Radiomics analysis of dual-energy CT-derived iodine maps for differentiating malignant from benign thyroid nodules.

Abstract

Many thyroid nodules are detected incidentally with the widespread use of sensitive imaging techniques; however, only a fraction of these nodules are malignant, resulting in unnecessary medical expenditures and anxiety. The major challenge is to differentiate benign thyroid nodules from malignant ones. The application of dual-energy computed tomography (DECT) and radiomics provides a new diagnostic approach. Studies applying radiomics from primary tumours on iodine maps to differentiate malignant from benign thyroid nodules are still lacking. To determine the ability of an iodine map-based radiomic nomogram in the venous phase for differentiating malignant thyroid nodules from benign nodules. A total of 141 patients with thyroid nodules who underwent DECT were enrolled and randomly assigned to the training and test cohorts between January 2018 and January 2019. The radiomic score (Rad-score) was derived from nine quantitative features of the iodine maps. Stepwise logistic regression analysis was used to develop radiomic, clinical and combined models. Age, normalized iodine concentration (NIC), and cyst changes were used to construct the clinical model. Receiver operating characteristic (ROC) curve analysis, sensitivity and specificity were performed to analyse the ability of the models to predict malignant thyroid nodules. Calibration analysis was used to test the fitness of the models. Decision curve analysis (DCA) and nomogram construction were also performed. According to the clinical model, age (0.989 [0.984, 0.995]; p<0.001), NIC (0.778 [0.640, 0.995]; p=0.01), and cyst changes (0.617 [0.507, 0.751]; p<0.001) were independently associated with malignant thyroid nodules. According to the combined model, age (0.994 [0.989, 0.999]; p=0.01), NIC (0.797 [0.674, 0.941]; p=0.008), cyst changes (0.786 [0.653, 0.947]; p=0.01), and the rad-score (1.106 [1.070, 1.143]; p<0.001) were independently associated with malignant thyroid nodules. The combined model achieved satisfactory discrimination in predicting malignant thyroid nodules and had greater predictive value in the training (AUC [areas under the curve], 0.96vs. 0.87; p=0.01) and test (AUC, 0.90vs. 0.79; p=0.04) cohorts than did the clinical model. The radiomics nomogram based on iodine maps is useful to distinguish malignant thyroid nodules from benign thyroid nodules.