Application of a combined radiomics nomogram based on CE-CT in the preoperative prediction of thymomas risk categorization

Abstract

ObjectiveThis study aimed to establish a combined radiomics nomogram to preoperatively predict the risk categorization of thymomas by using contrast-enhanced computed tomography (CE-CT) images.Materials and MethodsThe clinical, pathological, and CT data of 110 patients with thymoma (50 patients with low-risk thymomas and 60 patients with high-risk thymomas) collected in our Hospital from July 2017 to March 2022 were retrospectively analyzed. The study subjects were randomly divided into the training set (n = 77) and validation set (n = 33) in a 7:3 ratio. Radiomics features were extracted from the CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was performed to select 13 representative features. Five models, including logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), and gradient boosting decision tree (GBDT) were constructed to predict thymoma risks based on these features. A combined radiomics nomogram was further established based on the clinical factors and radiomics scores. The performance of the models was evaluated using receiver operating characteristic (ROC) curve, DeLong tests, and decision curve analysis.ResultsMaximum tumor diameter and boundary were selected to build the clinical factors model. Thirteen features were acquired by LASSO algorithm screening as the optimal features for machine learning model construction. The LR model exhibited the highest AUC value (0.819) among the five machine learning models in the validation set. Furthermore, the radiomics nomogram combining the selected clinical variables and radiomics signature predicted the categorization of thymomas at different risks more effectively (the training set, AUC = 0.923; the validation set, AUC = 0.870). Finally, the calibration curve and DCA were utilized to confirm the clinical value of this combined radiomics nomogram.ConclusionWe demonstrated the clinical diagnostic value of machine learning models based on CT semantic features and the selected clinical variables, providing a non-invasive, appropriate, and accurate method for preoperative prediction of thymomas risk categorization.

Diagnostic performance of radiomics model for preoperative risk categorization in thymic epithelial tumors: a systematic review and meta-analysis

“…They were all published in the recent three years, with nearly half of them published in the last years (2022). Except for three studies from Switzerland, Japan and the United States [ 22 , 25 , 28 ], the other studies all came from different provinces of China [ 16 – 21 , 23 , 24 , 26 , 27 ]. This might be related to the low incidence of TETs and the difficulty of conducting multi-center radiomics studies.…”

“…Several non-radiomics indicators were included in the clinical combined radiomics models, such as, gender, age, myasthenia gravis and regular imaging findings (tumor size, pleural effusion, pericardial effusion, infiltration, etc.) [ 18 , 19 , 21 , 23 ]. Although there was no statistical difference between the combined model and the radiomics model in the prediction efficiency, the absolute values were all improved in the studies.…”

Machine learning models combining computed tomography semantic features and selected clinical variables for accurate prediction of the pathological grade of bladder cancer

“…Linear correlation between each feature and the category label was evaluated through the optimal feature filter, and the 45 most relevant features from the 1,781 features were selected. The LASSO algorithm was used to select the most relevant feature from the 45 features ( 20 ) ( Figure 4 ). Finally, we selected a total of 15 most relevant features for the pathological grade of BCa ( Figure 5 ).…”

Multi-Sequence MRI Based Radiomics Nomogram for Prediction Expression of Programmed Death Ligand 1 in Thymic Epithelial Tumor

“…Radiomics methods have been widely applied with the extraction of numerous quantitative metrics on the entire tumor from radiological images including CT and MRI 8, 9 . Existing research recognize the critical role of radiomics in re ecting tissue heterogeneity, staging and risk strati cation of TET [10][11][12] . Besides, radiomics has been successfully employed to predict the level of PD-L1 in lung and esophageal tumors 13,14 .…”