使用多模式数据的机器学习模型准确地预测乳腺癌中化疗诱导的心脏毒性
Kundi Chen1, Yuqiong An1, Zhen Wang1
1Ultrasound Medical Center, Second Hospital of Lanzhou University, Lanzhou, China.
Frontiers in cardiovascular medicine
|January 22, 2026
概括
机器学习模型可以预测乳腺癌患者的化疗相关心脏功能障碍 (CTRCD). XGBoost模型识别了早期干预的高风险个体.
科学领域:
- 心脏病学 心脏病学
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
背景情况:
- 化疗相关心脏功能障碍 (CTRCD) 是乳腺癌治疗的一个重大挑战.
- 预测CTRCD风险对于患者管理和治疗优化至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测女性乳腺癌患者的CTRCD风险.
- 整合多式联运数据以提高预测准确度.
主要方法:
- 追溯分析了423名女性乳腺癌患者的数据 (人口统计,临床,心声,心电图,生物标志物).
- 数据分为培训 (70%) 和验证 (30%) 集.
- 利用七种特征选择方法和八种ML算法,包括XGBoost,用于模型开发和比较.
主要成果:
- CTRCD发生在26.24%的患者中.
- 确定了关键预测因素:年龄,低射出率,特定组合疗法,化疗周期和异常心电图.
- XGBoost模型实现了最高的性能,AUC为0.782.
结论:
- 开发的XGBoost模型显示了对CTRCD的强大预测能力.
- 这种ML工具可以帮助早期风险分层和及时临床管理CTRCD.
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