Ralph Highnam

Robust breast composition measurement - Volpara™

Volumetric breast composition measurements generally require accurate imaging physics data In this paper we describe a new method (VolparaTM) that uses relative (as opposed to absolute) physics modeling together with additional information derived from the image to substantially reduce the dependence on imaging physics data Results on 2,217 GE digital images, from a diversity of sites, show encouraging agreement with MRI data, as well as robustness to noise and errors in the imaging physics data.

Screen-film mammographic density and breast cancer risk: a comparison of the volumetric Standard Mammogram Form and the Interactive Threshold Measurement Methods

Background: Mammographic density is a strong risk factor for breast cancer, usually measured by an area-based threshold method that dichotomizes the breast area on a mammogram into dense and nondense regions. Volumetric methods of breast density measurement, such as the fully automated standard mammogram form (SMF) method that estimates the volume of dense and total breast tissue, may provide a more accurate density measurement and improve risk prediction. Methods: In 2000-2003, a case-control study was conducted of 367 newly confirmed breast cancer cases and 661 age-matched breast cancer-free controls who underwent screen-film mammography at several centers in Toronto, Canada. Conditional logistic regression was used to estimate odds ratios of breast cancer associated with categories of mammographic density, measured with both the threshold and the SMF (version 2.2β) methods, adjusting for breast cancer risk factors. Results: Median percent density was higher in cases than in controls for the threshold method (31% versus 27%) but not for the SMF method. Higher correlations were observed between SMF and threshold measurements for breast volume/area (Spearman correlation coefficient = 0.95) than for percent density (0.68) or for absolute density (0.36). After adjustment for breast cancer risk factors, odds ratios of breast cancer in the highest compared with the lowest quintile of percent density were 2.19 (95% confidence interval, 1.28-3.72; Pt <0.01) for the threshold method and 1.27 (95% confidence interval, 0.79-2.04; Pt = 0.32) for the SMF method. Conclusion: Threshold percent density is a stronger predictor of breast cancer risk than the SMF version 2.2β method in digitized images. Cancer Epidemiol Biomarkers Prev; 19(2); 418–28

Comparison of a new and existing method of mammographic density measurement: intramethod reliability and associations with known risk factors.

Background: Mammographic density is one of the strongest risk factors for breast cancer. It is commonly measured by an interactive threshold method that does not fully use information contained in a mammogram. An alternative fully automated standard mammogram form (SMF) method measures density using a volumetric approach. Methods: We examined between-breast and between-view agreement, reliability, and associations of breast cancer risk factors with the threshold and SMF measures of breast density on the same set of 1,000 digitized films from 250 women who attended routine breast cancer screening by two-view mammography in 2004 at a London population-based screening center. Data were analyzed using random-effects models on transformed percent density. Results: Median (interquartile range) percent densities were 12.8% (5.0-22.3) and 21.8% (18.4-26.6) in the threshold and SMF methods, respectively. There was no evidence of systematic differences between left-right breasts or between views in either method. Reliability of a single measurement was lower in the SMF than in the threshold method (0.77 versus 0.92 for craniocaudal and 0.68 versus 0.89 for mediolateral oblique views). Increasing body mass index and parity were associated with reduced density in both methods; however, an increase in density with hormone replacement therapy use was found only with the threshold method. Conclusion: Established properties of mammographic density were observed for SMF percent density; however, this method had poorer left-right reliability than the threshold method and has yet to be shown to be a predictor of breast cancer risk. (Cancer Epidemiol Biomarkers Prev 2007;16(6):1148–54)

Volumetric Assessment of Breast Tissue Composition from FFDM Images

We present first results from a new automated algorithm for the volumetric measurement of the composition of breast tissue from digital mammograms. The new algorithm overcomes issues in previous implementations through better segmentation and use of additional information We measure the success of the new algorithm using an overall quality metric based upon the results from a large multi-site, multi-vendor, multi-detector set of digitally acquired mammograms.

Breast cancer risk factors and a novel measure of volumetric breast density: cross-sectional study

We conducted a cross-sectional study nested within a prospective cohort of breast cancer risk factors and two novel measures of breast density volume among 590 women who had attended Glasgow University (1948–1968), replied to a postal questionnaire (2001) and attended breast screening in Scotland (1989–2002). Volumetric breast density was estimated using a fully automated computer programme applied to digitised film-screen mammograms, from medio-lateral oblique mammograms at the first-screening visit. This measured the proportion of the breast volume composed of dense (non-fatty) tissue (Standard Mammogram Form (SMF)%) and the absolute volume of this tissue (SMF volume, cm3). Median age at first screening was 54.1 years (range: 40.0–71.5), median SMF volume 70.25 cm3 (interquartile range: 51.0–103.0) and mean SMF% 26.3%, s.d.=8.0% (range: 12.7–58.8%). Age-adjusted logistic regression models showed a positive relationship between age at last menstrual period and SMF%, odds ratio (OR) per year later: 1.05 (95% confidence interval: 1.01–1.08, P=0.004). Number of pregnancies was inversely related to SMF volume, OR per extra pregnancy: 0.78 (0.70–0.86, P<0.001). There was a suggestion of a quadratic relationship between birthweight and SMF%, with lowest risks in women born under 2.5 and over 4 kg. Body mass index (BMI) at university (median age 19) and in 2001 (median age 62) were positively related to SMF volume, OR per extra kg m−2 1.21 (1.15–1.28) and 1.17 (1.09–1.26), respectively, and inversely related to SMF%, OR per extra kg m−2 0.83 (0.79–0.88) and 0.82 (0.76–0.88), respectively, P<0.001. Standard Mammogram Form% and absolute SMF volume are related to several, but not all, breast cancer risk factors. In particular, the positive relationship between BMI and SMF volume suggests that volume of dense breast tissue will be a useful marker in breast cancer studies.

Comparing measurements of breast density.

Breast density measurements can be made from mammograms using either area-based methods, such as the six category classification (SCC), or volumetric based methods, such as the standard mammogram form (SMF). Previously, we have shown how both types of methods generate breast density estimates which are generally close. In this paper, we switch our attention to the question of why, for certain cases, they provide widely differing estimates. First, we show how the underlying physical models of the breast employed in the methods need to be consistent, and how area-based methods are susceptible to projection effects. We then analyse a set of patients whose mammograms show large differences between their SCC and SMF assessments. More precisely, 12% of 657 patients were found to fall into this category. Of these, 2.7% were attributable to errors either in the SMF segmentation algorithms, human error in SCC categorization or poor image exposure. More importantly, 9.3% of the cases appear to be due to fundamental differences between the area- and volume-based techniques. We conclude by suggesting how we might remove half of those discrepancies by introducing a new categorization of the SMF estimates based on the breast thickness. We note however, that this still leaves 6% of patients with large differences between SMF and SCC estimates. We discuss why it might not be appropriate to assume SMF (or any volume measure) has a similar breast cancer risk prediction capability to SCC.

Mammographic compression – A need for mechanical standardization

BACKGROUND A lack of consistent guidelines regarding mammographic compression has led to wide variation in its technical execution. Breast compression is accomplished by means of a compression paddle, resulting in a certain contact area between the paddle and the breast. This procedure is associated with varying levels of discomfort or pain. On current mammography systems, the only mechanical parameter available in estimating the degree of compression is the physical entity of force (daN). Recently, researchers have suggested that pressure (kPa), resulting from a specific force divided by contact area on a breast, might be a more appropriate parameter for standardization. Software has now become available which enables device-independent cross-comparisons of key mammographic metrics, such as applied compression pressure (force divided by contact area), breast density and radiation dose, between patient populations. PURPOSE To compare the current compression practice in mammography between different imaging sites in the Netherlands and the United States from a mechanical point of view, and to investigate whether the compression protocols in these countries can be improved by standardization of pressure (kPa) as an objective mechanical parameter. MATERIALS AND METHODS We retrospectively studied the available parameters of a set of 37,518 mammographic compressions (9188 women) from the Dutch national breast cancer screening programme (NL data set) and of another set of 7171 compressions (1851 women) from a breast imaging centre in Pittsburgh, PA (US data set). Both sets were processed using VolparaAnalytics and VolparaDensity to obtain the applied average force, pressure, breast thickness, breast volume, breast density and average glandular dose (AGD) as a function of the size of the contact area between the breast and the paddle. RESULTS On average, the forces and pressures applied in the NL data set were significantly higher than in the US data set. The relative standard deviation was larger in the US data set than in the NL data set. Breasts were compressed with a force in the high range of >15 daN for 31.1% and >20 kPa for 12.3% of the NL data set versus, respectively, 1.5% and 1.7% of the US data set. In the low range we encountered compressions with a pressure of <5 daN for 21.1% and <5 kPa for 21.7% of the US data set versus, respectively, 0.05% and 0.6% in the NL data set. Both the average and the standard deviation of the AGD were higher in the US data set. CONCLUSION (1) Current mammographic breast compression policies lead to a wide range of applied forces and pressures, with large variations both within and between clinical sites. (2) Pressure standardization could decrease variation, improve reproducibility, and reduce the risk of unnecessary pain, unnecessary high radiation doses and inadequate image quality.

Comparing a new volumetric breast density method (Volpara TM ) to cumulus

Computer-aided thresholding programs, such as Cumulus, are seen as the gold standard for breast density measurement In this paper we compare a new volumetric breast density software package, VolparaTM to an experts BI-RADS visual assessment and Cumulus and show that all are closely related, whilst there is a less close relationship between Cumulus percent breast density and absolute volume of dense tissue These results support the further validation of this new method against breast cancer outcomes.

Breast density into clinical practice

It is well established that breast density is related to breast cancer risk; making that connection precise, and understanding how to use it in clinical practice, has been a major academic focus since the 1970s. However, it transpires that the first clinical uses of breast density have not been for risk prediction, rather they are for judging when to recommend further imaging. In this paper, we show how scientific research has had to be adapted in order to create the automated volumetric breast density assessment tool, Volpara®, to make it ready for actual clinical use and how it is impacting patient management.

The use of multi-scale monogenic signal on structure orientation identification and segmentation

A method of extracting salient image features in mammograms at multiple scales using the monogenic signal is presented. The derived local phase provides structure information (such as edge, ridge etc.) while the local amplitude encodes the local brightness and contrast information. Together with the simultaneously computed orientation, these three pieces of information can be used for mammogram segmentation including locating the inner breast edge which is important for quantitative breast density assessment. Due to the contrast invariant property of the local phase, the algorithm proves to be very reliable on an extensive datasets of images obtained from various sources and digitized by different scanners.