CT-based radiomics analysis for differentiation between thymoma and thymic carcinoma.

Background: The purpose of our study was to differentiate between thymoma and thymic carcinoma using a radiomics analysis based on the computed tomography (CT) image features. Methods: The CT images of 61 patients with thymic epithelial tumors (TETs) who underwent contrast-enhanced CT with slice thickness <1 mm were analyzed. Pathological examination of the surgical specimens revealed thymoma in 45 and thymic carcinoma in 16. Tumor volume and the ratio of major axis to minor axis were calculated using a computer-aided diagnostic software. Sixty-one different radiomics features in the segmented CT images were extracted, then filtered and minimized by least absolute shrinkage and selection operator (LASSO) regression to select the optimal radiomics features for predicting thymic carcinoma. The association between the quantitative values and a diagnosis of thymic carcinoma were analyzed with logistic regression models. Parameters identified as significant in univariate analysis were included in multiple analyses. Receiver-operating characteristic (ROC) curves were assessed to evaluate the diagnostic performance. Results: Thymic carcinoma was significantly predominant in men (P=0.001). Optimal radiomics features for predicting thymic carcinoma were as follows: gray-level co-occurrence matrix (GLCM)-homogeneity, GLCM-energy, compactness, large zone high gray-level emphasis (LZHGE), solidity, size of minor axis, and kurtosis. Multiple logistic regression analysis of these features revealed solidity and GLCM-energy as independent indicators associated with thymic carcinoma [odds ratio, 14.7 and 14.3; 95% confidence interval (CI): 1.6-139.0 and 3.0-68.7; and P=0.045 and 0.002, respectively]. Area under the curve (AUC) for diagnosing thymic carcinoma was 0.882 (sensitivity, 81.2%; specificity, 91.1%). Multivariate analysis adjusted for sex similarly revealed two features (solidity and GLCM-energy) as independent indicators associated with thymic carcinoma (odds ratio, 14.6 and 23.9; 95% CI: 2.4-89.2 and 1.9-302.8; P=0.004 and 0.014, respectively). Adjusted AUC for diagnosing thymic carcinoma was 0.921 (95% CI: 0.82-0.97): sensitivity, 62.5% and specificity, 100%. Conclusions: Two texture features (GLCM-energy and solidity) were significant predictors of thymic carcinoma.