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RECRUITINGOBSERVATIONAL

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

Important: This information is not medical advice. Talk to your doctor about whether a clinical trial is right for you.

About This Trial

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.

Who May Be Eligible (Plain English)

Who May Qualify: - 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 Who Should NOT Join This Trial: - patients with a postoperative diagnosis of non-urothelial carcinoma - poor quality of CT images - incomplete clinical and follow-up data Always talk to your doctor about whether this trial is right for you.

Original Eligibility Criteria

View original clinical language
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

Treatments Being Tested

OTHER

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.

Locations (1)

Department of Urology, The First Affiliated Hospital of Chongqing Medical University
Chongqing, Chongqing Municipality, China