Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Bladder Cancer
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Deep learning system for prognostication prediction in bladder cancer.
- Who it may be relevant to
- Registry conditions: Bladder Cancer. Basic parameters: No limits · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
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.
Detailed description
Bladder cancer can be difficult to diagnose and predict outcomes for, as the disease can vary greatly between patients. This research aims to develop a new system that uses artificial intelligence to analyze patient information, including images from surgery and scans. This system could then automatically predict a patient\'s overall survival and how likely they are to survive specifically from bladder cancer. This information could be used by doctors to make better treatment decisions for each patient.
Interventions
- 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.
Primary outcome measures
- Overall survival [Time frame: up to 10 years]
Secondary outcome measures (1)
- Recurrence free survival [Time frame: up to 10 years]
Eligibility criteria
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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Other
Study locations
China · 1 center
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University — Chongqing
Identifiers
NCT: NCT06389019 · BLCA_CMUFH · K2024-187-01