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Physical activity and mammographic breast density: a systematic review.

ABSTRACT: Studies show a protective relationship between physical activity and breast cancer risk across the life course from menarche to postmenopausal years. Mammographic breast density is a known and strong breast cancer risk factor. Whether the association of physical activity with breast cancer risk is mediated through mammographic breast density is poorly understood. This systematic review summarizes published studies that investigated the association between physical activity and mammographic breast density and discusses the methodological issues that need to be addressed. We included in this review studies that were published before October 31, 2011 that were accessible in full-text format and were published in English. We identified 20 studies through the PubMed Central, BioMed Central, Embase, and Scopus and using the search terms "physical activity and breast density" and "exercise and breast density" as well as through manual searches of the bibliographies of the articles identified in electronic searches. We found no evidence of association between physical activity and breast density across the studies by grouping them first by the timing of physical activity assessment (in adolescence, current/recent, past, and lifetime) and then by women's menopausal status (premenopausal and postmenopausal). Given the strength of the relationship between physical activity and breast cancer and the null findings of this review, it is unlikely that the effect of physical activity is mediated through an effect on breast density.

SUBMITTER: Yaghjyan L

PROVIDER: S-EPMC3641148 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Physical activity and mammographic breast density: a systematic review.

Yaghjyan Lusine L Colditz Graham A GA Wolin Kathleen K

Breast cancer research and treatment 20120720 2

Studies show a protective relationship between physical activity and breast cancer risk across the life course from menarche to postmenopausal years. Mammographic breast density is a known and strong breast cancer risk factor. Whether the association of physical activity with breast cancer risk is mediated through mammographic breast density is poorly understood. This systematic review summarizes published studies that investigated the association between physical activity and mammographic breas ...[more]

PMID: 22814722

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Project description:BackgroundMachine learning (ML) has become a vital part of medical imaging research. ML methods have evolved over the years from manual seeded inputs to automatic initializations. The advancements in the field of ML have led to more intelligent and self-reliant computer-aided diagnosis (CAD) systems, as the learning ability of ML methods has been constantly improving. More and more automated methods are emerging with deep feature learning and representations. Recent advancements of ML with deeper and extensive representation approaches, commonly known as deep learning (DL) approaches, have made a very significant impact on improving the diagnostics capabilities of the CAD systems.ObjectiveThis review aimed to survey both traditional ML and DL literature with particular application for breast cancer diagnosis. The review also provided a brief insight into some well-known DL networks.MethodsIn this paper, we present an overview of ML and DL techniques with particular application for breast cancer. Specifically, we search the PubMed, Google Scholar, MEDLINE, ScienceDirect, Springer, and Web of Science databases and retrieve the studies in DL for the past 5 years that have used multiview mammogram datasets.ResultsThe analysis of traditional ML reveals the limited usage of the methods, whereas the DL methods have great potential for implementation in clinical analysis and improve the diagnostic capability of existing CAD systems.ConclusionsFrom the literature, it can be found that heterogeneous breast densities make masses more challenging to detect and classify compared with calcifications. The traditional ML methods present confined approaches limited to either particular density type or datasets. Although the DL methods show promising improvements in breast cancer diagnosis, there are still issues of data scarcity and computational cost, which have been overcome to a significant extent by applying data augmentation and improved computational power of DL algorithms.

Project description:Introduction Mammographic density (MD), as assessed from film screen mammograms, is determined by the relative content of adipose, connective and epithelial tissue in the female breast. In epidemiological studies, a high percentage of MD confers a four to six fold risk elevation of developing breast cancer, even after adjustment for other known breast cancer risk factors. However, the biologic correlates of density are little known. Methods Gene expression analysis using whole genome arrays was performed on breast biopsies from 143 women; 79 women with no malignancy (healthy women) and 64 newly diagnosed breast cancer patients, both included from mammographic centres. Percent MD was determined using a previously validated, computerized method on scanned mammograms. Significance analysis of microarrays (SAM) was performed to identify genes influencing MD and generalized regression models were used to assess the independent contribution from different variables to MD. Results SAM-analysis identified 24 genes differentially expressed between samples from breasts with high and low MD. These genes included three uridine 5'-diphospho-glucuronosyltransferase (UGT) genes and the oestrogen receptor gene (ESR1). These genes were down-regulated in samples with high MD compared to those with low MD. The UGT gene products, which are known to inactivate oestrogen metabolites, were also down-regulated in tumour samples compared to samples from healthy individuals. Several single nucleotide polymorphisms (SNPs) in the UGT genes associated with the expression of UGT and other genes in their vicinity were identified. Conclusions Three UGT enzymes were lower expressed both in breast tissue biopsies from healthy women with high MD and in biopsies from newly diagnosed breast cancers. The association was strongest among young women and women using hormonal therapy. UGT2B10 predicts MD independently of age, hormone therapy and parity. Our results indicate that down-regulation of UGT genes in women exposed to female sex hormones is associated with high MD and might increase the risk of breast cancer.

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Physical activity and mammographic breast density: a systematic review.

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Physical activity and mammographic breast density: a systematic review.

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