用可解释AI预测癌患生存率，关键指标更清晰。
Predicting Survivability of Cancer Patients with Metastatic Patterns Using Explainable AI
- 用XGBoost模型结合临床与基因数据预测生存率
- AUC达0.82，关键影响因素包括转移部位数等
- 通过SHAP和生存分析提供医生可用的决策支持
癌症仍是全球主要健康挑战之一。本研究利用机器学习（ML）方法，基于涵盖27种癌症类型、共25,775名患者的综合MSK-MET数据集，预测具有转移模式的癌症患者生存率。评估了五种机器学习模型——XGBoost、朴素贝叶斯、决策树、逻辑回归和随机森林，经超参数调优与网格搜索后，XGBoost表现最佳，曲线下面积（AUC）为0.82。为提升模型可解释性，采用SHapley Additive exPlanations（SHAP）分析，识别出转移部位数量、肿瘤突变负荷、基因组改变分数及器官特异性转移等关键预测因子。进一步通过Kaplan-Meier曲线、Cox比例风险模型及XGBoost生存分析，确认了影响患者预后的显著因素，为临床提供可操作的个性化预后与治疗规划参考。
原文摘要 · Abstract (English)
Cancer remains a leading global health challenge and a major cause of mortality. This study leverages machine learning (ML) to predict the survivability of cancer patients with metastatic patterns using the comprehensive MSK-MET dataset, which includes genomic and clinical data from 25,775 patients across 27 cancer types. We evaluated five ML models-XGBoost, Naïve Bayes, Decision Tree, Logistic Regression, and Random Fores using hyperparameter tuning and grid search. XGBoost emerged as the best performer with an area under the curve (AUC) of 0.82. To enhance model interpretability, SHapley Additive exPlanations (SHAP) were applied, revealing key predictors such as metastatic site count, tumor mutation burden, fraction of genome altered, and organ-specific metastases. Further survival analysis using Kaplan-Meier curves, Cox Proportional Hazards models, and XGBoost Survival Analysis identified significant predictors of patient outcomes, offering actionable insights for clinicians. These findings could aid in personalized prognosis and treatment planning, ultimately improving patient care.
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