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AbstractObjectiveTo assess the efficacy and safety of different endoscopic surgical treatments for benign prostatic hyperplasia.DesignSystematic review and network meta-analysis of randomised controlled trials.Data sourcesA comprehensive search of PubMed, Embase, and Cochrane databases from inception to 31 March 2019.Study selectionRandomised controlled trials comparing vapourisation, resection, and enucleation of the prostate using monopolar, bipolar, or various laser systems (holmium, thulium, potassium titanyl phosphate, or diode) as surgical treatments for benign prostatic hyperplasia. The primary outcomes were the maximal flow rate (Qmax) and international prostate symptoms score (IPSS) at 12 months after surgical treatment. Secondary outcomes were Qmax and IPSS values at 6, 24, and 36 months after surgical treatment; perioperative parameters; and surgical complications.Data extraction and synthesisTwo independent reviewers extracted the study data and performed quality assessments using the Cochrane Risk of Bias Tool. The effect sizes were summarised using weighted mean differences for continuous outcomes and odds ratios for binary outcomes. Frequentist approach to the network meta-analysis was used to estimate comparative effects and safety. Ranking probabilities of each treatment were also calculated.Results109 trials with a total of 13 676 participants were identified. Nine surgical treatments were evaluated. Enucleation achieved better Qmax and IPSS values than resection and vapourisation methods at six and 12 months after surgical treatment, and the difference maintained up to 24 and 36 months after surgical treatment. For Qmax at 12 months after surgical treatment, the best three methods compared with monopolar transurethral resection of the prostate (TURP) were bipolar enucleation (mean difference 2.42 mL/s (95% confidence interval 1.11 to 3.73)), diode laser enucleation (1.86 (−0.17 to 3.88)), and holmium laser enucleation (1.07 (0.07 to 2.08)). The worst performing method was diode laser vapourisation (−1.90 (−5.07 to 1.27)). The results of IPSS at 12 months after treatment were similar to Qmax at 12 months after treatment. The best three methods, versus monopolar TURP, were diode laser enucleation (mean difference −1.00 (−2.41 to 0.40)), bipolar enucleation (0.87 (−1.80 to 0.07)), and holmium laser enucleation (−0.84 (−1.51 to 0.58)). The worst performing method was diode laser vapourisation (1.30 (−1.16 to 3.76)). Eight new methods were better at controlling bleeding than monopolar TURP, resulting in a shorter catheterisation duration, reduced postoperative haemoglobin declination, fewer clot retention events, and lower blood transfusion rate. However, short term transient urinary incontinence might still be a concern for enucleation methods, compared with resection methods (odds ratio 1.92, 1.39 to 2.65). No substantial inconsistency between direct and indirect evidence was detected in primary or secondary outcomes.ConclusionEight new endoscopic surgical methods for benign prostatic hyperplasia appeared to be superior in safety compared with monopolar TURP. Among these new treatments, enucleation methods showed better Qmax and IPSS values than vapourisation and resection methods.Study registrationCRD42018099583.

Cystoscopy remains the gold standard for diagnosing bladder lesions; however, its diagnostic accuracy is operator dependent and prone to missing subtle abnormalities such as carcinoma in situ or misinterpreting mimic lesions (tumor, inflammation, or normal variants). Artificial intelligence-based image-analysis systems are emerging, yet conventional models remain limited to single tasks and cannot produce explanatory reports or articulate diagnostic reasoning. Multimodal large language models (MM-LLMs) integrate visual recognition, contextual reasoning, and language generation, offering interpretive capabilities beyond conventional artificial intelligence. This study aims to rigorously evaluate state-of-the-art MM-LLMs for cystoscopic image interpretation and lesion classification using clinician-defined stress-test datasets enriched with rare, diverse, and challenging lesions, focusing on diagnostic accuracy, reasoning quality, and clinical relevance. Four MM-LLMs (OpenAI-o3 and ChatGPT-4o [OpenAI]; Gemini 2.5 Pro and MedGemma-27B [Google]) were evaluated under blinded, randomized procedures across two tasks: (1) free-text image interpretation for anatomic site, findings, lesion reasoning, and final diagnosis (n=401) and (2) seven-class tumor-like lesion classification (n=113) within a multiple-choice framework (cystitis, polyps, papilloma, papillary urothelial carcinoma, carcinoma in situ, non-urothelial carcinoma, and none of the above). Three raters independently scored outputs using a 5-point Likert scale, and classification metrics (accuracy, sensitivity, specificity, Youden J index (Youden J), and Matthews correlation coefficient [MCC]) were calculated for lesion detection, biopsy indication, and malignancy endpoints. For optimization, model performance was compared between zero-shot and text-based in-context learning prompts that were prefixed with brief descriptions of tumor features. The 401-image test set spanned 40 subcategories, with 322 (80.3%) containing abnormal findings in the image interpretation task. OpenAI-o3 demonstrated strong reasoning, with high satisfaction for anatomy (339/401, 84.5%) and findings (305/401, 76%), but lower satisfaction for lesion reasoning (211/401, 52.5%) and final diagnosis (193/401, 48.2%), indicating increasing difficulty with higher-order synthesis. Mean Likert score differences (OpenAI-o3 minus Gemini 2.5 Pro) were +0.27 for findings (adjusted P value: q=0.002), +0.24 for lesion reasoning (q=0.047), and +0.19 for final diagnosis. For clinically relevant endpoints in the full set, OpenAI-o3 achieved the most balanced performance, with lesion detection accuracy of 88.3%, sensitivity of 92%, specificity of 73.1%, Youden J of 0.650, and MCC of 0.635. In 7-class tumor-like lesion classification, OpenAI-o3 achieved accuracies of 73.5% for biopsy indication and 62.8% for malignancy, with a balanced sensitivity-specificity trade-off, outperforming other models. Notably, OpenAI-o3 performed best on prevalent malignant lesions. ChatGPT-4o and Gemini 2.5 Pro showed high sensitivity but low specificity, whereas MedGemma-27B underperformed. In-context learning improved OpenAI-o3 microaverage accuracy (40.7%→46.0%; MCC 0.311→0.370) but yielded only slight specificity gains and minimal accuracy change in other models, likely constrained by the absence of paired image-text context. MM-LLMs demonstrate meaningful assistive potential in generating interpretable cystoscopy free-text rationales and supporting biopsy triage and training. However, performance in difficult differential diagnoses remains modest and requires further optimization before safe clinical integration.

Diagnosing underlying causes of nonneurogenic male lower urinary tract symptoms associated with bladder outlet obstruction (BOO) is challenging. Video-urodynamic studies (VUDS) and pressure-flow studies (PFS) are both invasive diagnostic methods for BOO. VUDS can more precisely differentiate etiologies of male BOO, such as benign prostatic obstruction, primary bladder neck obstruction, and dysfunctional voiding, potentially outperforming PFS. These examinations' invasive nature highlights the need for developing noninvasive predictive models to facilitate BOO diagnosis and reduce the necessity for invasive procedures. We conducted a retrospective study with a cohort of men with medication-refractory, nonneurogenic lower urinary tract symptoms suspected of BOO who underwent VUDS from 2001 to 2022. In total, 2 BOO predictive models were developed-1 based on the International Continence Society's definition (International Continence Society-defined bladder outlet obstruction; ICS-BOO) and the other on video-urodynamic studies-diagnosed bladder outlet obstruction (VBOO). The patient cohort was randomly split into training and test sets for analysis. A total of 6 machine learning algorithms, including logistic regression, were used for model development. During model development, we first performed development validation using repeated 5-fold cross-validation on the training set and then test validation to assess the model's performance on an independent test set. Both models were implemented as paper-based nomograms and integrated into a web-based artificial intelligence prediction tool to aid clinical decision-making. Among 307 patients, 26.7% (n=82) met the ICS-BOO criteria, while 82.1% (n=252) were diagnosed with VBOO. The ICS-BOO prediction model had a mean area under the receiver operating characteristic curve (AUC) of 0.74 (SD 0.09) and mean accuracy of 0.76 (SD 0.04) in development validation and AUC and accuracy of 0.86 and 0.77, respectively, in test validation. The VBOO prediction model yielded a mean AUC of 0.71 (SD 0.06) and mean accuracy of 0.77 (SD 0.06) internally, with AUC and accuracy of 0.72 and 0.76, respectively, externally. When both models' predictions are applied to the same patient, their combined insights can significantly enhance clinical decision-making and simplify the diagnostic pathway. By the dual-model prediction approach, if both models positively predict BOO, suggesting all cases actually resulted from medication-refractory primary bladder neck obstruction or benign prostatic obstruction, surgical intervention may be considered. Thus, VUDS might be unnecessary for 100 (32.6%) patients. Conversely, when ICS-BOO predictions are negative but VBOO predictions are positive, indicating varied etiology, VUDS rather than PFS is advised for precise diagnosis and guiding subsequent therapy, accurately identifying 51.1% (47/92) of patients for VUDS. The 2 machine learning models predicting ICS-BOO and VBOO, based on 6 noninvasive clinical parameters, demonstrate commendable discrimination performance. Using the dual-model prediction approach, when both models predict positively, VUDS may be avoided, assisting in male BOO diagnosis and reducing the need for such invasive procedures.

The role of prostate cryoablation was still uncertain for patients with high-risk prostate cancer (PC). This study was designed to investigate 10-year disease-free survival and establish a nomogram in localized high-risk PC patients. Between October 2008 and December 2020, 191 patients with high-risk PC who received primary total prostate cryoablation (PTPC) were enrolled. The primary endpoint was biochemical recurrence (BCR), defined using Phoenix criteria. The performance of pre-operative and peri-operative nomograms was determined using the Harrell concordance index (C-index). Among the cohort, the median age and PSA levels at diagnosis were 71 years and 12.3 ng/mL, respectively. Gleason sum 8–10, stage ≥ T3a, and PSA > 20 ng/mL were noted in 27.2%, 74.4%, and 26.2% of patients, respectively. During the median follow-up duration of 120.4 months, BCR-free rates at 1, 3, 5, and 10 years were 92.6%, 76.6%, 66.7%, and 50.8%, respectively. The metastasis-free, cancer-specific, and overall survival rates were 89.5%, 97.4%, and 90.5% at 10 years, respectively. The variables in the pre-operative nomogram for BCR contained PSA at diagnosis, clinical stage, and Gleason score (C-index: 0.73, 95% CI, 0.67–0.79). The variables in the peri-operative nomogram for BCR included PSA at diagnosis, Gleason score, number of cryoprobes used, and PSA nadir (C-index: 0.83, 95% CI, 0.78–0.88). In conclusion, total prostate cryoablation appears to be an effective treatment option for selected men with high-risk PC. A pre-operative nomogram can help select patients suitable for cryoablation. A peri-operative nomogram signifies the importance of the ample use of cryoprobes and helps identify patients who may need early salvage treatment.

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.

Eastern Cooperative Oncology Group performance status (ECOG-PS) is a widely used functional status measure in oncology, yet its prognostic value in upper tract urothelial carcinoma remains unclear. In this multicenter study of 2473 patients undergoing radical nephroureterectomy, ECOG-PS ≥ 2 was independently associated with worse overall survival (hazard ratio [HR] 2.53, p  < 0.001), cancer-specific survival (HR 2.02, p  < 0.001), and disease-free survival (HR 1.50, p  = 0.003) than those with ECOG-PS 0–1. They also had a higher risk of major perioperative complications (odds ratio 2.46, p  < 0.001). These findings support ECOG-PS as a valuable preoperative risk stratification tool.

Background: Radical nephroureterectomy (RNU) is the standard treatment for upper tract urothelial carcinoma (UTUC). Minimally invasive techniques like robotic (RARNU) and laparoscopic (LRNU) RNU offer potential benefits over open surgery, but their comparative oncologic outcomes are debated. Methods: This retrospective, multicenter study analyzed 2037 Taiwanese patients undergoing RNU between 2010 and 2022. Missing data was addressed using multiple imputations. Overlap weighting was applied to balance patient characteristics between the RARNU and LRNU groups. Survival outcomes were compared using Kaplan-Meier analysis and Cox regression. Results: After excluding the missing data, 405 patients underwent RARNU, and 1262 underwent LRNU. After adjusting for baseline differences, both groups showed comparable rates of surgical complications, residual tumor, UTUC-related mortality, and disease recurrence. The median follow-up was similar (52.4 vs. 51.6 months, p = 0.91). Using Kaplan-Meier survival curve analysis, overall survival, cancer-specific survival, and disease-free survival were similar between the two groups. Conclusions: This study shows that RARNU for UTUC has comparable oncological and perioperative outcomes to pure LRNU. RARNU may be easier for surgeons to adopt, particularly those with limited laparoscopic experience.

Purpose: Interstitial cystitis/bladder pain syndrome (IC/BPS) is mysterious and difficult to diagnose without cystoscopic hydrodistention. This study aimed to explore non-invasive and highly reliable urine biomarkers to identify Hunner’s IC (HIC) and different non-Hunner’s IC (NHIC) subtypes. Methods: In total, 422 women with and without clinically diagnosed IC/BPS (n = 376 and 46, respectively) were retrospectively enrolled. Patients were diagnosed with HIC or NHIC by cystoscopic hydrodistention under anesthesia. Then, the maximal bladder capacity (MBC) and glomerulation grade were determined. Thirteen urine inflammatory cytokines, chemokines, and oxidative stress biomarkers based on the previously reported predictors of IC/BPS were assayed using commercial microsphere kits. The dataset was randomly divided into training (70%) and test (30%) sets for model construction and validation using logistic regression and stepwise variable selection techniques. To construct the predictive models, univariate analysis was performed to evaluate the discriminative power of each urinary biomarker, measured by the area under the curve (AUC). Biomarkers with AUC values < 0.6 were excluded from further modeling. Multivariate logistic regression was then employed, with variables selected through stepwise forward selection based on log-likelihood criteria. For dichotomization, cutoff values were determined using quartile ranges from the control group. The final model’s performance was assessed using AUC, accuracy, sensitivity, and specificity in both training and test sets. Results: By setting the screening criterion to AUC ≥ 0.60, the potential urinary biomarkers for identifying IC/BPS cases were eotaxin, monocyte chemoattractant protein-1, tumor necrosis factor-alpha (TNF-α), 8-hydroxy-2′-deoxyguanosine (8-OHdG), and 8-isoprostane. Those for identifying HIC from the IC/BPS cohort were interleukin (IL)-6, IL-8, interferon γ-inducible protein 10 (IP-10), and regulated on activation, normal T-cell expressed and secreted (RANTES). A diagnostic algorithm using a cluster of urinary biomarkers included TNF-α ≥ 0.95 pg/mL or 8-OHDG ≥ 22.34 pg/mL and 8-isoprastane ≥ 22.34 pg/mL for identifying IC/BPS from the overall cohort; for identifying HIC from the IC/BPS cohort, the urinary IP-10 ≥ 3.74 pg/mL or IP-10 ≥ 19.94 pg/mL was added. Conclusions: Using a cluster of urinary biomarkers such as TNF-α or 8-OHdG and 8-isoprostane can identify IC/BPS from a study cohort, and adding the urinary IP-10 can distinguish HIC from IC/BPS cases.

To determine the impacts of using ChatGPT to assist English as a foreign language (EFL) English college majors in revising essays and the possibility of leading to higher scores and potentially causing unfairness. A prospective, double-blinded, paired-comparison study was conducted in Feb. 2023. A total of 44 students provided 44 original essays and 44 ChatGPT-assisted revised essays, which were rated by two independent graders in a randomized and crossover fashion to minimize grading bias. The original and revision scores were paired for before-after comparison. Eight control essays were also rated by both graders to ensure inter-rater reliability. This study used a rigorous experimental design to confirm that ChatGPT-assisted revised essays led to significantly higher scores for EFL college English majors. Significant improvements were observed in all four dimensions of writing quality assessment, with the largest effects observed in vocabulary, followed by grammar, organization, and content. ChatGPT-assisted revised essays shifted the score curve from a normal distribution to a skewed distribution towards higher grades, with the greatest increase in revision scores seen among students who had lower original scores. This disproportionate improvement raises concerns about fairness in evaluation. The findings suggest that ChatGPT is effective in providing timely feedback to EFL English majors in an affordable manner, but it also highlights the potential for unfairness in writing evaluation. We should note that ChatGPT-assisted revisions do not reveal learners’ writing competence. Therefore, new forms of writing performance assessment should be implemented in EFL composition classes in this AI era.

Purpose Upper tract urothelial carcinoma (UTUC) presents a higher incidence rate in Taiwan compared to Western societies. The aim of this study is to investigate the potential of metformin in improving survival outcomes for patients with UTUC in Taiwan. Material and Methods This retrospective study included 940 patients with UTUC and type 2 diabetes from the Taiwan UTUC Collaboration Group, spanning 21 hospitals from July 1988 to September 2023. Patients were divided into two groups: those treated with metformin (n = 215) and those without metformin treatment (n = 725). Parameters analyzed included age, BMI, renal function, tumor grade and location, and pathological staging. Oncological outcomes measured were overall survival (OS), cancer‐specific survival (CSS), and bladder recurrence‐free survival (BRFS). Statistical analysis involved the use of Student's t‐test, Mann–Whitney test, Chi‐squared test, Fisher's exact test, and Cox proportional hazard regression. Results Significant differences were observed between the two groups in BMI, preoperative creatinine, eGFR, tumor location, tumor laterality, tumor size, and pathological grade and T stage. Patients treated with metformin exhibited a lower risk of CSS (HR = 0.619; p = 0.018) and improved OS (HR = 0.713; p = 0.024), although no significant association was found with BRFS (HR = 1.034; p = 0.791). The protective effect of metformin on OS was particularly significant in patients with advanced T stage, metastasis, and high‐grade tumors. Conclusion The study suggests that metformin use in UTUC patients with diabetes is associated with improved OS and CSS but not BRFS. The underlying mechanisms warrant further investigation. Repurposing metformin, a well‐established and safe drug, may develop new therapeutic strategies for UTUC.