Bai Xuexue, Feng Ming, Ma Wenbin, Wang Shiyong
Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, No. 1 Shuaifuyuan Hutong, Dongcheng District, Beijing, 100730, China.
Neurosurgery of The First Affiliated Hospital, Jinan University, Guangzhou, China.
Sci Rep. 2025 May 8;15(1):15990. doi: 10.1038/s41598-025-00758-0.
This study proposes a novel approach to predict the efficacy of bevacizumab (BEV) in treating peritumoral edema in metastatic brain tumor patients by integrating advanced machine learning (ML) techniques with comprehensive imaging and clinical data. A retrospective analysis was performed on 300 patients who received BEV treatment from September 2013 to January 2024. The dataset incorporated 13 predictive features: 8 clinical variables and 5 radiological variables. The dataset was divided into a training set (70%) and a test set (30%) using stratified sampling. Data preprocessing was carried out through methods such as handling missing values with the MICE method, detecting and adjusting outliers, and feature scaling. Four algorithms, namely Random Forest (RF), Logistic Regression, Gradient Boosting Tree, and Naive Bayes, were selected to construct binary classification models. A tenfold cross-validation strategy was implemented during training, and techniques like regularization, hyperparameter optimization, and oversampling were used to mitigate overfitting. The RF model demonstrated superior performance, achieving an accuracy of 0.89, a precision of 0.94, F1-score of 0.92, with both AUC-ROC and AUC-PR values reaching 0.91. Feature importance analysis consistently identified edema volume as the most significant predictor, followed by edema index, patient age, and tumor volume. Traditional multivariate logistic regression corroborated these findings, confirming that edema volume and edema index were independent predictors (p < 0.01). Our results highlight the potential of ML-driven predictive models in optimizing BEV treatment selection, reducing unnecessary treatment risks, and improving clinical decision-making in neuro-oncology.
本研究提出了一种新方法，通过将先进的机器学习（ML）技术与全面的影像和临床数据相结合，预测贝伐单抗（BEV）治疗转移性脑肿瘤患者瘤周水肿的疗效。对2013年9月至2024年1月接受BEV治疗的300例患者进行了回顾性分析。数据集纳入了13个预测特征：8个临床变量和5个放射学变量。使用分层抽样将数据集分为训练集（70%）和测试集（30%）。通过使用MICE方法处理缺失值、检测和调整异常值以及特征缩放等方法进行数据预处理。选择了四种算法，即随机森林（RF）、逻辑回归、梯度提升树和朴素贝叶斯，来构建二元分类模型。在训练过程中实施了十折交叉验证策略，并使用正则化、超参数优化和过采样等技术来减轻过拟合。RF模型表现出卓越的性能，准确率达到0.89，精确率达到0.94，F1分数达到0.92，AUC-ROC和AUC-PR值均达到0.91。特征重要性分析一致确定水肿体积是最显著的预测因子，其次是水肿指数、患者年龄和肿瘤体积。传统多变量逻辑回归证实了这些发现，确认水肿体积和水肿指数是独立预测因子（p < 0.01）。我们的结果突出了ML驱动的预测模型在优化BEV治疗选择、降低不必要的治疗风险以及改善神经肿瘤学临床决策方面的潜力。
