Bladder Cancer, Deep Learning, Ultrasound
Conditions
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.
Interventions
observational diagnostic model development
Sponsors
Peking University First Hospital
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
18 Years to 85 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Overall Diagnostic Accuracy | From may 2025 to may 2027 |
Countries
China
Contacts
Primary ContactZheng Zhang
Outcome results
None listed