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

False positive reduction in CADe using diffusing scale space

Segmentation is typically the first step in computer-aided-detection (CADe). The second step is false positive reduction which usually involves computing a large number of features with thresholds set by training over excessive data set. The number of false positives can, in principle, be reduced by extensive noise removal and other forms of image enhancement prior to segmentation. However, this can drastically affect the true positive results and their boundaries. We present a post-segmentation method to reduce the number of false positives by using a diffusion scale space. The method is illustrated using Integral Invariant scale space, though this is not a requirement. It is quite general, does not require any prior information, is fast and easy to compute, and gives very encouraging results. Experiments are performed both on intensity mammograms as well as on Volpara® density maps

Development of a phantom to test fully automated breast density software – a work in progress

Objectives: Mammographic density (MD) is an independent risk factor for breast cancer and may have a future role for stratified screening. Automated software can estimate MD but the relationship between breast thickness reduction and MD is not fully understood. Our aim is to develop a deformable breast phantom to assess automated density software and the impact of breast thickness reduction on MD. Methods: Several different configurations of poly vinyl alcohol (PVAL) phantoms were created. Three methods were used to estimate their density. Raw image data of mammographic images were processed using Volpara to estimate volumetric breast density (VBD%); Hounsfield units (HU) were measured on CT images; and physical density (g/cm3) was calculated using a formula involving mass and volume. Phantom volume versus contact area and phantom volume versus phantom thickness was compared to values of real breasts. Results: Volpara recognized all deformable phantoms as female breasts. However, reducing the phantom thickness caused a change in phantom density and the phantoms were not able to tolerate same level of compression and thickness reduction experienced by female breasts during mammography. Conclusion: Our results are promising as all phantoms resulted in valid data for automated breast density measurement. Further work should be conducted on PVAL and other materials to produce deformable phantoms that mimic female breast structure and density with the ability of being compressed to the same level as female breasts. Advances in knowledge: We are the first group to have produced deformable phantoms that are recognized as breasts by Volpara software

Impact of errors in recorded compressed breast thickness measurements on volumetric density classification using volpara v1.5.0 software

Purpose: Mammographic density has been demonstrated to predict breast cancer risk. It has been proposed that it could be used for stratifying screening pathways and recommending additional imaging. Volumetric density tools use the recorded compressed breast thickness (CBT) of the breast measured at the x-ray unit in their calculation, however the accuracy of the recorded thickness can vary. The aim of this study was to investigate whether inaccuracies in recorded CBT impact upon volumetric density classification and to examine whether the current quality control (QC) standard is sufficient for assessing mammographic density. Methods: Raw data from 52 digital screening mammograms were included in the study. For each image, the clinically recorded CBT was artificially increased and decreased to simulate measurement error. Increments of 1mm were used up to ±15% error of recorded CBT was achieved. New images were created for each 1mm step in thickness resulting in a total of 974 images which then had Volpara Density Grade (VDG) and volumetric density percentage assigned. Results: A change in VDG was recorded in 38.5% (n= 20) of mammograms when applying ±15% error to the recorded CBT and 11.5 % (n= 6) were within the QC standard prescribed error of ±5mm. Conclusion: The current QC standard of ±5mm error in recorded CBT creates the potential for error in mammographic density measurement. This may lead to inaccurate classification of mammographic density. The current QC standard for assessing mammographic density should be reconsidered

Measurement challenge : protocol for international case–control comparison of mammographic measures that predict breast cancer risk

Introduction: For women of the same age and body mass index, increased mammographic density is one of the strongest predictors of breast cancer risk. There are multiple methods of measuring mammographic density and other features in a mammogram that could potentially be used in a screening setting to identify and target women at high risk of developing breast cancer. However, it is unclear which measurement method provides the strongest predictor of breast cancer risk. Methods and analysis: The measurement challenge has been established as an international resource to offer a common set of anonymised mammogram images for measurement and analysis. To date, full field digital mammogram images and core data from 1650 cases and 1929 controls from five countries have been collated. The measurement challenge is an ongoing collaboration and we are continuing to expand the resource to include additional image sets across different populations (from contributors) and to compare additional measurement methods (by challengers). The intended use of the measurement challenge resource is for refinement and validation of new and existing mammographic measurement methods. The measurement challenge resource provides a standardised dataset of mammographic images and core data that enables investigators to directly compare methods of measuring mammographic density or other mammographic features in case/control sets of both raw and processed images, for the purposes of the comparing their predictions of breast cancer risk. Ethics and dissemination: Challengers and contributors are required to enter a Research Collaboration Agreement with the University of Melbourne prior to participation in the measurement challenge. The Challenge database of collated data and images are stored in a secure data repository at the University of Melbourne. Ethics approval for the measurement challenge is held at University of Melbourne (HREC ID 0931343.3)

Tracking ‘developing’ Focal Densities in Breast Quadrants

Abstract Background Focal density (FD) is a dense mammographic region that cannot be accurately identified as a mass without further examination. If a particular breast quadrant is significantly dense than others or has an increase in density over time, this could be associated with neoplasm especially in the presence of a tangible mass. We have developed a method to study and track quadrant-wise increase in FD over time. Method A set of 10 temporal patient cases collected over a period of up to 6 years were used. Each quarter of the breast is assigned a FD score, where quadrants are defined by first differentiating a border between the breast region and skin line. Then a nipple detection method is used to correctly identify nipples, including those ‘not in profile’. Afterward, the nipple location is used as a reference point to divide the breast into four quarters (see Figure 2). Further on, FDs are quantified [1] and a score assigned to each quadrant of the breast, and to the breast as a whole. Results Results show that our method can detect increase in FD over time in some quarters of breast; a finding that can be verified by Volpara density grade [2]. It can be seen (Figure-2) that Q1-left (upper-interior-UI) has a significantly higher FD score as compared others. Clinical evaluation for this BIRADS-C mammogram (Figure-1, left craniocaudal) confirms the presence of 6mm grade-4 screen detected invasive lobular carcinoma in the left UI quadrant of the breast. Figure-3 shows a FD comparison of all quadrats of the bilateral pair over the course of 6 years. Q1-left remained the densest throughout. Conclusion The study suggests that tracking FD (both ‘developing’ and ‘stable’) over time could potentially help in better understanding of the risk of breast cancer development in any particular quadrant of the breast

Breast compression across consecutive examinations among females participating in BreastScreen Norway

Objectives Breast compression is used in mammography to improve image quality and reduce radiation dose. However, optimal values for compression force are not known, and studies has found large variation in use of compression forces between breast centres and radiographers. We investigated breast compression, including compression force, compression pressure and compressed breast thickness across four consecutive full field digital mammography (FFDM) screening examinations for 25,143 subsequently screened women aged 50-69 years. Methods Information from women attending four consecutive screening examinations at two breast centres in BreastScreen Norway during January 2007 - March 2016 was available. We compared the changes in compression force, compression pressure and compressed breast thickness from the first to fourth consecutive screening examination, stratified by craniocaudal (CC) and mediolateral oblique (MLO) view. Results Compression force, compression pressure and compressed breast thickness increased relatively by 18.3%, 14.4% and 8.4% respectively, from first to fourth consecutive screening examination in CC view (p&lt;0.001 for all). For MLO view, the values increased relatively by 12.3% for compression force, 9.9% for compression pressure and 6.9% for compressed breast thickness from first to fourth consecutive screening examination (p&lt;0.001 for all). Conclusions We observed increasing values of breast compression parameters across consecutive screening examinations. Further research should investigate the effect of this variation on image quality and women’s experiences of discomfort and pain

Shape description and matching using integral invariants on eccentricity transformed images

Matching occluded and noisy shapes is a problem frequently encountered in medical image analysis and more generally in computer vision. To keep track of changes inside the breast, for example, it is important for a computer aided detection system to establish correspondences between regions of interest. Shape transformations, computed both with integral invariants (II) and with geodesic distance, yield signatures that are invariant to isometric deformations, such as bending and articulations. Integral invariants describe the boundaries of planar shapes. However, they provide no information about where a particular feature lies on the boundary with regard to the overall shape structure. Conversely, eccentricity transforms (Ecc) can match shapes by signatures of geodesic distance histograms based on information from inside the shape; but they ignore the boundary information. We describe a method that combines the boundary signature of a shape obtained from II and structural information from the Ecc to yield results that improve on them separately