融合MRI与临床指标，预测脑瘤术后早期复发风险
Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers
- 整合结构MRI与临床生物标志物的多模态学习框架
- 模型在时间依赖AUC和一致性指数上表现优异
- 适合临床医生用于个性化随访计划制定
脑瘤术后早期复发的准确预测仍是临床挑战。本研究提出一种多模态机器学习框架，结合结构MRI特征与临床生物标志物，以提升术后复发预测能力。采用四种机器学习算法——梯度提升机（GBM）、随机生存森林（RSF）、CoxBoost和XGBoost——并通过一致性指数（C-index）、时间依赖AUC、校准曲线及决策曲线分析验证模型性能。结果表明该模型具有良好预测能力，可为风险分层与个性化随访规划提供潜在工具。
原文摘要 · Abstract (English)
Accurately predicting early recurrence in brain tumor patients following surgical resection remains a clinical challenge. This study proposes a multi-modal machine learning framework that integrates structural MRI features with clinical biomarkers to improve postoperative recurrence prediction. We employ four machine learning algorithms -- Gradient Boosting Machine (GBM), Random Survival Forest (RSF), CoxBoost, and XGBoost -- and validate model performance using concordance index (C-index), time-dependent AUC, calibration curves, and decision curve analysis. Our model demonstrates promising performance, offering a potential tool for risk stratification and personalized follow-up planning.
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