Radiomics Signatures of Computed Tomography Imaging for Predicting Risk Categorization and Clinical Stage of Thymomas

Abstract

Purpose. The aim of this study is to develop and compare performance of radiomics signatures using texture features extracted from noncontrast enhanced CT (NECT) and contrast enhanced CT (CECT) images for preoperative predicting risk categorization and clinical stage of thymomas. Materials and Methods. Between January 2010 and October 2018, 199 patients with surgical resection and histopathologically confirmed thymoma were enrolled in this retrospective study. We extracted 841 radiomics features separately from volume of interest (VOI) in NECT and CECT images. The features with poor reproducibility and highly redundancy were removed. Then a least absolute shrinkage and selection operator method (LASSO) logistic regression model with 10-fold cross validation was used for further feature selection and radiomics signatures build. The predictive performances of radiomics signatures were assessed by receiver operating characteristic (ROC) analysis. The areas under the receiver operating characteristic curve (AUC) between radiomics signatures were compared by using Delong test. Result. In differentiating high risk thymomas from low risk thymomas, the AUC, sensitivity, and specificity were 0.801(95% CI 0.740–0.863), 0.752 and 0.767 for radiomics signature based on NECT images, and 0.827 (95% CI 0.771 -0.884), 0.798, and 0.722 for radiomics signature based on CECT images. But there was no significant difference (p=0.365) between them. In differentiating advanced stage thymomas from early stage thymomas, the AUC, sensitivity, and specificity were 0.829 (95%CI 0.757-0.900), 0.712, and 0.806 for radiomics signature based on NECT images and 0.860 (95%CI 0.803-0.917), 0.699, and 0.889 for radiomics signature based on CECT images. There was no significant difference (p=0.069) between them. The accuracy was 0.819 for radiomics signature based on NECT images, 0.869 for radiomics signature based on CECT images, and 0.779 for radiologists. Both radiomics signatures had a better performance than radiologists. But there was significant difference (p = 0.025) only between CECT radiomics signature and radiologists. Conclusion. Radiomics signatures based on texture analysis from NECT and CECT images could be utilized as noninvasive biomarkers for differentiating high risk thymomas from low risk thymomas and advanced stage thymomas from early stage thymoma. As a quantitative method, radiomics signature can provide complementary diagnostic information and help to plan personalized treatment for patients with thymomas.

Development and validation of a CT-texture analysis nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes

“…Yasaka K et al also found that the meanValue was a significant parameter for differentiating HTET from LTET with AUC of 0.89 [18]. Sphericity is the shape feature of the tumor and has been proven as the most significant factor for discriminating histologic subtypes in TET patients [29]. Busyness is a parameter of NGLDM, Fig.…”

Can computed tomography-based radiomics potentially discriminate between anterior mediastinal cysts and type B1 and B2 thymomas?

“…Only few scholars have conducted relevant research in the radiomics field. Wang et al [ 26 ] obtained the results that the AUCs were 0.829 and 0.860 for the radiomics signature based on unenhanced and enhanced CT images in differentiating advanced stage thymomas from early stage thymomas, respectively. However, Sui et al [ 27 ] believed that the unenhanced phase could better distinguish high-risk and low-risk thymomas than the enhanced phase, because more texture features were selected from the unenhanced phase than from the enhanced phase, and tumour heterogeneity was better detected in the unenhanced phase, the above studies were similar to our results that the enhanced CT and unenhanced CT radiomics can better differentiating the different stages of thymoma, the enrolled thymomas were confirmed by Masaoka clinical stage and WHO histologic classification.…”

Development and validation of a CT-TA nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes

“…Second, the texture features extracted in this study were only based on enhanced CT images, but not the plain CT. However, previous study showed that there was no significant difference between radiomics features based on plain CT images and ones based on enhanced CT images for predicting risk categorization of TETs [30]. Third, the tumor, node, metastasis (TNM) [39] or Masaoka [40] staging systems for TETs were not used in this study.…”

“…Yasaka K et al also found that the meanValue was a significant parameter for differentiating between HTET and LTET with AUC of 0.89 [21]. Sphericity is the shape features of tumors, and has been proven as the most significant affecting factor for discriminating histologic subtype in TET patients [30]. Busyness is a parameter of NGLDM, which measures the spatial frequency of changes in intensity between nearby voxels of different grey-levels.…”