可解释的机器学习模型预测了膀癌激进囊切除术后辅助疗法的反应
Jian Hou1, Yi Ding2, Runlin Feng3
1Department of Urology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
Frontiers in oncology
|November 17, 2025
概括
机器学习模型可以预测膀癌激进囊切除术后的辅助治疗反应. 关键预测因素包括瘤入侵和PD-L1/HER2表达,指导个性化治疗策略.
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:
- 激进囊切除术 (RC) 是膀癌的标准,但复发是常见的.
- 辅助疗法改善了结果,但由于瘤异质性,反应有所不同.
- 预测模型对于RC后的个性化治疗至关重要.
研究的目的:
- 开发和验证机器学习模型,以预测膀癌患者的辅助治疗反应.
- 确定影响治疗结果的关键病理,人口和分子特征.
- 加强个人化治疗策略,用于膀癌后RC.
主要方法:
- 对接受RC的膀癌患者的回顾性分析.
- 使用LASSO回归来选择特征,并使用9个机器学习算法来开发模型.
- 评估模型性能使用AUC和SHAP进行解释性.
主要成果:
- 一个随机森林模型实现了高预测性能 (AUC=0.92训练,0.74测试).
- 血管入侵,围神经入侵和PD-L1/HER2表达是关键的预测特征.
- 决策曲线分析表明了有利的临床效用.
结论:
- 整合多种特征的机器学习模型显示了预测辅助治疗反应的潜力.
- 外部验证揭示了性能局限性,强调需要进一步研究.
- 建议进行前性多中心研究,以提高模型的通用性和临床应用.
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