Deep learning radiomics for the prediction of epidermal growth factor receptor mutation status based on MRI in brain metastasis from lung adenocarcinoma patients.

backgroundEarly and accurate identification of epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients with brain metastases is critical for guiding targeted therapy. This study aimed to develop a deep learning radiomics model utilizing multi-sequence magnetic resonance imaging (MRI) to differentiate between EGFR mutant type (MT) and wild type (WT).

methodsIn this retrospective study, 288 NSCLC patients with confirmed brain metastases were enrolled, including 106 with EGFR MT and 182 with EGFR WT. All patients were randomly divided into a training dataset (75%) and a validation dataset (25%). Radiomics and deep learning features were extracted from the brain metastatic lesions using contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) MRI images. Features extraction and selection were performed using the least absolute shrinkage and selection operator (LASSO) and ResNet34. The predictive performance of the signatures for EGFR mutation status was assessed using receiver operating characteristic (ROC) curves and area under the curve (AUC) analyses.

resultsNo significant differences were found between the training and validation datasets. A four-feature radiomics signature (RS) demonstrated excellent predictive accuracy for EGFR MT, with α-binormal-based and empirical AUCs of 0.931 (95% CI: 0.880-0.940) and 0.926 (95% CI: 0.877-0.933), respectively. Incorporating deep learning signature (DLS) further enhanced the model's performance, achieving α-binormal-based and empirical AUCs of 0.943 (95% CI: 0.921-0.965) and 0.938 (95% CI: 0.914-0.962) in the training dataset. These findings were confirmed in the validation dataset, with AUCs of 0.936 (95% CI: 0.917-0.955) and 0.921 (95% CI: 0.901-0.941), demonstrating robust and consistent predictive performance.

conclusionsThe multi-sequence MRI-based deep learning radiomics model exhibited high efficacy in predicting EGFR mutation status in NSCLC patients with brain metastases. This approach, which integrates advanced radiological features with deep learning techniques, offers a non-invasive and accurate method for determining EGFR mutation status, potentially guiding personalized treatment decisions in clinical practice.