多基因机器学习预测新辅助化疗结果和乳腺癌复发
Lili Wang1,2,3, Xiaodong Zhang4, Jing Zhang5
1Department of Oncology, Molecular Oncology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350000, China.
BME frontiers
|January 29, 2026
概括
这项研究整合了多组学和机器学习,以预测乳腺癌患者的新辅助化疗反应和复发风险. 开发的模型在识别可能从治疗中受益的患者和那些有复发风险的患者方面显示出高准确性.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 生物信息学是一种生物信息学.
背景情况:
- 选择乳腺癌患者接受新辅助化疗 (NAC) 仍然具有挑战性.
- 准确预测NAC反应和复发风险对于个性化治疗策略至关重要.
研究的目的:
- 研究多组学 (MO) 数据与堆叠整体学习的整合,以预测乳腺癌 (BC) 中的NAC反应和复发风险.
- 评估结合临床病理学,基于MRI的内异质生物标志物和多组学数据的疗效,以改善预测.
主要方法:
- 对124名接受NAC.BC治疗的BC患者的回顾性分析.
- 从动态MRI中导出放射学特征 (RadSET,RadSITH) 的结果.
- 放射学特征与临床病理学指标的整合,使用堆叠整体学习来预测病理完整反应 (pCR) 和3年无病生存期 (DFS).
主要成果:
- 与单个放射学特征相比,多组学集成模型在pCR预测方面实现了显著更高的AUC (0.917).
- 该模型有效预测了NAC反应和术后复发风险.
- 弹性网考克斯模型在使用NAC前后变量时显示出强大的DFS预测性能.
结论:
- 与堆叠整体学习的多组学集成提供了一种强大的方法,用于预测乳腺癌中新辅助化疗反应.
- 这种方法提高了复发风险的预测,可能优化乳腺癌患者的临床决策.
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