Construction of a Deep Learning-Based Precise Diagnostic Framework for Bladder Tumors Using Ultrasound: A Multicenter, Ambispective Cohort Study

Recruiting now

Conditions studied: Deep Learning, Ultrasound, Bladder Cancer

In brief

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.

Key facts

Study ID

NCT07111364

Run by

Peking University First Hospital

People needed

400

Starts

2025-05-27

Expected to finish

2026-05-31

Last updated by the study team

2025-08-17

Who can join

Age: 18 and older, up to 85. Sex: any. Healthy volunteers: not accepted.

You may not qualify if…

• 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.

Where it is running

• Department of Urology, Peking University First Hospital — Beijing, China (enrolling)

Full record on ClinicalTrials.gov

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