RATIONALE AND OBJECTIVES: Breast density is a significant breast cancer risk factor that is measured from mammograms. However, uncertainty remains in both understanding its underlying physical properties as it relates to the breast and determining the optimal method for its measurement. A quantitative description of the information captured by the standard operator-assisted percentage of breast density (PD) measure was developed using full-field digital mammography (FFDM) images that were calibrated to adjust for interimage acquisition technique differences. MATERIALS AND METHODS: The information captured by the standard PD measure was quantified by developing a similar measure of breast density (PD(c)) from calibrated mammograms automatically by applying a static threshold to each image. The specific threshold was estimated by first sampling the probability distributions for breast tissue in calibrated mammograms. A percent glandular (PG) measure of breast density was also derived from calibrated mammograms. The PD, PD(c), and PG breast density measures were compared using both linear correlation (R) and quartile odds ratio measures derived from a matched case-control study. RESULTS: The standard PD measure is an estimate of the number of pixel values above a fixed idealized x-ray attenuation fraction. There was significant correlation (P < .0001) between the PD(c)-PD (r = 0.78), PD(c)-PG (r = 0.87), and PD-PG (r = 0.71) measures of breast density. Risk estimates associated with the lowest to highest quartiles for the PD(c) measure (odds ratio [OR]: 1.0 ref., 3.4, 3.6, and 5.6), and the standard PD measure (OR 1.0 ref., 2.9, 4.8, and 5.1) were similar and greater than that of the calibrated PG measure (OR 1.0 ref., 2.0, 2.4, and 2.4). CONCLUSIONS: The information captured by the standard PD measure was quantified as it relates to calibrated mammograms and used to develop an automated method for measuring breast density. These findings represent an initial step for developing an automated measure built on an established calibration platform. A fully developed automated measure may be useful for both research- and clinical-based risk applications.
RATIONALE AND OBJECTIVES: Breast density is a significant breast cancer risk factor that is measured from mammograms. However, uncertainty remains in both understanding its underlying physical properties as it relates to the breast and determining the optimal method for its measurement. A quantitative description of the information captured by the standard operator-assisted percentage of breast density (PD) measure was developed using full-field digital mammography (FFDM) images that were calibrated to adjust for interimage acquisition technique differences. MATERIALS AND METHODS: The information captured by the standard PD measure was quantified by developing a similar measure of breast density (PD(c)) from calibrated mammograms automatically by applying a static threshold to each image. The specific threshold was estimated by first sampling the probability distributions for breast tissue in calibrated mammograms. A percent glandular (PG) measure of breast density was also derived from calibrated mammograms. The PD, PD(c), and PG breast density measures were compared using both linear correlation (R) and quartile odds ratio measures derived from a matched case-control study. RESULTS: The standard PD measure is an estimate of the number of pixel values above a fixed idealized x-ray attenuation fraction. There was significant correlation (P < .0001) between the PD(c)-PD (r = 0.78), PD(c)-PG (r = 0.87), and PD-PG (r = 0.71) measures of breast density. Risk estimates associated with the lowest to highest quartiles for the PD(c) measure (odds ratio [OR]: 1.0 ref., 3.4, 3.6, and 5.6), and the standard PD measure (OR 1.0 ref., 2.9, 4.8, and 5.1) were similar and greater than that of the calibrated PG measure (OR 1.0 ref., 2.0, 2.4, and 2.4). CONCLUSIONS: The information captured by the standard PD measure was quantified as it relates to calibrated mammograms and used to develop an automated method for measuring breast density. These findings represent an initial step for developing an automated measure built on an established calibration platform. A fully developed automated measure may be useful for both research- and clinical-based risk applications.
Authors: Saskia van Engeland; Peter R Snoeren; Henkjan Huisman; Carla Boetes; Nico Karssemeijer Journal: IEEE Trans Med Imaging Date: 2006-03 Impact factor: 10.048
Authors: Norman F Boyd; Johanna M Rommens; Kelly Vogt; Vivian Lee; John L Hopper; Martin J Yaffe; Andrew D Paterson Journal: Lancet Oncol Date: 2005-10 Impact factor: 41.316
Authors: John J Heine; Michael J Carston; Christopher G Scott; Kathleen R Brandt; Fang-Fang Wu; Vernon Shane Pankratz; Thomas A Sellers; Celine M Vachon Journal: Cancer Epidemiol Biomarkers Prev Date: 2008-11 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: John J Heine; Christopher G Scott; Thomas A Sellers; Kathleen R Brandt; Daniel J Serie; Fang-Fang Wu; Marilyn J Morton; Beth A Schueler; Fergus J Couch; Janet E Olson; V Shane Pankratz; Celine M Vachon Journal: J Natl Cancer Inst Date: 2012-07-03 Impact factor: 13.506
Authors: Yuanjie Zheng; Brad M Keller; Shonket Ray; Yan Wang; Emily F Conant; James C Gee; Despina Kontos Journal: Med Phys Date: 2015-07 Impact factor: 4.071
Authors: Brad M Keller; Diane L Nathan; Yan Wang; Yuanjie Zheng; James C Gee; Emily F Conant; Despina Kontos Journal: Med Phys Date: 2012-08 Impact factor: 4.071
Authors: Erin E E Fowler; Autumn Smallwood; Cassandra Miltich; Jennifer Drukteinis; Thomas A Sellers; John Heine Journal: Phys Med Biol Date: 2018-12-19 Impact factor: 3.609
Authors: Erin E Fowler; Anders Berglund; Michael J Schell; Thomas A Sellers; Steven Eschrich; John Heine Journal: J Biomed Inform Date: 2020-03-12 Impact factor: 6.317