6th International Workshop on Breast Densitometry and Breast ...

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6 th <strong>International</strong> <strong>Workshop</strong> on Breast Densitometry
and Breast Cancer Risk Assessment
AN APPROACH TO THE STANDARDIZATION OF PERCENT MAMMOGRAPHIC
DENSITY TO ENABLE JOINT ANALYSIS OF DATA ACROSS MULTIPLE CENTERS
V. Shane Pankratz 1 , Christopher G. Scott 1 , Matthew R. Jensen 1 , Kimberly A. Bertrand 2,3 , Rulla M.
Tamimi 2,3 , Karla Kerlikowske 4 , Celine M. Vachon 5
1 Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic
College of Medicine, Rochester, MN; 2 Channing Division of Network Medicine, Department of Medicine,
Brigham and Women’s Hospital and Harvard Medical School, Boston, MA; 3 Department of
Epidemiology, Harvard School of Public Health, Boston, MA; 4 Departments of Epidemiology and
Biostatistics and General Internal Medicine Section, Department of Veterans Affairs, University of
California, San Francisco, CA; 5 Department of Health Sciences Research, Division of Epidemiology,
Mayo Clinic College of Medicine, Rochester, MN
Background: Percent mammographic density (MD) is one of the strongest risk factors for breast cancer
(BC). Because MD is often measured by a computer-assisted approach which requires human operators to
set thresholds, MD measures may differ when they are assessed by different readers, or even within a
single reader when measurements for a cohort are separated by long periods of time. This random
variability in MD measurements across readers and “batches” introduces measurement error in
epidemiologic analyses and may bias associations of interest. In order to integrate MD data obtained from
multiple readers across several reading epochs into a single pooled analysis, we developed an approach
that standardizes measures of MD across multiple readers and batches.
Methods: Data were obtained from six cohort or case-control studies, which were comprised of 3414
women with breast cancer and 7199 without. All underwent screening mammography and MD was
assessed from digitized film-screen mammograms using a computer-assisted threshold technique by three
different readers and across varying time frames. We developed a standardization approach that relies on
the age-dependent trends in MD. In this approach, we regress a transformed version of MD on age at
mammogram within each discrete set of MD reads, and extract the residuals from these regression
models. We re-center and re-scale these sets of residuals separately, such that their medians and
interquartile ranges were all equal to values estimated from a large set of MD reads obtained within one
reading epoch within a single cohort of individuals. We then add these standardized residuals to an age
trend that we estimated from all currently available data. We applied this standardization approach to the
data from these six studies and evaluated its effect on estimates of heterogeneity among the studies.
Results: Prior to standardization, differences in the MD reads among the six studies explained 13.6% of
the variability in percent MD, and we consistently observed evidence of significant heterogeneity among
studies when analyzing associations between MD and various outcomes of interest, including breast
cancer. We applied our standardization approach to the data from these studies to re-center and re-scale
their percent MD measurements. The effort resulted in distributions of percent MD that were more similar
across studies, while the rank ordering of within-study measurements was preserved. After
standardization, differences among studies explained 1.4% of the variability in MD, and there was no
longer evidence of significant heterogeneity among study sets in combined analyses of associations
between percent MD and breast cancer status.
ABSTRACTS
Conclusion: We implemented a standardization approach for MD that makes it possible to appropriately
combine MD measurements obtained across multiple readers. This approach may also be used to align
MD measurements obtained from studies other than those explicitly included in these analyses.
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