AI and Cancer Research ── Bladder ── 2026-11-09
Bladder cancer: treatment, trials and AI
- Abstract
BACKGROUND AND PURPOSE: After bladder-sparing chemoradiotherapy (CRT) for muscle-invasive bladder cancer (MIBC), residual microscopic disease may cause intravesical recurrence. This study aimed to develop machine learning models to predict pathological complete response (pCR) after CRT. MATERIALS AND METHODS: We retrospectively analyzed 285 patients with cT2-4N0M0 MIBC who underwent CRT followed by partial or radical cystectomy. CRT response was determined from cystectomy specimens. Patients were randomly assigned to a training cohort ( n = 228) and a test cohort ( n = 57) using a fixed random seed. A total of 102 machine learning models were evaluated in the training cohort. The model with the highest area under the receiver operating characteristic curve (AUC) in the training-cohort was selected and then evaluated in the test cohort. The model incorporated clinical T stage, tumor size, multiplicity, hydronephrosis, bladder neck involvement (BNI), concomitant carcinoma in situ (CIS), variant histology, grade, and progression. RESULTS: Clinical T stages were T2/3/4 in 169/105/11 patients, respectively. Hydronephrosis was observed in 69 patients. Transurethral resection before CRT revealed multiplicity, BNI, and concomitant CIS in 108, 54, and 34 patients, respectively. Overall, 178/285 patients (62%) achieved pCR. WeightedEnsemble_L4 was selected as the final model and had a test-cohort AUC of 0.829 (95% confidence interval [CI], 0.706-0.932). The calibration slope was 0.856 (95% CI, 0.469-1.639), and the wide CI indicated uncertainty in calibration. CONCLUSION: Machine learning based on clinicopathologic variables estimated pCR after CRT. This may support decision-making regarding bladder preservation in patients with MIBC.
Journal IF-equivalent: 2.1 (OpenAlex 2-year mean citedness, value as of 2026-11-08, retrieved 2026-11-09; not the official Clarivate IF)Reference: Fujiwara M, Tanaka H, Hirahara D, Saho T, Takaya E, Matsumoto S, et al. Machine learning models to predict pathological complete response after chemoradiotherapy in muscle-invasive bladder cancer: toward optimized indications for bladder preservation. Clinical and Translational Radiation Oncology. 2027 Jan;62:101320. doi:10.1016/j.ctro.2026.101320.Checked: Abstract only