在动态患者报告结果上使用机器学习方法,以集群与癌症治疗相关的症状
Nora Asper1, Hans Friedrich Witschel2, Louise von Stockar3
1Center for Dental Medicine and Faculty of Medicine, University of Zurich, 8032 Zurich, Switzerland.
Current oncology (Toronto, Ont.)
|June 25, 2025
概括
电子患者报告结果 (ePRO) 可以根据症状模式预测癌症类型. 这项研究表明,ePRO可以在没有临床医生的参与的情况下监测治疗疗效和治疗坚持.
科学领域:
- 在瘤学瘤学.
- 数字健康数字健康
- 机器学习在医学中的应用
背景情况:
- 电子患者报告结果 (ePRO) 在癌症护理中越来越多地用于监测和沟通.
- 通过ePROs追踪症状可以帮助早期检测不良反应和药物比较.
- 预测癌症类型和反映症状集群的ePRO模式的潜力尚未得到充分探索.
研究的目的:
- 调查ePRO中的模式是否可以预测潜在的癌症类型.
- 确定ePRO数据是否反映了瘤和治疗相关的症状集群 (SCs).
- 评估使用ePRO用于监测治疗轨迹和坚持治疗的可行性.
主要方法:
- 利用了226名癌症患者的数据,通过medidux应用报告了90多种症状.
- 根据频率和严重程度 (CTCAE) 将综合症状数据分为载体.
- 训练了一种后勤回归模型,从症状载体预测癌症类型,分析了一组乳腺癌患者.
主要成果:
- 机器学习模型确定了乳腺癌,肺癌和肠癌,其AUC值分别为0.74,0.63和0.78.
- 对于乳腺癌和肠癌 (AUC > 0.7),尽管数据集规模小,但预测性表现相当合理.
- 由于代表性有限,该模型无法识别前列腺癌和血淋巴癌.
结论:
- ePRO症状模式显示出预测癌症类型和反映症状集群的潜力.
- 这种方法可以使治疗坚持和疗效的动态监测,而无需额外的临床医生投入.
- 需要更大的队列来验证研究结果并建立强大的治疗概况.
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