目的:探讨不同肿瘤周围体积(VOIs)的CT影像组学预测肺腺癌患者表皮生长因子受体(EGFR)突变状态的价值。
方法:纳入779例经病理证实的肺腺癌患者的回顾性队列。640名患者被随机分为一组训练组,验证集,和内部测试集(3:1:1),其余139例患者被定义为外部测试集.在薄层CT图像上手动描绘肿瘤内VOI(VOI_I),和七个肿瘤周围的VOI(VOI_P)自动生成,并沿VOI_I扩展1、2、3、4、5、10和15mm。从每个VOI中提取1454个放射学特征。t检验,最小绝对收缩和选择运算符(LASSO),最小冗余最大相关性(mRMR)算法用于特征选择,其次是放射组学模型的构建(VOI_I模型,VOI_P模型和组合模型)。通过曲线下面积(AUC)评价模型的性能。
结果:399例患者被分类为EGFR突变(EGFR+),而380是野生型(EGFR-)。在训练集和验证集中,内部和外部测试集,VOI4(肿瘤内和瘤周4mm)模型实现了最佳预测性能,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)。
结论:从瘤周区域提取的影像组学可以在预测肺腺癌患者EGFR突变状态方面增加额外价值。与4毫米的最佳肿瘤范围。
OBJECTIVE: 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.
METHODS: 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).
RESULTS: 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).
CONCLUSIONS: 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.