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

Recruiting

• Conditions: Deep Learning; Ultrasound; Bladder Cancer

• Registration Number: NCT07111364
• Lead Sponsor: Peking University First Hospital

Brief Summary

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.

Detailed Description

Not available

Study Timeline

• Status Verified: 2025-05
• Study Start: 2025-05-27
• Study First Submitted: 2025-08-01
• Study First Submitted QC: 2025-08-01
• Study First Posted: 2025-08-08
• Last Update Submitted: 2025-08-13
• Last Update Posted: 2025-08-17
• Primary Completion: 2026-05-01
• Study Completion: 2026-05-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 400

Inclusion Criteria

① Suspected bladder mass detected by abdominal ultrasound (age ≥18 years);② Patients scheduled for surgical treatment of bladder tumors.

Exclusion Criteria

• 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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Overall Diagnostic Accuracy	From may 2025 to may 2027

Trial Locations

Locations (1)

Department of Urology, Peking University First Hospital; 🇨🇳 Beijing, China