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基于患者报告结果的机器学习模型的开发,以预测头癌的复发情况

Christopher M K L Yao¹, Katrina Hueniken², Shao Hui Huang³

¹Department of Otolaryngology - Head and Neck Surgery, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada; Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada.

|April 20, 2025

概括

使用患者报告结果的机器学习模型可以以中等准确度预测头癌复发. 这些模型可能有助于识别高风险患者,以便在治疗后进行更密切的监测.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
机器学习机器学习

背景情况:

头癌 (HNC) 的复发率很高,需要改进早期检测和监测策略.
患者报告结果 (PRO) 越来越被认为是患者护理需求和疾病进展的有价值预测指标.
机器学习 (ML) 提供了一种有前途的方法来分析复杂的PRO数据以预测HNC复发.

研究的目的:

使用PRO数据调查ML算法在预测HNC患者疾病进展方面的有效性.
为了确定HNC特定或一般的PRO在预测复发方面是否更有效.
确定最能预测HNC复发的关键PRO变量.

主要方法:

对1302名HNC患者的分析,这些患者在治疗后完成了症状评估问卷 (MDASI或ESAS).
应用ML模型,包括LASSO逻辑回归和随机森林 (RF),对基线和纵向PRO数据.
评估模型的预测性能,使用接收器操作曲线下的面积 (AUC) 进行十倍交叉验证.

主要成果:

在9.5%的HNC患者中观察到疾病复发.
使用基线MDASI数据的射频模型达到AUC约为0.675,疼痛,言语和口腔干燥被确定为关键预测因素.
结合疼痛,痛苦和情绪的非HPV模型在治疗后3个月达到0.71的AUC,在治疗后6个月达到0.70的AUC.

结论:

使用HNC特定的PROs的ML模型在识别患有疾病进展高风险的患者方面表现出中等的准确性.
需要使用更大的数据集进行进一步的前性研究来完善这些预测模型.
这些精细的模型有可能指导HNC患者的治疗后监测策略.

关键词:

头部和部癌症头部和部癌症机器学习机器学习患者报告的结果预测模型的预测模型监督监督监督监督监督监督监督监督监督监督监督监督

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