通过使用机器学习,根据常规收集的数据,支持对癌症患者进行分子分析的决定
Julia Kasprzak1, C Benedikt Westphalen2, Simon Frey3
1Comprehensive Cancer Center (CCC Munich LMU), LMU University Hospital Munich, Pettenkoferstraße 8a, Munich, Germany. Julia.Kasprzak@med.uni-muenchen.de.
Clinical and experimental medicine
|April 10, 2024
概括
一个新的机器学习模型可以帮助医生决定哪些癌症患者应该接受下一代测序 (NGS) 测试以进行向治疗,从而改善治疗决策.
科学领域:
- 在瘤学瘤学.
- 基因组学就是基因组学.
- 机器学习 机器学习
背景情况:
- 个性化医疗利用针对性疗法来治疗癌症.
- 对于下一代测序 (NGS) 测试决策缺乏标准化,导致潜在的过度使用或不足使用.
- 已经耗尽传统治疗的患者往往是分子向治疗的候选人.
研究的目的:
- 为临床医生开发关于NGS测试的决策支持工具.
- 帮助识别那些可以从分子分析中受益的患者.
主要方法:
- 在临床数据上训练了一种机器学习模型,以预测分子分析的必要性.
- 模型预测与分子瘤委员会 (MTB) 使用患者病例简报所做出的决定进行了验证.
主要成果:
- 该模型在13,587名没有分子分析的患者和440名具有分子分析的患者中进行了训练.
- 关键的预测特征包括患者年龄,身体状况,瘤类型,转移和先前的治疗方法.
- 该模型在15个验证案例中的9个中显示与MTB专家达成一致.
结论:
- 从历史数据开发的预测模型可以帮助临床医生确定分子分析的必要性.
- 该工具有可能优化向治疗在癌症护理中的使用.
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