Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome From Preoperative CT in Muscle Invasive Bladder Cancer
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 500
- 试验地点
- 1
- 主要终点
- Recurrence free survival（RFS)
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 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 pathologically confirmed MIBC after radical cystectomy;
- •contrast-CT scan less than two weeks before surgery;
- •complete CT image data and clinical data.
排除标准
- •patients who received neoadjuvant therapy;
- •non-urothelial carcinoma;
- •poor quality of CT images;
- •incomplete clinical and follow-up data.
结局指标
主要结局
Recurrence free survival（RFS)
时间窗: up to 10 years
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.
Overall survival（OS）
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
未报告次要终点
研究者
Mingzhao Xiao
Professor
First Affiliated Hospital of Chongqing Medical University