Bladder Cancer
Conditions
Keywords
Tomography, X-ray computed, Muscle-invasive bladder cancer, Radiomics, Deep Learning
Brief summary
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
OTHERdevelop 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
Sponsors
First Affiliated Hospital of Chongqing Medical University
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Overall survival（OS） | up to 10 years | 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. |
| Recurrence free survival（RFS) | up to 10 years | 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. |
Countries
China
Contacts
Primary ContactZongjie Wei
Outcome results
None listed