identified a high fistula rate in their study of patients who did not have stents introduced [45]. El-Sherbiny also found that non-stented repair in toilet-trained boys may be associated with higher rates of complication [46]. According to Bhat placing a stent in the bladder can avoid the urine extravasation encountered with a transurethral stent [47]. In 2018, Chua et  al. conducted a systematic meta-analysis of 20 studies (14 cohort, 6 RCTs) including a total of 2466 hypospadias repairs (1290 nonstented, 1176 stented) [48]. Despite concerns about methodological quality of the studies included, the overall pooled effect showed no difference in rates of early or late complications when comparing non-stented versus stented repairs (RR 0.83, 95% CI 0.46–1.50; RR 0.96, 95% CI 0.92, 1.48; respectively). Many different types of stents (e.g., Koyle, Zaontz, Firlit) have been developed and these are typically used according to surgeon discretion, without exerting an obvious impact on complication rates [49]. Nonetheless, a recent study demonstrated variable stiffness, strength, and heat resistance profiles for different stent materials, and postulated that stiffer stents can lead to postoperative discomfort, whereas softer stents can have issues with dislodgement, breaking, or kinking [50]. In the majority of previous studies, fewer complications were reported for patients undergoing stent-less hypospadias repair. However, it is important to note that all prior investigations considered only distal hypospadias, compared unequal numbers of patients in stented and stent-less results, and were significantly limited by the lack of long-term follow-up.

4  General Perioperative Considerations

59

4.1.4 Are Antibiotics Needed for Hypospadias Repair? Use of perioperative antibiotics in patients undergoing hypospadias repair is still a common practice among pediatric urologists. Traditionally, prophylactic antibiotics were given in clean-contaminated procedures to decrease rates of Urinary Tract Infection (UTI) and Surgical Site Infection (SSI) [51, 52]. However, Kanaroglou suggested that antibiotic prophylaxis does not offer significant advantages in distal hypospadias repair, even in the presence of a urethral stent [53]. Recently, clinical guidelines from the Centers for Disease Control have advocated against the use of prophylactic antibiotics for the prevention of postoperative SSI, even in the presence of a surgical drain (including for both clean and clean-contaminated procedures) [54]. However, a survey of practice trends among pediatric urologists revealed that >91% of SPU members use pre- and/or postoperative antibiotic prophylaxis for hypospadias repair [55]. With the rapid emergence of multidrug-resistant organisms, it is now strongly recommended to avoid unnecessary antibiotic use in the clinic. Many hospitals and healthcare facilities implement protocols to reduce antibiotic use and thereby minimize drug resistance. The American Urological Association recommends prophylactic antibiotics for patients undergoing surgical procedures with stent placement, though there is no particular protocol for hypospadias surgery using a stent [56]. Until recently, there were no well-established clinical guidelines for use of antibiotic prophylaxis in children undergoing hypospadias surgery. Seeking high-level evidence to determine the clinical value of antibiotic use, in April 2022 the PROPHY study [57] published findings from a randomized double-blind, placebo-controlled trial of 93 patients with midshaft to distal hypospadias (45 on prophylaxis, 48 on placebo). The authors did not detect any difference in symptomatic UTIs, SSIs, or urethroplasty complications, but were reluctant to conclude no benefit of prophylaxis given the sample sizes/study power. Another randomized study by Roth et al. found that routine antibiotic prophylaxis significantly decreased the risk of bacteriuria and pyuria, but did not impact rates of symptomatic UTI or other postoperative complications [58]. In a retrospective study of parenteral antibiotic prophylaxis, Doersch et al. found no benefit in preventing infectious complications after stented repair of distal hypospadias [59]. A recent systematic review by Chua et al. also found that postoperative antibiotic prophylaxis did not influence complication rates after stented repair of distal cases. However, asymptomatic bacteriuria was more prevalent in patients who did not receive antibiotic treatment [60]. For proximal hypospadias, the majority of pediatric urologists routinely use prophylactic antibiotics both pre- and postoperatively. The underlying rationale includes the need for lengthy surgical procedures, extensive tissue dissection, and prolonged duration of the stent remaining in situ. In conclusion, for most studies including both randomized trials and a systematic review, there was no superiority to using antibiotics in hypospadias repair. However, several of these studies failed to reach the desired sample size, and most involved only distal and mid-penile hypospadias. While current evidence is limited, there

60

T. Abbas et al.

appears to be little value in using postoperative antibiotic prophylaxis due to minimal impact on clinically significant complications of hypospadias repair.

4.1.5 What is the Role of Hyperbaric Oxygen Therapy in Hypospadias Repair? Wound healing is a complex process of multiple stages including angiogenesis, collagen synthesis, remodeling of the extracellular matrix, and tissue re-­epithelialization [61]. The surgical outcome of hypospadias repair is highly dependent on wound healing processes [62–64] that are in turn impacted by several complex variables (including vascularity/tissue perfusion, resolution of inflammation, and extent of scarring from previous surgeries) [65]. The phases of urethral healing are similar to dermal healing, but the duration of each phase is longer than for skin [66]. Hyperbaric Oxygenation Therapy (HBOT) has been used in plastic and reconstructive procedures to encourage wound healing by inducing fibroblast proliferation, promoting endothelial regeneration, and stimulating angiogenesis [67]. While HBOT application has been focused on cases of repeat hypospadias surgery, several reports have now shown that beneficial effects also extend to primary repair cases [68]. A recent systematic review and meta-analysis revealed that HBOT regimens typically vary from 10 to 30 sessions of 90–143 min duration using 2–2.5-­atmospheres absolute pressure. Study outcomes assessed included graft take versus failure when using a two-stage tubularized autograft surgical (STAG) approach, or overall repair failure during follow-up periods ranging from 3 months to 3 years [68]. The overall number needed to treat HBOT intervention to prevent surgical failure was 8, which was considered clinically acceptable and was therefore recommended for the management of complex hypospadias repairs (especially for patients who had prior failed procedures, likely with compromised tissue perfusion and significant scarring). Collectively, current evidence suggests that HBOT has a satisfactory safety profile and can decrease hypospadias repair complications and graft take failure. Nonetheless, future high-quality studies will be needed to strengthen the evidence base and identify which subgroups of hypospadias patients can benefit most from HBOT intervention.

4.1.6 Does Caudal Block Influence Urethroplasty Outcome? In reconstructive penile surgery, regional analgesia is classically applied to a dorsal nerve or via caudal block (CB). CBs are commonly performed in pediatric pelvic genitourinary surgery and are often also employed for hypospadias surgery [69]. Although post-anesthesia complications have been thoroughly reported, the debate is ongoing regards the association between CB and hypospadias repair complications. In particular, there is contradictory evidence as to whether CBs result in increased rates of urethrocutaneous fistula (UCF) and glans dehiscence following hypospadias repair. Some studies have reported an association between block type

4  General Perioperative Considerations

61

and incidence of complications [70, 71], possibly due to CB-induced penile engorgement causing tissue edema and impaired wound healing. However, the definitive mechanism underpinning CB influence on repair outcomes has never been established. Indeed, other investigators have been unable to observe any impact of CB on post-hypospadias complications [72]. A previous meta-analysis by Tanesco et  al. [73] identified an increase in postsurgical complications for patients who received CB, while in the largest series to date Adler et al. [74] did not detect any association between block type and incidence of post-repair complications (n  =  983 total patients). Similarly, a review by Zhu et al. [75] found that in non-randomized observational studies with a large number of patients, CBs were not associated with postoperative complications involving glans dehiscence or UCF (although the authors acknowledged the potential for selection bias and residual confounders with this approach). Furthermore, a recent meta-analysis of 3201 patients from 10 studies that met inclusion criteria confirmed that type of analgesic block is not associated with the risk of developing complications following primary hypospadias correction in children [76]. Based on the contradictory findings of previous studies, it is clear that the relationship between CB and hypospadias repair complications requires further evaluation to determine both association and underlying biological mechanism. It has been suggested that CB could cause systemic vasodilation and impact penile blood flow throughout surgical repair, and Kundra et al. have reported increased penile engorgement 10 min after CB administration [77]. Nonetheless, it is still speculative to conclude that blood engorgement would necessarily impair wound healing or increase rates of complication. Indeed, a recent study of children undergoing circumcision found no influence of CB on penile arterial or venous blood flow [78]. Previous studies proposing that CBs increase the risk of developing complications following hypospadias repair have a number of limitations, including small sample size and publication bias. Furthermore, the heterogeneity of surgical techniques employed for hypospadias repair is a major variable that can impact complication rates. Large, prospective, and multicenter randomized control trials could clarify the role of regional anesthesia in hypospadias surgical outcomes and overcome the limitations of earlier studies.

4.2 Conclusion Considerable efforts have recently been made to resolve various controversies in hypospadias management. While some useful conclusions and clinical implications can be drawn, previous studies feature numerous limitations that will need to be addressed in robust future investigations.

References 1. Abbas T, McCarthy L.  Foreskin and penile problems in childhood. Surgery (Oxford). 2016;34:221–5. https://doi.org/10.1016/j.mpsur.2016.03.007.

62

T. Abbas et al.

2. Kass EJ, Bolong D.  Single stage hypospadias reconstruction without fistula. J Urol. 1990;144:520–2. https://doi.org/10.1016/s0022-­5347(17)39510-­1. 3. Abbas TO, Vallasciani S, Elawad A, Elifranji M, Leslie B, Elkadhi A, et al. Plate Objective Scoring Tool (POST); an objective methodology for the assessment of urethral plate in distal hypospadias. J Pediatr Urol. 2020;16:675–82. https://doi.org/10.1016/j.jpurol.2020.07.043. 4. Abbas TO. The Plate Objective Scoring Tool (POST): further reflections and extended applications. Res Rep Urol. 2021;13:783–91. https://doi.org/10.2147/RRU.S321188. 5. Abbas TO, Braga LH, Spinoit AF, Salle JP. Urethral plate quality assessment and its impact on hypospadias repair outcomes: a systematic review and quality assessment. J Pediatr Urol. 2021;17(3):316–25. https://doi.org/10.1016/j.jpurol.2021.02.017. 6. Abbas TO. An objective hypospadias classification system. J Pediatr Urol. 2022;18(4):481-e1. https://doi.org/10.1016/J.JPUROL.2022.05.001. 7. Minevich E, Pecha BR, Wacksman J, Sheldon CA. Mathieu hypospadias repair: experience in 202 patients. J Urol. 1999;162:2141–2. 8. Snodgrass WT, Lorenzo A. Tubularized incised-plate urethroplasty for proximal hypospadias. BJU Int. 2002;89:90–3. 9. Uygur MC, Unal D, Tan MO, Germiyanoğlu C, Erol D. Factors affecting outcome of one-stage anterior hypospadias repair: analysis of 422 cases. Pediatr Surg Int. 2002;18:142–6. https:// doi.org/10.1007/s003830100657. 10. Yildiz T, Tahtali IN, Ates DC, Keles I, Ilce Z. Age of patient is a risk factor for urethrocutaneous fistula in hypospadias surgery. J Pediatr Urol. 2013;9:900–3. https://doi.org/10.1016/j. jpurol.2012.12.007. 11. Huang L-Q, Ge Z, Tian J, Ma G, Lu R-G, Deng Y-J, et al. Retrospective analysis of individual risk factors for urethrocutaneous fistula after onlay hypospadias repair in pediatric patients. Ital J Pediatr. 2015;41:35. https://doi.org/10.1186/s13052-­015-­0140-­8. 12. Marrocco G, Vallasciani S, Fiocca G, Calisti A. Hypospadias surgery: a 10-year review. Pediatr Surg Int. 2004;20:200–3. https://doi.org/10.1007/s00383-­004-­1147-­1. 13. Bush NC, Holzer M, Zhang S, Snodgrass W. Age does not impact risk for urethroplasty complications after tubularized incised plate repair of hypospadias in prepubertal boys. J Pediatr Urol. 2013;9:252–6. https://doi.org/10.1016/j.jpurol.2012.03.014. 14. Kim JK, Shiff M, Chua ME, Zu’bi F, Ming JM, Pokarowski M, et al. Time to event analysis for post-hypospadias repair complications: a single-surgeon experience. World J Urol. 2021;39:3913–9. https://doi.org/10.1007/s00345-­021-­03689-­3. 15. Tack LJW, Springer A, Riedl S, Tonnhofer U, Hiess M, Weninger J, et  al. Adolescent and young adult urogenital outcome following childhood hypospadias repair: perfection revisited. J Urol. 2021;206:734–44. https://doi.org/10.1097/JU.0000000000001869. 16. Dodson JL, Baird AD, Baker LA, Docimo SG, Mathews RI. Outcomes of delayed hypospadias repair: implications for decision making. J Urol. 2007;178:278–81. https://doi.org/10.1016/j. juro.2007.03.055. 17. Senkul T, Karademir K, neyt Işeri C, oan Erden D, Baykal K, neyt Adayener C. Hypospadias in adults. Urology. 2002;60:1059–62. https://doi.org/10.1016/s0090-­4295(02)02239-­2. 18. Immergut M, Boldus R, Yannone E, Bunge R, Flocks R.  The local application of testosterone cream to the prepubertal phallus. J Urol. 1971;105:905–6. https://doi.org/10.1016/ s0022-­5347(17)61657-­4. 19. Göllü Bahadır G, Ergün E, Telli O, Khanmammadov F, Çakmak AM. Hormone therapy in hypospadias surgery: a survey on the current practice in Turkey. Turk J Med Sci. 2016;46:1624–8. https://doi.org/10.3906/sag-­1507-­72. 20. Bastos AN, Oliveira LRS, Ferrarez CEPF, de Figueiredo AA, Favorito LA, Bastos Netto JM.  Structural study of prepuce in hypospadias—does topical treatment with t­estosterone produce alterations in prepuce vascularization? J Urol. 2011;185:2474–8. https://doi. org/10.1016/j.juro.2011.01.035. 21. Chao M, Zhang Y, Liang C.  Impact of preoperative hormonal stimulation on postoperative complication rates after hypospadias repair: a meta-analysis. Minerva Urol Nefrol. 2017;69:253–61. https://doi.org/10.23736/S0393-­2249.16.02634-­5.

4  General Perioperative Considerations

63

22. Mittal S, Eftekharzadeh S, Christianson SS, Hyacinthe N, Tan C, Weiss DA, et  al. Quantifying glans width changes in response to preoperative androgen stimulation in patients undergoing hypospadias repair. J Urol. 2022;207:1314–21. https://doi.org/10.1097/ JU.0000000000002481. 23. Wong NC, Braga LH. The influence of pre-operative hormonal stimulation on hypospadias repair. Front Pediatr. 2015;3:31. https://doi.org/10.3389/fped.2015.00031. 24. Chalapathi G, Rao KLN, Chowdhary SK, Narasimhan KL, Samujh R, Mahajan JK. Testosterone therapy in microphallic hypospadias: topical or parenteral? J Pediatr Surg. 2003;38:221–3. https://doi.org/10.1053/jpsu.2003.50047. 25. Netto JMB, Ferrarez CEPF, Schindler Leal AA, Tucci S, Gomes CA, Barroso U. Hormone therapy in hypospadias surgery: a systematic review. J Pediatr Urol. 2013;9:971–9. https://doi. org/10.1016/j.jpurol.2013.03.009. 26. Asgari SA, Safarinejad MR, Poorreza F, Asl AS, Ghanaie MM, Shahab E.  The effect of parenteral testosterone administration prior to hypospadias surgery: a prospective, randomized and controlled study. J Pediatr Urol. 2015;11(143):e1–6. https://doi.org/10.1016/j. jpurol.2014.12.014. 27. Babu R, Chakravarthi S.  The role of preoperative intra muscular testosterone in improving functional and cosmetic outcomes following hypospadias repair: a prospective randomized study. J Pediatr Urol. 2018;14:29.e1–6. https://doi.org/10.1016/j.jpurol.2017.07.009. 28. Sembiring G, Sigumonrong Y. Efficacy of preoperative testosterone therapy in hypospadias: a systematic review and meta-analysis. Ann Pediatr Surg. 2021;17:56. https://doi.org/10.1186/ s43159-­021-­00117-­4. 29. Snodgrass W, Bush N.  Recent advances in understanding/management of hypospadias. F1000Prime Rep. 2014;6:101. https://doi.org/10.12703/P6-­101. 30. Nerli RB, Koura A, Prabha V, Reddy M. Comparison of topical versus parenteral testosterone in children with microphallic hypospadias. Pediatr Surg Int. 2009;25:57–9. https://doi. org/10.1007/s00383-­008-­2278-­6. 31. Luo CC, Lin JN, Chiu CH, Lo FS. Use of parenteral testosterone prior to hypospadias surgery. Pediatr Surg Int. 2003;19:82–4. https://doi.org/10.1007/s00383-­002-­0717-­3. 32. Gearhart JP, Jeffs RD. The use of parenteral testosterone therapy in genital reconstructive surgery. J Urol. 1987;138:1077–8. https://doi.org/10.1016/s0022-­5347(17)43507-­5. 33. Gorduza D, Plotton I, Remontet L, Gay C-L, El Jani M, Cheikhelard A, et al. Preoperative topical estrogen treatment vs placebo in 244 children with midshaft and posterior hypospadias. J Clin Endocrinol Metab. 2020;105(7):2422–9. https://doi.org/10.1210/clinem/dgaa231. 34. Gilliver SC, Ashcroft GS.  Sex steroids and cutaneous wound healing: the contrasting influences of estrogens and androgens. Climacteric. 2007;10:276–88. https://doi. org/10.1080/13697130701456630. 35. Ashcroft GS, Mills SJ. Androgen receptor-mediated inhibition of cutaneous wound healing. J Clin Invest. 2002;110:615–24. https://doi.org/10.1172/JCI15704. 36. Calvin M.  Oestrogens and wound healing. Maturitas. 2000;34:195–210. https://doi. org/10.1016/s0378-­5122(99)00079-­1. 37. Khanna S, Shankar Raman V, Badwal S, Vinu Balraam KV. Quantification of the androgen and estrogen receptors in the penile tissues of hypospadias in comparison with normal children. Fetal Pediatr Pathol. 2022;42(2):175–86. https://doi.org/10.1080/15513815.2022.2104496. 38. Abbas T, Ibrahim T, AbdelKareem M, Ali M.  Pediatric ureteral stents. Cham: Springer International Publishing; 2022. p. 139–48. https://doi.org/10.1007/978-­3-­031-­04484-­7_12. 39. Steckler RE, Zaontz MR. Stent-free Thiersch–Duplay hypospadias repair with the Snodgrass modification. J Urol. 1997;158:1178–80. https://doi.org/10.1097/00005392-­199709000-­00125. 40. Hakim S, Merguerian PA, Rabinowitz R, Shortliffe LD, McKenna PH. Outcome analysis of the modified Mathieu hypospadias repair: comparison of stented and unstented repairs. J Urol. 1996;156:836–8. 41. Turial S, Enders J, Engel V, Schier F. Stent-free tubularized incised plate (TIP) repair of distal and mid-shaft hypospadias irrespective of age. Eur J Pediatr Surg. 2011;21:168–70. https://doi. org/10.1055/s-­0030-­1270457.

64

T. Abbas et al.

42. Keays MA, Dave S.  Current hypospadias management: diagnosis, surgical management, and long-term patient-centred outcomes. Can Urol Assoc J. 2017;11:S48–53. https://doi. org/10.5489/cuaj.4386. 43. Chalmers DJ, Siparsky GL, Wiedel CA, Wilcox DT.  Distal hypospadias repair in infants without a postoperative stent. Pediatr Surg Int. 2015;31:287–90. https://doi.org/10.1007/ s00383-­014-­3647-­y. 44. Waterman BJ, Renschler T, Cartwright PC, Snow BW, de Vries CR. Variables in successful repair of urethrocutaneous fistula after hypospadias surgery. J Urol. 2002;168:726–30. https:// doi.org/10.1016/S0022-­5347(05)64734-­9. 45. Manzoni G, Bracka A, Palminteri E, Marrocco G. Hypospadias surgery: when, what and by whom? BJU Int. 2004;94:1188–95. https://doi.org/10.1046/j.1464-­410x.2004.05128.x. 46. El-Sherbiny MT. Tubularized incised plate repair of distal hypospadias in toilet-­trained children: should a stent be left? BJU Int. 2003;92:1003–5. https://doi.org/10.1111/j.1464-­410x .2003.04513.x. 47. Bhat A. General considerations in hypospadias surgery. Indian J Urol. 2008;24:188–94. 48. Chua M, Welsh C, Amir B, Silangcruz JM, Ming J, Gnech M, et al. Non-stented versus stented urethroplasty for distal hypospadias repair: a systematic review and meta-analysis. J Pediatr Urol. 2018;14:212–9. https://doi.org/10.1016/j.jpurol.2017.11.023. 49. Lee LC, Schröder A, Bägli DJ, Lorenzo AJ, Farhat WA, Koyle MA.  Stent-related complications after hypospadias repair: a prospective trial comparing Silastic tubing and Koyle urethral stents. J Pediatr Urol. 2018;14:423.e1–5. https://doi.org/10.1016/j. jpurol.2018.08.002. 50. Rowe CK, Jamdee T, Foster C, Burke KA. Do the materials matter? A review of the literature and analysis of the materials properties of urethral stents for hypospadias repair. J Pediatr Urol. 2022;18(2):160–7. https://doi.org/10.1016/J.JPUROL.2022.01.003. 51. Kim JK, Chua ME, Ming JM, Braga LH, Smith GHH, Driver C, et  al. Practice variation on use of antibiotics: an international survey among pediatric urologists. J Pediatr Urol. 2018;14:520–4. https://doi.org/10.1016/j.jpurol.2018.04.018. 52. Glaser AP, Rosoklija I, Johnson EK, Yerkes EB. Prophylactic antibiotic use in pediatric patients undergoing urinary tract catheterization: a survey of members of the Society for Pediatric Urology. BMC Urol. 2017;17:76. https://doi.org/10.1186/s12894-­017-­0268-­5. 53. Kanaroglou N. Antibiotic prophylaxis in hypospadias repair: it’s time to re-evaluate. Can Urol Assoc J. 2014;8:241. https://doi.org/10.5489/cuaj.2352. 54. Michaelidis CI, Fine MJ, Lin CJ, Linder JA, Nowalk MP, Shields RK, et al. The hidden societal cost of antibiotic resistance per antibiotic prescribed in the United States: an exploratory analysis. BMC Infect Dis. 2016;16:655. https://doi.org/10.1186/s12879-­016-­1990-­4. 55. Hsieh MH, Wildenfels P, Gonzales ET. Surgical antibiotic practices among pediatric urologists in the United States. J Pediatr Urol. 2011;7:192–7. https://doi.org/10.1016/j.jpurol.2010.05.001. 56. Wolf JS, Bennett CJ, Dmochowski RR, Hollenbeck BK, Pearle MS, Schaeffer AJ, et  al. Best practice policy statement on urologic surgery antimicrobial prophylaxis. J Urol. 2008;179:1379–90. https://doi.org/10.1016/j.juro.2008.01.068. 57. Faasse MA, Farhat WA, Rosoklija I, Shannon R, Odeh RI, Yoshiba GM, et al. Randomized trial of prophylactic antibiotics vs. placebo after midshaft-to-distal hypospadias repair: the PROPHY study. J Pediatr Urol. 2022;18:171–7. https://doi.org/10.1016/j.jpurol.2022.01.008. 58. Roth EB, Kryger JV, Durkee CT, Lingongo MA, Swedler RM, Groth TW.  Antibiotic prophylaxis with trimethoprim-sulfamethoxazole versus no treatment after mid-to-distal hypospadias repair: a prospective, randomized study. Adv Urol. 2018;2018:7031906. https://doi. org/10.1155/2018/7031906. 59. Doersch KM, Logvinenko T, Nelson CP, Yetistirici O, Venna AM, Masoom SN, et  al. Is parenteral antibiotic prophylaxis associated with fewer infectious complications in stented, distal hypospadias repair? J Pediatr Urol. 2022;18(6):759–63. https://doi.org/10.1016/J. JPUROL.2022.05.003. 60. Chua ME, Kim JK, Rivera KC, Ming JM, Flores F, Farhat WA.  The use of postoperative prophylactic antibiotics in stented distal hypospadias repair: a systematic review and meta-­ analysis. J Pediatr Urol. 2019;15:138–48. https://doi.org/10.1016/j.jpurol.2018.10.012.

4  General Perioperative Considerations

65

61. de Gonzalez AC, Costa TF, de Andrade ZD, Medrado ARAP.  Wound healing—a literature review. An Bras Dermatol. 2016;91:614–20. https://doi.org/10.1590/abd1806-­4841.20164741. 62. Springer A. Assessment of outcome in hypospadias surgery—a review. Front Pediatr. 2014;2:2. https://doi.org/10.3389/fped.2014.00002. 63. Abbas TO, Ali TA, Uddin S.  Urine as a main effector in urological tissue engineering—a double-edged sword. Cell. 2020;9:538. https://doi.org/10.3390/cells9030538. 64. Abbas TO, Charles A, Ali M, Salle JLP.  Long-term fate of the incised urethral plate in Snodgrass procedure; a real concern does exist. Urol Case Rep. 2020;32:101216. https://doi. org/10.1016/J.EUCR.2020.101216. 65. Craig JR, Wallis C, Brant WO, Hotaling JM, Myers JB.  Management of adults with prior failed hypospadias surgery. Transl Androl Urol. 2014;3:196–204. https://doi.org/10.3978/j. issn.2223-­4683.2014.04.03. 66. Hofer MD, Cheng EY, Bury MI, Park E, Xu W, Hong SJ, et  al. Analysis of primary urethral wound healing in the rat. Urology. 2014;84:246.e1–7. https://doi.org/10.1016/j. urology.2014.04.012. 67. Oley MH, Oley MC, Iskandar AAA, Toreh C, Tulong MT, Faruk M. Hyperbaric oxygen therapy for reconstructive urology wounds: a case series. Res Rep Urol. 2021;13:841–52. https:// doi.org/10.2147/RRU.S331161. 68. Chua ME, Kim JJK, Ming JM, De Jesus MJ, See MC, Bagli DJ, et al. The utilization of hyperbaric oxygenation therapy in hypospadias repair: a systematic review and meta-analysis. Int Urol Nephrol. 2022;54:273–85. https://doi.org/10.1007/s11255-­021-­03096-­y. 69. Wiegele M, Marhofer P, Lönnqvist P-A.  Caudal epidural blocks in paediatric patients: a review and practical considerations. Br J Anaesth. 2019;122:509–17. https://doi.org/10.1016/j. bja.2018.11.030. 70. Kim MH, Im YJ, Kil HK, Han SW, Joe YE, Lee JH. Impact of caudal block on postoperative complications in children undergoing tubularised incised plate urethroplasty for hypospadias repair: a retrospective cohort study. Anaesthesia. 2016;71:773–8. https://doi.org/10.1111/ anae.13463. 71. Taicher BM, Routh JC, Eck JB, Ross SS, Wiener JS, Ross AK. The association between caudal anesthesia and increased risk of postoperative surgical complications in boys undergoing hypospadias repair. Paediatr Anaesth. 2017;27:688–94. https://doi.org/10.1111/pan.13119. 72. Alizadeh F, Amraei M, Haghdani S, Honarmand A.  The effect of caudal epidural block on the surgical complications of hypospadias repair in children aged 6–35 months: a randomized controlled trial. J Pediatr Urol. 2022;18:59.e1–6. https://doi.org/10.1016/j.jpurol.2021.11.009. 73. Tanseco PP, Randhawa H, Chua ME, Blankstein U, Kim JK, McGrath M, et al. Postoperative complications of hypospadias repair in patients receiving caudal block vs. non-caudal anesthesia: a meta-analysis. Can Urol Assoc J. 2018;13:E249–57. https://doi.org/10.5489/cuaj.5688. 74. Adler AC, Chandrakantan A, Lee AD, Koh CJ, Janzen NK, Austin PF. Effect of caudal vs. penile block on the incidence of hypospadias complications following primary repairs: a retrospective cohort study. J Urol. 2021;205:1454–9. https://doi.org/10.1097/JU.0000000000001448. 75. Zhu C, Wei R, Tong Y, Liu J, Song Z, Zhang S. Analgesic efficacy and impact of caudal block on surgical complications of hypospadias repair: a systematic review and meta-analysis. Reg Anesth Pain Med. 2019;44:259–67. https://doi.org/10.1136/rapm-­2018-­000022. 76. Adler AC, Bhatia VP, Chandrakantan A, Nathanson BH, Ouellette L, Austin PF. Association of analgesic block with the incidence of complications following hypospadias surgery; a meta-­ analysis. Urology. 2022;166:11–7. https://doi.org/10.1016/J. UROLOGY.2022.03.002. 77. Kundra P, Yuvaraj K, Agrawal K, Krishnappa S, Kumar LT.  Surgical outcome in children undergoing hypospadias repair under caudal epidural vs penile block. Paediatr Anaesth. 2012;22:707–12. https://doi.org/10.1111/j.1460-­9592.2011.03702.x. 78. Adler AC, Bhatia VP, Chandrakantan A, Austin PF.  Ultrasound assessment of penile blood flow following caudal block in children; a pilot study. Urology. 2022;167:207–10. https://doi. org/10.1016/j.urology.2022.03.029.

5

Management of Distal Hypospadias: New Insights and Stepwise Management Algorithm Tariq Abbas

Abstract

Several hundred different surgical procedures have been developed to enable hypospadias repair, but most have quickly fallen out of favor due to poor patient outcomes. Current limitations in the ability to optimize case selection and appraise surgical techniques may explain the persistence of relatively high complication rates. Several predictors are now considered key to decision-making and selecting the most appropriate surgical procedure, as well as predicting the risk of post-repair complications. Assessment of penile curvature is considered a cornerstone in the evaluation of patients with hypospadias since even minor variation in degree can alter the surgical approach employed. Several different classification systems have now been used to stratify hypospadias severity, all of which rely primarily on meatal position without precisely defining the true site of spongiosal bifurcation. Evidence-based protocols built on robust scientific data should therefore provide a strong platform for future improvements in hypospadias management. (See Video 5.1).

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/978-­981-­19-­7666-­7_5. T. Abbas (*) Pediatric Urology Section, Sidra Medicine, Doha, Qatar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 T. Abbas (ed.), Hypospadiology, https://doi.org/10.1007/978-981-19-7666-7_5

67

68

T. Abbas

Key Messages • Multiple perioperative factors and anatomical components of the hypospadiac penis are known to influence postoperative outcome. These features represent important predictive variables that may aid risk stratification and clinical decision-making. • Penile curvature assessment is a key step in evaluation and management of hypospadias that can significantly impact the choice of surgical approach as well as long-term results. • Previous hypospadias classification systems rely primarily on meatal position and do not precisely define the true site of spongiosal bifurcation. • Future improvements in hypospadias care will require standardized, evidence-­ based, and objective management algorithms.

5.1 Background and Challenges 5.1.1 How to Proceed with Distal Hypospadias? Urethral reconstruction can be approached in multiple different ways and is often guided by prior experience and skill level of the attending surgeons: these methods include urethral plate (UP) tubularization, UP augmentation, and UP replacement. In UP tubularization, the patient may undergo Thiersch–Duplay technique (simple tubularization), tubularized incised plate repair (TIP) (with hinging of the UP), or DTIP (with grafting of the incised area) [1]. Duplay is regarded as the most “natural” urethroplasty technique since this preserves very favorable urethral plates [2– 4]. The tubularized incised urethroplasty (TIP) method was popularized by Snodgrass in 1994 and quickly adopted by many surgeons worldwide for the repair of distal and even some proximal cases of hypospadias [5, 6]. Snodgrass and others hypothesize that the incised urethral plate undergoes complete epithelialization of

5  Management of Distal Hypospadias: New Insights and Stepwise Management…

69

the raw dorsal area [7, 8], while others suggest that at least part of the healing process involves fibrosis and retraction of the incised area (leading to progressive tension and fistula development) [9, 10]. Placement of inner preputial or oral mucosa graft (dorsal inlay graft; DTIP) over the incised area has been proposed to avoid this physiological mechanism [11–14]. Several key predictors are now considered essential to guide decision-making and selection of the most appropriate surgical repair technique, as well as predicting the risk of post-repair complications (see Chap. 1). These include severity of hypospadias, [15] urethral plate (UP) quality [16], and degree of penile curvature [17]. The following sections are planned to focus further details on these variables.

5.1.2 How to Classify Hypospadias? A critical first step in hypospadias management is to classify severity so that the most appropriate surgical repair method can be selected for each patient. While classification as mild or severe hypospadias may direct the surgeon to select a particular surgical approach [18], the precise anatomical phenotype can also shed light on the mechanisms underlying impaired embryonic/fetal development of the urethra. Smith [19] was the first to classify hypospadias according to the location of the urethral meatus, whereas Sheldon and Duckett [20] consider meatal position after chordee has been released. Several classification systems have since been used to stratify hypospadias severity, all of which rely similarly on meatal position. These classification systems lack precise definition of hypospadias severity and do not consider the true site of spongiosal bifurcation. This is problematic since some variants of distal hypospadias are associated with proximal spongiosal hypoplasia and penile curvature (which may necessitate a more complex surgical repair), whereas some apparently severe cases of proximal hypospadias are in fact less challenging for surgeons (when favorable anatomy is existing) [18, 21, 22]. It has long been appreciated by hypospadias surgeons that the external meatal position does not consistently reflect severity of the hypospadias defect or complexity of the surgical repair required [23, 24]. Additionally, Arlen et al. performed a multivariate analysis that confirmed meatal location alone does not impact rates of postoperative complication, highlighting the need to consider the entire hypospadias complex when determining severity [25]. Consequently, there is an unmet clinical need to develop more objective methods of assessing hypospadias severity [26].

5.1.3 How to Quantify the Urethral Plate Characteristics? UP quality appears to act as an independent factor influencing hypospadias repair outcomes [16], but current evaluation methods remain highly subjective. UP “quality” is often a subjective assessment which varies significantly between surgeons thus hampering precise scientific communication [2]. Several definitions have been used to define UP “width,” adding further complexity to the assessment process and

70

T. Abbas

emphasizing the lack of agreement between approaches. Arbitrary cut-off values for UP measurement would also be poorly suited to deal with variation in patient size, race, age, etc. Development of a more objective model that can standardize these assessments should lead to more accurate evaluation of hypospadias outcomes in future studies.

5.1.4 How to Quantify the Degree of Penile Curvature? Assessment of penile curvature (PC) is considered a cornerstone in the evaluation of patients with hypospadias. Accurate assessment of penile curvature is of paramount importance for surgical decision-making since even a minor variation in degree can alter repair method [27, 28]. Penile curvature is a key phenotypic feature of hypospadias, with men having probable ventral curvature expressing greater dissatisfaction with penis shape, increased difficulty with intercourse, and worse psychological impact [29]. Persistent or recurrent ventral penile curvature is also strongly associated with post-urethroplasty complications [30]. Traditionally, surgeons have used unaided visual inspection to estimate penile curvature, only recently progressing to the use of goniometry as promoted by Snodgrass and Bush. Reasons for PC in hypospadias include ventral penile axis embryological arrest, penile skin shortage, abnormally short urethral plate, and ventro-dorsal corporeal disproportion [3]. Despite clear clinical implications and prognostic value, [31, 32] assessment of PC is poorly consistent between surgeons, and no established methods exist for the rapid standardized evaluation of PC [33]. While there have been a limited number of publications that employed computer software or apps to assess the degree of curvature, each of these has applied different algorithms for quantification.

5.2

A Future Perspective Model

Aiming to overcome the significant challenges outlined above and also standardize our approach to hypospadias management, we built an algorithm based on the growing body of relevant literature as well as our own active research program (Fig. 5.1). Step 1: Protocol for Management of Primary Hypospadias Algorithm depicts our approach to managing patients with primary hypospadias. Redo cases have been excluded since they follow a different tailored protocol (see Sect. 5.3 below). Step 2: Classification of Anomaly Severity A critical early step is to classify hypospadias severity. While several different classification systems have previously been introduced, all of these rely heavily on meatal position which often does not reflect the exact location of the spongiosal defect. We therefore developed a new hypospadias classification system based on the level of

5  Management of Distal Hypospadias: New Insights and Stepwise Management…

71

Fig. 5.1  Suggested protocol for hypospadias management and clinical decision-making

bifurcation in the corpus spongiosum (BCS). To assess this, patients are evaluated in the supine position after full degloving of the penile skin thus allowing accurate determination of urethral hypoplasia. Indeed, Wong et al. [34] also defined hypospadias repair as distal, mid-shaft, or proximal based on meatal location at the time of urethroplasty (after cutback of the hypoplastic urethra). However, we opt to appraise severity prior to any intervention over the urethra thus allowing decision-­making to take place prior to any manipulation. The distance between the glandular knobs (B-B imaginary line) is considered a practical and relevant location to begin measuring the extent of the urethral defect (UD) while the urethral defect ratio (UDR) is calculated as UD divided by stretched penile length (SPL) (Fig. 5.2) (Eq. 5.1): Urethral Defect Ration ( UDR ) =

Urethral Defect ( UD ) . Stretched Penile Length ( SPL )

(5.1)

Once scoring is complete, hypospadias severity can be categorized into three grades: UDR 0–30°, whereas Pippi Salle et al. [7] performed two-stage autograft for curvatures greater than 50°. In a research setting, the IMPRESS study assessed the efficacy of collagenase injection for reducing curvature in patients with Peyronie’s disease [8], which relied on standardized goniometry measurements of PC to obtain FDA approval (the treatment arm achieved a mean curvature reduction of 17°, whereas the placebo arm

7  Penile Curvature Assessment in Hypospadias

95

achieved only 9.3° reduction). Accurate assessment of PC is therefore crucial to the development of more effective interventions for patients with hypospadias, but at present there are few reliable methods for precision quantification of PC hence communication of surgical outcomes in both the clinic and associated research literature remains poor.

7.1.2 How is Curvature Measured? Preoperative appraisal of PC relies on patient cooperation during outpatient clinic visits as well as the extent of the associated anatomical variables [9, 10]. PC can be estimated by unaided visual inspection (UVI), use of a goniometer, or with a computer program or app designed to quantify either captured or live images [11]. UVI and app-based measurements can also be aided by comparison with sample curvature models—like orchidometer use—to further improve the estimation of PC. When the penis consists of two straight segments joined by a hinge, with curvature due to flexion at the hinge, then PC can be accurately estimated using either a goniometer or protractor. While hingetype curvatures can be reliably and reproducibly assessed in this way, goniometerbased measurements are unsuitable for quantifying arc-type curvatures. In a previous study where pediatric urologists assessed PC in 3D plastic models using both a goniometer and visual inspection, mean errors were not significantly different between the two methods [12]. For example, mean error when assessing the 30° PC model was 10.8° for unaided visual inspection and 13.6° for the goniometer (individual measurements ranged from 20° to 70° for visual inspection, and from 25° to 85° for goniometer). This wide range in estimations, up to 55° out from the true extend of PC, highlights the challenges of obtaining accurate and reproducible measurements in congenital cases. These data further indicate that the use of a goniometer is no guarantee of precision. Using the same penile models as described above, researchers in Seattle [13] assessed a semiautomated algorithm for measuring PC in digital images. Pictures of each model were taken perpendicular to the direction of PC (i.e., at sunrise) and then at different angles relative to this location. While the study did not report results for specific degrees of curvature, the authors observed a mean error of 7.8° when the camera was directly perpendicular (at 0°) for all measurements with all models. A follow-on study used an app to construct 3D models from the 2D images described above prior to assessing PC extent [14]. Non-urologists were recruited to perform the measurements using an app in an iterative manner, similar to that used to measure testicular volume by orchidometer. This method resulted in a mean error of just 4.5°, almost half that of UVI/goniometry, and even slightly better than the semiautomated method described above. This type of measurement may therefore provide a more accurate and practical way of estimating PC. In summary, visual estimations, goniometers, and apps provide measurements with expected errors of ~7°–10° on average, i.e., 30° curvature could be estimated in the range of 20°–40° (although some estimations will still fall outside of this bracket).

96

C. Villanueva and T. Abbas

7.2 Current Challenges 7.2.1 Challenges of PC Measurement Almost all prior studies of PC have been conducted using HT curvatures. In the studies described above, penile models were constructed using computer software to segment a straight shaft into two equal portions, rotating each segment to the desired angle, and then smoothing the edges. While HT curvature provides the best chance of obtaining accurate measurements, these studies still achieved low levels of precision and reproducibility. It is therefore likely that AT curvatures will be subject to even greater degrees of measurement error. To our knowledge, no previous study has evaluated the accuracy/precision of measurements in either real penises or AT models. Below are 2D images (Fig. 7.1) of the HT models used in the studies referenced above and Fig. 7.2 shows 2D pictures of AT curvatures.

10

40

70

20

30

50

80

60

90

Fig. 7.1  2D images of the hinge-type PC models used in several published studies. For comparison

7  Penile Curvature Assessment in Hypospadias

10

20

40

70

97

30

50

60

80

90

Fig. 7.2  2D images of arc-type PC models corresponding to the models shown in Fig. 7.2

7.2.2 The Challenge of Arc-Type Curvatures Estimating curvature of an arc using a goniometer is extremely challenging, as illustrated in the example image (Fig. 7.3) from which a wide range of different measurements could be obtained. This is in stark contrast to HT curvature (Fig.  7.4) where accurate measurement is much easier to determine; A recent study using pictures of AT models showed that urologists tend to underestimate the degree of curvature in these cases (unpublished observations): the 30° picture was estimated at a mean of 23°, the 50° picture at a mean of 38°, and the 70° picture at a mean of 55°. These data demonstrate the difficulty of appraising AT curvature and the need for improved methods of PC assessment.

98

C. Villanueva and T. Abbas

Fig. 7.3  Arc-type 40° curvature. Difficult to ascertain which of the three measurements is the correct one

Fig. 7.4  Hinge-type 30° curvature easily measured with protractor, placed either ventrally, in the middle, or dorsally

7  Penile Curvature Assessment in Hypospadias

99

7.3 Future Perspectives There is a clear unmet need to develop a reliable, reproducible, objective, and standardized tool to measure PC in hypospadiology-related clinical practice and associated research. There are several significant limitations in the current most commonly used approaches for PC assessment (Fig. 7.5). It is critically important to standardize the variables affecting anthropometric measurements of the penis in order to achieve higher consistency in both the clinic and research settings [15–19]. Although 3D-printed penile models can help evaluate newly introduced tools for quantifying PC, a more promising approach could be computational validation, which allows appraisal of an essentially unlimited number of different anatomic configurations. Indeed, in most of the prior studies discussed here, high production costs restricted the use of 3D-printed penile models to multiples of ten with a limited number of degree variations. Advanced 3D mapping is another option for the creation of high-fidelity models that permit both real-time and retrospective analyses. These technologies offer greater precision than goniometry and can reliably evaluate angulation before, during, and after surgical correction. In a proof-of-­ concept study, Margolin et al. [20] recently investigated the accuracy of 3D photography by capturing measurements of curvature, length, circumference, and erect volume for a set of seven Peyronie’s disease models. For this method, an infrared camera was used to capture images while moving circumferentially around each model over a period 14 mm represents an independent determinant of favorable outcomes after repair. While preoperative hormonal stimulus does increase glans size and vascularity, this approach does not appear to yield better outcomes compared with patients who have not received testosterone [2]. However, current data on the effect of testosterone supplementation prior to hypospadias repair are limited and generally of poor quality. In the authors’ own experience, standard glansplasty in patients with 22; n = 35/43). Eight patients indicated mild ED (IIEF-5 between 17 and 21; n = 8/43), including 4 of whom had been operated more than three times. These findings also resemble our own assessments, in which all patients denied difficulty or pain with intercourse or ejaculation, and most indicated satisfaction with their sexual function. In our experience, most patients were more concerned about appearance of their penis rather than sexual function, which also seems to be reflected throughout the published literature. We have observed that most patients prefer to continue with regular follow-up in order to discuss their anxieties, genital changes, cosmesis, and functional issues. Several authors have documented this desire for longer follow-up, which appears to be driven by adolescent anxiety, poor body image, delayed complications, and patients’ misunderstanding of their medical history. Coping mechanisms represent an important part of psychological health in patients who have undergone hypospadias repair (particularly proximal cases), and in a unique study by Rynja et al. the authors found that these individuals were less likely to seek social support than the control group [35].

8.4 What and When? Surgery is currently the only treatment modality available for hypospadias patients, and rates of both early and late complications remain stubbornly high. Much of the literature that analyses hypospadias repair outcomes have focused on short-term outcomes in children, whereas long-term follow-up data remain scarce [36]. Certainly, issues such as recurrent curvature and strictures after puberty now seem to be more common than were previously realized. Barbagli reported that urethral stricture eventually occurs in 56–72% of patients with voiding disabilities after hypospadias repair [37], despite the fact that initial incidence is just 6–7% over a short-term follow-up period. Related to this, Kovell suggested that there is minimal need for revision surgery 6–10  years after initial hypospadias repair since most patients would not yet have completed the pubertal growth period when additional complications begin to appear [38]. Tack et  al. reported that long-term problems could be identified in patients even 20 years after an initial surgical procedure [39] (Fig. 8.4). A recent review by Tourchi et al. neatly summarizes the problem of not

114

A. Takvani and M. Bhat Time between primary repair and re-interventions First re-intervention Second re-intervention Third re-intervention

20,0 17,5

Time (years)

15,0 12,5 10,0 7,5 5,0 2,5 0,0 Distal hypospadias

Midshaft hypospadias Proximal hypospadias

Fig. 8.4  Time elapsed between initial hypospadias correction and subsequent re-interventions [40]

accounting for long-term results, and recommends more attention be paid to how different surgical techniques impact patients’ cosmetic, functional, and psychosexual outcomes both during and after puberty (as well as the consequences for the adult quality of life) [40]. The aim of surgery is to achieve a straight penis, with meatus at the tip, uninterrupted urinary flow, good cosmesis, and patient self-confidence. For distal hypospadias, surgical options include plate-preserving procedures such as tubularization of incised plate (TIP), glans approximation procedures (GAP), and meatal advancement and glanuloplasty incorporated (MAGPI). In patients with proximal hypospadias, these options include extended application of TIP, various flap-based methods, and graft urethroplasties (performed in either one or two stages). Technically, all flaps provide better blood supply than grafts hence better results are achieved with flap urethroplasties than with grafts [41]. With over 250 different procedures now described for hypospadias repair, it is not surprising that patient outcomes are extremely variable and complication rates remain high. Acute complications occur within 7–10 days, and as stated previously, long-term complications can surface at any point from adolescence into adulthood. In the next section, our aim is to discuss complication causes, incidence, timing, and preventive measures as reported in the literature and gained from our own experience. As discussed by Bhat et al. the literature on acute postoperative complications is currently sparse [36]. Common acute complications are bleeding/hematoma, urethrocutaneous fistula, wound edema/sepsis/infection, glans dehiscence, skin necrosis, penile torsion, inadvertent removal of urethral catheter/stent, bladder spasm, and meatal incrustation. In order of frequency, postoperative fistula is the most

8  Evaluating the Results of Hypospadias Repair: What? Why? When? And How?

115

common complication, followed by edema, meatal encrustation, and penile torsion [36]. There is a steep learning curve in hypospadias surgery and results improve significantly with increased experience of the surgeon [42, 43].

8.4.1 Short-Term Complications 8.4.1.1 Bleeding and Hematoma Significant hematoma is a potentially dangerous complication that can result in infection and/or devascularization of flaps and grafts, ultimately leading to failure of the surgical procedure [44]. In most cases arising due to poor hemostasis, bleeding is typically noted in the first 24–48 h after surgery and requires readmission and/ or prolonged admission. Occasionally, additional procedures under anesthesia are necessary to identify and control the source of bleeding. Typical causes of excessive bleeding are from the resected corpus spongiosum for chordee correction, trauma to the corpus cavernosum, or inadequate hemostasis while fashioning the glans flaps. Surgical hematoma can be prevented by controlling bleeding with the judicious use of bipolar electrocautery to minimize tissue necrosis. 8.4.1.2 Surgical Edema Postoperative edema may be excessive and can involve the penis as well as the scrotum. Incidence of edema as reported in the literature is approximately 11.11% [45]. In our experience, edema is aggravated following removal of pressure dressing, usually on day 5 after surgery. Edema can be prevented by careful tissue handling, keeping the vascular pedicle wide, and minimizing tissue mobilization, while also using a suction drain and compressive dressing to limit swelling. The dressing method has a significant role in the prevention of postoperative edema. Excessive pressure may compromise the blood supply to the flap and skin resulting in tissue necrosis, whereas insufficient pressure can lead to hematoma, edema, and infections that increase the risk of other complications [19]. 8.4.1.3 Urethrocutaneous Fistula (UF) Fistula formation is the most common complication, with typically affected sites being the coronal/sub-coronal level in TIP, or the site of anastomosis in flap urethroplasty. In our practice, we usually note occurrence of fistula in the second or third week postoperatively. In distal hypospadias repair, minor infection at sub-coronal suture line often leads to fistula formation upon stent/catheter removal. Incidence of fistula reportedly varies from 0 to 23% [46], but appears reduced in urethral plate preservation procedures such as TIP and inlay flap relative to other approaches including tube urethroplasty and inner preputial flap [36]. The causes of fistulae remain unknown, although it is likely that key drivers include local infection, ischemia, poor tissue healing, distal obstruction by meatal stenosis/encrustation, and ventral suture line tension in the neourethra. UF rate was significantly higher in patients where neourethra was constructed using 6/0 polyglactine (Vicryl) in a single layer, full thickness, uninterrupted fashion (16.6%), compared with patients

116

A. Takvani and M. Bhat

who underwent urethral anastomosis using 7/0 polydioxanone (PDS) in a subcuticular, uninterrupted fashion (4.9%, P 2 earlier interventions [19]. When tubularized urethral repairs were attempted in this patient population, tissue regeneration was not achieved and complications including graft contracture and stricture formation were later encountered [20]. A major advantage of acellular collagen material is readiness to use “off the shelf,” unlike seeded graft alternatives. Importantly, this eliminates the need for additional surgical procedures to facilitate cell/tissue harvesting. However, scaffolds that work well in healthy animal models are not necessarily so effective in a disease context or in human patients. In these settings, cells populating the grafts may be abnormal, and vascularization can be reduced by fibrosis and/or other pathological processes.

9  Tissue Engineering and Regenerative Medicine in Hypospadias Management

a

c

131

b

d

Fig. 9.1  Use information on the native tissue in strategy for tissue engineering. (a) Verhoef/ Elastin staining of native urethra/corpus spongiosum. Purple fibers indicate elastin. Scale bar 1 mm. (b) E. vWF staining as an endothelial marker. Small capillaries are found close to the epithelium of the urethra, indicated in brown staining. U represents urethral lumen, * vascular spaces in the corpus spongiosum. Scale bar 100 μm. (c) Strategy to generate vascularized TE construct, seed vascular progenitor cells in a hydrogel on a mesh scaffold, roll the scaffold around catheter. (d) Seed the lumen with urine tight epithelium. (Figure adapted from De Graaf et al. [12] (a and b with courtesy, CC-BY), c and d created in BioRender.com by author PdG)

Similar to tissue engineering for bladder reconstruction, TE for hypospadias repair has not yet entered clinical use [18]. We propose that this is largely due to a narrow focus on the urethral epithelium/mucosa only, whereas the native urethra consists of a multilayered structure integral to the corpus spongiosum [12, 21]. Urethral reconstructions may fail over the longer term due to the absence of the corpus spongiosum, leading to fistula or diverticula formation in hypospadias patients in particular [22]. The corpus spongiosum provides mechanical support and blood supply to the urethra and is thought to protect grafted penile skin from damage during sexual activity [23]. Therefore, engineered spongious tissue used in combination with urethral mucosa may provide a better blood supply and mechanical protection of the urethra. To our knowledge, only one group has described the use of grafts comprising both urethra and corpus spongiosum in an animal model (rabbit) [24, 25]. In this study, acellular corpus spongiosum matrix was combined with smooth muscle cells and keratinocytes for urethral reconstruction. For TE of corporal tissue (cavernosal, spongiosal, glandular), most approaches are based on decellularized tissue only due to the complex structure being restored [26].

132

G. Tsachouridis et al.

In our laboratory, we have developed a gel casting method to tissue engineer multilayered tubular constructs based on fiber-reinforced cell-laden hydrogels [15]. For this, a multi-chambered polydimethylsiloxane mold was cast with fiber-­ reinforced hydrogels containing smooth muscle cells (SMCs) and a co-culture of endothelial cells and pericytes. The cell-loaded hydrogels were rolled into a tubular construct by using a fiber mesh for guidance. Urothelial cells were seeded into the lumen where they were able to survive for 2 weeks. In the tubular construct, SMCs were viable and expressed elastin while endothelial cells formed capillary-like structures supported by pericytes, remaining stable for up to 7 weeks. This type of graft may eventually lead to improvements in reconstructive surgery due to provision of subepithelial vascularization thereby reducing graft failure [15]. Future research will focus on optimizing the epithelial lining of the lumen and culturing the graft to maturity in a bioreactor. While various TE-based approaches can restore subepithelial vascularization, the issue of mechanical support for the unique and complex structure of the corpus spongiosum is still not being addressed [14]. This 3D structure has proven difficult to replicate by electrospinning or other fiber-based biofabrication techniques, so researchers have typically resorted to the use of decellularized corpus spongiosum matrix instead [24, 25, 27]. Although this biological scaffold material has the same microstructure and molecular composition as native tissue, it also presents several barriers to clinical application. First, poor reproducibility due to variability of the decellularization process. Second, donor availability is a major issue, possibly solvable by postmortem donation of urogenital tissue, although this is surrounded by complex taboos [28]. Recent developments in 3D printing and biofabrication have enabled engineering of complex architectures such as the corpus spongiosum [14, 29], and might provide future solutions for hypospadias repair. Gel extrusion was one of the first approaches used to perform 3D bioprinting of cell-loaded hydrogels [30], and is now mostly used for applications in bone and cartilage regeneration. Existing hydrogels such as Gelatin Methacryloyl (GelMA) match the biomechanical properties of these hard tissues but lack the biochemical cues required by vascular cells as well as the flexibility needed to engineer corpus spongiosum. Enriching hydrogel printing inks with natural components of the extracellular matrix (ECM) can enhance cell compatibility [31]. However, the reduced stiffness needed for soft tissue engineering will require new printing techniques or a combination of different biofabrication approaches, like support of hydrogels with biomaterials like PCL/ PLCL. A recent study has shown that it is possible to make a urethral hybrid scaffold with mechanical qualities similar to those of native rabbit urethra [32]. The hydrogel was loaded with bladder epithelial cells and smooth muscle cells, which retained sufficient viability and proliferation (Fig. 9.2). Embedding in suspension solution (e.g., freeform reversible embedding of suspending hydrogels; FRESH [33]) makes it possible to print living cells into relatively soft materials (gelatin, collagen, or isolated ECM). Another novel approach is

9  Tissue Engineering and Regenerative Medicine in Hypospadias Management

a

d

b

e

133

c

f

Fig. 9.2 (a) The CT images of the urethra; (b) a scaffold with a spiral shape; (c) a 3D printing equipment; and (d) the bioprinted urethra’s end architecture. (e) The bioprinted urethra’s transactional picture was a full circle with three layers; (f) proliferation of cells inside the bioprinted urethra. (With permission from [32])

based on volumetric printing technologies: these enable the creation of entire objects at once, rather than via sequential addition of basic building blocks. With newly developed volumetric bioprinting techniques [34], large living tissue constructs can be generated by processing cell-friendly hydrogel-based bioresins with a volumetric, visible light laser-based printer. While currently being tested in other fields that require replacement of soft tissues such as liver and pancreas, these technologies also have major future potential in urology. As discussed above, “off the shelf” solutions have not proven effective in patients with complex history of >2 previous surgical interventions, but applications beyond simple inlay grafting of decellularized material may still warrant future study. In situ engineering, where the patient’s own body is used as a bioreactor to condition decellularized grafts, has been explored by the group of Kajbafzadeh [27, 35, 36]. This approach can also be applied to synthetic biomaterials that have been modified to promote in situ tissue maturation [37, 38]. In the case of heart valves, this in situ engineering approach has been shown to effectively fill corporal tissue with blood, and could therefore be adapted to support development of the corpus spongiosum (although alternative methods will be required to condition the urethral lumen, which is instead exposed to urine flow that lacks regenerative properties) [21, 39]. Preclinical studies are fundamental to developing new TE options for hypospadias treatment. Animal studies can help bridge the gap between preclinical and clinical research, but human penile anatomy is still distinct from most of the animal kingdom. Therefore, selecting appropriate animal models to test novel grafting approaches to hypospadias repair is not straightforward (see also “Chap. 3”: The rabbit model in preclinical hypospadias research: strengths and limitations for more details).

134

G. Tsachouridis et al.

A hypothetical approach could be humanized “urethra-on-a-chip” approaches, although these technologies are not yet sufficiently mature to replace animal testing of novel graft methods. At present, the combined urethra/corpus spongiosum environment is very challenging to reproduce in vitro due to the presence of intricate vascularization and specialized structures. For now, urethra-on-a-chip and organoid approaches are unable to reproduce this complex anatomy [40], but there is clear future potential to develop a life-size biofabricated penis with mechanical properties suitable for clinical applications.

9.3 Future Perspectives Given the intricacy of the urethra, it is extremely challenging to create grafts that accurately recapitulate all features and functions of the native organ. Variability of penile morphology in experimental animals also present challenges to research and development in this field. Consequently, a combination of approaches to fabrication and preclinical testing will be required to drive successful translation into the clinic. These efforts will depend on sustained collaboration of basic scientists and urology clinicians with specialists in biofabrication technologies [41]. In our opinion, progress in constructing these specialized tissues will require a deeper knowledge of the molecular anatomy of normal and hypospadiac urethras. While the urethral epithelium itself is critically important, the surrounding tissues also have major roles to play in supporting integration and healing. Since the urethra exhibits unique mechanical and functional properties, an ideal solution might involve TE-based generation of corresponding structures using cells derived from the patient to be treated.

9.4 Concluding Remarks Hypospadias encompasses a range of defects over an entire organ system and is not limited to a single damaged tissue. The urethra and corpus spongiosum combine to form a defined structure that requires support from surrounding tissues to perform key functions of micturition and reproduction. Beneath the epithelium is a well-­ organized vascular network, integral layer of smooth muscle cells, and elastic extracellular matrix that are each organized and orientated in specific ways. Recapitulating these complex features during reconstruction will be key to accelerating tissue healing and improving recovery from trauma or surgery. Current hypospadias tissue engineering approaches are not yet able to fully reproduce the anatomy and function of the human urethra. The tissue-engineered urethra should logically exhibit the same molecular and anatomical properties as healthy urethra, comprising the different layers of corpus spongiosum and with similar microcapillary vascularization present beneath the epithelium. Once these criteria can be fulfilled in a reliable fashion, TE-based solutions are likely to lead to significant improvements in patient outcomes.

9  Tissue Engineering and Regenerative Medicine in Hypospadias Management

135

References 1. Abbas T, McCarthy L.  Foreskin and penile problems in childhood. Surgery (Oxford). 2016;34(5):221–5. 2. Abbas TO. An objective hypospadias classification system. J Pediatr Urol. 2022;18(4):481-e1. 3. Spinoit AF, et al. Fertility and sexuality issues in congenital lifelong urology patients: male aspects. World J Urol. 2021;39(4):1013–9. 4. Bethell GS, et  al. Parental decisional satisfaction after hypospadias repair in the United Kingdom. J Pediatr Urol. 2020;16(2):164 e1–7. 5. Keays MA, Dave S. Current hypospadias management: diagnosis, surgical management, and long-term patient-centred outcomes. Can Urol Assoc J. 2017;11(1–2 Suppl):S48–53. 6. Rustad KC, et al. Strategies for organ level tissue engineering. Organogenesis. 2010;6(3):151–7. 7. Lanza R, et al. Principles of tissue engineering. 5th ed. Amsterdam: Elsevier; 2020. p. 1678. 8. Atala A, et al. Tissue-engineered autologous bladders for patients needing cystoplasty. Lancet. 2006;367(9518):1241–6. 9. Joseph DB, et al. Autologous cell seeded biodegradable scaffold for augmentation cystoplasty: phase II study in children and adolescents with spina bifida. J Urol. 2014;191(5):1389–95. 10. Sloff M, et al. Tissue engineering of the bladder—reality or myth? A systematic review. J Urol. 2014;192(4):1035–42. 11. Abbas TO, Yalcin HC, Pennisi CP.  From acellular matrices to smart polymers: degradable scaffolds that are transforming the shape of urethral tissue engineering. Int J Mol Sci. 2019;20(7):1763. 12. de Graaf P, et  al. The multilayered structure of the human corpus spongiosum. Histol Histopathol. 2018;33(12):1335–45. 13. Masri C, et al. Experimental characterization and constitutive modeling of the biomechanical behavior of male human urethral tissues validated by histological observations. Biomech Model Mechanobiol. 2018;17(4):939–50. 14. Ottenhof SR, et  al. Architecture of the corpus spongiosum: an anatomical study. J Urol. 2016;196:919–25. 15. van Velthoven MJJ, et al. Gel casting as an approach for tissue engineering of multilayered tubular structures. Tissue Eng Part C Methods. 2020;26(3):190–8. 16. Abbas TO, et al. Current status of tissue engineering in the management of severe hypospadias. Front Pediatr. 2017;5:283. 17. Versteegden LRM, et  al. Tissue engineering of the urethra: a systematic review and meta-­ analysis of preclinical and clinical studies. Eur Urol. 2017;72(4):594–606. 18. Casarin M, Morlacco A, Dal Moro F. Tissue engineering and regenerative medicine in pediatric urology: urethral and urinary bladder reconstruction. Int J Mol Sci. 2022;23(12):6360. 19. el-Kassaby A, AbouShwareb T, Atala A.  Randomized comparative study between buccal mucosal and acellular bladder matrix grafts in complex anterior urethral strictures. J Urol. 2008;179(4):1432–6. 20. le Roux PJ.  Endoscopic urethroplasty with unseeded small intestinal submucosa collagen matrix grafts: a pilot study. J Urol. 2005;173(1):140–3. 21. Abbas TO, Ali TA, Uddin S.  Urine as a main effector in urological tissue engineering—a double-edged sword. Cell. 2020;9(3):538. 22. Bhat A, et al. Comparison of variables affecting the surgical outcomes of tubularized incised plate urethroplasty in adult and pediatric hypospadias. J Pediatr Urol. 2016;12(2):108.e1–7. 23. Yiee JH, Baskin LS. Penile embryology and anatomy. Sci World J. 2010;10:1174–9. 24. Feng C, et  al. Reconstruction of three-dimensional neourethra using lingual keratinocytes and corporal smooth muscle cells seeded acellular corporal spongiosum. Tissue Eng Part A. 2011;17(23–24):3011–9. 25. Feng C, et al. Evaluation of the biocompatibility and mechanical properties of naturally derived and synthetic scaffolds for urethral reconstruction. J Biomed Mater Res A. 2010;94(1):317–25.

136

G. Tsachouridis et al.

26. de Vocht D, et al. A systematic review on cell-seeded tissue engineering of penile corpora. J Tissue Eng Regen Med. 2017;12(3):687–94. 27. Kajbafzadeh AM, et  al. Future prospects for human tissue engineered urethra transplantation: decellularization and recellularization-based urethra regeneration. Ann Biomed Eng. 2017;45(7):1795–806. 28. Caplan AL, et  al. The ethics of penile transplantation: preliminary recommendations. Transplantation. 2017;101(6):1200–5. 29. Chen MY, et  al. Current applications of three-dimensional printing in urology. BJU Int. 2020;125(1):17–27. 30. Mouser VHM, et al. Three-dimensional bioprinting and its potential in the field of articular cartilage regeneration. Cartilage. 2017;8(4):327–40. 31. Williams C, et al. Cardiac extracellular matrix-fibrin hybrid scaffolds with tunable properties for cardiovascular tissue engineering. Acta Biomater. 2015;14:84–95. 32. Zhang K, et  al. 3D bioprinting of urethra with PCL/PLCL blend and dual autologous cells in fibrin hydrogel: an in vitro evaluation of biomimetic mechanical property and cell growth environment. Acta Biomater. 2017;50:154–64. 33. Lee A, et al. 3D bioprinting of collagen to rebuild components of the human heart. Science. 2019;365(6452):482–7. 34. Bernal PN, et al. Volumetric bioprinting of complex living-tissue constructs within seconds. Adv Mater. 2019;31(42):e1904209. 35. Kajbafzadeh AM, et al. In vivo human corpus cavernosum regeneration: fabrication of tissue-­ engineered corpus cavernosum in rat using the body as a natural bioreactor. Int Urol Nephrol. 2017;49(7):1193–9. 36. Khorramirouz R, et al. Application of omentum as an in vivo bioreactor for regeneration of decellularized human internal mammary artery. J Biomed Mater Res A. 2017;105(10):2685–93. 37. Roelofs LAJ, et al. Bladder regeneration using multiple acellular scaffolds with growth factors in a bladder. Tissue Eng Part A. 2018;24(1–2):11–20. 38. Smits AIPM, Bouten CVC. Tissue engineering meets immunoengineering: prospective on personalized in situ tissue engineering strategies. Curr Opin Biomed Eng. 2018;6:17–26. 39. Zhang Y, et al. Urine derived cells are a potential source for urological tissue reconstruction. J Urol. 2008;180(5):2226–33. 40. Leung CM, et al. A guide to the organ-on-a-chip. Nat Rev Methods Prim. 2022;2(1):33. 41. Castilho M, et al. Multitechnology biofabrication: a new approach for the manufacturing of functional tissue structures? Trends Biotechnol. 2020;38(12):1316–28.

Artificial Intelligence in Hypospadiology: Role, Applications, and Benefits

10

Mohamed AbdulMoniem, Tariq Abbas, Amith Khandakar, Md Nazmul Islam Shuzan, Moajjem Hossain Chowdhury, and Muhammad E. H. Chowdhury

Abstract

Hypospadias is a complex multifactorial disorder that is impacted by both environmental and genetic variables, with varying degrees of severity and significant long-term functional effects if not treated properly. Typically, surgical guidelines founded on essential anatomical features aid in the decision-making process for urethroplasty. In the years thereafter, these methods have been tuned to the point that they may be utilized in clinical practice via risk assessment models, increasing diagnostic accuracy and streamlining workflow using artificial intelligence. It is also true that artificial intelligence (AI) has changed many facets of contemporary life, but its full potential has not yet been realized in pediatric urology and hypospadiology. Insufficient vast, high-quality, and publicly available datasets from a variety of institutes and locales may help explain the paucity of studies attempting to apply ML to hypospadiology. In this chapter, we will define key AI concepts, present a brief overview of AI’s use in medicine, explore exciting new AI-powered medical tools, and describe how to put AI to work solving specific medical issues. (See Video 10.1).

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/978-­981-­19-­7666-­7_10.

M. AbdulMoniem · A. Khandakar · M. N. I. Shuzan · M. H. Chowdhury · M. E. H. Chowdhury (*) Department of Electrical Engineering, Qatar University, Doha, Qatar e-mail: [email protected] T. Abbas Urology Division, Surgery Department, Sidra Medicine, Doha, Qatar © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 T. Abbas (ed.), Hypospadiology, https://doi.org/10.1007/978-981-19-7666-7_10

137

138

M. AbdulMoniem et al.

Key Messages • Current hypospadias classification methods are highly subjective, lack standardization, and offer poor inter/intra-observer agreement, leading to imprecise clinical decision-making. • The emergence of artificial intelligence (AI) has revolutionized many routine aspects of daily life and has a wide range of potential medical applications. • AI may provide the solution to standardizing decision-making in urethroplasty and uncover new opportunities to advance the field of hypospadiology. • Limited attempts have already been made to apply AI to hypospadiology, but a reliable, reproducible, and accurate method of assessing phenotype has yet to be realized.

10.1 Introduction Hypospadias is considered one of the most common congenital abnormalities of the male external genitalia, with a global incidence of 3.7 per 1000 live male births, and around 6000 new cases every year in the United States alone [1, 2]. Displaying a broad spectrum of manifestations, hypospadias can be observed either as an isolated defect or a phenotypic component of a more complex condition such as an intersex state [2]. Indeed, hypospadias is a complex multifactorial condition influenced by several environmental and genetic factors, resulting in highly variable severity and potential long-term functional consequences if not adequately treated [3]. To date, more than 20 different genes have been strongly associated with isolated hypospadias, supporting the concept that this condition is a common phenotypic outcome of multiple different genotypes [4].

10  Artificial Intelligence in Hypospadiology: Role, Applications, and Benefits

139

Numerous options for surgical repair have been introduced in the field of hypospadiology, where the primary aim is to achieve a straight penis with adequate caliber meatus at the apex of a reconfigured conical glans, ideally also maintaining an acceptable cosmetic outcome [2, 3]. However, despite the existence of multiple surgical approaches to hypospadias treatment, none can be considered optimal for successful repair in all cases [3]. Most of the available hypospadias outcome studies address surgical complications, whereas minimal work has been dedicated to studying the long-term functional outcomes of repair [3]. Distal hypospadias repairs are known to be more favorable when compared to proximal cases, especially when the latter is accompanied by severe ventral penile curvature. Approximately 5–10% of distal hypospadias repair cases result in complications, with a minority requiring a redo of urethroplasty [5]. Proximal hypospadias cases with severe curvature are generally associated with a higher complication rate of 15–56% [6, 7]. Surgical management of hypospadias has progressed significantly in recent years, leading to a marked reduction in complication rates [3], although several postoperative issues are still relatively common [8–10]. As a result, the identification of anatomical features known to be associated with the development of complications should factor into the choice of surgical approach. Decision-making for urethroplasty is typically aided by surgical guidelines based on key anatomical features, including the degree of ventral penile curvature, urethral plate (UP) width, glans size, and meatal location [3, 11, 12]. (See “Chap. 1” Toward an Ecosystem Model of Hypospadiology). However, according to current practices, the choice of hypospadias reconstruction procedure is still frequently determined by the surgeon’s experience, which is highly subjective and leads to nonempirical decision-making [2, 3]. Application of artificial intelligence (AI) could therefore enhance decision-making in urethroplasty by providing a standardized methodology for classifying hypospadias severity in an objective and reproducible manner.

10.2 Machine Learning Advances in the Field of Biomedical Engineering The term artificial intelligence (AI) was first coined in the 1950s, but various shortcomings in early models precluded widespread adoption and applications in medicine. Many of these constraints were solved in the early 2000s with the introduction of deep-learning neural networks. In the intervening years, refinement of these techniques has ushered in a new era of medicine, where AI can be applied to clinical practice through risk assessment models, boosting diagnostic accuracy, and increasing workflow efficiency. This section will explain the relevant terms in AI, provide a brief history of AI in medicine, discuss recent developments in AI-based medical tools, and outline the process of applying AI to defined medical problems.

140

M. AbdulMoniem et al.

10.2.1 The Relationship Between AI, ML, and DL Initial attempts to apply AI used expert domain knowledge together with conditional statements to grant programs a level of sentience. The need for advanced domain expertise makes this form of AI very expensive and complex to maintain. Furthermore, the algorithm bases decisions on hard-coded logic, which makes the system prone to mishandling outliers. This predicament urged the creation of AI that can learn from data, deriving high-level insight from low-level features, via a process now known as machine learning (ML) (Fig. 10.1). ML is a branch of artificial intelligence and computer science that focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving in accuracy over time. ML can be regarded as an algorithm that derives logic from data rather than the programmer. This type of algorithm is crucial for finding relationships in data that have not obviously been detected. Most ML algorithms require a feature matrix (specific parameters being input rather than raw data) alongside corresponding labels (outputs that the algorithm should predict). The feature matrix and corresponding labels are then used to train the ML algorithms to work independently for unseen data. Feature engineering is therefore a critical part of the ML process. Meaningful and rich features allow for a more accurate and robust model, which facilitates a wide range of applications in the clinical domain. Manual feature engineering can be challenging, especially in image and signal domains. In these cases, it is often better to use Deep Learning, or more specifically Deep Neural Networks, that provide additional layers of complexity. In this type of Machine Learning (ML) A subset of AI, where the focus is on the ability of machines to receive data and improve at a task with experience.

Deep Learning (DL)

Machine Learning (ML)

Artificial Intelligence (AI) Artificial Intelligence (AI) Any process where computers are used to mimic the cognitive abilities and functions of humans

Deep Learning (DL) A subset of ML. It deals with multi-layered neural networks that can learn by themselves from vast amount of data.

Fig. 10.1  Relationship between artificial intelligence (AI), machine learning (ML), and deep learning (DL)

10  Artificial Intelligence in Hypospadiology: Role, Applications, and Benefits

141

network, convolution layers are used to automate the process of feature extraction using learnable kernels, which can achieve impressive performance with both signals and images. Networks with convolution layers are called Convolutional Neural Networks (CNN) which are currently state-of-the-art in the image domain. At present, this architecture offers the best results for a minimal level of complexity. A large amount of data is required to train deep CNNs. Data collection is very expensive in the biomedical domain and often key information is simply not available. In some cases, Transfer Learning/Domain Adaptation can be used to learn patterns in big datasets such as ImageNet and then apply these to smaller datasets. In essence, this method derives coarse features from the big dataset and then learns fine features when trained on the small dataset of a specific domain (this final retraining process is termed fine-tuning).

10.2.2 A Brief History of AI in Medicine The period from the 1950s to the 1970s could be categorized as the early stages of AI.  Some key innovations in the broad field of AI include General Motors 1961 introduction of the first industrial robot arm capable of performing automatic die-­ casting [13]. Another landmark advance in 1964 was the development of the primitive chat-bot Eliza, which was able to conduct superficial conversations [14]. Despite many subsequent AI innovations in other fields, medicine has been sluggish to embrace this technology. During the early phase of AI development, medical uses were restricted to digitizing data, which ultimately provided a foundation for the future expansion of AI into multiple fields of medicine. A key step in the 1960s was The National Library of Medicine’s establishment of the Medical Literature Analysis and Retrieval System, as well as the web-based search engine PubMed, which created a primary digital resource for the subsequent acceleration of biomedicine. These resources helped drive the expansion of clinical informatics databases and medical record systems, which laid the groundwork for subsequent advances in medical AI. The AI field suffered from a relative lack of interest between 1970 and 2000 during which time both funding and research activity was significantly reduced (known as the “AI winter”). Nonetheless, some major advances in medical AI still took place during this period, including the creation of a glaucoma consultation program which demonstrated the viability of AI in clinical settings. The study team created a database that was regularly updated by collaborators, with model-building and consultation based on the causal-associational network “CASNET” approach [15], which aimed to inform doctors about disease presentation and how to treat individual patients. Created at Rutgers University, this system was first debuted to the public in Las Vegas at the 1976 Academy of Ophthalmology convention.

142

M. AbdulMoniem et al.

In the early 1970s, the AI system MYCIN was created to offer a list of likely bacterial infections and then propose appropriate antibiotic treatments tailored correctly for body weight, which was achieved using patient information entered by doctors and a knowledge base of roughly 600 separate rules. Refinement of the MYCIN rule-based system later created EMYCIN [16], and eventually lead to INTERNIST-1 which was built on the same framework but using a greater body of medical information [17], with the ultimate aim of guiding primary care physicians to make correct diagnoses. Since the 2000s, the AI community has experienced a marked resurgence, with deep neural networks being a major driver of progress in this field. Some notable works include Bakkar et al. [18], who used IBM Watson to identify novel RNA-­ binding proteins implicated in amyotrophic lateral sclerosis. Pharma Bot [19] was developed in 2015 to teach pediatric patients and their parents about different drug treatments while Mandy [20] was built in 2017 to automate patient intake procedures for a primary care clinic.

10.2.3 Guidelines for Applying AI in Biomedical Engineering The most important component of ML is high-quality input data. The following are general guidelines for creating appropriate datasets: • Datasets can comprise images, biomedical signals, or biomarkers [21, 22]. • The dataset should be clean without noisy signals or images that can disrupt the learning process. • For pretrained models in a given domain; a starting sample image size >200 is adequate. For training from scratch, a much larger sample size of >2000 is required. • For clinical or tabular datasets where parameters are considered as features or signal/image features are used, it is important that the number of features does not exceed the number of samples (rich feature engineering is key in these cases). • Labeled datasets with either class labels for categorization or metrics for regression will significantly improve model performance. • Datasets should represent the wider patient population as far as possible. A crucial step in ML projects is selecting the best algorithm for each application. For clinical work with biomarkers, Classical ML is the recommended choice, while for image domain transfer learning CNNs are superior. CNN-based approaches are also recommended for physiological signal datasets, although these methods do not offer the benefit of transfer learning (unless a deep-learning model is trained on a large signal dataset in the same domain).

10  Artificial Intelligence in Hypospadiology: Role, Applications, and Benefits

143

When commencing an ML project, it is vital to establish an appropriate evaluation protocol. In practice, this means partitioning the available data into: • training set: these data will only be used to train the model. • validation set: these data will only be used to select the best model and fine-tune the model. • test set: these data will only be used to evaluate the model and report results. Since most biomedical engineering datasets are small, a k-fold cross-validation approach is often taken (where k is usually 5). In this approach, the data are divided into fivefold, the first four of which are used for training and validation (a small part of the training set), while the fifth fold is used for testing. This process keeps repeating until each of the individual folds has been used for the testing process. Finally, overall results are collated, and predictions are compared to ground truth. This result is then reported. For model evaluation, many metrics are used for different ML paradigms. For classification tasks, the most common metrics are displayed below (TP, TN, FN, and FP denote the number of true positives, true negatives, false negatives, and false positives, respectively): • Accuracy is a basic metric used to assess the performance of a classification model. Accuracy can be defined simply as the total number of correct predictions out of the total samples. This metric is calculated using Eq. (10.1).



Accuracy =

TP + TN . TP + TN + FP + FN

(10.1)

• Precision calculates how many positive cases were correctly classified out of all the predicted positive cases. This metric reduces the incidence of false positives and is calculated using Eq. (10.2).



Precision =

TP . TP + FP

(10.2)

• Recall determines how many positive cases were correctly classified out of all positive cases. This metric is particularly important in healthcare since it is imperative to detect all positive cases. Recall is calculated using Eq. (10.3).



Recall =

TP . TP + FN

(10.3)

144

M. AbdulMoniem et al.

• F1-Score considers both false positives and false negatives. It can be stated as a weighted average of precision and recall. This metric is especially important when assessing models with imbalanced datasets and is calculated as shown in Eq. (10.4).



F1 - score = 2 ´

Precision ´ Recall 2 ´ TP = . Precision + Recall 2 ´ TP + FN + FP

(10.4)

• Receiver Operating Characteristics—Area Under the Curve (ROC-AUC): ROC curves plot true-positive rate against false-positive rate. The area under the curve is calculated to assess model performance, with scores ranging from 0.0 to 1.0 indicating poor performance and 1.0 representing best performance. For regression tasks, the most common metrics are displayed below (yi and yi represent ground truth and prediction respectively for the ith sample. N represents the number of samples): • Mean Squared Error (MSE) simply measures how close a regression line is to a set of data points and then squares these distances. This metric can be calculated using Eq. (10.5).

MSE =

1 N 2 å ( yi - yi ) . N i =1

(10.5)

• Mean Absolute Error (MAE) determines the average magnitude of errors in a set of prediction data in which the directions are not considered. MAE also measures the accuracy of continuous variables and is calculated using Eq. (10.6).

MAE =

1 N å yi - yi . N i =1

(10.6)

• Correlation coefficient (R) is a statistical technique that determines whether two variables are closely related (ground truth and predictions). R also indicates how close predictions are to the trend line. This metric can be calculated using Eq. (10.7).

R=

å å

N

( yi - mean ( yi )) ( yi - mean (y i))

i =1

N

( y - mean ( yi )) 2 ( yi -mean (y i)) 2 i =1 i

.

(10.7)

10  Artificial Intelligence in Hypospadiology: Role, Applications, and Benefits

145

For advanced ML and DL tasks, such as semantic segmentation, more sophisticated evaluation metrics are typically used, including: • Dice Similarity Coefficient (DSC) is a spatial overlap index that provides a quantitative measure of overlap between the predicted and the ground truth segmentation masks. The value of this metric is defined as twice the pixel area of overlap between ground truth and predicted mask (i.e., truly predicted positive pixels) divided by the total number of pixels in both images, as illustrated in Eq. (10.8): DSC =



2TP . 2TP + FP + FN

(10.8)

• Intersection over Union (IoU; also known as Jaccard Index) is defined by the area of overlap between predicted and ground truth segmentation masks expressed as a ratio relative to the area of union between the predicted and ground truth masks. This metric can adopt any value between 0 and 1, with perfect overlap between ground truth and predicted segmentation mask being signified by a unity IoU (whereas no overlap is indicated by IoU 0). Equation (10.9) can be used to compute this quantity: TP (10.9) . TP + FP + FN It should be noted that both IoU and DSC provide a quantitative evaluation of overlap between predicted and ground truth segmentation masks, with the primary distinction being that DSC favors truly predicted pixels by a factor of 2 when compared to IoU. Developing ML models is typically an empirical process that involves multiple rounds of training and refining. The main stages involved in building an AI model are illustrated in Fig. 10.2. IoU =

Data Splitting Best Model

Training Train Dataset

Train Model

Validate & Tune

Data Pre-processing Raw Dataset Test Dataset

Testing & Performance Evaluation

Fig. 10.2  Schematic diagram describing the main stages of developing a machine learning model

146

M. AbdulMoniem et al.

10.3 ML in Adult Urology Applications of ML in the field of urology span a wide variety of use cases, extending from AI-enabled diagnosis systems to patient outcome prediction models. Over the past few years, there has been increasing interest in using ML to enhance the ability of physicians to diagnose, analyze, and treat urological disorders. This has expedited the introduction of new approaches that will drive innovation in patient care through advances in diagnostics, treatment planning, and surgical skill assessment. ML has already been leveraged to enhance treatment of numerous conditions including urologic-oncology, urolithiasis, reproductive urology, renal transplant, pediatric urology, urogynecology, and benign prostate hyperplasia (BPH). In this section, the use of AI in different subspecialties of adult urology will be presented first, followed by a brief literature review regarding the use of AI in pediatric urology. Several important advancements have been made in the field of urolithiasis treatment over recent years, but decision-making and patient counseling are still far from trivial for practitioners. Key challenges include optimal treatment selection, accurate outcome prediction, and personalization of patient counseling. Multiple ML-based systems have been proposed to facilitate the prevention, diagnosis, and treatment of urolithiasis, aiming to offer a complete clinical management solution for kidney stone patients. Computed tomography (CT) and ultrasound (US) images were previously used to identify renal stones in Parakh et al. [23] and Selvarani et al. [24], respectively. In the former study, cascaded CNNs were used to detect urinary stones in 535 adult patients. The proposed framework consisted of two “inception-v3-like” CNN networks, such that the first was responsible for identifying the relevant portions of CT images where the urinary tract was captured, while the second was trained to classify images by the presence or absence of renal stones. By applying transfer learning, this system achieved an overall accuracy of 95% and sensitivity of 94%, with an AUC of 0.954. Selvarani et al. instead used a support vector machine (SVM) to predict the existence of renal stones using a dataset of 250 US images. The proposed classifier achieved 98.8% accuracy with a 1.8 false acceptance rate (FAR) and a 3.3 false rejection rate (FRR). In earlier work, Langkvist et al. [25] used 465 clinically acquired abdominal CT images for the detection of ureteral stones using raw pixels on 3D volumes. Their model achieved 100% sensitivity while maintaining a false-positive rate of 2.68. Additionally, Kazemi et al. [26] sought to improve the early detection of urolithiasis by developing a highly accurate decisionsupport system based on a dataset of 936 patients that spanned 42 separate features for each. In a study by Kriegshauser et al. [27], single-source dual-­energy CT (ssDECT) images were used instead of conventional CT images for the characterization of renal stones. They used a dataset comprised of 32 stones (24 of which were >5 mm) to distinguish uric acid from non-uric acid stones, and then attempted to classify subtypes of the 18 non-uric acid stones. Their model achieved 97% accuracy for the first task, with an overall accuracy of 72% reported when using a Naïve Bayes Tree classifier. Moreover, Eken et  al. [28] compared logistic regression with a simple artificial neural network (ANN) and genetic algorithm (GA) for the diagnosis of renal colic using a dataset collected from 227 individual patients. They showed that data mining approaches such as ANN can be used to predict renal colic in emergency settings and could provide an alternative to conventional multivariate analyses in biostatistics. Table  10.1 shows a summary of previous research in this domain.

Diagnosis of renal colic

Association between genetic Polymorphisms/patient habits and stone disease

Eken et al. [28]

Chiang et al. [46]

Kazemi et al. [26] Kriegshauser et al. [27]

Discriminant analysis (DA)/ artificial neural network (ANN)

256 subjects (151 patients/105 control)

Accuracy (uric/ non-uric acid): 97.1% Accuracy (non-uric acid subtypes): 72% AUC: 0.867 Sensitivity: 94.9% Precision: 78.4% Genetic variables: (ANN: 65% DA: 64%) Gen. and env. variables: (DA: 74% ANN: 89%)

Naïve Bayes tree classifier

Logistic regression, ANN, and GA

Accuracy: 97.1%

Accuracy: 95% AUC: 0.954 Sensitivity: 94% Precision: 95.9% Accuracy: 98.8% FAR: 1.8 FRR: 3.3 Sensitivity: 100% FAR: 2.68

Results

Ensemble model

227 patients

936 patients with 42 features 32 stones dataset

CNNs

Detection of ureteral stones using raw pixels on 3D volumes from CT scans Early detection of urolithiasis Characterization of renal stones using ssDECT images

Langkvist et al. [25]

465 abdominal CT images

Predict the existence of renal stones in US images

Selvarani et al. [24]

CNNs

Support vector machines (SVM)

535 adult patients

Detect urinary stones in CT images

Model/learning algorithm

250 US images

Size of the sample

Objective

Study Urolithiasis Parakh et al. [23]

Table 10.1  Artificial intelligence applications in urolithiasis and renal transplant

–

(continued)

Results are for ANN

–

–

–

–

–

Notes

10  Artificial Intelligence in Hypospadiology: Role, Applications, and Benefits 147

Outcome prediction after various forms of endourologic intervention

Predict the spontaneous passage of ureteral stones in patients with renal colic

Predict postoperative outcome of PCNL

Kadlec et al. [49]

Dal Moro et al. [50]

Shabaniyan et al. [51]

Aminsharifi et al. [48]

Objective Predict stone-free status in kidney stone patients after ESWL Predicting outcomes after PCNL

Study Seckiner et al. [47]

Table 10.1 (continued)

Nonlinear logistic regression

SVM

382 renal units

1163 renal colic patients (402 used in the study) 254 patients

Combination of sequential forward selection and Fisher’s DA and supervised learners SVM/KNN/ANN

SVM

Model/learning algorithm ANN

146 adult patients

Size of the sample 203 patients

(Stone-free status) Accuracy: 94.8% Sensitivity: 100.0% Specificity 88.9%

Accuracy: 80–95.1% Sensitivity: 82–97% AUC: 0.915 (Task 1) Accuracy: 69.6% Sensitivity: 75.3% Specificity 60.4% AUC: 0.749 (Task 2) Accuracy: 92.8% Sensitivity: 30% Specificity 98.3% AUC: 0.863 Sensitivity: 84.5% Specificity: 86.9%

Results Accuracy: 88.70%

–

–

Task 1: Stone-free status Task 2: Need for a secondary procedure

Percutaneous nephrolithotomy (PCNL) (min–max) Based on the task

Notes Extracorporeal shock wave lithotripsy (ESWL)

148 M. AbdulMoniem et al.

Predict treatment success after lithotripsy in ureteral stone cases Prediction of shock wave lithotripsy (SWL) success in renal stones using CT images

Choo et al. [53]

Differentiating stones from phleboliths

Abnormality detection of kidney in US images

Detect urinary tract stones using plain X-ray images

Kidney disease detection and segmentation (tumors, stones) using UT images

Jendeberg et al. [55]

Krishna et al. [56]

Kobayashi et al. [57]

Nithya et al. [58]

Mannil et al. [54]

Objective Predict the need for laser lithotripsy and expected duration of ureterorenoscopy

Study Huettenbrink et al. [52]

Accuracy: 92% Sensitivity: 94% Specificity 90% AUC: 0.95 Accuracy: 98.14% Sensitivity: 100% Specificity: 96.82% Sensitivity: 87.2% PPV: 0.662 F score: 0.752 Accuracy: 93.45% Sensitivity: 100% Specificity: 90%

Decision trees, KNN, ANN, Random Forest

CNNs

ANN/SVM

51 patients

ResNet (CNN)

ANN and segmentation using multi-kernel k-means clustering

1017 patients

100 images (40 normal, 30 tumor and 30 stone)

Train: 217 stones/167 phleboliths Test: 50 stones/50 phleboliths 508 images (250 normal, 138 stones, 120 cyst kidney)

AUC: 0.85

Decision tree analysis

791 patients

Results (Task 1) Accuracy: 96% (Task 2) Accuracy: 91% Accuracy: 92.29% AUC: 0.951

Model/learning algorithm ANN, SVM, k-nearest Neighbors (KNN), random Forest, Bayes classifiers

Size of the sample CT images from 474 patients

(continued)

Results of the classification task using ANN classifier

PPV: Positive predictive value

–

Results using random forest classifier with 3D texture analysis and stone-to-skin distance Majority vote accuracy: 93%

Notes ANN performance on Task 1: Use of lithotripsy Task 2: Time of surgery ≤30 min –

10  Artificial Intelligence in Hypospadiology: Role, Applications, and Benefits 149

Goldfarb et al. [60]

Decisional trees

Logistic regression/ tree-based model

37,407 records

Prediction of graft failure using body mass index (BMI) and other risk factors Prediction of 3-year graft survival based on pretransplant variables

Greco et al. [59]

194 renal transplant patients

Early noninvasive detection of renal transplant rejection

Abdeltawab et al. [31]

k-nearest neighbor (KNN) and naïve Bayes

Ensemble model

CNN

163,199 observations 2728 patients

Kidney transplant survival prediction Prediction of 5-year graft survival after transplantation

Model/learning algorithm

56 individuals

Size of the sample

Objective

Study Renal transplant Ethan et al. [29] Atallah et al. [30]

Table 10.1 (continued)

Accuracy: 65%

Concordance index: 0.724 Accuracy: 80.77 Sensitivity: 81.2% F score: 73.5 Accuracy: 92.9% Sensitivity: 93.3% Specificity: 92.3% Sensitivity: 88.2% Specificity: 73.8%

Results

–

–

–

–

–

Notes

150 M. AbdulMoniem et al.

10  Artificial Intelligence in Hypospadiology: Role, Applications, and Benefits

151

Renal transplant is considered the optimal solution for end-stage renal failure, but optimizing kidney transplant allocation is critical to improving outcomes due to the limited number of donors available. Prediction modeling for this type of surgical procedure is therefore a high priority, and numerous prior studies have attempted to predict transplant outcomes using ML. Mark et al. [29] proposed an ensemble model for kidney transplant survival prediction using 18 features. The proposed model achieved superior performance with a concordance index of 0.724, which surpassed that of previous models. Additionally, Atallah et al. [30] presented a novel method of predicting 5-year graft survival after transplantation. Their proposed model combined k-nearest neighbor (KNN) with naïve Bayes to achieve an average accuracy of 80.77% (F-measure 73.5%). Table 10.1 presents an overview of the literature in this sub-field. Several other interesting studies unrelated to outcome prediction have also been conducted in renal medicine. For example, Abdeltawab et  al. [31] introduced a deep-learning model for noninvasive early detection of renal transplant rejection using diffusion-weighted (DW) MRI volumes. The proposed system demonstrated 92.9% accuracy with 93.3% sensitivity and 92.3% specificity. AI has also been used successfully in other sub-fields of adult urology. For example, multiple systems have been developed for the detection of prostate cancer using clinical variables [32], MRI images [33, 34], and tissue microarray images [35]. Similarly, early detection of bladder cancer has been investigated using 3D features of MRI data [36], cystoscopic images [37], and surface atomic force microscopy (AFM) of cells collected from body fluids [38], as well as CT scan assessment of treatment responses [39]. Furthermore, AI has been also used to provide reliable diagnosis, prognosis, and treatment planning for renal cancer. Discrimination between angiomyolipoma and different types of renal cell cancer (RCC) has previously been achieved with high accuracy using three-phase CT scans [40, 41]. Contrasting with other studies, Coy et al. [42], instead converted volumetric CT images into a 2D image set and then trained a deep neural network to differentiate clear cell RCC from oncocytoma. Several other recent studies have considered different biomarkers and gene expression patterns to predict survival and disease prognosis in renal cancer [43, 44]. Another interesting application of AI in urology is in the diagnosis of clinically relevant hydronephrosis using renogram features [45].

10.4 ML in Pediatric Urology Although AI has revolutionized numerous aspects of modern life, this technology has yet to achieve full potential in the field of pediatric urology. Some attempts have been made to develop ML-based solutions for specific aspects of pediatric urology, but most applications have been repurposed from other related medical fields. For example, several use cases of ML in adult urology and radiology can be extended to cover pediatric urology-related tasks, such as renal imaging. However, there remain several distinct challenges in pediatric urology due to patient ages and the risk of long-term detrimental consequences hence it is vital to make accurate decisions in a standardized fashion. Figure 10.3 shows some of the common use cases of AI in the field of pediatric urology. Adoption of ML-based approaches in pediatric urology has only recently begun in earnest, with Bagli et al. [61] reporting the use of a 4-layer neural network to predict

152

M. AbdulMoniem et al.

Diagnosis Predication

Treatment Planning

AI can play a major role in early detection & diagnosis of urological diseases, such as urolithiasis, oncology & hypospadias

AI have been used in drug selection & rapid recovery schedules planning especially for brachytherapy.

AI in Pediatric Urology

AI presented promising potentials in patient outcome predictive analysis, benefiting from big biological data to forecast surgical outcomes.

Procedure Outcome Prediction

Improve surgical techniques by analyzing the patterns and help in reducing technical faults

Robotics Surgeries

Fig. 10.3  Common use cases of AI in pediatric urology

sonographic outcome after pyeloplasty in children with ureteropelvic junction obstruction (UPJO). A dataset consisting of 100 children was used to classify the postoperative results of pyeloplasty into four distinct classes; “significantly improved,” “improved,” “same,” or “worse” based on a set of 242 demographic, clinical, radiological, and surgical predictors. The network achieved a recall and precision of 100% thus displaying superior classification performance compared with classical linear regression. Additionally, Logvinenko et  al. [62] investigated the viability of using renal and bladder ultrasound (RBUS) images to predict voiding cystourethrogram (VCUG) abnormalities, such as vesicoureteric reflux and congenital urethral abnormalities. The investigation included the use of multivariate logistic models in addition to a single hidden layer with a 10-neuron neural network. They illustrated that RBUS cannot be used in isolation in the case of VCUG-identified abnormalities, since both methods provide different yet complementary information to the proposed model. Similarly, Papadopoulos et al. [63] tested whether a neural network architecture called Venn Prediction could be used to identify vesicoureteral reflux (VUR) disorder without the need for VCUG and achieved a sensitivity superior to other techniques. Moreover, Blum et al. developed a linear support vector machine (LSVM) classifier to predict early signs of clinically significant hydronephrosis caused by UPJO.  They used data from 55 patients with or without obstruction, including 45 extracted spatial and wavelet-based features from recorded drainage curves after administration of furosemide. Their model attained 93% accuracy in classification, with 91% sensitivity and 96% specificity. Other investigators have applied ML-based models to predict future risk of recurrent urinary tract infections (rUTIs) in children with VUR following an initial UTI [64], which would allow targeted VCUG for the highest risk cases. The model displayed a promising level of performance in identifying children who would benefit from VCUG, which represents a significant step toward personalized management of patients with an initial UTI. Table 10.2 provides a summary of the literature concerning ML use cases in pediatric urology.

153

10  Artificial Intelligence in Hypospadiology: Role, Applications, and Benefits Table 10.2  Artificial intelligence applications in pediatric urology Objective Study Pediatric urology Bagli et al. Predict [61] sonographic outcome after pyeloplasty in children with UPJO Logvinenko Use RBUS et al. [62] images to predict VCUG abnormalities

Papadopoulos Predict et al. [63] vesicoureteral reflux (VUR) disorder instead of traditional VCUG Blum et al. Predict early [45] signs of clinically significant hydronephrosis caused by UPJO Estrada et al. Predict future [64] risk of recurrent urinary tract infections (rUTIs) Kwong et al. Predict [65] progressive decline in renal function and the need for additional procedures in patients with PUV

Model/ Sample size algorithm 100 patients ANN

2259 patients

Multivariate logistic model/ANN

162 child patients

ANN

Results

Notes

Accuracy: 100% Sensitivity: 100% Precision: 100% (ANN) Sensitivity: 64% Specificity: 60%

UPJO: ureteropelvic junction obstruction

Accuracy: 84.07% Sensitivity: 72.00% Specificity: 86.89%

RBUS: renal and bladder ultrasound VCUG: voiding cystourethrogram Results using ANN and for all VUR grades –

Accuracy: 93% Sensitivity: 91% Specificity: 96%

–

500 Decision tree AUC: 0.761 participants

–

103 patients Random CKD c-index: survival forest 0.765 RRT c-index: 0.952 CIC c-index: 0.700

CKD: chronic kidney disease progression RRT: renal replacement therapy CIC: clean-­ intermittent-­ catheterization

55 patients Linear-SVM

(continued)

154

M. AbdulMoniem et al.

Table 10.2 (continued) Model/ algorithm Deep pretrained CNNs and SVM

Study Zheng et al. [66]

Objective Classify normal children’s kidneys and those with CAKUT

Sample size 100 children (50 control and 50 CAKUT)

Tokar et al. [67]

Predicting enuresis in children

Logistic 8071 elementary regression school students CNNs 180 pediatric patients

Lee et al. [68] Predicting the recurrence of UTI using (99mTc-­DMSA) renal scan Khondker et al. [69]

Assignment of VUR from VCUG

85 Renal units

Random forest

Keskinoğlu et al. [70]

Classification of VUR and UTI in children

611 pediatric patients

ANN

Yin et al. [71] Diagnosing children with posterior urethral valves (PUV)

157 Children

Pretrained CNNs

Smail et al. [72]

687 patients CNN

Grade hydronephrosis through Society for Fetal Urology (SFU) classification system

Results AUC: 0.92/0.88/0.92 Accuracy: 0.84/0.81/0.87 Specificity: 0.84/0.74/0.88 Sensitivity: 0.85/0.88/0.86 (Left, right, and bilateral abnormal kidney) Accuracy: 81.3% AUC: 0.81 Accuracy: 91.1% Specificity: 94.8% Sensitivity: 70.4% (External validation performance) Accuracy: 0.84 AUC: 0.88 AUC: 0.809 Precision: 0.779 Specificity: 0.418 Sensitivity: 0.939 AUC: 0.961 Accuracy: 0.925 Sensitivity: 0.873 Specificity: 0.986 Accuracy: Five-way: 51% Mild vs severe: 78% SFU II vs. SFU III: 71%

Notes CAKUT: congenital abnormalities of the kidney and urinary tract

–

–

–

–

–

–

10  Artificial Intelligence in Hypospadiology: Role, Applications, and Benefits

155

Table 10.2 (continued) Study Hobbs et al. [73]

Objective Identifies detrusor overactivity in urodynamic studies in the spina bifida population

Eroglu et al. [74]

Diagnose and grade VUR in VCUG images Selective guidance to continuous antibiotic prophylaxis in VUR patients

Bertsimas et al. [75]

Model/ Sample size algorithm 546 unique SVM patients

Results Time-based detrusor overactivity AUC: 0.919 Sensitivity: 0.842 Specificity: 0.864 Frequency-­ based detrusor overactivity AUC: 0.905 Sensitivity: 0.683 Specificity: 0.929 1228 Hybrid CNN AUC: 0.99 subject Accuracy: 96.9% 607 patients Decision trees AUC: 0.82

Notes –

–

–

10.5 Machine Learning in Hypospadias Like other sub-fields of pediatric urology, hypospadiology is no exception in being slow to adopt AI and ML-based applications to improve clinical care. Future implementation of AI and ML technologies may lead to much-needed standardization of treatment approaches and increased precision when assessing different aspects of the hypospadias ecosystem. (Please see “Chap. 1” Toward an Ecosystem Model of Hypospadiology). Figure 10.4 outlines the Hypospadias Ecosystem. Applying ML to large multicenter datasets may shed important new light on hypospadias etiology. Precise classification of this defect will also enable clinicians and researchers around the globe to communicate more effectively, leading to improvements in risk stratification and intraoperative decision-making, as well as providing a robust basis for long-term follow-up. Figure 10.5 presents some current applications of AI in the field of hypospadiology. Current severity grading systems for hypospadias suffer from inherent subjectivity, lack of standardization, and low inter/intra-observer agreement. Recent trends in medicine have included considerable efforts to eliminate subjectivity in diagnosis,

156

M. AbdulMoniem et al.

Me Pos atal itio n

s an Gl ize S

P Le eni ng le th

l Urethra sia la p o p y H

Phenotypes

ature

Uret hra Plate l

Curv

e e

ni

yp

qu

ch

e

Te

ic

Ex

et

pe

en

G

rie

Surgic al

nc

ot

Proc

Gen

ial Rac und kgro

Bac

edu Type re

Hypospadias Ecosystem

Urethr al Stent

g ssin

Dre

Outcomes

Fig. 10.4  Hypospadias ecosystem

Artificial Intelligence in Hypospadiology

Etiology and Parental Counselling Al can play a major role in genetic analysiss of large data and linkage between phenotypes, genotypes and clinical outcomes.

Diagnosis Predication Al can significantly aid in objective classification, objective quantification of key anatomical variables like urethral plate, penile curvature

Fig. 10.5  Application of AI in hypospadiology

Treatment Planning Al could be used to help in suitable selection of surgical procedures and better resource utilizations

Procedure Outcome Prediction Improve the accuracy of risk stratification and more insightful prediction based on longitudinal long-term outcome datasets

10  Artificial Intelligence in Hypospadiology: Role, Applications, and Benefits

157

which has contributed to the emergence of several different scoring systems for hypospadias. Glans-meatus-shaft (GMS) [76] and the plate objective scoring tool (POST) [77] are two of the most recent classification systems that have demonstrated strong inter and intra-observer reliability, while also significantly aiding the prediction of surgical outcomes. Despite attempts at standardization, many scoring tools are still affected by subjectivity in surgeon evaluation of hypospadias phenotypes. Variation in diagnosis and assessment of these conditions creates significant difficulties in preoperative counseling, intraoperative decision-making, comparison of postoperative outcomes, and dialogue between different centers. For these reasons, the use of novel AI and ML modalities to perform objective and standardized hypospadias classification is of great interest, with obvious opportunities to achieve superior patient care and unify classification and reporting methods. A limited number of studies in the hypospadiology literature have employed AI and ML to begin to address these issues. Fernandez et al. [1] used a model to localize and classify 1169 hypospadias images into distal or proximal cases, resulting in 71.6% of images being labeled as distal and 28.4% as proximal. The proposed system displayed an overall accuracy of 90%, equivalent to expert human classification of hypospadias phenotype. Recently, Abbas et al. developed a fully automated system for quantifying urethral plate quality in hypospadias [not published]. The proposed framework employs a state-of-the-art ML tool to identify five anatomical landmarks that define plate objective scoring tool (POST) score [78, 79]. The original study provides a detailed description of this framework which consists of three main stages (as shown in Fig. 10.6). The first stage includes a glans localization and detection network where the input image is cropped using the predicted bounding box thus isolating the glans (glanular area) from the image background. This provides a focused region of interest within the image to improve feature extraction and landmark detection in the later stages. Next, a deep convolutional neural network (CNN) architecture is used to predict the coordinates of the five anatomical landmarks that define POST score. Finally, the predicted landmarks are used to assess the quality of the urethral plate (UP) as well as hypospadias severity. A total of 691 images from pre-pubertal boys undergoing hypospadias repair were used in this study, which achieved a robust performance in glans localization and detection tasks (mean average precision 99.5% and overall sensitivity 99.1%). Additionally, a high-resolution deep network Hypospadias AI framework Glance Localization

POST: 0.89

POST landmark detection POST score calculation

Fig. 10.6  Schematic diagram of the automatic POST labelling framework as proposed in

158

M. AbdulMoniem et al. POST: 1.07

POST: 0.96

Hypospadias AI

Fig. 10.7  Sample qualitative evaluation of the automatic POST score framework

architecture achieved superior performance in detecting the locations of the five POST landmarks, with an average normalized mean error (NME) of 0.07152 and a failure rate of 2.5% (computed with a 0.2 NME threshold under real-time constraints). A sample qualitative evaluation of framework performance is presented in Fig. 10.7. Where other frameworks operate as a black box, a major advantage of the POST system is unambiguous interpretation of decision-making, since the deep NN only defines landmark locations which are subsequently used to compute validated POST ratios independently of the model. This system is currently deployed and can be used freely via the following website: https://hypospadias-ai.netlify.app. In a similar study, Abbas et al. [80, 81] investigated the feasibility of using an AI-based application to perform measurements of penile curvature (PC) using 2D images. In this study, nine 3D-printed penile models representing various curvature angles were used to generate a dataset for training and validation of the proposed framework. A total of 900 images with different backgrounds and lighting conditions were compiled for this study. The overall approach used for automatic PC angle estimation consisted of three stages: penile area localization, shaft segmentation, and curvature angle estimation. In the first stage, the well-established YOLOv5 localization network was used to crop input images and remove irrelevant parts while preserving the penile area. In the next stage, an encoder-decoder CNN was used to segment the penile shaft region from the rest of the image. Finally, a Hough-­ Transform-­based algorithm was used to estimate the angle between the distal and

10  Artificial Intelligence in Hypospadiology: Role, Applications, and Benefits

159

proximal limbs of the penis. This framework achieved 99.4% mean average precision in penile localization, while the segmentation network displayed superior performance of 98.41% DSC and 96.87% IoU (with an overall error of 8.53° reported across all nine curvature angles).

10.6 Challenges and Future Directions The limited number of studies that have attempted to integrate ML into hypospadiology can be partly explained by the lack of large high-quality and open-access datasets from different centers and geographical locations. Without these resources, it is challenging to establish whether developed models are generalizable between clinical settings via external validation. Obtaining appropriate ethical approvals is also a significant limitation for researchers at present. Another important challenge that must be resolved is adaptation of common standardization criteria, which will be necessary to accurately describe, collect, and report data, but also for validation of new ML models. Additionally, the lack of a universal scoring system for hypospadias severity and patient outcomes results in significant variability and ambiguity during model development. The use of ML systems can systematically reduce inter-rater variability and create a more standardized way to classify and report hypospadias cases. The ability of these frameworks to guide less experienced surgeons during intraoperative decision-­ making may also lead to significant improvements in postoperative outcomes. These benefits are in addition to the early identification and rapid diagnosis of congenital abnormalities, which should reduce the healthcare burden associated with late detection. It has long been established that hypospadias phenotypes include a significant genetic component (see also “Chap. 1” Toward an Ecosystem Model of Hypospadiology), but it is difficult to correlate these diverse genotypes with surgical success [82–84]. However, ML and AI-based systems may uncover new opportunities to predict the patient outcome based on more sophisticated anatomical and non-anatomical variables that are considered at present. Future work will concentrate on creating larger medical databases and using enhanced algorithms via smartphones or accessed through cloud storage (after appropriate permissions from regulatory bodies). While currently reliant on assessing anatomical images, rapid increases in computational capacity may eventually allow the use of more complex prediction algorithms that can be trained using multiple clinical variables as well as diverse biological, cellular, and genetic data. For example, the use of standard histology together with novel imaging technologies such as ultra-high frequency ultrasonography and thermal imaging [85] may be able to predict tissue quality and healing behavior. Furthermore, data such as genetic determinants of wound healing and hypospadias pathophysiology may prove tractable for AI training purposes thus leading to improved prediction of surgical outcomes and perhaps even longer term functional problems. Figure 10.8 illustrates the perceived integrated hypospadias dataset.

160

M. AbdulMoniem et al.

Fig. 10.8  An integrated dataset including imaging, genotypic, and phenotypic data. The hub provides a multifaceted search infrastructure, allowing users to query the extensive metadata associated with each dataset

References 1. Fernandez N, Lorenzo AJ, Rickard M, Chua M, Pippi-Salle JL, Perez J, Braga LH, Matava C.  Digital pattern recognition for the identification and classification of hypospadias using artificial intelligence vs experienced pediatric urologist. Urology. 2021;147:264–9. https://doi. org/10.1016/j.urology.2020.09.019. 2. Xinquan G, Yang L, Ming Z, Rui Z, Haitao F, Gang Z, Xia C. Selection of surgical procedures and microsurgical techniques for one-stage hypospadias repair. In: Proceedings 2011 international conference on human health and biomedical engineering, HHBE 2011. 2011. https://doi. org/10.1109/HHBE.2011.6027914. 3. Keays MA, Dave S. Current hypospadias management: diagnosis, surgical management, and long-term patient-centred outcomes. Can Urol Assoc J. 2017;11(1–2 Suppl 1):S48. https://doi. org/10.5489/cuaj.4386. 4. Köhler B, Lin L, Mazen I, Cetindag C, Biebermann H, Akkurt I, Rossi R, Hiort O, Grüters A, Achermann JC.  The spectrum of phenotypes associated with mutations in steroidogenic factor 1 (SF-1, NR5A1, Ad4BP) includes severe penoscrotal hypospadias in 46, XY males without adrenal insufficiency. Eur J Endocrinol. 2009;161(2):237–42. https://doi.org/10.1530/ EJE-­09-­0067.

10  Artificial Intelligence in Hypospadiology: Role, Applications, and Benefits

161

5. Snodgrass WT, Bush N, Cost N. Tubularized incised plate hypospadias repair for distal hypospadias. J Pediatr Urol. 2010;6(4):408–13. https://doi.org/10.1016/j.jpurol.2009.09.010. 6. Long CJ, Chu DI, Tenney RW, Morris AR, Weiss DA, Shukla AR, Srinivasan AK, Zderic SA, Kolon TF, Canning DA.  Intermediate-term followup of proximal hypospadias repair reveals high complication rate. J Urol. 2017;197(3 Part 2):852–8. https://doi.org/10.1016/j. juro.2016.11.054. 7. Castagnetti M, El-Ghoneimi A. Surgical management of primary severe hypospadias in children: systematic 20-year review. J Urol. 2010;184(4):1469–75. https://doi.org/10.1016/j. juro.2010.06.044. 8. Bethell GS, Chhabra S, Shalaby MS, et al. Parental decisional satisfaction after hypospadias repair in the United Kingdom. J Pediatr Urol. 2020;16(2):164-e1. https://doi.org/10.1016/j. jpurol.2020.01.005. 9. Spinoit AF, Waterschoot M, Sinatti C, et al. Fertility and sexuality issues in congenital lifelong urology patients: male aspects. World J Urol. 2021;39:1013–9. https://doi.org/10.1007/ s00345-­020-­03121-­2. 10. Lee OT, Durbin-Johnson B, Kurzrock EA. Predictors of secondary surgery after hypospadias repair: a population based analysis of 5000 patients. J Urol. 2013;190(1):251–6. https://doi. org/10.1016/j.juro.2013.01.091. 11. Holland AJA, Smith GHH.  Effect of the depth and width of the urethral plate on tubularized incised plate urethroplasty. J Urol. 2000;164(2):489–91. https://doi.org/10.1016/ S0022-­5347(05)67408-­3. 12. Sarhan O, Saad M, Helmy T, Hafez A. Effect of suturing technique and urethral plate characteristics on complication rate following hypospadias repair: a prospective randomized study. J Urol. 2009;182(2):682–6. https://doi.org/10.1016/j.juro.2009.04.034. 13. Moran ME.  Evolution of robotic arms. J Robot Surg. 2007;1(2):103–11. https://doi. org/10.1007/s11701-­006-­0002-­x. 14. Weizenbaum J.  ELIZA—a computer program for the study of natural language communication between man and machine. Commun ACM. 1966;9(1):36–45. https://doi. org/10.1145/365153.365168. 15. Weiss S, Kulikowski CA, Safir A. Glaucoma consultation by computer. Comput Biol Med. 1978;8(1):25–40. https://doi.org/10.1016/0010-­4825(78)90011-­2. 16. Shortliffe EH, Davis R, Axline SG, Buchanan BG, Green CC, Cohen SN.  Computer-­ based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system. Comput Biomed Res. 1975;8(4):303–20. https://doi. org/10.1016/0010-­4809(75)90009-­9. 17. Kulikowski CA. Beginnings of artificial intelligence in medicine (AIM): computational artifice assisting scientific inquiry and clinical art—with reflections on present AIM challenges. Yearb Med Inform. 2019;28(01):249–56. https://doi.org/10.1055/s-­0039-­1677895. 18. Bakkar N, Kovalik T, Lorenzini I, Spangler S, Lacoste A, Sponaugle K, Ferrante P, Argentinis E, Sattler R, Bowser R. Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis. Acta Neuropathol. 2018;135:227–47. https://doi.org/10.1007/s00401-­017-­1785-­8. 19. Comendador BEV, Francisco BMB, Medenilla JS, Nacion SMT, Serac TBE.  Pharmabot: a pediatric generic medicine consultant Chatbot. J Autom Control Eng. 2015;3(2):137–40. https://doi.org/10.12720/joace.3.2.137-­140. 20. Ni L, Lu C, Liu N, Liu J. MANDY: towards a smart primary care Chatbot application. Commun Comput Inf Sci. 2017;780:38–52. https://doi.org/10.1007/978-­981-­10-­6989-­5_4. 21. Moody GB, Mark RG. The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag. 2001;20(3):45–50. https://doi.org/10.1109/51.932724. 22. Russakovsky O, Deng J, Su H, et al. Imagenet large scale visual recognition challenge. Int J Comput Vis. 2015;115:211–52. https://doi.org/10.1007/s11263-­015-­0816-­y. 23. Parakh A, Lee H, Lee JH, Eisner BH, Sahani DV, Do S. Urinary stone detection on CT images using deep convolutional neural networks: evaluation of model performance and generalization. Radiol Artif Intell. 2019;1(4):e180066. https://doi.org/10.1148/ryai.2019180066.

162

M. AbdulMoniem et al.

24. Selvarani S, Rajendran P.  Detection of renal calculi in ultrasound image using meta-­ heuristic support vector machine. J Med Syst. 2019;43(9):300. https://doi.org/10.1007/ s10916-­019-­1407-­1. 25. Längkvist M, Jendeberg J, Thunberg P, Loutfi A, Lidén M. Computer aided detection of ureteral stones in thin slice computed tomography volumes using convolutional neural networks. Comput Biol Med. 2018;97:153–60. https://doi.org/10.1016/j.compbiomed.2018.04.021. 26. Kazemi Y, Mirroshandel SA. A novel method for predicting kidney stone type using ensemble learning. Artif Intell Med. 2018;84:117–26. https://doi.org/10.1016/j.artmed.2017.12.001. 27. Kriegshauser JS, Silva AC, Paden RG, He M, Humphreys MR, Zell SI, Fu Y, Wu T. Ex vivo renal stone characterization with single-source dual-energy computed tomography: a multiparametric approach. Acad Radiol. 2015;23(8):969–76. https://doi.org/10.1016/j.acra.2016.03.009. 28. Eken C, Bilge U, Kartal M, Eray O. Artificial neural network, genetic algorithm, and logistic regression applications for predicting renal colic in emergency settings. Int J Emerg Med. 2009;2:99–105. https://doi.org/10.1007/s12245-­009-­0103-­1. 29. Mark E, Goldsman D, Gurbaxani B, Keskinocak P, Sokol J. Using machine learning and an ensemble of methods to predict kidney transplant survival. PLoS One. 2019;14(1):e0209068. https://doi.org/10.1371/journal.pone.0209068. 30. Atallah DM, Badawy M, El-Sayed A, Ghoneim MA.  Predicting kidney transplantation outcome based on hybrid feature selection and KNN classifier. Multimed Tools Appl. 2019;78:20383–407. https://doi.org/10.1007/s11042-­019-­7370-­5. 31. Abdeltawab H, Shehata M, Shalaby A, et al. A novel CNN-based CAD system for early assessment of transplanted kidney dysfunction. Sci Rep. 2019;9(1):5948. https://doi.org/10.1038/ s41598-­019-­42431-­3. 32. Takeuchi T, Hattori-Kato M, Okuno Y, Iwai S, Mikami K. Prediction of prostate cancer by deep learning with multilayer artificial neural network. Can Urol Assoc J. 2019;13(5):E145. https://doi.org/10.5489/cuaj.5526. 33. Ishioka J, Matsuoka Y, Uehara S, et  al. Computer-aided diagnosis of prostate cancer on magnetic resonance imaging using a convolutional neural network algorithm. BJU Int. 2018;122(3):411–7. https://doi.org/10.1111/bju.14397. 34. Bonekamp D, Kohl S, Wiesenfarth M, et al. Radiomic machine learning for characterization of prostate lesions with MRI: comparison to ADC values. Radiology. 2018;289(1):128–37. https://doi.org/10.1148/radiol.2018173064. 35. Arvaniti E, Fricker KS, Moret M, Rupp N, Hermanns T, Fankhauser C, Wey N, Wild PJ, Rüschoff JH, Claassen M. Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Sci Rep. 2018;8(1):12054. https://doi.org/10.1038/s41598-­018-­30535-­1. 36. Xu X, Zhang X, Tian Q, et al. Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI. Int J Comput Assist Radiol Surg. 2017;12:645–56. https://doi.org/10.1007/s11548-­017-­1522-­8. 37. Eminaga O, Eminaga N, Semjonow A, Breil B. Diagnostic classification of cystoscopic images using deep convolutional neural networks. JCO Clin Cancer Inform. 2018;2:00126. https://doi. org/10.1200/cci.17.00126. 38. Sokolov I, Dokukin ME, Kalaparthi V, Miljkovic M, Wang A, Seigne JD, Grivas P, Demidenko E. Noninvasive diagnostic imaging using machine-learning analysis of nanoresolution images of cell surfaces: detection of bladder cancer. Proc Natl Acad Sci U S A. 2018;115(51):12920–5. https://doi.org/10.1073/pnas.1816459115. 39. Wu E, Hadjiiski LM, Samala RK, et  al. Deep learning approach for assessment of bladder cancer treatment response. Tomography. 2019;5(1):201–8. 10.18383/j.tom.2018.00036 40. Yan L, Liu Z, Wang G, Huang Y, Liu Y, Yu Y, Liang C. Angiomyolipoma with minimal fat: differentiation from clear cell renal cell carcinoma and papillary renal cell carcinoma by texture analysis on CT images. Acad Radiol. 2015;22(9):1115–21. https://doi.org/10.1016/j. acra.2015.04.004. 41. Feng Z, Rong P, Cao P, Zhou Q, Zhu W, Yan Z, Liu Q, Wang W.  Machine learning-based quantitative texture analysis of CT images of small renal masses: differentiation of angiomyolipoma without visible fat from renal cell carcinoma. Eur Radiol. 2018;28:1625–33. https:// doi.org/10.1007/s00330-­017-­5118-­z.

10  Artificial Intelligence in Hypospadiology: Role, Applications, and Benefits

163

42. Coy H, Hsieh K, Wu W, Nagarajan MB, Young JR, Douek ML, Brown MS, Scalzo F, Raman SS.  Deep learning and radiomics: the utility of Google TensorFlow™ inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT.  Abdom Radiol. 2019;44:2009–20. https://doi.org/10.1007/s00261-­019-­01929-­0. 43. Li P, Ren H, Zhang Y, Zhou Z.  Fifteen-gene expression based model predicts the survival of clear cell renal cell carcinoma. Medicine (United States). 2018;97(33):11839. https://doi. org/10.1097/MD.0000000000011839. 44. Kocak B, Durmaz ES, Ates E, Ulusan MB. Radiogenomics in clear cell renal cell carcinoma: machine learning-based high-dimensional quantitative CT texture analysis in predicting PBRM1 mutation status. Am J Roentgenol. 2019;212(3):W55–63. https://doi.org/10.2214/ AJR.18.20443. 45. Blum ES, Porras AR, Biggs E, Tabrizi PR, Sussman RD, Sprague BM, Shalaby-Rana E, Majd M, Pohl HG, Linguraru MG.  Early detection of ureteropelvic junction obstruction using signal analysis and machine learning: a dynamic solution to a dynamic problem. J Urol. 2018;199(3):847–52. https://doi.org/10.1016/j.juro.2017.09.147. 46. Chiang D, Chiang HC, Chen WC, Tsai FJ.  Prediction of stone disease by discriminant analysis and artificial neural networks in genetic polymorphisms: a new method. BJU Int. 2003;91(7):661–6. https://doi.org/10.1046/j.1464-­410X.2003.03067.x. 47. Seckiner I, Seckiner S, Sen H, Bayrak O, Dogan K, Erturhan S. A neural network-based algorithm for predicting stone-free status after ESWL therapy. Int Braz J Urol. 2017;43:1110–4. https://doi.org/10.1590/S1677-­5538.IBJU.2016.0630. 48. Aminsharifi A, Irani D, Tayebi S, Jafari Kafash T, Shabanian T, Parsaei H.  Predicting the postoperative outcome of percutaneous nephrolithotomy with machine learning system: software validation and comparative analysis with guy’s stone score and the CROES nomogram. J Endourol. 2020;34(6):692–9. https://doi.org/10.1089/end.2019.0475. 49. Kadlec AO, Ohlander S, Hotaling J, Hannick J, Niederberger C, Turk TM. Nonlinear logistic regression model for outcomes after endourologic procedures: a novel predictor. Urolithiasis. 2014;42:323–7. https://doi.org/10.1007/s00240-­014-­0656-­1. 50. Dal Moro F, Abate A, Lanckriet GRG, Arandjelovic G, Gasparella P, Bassi P, Mancini M, Pagano F.  A novel approach for accurate prediction of spontaneous passage of ureteral stones: support vector machines. Kidney Int. 2006;69(1):157–60. https://doi.org/10.1038/ sj.ki.5000010. 51. Shabaniyan T, Parsaei H, Aminsharifi A, Movahedi MM, Jahromi AT, Pouyesh S, Parvin H.  An artificial intelligence-based clinical decision support system for large kidney stone treatment. Australas Phys Eng Sci Med. 2019;42:771–9. https://doi.org/10.1007/ s13246-­019-­00780-­3. 52. Huettenbrink C, Hitzl W, Sascha P, Kubitz J, Popeneciu V, Ell J. Neural networks modeling for prediction of required resources for personalized endourologic treatment of urolithiasis. J Pers Med. 2022;12(5):784. 53. Choo MS, Uhmn S, Kim JK, Han JH, Kim DH, Kim J, Lee SH. A prediction model using machine learning algorithm for assessing stone-free status after single session shock wave lithotripsy to treat ureteral stones. J Urol. 2018;200(6):1371–7. https://doi.org/10.1016/j. juro.2018.06.077. 54. Mannil M, von Spiczak J, Hermanns T, Poyet C, Alkadhi H, Fankhauser CD. Three-dimensional texture analysis with machine learning provides incremental predictive information for successful shock wave lithotripsy in patients with kidney stones. J Urol. 2018;200(4):829–36. https://doi.org/10.1016/j.juro.2018.04.059. 55. Jendeberg J, Thunberg P, Lidén M.  Differentiation of distal ureteral stones and pelvic phleboliths using a convolutional neural network. Urolithiasis. 2021;49(1):41–9. https://doi. org/10.1007/s00240-­020-­01180-­z. 56. Divya Krishna K, Akkala V, Bharath R, Rajalakshmi P, Mohammed AM, Merchant SN, Desai UB.  Computer aided abnormality detection for kidney on FPGA based IoT enabled portable ultrasound imaging system. IRBM. 2016;37(4):189–97. https://doi.org/10.1016/j. irbm.2016.05.001.

164

M. AbdulMoniem et al.

57. Kobayashi M, Ishioka J, Matsuoka Y, et al. Computer-aided diagnosis with a convolutional neural network algorithm for automated detection of urinary tract stones on plain X-ray. BMC Urol. 2021;21:102. https://doi.org/10.1186/s12894-­021-­00874-­9. 58. Nithya A, Appathurai A, Venkatadri N, Ramji DR, Anna Palagan C.  Kidney disease detection and segmentation using artificial neural network and multi-kernel k-means clustering for ultrasound images. Measurement (Lond). 2020;149:106952. https://doi.org/10.1016/j. measurement.2019.106952. 59. Greco R, Papalia T, Lofaro D, Maestripieri S, Mancuso D, Bonofiglio R.  Decisional trees in renal transplant follow-up. Transplant Proc. 2010;42:1134–6. https://doi.org/10.1016/j. transproceed.2010.03.061. 60. Goldfarb-Rumyantzev AS, Scandling JD, Pappas L, Smout RJ, Horn S. Prediction of 3-year cadaveric graft survival based on pre-transplant variables in a large national dataset. Clin Transplant. 2003;17(6):485–97. https://doi.org/10.1046/j.0902-­0063.2003.00051.x. 61. Bägli DJ, Agarwal SK, Venkateswaran S, Shuckett B, Khoury AE, Merguerian PA, McLorie GA, Liu K, Niederberger CS.  Artificial neural networks in pediatric urology: prediction of sonographic outcome following pyeloplasty. J Urol. 1998;160(3 Part 2):980–3. https://doi. org/10.1016/S0022-­5347(01)62675-­2. 62. Logvinenko T, Chow JS, Nelson CP.  Predictive value of specific ultrasound findings when used as a screening test for abnormalities on VCUG. J Pediatr Urol. 2015;11(4):176-e1. https:// doi.org/10.1016/j.jpurol.2015.03.006. 63. Papadopoulos H, Anastassopoulos G.  Probabilistic prediction for the detection of vesicoureteral reflux. Commun Comput Inf Sci. 2013;14:253–62. https://doi. org/10.1007/978-­3-­642-­41013-­0_26. 64. Estrada CR, Nelson CP, Wang HH, Bertsimas D, Dunn J, Li M, Zhuo D. Targeted workup after initial febrile urinary tract infection: using a novel machine learning model to identify children most likely to benefit from voiding cystourethrogram. J Urol. 2019;202(1):144–52. https://doi. org/10.1097/JU.0000000000000186. 65. Kwong JC, Khondker A, Kim JK, et al. Posterior urethral valves outcomes prediction (PUVOP): a machine learning tool to predict clinically relevant outcomes in boys with posterior urethral valves. Pediatr Nephrol. 2022;37(5):1067–74. https://doi.org/10.1007/s00467-­021-­05321-­3. 66. Zheng Q, Furth SL, Tasian GE, Fan Y. Computer-aided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating ­texture image features and deep transfer learning image features. J Pediatr Urol. 2019;15(1):75-­e1. https://doi.org/10.1016/j.jpurol.2018.10.020. 67. Tokar B, Baskaya M, Celik O, Cemrek F, Acikgoz A. Application of machine learning techniques for enuresis prediction in children. Eur J Pediatr Surg. 2021;31(05):414–9. https://doi. org/10.1055/s-­0040-­1715655. 68. Lee H, Yoo B, Baek M, Choi JY. Prediction of recurrent urinary tract infection in paediatric patients by deep learning analysis of 99mTc-DMSA renal scan. Diagnostics. 2022;12(2):424. https://doi.org/10.3390/diagnostics12020424. 69. Khondker A, Kwong JCC, Rickard M, Skreta M, Keefe DT, Lorenzo AJ, Erdman L. A machine learning-based approach for quantitative grading of vesicoureteral reflux from voiding cystourethrograms: methods and proof of concept. J Pediatr Urol. 2022;18(1):78-e1. https://doi. org/10.1016/j.jpurol.2021.10.009. 70. Keskinoğlu A, Özgür S. The use of artificial neural networks for differential diagnosis between vesicoureteral reflux and urinary tract infection in children. J Pediatr Res. 2020;7:24650. https://doi.org/10.4274/jpr.galenos.2019.24650. 71. Yin S, Peng Q, Li H, Zhang Z, You X, Fischer K, Furth SL, Fan Y, Tasian GE. Multi-instance deep learning of ultrasound imaging data for pattern classification of congenital abnormalities of the kidney and urinary tract in children. Urology. 2020;142:183–9. https://doi.org/10.1016/j. urology.2020.05.019. 72. Smail LC, Dhindsa K, Braga LH, Becker S, Sonnadara RR. Using deep learning algorithms to grade hydronephrosis severity: toward a clinical adjunct. Front Pediatr. 2020;8:1. https://doi. org/10.3389/fped.2020.00001.

10  Artificial Intelligence in Hypospadiology: Role, Applications, and Benefits

165

73. Hobbs KT, Choe N, Aksenov LI, Reyes L, Aquino W, Routh JC, Hokanson JA. Machine learning for urodynamic detection of detrusor overactivity. Urology. 2022;159:247–54. https://doi. org/10.1016/j.urology.2021.09.027. 74. Eroglu Y, Yildirim K, Çinar A, Yildirim M. Diagnosis and grading of vesicoureteral reflux on voiding cystourethrography images in children using a deep hybrid model. Comput Methods Prog Biomed. 2021;210:106369. https://doi.org/10.1016/j.cmpb.2021.106369. 75. Bertsimas D, Li M, Estrada C, Nelson C, Scott Wang HH. Selecting children with vesicoureteral reflux who are most likely to benefit from antibiotic prophylaxis: application of machine learning to RIVUR. J Urol. 2021;205(4):1170–9. https://doi.org/10.1097/JU.0000000000001445. 76. Merriman LS, Arlen AM, Broecker BH, Smith EA, Kirsch AJ, Elmore JM. The GMS hypospadias score: assessment of inter-observer reliability and correlation with post-operative complications. J Pediatr Urol. 2013;9(6):707–12. https://doi.org/10.1016/j.jpurol.2013.04.006. 77. Abbas TO, Vallasciani S, Elawad A, Elifranji M, Leslie B, Elkadhi A, Pippi Salle JL. Plate Objective Scoring Tool (POST); an objective methodology for the assessment of urethral plate in distal hypospadias. J Pediatr Urol. 2020;16(5):675–82. https://doi.org/10.1016/j. jpurol.2020.07.043. 78. Abbas TO. The plate objective scoring tool (POST): Further reflections and extended applications. Res Rep Urol. 2021;2021:21188. https://doi.org/10.2147/RRU.S321188. 79. Abbas TO, , Braga LH, Spinoit AF, Salle JP (2021) Urethral plate quality assessment and its impact on hypospadias repair outcomes: a systematic review and quality assessment. J Pediatr Urol 17(3):316-325 https://doi.org/10.1016/j.jpurol.2021.02.017. 80. Abbas TO. Evaluation of penile curvature in patients with hypospadias; gaps in the current practice and future perspectives. J Pediatr Urol. 2022;18(2):151–9. https://doi.org/10.1016/j. jpurol.2021.12.015. 81. Abbas TO, AbdelMoniem M, Chowdhury MEH. Automated quantification of penile curvature using artificial intelligence. Front Artif Intell. 2022;5:954497. https://doi.org/10.3389/ frai.2022.954497. 82. Brouwers MM, van der Zanden LFM, de Gier RPE, Barten EJ, Zielhuis GA, Feitz WFJ, Roeleveld N.  Hypospadias: risk factor patterns and different phenotypes. BJU Int. 2010;105(2):254–62. https://doi.org/10.1111/j.1464-­410X.2009.08772.x. 83. van Rooij IALM, van der Zanden LFM, Brouwers MM, Knoers NVAM, Feitz WFJ, Roeleveld N.  Risk factors for different phenotypes of hypospadias: results from a Dutch case-control study. BJU Int. 2013;112(1):121–8. https://doi.org/10.1111/j.1464-­410X.2012.11745.x. 84. Woud SG, van Rooij IALM, van Gelder MMHJ, Olney RS, Carmichael SL, Roeleveld N, Reefhuis J. Differences in risk factors for second and third degree hypospadias in the national birth defects prevention study. Birth Defects Res A Clin Mol Teratol. 2014;100(9):703–11. https://doi.org/10.1002/bdra.23296. 85. Kukkonen TM, Binik YM, Amsel R, Carrier S.  Thermography as a physiological measure of sexual arousal in both men and women. J Sex Med. 2007;4(1):93–105. https://doi. org/10.1111/j.1743-­6109.2006.00399.x.