Erin E Fowler1, Autumn Smallwood2, Nadia Khan3, Cassandra Miltich1, Jennifer Drukteinis3, Thomas A Sellers1, John Heine4. 1. Cancer Epidemiology Department, Moffitt Cancer Center & Research Institute, 12902 Bruce B Downs Blvd, Mail Stop: MRC Can/Cont, Tampa, FL 33612. 2. Corporate Compliance Department, Moffitt Cancer Center & Research Institute, 12902 Bruce B Downs Blvd, Tampa, FL 33612. 3. No Affiliation. 4. Cancer Epidemiology Department, Moffitt Cancer Center & Research Institute, 12902 Bruce B Downs Blvd, Mail Stop: MRC Can/Cont, Tampa, FL 33612. Electronic address: john.heine@moffitt.org.
Abstract
RATIONALE AND OBJECTIVES: Mammographic density is an important risk factor for breast cancer, but translation to the clinic requires assurance that prior work based on mammography is applicable to current technologies. The purpose of this work is to evaluate whether a calibration methodology developed previously produces breast density metrics predictive of breast cancer risk when applied to a case-control study. MATERIALS AND METHODS: A matched case control study (n = 319 pairs) was used to evaluate two calibrated measures of breast density. Two-dimensional mammograms were acquired from six Hologic mammography units: three conventional Selenia two-dimensional full-field digital mammography systems and three Dimensions digital breast tomosynthesis systems. We evaluated the capability of two calibrated breast density measures to quantify breast cancer risk: the mean (PGm) and standard deviation (PGsd) of the calibrated pixels. Matching variables included age, hormone replacement therapy usage/duration, screening history, and mammography unit. Calibrated measures were compared to the percentage of breast density (PD) determined with the operator-assisted Cumulus method. Conditional logistic regression was used to generate odds ratios (ORs) from continuous and quartile (Q) models with 95% confidence intervals. The area under the receiver operating characteristic curve (Az) was also used as a comparison metric. Both univariate models and models adjusted for body mass index and ethnicity were evaluated. RESULTS: In adjusted models, both PGsd and PD were statistically significantly associated with breast cancer with similar Az of 0.61-0.62. The corresponding ORs and confidence intervals were also similar. For PGsd, the OR was 1.34 (1.09, 1.66) for the continuous measure and 1.83 (1.11, 3.02), 2.19 (1.28, 3.73), and 2.20 (1.26, 3.85) for Q2-Q4. For PD, the OR was 1.43 (1.16, 1.76) for the continuous measure and 0.84 (0.52, 1.38), 1.96 (1.19, 3.23), and 2.27 (1.29, 4.00) for Q2-Q4. The results for PGm were slightly attenuated and not statistically significant. The OR was 1.22 (0.99, 1.51) with Az = 0.60 for the continuous measure and 1.24 (0.78, 1.97), 0.98 (0.60, 1.61), and 1.26, (0.77, 2.07) for Q2-Q4 with Az = 0.60. CONCLUSION: The calibrated PGsd measure provided significant associations with breast cancer comparable to those given by PD. The calibrated PGm performed slightly worse. These findings indicate that the calibration approach developed previously replicates under more general conditions.
RATIONALE AND OBJECTIVES: Mammographic density is an important risk factor for breast cancer, but translation to the clinic requires assurance that prior work based on mammography is applicable to current technologies. The purpose of this work is to evaluate whether a calibration methodology developed previously produces breast density metrics predictive of breast cancer risk when applied to a case-control study. MATERIALS AND METHODS: A matched case control study (n = 319 pairs) was used to evaluate two calibrated measures of breast density. Two-dimensional mammograms were acquired from six Hologic mammography units: three conventional Selenia two-dimensional full-field digital mammography systems and three Dimensions digital breast tomosynthesis systems. We evaluated the capability of two calibrated breast density measures to quantify breast cancer risk: the mean (PGm) and standard deviation (PGsd) of the calibrated pixels. Matching variables included age, hormone replacement therapy usage/duration, screening history, and mammography unit. Calibrated measures were compared to the percentage of breast density (PD) determined with the operator-assisted Cumulus method. Conditional logistic regression was used to generate odds ratios (ORs) from continuous and quartile (Q) models with 95% confidence intervals. The area under the receiver operating characteristic curve (Az) was also used as a comparison metric. Both univariate models and models adjusted for body mass index and ethnicity were evaluated. RESULTS: In adjusted models, both PGsd and PD were statistically significantly associated with breast cancer with similar Az of 0.61-0.62. The corresponding ORs and confidence intervals were also similar. For PGsd, the OR was 1.34 (1.09, 1.66) for the continuous measure and 1.83 (1.11, 3.02), 2.19 (1.28, 3.73), and 2.20 (1.26, 3.85) for Q2-Q4. For PD, the OR was 1.43 (1.16, 1.76) for the continuous measure and 0.84 (0.52, 1.38), 1.96 (1.19, 3.23), and 2.27 (1.29, 4.00) for Q2-Q4. The results for PGm were slightly attenuated and not statistically significant. The OR was 1.22 (0.99, 1.51) with Az = 0.60 for the continuous measure and 1.24 (0.78, 1.97), 0.98 (0.60, 1.61), and 1.26, (0.77, 2.07) for Q2-Q4 with Az = 0.60. CONCLUSION: The calibrated PGsd measure provided significant associations with breast cancer comparable to those given by PD. The calibrated PGm performed slightly worse. These findings indicate that the calibration approach developed previously replicates under more general conditions.
Authors: Jane Ding; Ruth Warren; Iqbal Warsi; Nick Day; Deborah Thompson; Michael Brady; Christopher Tromans; Ralph Highnam; Douglas Easton Journal: Cancer Epidemiol Biomarkers Prev Date: 2008-05 Impact factor: 4.254
Authors: Norman Boyd; Lisa Martin; Anoma Gunasekara; Olga Melnichouk; Gord Maudsley; Chris Peressotti; Martin Yaffe; Salomon Minkin Journal: Cancer Epidemiol Biomarkers Prev Date: 2009-06 Impact factor: 4.254
Authors: Armando Manduca; Michael J Carston; John J Heine; Christopher G Scott; V Shane Pankratz; Kathy R Brandt; Thomas A Sellers; Celine M Vachon; James R Cerhan Journal: Cancer Epidemiol Biomarkers Prev Date: 2009-03-03 Impact factor: 4.254