Citations

Introduction: Ureteral stones are a common cause of emergency presentations. Definitive stone removal at the index encounter can reduce delays and repeat interventions. It is unknown whether fellowship-trained endourologists are more likely to perform definitive management than general/other subspecialist urologists. This multicenter retrospective cohort study evaluated the influence of endourology subspecialty training on the choice between upfront definitive stone treatment and initial ureteral decompression at first presentation. Patients managed with medical expulsive therapy alone were not included. Methods: Adults (≥18 years) presenting at 2022 to seven North American academic centers with symptomatic, noninfected, computed tomography-confirmed ureteral stones were included. Patients with infection, sepsis, positive nitrites, or intraoperative pyelonephritis were excluded from the study. Variables included demographics, stone size and location, subspecialty of the on-call urologist, and management type (definitive [ureteroscopy or shockwave lithotripsy] vs deferred [(initial stent]). Multivariate logistic regression was used to identify the predictors of definitive treatment. Results: Among the 395 patients, 190 (48.0%) underwent definitive management and 205 (52.0%) underwent deferred treatment. Distal stones were treated definitively more often than proximal stones were (51.1% vs 36.6%). An endourologist was on call for 150 encounters (38.0%); 64.7% of these patients received definitive treatment, compared with 38.0% under general/other subspecialist urologists. On multivariate analysis, endourologist on-call remained independently associated with definitive management (odds ratio [OR] 3.46, 95% confidence interval [CI] 2.19–5.47, p < 0.0001), as did distal stone location (OR: 2.46, 95% CI: 1.21–5.00, p = 0.013). Severe complications are rare, and none of the patients developed sepsis. Conclusions: Endourologists were more than three times more likely to perform definitive interventions for symptomatic, noninfected ureteral stones at initial presentation. Subspecialty expertise and timely operative access enable safe, efficient, single-stage management.

Purpose: Compared with white-light cystoscopy (WLC), blue-light cystoscopy (BLC) enhances detection of non–muscle-invasive bladder cancer (NMIBC), especially carcinoma in situ (CIS). However, its effects on health care resource utilization and cost burden have not been fully elucidated. This study assessed the economic implications of BLC vs WLC. Materials and Methods: We conducted a retrospective cohort analysis with claims data from the Optum Research Database between June 2011 and May 2023. Patients who underwent BLC or WLC were identified, and white-light patients were matched 6:1 to blue-light patients by index year and time between bladder cancer diagnosis and cystoscopy. Inverse probability of treatment weighting was used to adjust for baseline differences. Health care resource utilization and total and bladder cancer–specific costs were calculated on a per-patient-per-month basis. Results: The final cohort included 794 blue-light and 4764 white-light patients. Before weighting, claims coded for CIS were more frequent in blue-light patients (19.6% vs 8.8%; P < .001). Blue-light patients underwent more upper-tract imaging (71.8% vs 57.8%; P < .001), BCG (24.2% vs 15.8%; P < .001), and biomarker testing (65.0% vs 34.9%; P < .001). After weighting, blue-light patients had a higher mean number of bladder cancer–related ambulatory visits per month (1.3 vs 1.0; P = .004). However, neither all-cause health care resource utilization (3.47 vs 3.21; P = .11) nor total cost ($2987.93 vs $2886.16, per patient per month, P = .65) differed significantly between cohorts. Conclusions: BLC is associated with greater health care resource utilization for NMIBC without significantly elevating health care costs, suggesting that enhanced diagnostic strategies can be implemented without added financial burden in real-world practice.

Objectives To comprehensively review the available literature on prehabilitation and rehabilitation exercise, nutrition, and psychological support interventions for patients with kidney cancer (KC), to summarise the clinically relevant efficacy and cost‐effectiveness of interventions, to expose key knowledge gaps, and to inform future investigations and initiatives. Methods This review was performed according to the per Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) Extension for Scoping Reviews Guidelines. We included randomised controlled trials (RCTs) assessing programmes of prehabilitation or rehabilitation, exercise, psychological support, or nutrition components in patients with KC or KC caregivers from January 2004 to March 2022. Statistically significant positive (SS+) and negative (SS−) results were summarised. Results The systematic search yielded 10 968 records including 18 RCTs, involving 2774 unique subjects, 706 of whom were KC patients/survivors. None included caregivers or assessed cost‐effectiveness. Two interventions were implemented before surgery, one was implemented prior to initiation of systemic therapy, eight were implemented during systemic or radiation therapy, three were implemented after treatment, while implementation time was not specified for four interventions. Conclusion Most (14/18) RCTs involving exercise, nutrition, psychological support, or prehabilitative or rehabilitative programmes for KC performed to date demonstrated SS+ results. The evidence was most robust regarding previously evaluated psychological support, especially mindfulness‐based interventions (9/10 studies demonstrating SS+ improvement in primary outcomes), followed by therapeutically valid exercise with/without psychological support (5/8 RCTs demonstrated efficacy), then nutrition or pharmacological interventions (2/5 demonstrated efficacy). Level 1 evidence supports counselling patients and referral to mindfulness‐based psychological therapy along with physical therapy or physical medicine and rehabilitation, as well as consideration of preoperative carbohydrate drinks. No studies have examined impact on caregivers, or cost‐effectiveness, which are both critical areas for future study.

Prostate-specific antigen (PSA) screening has played a critical role in reducing prostate cancer mortality but remains controversial due to concerns regarding overdiagnosis, overtreatment, and inconsistent screening guidelines. In 2012, the United States Preventive Services Task Force (USPSTF) issued a grade D recommendation against PSA screening, leading to a national decline in PSA testing and prostate biopsy rates. This was followed by an increase in the incidence of metastatic prostate cancer at diagnosis. The USPSTF has since revised its recommendation to grade C, emphasizing shared decision-making for men aged 55 to 69 years. However, variability in screening recommendations across professional societies has resulted in clinical uncertainty, particularly for primary care physicians (PCPs), who frequently initiate PSA testing and urology referrals. This review provides an evidence-based overview of PSA screening tailored for the PCP. We performed a comprehensive literature review using Ovid MEDLINE, PubMed, Embase, and the Cochrane Library, including systematic reviews published between January 2020 and April 2025, and current guidelines from the American Urological Association (AUA), National Comprehensive Cancer Network (NCCN), European Association of Urology (EAU), and American Cancer Society (ACS). We summarize the evolving landscape of PSA screening and its integration with contemporary tools such as shared decision-making, multiparametric magnetic resonance imaging (mpMRI), and transperineally targeted biopsy. We advocate for initiating PSA screening between the ages of 45 and 49 in men with average or elevated risk, based on long-term evidence demonstrating the value of an early baseline PSA level and risk-stratified follow-up. This approach, coupled with improved diagnostic tools, can help PCPs navigate guideline variability and implement a rational, common-sense strategy to maximize early detection while minimizing harm.

458 Background: Renal angiomyolipoma (AML) is a common benign solid tumor of the kidney, typically found incidentally through imaging. These tumors can also present with symptoms like flank pain, hematuria, or retroperitoneal bleeding. AMLs are usually monitored and treated when they reach sizes between 4 and 6 cm based on clinical judgment, although the timing for intervention remains debated. This study aimed to investigate growth rates, assess radiographic features, and evaluate trends in clinical presentation. Methods: This multicenter, single-institution retrospective study reviewed patients diagnosed with renal AML at three Mayo Clinic locations (Rochester, Arizona, Jacksonville) from 1997 to 2021. Eligible patients had at least three abdominal scans over a minimum of two years and 36 months of follow-up. We reviewed radiologic reports, images, and charts to record demographics, tumor characteristics, imaging modalities, intervention rates, tumor sizes, and growth rates. The Mann-Whitney and Fisher exact tests were used for comparisons. Results: Among 839 patients, most were female (81.6%), with a median age of 58.55 years. Bleeding patients had significantly larger initial tumors (median 6.50 cm) compared to non-bleeding patients (median 1.20 cm), p < 0.001. Treatment with everolimus was more common in bleeding patients (12.2% vs. 2.1%), and the prevalence of tuberous sclerosis was higher in the bleeding group (19.5% vs. 6.3%). All bleeding patients underwent intervention, while 22.3% of the overall cohort received treatment. Follow-up duration and total scans did not differ significantly between groups. Over 12 months, non-bleeding patients had a median growth change of 0.00 cm [IQR: 0.00, 0.30], whereas bleeding patients showed a median growth change of 0.40 cm [IQR: 0.15, 1.00], p = 0.002. Conclusions: This study reveals significant differences in characteristics and growth patterns of renal angiomyolipomas between bleeding and non-bleeding patients. Bleeding patients had larger initial tumors and greater growth changes over time. Further studies on radiographic features may help identify lesions needing earlier intervention. Demographic and clinical characteristics of patients with renal AML: Comparison bleeding vs non-bleeding groups. Overall Non-bleeding Bleeding P-Value N 839 798 41 Gender (%) Female 685 (81.6) 650 (81.5) 35 (85.4) 0.793 Male 152 (18.1) 146 (18.3) 6 (14.6) Unknown 2 (0.2) 2 (0.3) 0 (0.0) Age (median [IQR]) 58.55 [47.04, 66.95] 58.72 [47.32, 67.19] 53.84 [42.88, 60.82] 0.015 Follow up (months) (median [IQR]) 45.30 [37.12, 89.15] 46.45 [36.32, 89.52] 39.70 [36.30, 61.63] 0.233 Total scans (median [IQR]) 3.00 [3.00, 5.00] 4.00 [3.00, 5.00] 3.00 [3.00, 4.00] 0.282 Initial size (cm) (median [IQR]) 1.30 [0.80, 2.70] 1.20 [0.70, 2.50] 6.50 [3.00, 9.30] <0.001 Everolimus (%) Yes 22 (2.6) 17 (2.1) 5 (12.2) 0.001 Tuberous sclerosis (%) Yes 58 (6.9) 50 (6.3) 8 (19.5) 0.003 Intervention (%) Yes 187 (22.3) 147 (18.4) 41 (100) <0.001

520 Background: The annual incidence of renal cancer is 0.02% among the general United States population. End stage renal disease (ESRD) and exposure to dialysis are thought to be the primary drivers of increased risk of renal cancer in renal transplant patients rather than immunosuppression. This has been evidenced by how the incidence of RCC in heart transplant recipients is no different than the normal population. In this study, we aim to characterize RCC incidence among kidney transplant patients (usually exposed to ESRD/dialysis) versus other solid organ transplant patients (usually unexposed to ESRD/dialysis). Methods: We used CPT codes to search for patients who had received a solid organ transplant (kidney, liver, heart, lung, pancreas) at all Mayo Clinic sites from 1/1/2018 to 1/1/2024. Within this cohort, we identified those who had been diagnosed with kidney cancer after initiating dialysis or receiving a transplant, and retrospectively extracted data related to demographics, cancer diagnosis, and transplant details. Results: We identified 10873 patients who had received a solid organ transplant (Table 1). Of these, 178 (1.76%) were diagnosed with kidney cancer after either starting dialysis or receiving a transplant. These 178 patients were of median age 56 years at time of cancer diagnosis (STD 12), 71% male, 70% white, 20% black, 4.5% Asian, 2.8% American Native. Of 5792 patients who had received a kidney transplant (with or without another solid organ transplant), 172 (2.97%) were diagnosed with kidney cancer after having started dialysis or receiving a kidney transplant. Of these 172, 156 (91%) were exposed to dialysis (107 primarily hemodialysis and 48 peritoneal). Of 4392 patients who had received a solid organ transplant other than kidney (liver, heart, lung, and/or pancreas), 6 (0.14%) were diagnosed with kidney cancer after having received a kidney transplant. Of these 6, none underwent dialysis. Conclusions: Patients with kidney transplants are at increased risk of kidney cancer compared to patients with non-kidney solid organ transplants (2.97% vs 0.14%). This supports prior evidence showing ESRD plays a significant role in the pathogenesis of kidney cancer. Patients who received solid organ transplants at Mayo Clinic from 2018-2024 and were subsequently diagnosed with kidney cancer. n % With RCC after TXP 178 1.7587% Cancer Diagnosis after Kidney HD/TXP 172 2.9696% Cancer Diagnosis after Other TXP 6 0.1386% Patients who received solid organ transplants at Mayo Clinic from 2018-2024 n % Unique Patients 10121 100% Kidney Transplant 5792 57.23% Alone 5088 50.27% &Liver 332 3.28% &Heart 119 1.18% &Lung 8 0.08% &Pancreas 245 2.42% Non-kidney Transplant (Liver, Heart, Lung, or Pancreas) 4329 42.77% TXP: Transplant, HD: Hemodialysis.

428 Background: Prostate cancer management presents a significant healthcare burden, with the need to efficiently triage patients for treatment. Our objective is to leverage large language models to predict physician-recommended treatment plans from unstructured clinical notes. By accurately predicting treatment plans, we aim to risk stratify and triage patients effectively, thereby optimizing the allocation of physician resources. Methods: 448 unstructured initial urology consultation patient notes following first positive prostate cancer biopsy were identified. The recommended and final treatments received were manually annotated to establish ground truth labels (Table 1). The dataset was split 80:20 for training and testing, preprocessed to remove plan sections and formatted into question-answer (QA) format. A domain-specific large language model (LLM) inspired by GPT and a specialized tokenizer (PCa- LLM) for prostate cancer terminology were developed. QA models were built using the PCa-LLM and compared with those using GPT-2 as the backbone to predict recommended and final treatments. Results: For the physician-recommended treatment plans, our LLM (PCa-LLM) showed superior performance with higher AUROC scores for curative vs. non-curative treatments (0.78 vs. 0.65), chemo-hormonal vs. other non-curative treatments (0.89 vs. 0.65), and surveillance vs. all other treatments (0.72 vs. 0.70), while both models achieved the same high AUROC of 0.99 for chemo-hormonal vs. all other treatments. For final treatments, PCa-LLM demonstrated better AUROC for curative vs. non-curative treatments (0.77 vs. 0.74) and chemo-hormonal vs. other non-curative treatments (0.71 vs. 0.66), while GPT2 outperformed PCa-LLM for surveillance vs. all other treatments (0.78 vs. 0.70). Both models achieved an AUROC of 0.99 for chemo-hormonal vs. all other treatments. Conclusions: PCa-LLM accurately predicted most treatment categories better than GPT2, with higher AUROC scores, and can be utilized to triage prostate cancer patients using initial consultation notes. Task Physician -Recommended Treatment Plan Final Treatment Received Curative Prostatectomy/Radiation 228 230 Non-Curative Focal Therapy 22 15 Active Surveillance 40 45 Chemo-hormonal 30 30 Model Predictions (AUROC) GPT2 PCa-LLM GPT2 PCa-LLM Curative vs. non-curative 0.65 0.78 0.74 0.77 Chemohormonal vs. other non-curative 0.65 0.89 0.66 0.71 Chemohormonal vs. all other 0.99 0.99 0.99 0.99 Surveillance vs. other non-curative 0.64 0.60 0.67 0.59 Surveillance vs. all other 0.70 0.72 0.78 0.70

Background The American Urological Association (AUA)/Society of Urology Oncology (SUO) guidelines recommend a repeat transurethral resection of bladder tumor (TURBT) for high-risk, non-invasive (HR Ta) nonmuscle invasive bladder cancer (NMIBC) patients. The evidence base for this recommendation is weak (grade C) and fraught with methodological shortcomings, such as the lack of adjuvant intravesical Bacillus Calmette Guerin (BCG) and single-center study designs. Objective We sought to evaluate the effect of repeat TURBT on recurrence-free survival at a population level in HR Ta NMIBC patients who completed BCG induction therapy. Methods High-grade Ta NMIBC patients who underwent TURBT for a ≥5 cm tumor were identified within the SEER-Medicare database. All patients completed induction BCG and were stratified into two groups: repeat TURBT within eight weeks of initial TURBT and a group without repeat TURBT (control group). The primary endpoint was the 3-year high-risk recurrence rate. Results A cohort of 604 patients was identified, with 93 (15.4%) undergoing a repeat TURBT within eight weeks of initial TURBT and 511 (84.6%) without a repeat TURBT. Patient demographic and clinical characteristics were similar overall. No significant difference in the 3-year recurrence rate was noted (repeat TURBT: 20.4% vs. control group: 15.7%, p = 0.25). After adjusting for demographic and clinical characteristics, no association between repeat TURBT and 3-year high-risk recurrence was observed (HR (95% CI): 1.27 (0.76, 2.11); p = 0.36). Conclusion Although our study contains several major limitations, our results suggest that repeat TURBT in large volume HG Ta NMIBC treated with induction BCG therapy was not associated with improved high-risk recurrence-free survival.

ABSTRACT Background Large language model (LLM) chatbots, a form of artificial intelligence (AI) that excels at prompt‐based interactions and mimics human conversation, have emerged as a tool for providing patients with information about urologic conditions. We aimed to examine the quality of information related to benign prostatic hyperplasia surgery from four chatbots and how they would respond to sample patient messages. Methods We identified the top three queries in Google Trends related to “treatment for enlarged prostate.” These were entered into ChatGPT (OpenAI), Bard (Google), Bing AI (Microsoft), and Doximity GPT (Doximity), both unprompted and prompted for specific criteria (optimized). The chatbot‐provided answers to each query were evaluated for overall quality by three urologists using the DISCERN instrument. Readability was measured with the built‐in Flesch–Kincaid reading level tool in Microsoft Word. To assess the ability of chatbots to answer patient questions, we prompted the chatbots with a clinical scenario related to holmium laser enucleation of the prostate, followed by 10 questions that the National Institutes of Health recommends patients ask before surgery. Accuracy and completeness of responses were graded with Likert scales. Results Without prompting, the quality of information was moderate across all chatbots but improved significantly with prompting (mean [SD], 3.3 [1.2] vs. 4.4 [0.7] out of 5; p < 0.001). When answering simulated patient messages, the chatbots were accurate (mean [SD], 5.6 [0.4] out of 6) and complete (mean [SD], 2.8 [0.3] out of 3). Additionally, 98% (39/40) had a median score of 5 or higher for accuracy, which corresponds to “nearly all correct.” The readability was poor, with a mean (SD) Flesch–Kincaid reading level grade of 12.1 (1.3) (unprompted). Conclusions LLM chatbots hold promise for patient education, but their effectiveness is limited by the need for careful prompting from the user and by responding at a reading level higher than that of most Americans (grade 8). Educating patients and physicians on optimal LLM interaction is crucial to unlock the full potential of chatbots.