Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Bladder Cancer
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 1,000
- 试验地点
- 1
- 主要终点
- Overall survival
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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
排除标准
- •patients with a postoperative diagnosis of non-urothelial carcinoma
- •poor quality of CT images
- •incomplete clinical and follow-up data
结局指标
主要结局
Overall survival
时间窗: 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.
次要结局
- Recurrence free survival(up to 10 years)
研究者
Mingzhao Xiao
Professor
First Affiliated Hospital of Chongqing Medical University