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

Bladder cancer is one of the most common malignancies of the genitourinary system worldwide. For muscle - invasive bladder cancer amenable to radical resection, the standard treatment is neoadjuvant therapy combined with radical cystectomy, with neoadjuvant therapy playing a crucial role. Currently, numerous studies have shown that cisplatin - based neoadjuvant chemotherapy can downstage tumors, reduce the risk of mortality in bladder cancer patients, improve survival rates, and enhance prognosis. Other treatment approaches such as neoadjuvant immunotherapy, targeted therapy, and combination therapies are also under investigation. However, responses to neoadjuvant therapy vary among bladder cancer patients, with some not achieving the desired therapeutic goals. Therefore, accurately predicting participants' response to treatment can provide an important reference for personalized and precise treatment of bladder cancer. In recent years, as advancements in computational power and data storage capacity, artificial intelligence (AI) has been widely applied in the field of digital diagnostics. AI technologies can extract and integrate a large number of features from multimodal data such as pathology, imaging, and clinical records, enabling precise disease diagnosis, prognosis assessment, and treatment prediction. In the field of tumor treatment prediction, multimodal AI technologies have achieved numerous breakthroughs, developing efficacy prediction models for tumors such as rectal and breast cancer based on imaging and pathological data, and validating the models' generalization capabilities through external validation. Therefore, the investigators plan to construct and validate a "Bladder Cancer Neoadjuvant Treatment Efficacy Prediction Model" based on multimodal data such as MRI images, digital pathology images, and clinical records of bladder cancer patients, and develop an AI - assisted prediction software for neoadjuvant treatment efficacy in bladder cancer. The investigators hope that this AI diagnostic model can serve as an auxiliary tool to assist clinicians in stratifying neoadjuvant treatment efficacy in bladder cancer patients, thereby formulating precise treatment plans, reducing the consumption of human and material resources, and seizing the optimal treatment opportunities.