Development and validation of a contrast-enhanced CT-based radiomics nomogram for differentiating mass-like thymic hyperplasia and low-risk thymoma.

PURPOSE: To explore the efficiency of a contrast-enhanced CT-based radiomics nomogram integrated with radiomics signature and clinically independent predictors to distinguish mass-like thymic hyperplasia (ml-TH) from low-risk thymoma (LRT) preoperatively.

METHODS

135 Patients with histopathology confirmed ml-TH (n = 65) and LRT (n = 70) were randomly divided into training set (n = 94) and validation set (n = 41) at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to obtain the optimal features. Based on the selected features, four machine learning models, support vector machine (SVM), logistic regression (LR), extreme gradient boosting (XGBOOST), and random forest (RF) were constructed. Multivariate logistic regression was used to establish a radiomics nomogram containing clinically independent predictors and radiomics signature. Receiver operating characteristic (ROC), DeLong test, and calibration curves were used to detect the performance of the radiomics nomogram in training set and validation set.

RESULTS

In the validation set, the area under the curve (AUC) value of LR (0.857; 95% CI: 0.741, 0.973) was the highest of the four machine learning models. Radiomics nomogram containing radiomics signature and clinically independent predictors (including age, shape, and net enhancement degree) had better calibration and identification in the training set (AUC: 0.959; 95% CI: 0.922, 0.996) and validation set (AUC: 0.895; 95% CI: 0.795, 0.996).

CONCLUSION

We constructed a contrast-enhanced CT-based radiomics nomogram containing clinically independent predictors and radiomics signature as a noninvasive preoperative prediction method to distinguish ml-TH from LRT. The radiomics nomogram we constructed has potential for preoperative clinical decision making.