AbstractPurposeWe investigated optimal peritumoral size and constructed predictive models for epidermal growth factor receptor (EGFR) mutation.MethodsA total of 164 patients with lung adenocarcinoma were retrospectively analyzed. Radiomic signatures for the intratumoral region and combinations of intratumoral and peritumoral regions (3, 5, and 7 mm) from computed tomography images were extracted using analysis of variance and least absolute shrinkage. The optimal peritumoral region was determined by radiomics score (rad‐score). Intratumoral radiomic signatures with clinical features (IRS) were used to construct predictive models for EGFR mutation. Combinations of intratumoral and 3, 5, or 7 mm‐peritumoral signatures with clinical features (IPRS3, IPRS5, and IPRS7, respectively) were also used to construct predictive models. Support vector machine (SVM), logistic regression (LR), and LightGBM models with five‐fold cross‐validation were constructed, and the receiver operating characteristics were evaluated. Area under the curve (AUC) of the training and test cohorts values were calculated. Brier scores (BS) and decision curve analysis (DCA) were used to evaluate the predictive models.ResultsThe AUC values of the SVM, LR, and LightGBM models derived from IRS were 0.783 (95% confidence interval: 0.602–0.956), 0.789 (0.654–0.927), and 0.735 (0.613–0.958) for training, and 0.791 (0.641–0.920), 0.781 (0.538–0.930), and 0.734 (0.538–0.930) for test cohort, respectively. Rad‐score confirmed that the 3 mm‐peritumoral size was optimal (IPRS3), and AUCs values of SVM, LR, and lightGBM models derived from IPRS3 were 0.831 (0.666–0.984), 0.804 (0.622–0.908), and 0.769 (0.628–0.921) for training and 0.765 (0.644–0.921), 0.783 (0.583–0.921), and 0.796 (0.583–0.949) for test cohort, respectively. The BS and DCA of the LR and LightGBM models derived from IPRS3 were better than those from IRS.ConclusionAccordingly, the combination of intratumoral and 3 mm‐peritumoral radiomic signatures may be helpful for predicting EGFR mutations.