Bladder cancer (BLCA), with its diverse histopathological features and varying patient outcomes, poses significant challenges in diagnosis and prognosis. Postoperative survival stratification based on radiomics feature and whole slide image feature may be useful for treatment decisions to improve prognosis. In this research, we aim to develop a deep learning-based prognostic-stratification system for automatic prediction of overall and cancer-specific survival in patients with BLCA.
patients with bladder cancer who had surgery like radical cystectomy or transurethral resection of bladder tumour (TURBT)
contrast-CT scan less than two weeks before surgery
complete CT image data and clinical data
complete whole slide image data
patients with a postoperative diagnosis of non-urothelial carcinoma
poor quality of CT images
incomplete clinical and follow-up data
We included patients who had surgery only or who had neoadjuvant chemotherapy before surgery. We excluded patients with a postoperative diagnosis of non-urothelial carcinoma.
Other
Retrospective
Deep learning system for prognostication prediction in bladder cancer
Other interventiondevelop and validate a deep learning system for prognostication prediction in bladder cancer based on CT radiomics and whole slide images.
Overall survival
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
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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Mingzhao Xiao
Lead sponsor
First Affiliated Hospital of Chongqing Medical University
Sponsor institution
