Objective: The aim of the study was to determine mammographic breast density changes during raloxifene therapy in postmenopausal patients
Materials And Methods: Fifty-five cases who were using raloxifen therapy were included in this retrospective analysis. Raloxifene was given for osteopenia and osteoporosis according to low bone mineral density measured by dual-energy X-ray absorptiometry (DEXA). None of the patients were using hormone replacement therapy 12 months before the initiation of raloxifene treatment or during the study. Mammographic breast density was determined by mammography before the initiation of raloxifene treatment (baseline) and after 12 to 16 months of therapy. The Breast Imaging Reporting and Data System (BI-RADS) breast density score was used for the evaluation of mammographic density.
Results: There was no change in mammographic breast density when the baseline and the first mammography taken after the initiation of therapy were compared (p = 0.32). There was no significant correlation between the duration of raloxifene treatment and mammographic density measured after raloxifene treatment (r = -0.158, p = 0.25). Only in one patient did the BI-RADS classification of 2 change to 3 after 12 months of therapy.
Conclusions: In conclusion, raloxifene therapy for 12 to 16 months does not increase mammographic breast density in postmenopausal women with low bone mass.
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Ann Surg Oncol
January 2025
Department of Radiology, University of Washington, Seattle, WA, USA.
Background: Ductal carcinoma in situ (DCIS) is overtreated, in part because of inability to predict which DCIS cases diagnosed at core needle biopsy (CNB) will be upstaged at excision. This study aimed to determine whether quantitative magnetic resonance imaging (MRI) features can identify DCIS at risk of upstaging to invasive cancer.
Methods: This prospective observational clinical trial analyzed women with a diagnosis of DCIS on CNB.
Comput Biol Med
January 2025
School of Automation Science and Engineering, South China University of Technology, Guangzhou, China. Electronic address:
Breast cancer poses a significant health threat worldwide. Contrastive learning has emerged as an effective method to extract critical lesion features from mammograms, thereby offering a potent tool for breast cancer screening and analysis. A crucial aspect of contrastive learning is negative sampling, where the selection of hard negative samples is essential for driving representations to retain detailed lesion information.
View Article and Find Full Text PDFBMC Res Notes
January 2025
Department of Surgery, Department of Clinical Sciences, Division of Surgery, Skåne University Hospital, Lund University, Lund, Sweden.
Objectives: Positive resection margins after breast-conserving surgery (BCS) most often demands a repeat surgery. To preoperatively identify patients at risk of positive margins, a multivariable model has been developed that predicts positive margins after BCS with a high accuracy. This study aimed to externally validate this prediction model to explore its generalizability and assess if additional preoperatively available variables can further improve its predictive accuracy.
View Article and Find Full Text PDFBreast 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 PDFJAMA 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.
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