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Enhancing detection of previously missed non-palpable breast carcinomas through artificial intelligence.
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.
Towards Automated Semantic Segmentation in Mammography Images for Enhanced Clinical Applications.
J Imaging Inform Med
December 2024
Tecgraf Institute and Department of Informatics, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
Mammography images are widely used to detect non-palpable breast lesions or nodules, aiding in cancer prevention and enabling timely intervention when necessary. To support medical analysis, computer-aided detection systems can automate the segmentation of landmark structures, which is helpful in locating abnormalities and evaluating image acquisition adequacy. This paper presents a deep learning-based framework for segmenting the nipple, the pectoral muscle, the fibroglandular tissue, and the fatty tissue in standard-view mammography images.
View Article and Find Full Text PDFUnderstanding BI-RADS Category 3.
Radiographics
January 2025
From the Department of Radiology, Division of Breast Imaging, UC San Diego Health, Koman Family Outpatient Pavilion, 9400 Campus Point Dr, #7316, La Jolla, CA 92037 (S.F., J.S., R.R.P., H.O.F.); and Department of Breast Imaging, Division of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (M.S.G., B.A.).
Article Synopsis
- The BI-RADS category 3 assessment indicates findings that are "probably benign," with a less than 2% chance of being cancerous, helping to reduce unnecessary breast biopsies.
- Despite its established guidelines for mammography, breast ultrasound, and emerging MRI uses, there is still confusion and misuse surrounding this category.
- Category 3 findings should be followed up with short-term imaging to monitor for changes, and it is not appropriate to use in screening studies without further diagnostic evaluations.
Artificial Intelligence in Breast Cancer Diagnosis and Treatment: Advances in Imaging, Pathology, and Personalized Care.
Life (Basel)
November 2024
Clinic of Endocrinology and Metabolic Diseases, St George University Hospital, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria.
Breast cancer is the most prevalent cancer worldwide, affecting both low- and middle-income countries, with a growing number of cases. In 2024, about 310,720 women in the U.S.
View Article and Find Full Text PDFCombining Contrast-Enhanced Mammography and Radioactive-Free Magnetic Seed Localization of Non-palpable Breast Tumors: A Feasibility Study.
J Cancer
October 2024
Zuyderland Medical Center, Department of Medical Imaging, Sittard-Geleen, the Netherlands.
Magnetic seed localization is a novel and reliable technique for perioperative localization of non-palpable breast cancers. However, due to susceptibility artifacts, magnetic seeds cannot be during response monitoring of neoadjuvant chemotherapy with MRI. Contrast-enhanced mammography (CEM) could provide an alternative modality for response monitoring while magnetic seeds are .
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