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A comparison of five methods of measuring mammographic density: a case-control study

  • Susan M. Astley1, 2Email author,
  • Elaine F. Harkness1, 2,
  • Jamie C. Sergeant3, 4,
  • Jane Warwick5,
  • Paula Stavrinos2,
  • Ruth Warren6,
  • Mary Wilson2,
  • Ursula Beetles2,
  • Soujanya Gadde2,
  • Yit Lim2,
  • Anil Jain2, 7,
  • Sara Bundred2,
  • Nicola Barr2,
  • Valerie Reece2,
  • Adam R. Brentnall8,
  • Jack Cuzick8,
  • Tony Howell2, 9 and
  • D. Gareth Evans2, 9, 10
Contributed equally
Breast Cancer Research201820:10

https://doi.org/10.1186/s13058-018-0932-z

Received: 22 June 2017

Accepted: 5 January 2018

Published: 5 February 2018

Abstract

Background

High mammographic density is associated with both risk of cancers being missed at mammography, and increased risk of developing breast cancer. Stratification of breast cancer prevention and screening requires mammographic density measures predictive of cancer. This study compares five mammographic density measures to determine the association with subsequent diagnosis of breast cancer and the presence of breast cancer at screening.

Methods

Women participating in the “Predicting Risk Of Cancer At Screening” (PROCAS) study, a study of cancer risk, completed questionnaires to provide personal information to enable computation of the Tyrer-Cuzick risk score. Mammographic density was assessed by visual analogue scale (VAS), thresholding (Cumulus) and fully-automated methods (Densitas, Quantra, Volpara) in contralateral breasts of 366 women with unilateral breast cancer (cases) detected at screening on entry to the study (Cumulus 311/366) and in 338 women with cancer detected subsequently. Three controls per case were matched using age, body mass index category, hormone replacement therapy use and menopausal status. Odds ratios (OR) between the highest and lowest quintile, based on the density distribution in controls, for each density measure were estimated by conditional logistic regression, adjusting for classic risk factors.

Results

The strongest predictor of screen-detected cancer at study entry was VAS, OR 4.37 (95% CI 2.72–7.03) in the highest vs lowest quintile of percent density after adjustment for classical risk factors. Volpara, Densitas and Cumulus gave ORs for the highest vs lowest quintile of 2.42 (95% CI 1.56–3.78), 2.17 (95% CI 1.41–3.33) and 2.12 (95% CI 1.30–3.45), respectively. Quantra was not significantly associated with breast cancer (OR 1.02, 95% CI 0.67–1.54). Similar results were found for subsequent cancers, with ORs of 4.48 (95% CI 2.79–7.18), 2.87 (95% CI 1.77–4.64) and 2.34 (95% CI 1.50–3.68) in highest vs lowest quintiles of VAS, Volpara and Densitas, respectively. Quantra gave an OR in the highest vs lowest quintile of 1.32 (95% CI 0.85–2.05).

Conclusions

Visual density assessment demonstrated a strong relationship with cancer, despite known inter-observer variability; however, it is impractical for population-based screening. Percentage density measured by Volpara and Densitas also had a strong association with breast cancer risk, amongst the automated measures evaluated, providing practical automated methods for risk stratification.

Keywords

Breast densityCase-controlRiskCancerPROCAS

Background

High mammographic density, the relative proportion of fibroglandular to fatty tissue in the breast, reduces the effectiveness of mammographic screening [14] and increases risk of developing breast cancer [5, 6]. The relationship of density with risk was established using expert visual assessment of film mammograms [7], with computer-assisted methods providing more reproducible estimates [8, 9]. With increasing uptake of full-field digital mammography (FFDM), the association between automated density assessment methods and cancer risk is under investigation [1012].

The most widely used method of assessing mammographic density in the USA is the Breast Imaging Reporting And Data System (BI-RADS) categorisation, where experts assign mammograms to one of four classes, the upper two being considered “dense”’ [13]. The class descriptors were changed in 2013 to better identify women whose cancers may be masked by dense parenchymal tissue [14]. Visual assessment of percentage density may be recorded on visual analogue scales (VAS), providing a continuous measure. This yielded a strong relationship with breast cancer risk for film mammograms, with an odds ratio (OR) of approximately 7 for 76–100% density relative to 0–25% [15]. Likewise, Boyd’s 6-class categorisation of percent visual density gave a relative risk in the highest category (> 75% dense) compared with the lowest of 6.05 (95% confidence interval (CI) = 2.82–12.97) in a case-control study with 354 cases [8]. Cumulus, a semi-automated thresholding method, was developed to improve reproducibility [8, 9] and has a well-established relationship with cancer risk [8, 12]. However, this method also requires trained observers, and whilst separating the breast from the mammogram background is reproducible, judgement of the best threshold to separate dense tissue from fat is less so. Boyd category, VAS and Cumulus are all relative, area-based methods, so density estimates can vary depending on breast positioning and patient weight [8, 16]. Weight change disproportionately alters the fatty component of the breast [17] and percentage density measures should be adjusted to take body mass index (BMI) into account [18].

Now that digital mammography is standard throughout the UK, volumetric measures of mammographic density, made by calibrating pixel values in the raw (“for processing”) FFDM image using a model of x-ray physics and imaging parameters [19], are now available. These can be expressed either in percentage terms (volumetric percent dense) or as absolute measures of dense and non-dense tissue.

The availability of fully automated density assessment paves the way for risk stratification in screening [20], allowing selection of the most appropriate imaging modality and screening frequency for the individual [21, 22]. The addition of mammographic density to breast cancer risk models based on other risk factors has demonstrated increased predictive power, depending on the method used for density estimation [2325]. It is therefore important to determine which density methods are suitable for risk-adapted screening; more accurate risk prediction will enable better targeting of risk-reducing interventions including chemoprevention and lifestyle modification [26, 27].

A previous case-control analysis, carried out in London, compared density measured in the unaffected (contralateral) breast in 414 women diagnosed with unilateral breast cancer at one hospital with that of 685 unmatched controls attending routine breast screening. Comparing the highest percentage density quintile with the lowest, and adjusting for age, BMI and reproductive variables, the strongest association with risk of developing breast cancer was for Volpara, with an OR of 8.26 (95% CI 4.28–15.96), followed by Quantra, OR 3.94 (2.26–6.86) and Cumulus, OR 3.38 (2.00–5.72) [13]. However, mammographic density was assessed at the time of detection of cancer, so the ability of density to predict women who would later develop the disease was not assessed. Here we address this by evaluating the association between five mammographic density methods and the presence of cancer at the time of screening, and the association between four mammographic density methods and cancer detected subsequently, either between screening rounds or at a later screen, using data from the Predicting Risk of Cancer At Screening study (PROCAS) [20].

Methods

Study design

Women invited to the Greater Manchester Breast Screening Service for routine 3-yearly mammographic screening between October 2009 and March 2015 were also invited to participate in the “Predicting Risk Of Cancer At Screening” (PROCAS) study, which aimed to provide women with a personalised risk estimate of their breast cancer risk based on mammographic density and classic breast cancer risk factors obtained via a questionnaire and quantified by the Tyrer-Cuzick risk score [28]. After October 2012 only women attending their first (prevalent round) screen were invited. At the time of recruitment informed consent was obtained from all participants.

In order to assess density using fully automated methods, the raw FFDM (for processing) image data from GE Senographe Essential mammography systems was obtained. Cancers (invasive and ductal carcinoma in situ) were identified through hospital records or through the North West Cancer Intelligence Service; women who moved out the area were considered ineligible. Two case-control datasets were created. In study 1, cases were women with breast cancer detected at the screen on entry to PROCAS and in study 2 cases were women who were breast cancer free at the screen on entry to PROCAS but had breast cancer detected subsequently, either between screening rounds or at a later screen. In these women we analysed the density of the screen on entry to PROCAS.

Three controls without cancer were matched to each cancer case based on age (±12 months), BMI category (missing, < 24.9, 25.0–29.9, 30+ kg/m2), hormone replacement therapy (HRT) use (current vs never/ever) and menopausal status (premenopausal, perimenopausal or postmenopausal). In both studies all controls had a subsequent cancer-free screening mammogram so it was unlikely that early signs of cancer were visible, and in study 2, controls were also matched on year of mammogram at entry.

Mammographic density measurement

Visual estimation of percentage density

Processed FFDM images were displayed on Planar Dome E5 5MP self-calibrating high-resolution monitors. Two of nineteen readers (usually a consultant radiologist or breast physician and an advanced practitioner radiographer) independently recorded density estimates on a paper form showing four 10-cm horizontal VAS, one for each view, labelled 0% and 100% at the ends of the scale. Forms were read using custom software and visual percentage density calculated. VAS readings were averaged between readers and views, and analysed in quintiles and as Boyd categories (0%, > 0–10%, > 10–25%, > 25–50%, > 50–75% and > 75%) [8]. Due to the small number of cases in the highest category (three in study 1 and six in study 2), the top two Boyd categories were combined for analysis. Intra-observer and inter-observer agreement for 120 mammograms randomly selected across deciles of VAS density scores, from the PROCAS study, were assessed by 11 readers, on two occasions, 3 years apart. The majority of readers had excellent intra-observer agreement (intraclass correlation coefficient (ICC) > 0.80), and inter-observer agreement for consistency was excellent (ICC = 0.82) and was substantial for absolute agreement (ICC = 0.69) [29].

Cumulus

Cumulus (Sunnybrook Health Sciences Centre, Toronto, ON, Canada) density assessment was undertaken by a single reader (JS) trained in August 2010 and validated by a member of the PROCAS team (JW) who had herself been trained by the group that developed the software. Reader performance was validated on test sets of data developed for this purpose by the trainers. Processed FFDM images were analysed. Cumulus was undertaken on a single contralateral mediolateral oblique (MLO) view of a subset of the study 1 dataset comprising 311 screen-detected cancers and their matched controls. The reader was blind to case-control status.

Quantra™

Quantra version 2.0 (Hologic Inc, Bedford, MA, USA) was used to assess density from the raw FFDM images for each view, each breast and each woman, giving breast and fibroglandular tissue volume (cm3), and the dense tissue area as a percentage of breast volume. It also provides a quantized BI-RADS-like score for each view and per breast.

Volpara™

Volpara Density Algorithm 1.5.0 (Volpara Health Technologies, Wellington, New Zealand) was also used to assess density from the raw FFDM images for each view, giving breast volume and fibroglandular tissue volume (cm3) and percentage density by volume. Volpara provided a macro, which produced per-patient results including Volpara Density Grade (VDG 4th and 5th Edition), designed to correlate with BI-RADS 4th and 5th Edition [15]. This also computes the percentage density of the two breasts following outlier removal.

Densitas™

Densitas version 2.0.0 (Densitas Inc, Halifax, NS, Canada) analyses processed FFDM images, giving breast and fibroglandular area (cm2) and percentage density by area for each image and per patient. It also produces per-patient measures of BIRADS 4th and 5th edition [15].

Statistical methods

In study 1, mammographic density was assessed in the contralateral breast in women with cancer and the breast on the same side in matched controls, whereas in study 2, density was assessed in both breasts at entry to PROCAS and the average was used.

Categorical data were compared using the chi-square test for proportions. For ordinal variables, a chi-square test for trend was also conducted. Continuous variables were assessed by the median and Mann-Whitney U test.

The relationship between density assessment and case-control status was analysed using conditional logistic regression. Density measures were modelled as quintiles based on the density distributions of controls, and also as continuous measures, transformed to approximately follow a normal distribution (square root transformation for VAS and Cumulus, and a logarithm transformation for Volpara, Quantra and Densitas). Univariate models were fitted initially, and multivariate models fitted to adjust for the logarithm 10-year Tyrer-Cuzick (v.6) risk score. In study 2 we also adjusted for parity, due to imbalance between cases and controls. We also performed an analysis in a subset of women who had been assessed using all density methods to determine which model performed best and differences between models were compared using the likelihood-ratio chi square. The matched concordance (mC) index, a modification of the concordance index (or area under the receiving operator characteristic curve (AUC)) for matched case-control studies, gives an average concordance index within matched groups (where 1.0 would indicate perfect discrimination after allowing for matching factors) with empirical bootstrap confidence intervals [30], was calculated to compare the discrimination performance of risk factors. All p values were two-sided. Analysis was performed in SPSS version 22 [31] and R 3.3.1 [32].

Results

Of the 57,905 women recruited to PROCAS, raw FFDM image data were available for 44,658 women (77%). Unavailability of raw FFDM images was predominantly due to the use of film mammography initially. There were 1004 cases of cancer occurring after consent up to November 2015, of which 704 were included in the analysis. The excluded women comprised 39 women with a pre-existing diagnosis of breast cancer, 13 with synchronous bilateral breast cancer, 118 with film mammograms and 130 with FFDM but for which raw image data was unavailable. Of the 704 women eligible for the analysis, 366 were women with breast cancer detected at the screen on entry to PROCAS (study 1) and 338 were women who were found to be breast cancer free at the screen on entry to PROCAS but had breast cancer detected subsequently, either between screening rounds or at a later screen (study 2). Of the latter, 114 women developed an interval cancer within 5–46 months of entry (IQR 13–31) and 224 women had breast cancer detected at a subsequent screen 17–55 months after entry (IQR 35–38).

Matching was satisfactory for both studies (Table 1). There was a difference in 10-year Tyrer-Cuzick score, with the score higher in cases (study 1, 2.95 vs 2.72, p = 0.003; study 2: 2.91 vs 2.63, p < 0.001). The reported rate of a previous breast biopsy in cases was 17.8% (study 1) and 22.5% (study 2), and in controls it was 14.5% (study 1) and 15.1% (study 2). The difference in biopsy rate between cases and controls was statistically significant in study 2 (p = 0.005), but was similar (in study 2) to the PROCAS study as a whole. In study 1 significantly fewer cases than controls reported being of “white” ethnic origin (91.3% vs 94.5%, p = 0.003), and fewer cases than controls reported having children in study 2 (85.8% vs 90.2%, p = 0.023).
Table 1

Demographics of study participants at time of recruitment to the Predicting Risk of Cancer At Screening (PROCAS) study*

 

Study 1

 

Study 2

 

PROCAS

Control subjects

Case subjects

 

Control subjects

Case subjects

 

(n = 57902)

(n = 1098)

(n = 366)

 

(n = 1014)

(n = 338)

 

Number

Percent

Number

Percent

P value

Number

Percent

Number

Percent

P value

Number

Percent

Age at consent (years)

 < 50

117

10.7

40

10.9

 

50

4.9

17

5.0

 

7173

12.4

 50–54

316

28.8

104

28.4

 

203

20.0

66

19.5

 

16962

29.3

 55–59

157

14.3

53

14.5

 

179

17.7

61

18.0

 

11046

19.1

 60–64

237

21.6

79

21.6

 

315

31.1

105

31.1

 

11223

19.4

 65–69

197

17.9

66

18.0

 

210

20.7

70

20.7

 

8552

14.8

 70+

74

6.7

24

6.6

1.000

57

5.6

19

5.6

1.000

2946

5.1

 Median (IQR)

58

51–64

58

51–64

0.988

61

55–65

61

55–65

0.965

57

51–63

Menopausal status

 Perimenopausal

170

15.5

57

15.6

 

145

14.3

49

14.5

 

10760

18.6

 Postmenopausal

747

68.0

248

67.8

 

769

75.8

256

75.7

 

37201

64.2

 Premenopausal

136

12.4

46

12.6

 

67

6.6

22

6.5

 

6869

11.9

 Unknown

45

4.1

15

4.1

1.000

33

3.3

11

3.3

1.000

3072

5.3

HRT use

 Never

699

63.7

250

68.3

 

518

51.1

177

52.4

 

36505

63.0

 Previous

342

31.1

93

25.4

 

379

37.4

122

36.1

 

16438

28.4

 Current

56

5.1

19

5.2

 

110

10.8

37

10.9

 

4421

7.6

 Unknown

1

0.1

4

1.1

0.302a

7

0.7

2

0.6

0.972

538

0.9

BMI (kg/m2)

 < 25

345

31.4

112

30.6

 

334

32.9

108

32.0

 

20774

35.8

 25–29

353

32.1

122

33.3

 

346

34.1

118

34.9

 

18969

32.8

 ≥ 30

327

29.8

105

28.7

 

272

26.8

88

26.0

 

14256

24.6

 Unknown

73

6.6

27

7.4

0.918

62

6.1

24

7.1

0.904

3933

6.8

 Median (IQR)

27.2

24.0–31.2

27.8

24.0–30.8

0.581

26.6

23.9–30.5

26.6

23.9–30.5

0.517

26.4

23.6–30.3

Ethnic origin

 White

1038

94.5

334

91.3

 

924

91.1

304

89.9

 

52689

91.0

 Other/unknown

50

4.6

32

8.7

0.003

90

8.9

34

10.1

0.511

5213

9.0

Year of mammogram

            

 2009

22

2.0

0

0.0

      

2372

4.1

 2010

607

55.3

43

11.7

 

196

19.3

70

20.7

 

14761

25.5

 2011

396

36.1

130

35.5

 

553

54.5

177

52.4

 

18350

31.7

 2012

70

6.4

129

35.2

 

264

26.0

86

25.4

 

14214

24.5

 2013

3

0.3

30

8.2

 

1

0.1

3

0.9

 

3499

6.0

 2014

0

0.0

33

9.0

 

0

0.0

2

0.6

 

3804

6.6

 2015

0

0.0

1

0.3

< 0.001b

    

0.769c

900

1.6

Initial Tyrer-Cuzick (10-year %)

 Median (IQR)

2.72

2.18 – 3.52

2.95

2.29–3.90

0.003

2.63

2.10–3.47

2.91

2.25–4.02

< 0.001

2.67

2.12–3.47

Family history

 None

803

73.1

249

68.0

 

754

74.4

229

67.8

 

42367

73.2

 FDR only

104

9.5

42

11.5

 

107

10.6

47

13.0

 

5749

9.9

 SDR only

147

13.4

51

13.9

 

126

12.4

42

12.4

 

7998

13.8

 FDR and SDR

44

4.0

24

6.6

0.112

27

2.7

20

5.9

0.008

1788

3.1

Current alcohol use

 No

283

25.8

89

24.3

 

273

26.9

93

27.5

 

15815

27.3

 Yes

797

72.6

269

73.5

 

726

71.6

241

71.3

 

41130

71.0

 Unknown

18

1.6

8

2.2

0.697

15

1.5

4

1.2

0.908

957

1.7

Any children

 No

128

11.7

43

11.7

 

98

9.7

45

13.3

 

7384

12.7

 Yes

970

88.3

323

88.3

 

915

90.2

290

85.8

 

50411

87.1

 Unknown

    

0.963

1

0.1

3

0.9

0.023d

107

0.2

Prior biopsy of breast

 No

910

82.9

284

77.6

 

837

82.5

252

74.6

 

47359

81.8

 Yes

159

14.5

65

17.8

 

153

15.1

76

22.5

 

8911

15.4

 Unknown

29

2.6

17

4.6

0.041

24

2.4

10

3.0

0.005

1632

2.8

*In study 1 cases are women with breast cancer detected at first screen on entry to the PROCAS study and in study 2 cases are women with breast cancer detected within PROCAS at a subsequent screen or between screening rounds

IQR interquartile range, HRT hormone replacement therapy, BMI body mass index, FDR first-degree relative, SDR second-degree relative

aExcludes unknown

b2013-2015 combined

c2012-2014 combined

dUnknown combined with no children

In study 1, VAS results were missing for 46 cases of cancer, Quantra failed to produce results for one case and one control, Volpara failed for one case, and Densitas failed for 6 cases and 62 controls. In study 2 there were missing density results for two cases of cancer assessed by VAS, for one case and one control assessed by Quantra and for 7 cases and 34 controls assessed by Densitas.

Study 1: screen-detected cancers

In study 1 after full adjustment, the strongest predictor of breast cancer risk was visually assessed density (Table 2, Fig. 1), with an odds ratio (OR) of 4.37 (95% CI 2.72–7.03) in the highest quintile of density compared with the lowest. When quantized in Boyd categories (Table 3), the adjusted OR of those with greater than 50% density was 6.73 (95% CI 3.64–12.45) compared to those with density 10% or lower. Volpara percent density provided the next strongest association with cancer, with an OR for the highest quintile of 2.42 (95% CI 1.56–3.78) (Table 2, Fig. 1). When quantized in Volpara Density Grades (VDG 5th edition), the OR of VDG4 was 4.39 (95% CI 2.28–8.48) compared with VDG1 (Table 3). Both visually assessed density and Volpara percent density showed a significant and clear trend with increasing density (χ2 trend 35.6, p < 0.001 and 11.2, p < 0.001, respectively). Percent density measured by Densitas and Cumulus was also statistically significant (Table 2, Fig. 1), with ORs of 2.17 (95% CI 1.41–3.33) and 2.12 (95% CI 1.30–3.45), respectively in the highest quintile of percent density compared with the lowest, and for Quantra there was no significant association (OR = 1.02, 95% CI 0.67–1.54). The relationship with dense volume is shown in Table 2; generally associations tended to be slightly lower than those for percent density. In the subset of women with all density measures VAS was a significantly better predictor of breast cancer risk than all other methods (Table 2, Additional file 1: Table S2). The matched concordance index for VAS was 0.651 (95% CI 0.611–0.691) demonstrating better discrimination between cases and controls than all other methods (Table 4).
Table 2

Risk of developing breast cancer by density measures (highest versus the lowest quintilea (referent))

  

Subset with data for all methodsb

 

Univariate

Adjustedc

Univariate

Adjustedc

 

OR

95% CI

OR

95% CI

OR

95% CI

OR

95% CI

Study 1

 

1.00

(referent)

1.00

(referent)

1.00

(referent)

1.00

(referent)

 VAS (%)

4.45

(2.77–7.15)

4.37

(2.72–7.03)

5.61

(3.29–9.56)

5.44

(3.18–9.29)

 Volpara gland volume (cm3)

2.13

(1.40–3.24)

2.09

(1.37–3.18)

2.00

(1.24–3.21)

1.97

(1.22–3.18)

 Volpara breast density (%)

2.44

(1.57–3.80)

2.42

(1.56–3.78)

2.38

(1.45–3.91)

2.41

(1.46–3.97)

 Cumulus dense area (cm2)

2.11

(1.32–3.38)

2.15

(1.34–3.45)

2.08

(1.28–3.27)

2.12

(1.30–3.45)

 Cumulus breast density (%)

2.09

(1.29–3.40)

2.12

(1.30–3.45)

2.20

(1.32–3.64)

2.23

(1.34–3.71)

 Quantra gland volume (cm3)

0.86

(0.58–1.30)

0.83

(0.55–1.25)

0.72

(0.45–1.15)

0.71

(0.44–1.13)

 Quantra breast density (%)

1.05

(0.70–1.59)

1.02

(0.67–1.54)

1.11

(0.70–1.77)

1.08

(0.68–1.72)

 Densitas dense area (cm2)

1.44

(0.96–2.16)

1.41

(0.93–2.12)

1.62

(1.01–2.59)

1.61

(1.00–2.58)

 Densitas breast density (%)

2.30

(1.50–3.52)

2.17

(1.41–3.33)

2.19

(1.35–3.56)

2.10

(1.29–3.41)

Study 2

 

1.00

(referent)

1.00

(referent)

1.00

(referent)

1.00

(referent)

 VAS (%)

4.54

(2.86–7.22)

4.48

(2.79–7.18)

4.41

(2.76–7.06)

4.36

(2.70–7.04)

 Volpara gland volume (cm3)

2.72

(1.79–4.14)

2.66

(1.74–4.08)

2.71

(1.77–4.14)

2.65

(1.72–4.09)

 Volpara breast density (%)

2.78

(1.74–4.44)

2.87

(1.77–4.64)

2.61

(1.62–4.19)

2.71

(1.67–4.39)

 Quantra gland volume (cm3)

1.36

(0.90–2.06)

1.28

(0.84–1.95)

1.32

(0.87–2.01)

1.24

(0.80–1.90)

 Quantra breast density (%)

1.32

(0.86–2.03)

1.32

(0.85–2.05)

1.30

(0.84–2.00)

1.32

(0.85–2.05)

 Densitas dense area (cm2)

2.34

(1.56–3.52)

2.23

(1.48–3.38)

2.29

(1.50–3.50)

2.14

(1.40–3.29)

 Densitas breast density (%)

2.45

(1.57–3.82)

2.34

(1.50–3.68)

2.44

(1.56–3.80)

2.34

(1.49–3.66)

OR odds ratio, CI confidence interval, VAS visual analogue scale, results in italics indicate statistically significant results (p<0.05)

aQuintiles based on distribution amongst controls

bStudy 1: 239 cases with 3 controls, 62 with 2 controls and 2 with 1 control; study 2: 296 cases with 3 controls, 31 with 2 controls and 2 with 1 control

cAdjusted for Tyrer-Cuzick score; study 2 also adjusted for parity

Figures in italics denote statistically significant results p<0.01

Fig. 1

Risk of developing cancer (odds ratios on a logarithm scale) by quintiles of percent density measures in study 1

Table 3

Density measures by categorical scales

  

Subset with data for all methodsb

Controls

Cases

Univariate

Adjusteda

Controls

Cases

Univariate

Adjusteda

N

Percent

N

Percent

OR

95% CI

OR

95% CI

N

Percent

N

Percent

OR

95% CI

OR

95% CI

Study 1

 VAS (mean) - Boyd categories (combining > 50–75% and > 75%)

  ≥ 0–10%

208

(18.9)

29

(9.1)

1.00

(referent)

1.00

(referent)

176

(21.1)

25

(8.3)

1.00

(referent)

1.00

(referent)

  > 10–25%

436

(39.7)

115

(35.9)

2.17

(1.37–3.42)

2.16

(1.37–3.40)

331

(39.7)

111

(36.9)

2.67

(1.63–4.38)

2.65

(1.62–4.34)

  > 25–50%

379

(34.5)

133

(41.6)

3.25

(2.05–5.18)

3.20

(2.01–5.09)

277

(33.3)

125

(41.5)

3.95

(2.38–6.54)

3.84

(2.31–6.36)

  > 50%

75

(6.8)

43

(13.4)

6.92

(3.74–12.79)

6.73

(3.64–12.45)

49

(5.9)

40

(13.3)

8.30

(4.28–16.07)

8.02

(4.13–15.56)

 Volpara Density Grades 4th Edition

  1 (<4.5)

496

(45.2)

124

(34.0)

1.00

(referent)

1.00

(referent)

400

(48.0)

108

(35.9)

1.00

(referent)

1.00

(referent)

  2 (4.5–<7.5)

339

(30.9)

131

(35.9)

1.78

(1.31–2.41)

1.74

(1.29–2.37)

258

(31.0)

113

(37.5)

1.90

(1.35–2.66)

1.88

(1.34–2.63)

  3 (7.5–<15.5)

229

(20.9)

85

(23.3)

1.85

(1.29–2.64)

1.86

(1.30–2.68)

153

(18.4)

64

(21.3)

1.92

(1.28–2.90)

1.94

(1.29–2.94)

  4 (15.5+)

34

(3.1)

25

(6.8)

4.18

(2.24–7.77)

4.07

(2.18–7.60)

22

(2.6)

16

(5.3)

3.73

(1.80–7.75)

3.58

(1.72–7.44)

 Volpara Density Grades 5th Edition

  1 (< 3.5)

274

(25.0)

62

(17.0)

1.00

(referent)

1.00

(referent)

216

(25.9)

50

(16.6)

1.00

(referent)

1.00

(referent)

  2 (3.5–<7.5)

561

(51.1)

193

(52.9)

1.68

(1.20–2.34)

1.68

(1.20–2.35)

442

(53.1)

171

(56.8)

1.88

(1.29–2.73)

1.89

(1.30–2.75)

  3 (7.5–<15.5)

229

(20.9)

85

(23.3)

2.00

(1.32–3.02)

2.04

(1.34–3.09)

153

(18.4)

64

(21.3)

2.22

(1.38–3.57)

2.27

(1.41–3.66)

  4 (15.5+)

34

(3.1)

25

(6.8)

4.44

(2.31–8.54)

4.39

(2.28–8.48)

22

(2.6)

16

(5.3)

4.26

(1.98–9.17)

4.14

(1.92–8.95)

Quantized density (BIRADS)

  1

54

(4.9)

27

(7.4)

1.00

(referent)

1.00

(referent)

47

(5.6)

21

(7.0)

1.00

(referent)

1.00

(referent)

  2

682

(62.2)

220

(60.3)

0.63

(0.39–1.02)

0.61

(0.38–0.99)

530

(63.6)

193

(64.1)

0.85

(0.50–1.44)

0.80

(0.47–1.37)

  3

317

(28.9)

102

(27.9)

0.63

(0.37–1.05)

0.58

(0.34–0.99)

226

(27.1)

75

(24.9)

0.77

(0.43–1.38)

0.71

(0.39–1.28)

  4

44

(4.0)

16

(4.4)

0.71

(0.33–1.52)

0.62

(0.29–1.33)

30

(3.6)

12

(4.0)

0.92

(0.39–2.17)

0.79

(0.33–1.88)

 Densitas: BIRADS 4

  0–24

387

(37.4)

105

(35.2)

1.00

(referent)

1.00

(referent)

326

(39.1)

94

(31.2)

1.00

(referent)

1.00

(referent)

  25–50

599

(57.8)

219

(60.8)

1.53

(1.15–2.04)

1.48

(1.11–1.98)

470

(56.4)

186

(61.8)

1.53

(1.12–2.08)

1.48

(1.09–2.03)

  51+

50

(4.8)

36

(10.0)

3.46

(2.04–5.87)

3.21

(1.88–5.47)

35

(4.2)

21

(7.0)

2.47

(1.34–4.55)

2.33

(1.26–4.30)

 Densitas: BIRADS 5

  A

194

(18.7)

46

(12.8)

1.00

(referent)

1.00

(referent)

171

(20.5)

37

(12.3)

1.00

(referent)

1.00

(referent)

  B

552

(53.3)

191

(53.1)

1.58

(1.08–2.30)

1.50

(1.03–2.19)

446

(53.5)

174

(57.8)

1.97

(1.29–2.99)

1.87

(1.23–2.85)

  C

278

(26.8)

117

(32.5)

2.01

(1.33–3.04)

1.92

(1.27–2.91)

206

(24.7)

85

(28.2)

2.12

(1.34–3.37)

2.03

(1.28–3.23)

  D

12

(1.2)

6

(1.7)

2.79

(0.94–8.24)

2.50

(0.84–7.48)

8

(1.0)

5

(1.7)

3.71

(1.09–12.66)

3.30

(0.95–11.42)

Study 2

 VAS (mean) - Boyd categories (combining >50–75% and >75%)

  ≥ 0–10%

193

(19.0)

34

(10.1)

1.00

(referent)

1.00

(referent)

180

(18.7)

32

(9.7)

1.00

(referent)

1.00

(referent)

  > 10–25%

401

(39.5)

103

(30.5)

1.61

(1.05–2.49)

1.55

(1.00–2.40)

383

(39.8)

100

(30.4)

1.61

(1.03–2.50)

1.55

(0.99–2.42)

  > 25–50%

356

(35.1)

154

(45.6)

2.92

(1.89–4.52)

2.87

(1.85–4.46)

329

(34.2)

152

(46.2)

3.02

(1.94–4.71)

2.97

(1.90–4.67)

  > 50%

64

(6.3)

45

(13.3)

5.52

(3.08–9.91)

5.45

(3.00–9.89)

60

(6.2)

45

(13.7)

5.65

(3.12–10.23)

5.63

(3.07–10.3)

 Volpara Density Grades 4th Edition

  1 (< 4.5)

474

(46.7)

126

(37.3)

1.00

(referent)

1.00

(referent)

447

(46.5)

123

(37.4)

1.00

(referent)

1.00

(referent)

  2 (4.5– < 7.5)

336

(33.1)

120

(35.5)

1.45

(1.07–1.96)

1.44

(1.06–1.95)

315

(32.7)

118

(35.9)

1.44

(1.06–1.95)

1.43

(1.05–1.95)

  3 (7.5– < 15.5)

167

(16.5)

73

(21.6)

1.96

(1.34–2.87)

2.08

(1.40–3.08)

156

(16.2)

69

(21.0)

1.81

(1.23–2.67)

1.92

(1.29–2.86)

  4 (15.5+)

37

(3.6)

19

(5.6)

2.47

(1.30–4.70)

2.61

(1.34–5.10)

34

(3.5)

19

(5.8)

2.56

(1.33–4.91)

2.73

(1.38–5.38)

 Volpara Density Grades 5th Edition

  1 (<3.5)

205

(20.2)

47

(13.9)

1.00

(referent)

1.00

(referent)

195

(20.3)

47

(14.3)

1.00

(referent)

1.00

(referent)

  2 (3.5–<7.5)

570

(56.2)

189

(55.9)

1.55

(1.07–2.24)

1.53

(1.06–2.23)

533

(55.4)

184

(55.9)

1.51

(1.04–2.19)

1.51

(1.04–2.21)

  3 (7.5–<15.5)

193

(19.0)

78

(23.1)

2.09

(1.33–3.28)

2.16

(1.36–3.43)

181

(18.8)

74

(22.5)

1.92

(1.22–3.04)

1.99

(1.25–3.17)

  4 (15.5+)

46

(4.5)

24

(7.1)

2.92

(1.53–5.58)

3.00

(1.54–5.86)

43

(4.5)

24

(7.3)

2.82

(1.47–5.40)

2.91

(1.48–5.72)

 Quantized density (BIRADS)

  1

32

(3.2)

12

(3.6)

1.00

(referent)

1.00

(referent)

32

(3.3)

12

(3.6)

1.00

(referent)

1.00

(referent)

  2

654

(64.5)

197

(58.5)

0.81

(0.39–1.70)

0.87

(0.42–1.82)

616

(64.0)

192

(58.4)

0.82

(0.39–1.71)

0.88

(0.42–1.83)

  3

287

(28.3)

108

(32.0)

1.05

(0.49–2.27)

1.12

(0.52–2.41)

267

(27.8)

105

(31.9)

1.04

(0.48–2.23)

1.11

(0.52–2.39)

  4

40

(3.9)

20

(5.9)

1.51

(0.59–3.88)

1.54

(0.59–4.01)

37

(3.8)

20

(6.1)

1.50

(0.58–3.87)

1.54

(0.59–4.02)

 Densitas: BIRADS 4

  0–24

354

(36.1)

95

(28.7)

1.00

(referent)

1.00

(referent)

345

(35.9)

95

(28.9)

1.00

(referent)

1.00

(referent)

  25–50

586

(59.8)

208

(62.8)

1.37

(1.02–1.83)

1.34

(1.00–1.80)

568

(59.0)

207

(62.9)

1.37

(1.02–1.83)

1.34

(1.00–1.80)

  51+

40

(4.1)

28

(8.5)

2.94

(1.65–5.24)

2.83

(1.55–5.16)

39

(4.1)

27

(8.2)

2.86

(1.60–5.11)

2.76

(1.51–5.05)

 Densitas: BIRADS 5

  A

96

(9.8)

25

(7.6)

1.00

(referent)

1.00

(referent)

94

(9.8)

25

(7.6)

1.00

(referent)

1.00

(referent)

  B

574

(58.6)

185

(57.9)

1.25

(0.77–2.01)

1.20

(0.74–1.94)

558

(58.0)

185

(56.2)

1.25

(0.77–2.01)

1.20

(0.74–1.94)

  C

294

(30.0)

110

(33.2)

1.47

(0.88–2.45)

1.46

(0.87–2.45)

284

(29.5)

108

(32.8)

1.46

(0.88–2.44)

1.45

(0.87–2.44)

  D

16

(1.6)

11

(3.3)

2.90

(1.14–7.42)

2.59

(0.99–6.78)

16

(1.7)

11

(3.3)

2.90

(1.13–7.41)

2.58

(0.99–6.76)

N number, OR odds ratio, CI confidence interval, VAS visual analogue scale, BIRADS Breast Imaging Reporting and Data System, results in italics indicate statistically significant results (p<0.05)

aAdjusted for Tyrer-Cuzick score; study 2 also adjusted for parity

bStudy 1: 239 cases with 3 controls, 62 with 2 controls and 2 with 1 control; study 2: 296 cases with 3 controls, 31 with 2 controls and 2 with 1 control

Table 4

Matched concordance index (mC)

 

Study 1

Study 2

 

mCa

95% CI

mCb

95% CI

VAS (%)

0.651

0.611

0.691

0.647

0.607

0.688

Volpara breast density (%)

0.571

0.528

0.618

0.575

0.534

0.615

Volpara gland volume (cm3)

0.553

0.513

0.591

0.586

0.546

0.627

Quantra breast density (%)

0.510

0.469

0.552

0.543

0.504

0.584

Quantra gland volume (cm3)

0.487

0.447

0.528

0.531

0.490

0.574

Densitas breast density (%)

0.571

0.526

0.612

0.587

0.548

0.628

Densitas dense area (cm2)

0.535

0.496

0.574

0.577

0.537

0.616

Cumulus breast density (%)

0.582

0.541

0.623

-

-

-

Cumulus dense area (cm2)

0.558

0.516

0.599

-

-

-

CI confidence interval, VAS visual analogue scale

aStudy 1: 239 cases with 3 controls, 62 with 2 controls, 2 with 1 control

bStudy 2: 296 with 3 controls, 31 with 2 controls, 2 with 1 control

Study 2: prior mammograms

In study 2 visually assessed density had the strongest association with subsequent cancer in the fully adjusted models, with an OR of 4.48 (95% CI 2.79–7.18) in the highest quintile of density compared with the lowest (Table 2). When quantized in Boyd categories (Table 3), the OR of those with density > 50% was 5.45 (95% CI 3.00–9.89) compared to those with density ≤ 10%. Volpara percent density had the next strongest association with cancer, with an OR for the highest quintile of 2.87 (95% CI 1.77–4.64) (Table 2, Fig. 2). When quantized in Volpara Density Grades (5th Edition), the OR of VDG4 was 3.00 (95% CI 1.54–5.86) compared with VDG1 (Table 3). Both visually assessed density and Volpara percent density showed a dose response relationship with increasing density (χ2 trend 42.7, p < 0.001 and 13.8, p < 0.001, respectively). For Densitas and Quantra, those with percentage density in the highest quintile had ORs of 2.34 (95% CI 1.50–3.68) and 1.32 (95% CI 0.85–2.05), respectively (Table 2, Fig. 2). VAS predicted breast cancer risk significantly better than all other density methods in the subset of women who had density measured by all four methods (Table 2, Additional file 1: Table S2). The matched concordance index for VAS was 0.647 (95% CI 0.607–0.688) demonstrating better discrimination between cases and controls than all other methods (Table 4).
Fig. 2

Risk of developing cancer (odds ratios on a logarithm scale) by quintiles of percent density measures for study 2

Discussion

Visual assessment of breast density recorded on a VAS was the strongest predictor of breast cancer risk, both in the contralateral breast of women with screen-detected cancers and in the average of bilateral mammographic views prior to the detection of cancer. It is unlikely that the presence of cancer influenced visual assessment in study 1, since a blinded re-read of images from the contralateral breast by four readers showed no evidence of bias [23] and the ORs were similar to those in study 2. There is strong association between the VAS and breast cancer despite known inter-observer variability [32]; since the average VAS score of two readers was used it is likely that cases falling into the top and bottom quintiles of density do so unequivocally.

Volpara and Densitas percent density had the next strongest associations with cancer in both studies, with categorisation into VDG having the largest odds ratio. Volpara, Quantra and Cumulus did not have as strong an association with breast cancer in study 1 as previously reported [13]. This may be due to differences in the approach used; Eng et al. analysed 414 cases from one hospital and 685 unmatched controls from a screening service based in London, adjusting for age, BMI and reproductive variables in the analysis, whilst we analysed 366 cases using Volpara and Quantra, and 311 using Cumulus, with 3 well-matched controls per case all recruited from the same screening programme. There were also a number of differences between the study populations, with our study population tending to be younger, with more women of white ethnicity and with higher BMI and being less likely to be postmenopausal and to have had children. Density distributions also differed across the two studies, with the current study having lower median (IQR) percent density assessed by Volpara (4.9, 3.5–7.4) and Quantra (11, 8–14), but higher percent density for Cumulus (20.3, 11.6–30.3) [13]. Our version of Volpara was later (1.5.0 vs 1.0) and we applied a Volpara macro for outlier rejection; our version of Quantra was also more recent (2.0 vs 1.3). For Cumulus, the difference might be due to reader experience.

Study 2 examined the relationship between mammographic density in mammograms prior to the detection of cancer, and in matched controls that subsequently remained cancer free. This enables us to evaluate which mammographic density methods are most appropriate for stratifying women attending breast screening. Whilst visual assessment was most strongly associated with cancer, it is unlikely to be used widely for population-based stratified screening; we conclude that Volpara or Densitas percentage density provide a pragmatic solution. However, we hypothesise that methods that measure purely the quantity or relative proportion of dense tissue do not fully capture the mammographic risk in the same way as visual assessment by experts, who can see not only the quantity of dense tissue but the location and pattern. The addition of algorithms that automatically quantify mammographic pattern to automated density software could potentially provide a solution that more closely reproduces visual assessment. Recent research in this area has proved promising [3336], although there is as yet no consensus as to the best method of encapsulating texture information within risk assessment.

Strengths and limitations

The strengths of this study include the ability to assess the relationship between several measures of mammographic density and risk of breast cancer. As well as examining the association between mammographic density and breast cancer risk, we were able to establish the temporal relationship in study 2. We also gathered detailed information in relation to a number of covariates (demographic, hormonal, reproductive, lifestyle and family history) via a self-reported questionnaire at entry to PROCAS [20]. Uptake to PROCAS was relatively low (38%), which may have biased the population to those with higher or lower risk, for example, the proportion of women in the PROCAS study who were overweight or obese was significantly lower than in the general population of Greater Manchester [37]. In addition, in study 1, due to the nature of the study design, whereby controls had to have had a subsequent cancer-free mammogram after entry to the PROCAS study, the year of mammogram in controls tended to be earlier than in cases, this may have had an impact on density measures due to changes in mammography technology, and the use of different mammographic machines over time.

Conclusions

Visual assessment of density, recorded on a VAS and averaged between two independent readers, is a strong predictor of breast cancer risk both in mammograms taken before the detection of cancer and in images of the opposite breast at the time of detection. Percentage density measured by Volpara and Densitas also showed a strong association with breast cancer risk amongst the automated measures evaluated, providing practical automated methods for risk stratification in personalized screening programmes.

Abbreviations

BIRADS: 

Breast Imaging Reporting And Data System

BMI: 

Body mass index

CI: 

Confidence interval

FFDM: 

Full- field digital mammography

HRT: 

Hormone replacement therapy

IQR: 

Interquartile range

ICC: 

Intraclass correlation coefficient

MLO: 

Mediolateral oblique

OR: 

Odds ratio

PROCAS: 

Predicting Risk of Cancer At Screening Study

VAS: 

Visual analogue scale

VDG: 

Volpara Density Grade

Declarations

Acknowledgements

We would like to thank the women who agreed to take part in the study, the study radiologists and advanced radiographic practitioners, and the study staff for recruitment and data collection.

Funding

This work was supported by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research programme (reference number RP-PG-0707-10031: “Improvement in risk prediction, early detection and prevention of breast cancer”) and the Prevent Breast Cancer (references GA09-003 and GA13-006). The views expressed are those of the author(s) and not necessarily those of the National Health Service (NHS), the NIHR, or the Department of Health.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Authors’ contributions

SMA conceived and designed the study, analysed and interpreted the data and drafted the manuscript. EFH performed the statistical analysis, interpreted the data and drafted the manuscript. JCS carried out the cumulus density assessment after training from JW. ARB participated in the statistical analysis. PS made substantial contributions to acquisition of questionnaire data. MW, UB, SG, YL, AJ, SB and NB made substantial contributions to acquisition of density data, each reading more than 4000 mammograms. VR was responsible for mammography reporting. RW, DGRE, AH and JC conceived and designed the study, analysed and interpreted the data, and helped to draft the manuscript. All authors were involved in critically revising the paper for intellectual content, and all read and approved the final manuscript.

Ethics approval and consent to participate

Ethics approval for the study was through the North Manchester Research Ethics Committee (09/H1008/81). Informed consent was obtained from all participants on entry to the PROCAS study.

Consent for publication

Not applicable.

Competing interests

Software licences for Volpara, Quantra and Densitas were provided free of charge under a research agreement by Volpara Health Technologies (Wellington, New Zealand), Hologic Inc (Marlborough, MA, USA) and Densitas Inc (Halifax, NS, Canada) respectively. The authors declare that they have no competing interests.

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Authors’ Affiliations

(1)
Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
(2)
Prevent Breast Cancer and Nightingale Breast Screening Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
(3)
Arthritis Research UK Centre for Epidemiology, Centre for Musculoskeletal Research, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
(4)
NIHR Manchester Musculoskeletal Biomedical Research Unit, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
(5)
Warwick Clinical Trials Unit, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
(6)
Department of Radiology, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK
(7)
School of Medical Sciences, University of Manchester, Manchester, UK
(8)
Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK
(9)
The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
(10)
Genomic Medicine, Division of Evolution and Genomic Science, Manchester Academic Health Sciences Centre, University of Manchester and Manchester University NHS Foundation Trust, Manchester, UK

References

  1. Day N, Warren R. Mammographic screening and mammographic patterns. Breast Cancer Res. 2000;2(4):247.View ArticlePubMedPubMed CentralGoogle Scholar
  2. Pisano ED, Gatsonis C, Hendrick E, Yaffe M, Baum JK, Acharyya S, et al. Diagnostic performance of digital versus film mammography for breast-cancer screening. New Engl J Med. 2005;353(17):1773–83.View ArticlePubMedGoogle Scholar
  3. van Gils CH, Otten JD, Verbeek AL, Hendriks JH, Holland R. Effect of mammographic breast density on breast cancer screening performance: a study in Nijmegen, The Netherlands. J Epidemiol Community Health. 1998;52(4):267–71.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Mandelson MT, Oestreicher N, Porter PL, White D, Finder CA, Taplin SH, White E. Breast density as a predictor of mammographic detection: comparison of interval-and screen-detected cancers. J Natl Cancer Inst. 2000;92(13):1081–7.View ArticlePubMedGoogle Scholar
  5. Andreas P, Graff RE, Ursin G, dos Santos Silva I, McCormack V, Baglietto L, et al. Mammographic Density Phenotypes and Risk of Breast Cancer: A Meta-analysis, JNCI: Journal of the National Cancer Institute. 2014;106(5). https://doi.org/10.1093/jnci/dju078.
  6. Boyd NF, Guo H, Martin LJ, Sun L, Stone J, Fishell E, et al. Mammographic density and the risk and detection of breast cancer. New Engl J Med. 2007;356(3):227–36.View ArticlePubMedGoogle Scholar
  7. Boyd NF, Martin LJ, Yaffe MJ, Minkin S. Mammographic density and breast cancer risk: current understanding and future prospects. Breast Cancer Res. 2011;13(6):223.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Boyd NF, Byng JW, Jong RA, Fishell EK, Little LE, Miller AB, 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(9):670–5.View ArticlePubMedGoogle Scholar
  9. Byng JW, Yaffe MJ, Jong RA, Shumak RS, Lockwood JS, Tritchler DL, Boyd NF. Analysis of mammographic density and breast cancer risk from digitized mammograms. Radiographics. 1998;18(6):1587–98.View ArticlePubMedGoogle Scholar
  10. Highnam R, Brady SM, Yaffe MJ, Karssemeijer N, Harvey J. Robust Breast Composition Measurement - VolparaTM. In: Martí J, Oliver A, Freixenet J, Martí R. (eds) Digital Mammography. IWDM 2010. Lecture Notes in Computer Science. Springer: Berlin. 2010;6136:342-49. https://doi.org/10.1007/978-3-642-13666-5_46.
  11. Ciatto S, Bernardi D, Calabrese M, Durnando M, Gentilini MA, Mariscotti G. A first evaluation of breast radiological density assessment by QUANTRA software as compared to visual classification. Breast. 2012;21(4):503–6.View ArticlePubMedGoogle Scholar
  12. 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(5):439.View ArticlePubMedPubMed CentralGoogle Scholar
  13. American College of Radiology. Breast Imaging Reporting and Data System (BI-RADS® ). 4th ed. Reston, VA: American College of Radiology, 2003.Google Scholar
  14. D’Orsi CJ, Sickles EA, Mendelson EB, Morris EA, et al. ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System. Reston: American College of Radiology; 2013.Google Scholar
  15. Duffy SW, Nagtegaal ID, Astley SM, Gillan MG, McGee MA, Boggis CR, et al. Visually assessed breast density, breast cancer risk and the importance of the craniocaudal view. Breast Cancer Res. 2008;10(4):1–7.View ArticleGoogle Scholar
  16. Donovan EO, Sergeant J, Harkness E, Morris J, Wilson M, Lim Y, et al. Use of Volumetric breast density measures for the prediction of weight and body mass index. In: Fujita H, Hara T, Muramatsu C, editors. Breast Imaging. IWDM. Cham: Springer; 2014. Lecture notes in computer science, vol 8539.Google Scholar
  17. Patel HG, Astley SM, Hufton AP, Harvie M, Hagan K, Marchant TE, et al. Automated Breast Tissue Measurement of Women at Increased Risk of Breast Cancer. In: Astley SM, Brady M, Rose C, Zwiggelaar R. (eds) Digital Mammography. IWDM 2006. Lecture Notes in Computer Science, vol 4046. Berlin: Springer. https://doi.org/10.1007/11783237_19.
  18. Boyd NF, Martin LJ, Sun L, Guo H, Chiarelli A, Hislop G, et al. Body size, mammographic density, and breast cancer risk. Cancer Epidemiol Biomarkers Prev. 2006;15:2086–92.View ArticlePubMedGoogle Scholar
  19. Highnam RP, Brady JM, Shepstone BJ. A representation for mammographic image processing. Medical Image Analysis. 1996;1(1):1-18 https://doi.org/10.1016/S1361-8415(01)80002-5.
  20. Evans DG, Warwick J, Astley SM, Stavrinos P, Sahin S, Ingham S, et al. Assessing individual breast cancer risk within the UK National Health Service Breast Screening Program: a new paradigm for cancer prevention. Cancer Prev Res. 2012;5(7):943–51.View ArticleGoogle Scholar
  21. Berg WA, Blume JD, Cormack JB, Mendelson EB, Lehrer D, Böhm-Vélez M, et al. Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer. JAMA. 2008;299(18):2151–63.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Haas BM, Kalra V, Geisel J, Raghu M, Durand M, Philpotts LE. Comparison of tomosynthesis plus digital mammography and digital mammography alone for breast cancer screening. Radiology. 2013;269(3):694–700.View ArticlePubMedGoogle Scholar
  23. Brentnall AR, Harkness EF, Astley SM, Donnelly LS, Stavrinos P, Sampson S, et al. Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort. Breast Cancer Res. 2015;17(1):147.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Chen J, Pee D, Ayyagari R, Graubard B, Schairer C, Byrne C, et al. Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density. J Natl Cancer Inst. 2006;98(17):1215–26.View ArticlePubMedGoogle Scholar
  25. Tice JA, Cummings SR, Ziv E, Kerlikowske K. Mammographic breast density and the Gail model for breast cancer risk prediction in a screening population. Breast Cancer Res Treat. 2005;94(2):115–22.View ArticlePubMedGoogle Scholar
  26. Cuzick J, Forbes J, Edwards R, Baum M, Cawthorn S, Coates A, et al. First results from the International Breast Cancer Intervention Study (IBIS-I): a randomised prevention trial. Lancet. 2002;360(9336):817–24.View ArticlePubMedGoogle Scholar
  27. Howe GR, Hirohata T, Hislop TG, Iscovich JM, Yuan JM, Katsouyanni K, et al. Dietary factors and risk of breast cancer: combined analysis of 12 case—control studies. J Natl Cancer Inst. 1990;82(7):561–9.View ArticlePubMedGoogle Scholar
  28. Tyrer J, Duffy SW, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med. 2004;23(7):1111–30.View ArticlePubMedGoogle Scholar
  29. Ang T, Harkness EF, Maxwell AJ, Lim YY, Emsley R, Howell A, et al. "Visual assessment of breast density using Visual Analogue Scales: observer variability, reader attributes and reading time", Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 1013608 (10 March 2017); https://doi.org/10.1117/12.2253797. http://dx.doi.org/10.1117/12.2253797.
  30. Brentnall AR, Cuzick J, Field J, Duffy SW. A concordance index for matched case-control studies with applications in cancer risk. Stat Med. 2015;34(3):396–405.View ArticlePubMedGoogle Scholar
  31. IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk: IBM Corp; 2013.Google Scholar
  32. Core Team R. R Foundation for Statistical Computing. Vienna: R: A language and environment for statistical computing; 2016. URL, https://www.R-project.org/.Google Scholar
  33. Chen X, Moschidis E, Taylor C, Astley S. Breast cancer risk analysis based on a novel segmentation framework for digital mammograms. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer; 2014. p. 536–43.Google Scholar
  34. Malkov S, Shepherd JA, Scott CG, Tamimi RM, Ma L, Bertrand KA, et al. Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status. Breast Cancer Res. 2016;18(1):122.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Kallenberg M, Petersen K, Nielsen M, Ng AY, Diao P, Igel C, et al. Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans Med Imaging. 2016;35(5):1322–31.View ArticlePubMedGoogle Scholar
  36. Ali MA, Czene K, Eriksson L, Hall P, Humphreys K. Breast tissue organisation and its association with breast cancer risk. Breast Cancer Res. 2017;19(1):103.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Evans DG, Astley S, Stavrinos P, Harkness E, Donnelly LS, 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. Programme Grants Appl Res. 2016;4(11).Google Scholar

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© The Author(s). 2018

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