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http://dx.doi.org/10.1148/radiol.252363 | DOI Listing |
Radiology
September 2025
Department of Radiology, Duke University Medical Center, Box 3808, Durham, NC 27710.
PLoS One
September 2025
Department of Diagnostic Imaging, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-ku, Sendai, Miyagi, Japan.
Purpose: To investigate the effectiveness of an integrated deep-learning (DL) algorithm, the Mixture of Radiological Findings Specific Experts (MoRFSE), in breast cancer classification by imitating the diagnostic decision-making process of radiologists.
Methods: A total of 2,764 mammographic images (1,462 breast cancer, 248 benign lesions, and 1,054 normal breast tissue) from the TOMPEI-CMMD were used. The MoRFSE comprises three DL models: a gate network for categorization (gNet) and two classification expert networks (cExp and mExp) specialized in capturing the distinct characteristics of calcifications and masses, respectively.
Eur J Breast Health
September 2025
University of Miami Hospital, Department of Radiology, Division of Breast Imaging, Miami, USA.
Screening mammography plays a critical role in the early detection of breast cancer. Suspicious breast calcifications on mammography often prompt further diagnostic evaluation due to concern for malignancy, worrying physicians and patients alike. Here, we present a case of a woman in her 70s whose annual screening mammogram with digital breast tomosynthesis demonstrated two new groups of microcalcifications, confirmed after recall with magnification views.
View Article and Find Full Text PDFRadiol Artif Intell
September 2025
Department of Imaging and Pathology, University Hospital Leuven, Herestraat 49, Box 7003, 3000 Leuven, Belgium.
Purpose To evaluate whether features extracted by Mirai can be aligned with mammographic observations, and contribute meaningfully to the prediction. Materials and Methods This retrospective study examined the correlation of 512 Mirai features with mammographic observations in terms of receptive field and anatomic location. A total of 29,374 screening examinations with mammograms (10,415 women, mean age at examination 60 [SD: 11] years) from the EMBED Dataset (2013-2020) were used to evaluate feature importance using a feature-centric explainable AI pipeline.
View Article and Find Full Text PDFJ Breast Imaging
August 2025
School of Medicine, Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Objective: To determine whether menopausal status and race affect the imaging features of triple-negative breast cancer (TNBC).
Methods: This institutional review board-approved retrospective study reviewed the clinicopathologic data and imaging features of patients diagnosed with invasive ductal TNBC from January 1, 2014, to March 30, 2023. There were 199 patients, of whom 67 (33.