An observational study in Bladder Cancer, sponsored by Mingzhao Xiao. Recruiting at 1 site in China. Per ClinicalTrials.gov, last updated 2025-05-28.
Sponsored by Mingzhao Xiao · Observational
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.
Bladder cancer can be difficult to diagnose and predict outcomes for, as the disease can vary greatly between patients. This research aims to develop a new system that uses artificial intelligence to analyze patient information, including images from surgery and scans. This system could then automatically predict a patient\'s overall survival and how likely they are to survive specifically from bladder cancer. This information could be used by doctors to make better treatment decisions for each patient.
1,616 studies on the registry are indexed under Urinary Bladder Neoplasms; 421 are open to participants now.
This study's planned enrollment of 1,000 is above the median of 180 across 374 observational studies indexed under Urinary Bladder Neoplasms.
Browse Urinary Bladder Neoplasms studies →Mingzhao Xiao is the lead sponsor of 3 studies on the registry; 2 are open to participants now.
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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.
Exclusion Criteria:
patients with bladder cancer who had surgery like radical cystectomy or transurethral resection of bladder tumour (TURBT).
Other: Deep learning system for prognostication prediction in bladder cancer
develop 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.
Time frame: up to 10 years
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
Time frame: up to 10 years
Plan to share: No — The datasets analyzed during the current study are not publicly available due to the privacy of patients but are available from the corresponding author on reasonable request.
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Mingzhao Xiao