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 compare the image quality of high-resolution diffusion-weighted imaging (DWI) using multiplexed sensitivity encoding (MUSE) versus reduced field-of-view (rFOV) techniques in endometrial cancer (EC) and to compare the diagnostic performance of these techniques with that of dynamic contrast-enhanced (DCE) MRI for assessing myometrial invasion of EC. Methods: MUSE-DWI and rFOV-DWI were obtained preoperatively in 58 women with EC. Three radiologists assessed the image quality of MUSE-DWI and rFOV-DWI. For 55 women who underwent DCE-MRI, the same radiologists assessed the superficial and deep myometrial invasion using MUSE-DWI, rFOV-DWI, and DCE-MRI. Qualitative scores were compared using the Wilcoxon signed-rank test. Receiver operating characteristic analysis was performed to compare the diagnostic performance. Results: Artifacts, sharpness, lesion conspicuity, and overall quality were significantly better with MUSE-DWI than with rFOV-DWI (p < 0.05). The area under the curve (AUC) of MUSE-DWI, rFOV-DWI, and DCE-MRI for the assessment of myometrial invasion were not significantly different except for significantly higher AUC of MUSE-DWI than that of DCE-MRI for superficial myometrial invasion (0.76 for MUSE-DWI and 0.64 for DCE-MRI, p = 0.049) and for deep myometrial invasion (0.92 for MUSE-DWI and 0.80 for DCE-MRI, p = 0.022) in one observer, and that of rFOV-DWI for deep myometrial invasion in another observer (0.96 for MUSE-DWI and 0.89 for rFOV-MRI, p = 0.048). Conclusion: MUSE-DWI exhibits better image quality than rFOV-DWI. MUSE-DWI and rFOV-DWI shows almost equivalent diagnostic performance compared to DCE-MRI for assessing superficial and deep myometrial invasion in EC although MUSE-DWI may be helpful for some radiologists.

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.

Objectives: To create prediction models (PMs) for distinguishing between benign and malignant liver lesions using quantitative data from dual-energy CT (DECT) without contrast agents. Materials and methods: This retrospective study included patients with liver lesions who underwent DECT, including non-contrast-enhanced scans. Benign lesions included hepatic hemangioma, whereas malignant lesions included hepatocellular carcinoma, metastatic liver cancer, and intrahepatic cholangiocellular carcinoma. Patients were divided into derivation and validation groups. In the derivation group, two radiologists calculated ten multiparametric data using univariate and multivariate logistic regression to generate PMs. In the validation group, two additional radiologists measured the parameters to assess the diagnostic performance of PMs. Results: The study included 121 consecutive patients (mean age 67.4 ± 13.8 years, 80 males), with 97 in the derivation group (25 benign and 72 malignant) and 24 in the validation group (7 benign and 17 malignant). Oversampling increased the benign lesion sample to 75, equalizing the malignant group for building PMs. All parameters were statistically significant in univariate analysis (all p < 0.05), leading to the creation of five PMs in multivariate analysis. The area under the curve for the five PMs of two observers was as follows: PM1 (slope K, blood) = 0.76, 0.74; PM2 (slope K, fat) = 0.55, 0.51; PM3 (effective-Z difference, blood) = 0.75, 0.72; PM4 (slope K, blood, fat) = 0.82, 0.78; and PM5 (slope K, effective-Z difference, blood) = 0.90, 0.87. PM5 yielded the best diagnostic performance. Conclusion: Multiparametric non-contrast-enhanced DECT is a highly effective method for distinguishing between liver lesions. Clinical relevance statement: The utilization of non-contrast-enhanced DECT is extremely useful for distinguishing between benign and malignant liver lesions. This approach enables physicians to plan better treatment strategies, alleviating concerns associated with contrast allergy, contrast-induced nephropathy, radiation exposure, and excessive medical expenses. Key Points: Distinguishing benign from malignant liver lesions with non-contrast-enhanced CT would be desirable. This model, incorporating slope K, effective Z, and blood quantification, distinguished benign from malignant liver lesions. Non-contrast-enhanced DECT has benefits, particularly in patients with an iodine allergy, renal failure, or asthma. Graphical