Knowledge of the MRI features of borderline epithelial ovarian tumor can aid in preoperative diagnosis; however, several benign and malignant tumors can mimic borderline tumors and pose diagnostic ... Borderline epithelial ovarian tumors are a distinct pathologic entity characterized by increased epithelial proliferation and nuclear atypia, but without frank stromal invasion. Borderline tumor (B... Created with BioRender.com

Objectives:To present and characterize CT and MR imaging findings of metastases to the parotid nodes.Methods:CT (n = 10) and MR (n = 11) images from 14 patients with metastases to the parotid nodes were reviewed. The primary tumour sites were the ocular adnexa in five patients, facial skin in four patients, upper aerodigestive tract in four patients and thyroid gland in one patient. CT and MR images were evaluated with emphasis on the size and number of parotid tumours, their location in the parotid gland, the presence of associated clinically pathological cervical nodes or previous history of cervical node metastasis, margin characteristics and the presence of central necrosis.Results:A total of 18 tumours were identified in 14 patients, with an average maximal cross-sectional diameter of 19 mm (7–44 mm). 12 patients had a single parotid tumour and 2 patients had unilateral multiple tumours; 12 tumours in 10 patients were located in the parotid tail, 6 tumours in 4 patients were located in the superficia...

Background: Strong correlation has been reported between tissue water diffusivity and tissue elasticity in the liver. The purpose of this study is to explore the capability of diffusion–based virtual MR elastography (VMRE) in the characterization of liver tumors by extending beyond liver fibrosis assessments. Methods: Fifty-four patients (56 liver tumors: hepatocellular carcinoma (HCC), 31; metastases, 25) who underwent MRE, diffusion-weighted imaging (DWI) (b: 0, 800 s/mm²), and VMRE (b: 200, 1500 s/mm²) were enrolled. The MRE shear modulus (µ_MRE), apparent diffusion coefficient (ADC), and shifted ADC (sADC) were obtained. Virtual stiffness (µ_diff) was estimated from the relationship between µ_MRE and sADC. A linear discriminant analysis combining VMRE and MRE to classify HCC and metastases was performed in a training cohort (thirty-two patients) to estimate a classifier (C), and evaluate its accuracy in a testing cohort (twenty-two patients). Pearson’s correlations between µ_MRE, sADC, and ADC were evaluated. In addition to the discriminant analysis, a receiver operating characteristic (ROC) curve was used to assess the discrimination capability between HCC and metastases. Results: The correlations between µ_MRE and sADC were significant for liver, HCC, and metastases (r = 0.91, 0.68, 0.71; all p < 0.05). Those between µ_MRE and ADC were weaker and significant only for metastases (r = 0.17, 0.20, 0.55). µ_diff values were not significantly different between HCC and metastases (p = 0.56). Areas under the curves (AUC) to differentiate HCC from metastases were as follows: VMRE, 0.46; MRE alone, 0.89; MRE + VMRE, 0.96. The classifier C also provided better performance than MRE alone, in terms of sensitivity (100 vs. 93.5%, respectively) and specificity (92 vs. 76%, respectively, p = 0.046). Conclusions: The correlation between sADC and µ_MRE was strong both in the liver and in tumors. However, VMRE alone could not classify HCC and metastases. The combination of MRE and VMRE, however, allowed discriminant performance between HCC and metastases.

Purpose: To compare image quality and visibility of anatomical structures on contrast-enhanced thin-slice abdominal CT images reconstructed using super-resolution deep learning reconstruction (SR-DLR), deep learning-based reconstruction (DLR), and hybrid iterative reconstruction (HIR) algorithms. Materials and methods: This retrospective study included 54 consecutive patients who underwent contrast-enhanced abdominal CT. Thin-slice images (0.5 mm thickness) were reconstructed using SR-DLR, DLR, and HIR. Objective image noise and contrast-to-noise ratio (CNR) for liver parenchyma relative to muscle were assessed. Two radiologists independently graded image quality using a 5-point rating scale for image noise, sharpness, artifact/blur, and overall image quality. They also graded the visibility of small vessels, main pancreatic duct, ureters, adrenal glands, and right adrenal vein on a 5-point scale. Results: SR-DLR yielded significantly lower objective image noise and higher CNR than DLR and HIR (P < .001). The visual scores of SR-DLR for image noise, sharpness, and overall image quality were significantly higher than those of DLR and HIR for both readers (P < .001). Both readers scored significantly higher on SR-DLR than on HIR for visibility for all structures (P < .01), and at least one reader scored significantly higher on SR-DLR than on DLR for visibility for all structures (P < .05). Conclusion: SR-DLR reduced image noise and improved image quality of thin-slice abdominal CT images compared to HIR and DLR. This technique is expected to enable further detailed evaluation of small structures.