Development and Validation of a Multimodal Fusion Artificial Intelligence Model for Predicting the Efficacy of Neoadjuvant Treatment of Bladder Cancer

This study is a multi-center observational study without interventions, including the construction of an AI predictive model, with retrospective and prospective testing. The study participants are bladder cancer patients who have undergone imaging examinations, been pathologically diagnosed, and received neoadjuvant treatment, with complete clinical and pathological data. The study plans to enroll 130 patients from our center, collecting corresponding imaging images, and gathering clinical and genomic data to build and internally validate a multimodal AI model. The model's generalization and robustness will be tested to explore the association between multimodal data and the efficacy of neoadjuvant treatment for bladder cancer. The aim is to assist clinicians in predicting and evaluating the efficacy of neoadjuvant treatment for bladder cancer, with the goal of improving patient diagnosis, treatment outcomes, and prognosis.

NCT Number	NCT06909643
Lead Sponsor	Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Conditions	Cancer, Neoadjuvant Therapy
Enrollment	469 participants
Start Date	2022-01-01
Primary Completion	2025-12-31 (estimated)
Study Completion	2025-12-31 (estimated)
Updated on ClinicalTrials.gov	2026-06-03