Clinical trial · Observational
Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome in Muscle Invasive Bladder Cancer
Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome From Preoperative CT in Muscle Invasive Bladder Cancer
NCT06092450CI-TRIAL-00090387recruitingClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Bladder Cancer | Malignant Bladder Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| develop and validate a deep learning radiomics model based on preoperative enhanced CT image | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- MIBC
- description
- patients with pathologically confirmed MIBC after radical cystectomy
- interventionNames
- Other: develop and validate a deep learning radiomics model based on preoperative enhanced CT image
Primary outcomes (2)
- measure
- Overall survival（OS）
- timeFrame
- up to 10 years
- description
- 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.
- measure
- Recurrence free survival（RFS)
- timeFrame
- up to 10 years
- description
- the time from the date of surgery to the date of first documented disease recurrence. Patients without recurrence at the time of analysis will be censored.
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
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.
References
Publications (1)
- DERIVEDWei 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