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Classifying the molecular subtype of breast cancer using vision transformer and convolutional neural network features.
Breast Cancer Res Treat
January 2025
Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, 1398 Shimamichou, Kita-Ku, Niigata, Japan.
Purpose: Identification of the molecular subtypes in breast cancer allows to optimize treatment strategies, but usually requires invasive needle biopsy. Recently, non-invasive imaging has emerged as promising means to classify them. Magnetic resonance imaging is often used for this purpose because it is three-dimensional and highly informative.
View Article and Find Full Text PDFExternal Validation of a 5-Factor Risk Model for Breast Cancer-Related Lymphedema.
JAMA Netw Open
January 2025
Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
Importance: Secondary lymphedema is a common, harmful side effect of breast cancer treatment. Robust risk models that are externally validated are needed to facilitate clinical translation. A published risk model used 5 accessible clinical factors to predict the development of breast cancer-related lymphedema; this model included a patient's mammographic breast density as a novel predictive factor.
View Article and Find Full Text PDFAge at first full-term pregnancy and other reproductive factors are associated with mammographic breast density in postmenopausal women: a study in Flanders, Belgium.
Cancer Res Commun
January 2025
University Hospitals Leuven, Leuven, Belgium.
This study evaluated the association between age at first full-term pregnancy (FFTP) and mammographic breast density (MBD) in postmenopausal women. 1,034 women, age 50-69y, were recruited from the Flemish (Belgium) population-based breast cancer screening program. Participants completed a questionnaire on lifestyle and reproductive factors.
View Article and Find Full Text PDFGaussian barebone mechanism and wormhole strategy enhanced moth flame optimization for global optimization and medical diagnostics.
PLoS One
January 2025
Department of Respiratory and Critical Care Medicine, The First Medical Centre of Chinese PLA General Hospital, Haidian District, Beijing, China.
Moth Flame Optimization (MFO) is a swarm intelligence algorithm inspired by the nocturnal flight mode of moths, and it has been widely used in various fields due to its simple structure and high optimization efficiency. Nonetheless, a notable limitation is its susceptibility to local optimality because of the absence of a well-balanced exploitation and exploration phase. Hence, this paper introduces a novel enhanced MFO algorithm (BWEMFO) designed to improve algorithmic performance.
View Article and Find Full Text PDFEvaluating the Impact of Changes in AI-derived Case Scores over Time on Digital Breast Tomosynthesis Screening Outcomes.
Radiol Artif Intell
January 2025
Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104.
Purpose To evaluate the change in DBT-AI (digital breast tomosynthesis-artificial intelligence) case scores over sequential screens. Materials and Methods This retrospective review included 21,108 female patients (mean age, 58.1 ± [SD] 11.
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