CT-Based Radiomics Signatures for Predicting the Risk Categorization of Thymic Epithelial Tumors

Abstract

ObjectivesThis study aims to assess the performance of radiomics approaches based on 3D computed tomography (CT), clinical and semantic features in predicting the pathological classification of thymic epithelial tumors (TETs).MethodsA total of 190 patients who underwent surgical resection and had pathologically confirmed TETs were enrolled in this retrospective study. All patients underwent non-contrast-enhanced CT (NECT) scans and contrast-enhanced CT (CECT) scans before treatment. A total of 396 hand-crafted radiomics features of each patient were extracted from the volume of interest in NECT and CECT images. We compared three clinical features and six semantic features (observed radiological traits) between patients with TETs. Two triple-classification radiomics models (RMs), two corresponding clinical RMs, and two corresponding clinical-semantic RMs were built to identify the types of the TETs. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were useful to evaluate the different models.ResultsOf the 190 patients, 83 had low-risk thymoma, 58 had high-risk thymoma, and 49 had thymic carcinoma. Clinical features (Age) and semantic features (mediastinal fat infiltration, mediastinal lymph node enlargement, and pleural effusion) were significantly different among the groups(P < 0.001). In the validation set, the NECT-based clinical RM (AUC = 0.770 for low-risk thymoma, 0.689 for high-risk thymoma, and 0.783 for thymic carcinoma; ACC = 0.569) performed better than the CECT-based clinical-semantic RM (AUC = 0.785 for low-risk thymoma, 0.576 for high-risk thymoma, and 0.774 for thymic carcinoma; ACC = 0.483).ConclusionsNECT-based and CECT-based RMs may provide a non-invasive method to distinguish low-risk thymoma, high-risk thymoma, and thymic carcinoma, and NECT-based RMs performed better.Advances in KnowledgeRadiomics models may be used for the preoperative prediction of the pathological classification of TETs.

Optimizing the radiomics-machine-learning model based on non-contrast enhanced CT for the simplified risk categorization of thymic epithelial tumors: A large cohort retrospective study

“…Compared with prior studies [18,20,23], a large patient cohort was enrolled and the enriched quantitative features were extracted from NECT images in the current study. We solely relied on radiomics extracted from the 3D NECT imaging modality with TETs and established triple-classification radiomics classifiers, achieving similar results with high ACC, which is consistent with a previous study [18]. Overall, 1218 features were extracted per lesion and 14 robust ML algorithms were employed.…”

“…This should be endorsed the potential use of radiomics as a valuable tool in the individualized assessment and treatment decision-making of TETs. It is also extensively applied in the field of simplified risk categorization of TETs based on CT images [18][19][20][21][22][23]. However, a solid conclusion is far from drew suffered from the following two reasons.…”

Contrast-enhanced CT-based radiomics model for differentiating risk subgroups of thymic epithelial tumors

“…Recently, six popular machine learning algorithms have been used to construct RMs: k-nearest neighbor (KNN), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), random forest (RF), logistic regression (LR), and decision tree (DT). Among them, the results using the LR algorithm were the most ideal in many CT-based radiomics studies to predict different risk subgroups of TETs or thymomas [ 18 , 21 , 22 ]. Therefore, in this study, we only chose LR algorithm.…”

“…Although several CT-based radiomics analyses have been used to identify the risk classification of thymic epithelial tumors, most studies were based on two-classification [ 16 , 17 ]. Only one study was based on triple classification, and the accuracy of the clinical-semantic radiomics model (RM) in the risk assessment of three subgroups in the validation group was only 48.3% [ 18 ]. Therefore, radiomics research based on triple classification needs further research.…”

CT-Based Radiomics Nomogram for Differentiation of Anterior Mediastinal Thymic Cyst From Thymic Epithelial Tumor

“…Radiomics methods are widely applied in the field of medicine to assist in disease diagnosis and prognosis (17,18). Radiomics methods have been recently utilized in predicting histological subtype classification and staging of thymic epithelial tumors (19)(20)(21)(22). However, studies that employ radiomics methods to differentiate thymic cysts from thymic epithelial tumors are limited.…”