A New Conception About Individualized Treatment Allocation for HCC-Using Machine Learning
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 4,991
- 主要终点
- overall survival
研究概览
简要总结
The current guidelines on hepatocellular carcinoma (HCC) aimed to build effective prognostic stratification strategies to guide therapeutic allocation; however, the current guidelines did not consider the simultaneous comparison of distinct therapies in similar populations. Here, the investigators aimed to develop and validate a new, integrated prognostic scheme for HCC patients using artificial intelligence (AI) to simulate the survival outcomes of patients allocated to different treatments.
详细描述
Given that liver resection (LR) and transarterial chemoembolization (TACE) are the mainstay curative and palliative therapies for HCC, respectively, patients who underwent LR or TACE were included in the study. Various prognostic AI algorithms were modeled using data from a large multi-institutional cohort, where LR and TACE were considered independent factors. The C-index, Brier score (BS), and area under the receiver operating characteristic curve (auROC) were calculated to estimate the AI models.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 100 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- 未提供
排除标准
- 未提供
结局指标
主要结局
overall survival
时间窗: 5 years
Overall survival (OS) was defined as the time interval between initial TACE or LR and all-cause death. Patients who survived up to the last follow-up date
次要结局
未报告次要终点