Yu Y X, Wang X M, Shi C, Hu S, Hu C H
Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou 215006, China.
Zhonghua Yi Xue Za Zhi. 2020 Mar 10;100(9):690-695. doi: 10.3760/cma.j.issn.0376-2491.2020.09.009.
To explore the value of CT radiomics quantitative features in the prediction of epidermal growth factor receptor (EGFR) mutation in lung cancer. The data of 144 patients, 75 males, 69 females, median age 54 (25-68 years), with EGFR gene test results in lung cancers diagnosed in the First Affiliated Hospital of Soochow University were retrospectively analyzed, including 81 patients, 39 males, 42 females, median age 52 (25-64)years old, with EGFR mutations and 63 patients,36 males,27 females,median age 56(32-68) years old,with EGFR wild types. According to a ratio of 2︰1, patients were randomly assigned to the training group and validation group. MaZda software was used to extract radiomics features including the gray level histogram (GLH), absolute gradient (GRA), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), auto-regressive model (ARM) and wavelets transform (WAV), and so on. Fisher coefficients (Fisher), classification error probability combined average correlation coefficients (POE+ACC) and mutual information (MI) were used to select 10 optimal features making up the optimal feature subsets. The optimal feature subsets were analyzed by using linear discriminant analysis (LDA) and nonlinear discriminant analysis (NDA) to calculate the accuracy, sensitivity and specificity in the differential diagnosis of EGFR mutant types and wild types in lung cancers. The prediction model was established using the optimal feature subsets with the highest accuracy in the training group with artificial neural network (ANN). The established prediction model was used to differentiate EGFR mutant types from wild types in the validation group. MaZda software extracted a total of 301 quantitative features in the CT images for the patients with EGFR mutant types and EGFR wild types in the training group. The optimal feature subsets obtained from Fisher-NDA and (POE+ACC)-NDA had the highest accuracy of 93.8%, in the differential diagnosis of the EGFR mutant types and EGFR wild types of lung cancer in the training group. The optimal feature subset prediction model obtained from Fisher-NDA had the accuracy, sensitivity and specificity of 83.3%, 86.7% and 77.8%, respectively, in the differential diagnosis of the EGFR mutant types and EGFR wild types of lung cancer in the validation group. The optimal subset of CT radiomics features has high accuracy in predicting EGFR mutations in lung cancer, providing a new method for predicting gene expression of lung cancer.
探讨CT影像组学定量特征在预测肺癌表皮生长因子受体（EGFR）突变中的价值。回顾性分析苏州大学附属第一医院确诊的144例肺癌患者的资料，其中男性75例，女性69例，中位年龄54岁（25 - 68岁），均有EGFR基因检测结果，包括81例EGFR突变患者，其中男性39例，女性42例，中位年龄52岁（25 - 64岁），以及63例EGFR野生型患者，其中男性36例，女性27例，中位年龄56岁（32 - 68岁）。按照2︰1的比例将患者随机分为训练组和验证组。采用MaZda软件提取影像组学特征，包括灰度直方图（GLH）、绝对梯度（GRA）、灰度共生矩阵（GLCM）、灰度游程长度矩阵（GLRLM）、自回归模型（ARM）和小波变换（WAV）等。利用Fisher系数（Fisher）、分类错误概率联合平均相关系数（POE+ACC）和互信息（MI）选择10个最优特征组成最优特征子集。采用线性判别分析（LDA）和非线性判别分析（NDA）对最优特征子集进行分析，计算在肺癌EGFR突变型和野生型鉴别诊断中的准确性、敏感性和特异性。在训练组中使用人工神经网络（ANN），以准确性最高的最优特征子集建立预测模型。将建立的预测模型用于验证组中EGFR突变型和野生型的鉴别。MaZda软件在训练组中为EGFR突变型和EGFR野生型患者的CT图像共提取了301个定量特征。在训练组肺癌EGFR突变型和EGFR野生型的鉴别诊断中，Fisher - NDA和（POE+ACC） - NDA获得的最优特征子集准确性最高，为93.8%。在验证组肺癌EGFR突变型和EGFR野生型的鉴别诊断中，Fisher - NDA获得的最优特征子集预测模型的准确性、敏感性和特异性分别为83.3%、86.7%和77.8%。CT影像组学特征的最优子集在预测肺癌EGFR突变方面具有较高准确性，为预测肺癌基因表达提供了一种新方法。
