Department of Radiology, The Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China.
Infervision, Chaoyang District, Beijing, 100025, China.
Radiol Med. 2023 Dec;128(12):1483-1496. doi: 10.1007/s11547-023-01722-6. Epub 2023 Sep 25.
To investigate the value of Computed Tomography (CT) radiomics derived from different peritumoral volumes of interest (VOIs) in predicting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients.
A retrospective cohort of 779 patients who had pathologically confirmed lung adenocarcinoma were enrolled. 640 patients were randomly divided into a training set, a validation set, and an internal testing set (3:1:1), and the remaining 139 patients were defined as an external testing set. The intratumoral VOI (VOI_I) was manually delineated on the thin-slice CT images, and seven peritumoral VOIs (VOI_P) were automatically generated with 1, 2, 3, 4, 5, 10, and 15 mm expansion along the VOI_I. 1454 radiomic features were extracted from each VOI. The t-test, the least absolute shrinkage and selection operator (LASSO), and the minimum redundancy maximum relevance (mRMR) algorithm were used for feature selection, followed by the construction of radiomics models (VOI_I model, VOI_P model and combined model). The performance of the models were evaluated by the area under the curve (AUC).
399 patients were classified as EGFR mutant (EGFR+), while 380 were wild-type (EGFR-). In the training and validation sets, internal and external testing sets, VOI4 (intratumoral and peritumoral 4 mm) model achieved the best predictive performance, with AUCs of 0.877, 0.727, and 0.701, respectively, outperforming the VOI_I model (AUCs of 0.728, 0.698, and 0.653, respectively).
Radiomics extracted from peritumoral region can add extra value in predicting EGFR mutation status of lung adenocarcinoma patients, with the optimal peritumoral range of 4 mm.
探讨不同瘤周感兴趣区（VOI）的 CT 放射组学在预测肺腺癌患者表皮生长因子受体（EGFR）突变状态中的价值。
回顾性纳入 779 例经病理证实为肺腺癌的患者。640 例患者随机分为训练集、验证集和内部测试集（3:1:1），其余 139 例患者为外部测试集。在薄层 CT 图像上手动勾画肿瘤内 VOI（VOI_I），并沿 VOI_I 分别以 1、2、3、4、5、10 和 15mm 向外扩展生成 7 个瘤周 VOI（VOI_P）。从每个 VOI 中提取 1454 个放射组学特征。采用 t 检验、最小绝对收缩和选择算子（LASSO）和最小冗余最大相关性（mRMR）算法进行特征选择，然后构建放射组学模型（VOI_I 模型、VOI_P 模型和联合模型）。采用曲线下面积（AUC）评估模型性能。
399 例患者被归类为 EGFR 突变型（EGFR+），380 例为野生型（EGFR-）。在训练集、验证集、内部和外部测试集中，VOI4（肿瘤内和肿瘤旁 4mm）模型的预测性能最佳，AUC 分别为 0.877、0.727 和 0.701，优于 VOI_I 模型（AUC 分别为 0.728、0.698 和 0.653）。
从瘤周区域提取的放射组学特征可在预测肺腺癌患者 EGFR 突变状态方面提供额外价值，最佳瘤周范围为 4mm。
