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

Summary

This study is a multi-center observational study without interventions, including the construction of an AI diagnostic model and retrospective testing of a multi-center cohort. 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.

Eligibility

Inclusion Criteria:

* Bladder occupying lesions, with histopathological confirmation of bladder cancer after resection.
* Planned neoadjuvant therapy and radical cystectomy.

Exclusion Criteria:

* Patients who have not undergone standard bladder imaging examinations or have missing imaging or pathological data.
* Patients who have received local treatments (such as interventional embolization) or systemic treatments (such as radiotherapy, chemotherapy, immunotherapy, or targeted therapy).
* Poor quality of imaging or pathological images.

Conditions2

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