A Deep Learning Model Based on Contrast-enhanced Ultrasound to Aid Clinical Decisions and Predict Biological Biomarker and Prognosis of Hepatocellular Carcinoma
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 1
- 试验地点
- 1
- 主要终点
- Recurrence-free survival (RFS)
研究概览
简要总结
Developing a deep learning model based on contrast-enhanced ultrasound (CEUS) to predict the prognosis of hepatocellular carcinoma (HCC) and aid choose operation decisions
详细描述
Collecting CEUS and clinical data of HCC from different institutions retrospectively.
Developing a deep learning model based on CEUS to predict the prognosis of HCC. Developing a deep learning model based on CEUS to choose a better operation (ablation or surgery) of HCC patients.
Then, validating the deep learning model in the prospective data.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •patients with HCC (Ia, Ib, IIa stage) China liver cancer staging who underwent resection or ablation
- •without macro-vascular invasion
- •Child-Pugh A/B grade
- •HCC is proved by pathological examination or two enhanced imaging
- •CEUS (Sonovue or Sonozoid) images are performed two weeks before the operation
- •Invasive biomarker or prognosis of HCC available
- •CEUS images are included in at least three stages (Arterial phase, Portal phase, and Late phase)
排除标准
- •postop follow-up loss or expired less than 3 months
- •patients with co-malignancy
- •poor images quality for analyzing
结局指标
主要结局
Recurrence-free survival (RFS)
时间窗: Immediately after the surgery or ablation
Recurrence-free survival is defined as the time elapsed between a predefined point in time (the date of diagnosis, randomization or the intervention) and any recurrence (local, regional, or distant) or death due to any cause (death is an event).
次要结局
- Recurrence(Immediately after the surgery or ablation)
研究者
Ping Liang
Chief of department of interventional ultrasound
Chinese PLA General Hospital