Development and Validation of a Nomogram Based on Multimodality Ultrasonography Images for Differentiating Malignant from Benign American College of Radiology Thyroid Imaging, Reporting and Data System (TI-RADS) 3–5 Thyroid Nodules

Abstract

ObjectivesTo develop and validate a predictive nomogram based on combined image features of gray-scale ultrasonography (US), elastosonography (ES), and contrast-enhanced US (CEUS) to differentiate malignant from benign ACR TI-RADS3-5 thyroid nodules. Materials and MethodsAmong 2767 thyroid nodules scanned by CEUS in * Hospital between April 2014 and November 2018, 669 nodules classified as ACR TI-RADS 3-5 were included with confirmed diagnosis and ES examination. 455 nodules were set as a training cohort and 214 as a validation cohort. Images were categorized as gray-scale US ACR TI-RADS3, TI-RADS4, TI-RADS5; ES patterns of ES-1, ES-2; and CEUS patterns of either heterogeneous hypoenhancement, concentric hypoenhancement, homogeneous hyper/isoenhancement, no-perfusion, hypoenhancement with sharp margin, island-like enhancement, or ring-like enhancement. Based on multivariate logistic regression analysis, a predictive nomogram model was developed and validated by receiver operating characteristic curve analysis. ResultsIn the training cohort, ACR TI-RADS4-5, ES-2, heterogeneous hypoenhancement, concentric hypoenhancement, and homogeneous hyper/isoenhancement were selected as predictors of malignancy by univariate logistic regression analysis. A predictive nomogram (combining indices of ACR TI-RADS, ES and CEUS) showed excellent predictive ability for differentiating malignant from benign lesions in the training cohort (AUC, 0.93 [95% CI 0.90-0.95]). The prediction nomogram model demonstrated a sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 0.84, 0.88, 0.91 and 0.81.In the validation cohort, the AUC of the prediction nomogram model was significantly higher than those of the single modalities (P<0.005) . The AUCs of the validation cohort were 0.93 (95% CI 0.89-0.96) and 0.93 (95% CI 0.89-0.97) respectively for senior and junior radiologists. The prediction nomogram model has a sensitivity, specificity, PPV and NPV of 0.86, 0.87, 0.87 and 0.86. ConclusionsA predictive nomogram model combining ACR TI-RADS, ES, and CEUS showed potential clinical utility in differentiating malignant from benign ACR TI-RADS3-5 thyroid nodules.