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可解释的ML模型可以更深入地了解局部前列腺癌治疗决策

Jang Hee Han¹, Sungyup Lee², Byounghwa Lee²

¹Department of Urology, Seoul National University Hospital, Seoul, Republic of Korea.

Scientific reports

|July 17, 2023

概括

这项研究开发了一个模型来预测局部性前列腺癌治疗决策,结合了各种患者因素. 该模型实现了良好的准确性,改善了对复杂治疗选择的理解.

科学领域:

泌尿器科泌尿器科泌尿器科泌尿器科
在瘤学瘤学.
医疗信息学医疗信息学

背景情况:

现有的前列腺癌 (PCa) 治疗决策辅助器缺乏一致性和确定性.
了解影响治疗选择的多方面的因素对于局部PCa患者至关重要.

研究的目的:

开发和验证局部前列腺癌治疗决策的预测模型.
确定影响治疗选择的关键瘤,人口,社会经济和地理因素.
在PCa治疗决策过程中可视化复杂的特征相互作用.

主要方法:

利用了一个大型的现实世界数据库 (SEER-PWW),其中包括255,837名被诊断为局部PCa的男性 (2010-2015年).
开发了两个预测模型:一个用于整个队列 (AS/WW,RP,RT),一个用于低风险患者 (AS/WW).
使用多类AUC评估模型歧视,并使用Shapley添加式解释 (SHAP) 进行解释.

主要成果:

整体决策预测模型的多类AUC为0.77;低风险模型的AUC为0.74.
瘤学变量显著影响了急性前列腺切除术 (RP) 决策.
地理因素对放射治疗 (RT) 决策有很大的影响.

结论:

开发了准确的预测模型,用于使用现实数据选择局部性前列腺癌治疗.
证明了包括地理位置在内的各种因素对治疗决策的重大影响.
可视化非线性特征交互,为复杂的PCa治疗决策过程提供了洞察力.

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