No pan‐India‐specific guidelines exist for the management of urological cancers. Although western guidelines are useful for informing management strategies, they do not account for the nuances of management in the Indian context. A modified Delphi method was used to provide a framework for the systematic development of India‐centric guidelines for the management of three uro‐oncology disease states: small renal masses, non‐muscle invasive bladder cancer and high‐risk/locally advanced prostate cancer.

ObjectivesTo assess the role of ultrasensitive PSA values (usPSA) after radical prostatectomy in predicting the subsequent biochemical recurrence (BCR).Material and methodsThe study included 1836 patients who underwent open or robot‐assisted RP at Turku University Hospital between 2003 and 2018. Exclusion criteria involved patients with adjuvant treatments and those who did not reach a PSA nadir <0.1 ng/ml, resulting in a final cohort of 1313 patients. The prognostic impact of the optimal usPSA nadir cut‐off value 6 months after RP was investigated to predict subsequent BCR for the whole cohort (N = 1313). The optimal usPSA cut‐off value was determined for patients at 3–5 years post‐surgery (N = 806) and beyond 5 years (N = 493) of follow‐up. We used the area under the curve (AUC) calculation and the Kaplan–Meier method.ResultsIn a cohort with a median age of 64, primarily featuring Gleason score 7 prostate cancer. uPSA nadir of 0.01 ng/ml (AUC = 0.80) at the first monitoring post‐surgery emerged as the optimal cut‐off for identifying subjects at low (80%) or high (20%) risk of BCR within the first 3 years. Beyond this period, uPSA values during the first 3 [(AUC = 0.89; 3–5 years post‐surgery) and (AUC = 0.81; beyond 5 years)] and 5 post‐surgery years (AUC = 0.85) outperformed uPSA nadir in predicting subsequent BCR. Notably, EAU‐defined high‐risk patients with low uPSA nadir maintained substantial BCR‐free survival.ConclusionIn conclusion, a low usPSA predicts minimal BCR risk over the next 2–3 years post‐measurement. Patients with low usPSA can benefit from reduced post‐surgery PSA monitoring at 2‐ to 3‐year intervals without compromising outcomes. This strategic approach optimizes resource allocation in busy urological outpatient clinics, especially valuable in publicly reimbursed healthcare systems like Finland.

PurposeThis study aimed to report a newly developed, high‐fidelity synthetic simulator to simulate excision and primary anastomotic (EPA) bulbar urethroplasty and its clinical use for new practitioners in shortening the learning curve.Material and MethodsThe bulbar urethral anastomosis simulator consists of several standardized components created according to the actual size of the male patient. Interns, novice residents, and fellows inexperienced with urethral reconstruction (n = 10, 5, 5) from different medical centres were invited to participate in the training programme. Two reconstructive urology experts monitored each practice. Following the training, three kinds of validity testing were used to assess the simulator: face, content, and construct. In the intern group, the task performance in the first five training sessions and the last five training ones were compared using a self‐control approach. In the resident and fellow group, the real surgical data, including estimated blood loss, operative duration, and 6‐month post‐operative success rate of trainees after training, are plotted, which are compared with that of reconstructive urology experts (n = 5) included retrospectively to study the effectiveness of the simulator in shortening the learning curve.ResultsThe overall mean satisfaction rate for the simulators was inspiring and evaluated by experts. In the intern group, significant improvement can be achieved through 10 training sessions (p < 0.05). In clinical practice, the intraoperative indicators and surgical success rate of both the training groups showed the tendency to close or even better than those in the expert group. In terms of the learning curve, training groups performed better compared with experts in the early stages of their careers.ConclusionsIn conclusion, this synthetic training simulator for bulbar urethral anastomosis is novel, effective, and convenient for beginners of different groups. The training course can bridge the gap between preclinical use and actual surgery via this simulator.

PurposeThe purpose of this study is to investigate the impact of Mayo Adhesive Probability (MAP) score and body mass index (BMI) on renal function decline after robotic assisted partial nephrectomy (RAPN).MethodsWe queried our prospective database for patients who underwent RAPN between January 2018 and December 2023. Outcomes were development of de novo CKD‐S3 (estimated glomerular filtration rate [eGFR] < 60 ml/min/1.73 m2). Multivariable analysis (MVA) via Cox regression identified predictors for CKD‐S3. Kaplan–Meier Analyses was fitted for survival assessment. Finally, multivariable linear regression was utilized to identify predictors of delta eGFR at last follow‐up (preoperative eGFR—last eGFR).ResultsTwo‐hundred fifty‐eight patients were analysed (obese n = 49 [19%]; MAP score 0–2 = 135 [52.33%]; MAP score 3–5 = 123 [47.6%]) with a median follow‐up of 33 (IQR 20–42) months. MVA revealed, high MAP score (HR 2.29, p = 0.019), increasing RENAL score (HR 1.26, p = 0.009), increasing age (HR 1.04, p = 0.003), obesity (HR 2.38, p = 0.006) and diabetes mellitus (HR 2.38, p = 0.005) as associated with increased risk of development of CKD‐S3, while trifecta achievement was not (p = 0.63). Comparing low MAP score versus high MAP score 4‐year CKD‐S3 free survival was 87.8% versus 56.1% (p < 0.001). Multivariable linear regression showed that high MAP score (coefficient 6.64, p = 0.001) and BMI (coefficient 0.51, p = 0.011) were significantly associated with increased delta eGFR at last follow up.ConclusionsMAP score and increasing BMI are predictor for long term renal functional detrimental. These insights may call consideration for closer follow‐up or greater medical scrutiny prior surgery in obese patients and with elevated MAP score. Further investigations are requisite.

ObjectiveThe objective of this study is to compare detection rates of extracapsular extension (ECE) of prostate cancer (PCa) using artificial intelligence (AI)‐generated cancer maps versus MRI and conventional nomograms.Materials and methodsWe retrospectively analysed data from 147 patients who received MRI‐targeted biopsy and subsequent radical prostatectomy between September 2016 and May 2022. AI‐based software cleared by the United States Food and Drug Administration (Unfold AI, Avenda Health) was used to map 3D cancer probability and estimate ECE risk. Conventional ECE predictors including MRI Likert scores, capsular contact length of MRI‐visible lesions, PSMA T stage, Partin tables, and the “PRedicting ExtraCapsular Extension” nomogram were used for comparison.Postsurgical specimens were processed using whole‐mount histopathology sectioning, and a genitourinary pathologist assessed each quadrant for ECE presence. ECE predictors were then evaluated on the patient (Unfold AI versus all comparators) and quadrant level (Unfold AI versus MRI Likert score). Receiver operator characteristic curves were generated and compared using DeLong's test.ResultsUnfold AI had a significantly higher area under the curve (AUC = 0.81) than other predictors for patient‐level ECE prediction. Unfold AI achieved 68% sensitivity, 78% specificity, 71% positive predictive value, and 75% negative predictive value. At the quadrant level, Unfold AI exceeded the AUC of MRI Likert scores for posterior (0.89 versus 0.82, p = 0.003), anterior (0.84 versus 0.80, p = 0.34), and all quadrants (0.89 versus 0.82, p = 0.002). The false negative rate of Unfold AI was lower than MRI in both the anterior (−60%) and posterior prostate (−40%).ConclusionsUnfold AI accurately predicted ECE risk, outperforming conventional methodologies. It notably improved ECE prediction over MRI in posterior quadrants, with the potential to inform nerve‐spare technique and prevent positive margins. By enhancing PCa staging and risk stratification, AI‐based cancer mapping may lead to better oncological and functional outcomes for patients.

BackgroundThe objective of this study is to assess whether urethral preservation can be performed safely using frozen section analysis (FSA) of the urethral stump on urethral recurrence after radical cystectomy.MethodsBetween June 2012 and July 2022, we investigated consecutive male patients who underwent urethral FSA during radical cystectomy for urothelial carcinoma. For FSA‐abnormal cases, urethrectomy was performed, and for FSA‐normal cases, the urethra was preserved. The diagnostic accuracy of FSA was assessed in comparison with the pathological findings of the permanent sections of the same tissue. Postoperatively, computed tomography and urinary cytology were performed as routine surveillance of recurrence.ResultsOf the 77 patients included in this study, three patients with abnormal FSA underwent concurrent urethrectomy. The negative predictive value of urethral FSA was 100%. With a median postoperative follow‐up of 38 months (interquartile ranges 21–71), no urethral recurrence was observed.ConclusionsFSA may be useful in determining the indication for urethrectomy.

Advancements in artificial intelligence (AI) have shown promise in standardizing medical imaging evaluations, particularly in detecting prostate cancer (PCa) on MRI.1 Though MRI-based AI algorithms have been developed to detect PCa in untreated glands,2, 3 little research exists on the efficacy of such models after prostate ablation. While focal therapy (FT) targets and destroys localized PCa, it usually distorts prostate anatomy, making it difficult to evaluate on MRI.4 Our study investigates the efficacy of a biparametric MRI (bpMRI)-based deep learning algorithm for post-FT PCa identification. This retrospective cohort study utilized post-FT prostate bpMRIs from an IRB-approved clinical trial (NCT03354416). MRIs were evaluated with a previously developed AI model, a 3D U-Net-based deep neural network that can detect suspicious lesions on untreated prostate bpMRIs based on T2-weighted images, apparent diffusion coefficient maps and high b-value diffusion-weighted images (Figure 1A–C).5 This algorithm was originally trained using a diverse MRI dataset obtained from treatment-naïve patients. AI output consisted of PCa-suspicious lesion prediction maps overlayed on T2-weighted MRI (Figure 1D). Predictions were compared to MRI/transrectal ultrasound fusion-guided and systematic prostate biopsies. A patient-level analysis was performed where if at least one location containing Gleason Grade ≥1 disease was detected by the AI, this was a true positive. If an AI prediction was made in an area that turned out to be benign on biopsy, this was a false positive, even if biopsy revealed malignancy in a different region of the prostate. Patients with biopsy-proven PCa lesions that were not predicted by AI were false negatives. If AI made no predictions in a patient with a fully benign prostate biopsy, this was a true negative. AI performance metrics included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and overall accuracy. Of the 40 included patients, the median time to post-FT MRI was 2.5 years, and 25 patients had PCa at biopsy. AI made 33 unique lesion predictions across 24 patients. Of these, 16 patients (67%) had one lesion prediction, 7 patients (29%) had two lesion predictions and 1 patient (4%) had three lesion predictions. Across all AI predictions in this cohort, 9 patients (22.5%) had true positives, 15 patients (37.5%) had false positives, 10 patients (25%) had false negatives and 6 patients (15%) had true negatives. The AI's overall sensitivity was 47.4% with a specificity of 28.6%. The PPV and NPV were both 37.5%. Overall, the AI achieved an accuracy of 37.5%. The performance characteristics of this model are listed in Table 1. Our AI reached a moderate level of sensitivity. Despite low specificity and overall accuracy, this is a noteworthy finding, as this AI algorithm was trained on treatment-naïve glands and not on post-FT images. The 47% sensitivity rate underscores its potential and future effectiveness if specific training with post-FT images could be achieved. This compares favourably to radiologist interpretations of MRI post-FT, with some series demonstrating sub-50% sensitivity.6, 7 Additionally, post-FT MRI analysis typically relies on dynamic contrast-enhanced (DCE) imaging over typical bpMRI sequences.8 However, our AI is based on bpMRI and does not include DCE MRI, so additional training on DCE data would likely require a complete renovation of the AI model. Besides reliance on bpMRI, another limitation was the use of targeted and systematic prostate biopsies as the ground truth. While having whole gland specimens could have demonstrated whether lesions detected only by the AI were true or false positives, this may have resulted in a selection bias in our study population, as not all patients undergo surgery. Additionally, targeted biopsies were performed based on original prospective MRI read-outs and not AI predictions. A standard-of-care system for radiologist analysis of post-FT images has yet to be established, although the PI-FAB system shows promise.8 Future AI algorithms will merit comparison to such standardized systems of interpretation. In conclusion, the performance of this model in the post-FT setting is noteworthy given the limitations of its training data and may already perform similarly to radiologist reads, although further research is necessary. This study provides motivation to improve the performance of a general AI model for prostate cancer lesion detection and serves as an initial step in understanding the potential role of AI in PCa detection in post-FT patients. The authors declare no conflict of interest.

Introduction and aimsRetroperitoneal fibrosis (RPF) is a fibroinflammatory disease in which patients may suffer obstructive uropathy (OU). The optimum treatment strategy for RPF with secondary OU is currently unclear, and the aim of this literature review is to assess the methods used to treat this patient cohort.MethodsMedline, Embase, Cinahl, the Cochrane Library and PubMed were systematically searched to find studies assessing treatment outcomes in this patient cohort. After reviewing the studies' titles, abstracts and full texts, 12 were found that matched our search aims. Data from these publications were analysed and reported.ResultsThe demographic and symptomatic features of patients across the 12 studies were representative of the general RPF population. No randomised control trials (RCTs) were found, and just one study formally compared outcomes between patients who underwent different treatment strategies. Many of the studies concluded that using medical and surgical methods in combination led to positive outcomes; whereas, others found positive outcomes following a variety of regimens. Many studies also highlighted, however, that significant minorities required further treatment after initial therapy. Conclusions regarding optimum treatment methods were limited as most publications did not formally compare outcomes following different strategies and had an observational study design.ConclusionAlthough positive outcomes were commonly seen following medical, surgical and a combination of treatments, the literature currently lacks research formally comparing outcomes after assigning specific treatment protocols to groups of RPF patients. More research is therefore required to determine how to best manage RPF leading to secondary OU.