中国临床医学影像杂志2026，Vol.37Issue(5)：309-313,5.DOI:10.12117/jccmi.2026.05.002
多模态MRI影像组学模型预测非小细胞肺癌脑转移瘤EGFR突变状态的研究
Research on prediction of EGFR mutation status in brain metastases of non-small cell lung cancer using a multimodal MRI radiomics model
摘要
Abstract
Objective:To determine the predictive value of multimodal MRI radiomics models in assessing epidermal growth factor receptor(EGFR)mutation status in non-small cell lung cancer(NSCLC)patients with brain metastases,ultimately providing imaging evidence to guide personalized targeted therapy.Methods:A total of 97 patients with brain metastases were retrospectively included and randomly divided into a training group(40 with mutations and 38 with wild-type)and test group(10 with mutations and 9 with wild-type).The imaging and related clinical data from the pre-treatment MRI scans,including the plain and enhanced scans,were collected using the medical record system.The radiomics features were extracted from the T1WI enhancement,apparent diffusion coefficient(ADC)and T2-fluid attenuated inversion recovery sequence(T2-FLAIR)images.Feature reduction and selection were performed using variance selection and univariate selection methods.Refined features were utilized to build three classifiers-support vector machine(SVM),random forest(RF)and logistic regression(LR)to establish radiomic prediction models.The predictive efficacy of these models for EGFR mutation status was evaluated using receiver op-erating characteristic(ROC)curves.Results:The LR classifier exhibited the best predictive performance in the combined se-quences of T1WI enhancement,ADC,and T2-FLAIR images.The training group showed an area under the curve(AUC)of 0.854,with sensitivity,specificity,and accuracy of 78%,74%,and 69%.In contrast,the testing group achieved an AUC of 0.833,a sensitivity of 83%,a specificity of 71%,and an accuracy of 71%.Moreover,the LR analysis confirmed that both the T1WI enhancement and T2-FLAIR radiomics models showed effective predictive performance,whereas the ADC model yielded less satisfactory results.Conclusions:The radiomics models incorporating the T1WI enhancement and T2-FLAIR imaging can be used to predict the mutation status of EGFR,with the LR combined classifier to exhibit the best predictive performance in the test set(AUC=0.833).This model offers a non-invasive tool for predicting EGFR mutation status in patients with brain metastases who have limited tissue for detection.关键词
癌,非小细胞肺/脑肿瘤/肿瘤转移/磁共振成像Key words
Carcinoma,Non-Small-Cell Lung/Brain Neoplasms/Neoplasm Metastasis/Magnetic Resonance Imaging分类
医药卫生引用本文复制引用
陈杰云,黄锦祥,林晓莹..多模态MRI影像组学模型预测非小细胞肺癌脑转移瘤EGFR突变状态的研究[J].中国临床医学影像杂志,2026,37(5):309-313,5.基金项目
福建省自然科学基金计划项目资助(2022J011463). （2022J011463）
