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Recruiting Observational

Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Bladder Cancer

NCT06389019 · tracked via the Priya Life Science China tracker
Phase
Observational
Started
2024-01-01
Last updated
2025-05-28

Condition(s) studied

Bladder Cancer

Investigational drug(s) / intervention(s)

Deep learning system for prognostication prediction in bladder cancer

Deep learning system for prognostication prediction in bladder cancer: develop and validate a deep learning system for prognostication prediction in bladder cancer based on CT radiomics and whole slide images.

Study summary

Bladder cancer (BLCA), with its diverse histopathological features and varying patient outcomes, poses significant challenges in diagnosis and prognosis. Postoperative survival stratification based on radiomics feature and whole slide image feature may be useful for treatment decisions to improve prognosis. In this research, we aim to develop a deep learning-based prognostic-stratification system for automatic prediction of overall and cancer-specific survival in patients with BLCA.

Eligibility

Sex
ALL
Min age
—
Max age
—
Healthy volunteers
No
Inclusion Criteria: * patients with bladder cancer who had surgery like radical cystectomy or transurethral resection of bladder tumour (TURBT) * contrast-CT scan less than two weeks before surgery * complete CT image data and clinical data * complete whole slide image data Exclusion Criteria: * patients with a postoperative diagnosis of non-urothelial carcinoma * poor quality of CT images * incomplete clinical and follow-up data

Primary outcome measure(s)

  • Overall survival — up to 10 years
    the time from the date of surgery to death from any cause or the date of last contact (censored observation) at the date of data cut-off.

Trial sites (1)

FacilityCityRegionStatus
Department of Urology, The First Affiliated Hospital of Chongqing Medical University Chongqing Chongqing Municipality Recruiting
Official registry record

This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.

View NCT06389019 on ClinicalTrials.gov ↗ ← All trials in China