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使用可解释的随机森林模型,预测患上上皮质卵巢癌的患者无进展生存率

Lian Jian¹, Xiaoyan Chen², Pingsheng Hu¹

¹Department of Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan, China.

|August 21, 2024

概括

这项研究开发了一种用于表皮卵巢癌的可解释机器学习模型,使用临床数据和放射学来预测无进展生存率. 该模型结合了癌症抗原-125水平和瘤阶段,为患者风险评估提供了更好的临床解释性.

科学领域:

在瘤学瘤学.
放射学放射学是一门学科.
人工智能的人工智能

背景情况:

预后模型对于个性化癌症护理至关重要,但以前用于上皮卵巢癌的AI模型缺乏可解释性.
这项研究解决了在预测患者结果方面需要透明AI的需求.

研究的目的:

开发一种可解释的机器学习模型,用于预测卵巢上皮癌患者的无进展生存率.
整合临床变量和放射学特征,以提高预后准确度.

主要方法:

用对比度增强CT扫描对102名上皮卵巢癌患者进行了回顾性分析.
2074个放射性特征的提取和使用最小绝对收缩和选择运算符 (LASSO) 的逻辑回归进行选择.
随机森林模型的开发和使用Shapley添加式解释 (SHAP) 的解释.

主要成果:

多变量考克斯分析确定了癌症抗原-125 (CA-125) 水平,瘤阶段和放射性评分 (Radscore) 作为无进展生存的独立预测因素.
组合模型在培训中实现了0.812的AUC,在验证队伍中达到0.772.
SHAP分析显示Radscore,瘤阶段和CA-125是最有影响力的特征.

结论:

结合临床和放射学数据的可解释AI模型可以有效地预测上皮卵巢癌的无进展生存率.
基于SHAP的解释增强了临床医生对模型预测的理解,促进了更好的患者管理.
这种方法改善了卵巢癌患者的个性化风险评估和治疗指南.

关键词:

计算机断层扫描 (CT) 是一种计算机断层扫描.卵巢癌是发生在卵巢中的癌症.预测预后预测预测无线电学 (Radiomics) 是一种放射学.随机的森林随机的森林

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