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

Sponsor

Mingzhao Xiao

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

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)

Facility	City	Region	Status
Department of Urology, The First Affiliated Hospital of Chongqing Medical University	Chongqing	Chongqing Municipality	Recruiting

More Mingzhao Xiao trials in China

Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Upper Tract Urothelial Carcinoma Active, not recruiting

Contrast-enhanced CT-based Deep Learning Model for Preoperative Prediction of Disease-free Survival (DFS) in Localized Clear Cell Renal Cell Carcinoma (ccRCC) Recruiting

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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