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Development and Validation of a Multimodal Fusion Artificial Intelligence Model for Predicting the Efficacy of Neoadjuvant Treatment of Bladder Cancer
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Summary
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.
Key Details
Gender
All
Age Range
Any - Any
Study Type
OBSERVATIONAL
Enrollment
469
Start Date
2022-01-01
Completion Date
2025-12-31
Last Updated
2026-06-03
Healthy Volunteers
No
Conditions
Interventions
Artificial intelligence (AI)-based predictive model
Collect magnetic resonance imaging and pathological slides of resected tumor of the enrolled patients. Analyze the data using the AI model to generate predictive results (sensitive or insensitive to the neoadjavant therapy). No intervention to patients would be performed in this diagnostic test study.
Locations (1)
Sun Yat-sen Memorial Hospital of Sun Yat-sen University
Guangzhou, Guangdong, China