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.
Study Type
OBSERVATIONAL
Enrollment
500
develop and validate a deep learning radiomics model based on preoperative enhanced CT to predict postoperative survival in MIBC
Department of Urology, The First Affiliated Hospital of Chongqing Medical University
Chongqing, Chongqing Municipality, China
RECRUITINGOverall 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.
Time frame: up to 10 years
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.
Time frame: up to 10 years
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