Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome in Muscle Invasive 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: develop and validate a deep learning radiomics model based on preoperative enhanced CT image.
- 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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Official title
Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome From Preoperative CT in Muscle Invasive Bladder Cancer
Overview
Muscle invasive bladder cancer (MIBC) has a poor prognosis even after radical cystectomy. Postoperative survival stratification based on radiomics and deep learning may be useful for treatment decisions to improve prognosis. This study was aimed to develop and validate a deep learning radiomics model based on preoperative enhanced CT to predict postoperative survival in MIBC.
Interventions
- Other develop and validate a deep learning radiomics model based on preoperative enhanced CT image
develop and validate a deep learning radiomics model based on preoperative enhanced CT to predict postoperative survival in MIBC
Primary outcome measures
- Overall survival（OS） [Time frame: up to 10 years]
- Recurrence free survival（RFS) [Time frame: up to 10 years]
Eligibility criteria
Inclusion criteria
- patients with pathologically confirmed MIBC after radical cystectomy;
- contrast-CT scan less than two weeks before surgery;
- complete CT image data and clinical data.
Exclusion criteria
- patients who received neoadjuvant therapy;
- 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
- Cohort
Study locations
China · 1 center
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University — Chongqing
Publications
- Wei Z, Xv Y, Liu H, Li Y, Yin S, Xie Y, Chen Y, Lv F, Jiang Q, Li F, Xiao M. A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study. Int J Surg. 2024 May 1;110(5):2922-2932. doi: 10.1097/JS9.0000000000001194. PMID 38349205
Identifiers
NCT: NCT06092450 · AI-BLCA · 2022-K508