CT-Based Radiomics Models for Differentiation of Benign and Malignant Thyroid Nodules: A Multicenter Development-and-Validation.

Background: CT is increasingly detecting thyroid nodules. Prior studies indicated a potential role of CT-based radiomics models in characterizing thyroid nodules, although lacked external validation. Objectives: To develop and validate a CT-based radiomics model for the differentiation of benign and malignant thyroid nodules. Methods: This retrospective study included 378 patients (mean age, 46.3±13.9 years; 86 men, 292 women) with 408 resected thyroid nodules (145 benign, 263 malignant) from two centers (center 1: 293 nodules, January 2018-December 2022; center 2: 115 nodules, January 2020-December 2022), who underwent preoperative multiphase neck CT (noncontrast, arterial, and venous phases). Nodules from center 1 were divided into training (n=206) and internal validation (n=87) sets; all nodules from center 2 formed an external validation set. Radiologists assessed nodules for morphologic CT features. Nodules were manually segmented on all phases, and radiomic features were extracted. Conventional (clinical and morphologic CT), noncontrast radiomics, arterial-phase radiomics, venous-phase radiomics, multiphase radiomics, and combined (clinical, morphologic, and multiphase radiomics) models were established using feature selection methods and evaluated by ROC curve analysis, calibration curves, and decision-curve analysis. Results: The combined model included patient age, three morphologic features (cystic change, edge interruption sign, abnormal cervical lymph nodes), and 28 radiomic features (from all three phases). In the external validation set, the combined model had AUC of 0.923 and, at an optimal threshold derived in the training set, sensitivity of 84.0%, specificity of 94.1%, and accuracy of 87.0%. In the external validation set, AUC was significantly higher for the combined model than for the conventional model (0.827), noncontrast radiomics model (0.847), arterial-phase radimoics model (0.826), venous-phase radiomics model (0.773), and multiphase radiomics model (0.824) (all p<.05). In the external validation set, the calibration curves indicated lowest (i.e., best) Brier score for the combined model; in decision-curve analysis, the combined model had the highest net benefit for most of the range of threshold probabilities. Conclusion: A combined model incorporating clinical, morphologic CT, and multiphasic radiomics CT features, exhibited robust performing in differentiating benign and malignant thyroid nodules. Clinical Impact: The combined radiomics model may help guide further management for thyroid nodules detected on CT.