Clinical and epidemiological issues and applications of mammographic density

The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the authorMammographic density, the amount of radiodense tissue on a mammogram, is a strong risk factor for breast cancer, with properties that could be an asset in screening and prevention programmes. Its use in risk prediction contexts is currently limited, however, mainly due to di culties in measuring and interpreting density. This research investigates rstly, the properties of density as an independent marker of breast cancer risk and secondly, how density should be measured. The rst question was addressed by analysing data from a chemoprevention trial, a trial of hormonal treatment, and a cohort study of women with a family history of breast cancer . Tamoxifen-induced density reduction was observed to be a good predictor of breast cancer risk reduction in high-risk una ected subjects. Density and its changes did not predict risk or treatment outcome in subjects with a primary invasive breast tumour. Finally absolute density predicted risk better than percent density and showed a potential to improve existing risk-prediction models, even in a population at enhanced familial risk of breast cancer. The second part of thesis focuses on density measurement and in particular evaluates two fully-automated volumetric methods, Quantra and Volpara. These two methods are highly correlated and in both cases absolute density (cm3) discriminated cases from controls better than percent density. Finally, we evaluated and compared di erent measurement methods. Our ndings suggested good reliability of the Cumulus and visual assessments. Quantra volumetric estimates appeared negligibly a ected by measurement error, but were less variable than visual bi-dimensional ones, a ecting their ability to discriminate cases from controls. Overall, visual assessments showed the strongest association with breast cancer risk in comparison to computerised methods. Our research supports the hypothesis that density should have a role in personalising screening programs and risk management. Volumetric density measuring methods, though promising, could be improved.Cancer Research U

Adjusting for BMI in analyses of volumetric mammographic density and breast cancer risk

Abstract Background Fully automated assessment of mammographic density (MD), a biomarker of breast cancer risk, is being increasingly performed in screening settings. However, data on body mass index (BMI), a confounder of the MD–risk association, are not routinely collected at screening. We investigated whether the amount of fat in the breast, as captured by the amount of mammographic non-dense tissue seen on the mammographic image, can be used as a proxy for BMI when data on the latter are unavailable. Methods Data from a UK case control study (numbers of cases/controls: 414/685) and a Norwegian cohort study (numbers of cases/non-cases: 657/61059), both with volumetric MD measurements (dense volume (DV), non-dense volume (NDV) and percent density (%MD)) from screening-age women, were analysed. BMI (self-reported) and NDV were taken as measures of adiposity. Correlations between BMI and NDV, %MD and DV were examined after log-transformation and adjustment for age, menopausal status and parity. Logistic regression models were fitted to the UK study, and Cox regression models to the Norwegian study, to assess associations between MD and breast cancer risk, expressed as odds/hazard ratios per adjusted standard deviation (OPERA). Adjustments were first made for standard risk factors except BMI (minimally adjusted models) and then also for BMI or NDV. OPERA pooled relative risks (RRs) were estimated by fixed-effect models, and between-study heterogeneity was assessed by the I 2 statistics. Results BMI was positively correlated with NDV (adjusted r = 0.74 in the UK study and r = 0.72 in the Norwegian study) and with DV (r = 0.33 and r = 0.25, respectively). Both %MD and DV were positively associated with breast cancer risk in minimally adjusted models (pooled OPERA RR (95% confidence interval): 1.34 (1.25, 1.43) and 1.46 (1.36, 1.56), respectively; I 2 = 0%, P >0.48 for both). Further adjustment for BMI or NDV strengthened the %MD–risk association (1.51 (1.41, 1.61); I 2 = 0%, P = 0.33 and 1.51 (1.41, 1.61); I 2 = 0%, P = 0.32, respectively). Adjusting for BMI or NDV marginally affected the magnitude of the DV–risk association (1.44 (1.34, 1.54); I 2 = 0%, P = 0.87 and 1.49 (1.40, 1.60); I 2 = 0%, P = 0.36, respectively). Conclusions When volumetric MD–breast cancer risk associations are investigated, NDV can be used as a measure of adiposity when BMI data are unavailable

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

Relationships between mammographic density, tissue microvessel density, and breast biopsy diagnosis

Association between microvessel density (MVD) and tumor characteristics among breast cancer cases (n = 44). (DOC 54 kb

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

The Short-Term Effect of Weight Loss Surgery on Volumetric Breast Density and Fibroglandular Volume

Purpose: Obesity and breast density are both associated with an increased risk of breast cancer and are potentially modifiable. Weight loss surgery (WLS) causes a significant reduction in the amount of body fat and a decrease in breast cancer risk. The effect of WLS on breast density and its components has not been documented. Here, we analyze the impact of WLS on volumetric breast density (VBD) and on each of its components (fibroglandular volume and breast volume) by using three-dimensional methods. Materials and Methods: Fibroglandular volume, breast volume, and their ratio, the VBD, were calculated from mammograms before and after WLS by using Volpara™ automated software. Results: For the 80 women included, average body mass index decreased from 46.0 ± 7.22 to 33.7 ± 7.06 kg/m2. Mammograms were performed on average 11.6 ± 9.4 months before and 10.1 ± 7 months after WLS. There was a significant reduction in average breast volume (39.4 % decrease) and average fibroglandular volume (15.5 % decrease), and thus, the average VBD increased from 5.15 to 7.87 % (p < 1 × 10−9) after WLS. When stratified by menopausal status and diabetic status, VBD increased significantly in all groups but only perimenopausal and postmenopausal women and non-diabetics experienced a significant reduction in fibroglandular volume. Conclusions: Breast volume and fibroglandular volume decreased, and VBD increased following WLS, with the most significant change observed in postmenopausal women and non-diabetics. Further studies are warranted to determine how physical and biological alterations in breast density components after WLS may impact breast cancer risk.ECU Open Access Publishing Support Fun

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. Measurement of mammographic density

Mammographic density has been strongly associated with increased risk of breast cancer. Furthermore, density is inversely correlated with the accuracy of mammography and, therefore, a measurement of density conveys information about the difficulty of detecting cancer in a mammogram. Initial methods for assessing mammographic density were entirely subjective and qualitative; however, in the past few years methods have been developed to provide more objective and quantitative density measurements. Research is now underway to create and validate techniques for volumetric measurement of density. It is also possible to measure breast density with other imaging modalities, such as ultrasound and MRI, which do not require the use of ionizing radiation and may, therefore, be more suitable for use in young women or where it is desirable to perform measurements more frequently. In this article, the techniques for measurement of density are reviewed and some consideration is given to their strengths and limitations