中国肺癌杂志2025，Vol.28Issue(2)：105-113,9.DOI:10.3779/j.issn.1009-3419.2025.102.05
通过机器学习探究外周血相关指标对非小细胞肺癌EGFR突变及预后的预测价值研究
Exploration of the Predictive Value of Peripheral Blood-related Indicators for EGFR Mutations and Prognosis in Non-small Cell Lung Cancer Using Machine Learning
摘要
Abstract
Background and objective Epidermal growth factor receptor(EGFR)sensitive mutation is one of the effective targets of targeted therapy for non-small cell lung cancer(NSCLC).However,due to the difficulty of obtaining some primary tissues and the economic factors in some underdeveloped areas,some patients cannot undergo traditional genetic test-ing.The aim of this study is to establish a machine learning(ML)model using non-invasive peripheral blood markers to explore the biomarkers closely related to EGFR mutation status in NSCLC and evaluate their potential prognostic value.Methods 2642 lung cancer patients who visited Jiangsu Cancer Hospital from November 2016 to May 2023 were retrospectively enrolled and finally 175 NSCLC patients with complete follow-up data were included in the study.The ML model was constructed based on peripheral blood indicators and divided into training set and test set according to the ratio of 8:2.Unsupervised learn-ing algorithms were used for clustering blood features and mutual information method for feature selection,and an ensemble learning algorithm based on Shapley value was designed to calculate the contribution of each feature to the model prediction result.The receiver operating characteristic(ROC)curve was used to evaluate the predictive ability of the model.Results Through the feature extraction and contribution analysis of the predictive results of the interpretable ML model based on the Shapley value,the top ten indicators with the highest contribution were:pathological type,phosphorus,eosinophils,monocyte count,activated partial thromboplastin time,potassium,total bilirubin,sodium,eosinophil percentage,and total cholesterol.The area under the curve(AUC)of the model was 0.80.In addition,patients with hyponatremia and squamous cell carcinoma group had a poor prognosis(P<0.05).Conclusion The interpretable model constructed in this study provides a new approach for the prediction of EGFR mutation status in NSCLC patients,which provides a scientific basis for the diagnosis and treat-ment of patients who cannot undergo genetic testing.关键词
肺肿瘤/表皮生长因子受体/机器学习/预测模型Key words
Lung neplasms/Epidermal growth factor receptor/Machine learning/Predictive model引用本文复制引用
付书磊,温少迪,张佳强,杜晓月,李茹,沈波..通过机器学习探究外周血相关指标对非小细胞肺癌EGFR突变及预后的预测价值研究[J].中国肺癌杂志,2025,28(2):105-113,9.基金项目
本研究受国家自然科学基金项目(No.82272863)和齐鲁临床研究基金项目(No.2024KF0228)资助 This study was supported by the grants from National Natural Science Foundation of China(No.82272863)and Qilu Clinical Research Fund Project(No.2024KF0228)(Both to Bo SHEN). （No.82272863）
