It is not clear to what extent mammographic density represents a risk factor for breast cancer among women with moderate risk for disease. We conducted a population-based study to estimate the independent effect of breast density on breast cancer risk and to evaluate the potential of breast density as a marker of risk in an intermediate risk population. From November 2006 to April 2009, data that included American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) breast density categories and risk information were collected on 52,752 women aged 50-69 years without previously diagnosed breast cancer who underwent screening mammography examination. A total of 257 screen-detected breast cancers were identified. Logistic regression was used to assess the effect of breast density on breast carcinoma risk and to control for other risk factors. The risk increased with density and the odds ratio for breast cancer among women with dense breast (heterogeneously and extremely dense breast), was 1.9 (95% confidence interval, 1.3-2.8) compared with women with almost entirely fat breasts, after adjustment for age, body mass index, age at menarche, age at menopause, age at first childbirth, number of live births, use of oral contraceptive, family history of breast cancer, prior breast procedures, and hormone replacement therapy use that were all significantly related to breast density (p < 0.001). In multivariate model, breast cancer risk increased with age, body mass index, family history of breast cancer, prior breast procedure and breast density and decreased with number of live births. Our finding that mammographic density is an independent risk factor for breast cancer indicates the importance of breast density measurements for breast cancer risk assessment also in moderate risk populations.
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Sci Rep
December 2024
Department of Clinical Pharmacy, Baoshan Hospital Affiliated to, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
This study investigates the potential treatment of breast cancer utilizing Gentiana robusta King ex Hook. f. (QJ) through an integrated approach involving network pharmacology, molecular docking, and molecular dynamics simulation.
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December 2024
Department of Breast Surgery, Second Affiliated Hospital of Dalian Medical University, No. 467 Zhongshan Road, Shahekou District, Dalian, China.
Early prediction of patient responses to neoadjuvant chemotherapy (NACT) is essential for the precision treatment of early breast cancer (EBC). Therefore, this study aims to noninvasively and early predict pathological complete response (pCR). We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model.
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