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利用可解释的机器学习来预测高度血清性卵巢癌的无进展生存率:来自前性队列研究的见解

Zhuo Chen^1,2,3,4, Hui Ouyang^3,5, Botao Sun^3,5

¹Department of Gynecology, Xiangya Hospital, Central South University, Changsha, Hunan Province, China.

International journal of surgery (London, England)

|January 29, 2025

概括

这项研究开发了一种可解释的机器学习模型,用于预测高度血清性卵巢癌 (HGSOC) 患者的无进展生存期 (PFS). 该模型可以通过Web应用程序访问,识别关键预测因素并帮助制定个性化治疗策略.

科学领域:

在瘤学瘤学.
机器学习机器学习
生物统计学生物统计学

背景情况:

高度血清性卵巢癌 (HGSOC) 的疾病进展率很高,现有的预后工具显示出局限性.
准确预测无进展生存 (PFS) 对于有效的HGSOC患者管理至关重要.
需要改进,可解释的模型来预测HGSOC中的PFS.

研究的目的:

开发和验证一种可解释的机器学习 (ML) 模型,用于预测HGSOC患者的PFS.
确定影响HGSOC中PFS的关键临床和分子特征.
为预测模型的临床应用创建一个用户友好的工具.

主要方法:

使用310名HGSOC患者的前性队列开发了9个ML算法用于PFS预测.
选择了最佳模型,并使用引导方法进行内部验证.
沙普利增量解释 (SHAP) 用于模型解释性和特征重要性分析.

主要成果:

随机生存森林 (RSF) 模型实现了0.755的C指数,证明了卓越的预测性能.
SHAP分析确定了瘤残留,HE4,FIGO阶段,T阶段,CA125,年龄,瘤体积,血小板数量和BMI作为PFS的显著预测因素.
确定了预测因素之间的非线性关系和相互作用效应,为PFS风险提供了更深入的见解.

结论:

在HGSOC中成功开发了用于PFS预测的可解释的ML模型,其性能优于现有的方法.
开发的模型及其交互式网络工具增强了个性化HGSOC患者管理的临床实用性.
这种方法有可能改善HGSOC的治疗决策和患者结果.

关键词:

沙普利添加剂的解释闪闪发光的应用程序机器学习是机器学习.预测模型预测模型血清性卵巢癌是发生在卵巢癌的.

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