AR: Research sets stage for standard breast density measures
The sum of three breast density measurements may be greater than the parts, according to a study in this month's Academic Radiology. Researchers determined “three measures of breast density capture different attributes of the same data field,” findings that could inform development of a standardized quantitative measure of breast density.
Although physicians have realized that breast density represents a significant breast cancer risk factor, data indicating the efficacy of calibrating mammograms for risk assessments using breast cancer status as the end point are scant.
“If calibration can be optimized to improve the precision with which breast density is measured, a more accurate estimate of the magnitude of association between breast density and breast cancer may be obtained,” wrote John J. Heine, PhD, of H. Lee Moffitt Cancer Center & Research Institute in Tampa, Fla., and colleagues.
Heine and colleagues evaluated spatial variation in mammograms calibrated to account for x-ray acquisition technique differences using digital mammography. The matched case-control study assessed three breast density measures and their association with breast cancers: average of the calibrated mammogram (PG), standard deviation of the calibrated mammograms (PGSD) and the standard PD measure derived from the raw data (no calibration).
The study population comprised 123 first-time, unilateral breast cancer cases diagnosed between September 2007 and July 2010 and controls matched on age, hormone replacement therapy use and screening history.
The researchers used estimates for the combined case-control glandular and adipose tissue components to create a model to explain the relationships between the three measures.
Heine and colleagues reported three new findings. “[T]he PGSD measure showed greater magnitude of association with breast cancer than the other measures in a side-by-side comparison … Second, the work provides evidence for the correlation between the measures.”
The researchers further determined that the three measures characterize different attributes of breast density, and noted that the relationship provides a partial explanation for the positive correlation between the measures.
The third finding indicated breast area may be a confounding factor for both calibrated measurements and the PD measure.
Heine and colleagues acknowledged that calibration may entail “considerable initial effort to develop its infrastructure,” and pointed out that data suggest calibrated measures do not yield associations with breast cancer beyond that of the PD measure. However, calibration may offer additional clinical utility, they continued, by automating the PD measurement process. In addition, the normalized representation of the breast produced by calibration may yield a quantitative benefit, summed Heine et al.
Although physicians have realized that breast density represents a significant breast cancer risk factor, data indicating the efficacy of calibrating mammograms for risk assessments using breast cancer status as the end point are scant.
“If calibration can be optimized to improve the precision with which breast density is measured, a more accurate estimate of the magnitude of association between breast density and breast cancer may be obtained,” wrote John J. Heine, PhD, of H. Lee Moffitt Cancer Center & Research Institute in Tampa, Fla., and colleagues.
Heine and colleagues evaluated spatial variation in mammograms calibrated to account for x-ray acquisition technique differences using digital mammography. The matched case-control study assessed three breast density measures and their association with breast cancers: average of the calibrated mammogram (PG), standard deviation of the calibrated mammograms (PGSD) and the standard PD measure derived from the raw data (no calibration).
The study population comprised 123 first-time, unilateral breast cancer cases diagnosed between September 2007 and July 2010 and controls matched on age, hormone replacement therapy use and screening history.
The researchers used estimates for the combined case-control glandular and adipose tissue components to create a model to explain the relationships between the three measures.
Heine and colleagues reported three new findings. “[T]he PGSD measure showed greater magnitude of association with breast cancer than the other measures in a side-by-side comparison … Second, the work provides evidence for the correlation between the measures.”
The researchers further determined that the three measures characterize different attributes of breast density, and noted that the relationship provides a partial explanation for the positive correlation between the measures.
The third finding indicated breast area may be a confounding factor for both calibrated measurements and the PD measure.
Heine and colleagues acknowledged that calibration may entail “considerable initial effort to develop its infrastructure,” and pointed out that data suggest calibrated measures do not yield associations with breast cancer beyond that of the PD measure. However, calibration may offer additional clinical utility, they continued, by automating the PD measurement process. In addition, the normalized representation of the breast produced by calibration may yield a quantitative benefit, summed Heine et al.