在C型肝炎病毒持续病毒学反应后肝细胞癌的预测使用随机生存森林模型
Hikaru Nakahara1,2, Atsushi Ono1, C Nelson Hayes1
1Department of Gastroenterology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan.
JCO clinical cancer informatics
|December 18, 2024
概括
机器学习准确地预测了在获得持续病毒反应 (SVR) 的患者中肝细胞癌 (HCC) 风险. 这使得个性化查策略成为可能,改善了患者的治疗结果和医疗保健效率.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 机器学习在医学中的应用
- 在瘤学瘤学.
背景情况:
- 目前针对持续病毒反应 (SVR) 患者的肝细胞癌 (HCC) 查指南不考虑个体患者的风险.
- 需要个性化查协议,以优化在SVR人群中早期检测HCC.
研究的目的:
- 开发和验证一种机器学习模型,用于预测SVR后患者的HCC发病率.
- 根据个体患者的风险,实现量身定制的HCC查策略.
主要方法:
- 一个随机生存森林 (RSF) 模型是使用来自1028名SVR患者的临床数据开发的.
- 模型的性能使用哈雷尔的c指数进行评估,并在737名SVR患者的独立队列中得到验证.
- 沙普利增量解释法 (SHAP) 用于特征解释,卡普兰-梅尔分析比较了风险组之间的HCC发病率.
主要成果:
- 该RSF模型实现了高预测准确性,其c指数分数在导出队列中为0.90,在验证队列中为0.80.
- 确定的关键预测因素包括血小板计数,胺甲基酶,性别,年龄和ALT.
- 将风险分为四个风险组 (低风险,中风险,高风险,非常高风险) 的分层显示出明显的5年累积HCC发病率,在衍生队列中从0%到54%以上.
结论:
- 整合RSF和SHAP提供了一种准确的方法来对SVR后患者的HCC风险进行分类.
- 这种方法有助于实施个性化的HCC查协议.
- 个性化查可以带来更高效和更具成本效益的患者护理.
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