Deep-learning ultrasound system for precise diagnosis of bladder tumors
Official title: Construction of a Deep Learning-Based Precise Diagnostic Framework for Bladder Tumors Using Ultrasound
Run by Peking University First Hospital · NCT07111364 · Data updated 2026-11-12
This project will try a deep-learning system that reads ultrasound images to automatically segment bladder tumors, estimate T-stage, and predict tumor grade for adults with suspected bladder masses.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years to 85 Years |
| Sex | All |
| Enrollment | 400 (estimated) |
| Sponsor | Peking University First Hospital Academic / other |
| Drugs / interventions | chemotherapy |
| Locations | 1 site (Beijing) |
| Status | Recruiting |
| Started | May 27, 2025 |
| Main results expected | May 1, 2026 |
| Study ends | May 31, 2026 |
| Registry record updated | August 17, 2025 |
| Trial ID | NCT07111364 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults (≥18 years) with a suspected bladder mass on ultrasound who are scheduled for surgical treatment and can undergo adequate abdominal or transrectal ultrasound are ideal candidates.
✕Probably not a fit
Patients older than 85, unable to obtain technically adequate ultrasound images, with recent bladder therapies, indwelling urinary devices, or without pathologically confirmed urothelial carcinoma are unlikely to benefit.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
Researchers will build and train a deep-learning model using ultrasound images collected from patients with suspected bladder masses, combining retrospective and prospective cohorts. The system is designed to perform automated tumor segmentation, predict T-stage, and estimate pathological grade, with surgical pathology serving as the reference standard. The aim is to improve the objectivity, accuracy, and efficiency of ultrasound-based diagnosis while reducing dependence on operator experience. Patients with adequate preoperative abdominal or transrectal ultrasound images who undergo surgery will be included, while those with recent treatments, indwelling devices, or poor image quality will be excluded.
Why it matters
Potential benefit: If successful, the tool could give faster, more objective ultrasound-based tumor assessments that help guide treatment decisions and optimize use of clinical resources.
How similar studies have gone: Deep-learning models applied to ultrasound and other imaging modalities have shown promising results for tumor detection and grading, but ultrasound-based DL specifically for bladder T-staging and grade prediction remains relatively novel and less validated.
Who can join: the full rules
Show full inclusion / exclusion criteria
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.
Where it's running
-
Department of Urology, Peking University First Hospital
Beijing, China Recruiting
Study contacts
- Study coordinator: Zheng Zhang
- Email: doczhz@aliyun.com
- Phone: +86 139 0137 1490
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.