PURPOSE: This study describes the design and characteristics of a highly accurate, precise, and automated single-energy method to quantify percent fibroglandular tissue volume (%FGV) and fibroglandular tissue volume (FGV) using digital screening mammography. METHODS: The method uses a breast tissue-equivalent phantom in the unused portion of the mammogram as a reference to estimate breast composition. The phantom is used to calculate breast thickness and composition for each image regardless of x-ray technique or the presence of paddle tilt. The phantom adheres to the top of the mammographic compression paddle and stays in place for both craniocaudal and mediolateral oblique screening views. We describe the automated method to identify the phantom and paddle orientation with a three-dimensional reconstruction least-squares technique. A series of test phantoms, with a breast thickness range of 0.5-8 cm and a %FGV of 0%-100%, were made to test the accuracy and precision of the technique. RESULTS: Using test phantoms, the estimated repeatability standard deviation equaled 2%, with a +/-2% accuracy for the entire thickness and density ranges. Without correction, paddle tilt was found to create large errors in the measured density values of up to 7%/mm difference from actual breast thickness. This new density measurement is stable over time, with no significant drifts in calibration noted during a four-month period. Comparisons of %FGV to mammographic percent density and left to right breast %FGV were highly correlated (r=0.83 and 0.94, respectively). CONCLUSIONS: An automated method for quantifying fibroglandular tissue volume has been developed. It exhibited good accuracy and precision for a broad range of breast thicknesses, paddle tilt angles, and %FGV values. Clinical testing showed high correlation to mammographic density and between left and right breasts.
PURPOSE: This study describes the design and characteristics of a highly accurate, precise, and automated single-energy method to quantify percent fibroglandular tissue volume (%FGV) and fibroglandular tissue volume (FGV) using digital screening mammography. METHODS: The method uses a breast tissue-equivalent phantom in the unused portion of the mammogram as a reference to estimate breast composition. The phantom is used to calculate breast thickness and composition for each image regardless of x-ray technique or the presence of paddle tilt. The phantom adheres to the top of the mammographic compression paddle and stays in place for both craniocaudal and mediolateral oblique screening views. We describe the automated method to identify the phantom and paddle orientation with a three-dimensional reconstruction least-squares technique. A series of test phantoms, with a breast thickness range of 0.5-8 cm and a %FGV of 0%-100%, were made to test the accuracy and precision of the technique. RESULTS: Using test phantoms, the estimated repeatability standard deviation equaled 2%, with a +/-2% accuracy for the entire thickness and density ranges. Without correction, paddle tilt was found to create large errors in the measured density values of up to 7%/mm difference from actual breast thickness. This new density measurement is stable over time, with no significant drifts in calibration noted during a four-month period. Comparisons of %FGV to mammographic percent density and left to right breast %FGV were highly correlated (r=0.83 and 0.94, respectively). CONCLUSIONS: An automated method for quantifying fibroglandular tissue volume has been developed. It exhibited good accuracy and precision for a broad range of breast thicknesses, paddle tilt angles, and %FGV values. Clinical testing showed high correlation to mammographic density and between left and right breasts.
Authors: Olga Pawluczyk; Bindu J Augustine; Martin J Yaffe; Dan Rico; Jiwei Yang; Gordon E Mawdsley; Norman F Boyd Journal: Med Phys Date: 2003-03 Impact factor: 4.071
Authors: N F Boyd; J W Byng; R A Jong; E K Fishell; L E Little; A B Miller; G A Lockwood; D L Tritchler; M J Yaffe Journal: J Natl Cancer Inst Date: 1995-05-03 Impact factor: 13.506
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: Vicki Hart; Katherine W Reeves; Susan R Sturgeon; Nicholas G Reich; Lynnette Leidy Sievert; Karla Kerlikowske; Lin Ma; John Shepherd; Jeffrey A Tice; Amir Pasha Mahmoudzadeh; Serghei Malkov; Brian L Sprague Journal: Cancer Epidemiol Biomarkers Prev Date: 2015-08-27 Impact factor: 4.254
Authors: John A Shepherd; Karla Kerlikowske; Lin Ma; Frederick Duewer; Bo Fan; Jeff Wang; Serghei Malkov; Eric Vittinghoff; Steven R Cummings Journal: Cancer Epidemiol Biomarkers Prev Date: 2011-05-24 Impact factor: 4.254
Authors: Hannah Oh; Zeina G Khodr; Mark E Sherman; Maya Palakal; Ruth M Pfeiffer; Laura Linville; Berta M Geller; Pamela M Vacek; Donald L Weaver; Rachael E Chicoine; Roni T Falk; Hisani N Horne; Daphne Papathomas; Deesha A Patel; Jackie Xiang; Xia Xu; Timothy Veenstra; Stephen M Hewitt; John A Shepherd; Louise A Brinton; Jonine D Figueroa; Gretchen L Gierach Journal: Horm Cancer Date: 2016-05-02 Impact factor: 3.869
Authors: Gretchen L Gierach; Deesha A Patel; Roni T Falk; Ruth M Pfeiffer; Berta M Geller; Pamela M Vacek; Donald L Weaver; Rachael E Chicoine; John A Shepherd; Amir Pasha Mahmoudzadeh; Jeff Wang; Bo Fan; Sally D Herschorn; Xia Xu; Timothy Veenstra; Barbara Fuhrman; Mark E Sherman; Louise A Brinton Journal: Horm Cancer Date: 2015-03-11 Impact factor: 3.869