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Background: Monitoring metabolites of tamoxifen, such as endoxifen, has been suggested as a strategy to ascertain therapeutic effect of tamoxifen therapy, but clinical guidelines are missing. Herein, we aim to investigate the outcome of endoxifen concentrations of low-dose tamoxifen, using change in mammographic breast density as a proxy for therapy response.
Methods: In the randomized KARISMA-Tam (Karolinska Mammography project for risk prediction of breast cancer -Intervention Study with Tamoxifen) trial, including 5 doses of tamoxifen, measurements of plasma endoxifen concentrations, determination of CYP2D6 metabolizer status, and mammographic breast density change over the trial period were carried out. Association between endoxifen concentrations and relative mammographic breast density change after 6 months treatment was analyzed using linear regression in a spline model.
Results: A total of 824 women (335 premenopausal, 489 postmenopausal) were included. In analyses of premenopausal women, a spline model described a mammographic breast density decrease, equivalent to the mean (-18.5%) seen in women exposed to 20 mg tamoxifen, at endoxifen concentrations of 2-3 ng/mL. The mammographic breast density decrease reached a nadir at endoxifen levels of 3 ng/mL and did not decrease further at higher endoxifen concentrations. Most intermediate and normal tamoxifen metabolizers (about 90% of all participants) reached an endoxifen concentration of more than 2 ng/mL at tamoxifen doses of 5 and 10 mg. No mammographic breast density decrease was seen in the postmenopausal group.
Conclusions: We have identified a possible window of effect on mammographic breast density at endoxifen concentrations of 2-3 ng/mL in premenopausal women, which corresponds to the doses of 5 and 10 mg tamoxifen. Because mammographic breast density change was used as a surrogate marker for therapy response, results should be confirmed using clinically established outcomes measures.
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http://dx.doi.org/10.1093/jnci/djae280 | DOI Listing |
IEEE Trans Med Imaging
September 2025
Mammography is a primary method for early screening, and developing deep learning-based computer-aided systems is of great significance. However, current deep learning models typically treat each image as an independent entity for diagnosis, rather than integrating images from multiple views to diagnose the patient. These methods do not fully consider and address the complex interactions between different views, resulting in poor diagnostic performance and interpretability.
View Article and Find Full Text PDFDigit Health
September 2025
Department of Respiratory and Critical Care Medicine, The Sixth Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Objective: Accurate segmentation of breast lesions, especially small ones, remains challenging in digital mammography due to complex anatomical structures and low-contrast boundaries. This study proposes DVF-YOLO-Seg, a two-stage segmentation framework designed to improve feature extraction and enhance small-lesion detection performance in mammographic images.
Methods: The proposed method integrates an enhanced YOLOv10-based detection module with a segmentation stage based on the Visual Reference Prompt Segment Anything Model (VRP-SAM).
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 PDFJ Radiol Prot
September 2025
Radiological Physics & Advisory Division, Bhabha Atomic Research Centre, Mumbai, Maharashtra, INDIA.
Purpose This foundational study aims to support the development of regional Diagnostic Reference Levels (DRLs) for mammography screening in India by estimating Mean Glandular Dose (MGD) using appropriate breast-equivalent phantoms across Computed Mammography (CR), Digital Mammography (DR), and Digital Breast Tomosynthesis (DBT) systems. Additionally, system-displayed MGD values were compared with calculated MGDs to evaluate their accuracy for routine clinical use. Methods A total of 5,000 mammographic views were collected across CR, DR, and DBT units.
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