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)

Department of Urology, Peking University First Hospital

Beijing, China

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NCT07111364


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