Multiregional radiomics of brain metastasis can predict response to EGFR-TKI in metastatic NSCLC

To develop radiomics signatures from multiparametric magnetic resonance imaging (MRI) scans to detect epidermal growth factor receptor (EGFR) mutations and predict the response to EGFR-tyrosine kinase inhibitors (EGFR-TKIs) in non-small cell lung cancer (NSCLC) patients with brain metastasis (BM). We included 230 NSCLC patients with BM treated at our hospital between January 2017 and December 2021 and 80 patients treated at another hospital between July 2014 and October 2021 to form the primary and external validation cohorts, respectively. All patients underwent contrast-enhanced T1-weighted (T1C) and T2-weighted (T2W) MRI, and radiomics features were extracted from both the tumor active area (TAA) and peritumoral edema area (POA) for each patient. The least absolute shrinkage and selection operator (LASSO) was used to identify the most predictive features. Radiomics signatures (RSs) were constructed using logistic regression analysis. For predicting the EGFR mutation status, the created RS-EGFR-TAA and RS-EGFR- POA showed similar performance. By combination of TAA and POA, the multi-region combined RS (RS-EGFR-Com) achieved the highest prediction performance, with AUCs of 0.896, 0.856, and 0.889 in the primary training, internal validation, and external validation cohort, respectively. For predicting response to EGFR-TKI, the multi-region combined RS (RS-TKI-Com) generated the highest AUCs in the primary training (AUC = 0.817), internal validation (AUC = 0.788), and external validation (AUC = 0.808) cohort, respectively. Our findings suggested values of multiregional radiomics of BM for predicting EGFR mutations and response to EGFR-TKI. The application of radiomic analysis of multiparametric brain MRI has proven to be a promising tool to stratify which patients can benefit from EGFR-TKI therapy and to facilitate the precise therapeutics of NSCLC patients with brain metastases.