Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Bladder Cancer
Mingzhao Xiao
1,000 participants
Jan 1, 2024
OBSERVATIONAL
Conditions
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
Inclusion Criteria4
- 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 Criteria3
- patients with a postoperative diagnosis of non-urothelial carcinoma
- poor quality of CT images
- incomplete clinical and follow-up data
Interventions
develop and validate a deep learning system for prognostication prediction in bladder cancer based on CT radiomics and whole slide images.
Locations(1)
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NCT06389019