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Background: To further investigate the differential diagnosis of thyroid nodules using dual-energy computed tomography (DECT) and explore the relationship between DECT parameters and lymph node metastasis in thyroid carcinoma for clinical practice, especially difficult diagnosis by routine imaging examination.Methods: A total of 150 patients with thyroid nodules who underwent preoperative DECT and Thyroid Imaging Report and Data System (TIRADS) classification were enrolled in this study, including 96 patients with malignant tumors and 54 with benign tumors. The DECT parameters were got form regions of interest (ROI) by an experienced radiologist team and thyroid nodules and lymph node status of all patients were identified by cytology and histopathology. Statistical analyses were performed using Student's t-test, Chisquared test, and receiver operating characteristic (ROC) curves.Results: In the differential diagnosis of benign and malignant thyroid nodules, the optimal iodine concentration (IC) and normalized iodine concentration (NIC) cut-off values were IC a (2.835 mg/mL), NIC 1a (0.690), and their corresponding area under the curve (AUC) were 0.940, 0.954 respectively; meantime, the optimal computed tomography (CT) value and slope of the spectral Hounsfield unit curve (λ HU ) cut-off values were 70 keVa (125.05 HU) and λ HU2a (1.405), and their corresponding AUC were 0.955, 0.941 respectively. For lymph node status (with or without lymph node metastasis), the optimal IC and NIC thresholds were IC a (1.715 mg/mL) and NIC 2a (0.155), and their corresponding AUC were 0.717, 0.720 respectively; meanwhile, the optimal CT value and λ HU thresholds were 70 keVv (89.635 HU) and λ HU2v(1.185), and their corresponding AUC were 0.729, 0.641 respectively.Conclusions: Base on our study, we think DECT is useful in differentiating malignant from benign thyroid nodules, which has potential value in the indirect prediction of lymph node metastasis in thyroid carcinoma.

Background: Distinguishing exophytic renal urothelial carcinoma (ERUC) from exophytic renal clear-cell carcinoma (ERCCC) with collecting system invasion may be difficult as they involve similar locations and collecting system invasion. Objective: To characterize the clinical data and computed tomography (CT) features that can aid in differentiating ERUC from ERCCC. Methods: Data from 17 patients with ERUC and 222 patients with ERCCC were retrospectively assessed. CT and clinical features exhibiting significant differences in t-tests/Mann-Whitney U-test and chi-square tests/Fisher’s exact tests were analyzed using receiver operating characteristic (ROC) curves. Variables with area under the curve (AUC) <0.7 were excluded. Univariate logistic regressions analysis was used to analyze the associations of CT and clinical features with ERUC or ERCCC. Variables with odds ratio (OR) values being close to 1 in univariate logistic regression were excluded from multivariate logistic regression. A predictive model was then constructed based on the predictors (p<0 in multivariate logistic regression). Differential diagnostic performance was assessed AUC values. Results: Multivariate logistic regression analysis identified preserving reniform contour (OR: 45.27, 95% confidence interval [CI]: 4.982–411.390) and infiltrative growth pattern (OR: 21.741, 95% CI: 1.898–249.049) as independent predictors that can be used to distinguish ERUC from ERCCC. AUC values for preserving reniform contour , infiltrative growth pattern, and Model-1 were 0.907 (95% CI: 0.817-0.998), 0.837 (95% CI: 0.729-0.946), and 0.947 (95% CI: 0.874–1) respectively. Conclusion: The independent predictors and predictive model may play an important role in preoperative differentiation between ERUC and ERCCC.

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