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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:Objectives Mammographic density is a well-defined risk factor for breast cancer and having extremely dense breast tissue is associated with a one-to six-fold increased risk of breast cancer. However, it is questioned whether this increased risk estimate is applicable to current breast density classification methods. Therefore, the aim of this study was to further investigate and clarify the association between mammographic density and breast cancer risk based on current literature. Methods Medline, Embase and Web of Science were systematically searched for articles published since 2013, that used BI-RADS lexicon 5th edition and incorporated data on digital mammography. Crude and maximally confounder-adjusted data were pooled in odds ratios (ORs) using random-effects models. Heterogeneity regarding breast cancer risks were investigated using I2 statistic, stratified and sensitivity analyses. Results Nine observational studies were included. Having extremely dense breast tissue (BI-RADS density D) resulted in a 2.11-fold (95% CI 1.84–2.42) increased breast cancer risk compared to having scattered dense breast tissue (BI-RADS density B). Sensitivity analysis showed that when only using data that had adjusted for age and BMI, the breast cancer risk was 1.83-fold (95% CI 1.52–2.21) increased. Both results were statistically significant and homogenous. Conclusions Mammographic breast density BI-RADS D is associated with an approximately two-fold increased risk of breast cancer compared to having BI-RADS density B in general population women. This is a novel and lower risk estimate compared to previously reported and might be explained due to the use of digital mammography and BI-RADS lexicon 5th edition. Highlights • So far, breast density was linked to a one-to sixfold increased breast cancer risk.• Prior risk estimates were based on old BI-RADS lexicon and screen-film mammography.• We included only studies using digital mammography and BI-RADS lexicon 5th edition.• Breast density D is associated with a two-fold increased risk of breast cancer.• This is a lower risk estimate compared to previously reported studies.

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.

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Physical activity and mammographic breast density: a systematic review.

Publications

Physical activity and mammographic breast density: a systematic review.

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