Preoperative Prediction of Cervical Nodal Metastasis in Papillary Thyroid Carcinoma: Value of Quantitative Dual-Energy CT Parameters and Qualitative Morphologic Features.

OBJECTIVE. The purpose of our study was to assess the value of combining quantitative dual-energy CT (DECT) parameters with qualitative morphologic parameters for the preoperative prediction of cervical nodal metastasis from papillary thyroid carcinoma (PTC). MATERIALS AND METHODS. Thirty-five patients with pathologically proven PTC underwent single-phase contrast-enhanced DECT before thyroidectomy and cervical lymphadenectomy. Analyses of quantitative DECT parameters and qualitative morphologic features of metastatic and benign lymph nodes (LNs) were independently performed. The diagnostic performances of using only quantitative parameters, only morphologic features, and their combination for predicting cervical nodal metastasis were statistically calculated with ROC curves and logistic regression models. RESULTS. A total of 206 LNs, 80 metastatic and 126 benign, were included. The best single performer in DECT was the normalized iodine concentration in the venous phase, which had low sensitivity (62.5%) but high specificity (85.7%), for diagnosing metastatic cervical LNs. On the other hand, the best single performer in qualitative morphologic parameters was using the criterion of shortest diameter of greater than 5 mm, which had low specificity (69.8%) but high sensitivity (86.3%). Combining these two parameters improved the AUC, sensitivity, and specificity to 0.846, 86.3%, and 72.2%, respectively. The combination of multiple quantitative DECT parameters and all morphologic data further improved AUC, sensitivity, and specificity to 0.878, 87.5%, and 73.8%, respectively, which was significant compared with the use of any single parameter. CONCLUSION. The combination of quantitative DECT parameters with morphologic data improves performance in the preoperative diagnosis of metastatic cervical LNs in patients with PTC.

Diagnostic Performance of MR Imaging-based Features and Texture Analysis in the Differential Diagnosis of Ovarian Thecomas/Fibrothecomas and Uterine Fibroids in the Adnexal Area.

RATIONALE AND OBJECTIVES: To investigate the value of MRI-based features and texture analysis (TA) in the differential diagnosis between ovarian thecomas/fibrothecomas (OTCA/f-TCAs) and uterine fibroids in the adnexal area (UF-iaas). MATERIALS AND METHODS: This retrospective study included 16 OTCA/f-TCA and 37 UF-iaa patients who underwent conventional MRI and DWI between August 2014 and September 2018. Three-dimensional TA was performed with T2-weighted MRI. The clinical, MRI-based and texture features were compared between OTCA/f-TCAs and UF-iaas. Multivariate logistic regression analysis was used for filtering the independent discriminative features and constructing the discriminating model. ROCs were generated to analyse MRI-based features, texture features and their combination for discriminating between the two diseases. RESULTS: Six imaging-based features (ipsilateral ovary detection, arterial period enhancement, lesion components, peripheral cysts, "whorl signs", mean ADCs) and six texture features (Histogram-energy, Histogram-entropy, Histogram-kurtosis, GLCM-energy, GLCM-entropy, and Haralick correlation) were significantly different between OTCA/f-TCAs and UF-iaas (p < 0.05). Multivariate analysis of the MRI-based features revealed that arterial period enhancement (OR = 0.104), peripheral cysts (OR = 16.513), and whorl signs (OR = 0.029) were independent features for discriminating between OTCA/f-TCAs and UF-iaas (p < 0.05). Multivariate analysis of the texture features showed that Histogram-energy and GLCM-energy were independent features for discriminating between OTCA/f-TCAs and UF-iaas (p < 0.05). The area under the curve of imaging-based diagnosis was 0.85, and the combination of imaging-based diagnosis and TA improved the area under the curve to 0.87, with higher accuracy, specificity and sensitivity of 86%, 92%, and 84%, respectively (p < 0.05). CONCLUSIONS: MRI-based features can be useful in differentiating OTCA/f-TCAs from UF-iaas. Furthermore, combining imaging-based diagnosis and TA can improve diagnostic performance.