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

NCT07111364 | Recruiting DMC

• 1 other identifier: BCA-AI-US
• Study Type: observational
• Target: 400
• Locations: 1 country
• Sites: 1

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.

Conditions

Deep Learning; Ultrasound; Bladder Cancer

Outcome Measures

Primary Outcomes (1)

• Overall Diagnostic Accuracy

  From may 2025 to may 2027

Interventions

observational diagnostic model development OTHER

observational diagnostic model development

Eligibility Criteria

• Age: 18 Years - 85 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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.

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.

Sponsors & Collaborators

• Peking University First Hospital: lead

Study Sites (1)

Department of Urology, Peking University First Hospital

Beijing, 100034, China

RECRUITING

MeSH Terms

Conditions

Urinary Bladder Neoplasms

Condition Hierarchy (Ancestors)

Urologic Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Urinary Bladder Diseases; Urologic Diseases; Male Urogenital Diseases

Central Study Contacts

Zheng Zhang

CONTACT

+86 139 0137 1490; doczhz@aliyun.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 1, 2025
• First Posted: August 8, 2025
• Study Start: May 27, 2025
• Primary Completion: May 1, 2026
• Study Completion (Estimated): May 31, 2026
• Last Updated: August 17, 2025

Record last verified: 2025-05

Locations

CN (1)