基于机器学习的预测,预测食道状细胞癌的生存预后
Kaijiong Zhang1, Bo Ye1, Lichun Wu1
1Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
Scientific reports
|August 19, 2023
概括
这项研究开发了一种机器学习模型,用于预测食道状细胞癌 (ESCC) 患者的存活率,超过目前的分期系统,并帮助个性化治疗策略.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 对于食道状细胞癌 (ESCC) 的预后工具缺乏准确性,用于个性化患者管理.
- 机器学习 (ML) 提供了在ESCC中改善生存预测的潜力.
研究的目的:
- 开发和验证基于ML的ESCC患者生存预测模型.
- 确定影响ESCC患者生存的关键临床特征.
主要方法:
- 六种ML方法 (Rpart,弹性网,GBM,随机森林,GLMboost,ML扩展的CoxPH) 用于构建风险预测模型.
- 在1954名ESCC患者身上训练了模型,并在487名ESCC患者身上验证了模型.
- 使用一致性指数 (C指数) 评估性能.
主要成果:
- N阶段,T阶段,手术边缘,瘤等级,瘤长度,性别,MPV,AST,FIB和Mg被确定为至关重要的生存预测因素.
- 用ML扩展的CoxPH,弹性网和随机森林模型显示出优异的性能.
- 考克斯PH模型的风险得分有效地将患者分为低风险,中等风险和高风险组,具有明显的3年总生存率 (80.8%,58.2%,29.5%).
- 与AJCC第8阶段相比,ML模型显示出更好的区分能力和净收益.
结论:
- 开发的ML风险模型准确预测ESCC患者的生存率.
- 这个模型可以帮助风险分层,并指导临床决策,以实现个性化的患者管理.
- 考克斯PH方法适用于ESCC的解释性预后研究.
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