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Original Research| Volume 88, P48-56, January 2018

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

Open AccessPublished:November 28, 2017DOI:https://doi.org/10.1016/j.ejca.2017.10.022
Mammographic density and breast cancer risk in breast screening assessment cases and women with a family history of breast cancer
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      Highlights

      • •
        Three different breast density measures compared: visual, Quantra and Volpara.
      • •
        All density measures showed a positive association with presence of cancer.
      • •
        The strongest effect was seen with Volpara absolute density measure.
      • •
        A stronger effect of Volpara density on risk was observed for large and grade 3 cancers.

      Abstract

      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.

      Keywords

      1. Introduction

      High breast density has been shown to be a strong, independent risk factor for breast cancer [
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      Individual and combined effects of age, breast density, and hormone replacement therapy use on the accuracy of screening mammography.
      Ann Intern Med. 2003; 138: 168-175
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      Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis.
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      • Boyd N.F.
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      • Fishell E.
      • et al.
      Mammographic density and the risk and detection of breast cancer.
      N Engl J Med. 2007; 356: 227-236
      ,
      • Boyd N.
      • Martin L.
      • Gunasekara A.
      • Melnichouk O.
      • Maudsley G.
      • Peressotti C.
      • et al.
      Mammographic density and breast cancer risk: evaluation of a novel method of measuring breast tissue volumes.
      Cancer Epidemiol Biomarkers Prev. 2009; 18: 1754-1762
      ]. It has been reported that women with a high breast density compared to women with a low breast density have a four- to sixfold increased risk of developing the disease [
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      • Ursin G.
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      • et al.
      Mammographic features and breast cancer risk: effects with time, age, and menopause status.
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      • Fentiman I.S.
      • et al.
      Mammographic features and subsequent risk of breast cancer: a comparison of qualitative and quantitative evaluations in the Guernsey prospective studies.
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      ,
      • Ziv E.
      • Shepherd J.
      • Smith-Bindman R.
      • Kerlikowske K.
      Mammographic breast density and family history of breast cancer.
      J Natl Cancer Inst. 2003; 95: 556-558
      ]. High breast density has also been linked to cancers which are larger and have positive lymph nodes, although the reported results vary considerably [
      • Aiello E.J.
      • Buist D.S.
      • White E.
      • Porter P.L.
      Association between mammographic breast density and breast cancer tumor characteristics.
      Cancer Epidemiol Biomarkers Prev. 2005; 14: 662-668
      ,
      • Sala E.
      • Solomon L.
      • Warren R.
      • McCann J.
      • Duffy S.
      • Luben R.
      • et al.
      Size, node status and grade of breast tumours: association with mammographic parenchymal patterns.
      Eur Radiol. 2000; 10: 157-161
      ,
      • Roubidoux M.A.
      • Bailey J.E.
      • Wray L.A.
      • Helvie M.A.
      Invasive cancers detected after breast cancer screening yielded a negative result: relationship of mammographic density to tumor prognostic factors.
      Radiology. 2004; 230: 42-48
      ,
      • Kerlikowske K.
      • Zhu W.
      • Hubbard R.A.
      • Geller B.
      • Dittus K.
      • Braithwaite D.
      • et al.
      Outcomes of screening mammography by frequency, breast density, and postmenopausal hormone therapy.
      JAMA Intern Med. 2013; 173: 807-816
      ,
      • Duffy S.W.
      • Tabar L.
      • Smith R.A.
      • Krusemo U.B.
      • Prevost T.C.
      • Chen H.H.
      Risk of breast cancer and risks with breast cancer: the relationship of histologic type with epidemiology, disease progression and survival.
      Semin Breast Dis. 1999; 2: 292-300
      ] and high breast density has been found in women with cancers diagnosed outside of the screening programme [
      • Carney P.A.
      • Miglioretti D.L.
      • Yankaskas B.C.
      • Kerlikowske K.
      • Rosenberg R.
      • Rutter C.M.
      • et al.
      Individual and combined effects of age, breast density, and hormone replacement therapy use on the accuracy of screening mammography.
      Ann Intern Med. 2003; 138: 168-175
      ,
      • Boyd N.F.
      • Guo H.
      • Martin L.J.
      • Sun L.
      • Stone J.
      • Fishell E.
      • et al.
      Mammographic density and the risk and detection of breast cancer.
      N Engl J Med. 2007; 356: 227-236
      ,
      • Mandelson M.T.
      • Oestreicher N.
      • Porter P.L.
      • White D.
      • Finder C.A.
      • Taplin S.H.
      • et al.
      Breast density as a predictor of mammographic detection: comparison of interval- and screen-detected cancers.
      J Natl Cancer Inst. 2000; 92: 1081-1087
      ,
      • Kerlikowske K.
      • Grady D.
      • Barclay J.
      • Sickles E.A.
      • Ernster V.
      Effect of age, breast density, and family history on the sensitivity of first screening mammography.
      JAMA. 1996; 276: 33-38
      ,
      • Chiarelli A.M.
      • Kirsh V.A.
      • Klar N.S.
      • Shumak R.
      • Jong R.
      • Fishell E.
      • et al.
      Influence of patterns of hormone replacement therapy use and mammographic density on breast cancer detection.
      Cancer Epidemiol Biomarkers Prev. 2006; 15: 1856-1862
      ]. One possible explanation for the latter is a masking bias, in that dense breast tissue could render breast cancers less sensitive to screen detection, leading to a higher incidence of breast cancer in those previously screened negative. A number of studies, however, indicate that this is only partly responsible for the observed increased cancer risk with high density [
      • McCormack V.A.
      • dos Santos Silva I.
      Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis.
      Cancer Epidemiol Biomarkers Prev. 2006; 15: 1159-1169
      ,
      • Vachon C.M.
      • van Gils C.H.
      • Sellers T.A.
      • Ghosh K.
      • Pruthi S.
      • Brandt K.R.
      • et al.
      Mammographic density, breast cancer risk and risk prediction.
      Breast Cancer Res. 2007; 9: 217
      ,
      • van Gils C.H.
      • Otten J.D.
      • Verbeek A.L.
      • Hendriks J.H.
      Mammographic breast density and risk of breast cancer: masking bias or causality?.
      Eur J Epidemiol. 1998; 14: 315-320
      ]. Indeed, density has been shown to be a risk factor for screen-detected as well as symptomatic cancers [
      • Boyd N.F.
      • Guo H.
      • Martin L.J.
      • Sun L.
      • Stone J.
      • Fishell E.
      • et al.
      Mammographic density and the risk and detection of breast cancer.
      N Engl J Med. 2007; 356: 227-236
      ,
      • Vachon C.M.
      • van Gils C.H.
      • Sellers T.A.
      • Ghosh K.
      • Pruthi S.
      • Brandt K.R.
      • et al.
      Mammographic density, breast cancer risk and risk prediction.
      Breast Cancer Res. 2007; 9: 217
      ].
      There is no consensus on the most useful measure of breast composition in risk prediction, risk management and surveillance decisions. One meta-analysis found that absolute rather than proportional estimates of breast density are more strongly predictive of risk [
      • McCormack V.A.
      • dos Santos Silva I.
      Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis.
      Cancer Epidemiol Biomarkers Prev. 2006; 15: 1159-1169
      ], whereas another found the opposite [
      • Pettersson A.
      • Graff R.E.
      • Ursin G.
      • Santos Silva I.D.
      • McCormack V.
      • Baglietto L.
      • et al.
      Mammographic density phenotypes and risk of breast cancer: a meta-analysis.
      J Natl Cancer Inst. 2014; 106
      ].
      Younger, pre- or perimenopausal women are known to have a higher proportion of dense breast tissue, as breast density decreases with age [
      • Kolb T.M.
      • Lichy J.
      • Newhouse J.H.
      Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations.
      Radiology. 2002; 225: 165-175
      ,
      • Fletcher S.W.
      • Elmore J.G.
      Clinical practice. Mammographic screening for breast cancer.
      N Engl J Med. 2003; 348: 1672-1680
      ]. The National Health Service Breast Screening Programme (NHSBSP) in the United Kingdom (UK) invites women aged 50–70 every 3 years for two-view digital mammography which is double read [
      • Health & Social Care Information Centre
      Breast Screening Programme, England Statistics for 2014–15.
      2016
      ]. Extension of the age range to 47–73 is currently under investigation. Women at moderate risk with a significant family history of breast cancer may be screened annually from age 40 [
      • National Collaborating Centre for Cancer. Clinical Guideline
      Familial breast cancer: classification and care of people at risk of familial breast cancer and management of breast cancer and related risks in people with a family history of breast cancer.
      2013
      ].
      Issues outstanding in breast density include:
      • •
        identifying the breast density measure (percent density, absolute quantity of dense tissue) most strongly associated with breast cancer;
      • •
        the method of measurement (visual, automated volumetric measures, automated area measures) most strongly associated with cancer;
      • •
        age and tumour-specific associations with risk;
      • •
        the extent to which density contributes risk information in subjects already known to be at higher risk of breast cancer, such as women attending for screening who are recalled for assessment due to a suspicious mammographic finding (and which measure of density is most suitable in this population).
      Also, it is worth noting that the identification of mammographic density as a risk factor took place in the predigital era, and most of the studies demonstrating the effect of density on breast cancer risk pertain to measures from film/screen mammography. There is a current need to demonstrate and validate measures of breast composition from digital mammography which are equally strongly associated with breast cancer risk.
      In this study, we assess the associations of visual percent density assessment and automated volumetric breast composition measures with breast cancer risk in women recalled for assessment in the general population screening programme and in women aged 40–50 years under increased mammographic surveillance due to a family history of breast cancer. Women in the latter category are those at moderate or high familial risk of breast cancer, defined as a lifetime risk of at least 17% [
      • National Collaborating Centre for Cancer. Clinical Guideline
      Familial breast cancer: classification and care of people at risk of familial breast cancer and management of breast cancer and related risks in people with a family history of breast cancer.
      2013
      ].

      2. Materials and methods

      In the TOMMY trial (TOMosynthesis with digital MammographY in the UK NHS Breast Screening Programme), participants were recruited from six centres [
      • Gilbert F.J.
      • Tucker L.
      • Gillan M.G.
      • Willsher P.
      • Cooke J.
      • Duncan K.A.
      • et al.
      The TOMMY trial: a comparison of TOMosynthesis with digital MammographY in the UK NHS Breast Screening Programme – a multicentre retrospective reading study comparing the diagnostic performance of digital breast tomosynthesis and digital mammography with digital mammography alone.
      Health Technol Assess. 2015; 19 (i-xxv): 1-136
      ]. They comprised women aged 47–73 years recalled to an assessment clinic and also women below 50 years of age with a family history of breast cancer who attended annual mammography screening. Data were available for 6020 breast screening assessment cases (of whom 1158 had breast cancer) and 1040 family history screenees (of whom two had breast cancer), who had been recruited between February 2011 and August 2013. On recruitment, each woman had a two-dimensional (2D) mammogram as part of the digital breast tomosynthesis (DBT) examination. Both the DBT and the standard 2D imaging were performed as a single procedure at the same breast compression on a Hologic Selenia Dimensions Digital Mammography Unit (Hologic Inc., Bedford, MA, United States of America ). These research images were read by trained radiologists blinded to the knowledge of cancer status of the woman-screenee, using full field digital mammography (2D) and the DBT. To score visual density, readers used a visual analogue scale (VAS), requiring them to make a mark on a 10-cm line which was subsequently converted to a percentage score between 0% and 100% [
      • Sukha A.
      • Berks M.
      • Morris J.
      • Boggis C.
      • Wilson M.
      • Barr N.
      • et al.
      Visual assessment of density in digital mammograms.
      in: Martí J. Oliver A. Freixenet K. Martí R. Digital mammography. Proceedings of the 10th International Workshop, IWDM June 16–18; 2010. Springer, Girona, Catalonia, Spain: Heidelberg2010: 466-473
      ]. Visual percent density was estimated for each woman by one of 26 image readers using information from the available mammograms from the examination without knowledge of cancer status (although the readers were of course able to see abnormalities). In the family history cases, density was also scored by an additional reader and the mean of the two results was used. Although visual assessment of density is subject to inter- and intraobserver variability [
      • Boyd N.
      • Martin L.
      • Gunasekara A.
      • Melnichouk O.
      • Maudsley G.
      • Peressotti C.
      • et al.
      Mammographic density and breast cancer risk: evaluation of a novel method of measuring breast tissue volumes.
      Cancer Epidemiol Biomarkers Prev. 2009; 18: 1754-1762
      ,
      • Nicholson B.T.
      • LoRusso A.P.
      • Smolkin M.
      • Bovbjerg V.E.
      • Petroni G.R.
      • Harvey J.A.
      Accuracy of assigned BI-RADS breast density category definitions.
      Acad Radiol. 2006; 13: 1143-1149
      ], reasonable agreement was observed between the readers, with absolute differences of less than 10% in 70% of cases [
      • Morrish O.W.
      • Tucker L.
      • Black R.
      • Willsher P.
      • Duffy S.W.
      • Gilbert F.J.
      Mammographic breast density: comparison of methods for quantitative evaluation.
      Radiology. 2015; 275: 356-365
      ]. The readers had a minimum of two years' experience of reading at least 5000 cases annually in the NHSBSP.
      In addition to radiological, clinical and pathological data, we also measured breast density using two automated volumetric tools, Volpara™ version 1.4.2 [
      • Highnam R.
      • Sauber N.
      • Destounis S.
      • Harvey J.
      • McDonald D.
      Breast density into clinical practice.
      in: Maidment A.D.A. Bakic P.R. Gavenonis S. Breast density. Proceedings of the 11th International Workshop, IWDM July 8–11; 2012. Springer-Verlag, Philadelphia, PA, USA: Berlin2012: 466-473
      ] and Quantra™ version 2.0 [
      • Skippage P.
      • Wilkinson L.
      • Allen S.
      • Roche N.
      • Dowsett M.
      • A'Hern R.
      Correlation of age and HRT use with breast density as assessed by Quantra™.
      Breast J. 2013; 19: 79-86
      ] and by visual assessment. All breast density measures were performed on 2D mammography.
      Age was coded for 6985 (99%) of the 7060 cases. Ages of the subjects ranged from 29 to 85, with 96% of subjects aged 40–70. Volpara breast composition data were available for 7019 of the 7060 cases (1157 of the 1160 cancer patients and 5862 non-cancer patients). Corresponding Quantra data were available for 7005 of the cases (1156 cancer patients and 5849 non-cancer patients). Visually assessed percent density was available for 6969 cases (including 1153 cancer patients). None of the three methods gave a complete set of results for all cases as the software tools did not produce scores for every image analysed and other clinical pressures occasionally took precedence over the requirement to give a density score. However, this occurred in only 0.6% and 0.8% of cases in Volpara and Quantra respectively.
      The output of both software tools gave measurements of total breast volume, dense fibroglandular volume and percent volumetric breast density for each image. The craniocaudal (CC) and the mediolateral-oblique (MLO) images of each breast were analysed. To obtain a single score for each woman, the CC and MLO scores were utilised as follows. For cases where no cancer was assessed as being present, the largest breast volume and fibroglandular volume for each breast (either from the CC or MLO view) were determined and the average of each of these volumes of the two breasts were calculated. For cases where cancer was confirmed, results were used from the contralateral breast. If no contralateral data were available, results from the affected breast were used. This occurred for one cancer case in the Quantra data (0.1% of cancers) and 14 cases in the Volpara (1% of cancers). Volumetric percent density was calculated, as 100 times the ratio of the fibroglandular tissue volume to the overall breast volume.
      To evaluate the association of breast composition measures with risk, data were analysed by logistic regression with breast cancer as the outcome variable and the various density and volume measures as predictor variables, adjusted for age. A major negative confounder of area or volumetric percent density is body mass index (BMI). In the NHSBSP, weight and height are not traditionally recorded. We had, however, weight and height data for a small subset of 178 recruits for which we calculated BMI. While this did not provide sufficient data to adjust the regression models, we analysed this subset and found that:
      • (1)
        BMI and total breast volume as measured by Volpara had very similar negative correlations with percent density measures; and
      • (2)
        within this subset of the data, adjusting the effects of percent density measures on breast cancer risk for total breast volume gave almost identical results to adjusting for BMI.
      See Appendix for detailed numerical results.
      We then compared the predictive potential of the measures using standardised logistic regression coefficients, so that all measures pertained to the same scale. Using the most predictive measure, we then estimated effects in subgroups of age, invasive status, node status, size and grade of the cancers diagnosed, all determined histologically, radiological features (mass, calcification, or either asymmetry or architectural distortion, as determined by the readers), and detection status by 2D mammography and DBT. Data were analysed using STATA version 10.0 [
      • StataCorp
      Stata statistical software: release 10.
      StataCorp LP, College Station, TX2007
      ].

      3. Results

      Table 1 shows the mean and standard deviation of breast composition measures using Volpara, by age, cancer status and non-cancer source (assessment or family history screenee). The dense tissue volume was generally higher in cancer cases than in non-cancer cases, and declined with age in all groups. The percent density showed the same tendencies, although less markedly. Table 2 shows the corresponding figures for Quantra, exhibiting a similar pattern. Table 3 shows the mean and standard deviation of visually assessed percent density by age, cancer status and non-cancer source. This showed a distinct decline with age for both cancer and non-cancer cases. However, in those aged 60 or over, the cancer cases had a slightly lower percent density than the non-cancer cases.
      Table 1Means (SD's) of breast composition measures using the Volpara volumetric breast density measurement (Volpara Health Technologies Ltd), by age and diagnostic group, in 6944 assessment cases, including 1149 cancers in the UK Breast Screening Programme.
      Age (years)Breast composition measureMean (SD) for population
      CancersAssessment

      non-cancers      		Family history

      non-cancers      		All

      non-cancers
      <50Breast volume (cm3)1063 (663)1027 (673)1041 (668)1037 (669)
      Dense volume (cm3)111 (53)101 (67)101 (61)101 (63)
      % Density13 (7)12 (6)12 (7)12 (7)
      No. of subjects293139421255
      50–59Breast volume (cm3)1153 (670)1034 (614)983 (597)1033 (613)
      Dense volume (cm3)94 (54)84 (50)84 (51)84 (50)
      % Density10 (6)9 (5)10 (5)10 (5)
      No. of subjects4603092433135
      ≥60Breast volume (cm3)1092 (562)1010 (557)582 (75)1009 (556)
      Dense volume (cm3)77 (44)73 (43)53 (20)73 (43)
      % Density8 (4)8 (4)9 (2)8 (4)
      No. of subjects660140231405
      Table 2Means (SD's) of breast composition measures using Quantra volumetric breast density measurement (Hologic), by age and diagnostic group, in 6930 assessment cases, including 1148 cancers in the UK Breast Screening Programme.
      Age (years)Breast composition measureMean (SD) for population
      CancersAssessment

      non-cancers      		Family history

      non-cancers      		All

      non-cancers
      <50Breast volume (cm3)1118 (723)1075 (696)1088 (681)1085 (685)
      Dense volume (cm3)143 (99)142 (128)137 (99)138 (107)
      % Density14 (7)14 (7)14 (7)14 (7)
      No. of subjects293139381251
      50–59Breast volume (cm3)1195 (670)1079 (636)1044 (602)1078 (636)
      Dense volume (cm3)131 (93)114 (99)111 (82)114 (88)
      % Density12 (6)11 (6)13 (9)11 (6)
      No. of subjects4593088433131
      ≥60Breast volume (cm3)1126 (582)1054 (570)612 (121)1052 (570)
      Dense volume (cm3)104 (71)97 (76)57 (41)97 (76)
      % Density9 (5)9 (5)9 (5)9 (5)
      No. of subjects660139731400
      Table 3Means (SD's) of visually assessed percent density, by age and diagnostic group, in 6969 assessment cases, including 1153 cancers in the UK Breast Screening Programme.
      Age (years)QuantityMean (SD) for population
      CancersAssessment

      non-cancers      		Family history

      non-cancers      		All

      non-cancers
      <50% Density46 (19)43 (18)42 (22)42 (21)
      No. of subjects293149151229
      50–59% Density42 (22)40 (21)37 (18)40 (21)
      No. of subjects4613067413108
      ≥60% Density33 (19)35 (20)40 (22)35 (20)
      No. of subjects6631432471479
      Table 4 shows the age-adjusted standardised logistic regression coefficients for the automated measures of dense tissue volume and the visually assessed percent density. The three measures that used percentages were also adjusted for Volpara total breast volume. The strongest effect in terms of both coefficient and significance was that of Volpara absolute dense tissue volume, corresponding to a 3% increase in the odds of cancer per additional 10 cm3 of dense tissue (95% CI 1–5%). The effect of Quantra dense tissue volume was slightly weaker but very similar. The confidence intervals on the two standardised estimates indicate that the difference is compatible with chance.
      Table 4Age-adjusted standardised logistic regression coefficients for effects of Volpara (Volpara Health Technologies Ltd), Quantra (Hologic) and visually assessed breast composition measures on risk of breast cancer in approximately 6900 (varying depending on numbers with missing data) assessment cases in the UK Breast Screening Programme.
      Breast composition measureStandardised logistic regression coefficient95% CIExact significance
      VolparaAbsolute dense volume0.160.09–0.22p = 0.000002
      Percent dense volume0.090.00–0.17p = 0.04
      QuantraAbsolute dense volume0.150.09–0.22p = 0.000003
      Percent dense volume0.140.06–0.21p = 0.0003
      VisualPercent dense area0.090.01–0.16p = 0.02
      Table 5 shows the age-adjusted odds ratios by quintile of the two measures of dense tissue volume, both showing a moderate but highly significant increase in risk across quintiles. There was an approximate doubling of risk for the highest quintile compared to the lowest.
      Table 5Odds ratios for breast cancer by quintile of absolute dense volume by Volpara (Volpara Health Technologies Ltd) and Quantra (Hologic) in approximately 6900 (depending on numbers with missing data) assessment cases in the UK Breast Screening Programme.
      Volpara dense volume (cm3)OR95% CIQuantra dense volume (cm3)OR95% CI
      <481.00–<541.00–
      48–63.991.451.17–1.7954–78.991.461.17–1.82
      64–82.991.531.23–1.9079–110.991.651.32–2.05
      83–114.991.501.19–1.86111–159.991.631.30–2.04
      ≥1151.881.50–2.35≥1602.061.65–2.57
      Table 6 shows the results of subgroup analyses of the association of Volpara dense tissue volume with breast cancer risk. For the most part, the effect of the volume of dense tissue was similar in subgroups to that overall, but a number of observations arise. The increased risk with this measure was for the most part apparent within the subgroups considered. The effect was slightly higher in the presence of calcifications, in tumours missed by 2D mammography, in node positive tumours, in larger tumours (>20 mm, and to a lesser extent in tumours of size 11–20 mm) and in grade 3 cancers. For the radiological indications, the effect of density on risk of tumours appearing as calcifications was statistically significant, and for tumours appearing as either asymmetry or architectural distortion the effect was of borderline significance.
      Table 6Odds ratios per 10 cm3 of dense tissue as measured by Volpara (Volpara Health Technologies Ltd) within subgroups of 7019 assessment cases in the UK Breast Screening Programme.
      VariableSubgroupOR per 10 cm395% CISignificance
      Age (years)<501.020.97–1.07p = 0.3
      50–591.041.02–1.07p < 0.001
      ≥601.021.00–1.05p = 0.03
      Dominant radiological featureMass1.000.96–1.04p = 0.8
      Calcification1.041.02–1.07p < 0.001
      Asymmetry/architectural distortion1.031.00–1.06p = 0.03
      Recalled by 2D mammographyNo1.041.00–1.07p = 0.01
      Yes1.031.01–1.05p < 0.001
      Recalled by 2D + DBTNo1.010.96–1.06p = 0.5
      Yes1.031.01–1.05p = 0.001
      Recalled by synthetic 2D + DBTNo1.020.98–1.06p = 0.4
      Yes1.031.00–1.05p = 0.002
      Invasive statusNon-invasive1.031.00–1.06p = 0.01
      Invasive1.031.01–1.05p < 0.001
      Node status of tumour (invasive only)Negative1.021.00–1.05p = 0.01
      Positive1.031.01–1.06p < 0.001
      Size of tumour

      (invasive only)      		1–10 mm1.010.98–1.04p = 0.6
      11–20 mm1.031.00–1.05p = 0.004
      >20 mm1.061.03–1.08p < 0.001
      Histological grade of tumour (invasive only)11.031.00–1.06p = 0.01
      21.021.00–1.05p = 0.02
      31.041.00–1.08p = 0.001
      Total population–1.031.01–1.05p < 0.001
      Notes Asymmetry/architectural distortion: forms of asymmetrical breast density visible from the mammogram.

      4. Discussion

      We found that automated volumetric measures of mammographic density added significantly to estimation of breast cancer risk in subjects already known to be at enhanced risk due to a screening finding or to family history. This adds to the evidence of breast density as a robust predictor of breast cancer risk. Notably, we found that the automated absolute measures were more strongly predictive of risk in this population than visually assessed percent density. The fact that automated measures were predictive indicates that density may have a role in risk management at population level. The NHSBSP screens more than two million women per year and, clearly to be practicable, any breast composition risk marker would have to be automatically derived with minimal human resource implications. Both commercially available products, Volpara and Quantra, showed predictive potential, with Volpara slightly stronger. Our risk gradients were not as strong as observed by others [
      • 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
      ]. This may be due to the fact that our non-cancer cases were at enhanced risk due to recall for assessment or family history and therefore may have had higher breast density than general population controls. Also, our study data did not include interval cancers. There are a higher proportion of interval cancers in dense breasts, and if these had been included this would have likely increased the risk gradient to the expected level. In a single Dutch screening centre of women in the 50–75 year old category, including interval cancers, the highest quartile of absolute density had a 2.5-fold risk compared to the lowest quartile [
      • Wanders J.O.P.
      • Holland K.
      • Karssemeijer N.
      • Peeters P.H.M.
      • Veldhuis W.B.
      • Mann R.M.
      • et al.
      The effect of volumetric breast density on the risk of screen-detected and interval breast cancers: a cohort study.
      Breast Cancer Res. 2017; 19: 67
      ].
      We did not have data on BMI, except for a small minority of cancers, so could not adjust for this in our analysis. However, as reported in the Appendix, in the subset with BMI data, total breast volume as assessed by Volpara displayed the same properties as BMI in terms of correlation with other breast composition measures and of adjustment of percent density measures. This raises an interesting issue. Traditionally, estimates of the effect of percent mammographic density on breast cancer risk are adjusted for BMI as the two are known to be strongly negatively confounded. The reason for this confounding may be the structural negative relationship between percent density and total breast size, since the latter is essentially the denominator of the former. Thus, BMI may be a surrogate for total breast volume rather than the reverse. In any case, results in the Appendix suggest that adjustment for total breast volume achieves the same effect in this context as adjustment for BMI.
      The finding that breast density is associated with increased risk of breast cancer in this specific population already known to be at enhanced risk is novel, but consistent with the literature. While studies vary in their findings as to which measure of density is most predictive of risk, the finding that increased levels of density are associated with increased risk of breast cancer is almost universal [
      • Carney P.A.
      • Miglioretti D.L.
      • Yankaskas B.C.
      • Kerlikowske K.
      • Rosenberg R.
      • Rutter C.M.
      • et al.
      Individual and combined effects of age, breast density, and hormone replacement therapy use on the accuracy of screening mammography.
      Ann Intern Med. 2003; 138: 168-175
      ,
      • McCormack V.A.
      • dos Santos Silva I.
      Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis.
      Cancer Epidemiol Biomarkers Prev. 2006; 15: 1159-1169
      ,
      • Boyd N.F.
      • Byng J.W.
      • Jong R.A.
      • Fishell E.K.
      • Little L.E.
      • Miller A.B.
      • et al.
      Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study.
      J Natl Cancer Inst. 1995; 87: 670-675
      ,
      • Boyd N.F.
      • Guo H.
      • Martin L.J.
      • Sun L.
      • Stone J.
      • Fishell E.
      • et al.
      Mammographic density and the risk and detection of breast cancer.
      N Engl J Med. 2007; 356: 227-236
      ,
      • Boyd N.
      • Martin L.
      • Gunasekara A.
      • Melnichouk O.
      • Maudsley G.
      • Peressotti C.
      • et al.
      Mammographic density and breast cancer risk: evaluation of a novel method of measuring breast tissue volumes.
      Cancer Epidemiol Biomarkers Prev. 2009; 18: 1754-1762
      ,
      • Vachon C.M.
      • van Gils C.H.
      • Sellers T.A.
      • Ghosh K.
      • Pruthi S.
      • Brandt K.R.
      • et al.
      Mammographic density, breast cancer risk and risk prediction.
      Breast Cancer Res. 2007; 9: 217
      ,
      • Ursin G.
      • Ma H.
      • Wu A.H.
      • Bernstein L.
      • Salane M.
      • Parisky Y.R.
      • et al.
      Mammographic density and breast cancer in three ethnic groups.
      Cancer Epidemiol Biomarkers Prev. 2003; 12: 332-338
      ,
      • Byrne C.
      • Schairer C.
      • Wolfe J.
      • Parekh N.
      • Salane M.
      • Brinton L.A.
      • et al.
      Mammographic features and breast cancer risk: effects with time, age, and menopause status.
      J Natl Cancer Inst. 1995; 87: 1622-1629
      ,
      • Torres-Mejia G.
      • De Stavola B.
      • Allen D.S.
      • Perez-Gavilan J.J.
      • Ferreira J.M.
      • Fentiman I.S.
      • et al.
      Mammographic features and subsequent risk of breast cancer: a comparison of qualitative and quantitative evaluations in the Guernsey prospective studies.
      Cancer Epidemiol Biomarkers Prev. 2005; 14: 1052-1059
      ,
      • Ziv E.
      • Shepherd J.
      • Smith-Bindman R.
      • Kerlikowske K.
      Mammographic breast density and family history of breast cancer.
      J Natl Cancer Inst. 2003; 95: 556-558
      ,
      • Aiello E.J.
      • Buist D.S.
      • White E.
      • Porter P.L.
      Association between mammographic breast density and breast cancer tumor characteristics.
      Cancer Epidemiol Biomarkers Prev. 2005; 14: 662-668
      ,
      • Sala E.
      • Solomon L.
      • Warren R.
      • McCann J.
      • Duffy S.
      • Luben R.
      • et al.
      Size, node status and grade of breast tumours: association with mammographic parenchymal patterns.
      Eur Radiol. 2000; 10: 157-161
      ]. It has generally been observed that quantitative measures of density are stronger predictors of breast cancer risk than qualitative [
      • McCormack V.A.
      • dos Santos Silva I.
      Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis.
      Cancer Epidemiol Biomarkers Prev. 2006; 15: 1159-1169
      ,
      • Torres-Mejia G.
      • De Stavola B.
      • Allen D.S.
      • Perez-Gavilan J.J.
      • Ferreira J.M.
      • Fentiman I.S.
      • et al.
      Mammographic features and subsequent risk of breast cancer: a comparison of qualitative and quantitative evaluations in the Guernsey prospective studies.
      Cancer Epidemiol Biomarkers Prev. 2005; 14: 1052-1059
      ]. It is known that density also impairs mammographic accuracy, which can mean that some tumours are missed at screening due to masking by high levels of density, and therefore subsequent incidence in this group is increased [
      • Roubidoux M.A.
      • Bailey J.E.
      • Wray L.A.
      • Helvie M.A.
      Invasive cancers detected after breast cancer screening yielded a negative result: relationship of mammographic density to tumor prognostic factors.
      Radiology. 2004; 230: 42-48
      ]. However, results from several studies indicate that there is also an effect of increased risk of breast cancer due to density which is not attributable to a masking phenomenon [
      • McCormack V.A.
      • dos Santos Silva I.
      Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis.
      Cancer Epidemiol Biomarkers Prev. 2006; 15: 1159-1169
      ,
      • Boyd N.F.
      • Guo H.
      • Martin L.J.
      • Sun L.
      • Stone J.
      • Fishell E.
      • et al.
      Mammographic density and the risk and detection of breast cancer.
      N Engl J Med. 2007; 356: 227-236
      ,
      • Vachon C.M.
      • van Gils C.H.
      • Sellers T.A.
      • Ghosh K.
      • Pruthi S.
      • Brandt K.R.
      • et al.
      Mammographic density, breast cancer risk and risk prediction.
      Breast Cancer Res. 2007; 9: 217
      ,
      • van Gils C.H.
      • Otten J.D.
      • Verbeek A.L.
      • Hendriks J.H.
      Mammographic breast density and risk of breast cancer: masking bias or causality?.
      Eur J Epidemiol. 1998; 14: 315-320
      ]. Recent results suggest that absolute measures of dense tissue area or volume have greater predictive power than percentage measures [
      • Haars G.
      • van Noord P.A.
      • van Gils C.H.
      • Grobbee D.E.
      • Peeters P.H.
      Measurements of breast density: no ratio for a ratio.
      Cancer Epidemiol Biomarkers Prev. 2005; 14: 2634-2640
      ,
      • Stone J.
      • Warren R.M.
      • Pinney E.
      • Warwick J.
      • Cuzick J.
      Determinants of percentage and area measures of mammographic density.
      Am J Epidemiol. 2009; 170: 1571-1578
      ,
      • Stone J.
      • Ding J.
      • Warren R.M.
      • Duffy S.W.
      • Hopper J.L.
      Using mammographic density to predict breast cancer risk: dense area or percentage dense area.
      Breast Cancer Res. 2010; 12: R97
      ], but this is not universally observed [
      • Yaghjyan L.
      • Colditz G.A.
      • Rosner B.
      • Tamimi R.M.
      Mammographic breast density and subsequent risk of breast cancer in postmenopausal women according to the time since the mammogram.
      Cancer Epidemiol Biomarkers Prev. 2013; 22: 1110-1117
      ].
      In this study, we found that absolute dense tissue volume was a stronger predictor than percent density. We also found that dense tissue volume was slightly more strongly predictive of cancers with unfavourable prognostic factors such as larger than 20 mm in size, grade 3 and node positive cancers. Confidence intervals were relatively wide in these subgroups, so interpretation should be done cautiously. Whether this is due to chance, to a true difference in the biological effect of high levels of density or to the masking effect of dense tissue remains to be seen, but it has been observed elsewhere [
      • Aiello E.J.
      • Buist D.S.
      • White E.
      • Porter P.L.
      Association between mammographic breast density and breast cancer tumor characteristics.
      Cancer Epidemiol Biomarkers Prev. 2005; 14: 662-668
      ,
      • Sala E.
      • Solomon L.
      • Warren R.
      • McCann J.
      • Duffy S.
      • Luben R.
      • et al.
      Size, node status and grade of breast tumours: association with mammographic parenchymal patterns.
      Eur Radiol. 2000; 10: 157-161
      ,
      • Roubidoux M.A.
      • Bailey J.E.
      • Wray L.A.
      • Helvie M.A.
      Invasive cancers detected after breast cancer screening yielded a negative result: relationship of mammographic density to tumor prognostic factors.
      Radiology. 2004; 230: 42-48
      ,
      • Kerlikowske K.
      • Zhu W.
      • Hubbard R.A.
      • Geller B.
      • Dittus K.
      • Braithwaite D.
      • et al.
      Outcomes of screening mammography by frequency, breast density, and postmenopausal hormone therapy.
      JAMA Intern Med. 2013; 173: 807-816
      ,
      • Bertrand K.A.
      • Scott C.G.
      • Tamimi R.M.
      • Jensen M.R.
      • Pankratz V.S.
      • Norman A.D.
      • et al.
      Dense and nondense mammographic area and risk of breast cancer by age and tumor characteristics.
      Cancer Epidemiol Biomarkers Prev. 2015; 24: 798-809
      ,
      • Yaghjyan L.
      • Pettersson A.
      • Colditz G.A.
      • Collins L.C.
      • Schnitt S.J.
      • Beck A.H.
      • et al.
      Postmenopausal mammographic breast density and subsequent breast cancer risk according to selected tissue markers.
      Br J Cancer. 2015; 113: 1104-1113
      ]. Again, inclusion of interval cancers (not possible in this study) would clarify issues of masking. A number of other studies, however, have not found a stronger association with less favourable biological tumour attributes [
      • Antoni S.
      • Sasco A.J.
      • dos Santos Silva I.
      • McCormack V.
      Is mammographic density differentially associated with breast cancer according to receptor status? A meta-analysis.
      Breast Cancer Res Treat. 2013; 137: 337-347
      ,
      • Ding J.
      • Warren R.
      • Girling A.
      • Thompson D.
      • Easton D.
      Mammographic density, estrogen receptor status and other breast cancer tumor characteristics.
      Breast J. 2010; 16: 279-289
      ,
      • Phipps A.I.
      • Buist D.S.
      • Malone K.E.
      • Barlow W.E.
      • Porter P.L.
      • Kerlikowske K.
      • et al.
      Breast density, body mass index, and risk of tumor marker-defined subtypes of breast cancer.
      Ann Epidemiol. 2012; 22: 340-348
      ,
      • Pollán M.
      • Ascunce N.
      • Ederra M.
      • Murillo A.
      • Erdozáin N.
      • Alés-Martínez J.
      • et al.
      Mammographic density and risk of breast cancer according to tumor characteristics and mode of detection: a Spanish population-based case-control study.
      Breast Cancer Res. 2013; 15: R9
      ]. Two of the latter studies did find an increased effect of density on interval cancers which would be expected to be larger and more likely to be node positive [
      • Ding J.
      • Warren R.
      • Girling A.
      • Thompson D.
      • Easton D.
      Mammographic density, estrogen receptor status and other breast cancer tumor characteristics.
      Breast J. 2010; 16: 279-289
      ,
      • Pollán M.
      • Ascunce N.
      • Ederra M.
      • Murillo A.
      • Erdozáin N.
      • Alés-Martínez J.
      • et al.
      Mammographic density and risk of breast cancer according to tumor characteristics and mode of detection: a Spanish population-based case-control study.
      Breast Cancer Res. 2013; 15: R9
      ]. The result may be particular to screen-detected cancers, since Ding et al. [
      • Ding J.
      • Warren R.
      • Girling A.
      • Thompson D.
      • Easton D.
      Mammographic density, estrogen receptor status and other breast cancer tumor characteristics.
      Breast J. 2010; 16: 279-289
      ] did find an increased effect of density on risk of larger tumours among their screen-detected cancers. This may have management or diagnostic implications. In our set of screen-detected cancers, larger, node positive cancers were found in the breasts with highest density. Some of these may have been missed at previous screens as a result of high density and had diagnosis considerably delayed. The UK breast screening programme has a relatively long three yearly screening interval. Therefore, screening frequency could be increased for women with more dense fibroglandular tissue in order to find the tumours at a smaller size, or DBT could be used in those women with highest breast volumetric density, as the addition of DBT was found to improve diagnostic accuracy in women with dense breasts in the TOMMY trial [
      • Gilbert F.J.
      • Tucker L.
      • Gillan M.G.
      • Willsher P.
      • Cooke J.
      • Duncan K.A.
      • et al.
      The TOMMY trial: a comparison of TOMosynthesis with digital MammographY in the UK NHS Breast Screening Programme – a multicentre retrospective reading study comparing the diagnostic performance of digital breast tomosynthesis and digital mammography with digital mammography alone.
      Health Technol Assess. 2015; 19 (i-xxv): 1-136
      ]. The adequacy of screening frequency depending on breast density (and possibly on other risk factors) is being currently studied by others, such as the PROCAS study [
      • Evans D.G.
      • Astley S.
      • Stavrinos P.
      • Harkness E.
      • Donnelly L.S.
      • Dawe S.
      • et al.
      Improvement in risk prediction, early detection and prevention of breast cancer in the NHS Breast Screening Programme and family history clinics: a dual cohort study.
      2016 Aug
      ].
      It is worth noting that women with dense breasts were more likely to undergo biopsy. In the lowest quintile of Volpara absolute dense volume, 33% of the assessment cases had a biopsy, whereas in the highest quintile, the figure was 41%. It is possible that the availability of DBT (and perhaps other imaging modalities) at assessment might avoid some unnecessary biopsies. Of the non-cancer cases who underwent biopsy, 39% were not marked for recall in rereading by DBT (compared to 29% in rereading with 2D mammography).
      The study population was a mix of breast screening assessment cases (85%) and family history screening cases (15%), who were all women at a higher risk of breast cancer by definition. In the TOMMY trial [
      • Gilbert F.J.
      • Tucker L.
      • Gillan M.G.
      • Willsher P.
      • Cooke J.
      • Duncan K.A.
      • et al.
      The TOMMY trial: a comparison of TOMosynthesis with digital MammographY in the UK NHS Breast Screening Programme – a multicentre retrospective reading study comparing the diagnostic performance of digital breast tomosynthesis and digital mammography with digital mammography alone.
      Health Technol Assess. 2015; 19 (i-xxv): 1-136
      ], family history screenees were included to provide a group of cases with higher breast density as a result of their lower average age for subanalysis of the impact of breast density on the diagnostic accuracy of DBT, and have therefore been kept in our analysis. Results were essentially unchanged when we excluded the family history screenees, so they apply specifically to women recalled for assessment due to a suspicious screening mammogram. One would expect a smaller effect of density on risk in assessment cases as these would be likely to have higher levels of density on average than the general population, as women with denser breasts have mammography results harder to read and tend to be recalled more often [
      • Moshina N.
      • Ursin G.
      • Roman M.
      • Sebuødegård S.
      • Hofvind S.
      Positive predictive values by mammographic density and screening mode in the Norwegian Breast Cancer Screening Program.
      Eur J Radiol. 2016; 85: 248-254
      ]. Our results indicate that the effect in this group, while smaller than observed in the general screened population [
      • McCormack V.A.
      • dos Santos Silva I.
      Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis.
      Cancer Epidemiol Biomarkers Prev. 2006; 15: 1159-1169
      ], is by no means negligible. There may be a role for density in the subsequent surveillance and risk management of women recalled for assessment but found not to have breast cancer. Optimisation of the screening technique such as the addition of DBT or increased frequency of screening may be relevant. Alternatively, women could be counselled and offered strategies to reduce their volume of fibroglandular tissue by other lifestyle changes such as stopping hormone replacement therapy, or by primary chemoprevention.
      In conclusion, we found that dense breast tissue volume as measured by automated methods was a significant predictor of breast cancer risk in women with a suspicious screening mammogram or a family history. This is consistent with findings that various measures of density can add predictive power to currently used breast cancer risk assessment tools [
      • Assi V.
      • Massat N.J.
      • Thomas S.
      • MacKay J.
      • Warwick J.
      • Kataoka M.
      • et al.
      A case-control study to assess the impact of mammographic density on breast cancer risk in women aged 40–49 at intermediate familial risk.
      Int J Cancer. 2015; 136: 2378-2387
      ,
      • Warwick J.
      • Birke H.
      • Stone J.
      • Warren R.M.
      • Pinney E.
      • Brentnall A.R.
      • 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
      ]. The fully automated methods can be used with little addition to human resource costs. Density is likely to have a role in risk management both in a population screening context and in management and surveillance of women at increased risk of breast cancer, and in particular can assist in identifying populations who might benefit from enhanced surveillance or primary prevention interventions [
      • Warwick J.
      • Birke H.
      • Stone J.
      • Warren R.M.
      • Pinney E.
      • Brentnall A.R.
      • 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
      ].

      Financial support

      The TOMMY trial was supported by a grant from the NIHR Health Technology Assessment Programme.

      Conflict of interest statement

      SWD has received research grant funding from Philips. JC has developed educational materials for Hologic. MJM has received research grant funding from Hologic. FJG has received research grant funding from GE Healthcare and payment for lectures from Bracco.

      Role of the funding source

      This work was supported by the National Institute for Health Research's Health Technology Assessment Programme [grant number 09/22/182]. The funder had no involvement in the design, conduct, analysis or interpretation.

      Acknowledgements

      We thank Ralph Highnam (Volpara Health Technologies Ltd.) and Ashwini Kshirsagar (Hologic) for their insights into the operation of their respective products, and large numbers of radiological and informatics staff in our institutions who made the study possible.

      Appendix A. Supplementary data

      The following is the supplementary data related to this article:

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      Article info

      Publication history

      Published online: November 28, 2017
      Accepted: October 22, 2017
      Received in revised form: October 13, 2017
      Received: November 7, 2016

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      DOI: https://doi.org/10.1016/j.ejca.2017.10.022

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