Background: Diffusion weighted imaging (DWI) has a good diagnostic value for malignant thyroid nodules, but\nthe published protocols suffer from flaws and focus on the apparent diffusion coefficient (ADC). This study\ninvestigated the diagnostic performance of multiple MRI parameters in differentiating malignant from benign\nthyroid nodules.\nMethods: This was a retrospective study of 181 consecutive patients (148 benign and 111 malignant nodules,\nconfirmed by pathological results). The patients underwent conventional MRI, DWI, and dynamic contrast-enhanced\nMRI before surgery. The chi-square test and the Student t test were used to compare the conventional features\nand ADC value between malignant and benign groups. Multivariate logistic regression was used to identify the\nindependent predictors and to construct a model. Receiver operator characteristic (ROC) curve analysis was used\nto assess the diagnostic performance of the independent variables and model.\nResults: Tumor diameter, ADC value, cystic degeneration, pseudocapsule sign, high signal cystic area on T1-\nweighted imaging, ring sign in the delayed phase, and irregular shape showed significant differences between\ntwo groups (all P < 0.05). The multivariable analysis revealed that ADC value (OR = 694.006, P < 0.001), irregular\nshape (OR = 32.798, P < 0.001), ring sign in the delayed phase (OR = 20.381, P = 0.004), and cystic degeneration\n(OR = 8.468, P = 0.016) were independent predictors. Among them, ADC performed the best in discriminating\nbenign from malignant nodules, with an area under the curve (AUC) of 0.95, 0.90 sensitivity, and 0.91 specificity.\nWhen the independent factors were combined, the diagnostic performance was improved with an AUC of 0.99, 0.\n97 sensitivity, and 0.95 specificity.\nConclusions: ADC value could discriminate between benign and malignant thyroid nodules with a good\nperformance. Subjective features such as the ring sign, irregular shape, and cystic degeneration associated\nwith malignant thyroid nodules could provide complementary information for differentiation.
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