Previous studies have reported that tamoxifen use is associated with a decrease in mammographic breast density. This is a potentially valuable finding since mammographic sensitivity is limited by breast density. Anything that reduces breast density would theoretically enhance the sensitivity of mammography for the detection of breast cancer in women at an earlier stage when it is more curable. We performed a retrospective study investigating the potential effect of tamoxifen on breast density. The data for this retrospective study were collected from the records of 52 charts from a single medical oncologist. Patients with breast cancer were selected regardless of stage or age at the time of diagnosis or treatment, as long as their charts had records of bilateral mammograms. For each breast on each woman, both mediolateral oblique and craniocaudal views were reviewed independently by two radiologists on two separate occasions to obtain inter- and intraobserver variability. Two methods of classifying breast density were used: the Breast Imaging Reporting and Data System (BI-RADS), and measurements of percent density. Only age and menopausal status were found to be associated with breast density. There was no correlation between breast density and tamoxifen use (past or present). Our study shows no association between tamoxifen use and breast density. We confirm previous observations that breast density is inversely correlated with age and postmenopausal status.
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http://dx.doi.org/10.1111/j.1075-122X.2004.21332.x | DOI Listing |
Br J Radiol
December 2024
Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141, Milan, Italy.
Neoadjuvant Therapy (NT) has become the gold standard for treating locally advanced Breast Cancer (BC). The assessment of pathological response (pR) post-NT plays a crucial role in predicting long-term survival, with Contrast-Enhanced Magnetic Resonance Imaging (MRI) currently recognised as the preferred imaging modality for its evaluation. Traditional imaging techniques, such as Digital Mammography (DM) and Ultrasonography (US), encounter difficulties in post-NT assessments due to breast density, lesion changes, fibrosis, and molecular patterns.
View Article and Find Full Text PDFFront Oncol
December 2024
Department of Pathology, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China.
Background: Signet-ring cell carcinoma (SRCC) originates from undifferentiated stem cells in the neck of glands within the lamina propria of the mucosa. Primarily affecting the stomach, SRCC can also involve the breast, pancreas, gallbladder, colon, and bladder, although these cases are rare. SRCC of the prostate is extremely rare, and diagnosing it pelvic puncture is particularly challenging.
View Article and Find Full Text PDFFront Immunol
December 2024
Translational Research Unit, Montpellier Cancer Institute Val d'Aurelle, Montpellier, France.
Background: In triple-negative breast cancer (TNBC), the most immunogenic breast cancer type, tumor-infiltrating lymphocytes (TILs) are an independent prognostic factor. Tertiary lymphoid structures (TLS) are an important TILs source, but they are not integrated in the current prognostic criteria.
Methods: In this retrospective study, TLS were assessed in hematein-eosin-saffron-stained (HES) histological sections from 397 early, chemotherapy-naive TNBC samples after primary surgical resection.
J Ultrasound
December 2024
Department of Radiology, Research Institute of Radiology, Asan Medical Center, College of Medicine, University of Ulsan, Olympic-ro 43-gil, Songpa-gu, 05505, Seoul, Republic of Korea.
Purpose: To determine how often non-mass lesions are seen in screening breast ultrasounds, and analyze their ultrasound features according to the ultrasound lexicon to find features suggestive of malignant non-mass lesions.
Methods: This study is a single center retrospective study for nonmass lesions on screening breast ultrasound. Among 21,604 patients who underwent screening breast US, there were 279 patients with nonmass lesions.
J Pathol Inform
January 2025
U.S. Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, Silver Spring, MD, United States of America.
Objective: With the increasing energy surrounding the development of artificial intelligence and machine learning (AI/ML) models, the use of the same external validation dataset by various developers allows for a direct comparison of model performance. Through our High Throughput Truthing project, we are creating a validation dataset for AI/ML models trained in the assessment of stromal tumor-infiltrating lymphocytes (sTILs) in triple negative breast cancer (TNBC).
Materials And Methods: We obtained clinical metadata for hematoxylin and eosin-stained glass slides and corresponding scanned whole slide images (WSIs) of TNBC core biopsies from two US academic medical centers.
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