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可解释和可视化的机器学习模型可以预测前列腺癌的生化复发

Wenhao Lu^1,2,3,4, Lin Zhao⁵, Shenfan Wang⁵

¹Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-Constructed By the Province and Ministry, Guangxi Medical University, No. 22, Shuangyong Road, Qingxiu District, Nanning City, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China.

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概括

可解释的机器学习模型可以准确地预测前列腺癌复发. 这些模型通过可视化关键预后因素,提高临床效用,帮助个性化治疗决策.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
生物统计学生物统计学

背景情况:

机器学习 (ML) 模型在预测预后方面表现有希望,但由于其"黑子"性质,往往缺乏临床解释性.
可解释的AI (XAI) 技术对于将复杂的ML模型转化为临床可行的见解至关重要.

研究的目的:

开发和验证可解释和可视化的ML模型,用于预测前列腺癌 (PCa) 中的生化复发 (BCR).
增强基于ML的预后工具的临床实用性,用于PCa管理.

主要方法:

对647名PCa患者的回顾性分析.
使用LASSO回归来确定11个与BCR相关的临床参数的特征选择.
使用考克斯回归和五个ML算法 (RSF,SSVM,sTree,GBDT,XGBoost) 的预测模型的开发.
通过一致性指数 (C指数) 和决策曲线分析 (DCA) 评估模型性能.
使用沙普利增量解释 (SHAP) 值进行解释性分析.

主要成果:

随机生存森林 (RSF) 模型获得了最高的C指数 (0.846),超过了其他ML模型和Cox回归.
决策曲线分析表明RSF模型具有优越的临床实用性.
在RSF模型中,SHAP值对每个特征的贡献提供了明确的见解.

结论:

开发的可解释的ML模型,特别是RSF,为预测PCa BCR提供了可靠的框架.
这些模型可以作为识别患有复发风险的患者的参考,并为个性化治疗策略提供信息.

关键词:

生物化学复发的发生.机器学习是机器学习.预后预后预测预测前列腺癌是什么意思前列腺癌是什么意思在SHAP中,价值是SHAP值.

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