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Full Field Digital Mammography and Breast Density

Posted Oct 31, 2011 Updated Apr 10, 2023
By John J. Heine PhD , Erin E.E. Fowler BS , Chris I. Flowers MD
10 min read

Rationale and Objectives

Mammographic breast density is an important and widely accepted risk factor for breast cancer. A statement about breast density in the mammographic report is becoming a requirement in many States. However, there is significant inter-observer variation between radiologists in their interpretation of breast density. A properly designed automated system could provide benefits in maintaining consistency and reproducibility. We have developed a new automated and calibrated measure of breast density using full field digital mammography (FFDM). This new measure assesses spatial variation within a mammogram and produced significant associations with breast cancer in a small study. The costs of this automation are delays from advanced image and data analyses before the study can be processed. We evaluated this new calibrated variation measure using a larger dataset than previously. We also explored the possibility of developing an automated measure from unprocessed (raw data) mammograms as an approximation for this calibrated breast density measure.

Materials and Methods

A case-control study comprised of 160 cases and 160 controls matched by age, screening history, and hormone replacement therapy was used to compare the calibrated variation measure of breast density with three variants of a noncalibrated measure of spatial variation. The operator-assisted percentage of breast density measure (PD) was used as a standard reference for comparison. Odds ratio (OR) quartile analysis was used to compare these measures. Linear regression analysis was applied to assess the calibration’s impact on the raw pixel distribution.

Results

All breast density measures showed significant breast cancer associations. The calibrated spatial variation measure produced the strongest associations (OR: 1.0 [ref.], 4.6, 4.3, 7.4). The associations for PD were diminished in comparison (OR: 1.0 [ref.], 2.7, 2.9, 5.2). Two additional non-calibrated measures restricted in region size also showed significant associations (OR: 1.0 [ref.], 2.9, 4.4, 5.4), and (OR: 1.0 [ref.], 3.5, 3.1, 4.9). Regression analyses indicated the raw image mean is influenced by the calibration more so than its standard deviation.

Conclusion

Breast density measures can be automated. The associated calibration produced risk information not retrievable from the raw data representation. Although the calibrated measure produced the stronger association, the non-calibrated measures may offer an alternative to PD and other operator based methods after further evaluation, because they can be implemented automatically with a simple processing algorithm.

Mammographic breast density is a significant factor for breast cancer risk that has been studied for many years . A statement about breast density is a part of the radiological report according to the fourth edition of the Breast Imaging Reporting and Data System (BI-RADS) . BI-RADS breaks down the estimate of breast density into quartiles: from almost entirely fat (0%–25% glandular tissue) to extremely dense (75%–100% glandular tissue). The breast density part of this report is meant to guide referring physicians to the risk of a cancer being obscured by the background tissue. The downside is that there is significant variation in the way breast density is reported from the 2D examination read by the radiologist . BI-RADS breast density is also used as a measure of risk in research as a coarse approximation for the percentage of breast density measure. Breast density as a breast cancer risk factor is not currently used in clinical practice due to the lack of standardization and automation of its measurement . The attributes of an automated breast density measure for clinical applications should have a high degree of replication and translate across imaging platforms without extensive modification.

There are various methods used to assess breast density, as reviewed previously . For the most part, the breast density and breast cancer associations have been developed with measurements that did not consider the inter-image acquisition technique differences. In particular, the operator-assisted percentage of breast density approach (or PD) has shown repeatedly to correlate well with breast cancer without considering the acquisition technique. Methods for automating PD are not widely used . An alternative method of assessing breast density is to calibrate, or adjust, for the acquisition technique differences .

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Methods

Study Population

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Spatial Variation Breast Density

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Figure 1, From left to right, images with the largest box area/breast area ratio, image with the medium ratio, and the image with the smallest ratio ( right ). The image areas from left to right in pixel units are 2,426,894, 1,324,519 and 386,023. The outlined box is 3 × 3 cm 2 (300 × 300 pixels) and is vertically centered on the segmented image vertical centroid coordinate. The raw image standard deviation from the fixed box (R SDX ) breast density was derived from this region. These images are processed clinical display images. The authors use these as raw image surrogates for display purposes because the raw images are not useful for display illustrations without manipulation.

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Percentage of Breast Density (PD)

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Breast Cancer Association Comparisons

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Calibration Assessment

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Results

Breast Density Measurement Association

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Table 1

Patient Characteristics

Characteristic Case n Case Mean/SD or % Control n Control Mean SD or % Age 160 58.5/10.6 160 58.5/10.5 HRT Never used 84 52.5% 88 55.0% 1–5 years 26 16.3% 23 14.4% 6–10 years 17 10.6% 17 10.6% 11–15 years 12 7.5% 10 6.3% >15 years 21 13.1% 22 13.8% BMI (kg/m 2 ) 159 ∗ 26.4/4.5 160 25.3/4.3 Breast area (pixels) 160 1,392,643/478,251 160 1,318,957/407,717 Menopausal (post) 123 76.9% 115 71.9%

This table provides the number ( n ) of cases and controls in the hormone replacement therapy (HRT) stratifications by years and for the other measures. The mean and standard deviation (SD) for the age, body mass index (BMI), breast area distributions, and menopausal status (postmenopausal or not) breakdown by case-control group are also provided.

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Table 2

Breast Density Measurement Associations

Quartile OR (95% CI) First (n 1 ) Second (n 2 ) Third (n 3 ) Fourth (n 4 ) Az PD n = 160 23 41 39 57 BMI adjusted 1.00 (ref.) 2.50 (1.12–5.56) 2.85 (1.26–6.45) 4.94 (2.10–11.62) 0.630 BMI, area adjusted 1.00 (ref.) 2.57 (1.15–5.75) 2.87 (1.26–6.53) 5.17 (2.17–12.33) 0.637 BMI, area, menopausal adjusted 1.00 (ref.) 2.70 (1.19–6.14) 2.94 (1.27–6.80) 5.24 (2.18–12.59) 0.643 PG SD n = 160 22 53 38 47 BMI adjusted 1.00 (ref.) 3.36 (1.58–7.13) 2.89 (1.24–6.74) 4.84 (1.95–12.02) 0.637 BMI, area adjusted 1.00 (ref.) 3.80 (1.75–8.24) 3.56 (1.46–8.69) 6.18 (2.34–16.28) 0.639 BMI, area, menopausal adjusted 1.00 (ref.) 4.63 (2.05–10.47) 4.26 (1.69–10.76) 7.43 (2.72–20.28) 0.651 R SD n = 160 26 42 41 51 BMI adjusted 1.00 (ref.) 1.81 (0.89–3.67) 2.11 (0.97–4.61) 2.78 (1.29–5.97) 0.617 BMI, area adjusted 1.00 (ref.) 2.07 (0.99–4.30) 2.59 (1.13–5.94) 3.48 (1.52–7.93) 0.625 BMI, area, menopausal adjusted 1.00 (ref.) 2.17 (1.03–4.57) 2.86 (1.22–6.72) 3.76 (1.62–8.70) 0.634 R SDL n = 160 21 39 44 56 BMI adjusted 1.00 (ref.) 2.41 (1.15–5.08) 3.39 (1.52–7.58) 4.37 (1.94–9.85) 0.636 BMI, area adjusted 1.00 (ref.) 2.66 (1.24–5.68) 3.82 (1.67–8.76) 4.95 (2.14–11.46) 0.646 BMI, area, menopausal adjusted 1.00 (ref.) 2.89 (1.34–6.27) 4.44 (1.88–10.49) 5.38 (2.29–12.63) 0.654 R SDX n = 160 24 55 36 45 BMI adjusted 1.00 (ref.) 2.96 (1.44–6.10) 2.29 (1.06–4.95) 3.57 (1.48–8.59) 0.639 BMI, area adjusted 1.00 (ref.) 3.35 (1.58–7.09) 2.89 (1.26–6.64) 4.74 (1.81–12.40) 0.639 BMI, area, menopausal adjusted 1.00 (ref.) 3.50 (1.64–7.47) 3.11 (1.34–7.23) 4.87 (1.85–12.84) 0.650

This table gives the quartile odds ratio (OR) stratifications and area under the receiver operating characteristic curve (Az) quantities for each of the five breast density measures: 1) operator-assisted percentage of breast density (PD), 2) calibrated standard deviation (PG SD ), 3) raw image standard deviation (R SD ), 4) raw image standard deviation from the reduced breast area (R SDL ), and 5) raw image standard deviation from the fixed box (R SDX ). The number of cases in each stratification (n 1 -n 4 ) is listed in the top row. 95% confidence intervals (CIs) are provided parenthetically next to each OR. In the analysis, we controlled for body mass (BMI), breast area (area), and menopausal status.

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Figure 2, The breast area frequency histogram. This shows the frequency histogram for the breast area measured in 10 5 pixel units (ie, the bin-width used for the horizontal axis). The symmetric behavior indicates most images are similar to the middle image in Figure 1 .

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Correlation Comparisons

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Figure 3, Mean regression. This plot shows the calibrated mean values modeled as a linear function of the raw image mean values ( dashes ). The regression fitted lines ( solid ) show the two measures are not described well by this relationship, indicating the calibration has a strong influence. The data were modeled with all the points ( line with the longer length ) and with three outliers removed ( line with the shorter length ) from the right. The respective slope (m) and standard errors for each plot were m = 0.026 ± 0.004 and 0.047 ± 0.005 with r 2 = 0.12 and 0.19.

Figure 4, Standard deviation regression. This plot shows the calibrated standard deviation modeled as a linear function of the raw image standard deviation (dashes). The slope and standard error were m = 0.042 ± 0.002. The regression fitted line (solid) shows the two measures are highly correlated (R 2 = 0.73) indicating that the calibration re-scales the standard deviation quantities while approximately maintains the internal distances between the samples.

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Discussion

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Conclusions

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Academic Radiology, Volume 18, Issue 11
Journals General Radiology
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