Objective: Our single-center retrospective study aimed to evaluate the relationship between magnetic resonance (MR)-directed ultrasound (MDUS) detectability and MRI findings of non-mass enhancement (NME) lesions, regarding the morphologic and enhancement features, the distance from the skin and nipple, and the presence of concomitant landmarks. Methods: A total of 350 MRI-detected NME lesions that were determined between January 2015 and May 2019 and subsequently underwent MDUS were analyzed. The MRI findings, biopsy results, and follow-up outcomes of lesions were recorded. The correlation between the MRI findings of the lesions and MDUS detectability was analyzed. Results: 114 (32.6%) of the 350 lesions had a counterpart in the MDUS. Respectively, 66 (37.9%), 38 (43.2%) and 59 (38.3%) of the lesions detected in MDUS were larger than 20 mm in size, with a distance of less than 20 mm to the nipple and 15 mm to the skin. The lesion size and lesion distance to the nipple and skin were significantly associated with a ultrasound correlate (p < 0.05). The MDUS detection rate was significantly higher in NME lesions with MR findings including diffuse distribution (p < 0.001), clustered-ring enhancement pattern (p < 0.001), washout kinetic curve (p = 0.006), and MR-BIRADS category 5 (p < 0.001). Multivariate logistic regression showed that only the clustered-ring enhancement pattern was significantly associated with an MDUS correlation (p < 0.001). Conclusion: Statistically significant correlations were found between the size, distance to the nipple and skin, distribution pattern, enhancement pattern and kinetic curve of the NME lesions on MRI and ultrasound detectability. Advances in knowledge: We found that clustered-ring enhancement patterns were significantly more frequent in MR-directed ultrasound detectable lesions.

Background/Objectives: Segmental non-mass enhancement (NME) is the breast MRI distribution pattern with the highest positive predictive value (PPV) for malignancy. Despite its diagnostic relevance, its imaging characteristics have rarely been examined in isolation, leaving uncertainty in clinical practice. This multicenter study aimed to evaluate multiparametric MRI features—including internal enhancement pattern, dynamic contrast enhancement (DCE) kinetics, and diffusion restriction—in segmental NME to identify malignancy predictors. Methods: This retrospective cohort reviewed 14,834 breast MRI reports from five institutions (September 2017–February 2024), identifying 103 women (mean age, 44.4 ± 9.9 years) with segmental NME (70 malignant, 33 benign). MRI was performed at 1.5T or 3T using standardized protocols. Two breast radiologists, blinded to pathology, assessed internal enhancement, DCE kinetics, diffusion restriction, and short tau inversion recovery (STIR) features according to BI-RADS. Statistical analyses included chi-square/Fisher’s tests and logistic regression. Results: Clustered ring enhancement (CRE) was significantly associated with malignancy (p = 0.004). Fast initial-phase enhancement (p < 0.001) and delayed-phase washout (p = 0.011) also correlated with malignancy. On multivariate analysis, fast initial-phase enhancement remained an independent predictor (odds ratio [OR] = 5.133, p = 0.031), whereas slow enhancement predicted benignity (OR = 0.194, p = 0.020). Histologies included ductal carcinoma in situ, invasive ductal carcinoma, granulomatous mastitis, and benign hyperplastic lesions. Conclusions: This study, focusing exclusively on segmental NME, identifies CRE, fast initial-phase enhancement, and washout kinetics as reliable imaging biomarkers. Incorporating these features into breast MRI interpretation may improve diagnostic accuracy, risk stratification, and management decisions.

Aim: Diagnostic arthroscopy is an invasive and an expensive method using for the diagnosis of meniscal tears. The aim of this study was to determine the value of knee magnetic resonance imaging in the diagnosis of meniscal tears and its role in the prevention of unnecessary diagnostic arthroscopy. Methods: A total of 105 patients who underwent knee magnetic resonance imaging and arthroscopy due to meniscus injury were included in the study. Fifty-nine patients were examined using a 1.5 Tesla magnetic resonance scanner and 46 were examined using a 3.0 Tesla magnetic resonance scanner. Magnetic resonance imaging findings were evaluated retrospectively in workstations by two radiologists experienced in musculoskeletal magnetic resonance imaging. Meniscal tears were reported as anterior horn tear, corpus tear or posterior horn tear. Meniscal tears were classified by using surgical classification. Each patient’s magnetic resonance images were evaluated with a consensus and compared with the arthroscopic diagnosis. Results: Meniscal tears were detected in 96 out of 106 knees on arthroscopy. By using arthroscopy as the gold standard for diagnosis of meniscal tears, the sensitivity, specificity, and accuracy values of the magnetic resonance imaging evaluation were found as 85.71% (95% CI: 77.84-91.61), 93% (95% CI: 86.11-97.14) and 89.15% (95% CI: 84.17-93), respectively. These values of magnetic resonance imaging were found high in the diagnosis of meniscal tears. Conclusion: Magnetic resonance imaging is an effective imaging method in the diagnosis of meniscal tears. Although the resolution of 3.0 Tesla magnetic resonance imaging is higher, 1.5 Tesla magnetic resonance imaging is sufficient in routine meniscus tear diagnosis.

AIM: We aimed to evaluate changes in patient numbers, procedures performed, and the result of imaging procedures in the breast imaging unit of a non-pandemic hospital in Turkey during the COVID-19 pandemic. MATERIAL AND METHOD: The study retrospectively evaluated 2010 and 740 patients who presented to our unit in the pandemic and non-pandemic periods, respectively. Patient sex and age, neoadjuvant chemotherapy story, imaging modalities used, final BI-RADS classification, and biopsy and/or wire localization procedures were compared between the periods. RESULTS: There were significant decreases in the number of patients who presented to our unit and the numbers of mammography, ultrasound, MRI, biopsy, and wire localization procedures performed during the pandemic period (p

Background/Objectives: Segmental non-mass enhancement (NME) is the breast MRI distribution pattern with the highest positive predictive value (PPV) for malignancy. Despite its diagnostic relevance, its imaging characteristics have rarely been examined in isolation, leaving uncertainty in clinical practice. This multicenter retrospective cohort study aimed to evaluate multiparametric MRI features—including internal enhancement pattern, dynamic contrast-enhanced (DCE) kinetics, and diffusion restriction—in segmental NME to identify malignancy predictors. Methods: This retrospective cohort review included 14,834 breast MRI reports from five institutions (September 2017–February 2024), identifying 103 women (mean age, 44.4 ± 9.9 years) with segmental NME (70 malignant, 33 benign). MRI was performed at 1.5 T or 3 T using standardized protocols. Two breast radiologists, blinded to pathology, assessed internal enhancement, DCE kinetics, diffusion restriction, and short tau inversion recovery (STIR) features according to BI-RADS. Statistical analyses included chi-square/Fisher’s tests and logistic regression. Results: Clustered ring enhancement (CRE) was significantly associated with malignancy (p = 0.004). Fast initial-phase enhancement (p < 0.001) and delayed-phase washout (p = 0.011) also correlated with malignancy. On multivariate analysis, fast initial-phase enhancement remained an independent predictor (odds ratio [OR] = 5.133, p = 0.031), whereas slow enhancement predicted benignity (OR = 0.194, p = 0.020). Histologies included ductal carcinoma in situ, invasive ductal carcinoma, granulomatous mastitis, and benign hyperplastic lesions. Conclusions: This study, focusing exclusively on segmental NME, identifies CRE, fast initial-phase enhancement, and washout kinetics as reliable imaging biomarkers. Incorporating these features into breast MRI interpretation may improve diagnostic accuracy, risk stratification, and management decisions.