- Research article
- Open Access
Mammographic density assessed on paired raw and processed digital images and on paired screen-film and digital images across three mammography systems
Breast Cancer Researchvolume 18, Article number: 130 (2016)
Inter-women and intra-women comparisons of mammographic density (MD) are needed in research, clinical and screening applications; however, MD measurements are influenced by mammography modality (screen film/digital) and digital image format (raw/processed). We aimed to examine differences in MD assessed on these image types.
We obtained 1294 pairs of images saved in both raw and processed formats from Hologic and General Electric (GE) direct digital systems and a Fuji computed radiography (CR) system, and 128 screen-film and processed CR-digital pairs from consecutive screening rounds. Four readers performed Cumulus-based MD measurements (n = 3441), with each image pair read by the same reader. Multi-level models of square-root percent MD were fitted, with a random intercept for woman, to estimate processed–raw MD differences.
Breast area did not differ in processed images compared with that in raw images, but the percent MD was higher, due to a larger dense area (median 28.5 and 25.4 cm2 respectively, mean √dense area difference 0.44 cm (95% CI: 0.36, 0.52)). This difference in √dense area was significant for direct digital systems (Hologic 0.50 cm (95% CI: 0.39, 0.61), GE 0.56 cm (95% CI: 0.42, 0.69)) but not for Fuji CR (0.06 cm (95% CI: −0.10, 0.23)). Additionally, within each system, reader-specific differences varied in magnitude and direction (p < 0.001). Conversion equations revealed differences converged to zero with increasing dense area. MD differences between screen-film and processed digital on the subsequent screening round were consistent with expected time-related MD declines.
MD was slightly higher when measured on processed than on raw direct digital mammograms. Comparisons of MD on these image formats should ideally control for this non-constant and reader-specific difference.
Mammographic density (MD), a measure of the radiodense tissue in the breast, is a strong marker of breast cancer (BC) risk . MD is increasingly being incorporated into BC research and clinical practice, for example in BC risk prediction models , as a marker for the effectiveness of therapeutic drugs mediated through MD , and in risk-based stratification for tailored BC screening regimens . To enable these applications, estimates of differences in MD between women and within women over time are needed. However, obtaining directly comparable MD measurements is challenged by the fact that no single MD measurement tool is used universally; there are more than 10 quantitative methods currently in use [5–8]. Further, for the widely used threshold method, MD measurements are affected by well-documented reader variability [9, 10]. Less studied is the influence of the type of mammogram used for MD measurements. Images originate from a variety of imaging modalities and mammography systems; that is, from older screen-film mammography (SFM) or more recently from digital mammography.
Image quality differs between SFM and digital mammography—for example, in terms of object visibility and spatial resolution —and thus a reader’s assessment of threshold-based MD may also differ between these modalities. Further, digital images are acquired in a raw (‘for processing’) format, in which the greyscale is proportional to X-ray attenuation. The processed (‘for presentation’) image is a manipulation of the raw image to aid tumour detection, based on manufacturer-specific algorithms which are generally unspecified and thus irreversible. Because processing may suppress or enhance image features such as dense tissue, MD measurements may systematically differ between the original raw and the processed images. The raw image is often deleted and only a processed format is available for MD measurements. Further, differences in MD between raw and processed images may vary by the type of digital mammography; that is, computed radiography (CR, a digital extension of screen film) or direct digital.
Two previous studies of MD in raw–processed pairs showed different results. From a General Electric (GE) Senographe 2000D model, percent MD (PMD) was higher in processed than in raw images ; whereas on images captured on a GE Senographe DS model , PMD was lower in processed than in raw images for one reader, but not different for another reader. We are not aware of raw–processed MD comparisons for other mammography systems.
In the present study, we extended the examination of MD across three widely used digital mammography systems (GE and Hologic, both direct digital, and Fuji, a CR system) by comparing threshold-based MD measurements for the same mammogram saved in both raw and processed formats and estimating MD conversion equations between these formats. In a similar fashion, we examined differences in MD between digitized SFM and processed CR-digital images taken from the same woman during consecutive screening rounds.
Source of images
For raw–processed MD comparisons, we included women who had both raw and processed image pairs available; that is, the same mammogram from a single screening session was saved in both formats. To examine different digital mammography system manufacturers (hereafter ‘systems’) we acquired six sets from three systems (Table 1): two direct digital systems (Hologic: sets H1, H2 and H3; and GE: sets G1 and G2) and a Fuji CR system (set F1). Hologic images were all captured on Lorad Selenia models whereas the GE images were captured on different models; Senographe 2000D, DS and Essential. Image sets originated from the Chilean Cohort Study of Breast Cancer Risk  (set H1), the Bahcesehir Mammographic Screening project in Turkey  (set H2), screening mammograms from the H. Lee Moffitt Cancer Center, Florida, USA (sets H3 and G1)  and the East London Breast Screening Programme, UK (set G2) . These five sets reflect populations with nearly 3-fold differences in BC incidence rates . In contrast, set F1 is a pooled resource of anonymized Fuji CR images taken for 100 women in 2008, on which both right craniocaudal (CC) and left CC images were saved in both formats (400 images). Other than age for 47 women, no other information was known about these women. Thus whilst all other sets were from BC-free women, we cannot guarantee this status for set F1. All mammograms were taken between 2007 and 2013. Two sets, H1 and G2, also contributed to the International Consortium on Mammographic Density (ICMD) .
For the comparison of MD assessed on SFM and digital mammography (Table 1, set F2, BreastScreen Victoria, Australia), we obtained pairs of view and laterality-matched films for the same 139 woman who were screened on SFM at one screening round and on a digital CR Fuji system at the next, a median of 2.1 years later (range 1.2–2.5 years).
Ethics approvals were obtain from IARC (IEC 12–34 for the ICMD) and from contributing studies.
To improve readability of raw images, greyscale levels were transformed using a log-inversion implemented in Niftyview . This process creates a ‘positive’ image out of the raw ‘negative’ and restores the approximately linear relationship between image intensity and tissue density exhibited by SFM. MD was measured in Cumulus version 3 or 6, in which the reader selects the threshold to dichotomize dense and non-dense pixels. These versions give equivalent MD measurements, but differ in ease of use for the reader. Measures obtained are areas (cm2) of the breast, the dense area (DA) and the non-dense area, and PMD, calculated as:
Image sets were read by four experienced readers (VAM, Id-S-S, NFB and JH) in combinations dependent on permissions for inter-institutional image transfers. Sets H1, H2 and G2 were distributed randomly into 12 batches of 100 images (six raw and six processed batches) and allocated randomly to three readers. Each pair was read by the same reader. Each batch included three within-batch repeats and five images from each batch were repeated in the other two readers’ batches. The Fuji images (F1) and the SFM-digital image set (F2) were mainly read by a single reader. Sets H3 and G1 were not transferred between institutions, but had been measured previously by one reader as published previously .
Twelve image pairs were excluded because one or both images were indicated for exclusion upon MD measurement (e.g. due to low image quality, breast implants).
The primary outcome is PMD (%), and secondary outcomes are DA and breast area. For each of these, we used a square-root transformation (e.g. √PMD) to normalize distributions . The interpretation of these measures can be aided by considering each area as a square, thus √DA and √breast area are the width in centimetres of the square. Similarly, √PMD can be thought of as the width of the dense square for a 10 cm × 10 cm breast area.
For each image format, within-reader reliability of √MD was assessed using the intraclass correlation coefficient:
Between-women variance (σ2 b) and within-reader variance (σ2 w) were estimated in ANOVA models fitted on sets H1, H2, G2, F1 and F2 and all of the ICMD measurements combined. Sets H3 and G1 did not have within-reader repeats.
To estimate within-pair raw–processed differences in MD, we fitted multi-level normal-error regression models of √MD, where the fixed effect of image format was level 1 and a random intercept for woman was level 2. The assumption of a constant difference in √MD across the MD range was examined using Bland–Altman plots. Subgroup analyses were conducted by reader, system, model and processing software version, and by PMD and breast area categories and possible effect modification tested using likelihood ratio tests. These potential effect modifiers are features of the image or of the imaging process; woman-level characteristics such as body mass index (BMI) or age were not investigated, because potential effect modification would be mediated through image characteristics.
A similar approach was used to compare SFM and digital processed images for set F2.
Calibration equations for conversion between MD measured on raw and processed images, and vice versa, were based on √DA because all √PMD differences were driven through √DA whilst the change in √breast area was negligible (<1 mm). Standard regression models were not used as they assume error only in the dependent variable, which results in a fitted model that is not reversible (i.e. predicting raw from processed would give a different outcome to predicting processed from raw). Because there is measurement error in MD assessment on both raw and processed films, we applied a reversible conversion method. The principle of this calibration method was to maintain, for each reader and system combination, equality of the standard normal z scores of √DA whether they were assessed on a processed image (z p) or a raw image (z r):
where x̄ and s are the mean and standard deviation for the image type respectively. This method yields the following conversion equation:
In total, 1294 raw–processed digital image pairs (2588 images) were analysed: 676 pairs captured on Hologic Lorad Selenia direct digital systems (CC and mediolateral oblique (MLO)), 418 on GE Senographe direct digital systems (CC and MLO) and 200 from Fuji CR (CC only) (Table 1). For digital image pairs, women were aged from 26 to 87 years at mammography (mean 55.1, SD 12.8) and the median BMI was 26.2 kg/m2 but varied between sets. Median overall PMD ranged between 15.4 and 24.8% and median DA ranged between 23.6 and 30.4 cm2 (Table 2) and reader-specific median measures are given in (Additional file 1: Table S1). Visual examination of sample raw–processed image pairs shows different degrees of accentuation of breast features and of the skin edge (Fig. 1).
Within-reader reliability of PMD was slightly higher in SFM (ICC 0.94, 95% confidence interval (CI): 0.93, 0.95) than in raw digital (ICC 0.91, 95% CI: 0.89, 0.93) or processed digital (ICC 0.89, 95% CI: 0.88, 0.91) images. This difference generally held across readers (Table 3) and was driven by higher within-reader repeatability from SFM than when measuring from digital images. In contrast, whilst readers 1 and 3 had higher ICCs for PMD and DA assessed on raw images than on processed images, this was reversed for reader 2. Reader 1 ICCs for PMD and DA did not differ between image formats for the Fuji CR or Hologic systems, whereas for GE images the ICCs were lower on processed than on raw images. Throughout, ICCs for PMD predominantly reflected those for DA because breast area ICCs were near 100% for all image formats, readers and systems (Table 3). Based on the subset of images that were read by all readers, mean raw-processed MD measures and correlation coefficients by reader are given in Additional file 2: Table S2 and correlations between readers by image type in (and Additional File 3: Table S3.
For processed–raw digital image pairs, the median PMD was higher when measured on processed images than on raw images, by 1.7–3.3 absolute percentage points depending on the system (Table 2). Similarly, the median DA was larger by 1.6–4.6 cm2, whereas the median breast area was similar. Regression results were similar: √PMD was 0.34 cm (95% CI: 0.28, 0.40) larger in processed images than in raw images, whilst √DA was 0.44 cm (95% CI: 0.36, 0.52) larger and √breast area did not differ (0.01 cm; 95% CI: −0.01, 0.02) (Table 4). These differences in PMD were approximately one-fifth of the between-women SD (Table 3). For a given reader, PMD and DA differences varied in magnitude between systems (heterogeneity p < 0.01 for readers 1–3, p = 0.21 for reader 4), and for a given system the differences varied in both magnitude and direction between readers (p < 0.001 for each system). Specifically, for readers 1, 3 and 4, √PMD was larger in processed than in raw images by 0.4–0.9 cm (reader 1), 0.1–0.7 cm (reader 3) and 0.4–0.6 cm (reader 4), depending on the system. In contrast, √PMD in processed compared with raw images for reader 2 was either not different (GE) or was smaller (Fuji CR system and Hologic). Mean √DA from processed images was 0.9 (95% CI: 0.7, 1.1) higher for reader 2 and 0.9 (95% CI: 0.7, 1.1) higher for reader 3 compared with reader 1. Between-reader differences were larger for raw images; mean √DA was 2.3 (95% CI: 1.9, 2.8) higher for reader 2 and 1.9 (95% CI: 1.4, 2.3) higher for reader 3 compared with reader 1. For SFM, between-reader differences were slightly smaller; mean √DA was 1.3 (95% CI: 1.1, 1.4) higher for reader 2 and 0.7 (95% CI: 0.5, 0.8) higher for reader 3 compared with reader 1. Breast area differences also varied between system–reader combinations, but average differences were extremely small in magnitude (<1.2 mm √breast area). Differences by model or processing software within a system were not significant (data not shown). Effect modification of DA and PMD differences by categories of PMD or of breast area (categories defined by the raw image) were significant (p < 0.001 for both). The differences tended to decrease with increasing PMD, but they increased with increasing breast area (Additional File 4: Table S4).
Most scatter plots (Fig. 2) showed that differences in DA on processed images compared with raw images are larger at lower DAs, and converge towards no difference in breasts with a √DA of ≥5 cm. Bland–Altman plots also revealed that processed–raw differences in √PMD and √DA (Additional File 5: Figure S1) were not constant across the underlying MD range. However differences were constant on the standardized scale (shown for DA in Additional File 6: Figure S2), and thus calibration equations were based on standardized values of DA in the two image types. Figure 2 (Additional file 7: Information 1) presents these reader-specific and system-specific calibration equations for DA. Differences were very small for the Fuji CR and were larger and of a similar magnitude between the direct digital systems. For all readers combined, conversion equations from raw DA to their processed equivalent are as follows:
Hologic: processed √DA = 5.252 + 0.719 (raw √DA – 4.751)
GE: processed √DA = 5.081 + 0.872 (raw √DA – 4.523)
Fuji: processed √DA = 5.694 + 1.107 (raw √DA – 5.633)
After correcting DA, the corrected non-dense area and PMD would then be calculated using the original breast area and preserving the original definitions:
Equations to generate √DA, as if measured on a raw image, from DA measured on a processed image are provided in (Additional file 7: Information 1).
For the processed–SFM set (F2), comparing MD measured on the processed digital image with that on the earlier SFM, √breast area was 0.17 cm larger (95% CI: 0.06, 0.28) and √DA was 0.17 cm smaller (95% CI: 0.01, 0.33).
In the present study, we compared Cumulus-assessed MD measures (PMD, breast area and DA) on the same digital mammograms saved in processed and raw formats. Overall, we observed higher MD in the former image type, a difference that was not entirely consistent either in magnitude or direction across four readers for a given mammography system. Differences in MD assessed on raw and processed images were small for the CR system, but larger for direct digital systems. Differences between SFM and CR-digital images appeared to be small, although the latter were not time-matched comparisons. Readers had higher MD repeatability for SFM images than for raw or processed digital images. This may be because readers had more experience of reading from SFM images, or because density is more easily visualized in SFM images.
Comparison and plausibility
Readers noted several appearance qualities of processed images that may affect the MD assessment, such as ‘thickened breast edge’ or ‘faded parenchyma’. Processing algorithms involve multiple steps designed to clarify the image, enhance suspected lesions and reduce noise—this noise may be dense tissue, therefore it has been hypothesized that density would be lower in processed images. However, this and similar studies generally found higher MD on processed images, particularly at lower density levels. Enhancement of light/dark transitions and accentuation of the breast edge may contribute to this increase. Differences in PMD were almost entirely driven by changes in the DA because breast area altered minimally. Our results are also consistent with those of Keller et al. , and Martin et al. , who reported that differences were highly reader dependent. Unsurprisingly, Vachon et al.’s results , which comprised 14% of our raw–processed pairs, also found that PMD was overestimated in less dense breasts in processed compared with raw GE images. Studies that compared MD using the BIRADS classification did not find differences by image type , but differences may be too small to be detected using a broad categorical classification.
Differences in MD assessment between SFM and Fuji CR were not assessed optimally, because they were based on films taken 2 years apart. While there was no breast area difference in the time-matched images, over this time interval the breast area increased indicating measurable age-related changes. The magnitude of this increase (0.17 cm √breast area) was consistent with the expected within-woman changes (0.16 cm over 2 years) found in a previous SFM-only longitudinal study . Similarly, the decline in DA was only slightly larger than would be expected from age-related changes (−0.13 cm √DA), suggesting that any differences due to image formats were small (at most 0.04 cm). However, similar studies comparing PMD in SFM and digital mammography reported that PMD was higher in SFM images than in raw or processed digital images , including one in which the digital and the SFM were taken on the same day . In both studies the differences were larger than for the present study, possibly because they were comparing SFM with direct digital and not with CR as in the present study. Breast area was also higher in digital images taken on the same day as SFM images, indicating that lower PMD assessment may be a product of both underestimation of DA and overestimation of breast area in digital images compared with SFM images. Harvey  hypothesized that more subcutaneous fat is included in digital measurements because the breast edge can be seen and delimited more precisely, but only PMD was reported in that study. In the present study, small differences between SFM and CR may reflect these closely related imaging technologies; CR systems are additions to SFM systems, using phosphor plates and a separate reader to create digital images, whereas the direct digital image is created at the point of image capture . Thus, CR images have lower spatial resolution and more image noise than direct digital images . The improved image quality in direct digital allows for more complex multi-functional processing algorithms, which may account for the larger raw–processed differences in direct digital images compared with CR images.
Strengths and limitations
This is the first study to compare raw and processed images, using the same design and analytic approach, captured on several widely used mammography systems. Comparisons of MD across multiple systems are important because it is unlikely that all women in a study, or the same woman followed for several years, will be screened on the same mammography machine. Nevertheless, several design features would have improved the study; by including CC views alongside MLO for all images, and including other widely used mammography systems such as Siemens, and other CR systems. We were limited by the lack of information on manipulations performed by processing algorithms which are proprietary to manufacturers. Multiple readers are a further strength, being reflective of clinical and research settings—between-reader differences in raw–processed calibration highlight the need to recognize and quantify these differences where possible. Further, we used a reversible statistical method for processed–raw MD conversions; that is, neither raw nor processed MD is considered the error-free independent variable, which would not have been the case had a simple regression method been used. Finally, the women included in this study came from countries with a wide range of BC incidence rates, and thus the results should be generalizable to women across the BC risk spectrum.
Relevance and implications
The potential impact of raw–processed differences in MD from direct-digital systems (3.3 percentage points) will depend on the application. When investigating MD as a predictor of BC risk, differences are unlikely to introduce substantial misclassification between very low density (<10%) and very high density (e.g. >50%) and would thus have a small impact on relative risk estimates. For investigations of determinants of MD or changes in MD, raw–processed differences are of a magnitude similar to 10 years of aging or the menopause-related PMD change (as assessed within ICMD) and depend greatly on the reader. Thus, in the screening or clinical setting when assessing MD change over time for the same woman, it is important that the same reader reads the woman’s repeat mammograms. If the calibration equations presented in this article are to be used in the screening or clinical settings, they will need to be validated, particularly for different readers. In studies comparing PMD across raw and processed image types, correcting for these differences is thus important and would ideally be made using reader-specific and system-specific calibrations. Even if all images are of the same type (raw or processed) it is necessary to calibrate between readers. Comparability of raw images between systems has not been assessed and difference in acquisition between systems may be present. The repeated finding across studies of large between-reader differences in MD, in addition to their time-intensive nature, again emphasizes the need for fully-automated methods of MD measurement. Four such fully automated quantitative methods were recently evaluated for BC risk prediction, alongside Cumulus . Although such methods eliminate between-reader variations in readings, many only work on a single image type (often raw digital images ), but others can be applied across multiple types [8, 26]. It is possible that there would be between-system differences in automated measures, particularly volumetric measures due to differences in breast positioning and therefore breast thickness , but not all studies have found this . In the future, as further processing algorithms are developed, MD differences between raw and processed images are likely not only to persist but also to change. However, as digital storage becomes cheaper and faster, such problems may be overcome if raw images are systematically stored and MD is consistently measured on them. In a similar fashion, a consistent and fully-automated MD measurement tool could be applied to the raw image bank to provide MD data in an efficient and systematic manner.
Processed ‘for presentation’ direct digital mammograms have, on average, a higher Cumulus-assessed PMD and dense area compared with their corresponding raw ‘for processing’ images, whilst such differences were small for CR systems. Raw–processed differences in the direct digital systems depended on mammography system and to a large extent on reader, as did absolute density readings for a given image type. Controlling for these factors is necessary when comparing density readings across image types. For detection of small differences in density (e.g. within-woman changes), reader-specific processed to raw calibration, or restriction of comparisons to readings made by the same reader and on the same image type may be necessary.
Body mass index
Intra-class correlation coefficient
International Consortium on Mammographic Density
Percent mammographic density
Boyd NF, Guo H, Martin LJ, Sun L, Stone J, Fishell E, et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med. 2007;356:227–36.
Warwick J, Birke H, Stone J, Warren RM, Pinney E, Brentnall AR, et al. Mammographic breast density refines Tyrer-Cuzick estimates of breast cancer risk in high-risk women: findings from the placebo arm of the International Breast Cancer Intervention Study I. Breast Cancer Res. 2014;16:451.
Cuzick J, Warwick J, Pinney E, Duffy SW, Cawthorn S, Howell A, et al. Tamoxifen-induced reduction in mammographic density and breast cancer risk reduction: a nested case–control study. J Natl Cancer Inst. 2011;103:744–52.
Schousboe JT, Kerlikowske K, Loh A, Cummings SR. Personalizing mammography by breast density and other risk factors for breast cancer: analysis of health benefits and cost-effectiveness. Ann Intern Med. 2011;155:10–20.
Nickson C, Arzhaeva Y, Aitken Z, Elgindy T, Buckley M, Li M, et al. AutoDensity: an automated method to measure mammographic breast density that predicts breast cancer risk and screening outcomes. Breast Cancer Res. 2013;15:R80.
Nguyen TL, Aung YK, Evans CF, Yoon-Ho C, Jenkins MA, Sung J, et al. Mammographic density defined by higher than conventional brightness threshold better predicts breast cancer risk for full-field digital mammograms. Breast Cancer Res. 2015;17:142.
Eng A, Gallant Z, Shepherd J, McCormack V, Li J, Dowsett M, et al. Digital mammographic density and breast cancer risk: a case–control study of six alternative density assessment methods. Breast Cancer Res. 2014;16:439.
Keller BM, Chen J, Daye D, Conant EF, Kontos D. Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: comparison of fully automated area and volumetric density measures in a case–control study with digital mammography. Breast Cancer Res. 2015;17:117.
Woolcott CG, Conroy SM, Nagata C, Ursin G, Vachon CM, Yaffe MJ, et al. Methods for assessing and representing mammographic density: an analysis of 4 case–control studies. Am J Epidemiol. 2014;179:236–44.
Keller BM, Nathan DL, Gavenonis SC, Chen J, Conant EF, Kontos D. Reader variability in breast density estimation from full-field digital mammograms: the effect of image postprocessing on relative and absolute measures. Acad Radiol. 2013;20:560–8.
Kuzmiak CM, Pisano ED, Cole EB, Zeng D, Burns CB, Roberto C, et al. Comparison of full-field digital mammography to screen-film mammography with respect to contrast and spatial resolution in tissue equivalent breast phantoms. Med Phys. 2005;32:3144–50.
Vachon CM, Fowler EE, Tiffenberg G, Scott CG, Pankratz VS, Sellers TA, et al. Comparison of percent density from raw and processed full-field digital mammography data. Breast Cancer Res. 2013;15:R1.
Garmendia ML. Alarming weight gain in women of a post-transitional country. 2014.
Kayhan A, Gurdal SO, Ozaydin N, Cabioglu N, Ozturk E, Ozcinar B, et al. Successful first round results of a Turkish breast cancer screening program with mammography in Bahcesehir, Istanbul. Asian Pac J Cancer Prev. 2014;15:1693–7.
Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136:E359–86.
McCormack VA, Burton A, Dos-Santos-Silva I, Hipwell JH, Dickens C, Salem D, et al. International Consortium on Mammographic Density: methodology and population diversity captured across 22 countries. Cancer Epidemiol. 2016;40:141–51.
University College London CfMIC. NiftyView. NiftyView and NiftK Translational Medical Imaging Platform. 2015. http://cmictig.cs.ucl.ac.uk/research/software/software-nifty. Accessed Feb 2014.
Mariapun S, Li J, Yip CH, Taib NA, Teo SH. Ethnic differences in mammographic densities: an Asian cross-sectional study. PLoS One. 2015;10, e0117568.
Martin LJ, Gunasekara A, Melnichouk O, Minkin S, Peressotti C, Mawdsley G, et al. Comparison of mammographic density measured using film and digital mammograms. In: 5th International Workshop on Breast Densitometry, San Francisco, US. June 2011. Abstract.
Keller BM, Nathan DL, Wang Y, Zheng Y, Gee JC, Conant EF, et al. Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation. Med Phys. 2012;39:4903–17.
McCormack VA, Perry NM, Vinnicombe SJ, Dos Santos Silva I. Changes and tracking of mammographic density in relation to Pike's model of breast tissue aging: a UK longitudinal study. Int J Cancer. 2010;127:461.
Harvey JA. Quantitative assessment of percent breast density: analog versus digital acquisition. Technol Cancer Res Treat. 2004;3:611–6.
Bosmans H, De HA, Lemmens K, Zanca F, Thierens H, Van OC, et al. Technical and clinical breast cancer screening performance indicators for computed radiography versus direct digital radiography. Eur Radiol. 2013;23:2891–8.
Chiarelli AM, Edwards SA, Prummel MV, Muradali D, Majpruz V, Done SJ, et al. Digital compared with screen-film mammography: performance measures in concurrent cohorts within an organized breast screening program. Radiology. 2013;268:684–93.
Jefferys M, Harvey J, Highnam R. Comparing a new volumetric breast density method (Volpara™) to Cumulus. In: Martí J, Oliver A, Freixenet J, Martí R, editors. Digital Mammography: 10th International Workshop, IWDM 2010; Girona, Catalonia, Spain; June 16–18, 2010. Berlin: Springer; 2010. p. 408–13.
Busana MC, Eng A, Denholm R, Dowsett M, Vinnicombe S, Allen S, et al. Impact of type of full-field digital image on mammographic density assessment and breast cancer risk estimation: a case–control study. Breast Cancer Res. 2016;18:96.
Tyson AH, Mawdsley GE, Yaffe MJ. Measurement of compressed breast thickness by optical stereoscopic photogrammetry. Med Phys. 2009;36:569–76.
Damases CN, Brennan PC, McEntee MF. Mammographic density measurements are not affected by mammography system. J Med Imaging (Bellingham). 2015;2:015501.
The authors would like to thank BreastScreen Victoria and Dr Ralph Highnam for the facilitation of image acquisition. Previous studies were supported by: Australia—Australian National Breast Cancer Foundation (to JSt), MCCS by VicHealth, Cancer Council Victoria and Australian NHMRC grants 209057, 251553 and 504711, and cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database; Canada—National Cancer Institute of Canada (to NFB); Chile—Fondecyt 11100238 (to MLG), 1120326, 1130277 and 3130532, World Cancer Research Fund 2010/245, Ellison Medical Foundation Grant (to AP); Iran—Isfahan University of Medical Sciences, and assistance from Dr Vida Razavi and Dr Shamila Razavi; Israel—The Israel Cancer Association; Republic of Korea—Asan Medical Center, Seoul, Republic of Korea, Grant No. 2010–0811; Malaysia—Sime Darby LPGA Tournament, Ministry of Education University Malaya High Impact Research Grant UM.C/HIR/MOHE/06 and University Malaya Research Grant (UMRG Grant No.: RP046B-15HTM); Mexico—Ministry of Education of Mexico and ISSSTE’s Medical Directorate staff and regional office in Jalisco for technical and administrative support, National Council of Science and Technology (Mexico) and the American Institute for Cancer Research (10A035); the Netherlands—EPIC-NL-Europe against Cancer Programme of the European Commission (SANCO), Dutch Ministry of Health, Dutch Cancer Society, ZonMW the Netherlands Organisation for Health Research and Development, and the World Cancer Research Fund (WCRF); Poland—Polish–Norwegian Research Programme (PNRF–243–AI–1/07); Singapore—Clinician Scientist Award from National Medical Research Council and National University Cancer Institute Singapore (NCIS) Centre grant programme from National Medical Research Council; South Africa—Pink Drive; Spain—Spain’s Health Research Fund (Fondo de Investigacion Santiaria) PI060386 and PS09/0790, and Spanish Federation of Breast Cancer Patients (FECMA) EPY1169-10; Turkey—Roche Mustahzarlari San. A.S., Istanbul, Turkey; UK—EPSRC and EP/K020439/1 (JHH), Breast Cancer Campaign (2007MayPR23), Cancer Research UK (G186/11; C405/A14565), Da Costa Foundation; USA—National Cancer Institute R01CA85265, R37 CA54281, R01 CA97396, P50 CA116201, R01 CA177150 and R01 CA140286, Cancer Center Support Grant CA15083, CA131332, CA124865, UM1 CA186107 and UM1 CA176726 and the Susan G. Komen Foundation.
This work was supported by the US National Cancer Institute at the National Institutes of Health (R03CA167771) and by the International Agency for Research on Cancer.
Availability of data and materials
The data that support the findings of this study are available from the ICMD. Upon agreement of ICMD collaborators, these data can be accessed and analysed at IARC.
ABur, GB, JSt, RMT, JHe, CV, VO, AP, MLG, CS, JHH, CD, JSc, MEA, KB, AK, GGG, JHo, BP-G, MP, S-HT, SM, NAMT, ML, RL-R, MR, IR, AAF, GU, SQ, HM, EL, RS, MS, JWL, JK, DS, RK, MH, HM, K-SC, CN, SV, RN, CHvG, JOPW, BP, ABuk, SA, SV, SM, AMC, LL, GM, MJY, NFB, Id-S-S and VAM made substantial contributions to acquisition of data, and critical review of the manuscript. ABur and VAM conceived of the study, coordinated the data acquisition and analysis, and drafted the manuscript. VAM, Id-S-S and NFB assessed MD on the acquired images. GB participated in planning the statistical methods. ABur, GB, JSt, RMT, NFB, Id-S-S and VAM made substantial contributions to conception and design, analysis and interpretation of data. All authors read and approved the final manuscript.
ML received a non-restricted investigator-initiated grant from AstraZeneca and minor support from Swiss Re.
Consent for publication
Ethics approval and consent to participate
Ethics approvals were obtained from IARC (IEC 12–34 for the ICMD) for the consortium and from each contributing study from local institutions. Original studies had written informed consent from individual women (all studies), or a waiver to access anonymized images and data (Egypt, Turkey and Israel).
is Table S1 presenting percent density, dense area and total breast area in raw–processed image pairs and in SFM–processed digital image pairs, by reader. (DOC 33 kb)
is Table S2 presenting mean MD measures of inter-reader repeats, by reader and image type. (DOC 29 kb)
is Table S3 presenting correlation of MD measures in inter-reader repeats. (DOC 29 kb)
is Table S4 presenting mean differences in MD measures between processed images and the corresponding raw digital image, by percent density and breast area categories. (DOC 30 kb)
is Figure S1 showing Bland–Altman plots for vMD measures, by mammography system and reader for: (A) percent mammographic density, (B) dense area and (C) breast area. Y axes to the same scale for comparisons. (DOCX 138 kb)
is Figure S2 showing Bland–Altman plots for within-system and within-reader standardized vDA measures. (DOCX 79 kb)
is Information 1 showing calibration equations for the conversion of raw vDA to processed vDA, and Information 2 showing calibration equations for the conversion of processed vDA to raw vDA. (DOC 41 kb)