98%
921
2 minutes
20
Rationale: Mucoepidermoid carcinoma (MEC) of the breast is a rare entity, with an estimated incidence of only 0.2% to 0.3% of all primary breast tumors. The radiological features of breast MEC have scarcely been investigated mainly because of its rarity. In this article, we present a case of breast MEC diagnosed at our hospital and review the literature, focusing on radiological findings and radiologic-pathologic correlations that could improve clinical management of this entity. To the best of our knowledge, our study is the first review of the literature that focuses on the radiological features of breast MEC.
Patient Concerns: A 47-year-old premenopausal woman presented with a painless palpable mass in the right breast.
Diagnosis: Mammography and ultrasonography revealed a mass with suspicious malignant features, which was categorized as Breast Imaging Reporting and Data System category 4c. A 14-gauge core-needle biopsy revealed an intermediate-grade MEC of the breast. The patient underwent breast magnetic resonance imaging and chest computed tomography for preoperative evaluation. Postoperative histopathological examination confirmed a diagnosis of intermediate-grade MEC. The clinical staging was T2N0M0.
Interventions: The patient underwent breast-conserving surgery, adjuvant chemotherapy, radiotherapy, and hormonal therapy.
Outcomes: No evidence of recurrence has been reported over 37 months.
Lessons: The imaging characteristics of breast MEC were variable, and there were no specific radiological features for diagnosis. The presence of cystic components on radiological imaging is likely to be an indicator of a low-grade tumor and better prognosis, although the number of reported cases is limited.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9239627 | PMC |
http://dx.doi.org/10.1097/MD.0000000000029745 | DOI Listing |
Radiol Med
September 2025
Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141, Milan, Italy.
Metastatic involvement (MB) of the breast from extramammary malignancies is rare, with an incidence of 0.09-1.3% of all breast malignancies.
View Article and Find Full Text PDFJ Neuromuscul Dis
September 2025
Department of Neurology, University of Kansas Medical Center, Kansas City, KS, USA.
Background: Electrical impedance myography (EIM) has been proposed as an efficient, non-invasive biomarker of muscle composition in facioscapulohumeral muscular dystrophy (FSHD).
Objective: We investigate whether EIM parameters are associated with muscle structure measured by magnetic resonance imaging (MRI), muscle histology, and transcriptomic analysis as well as strength at the individual leg muscle level.
Methods: We performed a multi-center cross-sectional study enrolling 33 patients with FSHD.
Neuroradiology
September 2025
Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Purpose: To develop and validate an integrated model based on MR high-resolution vessel wall imaging (HR-VWI) radiomics and clinical features to preoperatively assess periprocedural complications (PC) risk in patients with intracranial atherosclerotic disease (ICAD) undergoing percutaneous transluminal angioplasty and stenting (PTAS).
Methods: This multicenter retrospective study enrolled 601 PTAS patients (PC+, n = 84; PC -, n = 517) from three centers. Patients were divided into training (n = 336), validation (n = 144), and test (n = 121) cohorts.
J Thorac Imaging
September 2025
Department of Radiology, the Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University.
Purpose: To establish an explainable machine learning (ML) approach using patient-related and noncontrast chest CT-derived features to predict the contrast material arrival time (TARR) in CT pulmonary angiography (CTPA).
Materials And Methods: This retrospective study included consecutive patients referred for CTPA between September 2023 to October 2024. Sixteen clinical and 17 chest CT-derived parameters were used as inputs for the ML approach, which employed recursive feature elimination for feature selection and XGBoost with SHapley Additive exPlanations (SHAP) for explainable modeling.
Int J Surg
September 2025
Department of Radiology, Hainan Cancer Hospital, Hainan, China.