Editor’s Note: The following is from the April 2025 issue of The Journal of Urology^®. Visit The Journal of Urology Current Issue page to access these articles. Reprinted with permission from Siemens DR, Routh JC. Editors’ Choice. J Urol. 2025;213(4):403-404. doi:10.1097/JU.0000000000004444

Creation and Validation of Staging Systems and Prognostic Models in Urology

Staging systems and prognostic models are critical tools in the management of disease, offering a structured framework to classify severity and risk, to guide treatment decisions, and with the potential to predict outcomes for our patients. Although many of these tools exist in urology and are common points of communication in our literature, their uptake and utility in routine practice is likely relatively low.^1,2

These tools can fall short in capturing the full complexity and individual patient variability of urological conditions. Many are derived from limited patient cohorts reducing their utility to contemporary or diverse populations. Continuous refinement, validation across broader populations, and integration of novel biomarkers are essential to ensure they remain relevant. More recently, machine learning–based models have the potential to improve the effectiveness of prognostic tools, but they also introduce challenges that could limit adoption because they are seen as “black boxes,” lacking the transparency in decision-making. Finally, many of our tools are duplications for common diseases and do not address less common and understudied conditions, where clinicians could use more standardized communication to support patient counseling and multi-institutional research collaboration.

These themes are addressed in 3 different manuscripts in this issue of The Journal of Urology^®. First, Robert et al³ aimed to validate existing prognostic models for recurrence, cancer-specific mortality, and all-cause mortality in patients with nonmetastatic kidney cancer treated surgically. Using data from 7174 patients in a Canadian, multicentered collaborative, the performance of many different models were expertly analyzed at 5 years after surgery. The authors demonstrate considerable variability in model performance, but among those tested, they conclude that the Mayo Clinic prediction models demonstrated superior performance in guiding adjuvant therapy decisions.

Next, Erickson et al⁴ develop and validate a staging system for anterior urethral stricture disease based on the Length (L), Urethral Segment (S), and Etiology (E) classification system. The team created a novel Urethroplasty Triad Score to stratify stricture severity by evaluating functional outcomes, urethral meatus location, and the number of surgeries required. The staging system, consisting of 5 stages and 10 substages, demonstrated a clear progression in severity as reflected by decreasing Urethroplasty Triad Scores. Validation in a separate cohort confirmed the system’s reliability. The authors conclude that the LSE staging system will enhance communication about stricture complexity for these patients and facilitate standardized reporting for future research.

Finally, Lange et al⁵ address the variability in both the feasibility and outcomes of endoscopic management of upper tract urothelial carcinoma (UTUC) by developing a standardized assessment tool, the Upper TRACT Endometry Score. Using a modified Delphi method, international experts identified 5 consensus categories to construct the scoring system, which was validated retrospectively using data from a cohort of patients with UTUC undergoing endoscopic management. The analysis showed that higher scores were associated with an increased likelihood of requiring more intensive interventions. The authors conclude that the Upper TRACT Endometry Score, after further validation, could be used as a simple tool for counseling patients and standardizing reporting of factors influencing conservative management in UTUC.

Since the initial formation of the Intergroup Rhabdomyosarcoma Study Group over 50 years ago, treatment regimens have steadily evolved from radical surgery to an emphasis on organ-sparing surgery, chemotherapy, and radiation. While mortality has clearly improved during that evolution, what is missing is the impact of those treatments on the survivors; just because a bladder or vagina remains in situ after cancer treatment, it does not necessarily function properly. Functional outcomes have unfortunately remained frustratingly elusive. Saunders et al⁶ have gone a long way to filling that gap; they performed a qualitative analysis of survivors of childhood genitourinary rhabdomyosarcoma, particularly examining their perspectives on continence, sexuality, body image, and relationships. This paper should be mandatory reading for any clinician involved in the care of individuals with these rare tumors because it has something to offer both when shaping a treatment plan at the tumor board and after the child has progressed on to becoming a cancer survivor.

On a more quantitative note, it can sometimes be difficult to balance the burdens of surveillance with the need to prevent calamity (or at least a UTI) in infants with primary obstructive megaureter. If only clinicians could enumerate risk factors for children who are more likely to progress and could then titrate their surveillance regimen in a risk-adjusted manner to that end, Khondker et al⁷ examined a large, single-institution cohort of children with primary obstructive megaureter and defined risk factors for nonresolution after censoring for both surgical intervention and loss to follow-up. Whether these risk factors are generalizable to other centers and practice patterns will be important to assess in the future, but this is a key first step.

1. Kim SP, Karnes RJ, Nguyen PL, et al. Clinical implementation of quality of life instruments and prediction tools for localized prostate cancer: results from a national survey of radiation oncologists and urologists. J Urol. 2013;189(6):2092-2098. doi:10.1016/j.juro.2012.11.174
2. Forbes CM, McCoy AB, Hsi RS. Clinician versus nomogram predicted estimates of kidney stone recurrence risk. J Endourol. 2021;35(6):847-852. doi:10.1089/end.2020.0978
3. Robert A, Mallick R, McIsaac DI, et al. Validation of prognostic models for renal cell carcinoma recurrence, cancer-specific mortality and all-cause mortality. J Urol. 2025;213(4):455-466. doi:10.1097/JU.0000000000004348
4. Erickson BA, Tuong MN, Zorn AN, et al. Development and validation of the Length, Segment, and Etiology anterior urethral stricture disease staging system using longitudinal urethroplasty outcomes data from the trauma and urologic reconstruction network of surgeons. J Urol. 2025;213(4):512-523. doi:10.1097/JU.0000000000004369
5. Lange S, Reinhardt A, Igel D, et al. The Upper TRACT Endometry Score: development and internal validation of an objective measure of variables that impact endoscopic procedures for upper tract urothelial carcinoma. J Urol. 2025;213(4):467-474. doi:10.1097/JU.0000000000004383
6. Saunders RA, Balthazar AK, Jaeger CD, et al. Long-term urinary and sexual outcomes in pediatric genitourinary rhabdomyosarcoma survivors: a qualitative study. J Urol. 2025;213(4):494-503. doi:10.1097/JU.0000000000004374
7. Khondker A, Kim JK, Ahmad I, et al. Spontaneous resolution of primary obstructive megaureter: risk stratification and prediction based on early sonographic factors. J Urol. 2025;213(4):485-493. doi:10.1097/JU.0000000000004355