Mammographic-pathologic correlation of suspicious microcalcifications is essential for optimal diagnosis and local staging of early breast carcinoma. Loss of microcalcifications during histologic sectioning has been suggested as one reason for the occasional lack of microscopic visualization of microcalcifications in routinely processed breast biopsy specimens obtained for suspicious mammographic microcalcifications. Two case reports utilizing radiography of histologic shavings of stereotactic core biopsies and surgical excisional biopsies of mammographic microcalcifications provide concrete evidence of the loss of large calcific particles during the microtome process.
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http://dx.doi.org/10.1111/j.1075-122X.2004.21300.x | DOI Listing |
Eur J Radiol Open
June 2025
Radiology Department, National Cancer Institute, Cairo University, Egypt.
Purpose: To investigate the impact of artificial intelligence (AI) reading digital mammograms in increasing the chance of detecting missed breast cancer, by studying the AI- flagged early morphology indictors, overlooked by the radiologist, and correlating them with the missed cancer pathology types.
Methods And Materials: Mammograms done in 2020-2023, presenting breast carcinomas (n = 1998), were analyzed in concordance with the prior one year's result (2019-2022) assumed negative or benign. Present mammograms reviewed for the descriptors: asymmetry, distortion, mass, and microcalcifications.
Radiographics
February 2025
From the Washington University School of Medicine, Mallinckrodt Institute of Radiology, 510 S Kingshighway Blvd, St. Louis, MO 63110.
Annual review of false-negative (FN) mammograms is a mandatory and critical component of the Mammography Quality Standards Act (MQSA) annual mammography audit. FN review can help hone reading skills and improve the ability to detect cancers at mammography. Subtle architectural distortion, asymmetries (seen only on one view), small lesions, lesions with probably benign appearance (circumscribed regular borders), isolated microcalcifications, and skin thickening are the most common mammographic findings when the malignancy is visible at retrospective review of FN mammograms.
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December 2024
Institute of Diagnostic and Interventional Radiology, GZO Regional Health Center, 8620 Wetzikon, Switzerland.
Objective: This study develops a BI-RADS-like scoring system for vascular microcalcifications in mammographies, correlating breast arterial calcification (BAC) in a mammography with coronary artery calcification (CAC), and specifying differences between microcalcifications caused by BAC and microcalcifications potentially associated with malignant disease.
Materials And Methods: This retrospective single-center cohort study evaluated 124 consecutive female patients (with a median age of 57 years). The presence of CAC was evaluated based on the Agatston score obtained from non-enhanced coronary computed tomography, and the calcifications detected in the mammography were graded on a four-point Likert scale, with the following criteria: (1) no visible or sporadically scattered microcalcifications, (2) suspicious microcalcification not distinguishable from breast arterial calcification, (3) minor breast artery calcifications, and (4) major breast artery calcifications.
Ann Surg Oncol
January 2025
Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA.
Background: Flat epithelial atypia (FEA), a rare breast proliferative lesion, is often diagnosed following core biopsy (CB) of mammographic microcalcifications. In the prospective multi-institution TBCRC 034 trial, we investigate the upgrade rate to ductal carcinoma in situ (DCIS) or invasive cancer following excision for patients diagnosed with FEA on CB.
Patients And Methods: Patients with a breast imaging reporting and data system (BI-RADS) ≤ 4 imaging abnormality and a concordant CB diagnosis of FEA were identified for excision.
Phys Med
January 2025
Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
Purpose: Inflammatory breast cancer (IBC) is a rare and aggressive type of breast cancer, as many physicians may not be aware of it in terms of symptoms and diagnosis. Mammography is the first choice in breast screenings and diagnosis. Because of a lack of expertise and imaging datasets, IBC portrayal and machine learning-based diagnosis systems have not yet been studied thoroughly.
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