Chen Jianing, Wang Li, Liu Li, Wang Qi, Zhao Jing, Yu Xin, Zhang Shiji, Su Chunxia
Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Clinical Research Center, Shanghai Pulmonary Hospital, Shanghai, 200433, China.
Discov Oncol. 2025 Jul 29;16(1):1438. doi: 10.1007/s12672-025-03298-1.
Brain metastasis (BM) stands as a significant contributor to mortality among cancer patients. The factors contributing to BM remain incompletely elucidated. Accurate prediction of BM occurrence is essential for effective disease prevention and control during patient management.
Clinical characteristics from a cohort of 39,930 non-small cell lung cancer (NSCLC) patients were meticulously collected. 12 candidate variables were screened out through Least absolute shrinkage and selection operator (LASSO) regression analysis. This dataset was divided into training, testing, and validation sets in a ratio of 6:2:2. Additionally, 276 NSCLC patient records from Shanghai Pulmonary Hospital (SPH) were used as an external cohort. Subsequently, seven machine learning models were constructed employing diverse algorithms, namely Logistic Regression (LR), Classification and Regression Tree (CART), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting (XGBOOST). SHapley Additive exPlanations (SHAP) analysis applied to visualize the importance of each feature. The evaluation of these models was based on the area under the receiver-operating characteristic curve (ROC), facilitating a comprehensive comparison of their predictive performances. The GBM model, demonstrating superior accuracy, was selected for brain metastasis (BM) prediction. A dedicated website was developed to visualize and communicate the predictive model's outcomes.
Through a comprehensive evaluation incorporating metrics such as the area under the curve (AUC), accuracy, sensitivity, specificity, Kappa value, and calibration curve, the model generated by the Gradient Boosting Machine (GBM) algorithm demonstrated exceptional and near-perfect performance in both the validation set (AUC: 0.8276) and the test set (AUC: 0.8301), however, performance was lower in the SPH cohort (AUC: 0.6100). To enhance interpretability, the relative importance of the 12 candidate variables was ranked, providing clinicians with a clear understanding of how each factor contributes to the prediction of BM risk.
Successfully developed was a machine learning model, characterized by high accuracy and precision, along with a web-based predictor tailored for NSCLC patients. This advancement enables the accurate prediction of BM risk, facilitating the implementation of targeted preventive measures for individuals deemed at high risk.
脑转移（BM）是癌症患者死亡的一个重要因素。导致脑转移的因素仍未完全阐明。准确预测脑转移的发生对于患者管理期间有效的疾病预防和控制至关重要。
精心收集了39930例非小细胞肺癌（NSCLC）患者的临床特征。通过最小绝对收缩和选择算子（LASSO）回归分析筛选出12个候选变量。该数据集按6:2:2的比例分为训练集、测试集和验证集。此外，来自上海肺科医院（SPH）的276例NSCLC患者记录用作外部队列。随后，采用多种算法构建了七个机器学习模型，即逻辑回归（LR）、分类与回归树（CART）、随机森林（RF）、支持向量机（SVM）、K近邻（KNN）、梯度提升机（GBM）和极端梯度提升（XGBOOST）。应用SHapley加法解释（SHAP）分析来可视化每个特征的重要性。这些模型的评估基于受试者操作特征曲线（ROC）下的面积，便于对其预测性能进行全面比较。选择表现出卓越准确性的GBM模型用于脑转移（BM）预测。开发了一个专门的网站来可视化和传达预测模型的结果。
通过综合评估纳入曲线下面积（AUC）、准确性、敏感性、特异性、Kappa值和校准曲线等指标，梯度提升机（GBM）算法生成的模型在验证集（AUC：0.8276）和测试集（AUC：0.8301）中均表现出卓越且近乎完美的性能，然而，在SPH队列中的性能较低（AUC：0.6100）。为了提高可解释性，对12个候选变量的相对重要性进行了排名，使临床医生清楚了解每个因素如何对脑转移风险预测产生影响。
成功开发了一种机器学习模型，其特点是具有高精度和准确性，以及为NSCLC患者量身定制的基于网络的预测器。这一进展能够准确预测脑转移风险，便于对被认为高风险的个体实施针对性预防措施。
