We sought to examine the effect of tumor location on the prognosis of patients with upper tract urothelial carcinoma (UTUC) treated with radical nephroureterectomy (RNU). This retrospective study came from the Taiwan UTUC Collaboration Group, which consisted of 2658 patients at 15 institutions in Taiwan from 1988 to 2019. Patients with kidney-sparing management, both renal pelvic and ureteral tumors, as well as patients lacking complete data were excluded; the remaining 1436 patients were divided into two groups: renal pelvic tumor (RPT) and ureteral tumor (UT), with 842 and 594 patients, respectively. RPT was associated with more aggressive pathological features, including higher pathological T stage (p < 0.001) and the presence of lymphovascular invasion (p = 0.002), whereas patients with UT often had synchronous bladder tumor (p < 0.001), and were more likely to bear multiple lesions (p = 0.001). Our multivariate analysis revealed that UT was a worse prognostic factor compared with RPT (overall survival: HR 1.408, 95% CI 1.121–1.767, p = 0.003; cancer-specific survival: HR 1.562, 95% CI 1.169–2.085, p = 0.003; disease-free survival: HR 1.363, 95% CI 1.095–1.697, p = 0.006; bladder-recurrence-free survival: HR 1.411, 95% CI 1.141–1.747, p = 0.002, respectively). Based on our findings, UT appeared to be more malignant and had a worse prognosis than RPT.

Objectives: To assess the quality and alignment of ChatGPT's cancer treatment recommendations (RECs) with National Comprehensive Cancer Network (NCCN) guidelines and expert opinions. Methods: Three urologists performed quantitative and qualitative assessments in October 2023 analyzing responses from ChatGPT-4 and ChatGPT-3.5 to 108 prostate, kidney, and bladder cancer prompts using two zero-shot prompt templates. Performance evaluation involved calculating five ratios: expert-approved/expert-disagreed and NCCN-aligned RECs against total ChatGPT RECs plus coverage and adherence rates to NCCN. Experts rated the response's quality on a 1-5 scale considering correctness, comprehensiveness, specificity, and appropriateness. Results: ChatGPT-4 outperformed ChatGPT-3.5 in prostate cancer inquiries, with an average word count of 317.3 versus 124.4 ( p < 0.001) and 6.1 versus 3.9 RECs ( p < 0.001). Its rater-approved REC ratio (96.1% vs. 89.4%) and alignment with NCCN guidelines (76.8% vs. 49.1%, p = 0.001) were superior and scored significantly better on all quality dimensions. Across 108 prompts covering three cancers, ChatGPT-4 produced an average of 6.0 RECs per case, with an 88.5% approval rate from raters, 86.7% NCCN concordance, and only a 9.5% disagreement rate. It achieved high marks in correctness (4.5), comprehensiveness (4.4), specificity (4.0), and appropriateness (4.4). Subgroup analyses across cancer types, disease statuses, and different prompt templates were reported. Conclusions: ChatGPT-4 demonstrated significant improvement in providing accurate and detailed treatment recommendations for urological cancers in line with clinical guidelines and expert opinion. However, it is vital to recognize that AI tools are not without flaws and should be utilized with caution. ChatGPT could supplement, but not replace, personalized advice from healthcare professionals.

Yuan LH, et al. World J Mens Health. 2024 Dec;42:e104. https://doi.org/10.5534/wjmh.240173

The integration of artificial intelligence (AI) into functional urology has recently demonstrated the potential to enhance decision-making and optimize the treatment process. This study systematically reviewed literature on AI applications retrieved from Google Scholar and PubMed between 2020 and 2024 on AI applications aimed at reducing urodynamics (UDS) invasiveness, enhancing diagnostics, and predicting treatment outcomes. A total of 248 records were initially identified, and 31 articles were included after exclusions. In noninvasive diagnostics, some neural network (NN)-based uroflowmetry (UFM) systems demonstrated diagnostic accuracies exceeding 90% and strong correlations to invasive pressure-flow studies. Innovations such as sound-based UFM and dual machine learning (ML) prediction models reduced the need for invasive testing by over 30%, enhancing patient comfort and streamlining clinical workflows. For enhancing UDS diagnoses, ML and NNs improved diagnostic accuracy for complex conditions such as detrusor overactivity and bladder outlet obstruction, achieving sensitivities and specificities above 80% in multiple studies. Deep learning further advanced automated UDS data analysis, demonstrating robust performance in identifying intricate patterns. AI also facilitates prognosis and treatment response prediction. Models incorporating clinical, demographic, and imaging data achieved high accuracies in predicting outcomes, such as incontinence after prostatectomy and treatment efficacy for overactive bladder. For benign prostatic hyperplasia, treatment response prediction identified nonresponders early, reducing ineffective therapies. Despite these advancements, challenges persist. Most studies are constrained by small sample sizes, limited external validation, and the absence of standardized, multicenter databases. Addressing these issues is crucial to improve AI’s generalizability and clinical impact. This review highlights the expanding role of AI in urodynamics, emphasizing its potential to revolutionize diagnostics, enhance patient care, and personalize treatment strategies. Future research should emphasize multicenter collaborations, data standardization, and rigorous validation to ensure the safe and effective implementation of AI-driven solutions in functional urology.