This study aims to develop an ultrasound image-based deep learning system to enable automatic segmentation, T-staging, and pathological grading prediction of bladder tumors. It seeks to enhance the objectivity, accuracy, and efficiency of bladder cancer diagnosis, reduce reliance on physician experience, and provide support for precision medicine and resource optimization.
None
Age >85 years;
Patients unable to undergo abdominal/transrectal ultrasound (e.g., uncooperative individuals, technically inadequate images);
History of bladder tumor surgery, radiotherapy, chemotherapy, or systemic therapy within 3 months; ④ Patients with indwelling medical devices (e.g., double-J ureteral stents, urinary catheters);
Failure to undergo bladder tumor surgery within 2 weeks post-ultrasound; ⑥ Non-urothelial carcinoma or pathologically unconfirmed diagnoses.
This study consecutively enrolled patients with suspected bladder tumors prospectively registered at the Department of Urology, Peking University First Hospital and Shanxi Province Cancer Hospital between May 2025 and May 2027.
Cohort
Other
observational diagnostic model development
Other interventionobservational diagnostic model development
Overall Diagnostic Accuracy
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