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.
patients with pathologically confirmed MIBC after radical cystectomy;
contrast-CT scan less than two weeks before surgery;
complete CT image data and clinical data.
patients who received neoadjuvant therapy;
non-urothelial carcinoma;
poor quality of CT images;
incomplete clinical and follow-up data.
patients with pathologically confirmed MIBC who underwent radical cystectomy
Cohort
Retrospective
develop and validate a deep learning radiomics model based on preoperative enhanced CT image
Other interventiondevelop and validate a deep learning radiomics model based on preoperative enhanced CT to predict postoperative survival in MIBC
Overall survival（OS）
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)
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.
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