我们研究了肿瘤周围最佳大小并构建了表皮生长因子受体（EGFR）突变的预测模型。共回顾分析了164例肺腺癌患者。利用方差分析和最小绝对缩减提取计算机断层扫描图像中增强区域和增强区域与周围组合（3、5和7 mm）的放射组学特征。放射组学评分（rad-score）确定了最佳的周围组合。利用带有临床特征的肿瘤内部放射组学特征（IRS）构建了EGFR突变的预测模型。利用带有3、5或7 mm周围签名的肿瘤内部签名与临床特征（IPRS3、IPRS5和IPRS7）的组合也用于构建预测模型。使用支持向量机、逻辑回归和LightGBM模型进行五倍交叉验证，并评估接收者操作特征曲线。计算了训练和测试队列的曲线下面积（AUC）。使用Brier分数（BS）和决策曲线分析（DCA）评估了预测模型。从IRS中得出的SVM、LR和LightGBM模型的AUC值分别为0.783（95％置信区间：0.602-0.956）、0.789（0.654-0.927）和0.735（0.613-0.958），测试队列为0.791（0.641-0.920）、0.781（0.538-0.930）和0.734（0.538-0.930）。Rad-score确认了最佳的3 mm周围大小（IPRS3），从IPRS3中得出的SVM、LR和LightGBM模型的AUC值分别为0.831（0.666-0.984）、0.804（0.622-0.908）和0.769（0.628-0.921），测试队列为0.765（0.644-0.921）、0.783（0.583-0.921）和0.796（0.583-0.949）。从IPRS3中得出的LR和LightGBM模型的BS和DCA优于IRS。因此，肿瘤内部和3 mm周围放射组学签名的组合可能有助于预测EGFR突变。© 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.

We investigated optimal peritumoral size and constructed predictive models for epidermal growth factor receptor (EGFR) mutation.A 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.The 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.Accordingly, the combination of intratumoral and 3 mm-peritumoral radiomic signatures may be helpful for predicting EGFR mutations.© 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.