AI and Cancer Research ── Bladder ── 2026-10-30
Bladder cancer: treatment, trials and AI
- Abstract
Objective: Artificial intelligence (AI) chatbots are increasingly used as accessible sources of medical information. However, concerns remain regarding the quality, reliability, readability, and clinical applicability of AI-generated health content. Intravesical Bacillus Calmette-Guérin (BCG) therapy is a standard adjuvant treatment for intermediate- and high-risk non-muscle invasive bladder cancer (NMIBC) and requires adequate patient understanding of treatment procedures and potential adverse effects. This study aimed to evaluate the quality, reliability, readability, and guideline adherence of information provided by commonly used AI chatbots regarding intravesical BCG therapy.
Methods: This cross-sectional methodological study evaluated responses generated by four AI chatbots (ChatGPT, Copilot, Gemini, and Perplexity) to a standardized question regarding intravesical BCG therapy. Responses were independently assessed by five urologists using validated tools including DISCERN, PEMAT-P, WRR, and the Coleman-Liau Index (CLI). Compliance with European Association of Urology (EAU) guidelines was evaluated using a 5-point Likert scale.
Results: Responses ranged from 454 to 550 words and required a university-level reading ability (CLI range: 16.02–19.35). DISCERN scores indicated moderate information quality across all chatbots, with Gemini demonstrating the numerically highest median score (51 [47–59]). PEMAT-P actionability scores were generally low (median 40%). Guideline adherence was moderate, with all chatbots receiving a median Likert score of 3.
Conclusion: AI chatbots provide moderate-quality information regarding intravesical BCG therapy but require high reading-grade levels and offer limited actionable guidance. AI-generated information may support patient education but should not replace physician counseling.
Journal IF-equivalent: 0.3 (OpenAlex 2-year mean citedness, value as of 2026-10-29, retrieved 2026-10-30; not the official Clarivate IF)Reference: Zengin S, Boyaci C, Olgun A, Kutluay O, Gul A. Evaluation of Patient Information Provided by Artificial Intelligence Chatbots on Intravesical BCG Therapy for Bladder Cancer: A Cross-Sectional Methodological Study. New J Urol. 2026 Oct 28;21(3):131-7. doi:10.33719/nju1929548.Checked: Abstract only - Abstract
Background: Bladder neuroendocrine carcinoma (BNEC) is a rare yet highly aggressive malignancy with an extremely poor prognosis. The current TNM staging system provides limited prognostic information and validated prognostic prediction tools are lacking. This study aimed to develop and validate a machine learning (ML)-based model for individualised survival prediction in BNEC patients using large-scale population data.
Methods: Data from pathologically confirmed BNEC patients from 2004 to 2021 were extracted from the US Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors. On the basis of the independent prognostic factors, eight machine learning models, namely, random forest (RF), gradient boosting machine (GBM), LASSO-Cox, CoxBoost, support vector machine (SVM), XGBoost, SuperPC, and plsRcox, were constructed using the training set (70% of the total sample). Model performance was comprehensively evaluated on the validation set (30% of total samples) using the C-index, time-dependent receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).
Results: A total of 2117 patients with BNEC were included in the final analysis. Multivariate analysis revealed age >74 years (HR = 1.51, p<0.001), male sex (HR = 1.17, p=0.038), advanced T stage (T2–T4), positive nodal status (N1–N2), distant metastasis (M1, HR = 1.99, p<0.001), surgery (TURBT: HR = 0.75, P = 0.017; RC: HR = 0.36, P<0.001), radiotherapy (HR = 0.79, p=0.002), and chemotherapy (HR = 0.43, p<0.001) as independent prognostic factors. Among the eight machine learning models, the GBM algorithm demonstrated the best performance in both the training and validation sets, with AUC values for 1-year, 3-year, and 5-year survival prediction being 0.846/0.737, 0.825/0.730, and 0.816/0.742, respectively. Decision curve analysis (DCA) demonstrated greater clinical net benefit across a broad range of threshold probabilities, while the calibration curves indicated high agreement between the predicted and observed outcomes. Risk stratification based on the GBM-derived risk score revealed significant differences in survival between the high-risk and low-risk groups (p < 0.0001).
Conclusion: The GBM-based machine learning prognostic model demonstrated comparatively favorable performance in predicting survival outcomes among patients with BNEC. This model may provide a useful tool for individualized prognostic assessment and risk stratification, although further validation in multicentre prospective cohorts is warranted.
Journal IF-equivalent: 2.7 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)Reference: Qi S, Dong W, Shi Z, Sun Y, Zhang D, Liu C, et al. Development and validation of a machine learning-based model for predicting survival in patients with bladder neuroendocrine carcinoma: a large retrospective study. Front Oncol. 2026 Oct 27;16:1956255. doi:10.3389/fonc.2026.1956255.Checked: Abstract only