الملخص
Objective:To explore the value of multi-parametric MRI for thyroid gland in differentiating benign and malignant thyroid nodules.Methods:From December 2018 to May 2020, 78 patients with 91 post-surgically pathologically confirmed thyroid nodules were enrolled in Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. For each patient, the following MRI findings were obtained including the nodules′ location, size, shape, margin, signal intensity, cystic change, degree and pattern of contrast enhancement, involvement of surrounding structure, and ADC values. The time-intensity curve (TIC) were plotted and subtyped based on dynamic contrast enhancement MRI. The MRI findings between the benign and malignant thyroid nodules were compared using Mann-Whitney U test, χ 2 test or Fisher exact test. Multiple logistic regression analysis was used to select independent predictive variables and build a combined model, and the ROC curve was used to evaluate the diagnostic performance of each MRI finding and the combined model. Results:Between the benign and malignant thyroid nodules, the significant differences were found in size, shape, margin, presence of cystic changes, T 1WI signal intensity, ADC value, enhancement homogeneity, TIC subtypes and presence of thyroid capsule involvement ( P<0.05). Multivariate logistic analysis showed that ill-defined margin (OR=77.61), no presence of cystic changes (OR=36.11) and difference between TIC subtypes (OR=83.41) were independent predictive variables, and the area under the ROC curve (AUC) was 0.879, 0.788, and 0.751, respectively. The AUC, sensitivity and specificity of the combined model were 0.977, 0.986, and 0.904, respectively. Conclusions:Thyroid multi-parametric MRI derived findings can be used for the differential diagnosis of benign and malignant nodules. Combined with the independent risk factors with ill-defined margin, no presence of cystic changes, TIC of type plateau or washout, the diagnostic model has a higher diagnostic efficiency.