Detection of EGFR mutations in early-stage lung adenocarcinoma by machine learning-based radiomics

BACKGROUND: We hypothesized that epidermal growth factor receptor (EGFR) mutations could be detected in early-stage lung adenocarcinoma using radiomics. METHODS: This retrospective study included consecutive patients with clinical stage I/II lung adenocarcinoma who underwent curative-intent pulmonary resection from March–December 2016. Using preoperative enhanced chest computed tomography, 3,951 radiomic features were extracted in total from the tumor (area within the tumor boundary), tumor rim (area within ±3 mm of the tumor boundary), and tumor exterior (area between +10 mm outside the tumor and tumor boundary). A machine learning-based radiomics model was constructed to detect EGFR mutations. The combined model incorporated both radiomic and clinical features (gender and smoking history). The performance was validated with five-fold cross-validation and evaluated using the mean area under the curve (AUC). RESULTS: Of 99 patients (mean age, 66±11 years; female, 66.6%; clinical stage I/II, 89.9%/10.1%), EGFR mutations in the surgical specimen were detected in 46 (46.5%). A median of 4 (range, 2 to 8) radiomic features was selected for each validation session. The mean AUCs in the radiomics and combined models were 0.75 and 0.83, respectively. The two top-ranked features in the combined model were the radiomic features extracted from the tumor exterior and the tumor, indicating a higher impact of radiomic features over relevant clinical features. CONCLUSIONS: Radiomic features, including those in the peri-tumoral area, may help detect EGFR mutations in lung adenocarcinomas in preoperative settings. This non-invasive image-based technology could help guide future precision neoadjuvant therapy.