RecruitingNCT07111364
Construction of a Deep Learning-Based Precise Diagnostic Framework for Bladder Tumors Using Ultrasound: A Multicenter, Ambispective Cohort Study
Construction of a Deep Learning-Based Precise Diagnostic Framework for Bladder Tumors Using Ultrasound
Sponsor
Peking University First Hospital
Enrollment
400 participants
Start Date
May 27, 2025
Study Type
OBSERVATIONAL
Conditions
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.
Eligibility
Min Age: 18 YearsMax Age: 85 Years
Exclusion Criteria4
- 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.
Interventions
OTHERobservational diagnostic model development
observational diagnostic model development
Locations(1)
View Full Details on ClinicalTrials.gov
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NCT07111364
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