Background risk of breast cancer and the association between physical activity and mammographic density

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The Relationship between Body Mass Index and Mammographic Density during a Premenopausal Weight Loss Intervention Study.

We evaluated the association between short-term change in body mass index (BMI) and breast density during a 1 year weight-loss intervention (Manchester, UK). We included 65 premenopausal women (35-45 years, ≥7 kg adult weight gain, family history of breast cancer). BMI and breast density (semi-automated area-based, automated volume-based) were measured at baseline, 1 year, and 2 years after study entry (1 year post intervention). Cross-sectional (between-women) and short-term change (within-women) associations between BMI and breast density were measured using repeated-measures correlation coefficients and multivariable linear mixed models. BMI was positively correlated with dense volume between-women (r = 0.41, 95%CI: 0.17, 0.61), but less so within-women (r = 0.08, 95%CI: -0.16, 0.28). There was little association with dense area (between-women r = -0.12, 95%CI: -0.38, 0.16; within-women r = 0.01, 95%CI: -0.24, 0.25). BMI and breast fat were positively correlated (volume: between r = 0.77, 95%CI: 0.69, 0.84, within r = 0.58, 95%CI: 0.36, 0.75; area: between r = 0.74, 95%CI: 0.63, 0.82, within r = 0.45, 95%CI: 0.23, 0.63). Multivariable models reported similar associations. Exploratory analysis suggested associations between BMI gain from 20 years and density measures (standard deviation change per +5 kg/m2 BMI: dense area: +0.61 (95%CI: 0.12, 1.09); fat volume: -0.31 (95%CI: -0.62, 0.00)). Short-term BMI change is likely to be positively associated with breast fat, but we found little association with dense tissue, although power was limited by small sample size

Breast cancer risk associated with changes in mammographic density.

PhD ThesisBreast cancer is the most common cancer in the UK, and mammographic density (‘density’) is one of its strongest known risk factors. At present, most research focuses on static measures of density to determine population effects. The central hypothesis of this thesis is that repeated measures of density are more valuable for personalised breast cancer prevention. This hypothesis was tested through the following research. Study-I investigated within-women associations between body mass index (BMI) and density, to assess whether density (visual/Cumulus/volumetric ‘Stepwedge’) acts as a mediator for breast cancer risk reduction during a premenopausal weight-loss intervention (n=65). Study-II evaluated the benefit of using a woman’s longitudinal history of (BI-RADS) density to improve breast cancer risk estimation (n=132,439). Study-III was a Cochrane systematic review investigating the association between endocrine therapy-induced density reduction and breast cancer risk and mortality. Studies-IV and V (n=575) evaluated visually-assessed density reduction with prophylactic anastrozole during the International Breast Cancer Intervention Study-II, and its use as a biomarker for concurrent breast cancer risk reduction, respectively. In Study-I, change in BMI was associated with change in breast fat but not dense tissue, negating density reduction as a biomarker for risk reduction with weight-loss. In Study-II, longitudinal density provided approximately a quarter more statistical information than most recent density and improved discriminatory accuracy. Study-III found evidence that density reduction may be a biomarker for reduction in risk and mortality with tamoxifen, but the level of evidence was limited by some study quality issues. Study-IV indicated that preventive anastrozole might marginally reduce density, but statistical significance was not obtained. In Study-V, sample size was too small to draw definitive conclusions. Overall, changes in density were useful for the study of breast cancer risk and should be considered for personalised breast cancer prevention strategies

Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.

BackgroundTo determine if mammographic features from deep learning networks can be applied in breast cancer to identify groups at interval invasive cancer risk due to masking beyond using traditional breast density measures.MethodsFull-field digital screening mammograms acquired in our clinics between 2006 and 2015 were reviewed. Transfer learning of a deep learning network with weights initialized from ImageNet was performed to classify mammograms that were followed by an invasive interval or screen-detected cancer within 12 months of the mammogram. Hyperparameter optimization was performed and the network was visualized through saliency maps. Prediction loss and accuracy were calculated using this deep learning network. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were generated with the outcome of interval cancer using the deep learning network and compared to predictions from conditional logistic regression with errors quantified through contingency tables.ResultsPre-cancer mammograms of 182 interval and 173 screen-detected cancers were split into training/test cases at an 80/20 ratio. Using Breast Imaging-Reporting and Data System (BI-RADS) density alone, the ability to correctly classify interval cancers was moderate (AUC = 0.65). The optimized deep learning model achieved an AUC of 0.82. Contingency table analysis showed the network was correctly classifying 75.2% of the mammograms and that incorrect classifications were slightly more common for the interval cancer mammograms. Saliency maps of each cancer case found that local information could highly drive classification of cases more than global image information.ConclusionsPre-cancerous mammograms contain imaging information beyond breast density that can be identified with deep learning networks to predict the probability of breast cancer detection

Mammographic density, breast cancer risk and risk prediction

In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models

The effects of menopausal vasomotor symptoms and changes in anthropometry on breast cancer etiology

One of the strongest predictors of breast cancer risk is mammographic density; however, incomplete understanding of the mechanisms relating density to risk has limited its use as a marker for breast cancer susceptibility. Hormone fluctuations during the menopausal transition may influence declines in mammographic density and may also trigger the onset of menopausal vasomotor symptoms (VMS), which have been associated with lower breast cancer risk. The effects of hormone changes on density, VMS, and breast cancer risk are complicated by external factors such as changing body mass and hormone therapy use during the menopausal transition. We evaluated the association between change in BMI and change in mammographic density using volumetric measurement methods. We found that an annual increase in BMI was associated with a decrease in absolute dense volume and percent dense volume. Longitudinal studies of density and breast cancer, or those using density to reflect breast cancer risk, should consider controlling for BMI gain/loss to understand the independent relationship between density and risk. We further investigated the association of VMS and percent mammographic density. We observed no overall association, but found some evidence of an inverse relationship among perimenopausal women and those using hormone therapies. This suggests that an association between VMS and breast cancer risk is not strongly mediated by changes in breast density. Finally, we evaluated VMS and incident breast cancer risk. VMS were associated with a 38% reduction in risk. Adjustment for endogenous hormone levels did not alter our results, suggesting that endogenous hormones play a lesser role in the association between VMS and breast cancer risk than previously hypothesized. These studies further our understanding of breast cancer etiology. If confirmed, the association between VMS and breast cancer risk could propose VMS as an easily measured factor that could enhance risk prediction. Our findings that this association is not strongly mediated through breast density nor endogenous hormone levels raise provocative questions regarding the mechanisms that link VMS to breast cancer risk. Extending our knowledge of breast cancer etiology through new measurement methods and risk factors may lead to improved risk prediction and opportunities for disease prevention

Mammographic density and breast cancer risk in breast screening assessment cases and women with a family history of breast cancer.

BACKGROUND: Mammographic density has been shown to be a strong independent predictor of breast cancer and a causative factor in reducing the sensitivity of mammography. There remain questions as to the use of mammographic density information in the context of screening and risk management, and of the association with cancer in populations known to be at increased risk of breast cancer. AIM: To assess the association of breast density with presence of cancer by measuring mammographic density visually as a percentage, and with two automated volumetric methods, Quantra™ and VolparaDensity™. METHODS: The TOMosynthesis with digital MammographY (TOMMY) study of digital breast tomosynthesis in the Breast Screening Programme of the National Health Service (NHS) of the United Kingdom (UK) included 6020 breast screening assessment cases (of whom 1158 had breast cancer) and 1040 screened women with a family history of breast cancer (of whom two had breast cancer). We assessed the association of each measure with breast cancer risk in these populations at enhanced risk, using logistic regression adjusted for age and total breast volume as a surrogate for body mass index (BMI). RESULTS: All density measures showed a positive association with presence of cancer and all declined with age. The strongest effect was seen with Volpara absolute density, with a significant 3% (95% CI 1-5%) increase in risk per 10 cm3 of dense tissue. The effect of Volpara volumetric density on risk was stronger for large and grade 3 tumours. CONCLUSIONS: Automated absolute breast density is a predictor of breast cancer risk in populations at enhanced risk due to either positive mammographic findings or family history. In the screening context, density could be a trigger for more intensive imaging

Volumetric mammographic density: heritability and association with breast cancer susceptibility loci.

BACKGROUND: Mammographic density is a strong heritable trait, but data on its genetic component are limited to area-based and qualitative measures. We studied the heritability of volumetric mammographic density ascertained by a fully-automated method and the association with breast cancer susceptibility loci. METHODS: Heritability of volumetric mammographic density was estimated with a variance component model in a sib-pair sample (N pairs = 955) of a Swedish screening based cohort. Associations with 82 established breast cancer loci were assessed in an independent sample of the same cohort (N = 4025 unrelated women) using linear models, adjusting for age, body mass index, and menopausal status. All tests were two-sided, except for heritability analyses where one-sided tests were used. RESULTS: After multivariable adjustment, heritability estimates (standard error) for percent dense volume, absolute dense volume, and absolute nondense volume were 0.63 (0.06) and 0.43 (0.06) and 0.61 (0.06), respectively (all P < .001). Percent and absolute dense volume were associated with rs10995190 (ZNF365; P = 9.0 × 10(-6) and 8.9 × 10(-7), respectively) and rs9485372 (TAB2; P = 1.8 × 10(-5) and 1.8 × 10(-3), respectively). We also observed associations of rs9383938 (ESR1) and rs2046210 (ESR1) with the absolute dense volume (P = 2.6 × 10(-4) and 4.6 × 10(-4), respectively), and rs6001930 (MLK1) and rs17356907 (NTN4) with the absolute nondense volume (P = 6.7 × 10(-6) and 8.4 × 10(-5), respectively). CONCLUSIONS: Our results support the high heritability of mammographic density, though estimates are weaker for absolute than percent dense volume. We also demonstrate that the shared genetic component with breast cancer is not restricted to dense tissues only.This work was supported by the Swedish Research Council (grant no. 521-2011- 3187) and Swedish Cancer Society (grant no. CAN 2013/469). The KARolinska MAmmography project for risk prediction of breast cancer study was supported by Märit and Hans Rausing’s Initiative Against Breast Cancer and the Cancer and Risk Prediction Center CRisP (http://ki.se/en/meb/crisp), a Linneus Centre (Contract ID 70867902) financed by the Swedish Research Council. KH is supported by the Swedish Research Counsil (grant no. 521-2011-3205) and JL is a UNESCO-L’OREAL International Fellow.This is the accepted manuscript. The final version is available from OUP at http://dx.doi.org/10.1093/jnci/dju33