Mining whole‐brain information with deep learning to predict EGFR mutation and subtypes in brain‐metastatic NSCLC: A multicenter study

Background Epidermal growth factor receptor (EGFR) and its mutation subtypes play a pivotal role in the treatment of non‐small cell lung cancer (NSCLC) patients. Therefore, developing an accurate, noninvasive quantitative method to predict EGFR genotype is crucial for personalized treatment. Purpose To explore a deep learning‐based method with whole‐brain information for predicting EGFR mutation and subtypes utilizing MRI images in NSCLC patients presenting with brain metastasis (BM). Methods This study enrolled 293 patients with BM. A primary set was built with 170 patients from Center 1 (between January 2017 and December 2021). External sets were constructed with 62 patients from Center 2 (between July 2014 and October 2021) and 61 patients from Center 3 (between January 2020 and October 2022). All patients underwent contrast‐enhanced T1‐weighted (T1CE) and T2‐weighted (T2W) brain MRI scans before genetic testing. An EGFR site recognition network (ESR‐Net) was developed by mining whole‐brain information to predict EGFR mutations and subtypes. The ESR‐Net integrated deformable convolution and an auxiliary network to seek informative mutation features and enhance tumor features, respectively. Predictive performances of deep learning models were assessed using area under the curve (AUC) analysis. Results For the prediction of EGFR mutations, the ESR‐Net demonstrated superior performance with AUCs ranging from 0.835 to 0.840 across primary and external validation sets, surpassing conventional state‐of‐the‐art methodologies. Furthermore, the ESR‐Net exhibited AUCs ranging from 0.858 to 0.904 for predicting EGFR exon 19 (Del19) mutation and 0.838 to 0.903 for predicting EGFR exon 21 (L858R) mutation across primary and external sets. Conclusions The developed ESR‐Net demonstrates promising potential for early detection of EGFR mutations and subtypes with multicenter data, which may promote optimal treatment management for patients with brain metastatic NSCLC.