BACKGROUND: Current models for assessing breast cancer risk are complex and do not include breast density, a strong risk factor for breast cancer that is routinely reported with mammography. OBJECTIVE: To develop and validate an easy-to-use breast cancer risk prediction model that includes breast density. DESIGN: Empirical model based on Surveillance, Epidemiology, and End Results incidence, and relative hazards from a prospective cohort. SETTING: Screening mammography sites participating in the Breast Cancer Surveillance Consortium. PATIENTS: 1,095,484 women undergoing mammography who had no previous diagnosis of breast cancer. MEASUREMENTS: Self-reported age, race or ethnicity, family history of breast cancer, and history of breast biopsy. Community radiologists rated breast density by using 4 Breast Imaging Reporting and Data System categories. RESULTS: During 5.3 years of follow-up, invasive breast cancer was diagnosed in 14,766 women. The breast density model was well calibrated overall (expected-observed ratio, 1.03 [95% CI, 0.99 to 1.06]) and in racial and ethnic subgroups. It had modest discriminatory accuracy (concordance index, 0.66 [CI, 0.65 to 0.67]). Women with low-density mammograms had 5-year risks less than 1.67% unless they had a family history of breast cancer and were older than age 65 years. LIMITATION: The model has only modest ability to discriminate between women who will develop breast cancer and those who will not. CONCLUSION: A breast cancer prediction model that incorporates routinely reported measures of breast density can estimate 5-year risk for invasive breast cancer. Its accuracy needs to be further evaluated in independent populations before it can be recommended for clinical use.
BACKGROUND: Current models for assessing breast cancer risk are complex and do not include breast density, a strong risk factor for breast cancer that is routinely reported with mammography. OBJECTIVE: To develop and validate an easy-to-use breast cancer risk prediction model that includes breast density. DESIGN: Empirical model based on Surveillance, Epidemiology, and End Results incidence, and relative hazards from a prospective cohort. SETTING: Screening mammography sites participating in the Breast Cancer Surveillance Consortium. PATIENTS: 1,095,484 women undergoing mammography who had no previous diagnosis of breast cancer. MEASUREMENTS: Self-reported age, race or ethnicity, family history of breast cancer, and history of breast biopsy. Community radiologists rated breast density by using 4 Breast Imaging Reporting and Data System categories. RESULTS: During 5.3 years of follow-up, invasive breast cancer was diagnosed in 14,766 women. The breast density model was well calibrated overall (expected-observed ratio, 1.03 [95% CI, 0.99 to 1.06]) and in racial and ethnic subgroups. It had modest discriminatory accuracy (concordance index, 0.66 [CI, 0.65 to 0.67]). Women with low-density mammograms had 5-year risks less than 1.67% unless they had a family history of breast cancer and were older than age 65 years. LIMITATION: The model has only modest ability to discriminate between women who will develop breast cancer and those who will not. CONCLUSION: A breast cancer prediction model that incorporates routinely reported measures of breast density can estimate 5-year risk for invasive breast cancer. Its accuracy needs to be further evaluated in independent populations before it can be recommended for clinical use.
Authors: M H Gail; L A Brinton; D P Byar; D K Corle; S B Green; C Schairer; J J Mulvihill Journal: J Natl Cancer Inst Date: 1989-12-20 Impact factor: 13.506
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: C Byrne; C Schairer; J Wolfe; N Parekh; M Salane; L A Brinton; R Hoover; R Haile Journal: J Natl Cancer Inst Date: 1995-11-01 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: Amanda I Phipps; Christopher I Li; Karla Kerlikowske; William E Barlow; Diana S M Buist Journal: Cancer Epidemiol Biomarkers Prev Date: 2010-05-25 Impact factor: 4.254
Authors: Karla Kerlikowske; Andrea J Cook; Diana S M Buist; Steve R Cummings; Celine Vachon; Pamela Vacek; Diana L Miglioretti Journal: J Clin Oncol Date: 2010-07-19 Impact factor: 44.544
Authors: P A Fasching; A B Ekici; D L Wachter; A Hein; C M Bayer; L Häberle; C R Loehberg; M Schneider; S M Jud; K Heusinger; M Rübner; C Rauh; M R Bani; M P Lux; R Schulz-Wendtland; A Hartmann; M W Beckmann Journal: Geburtshilfe Frauenheilkd Date: 2013-12 Impact factor: 2.915
Authors: Merlise A Clyde; Rachel Palmieri Weber; Edwin S Iversen; Elizabeth M Poole; Jennifer A Doherty; Marc T Goodman; Roberta B Ness; Harvey A Risch; Mary Anne Rossing; Kathryn L Terry; Nicolas Wentzensen; Alice S Whittemore; Hoda Anton-Culver; Elisa V Bandera; Andrew Berchuck; Michael E Carney; Daniel W Cramer; Julie M Cunningham; Kara L Cushing-Haugen; Robert P Edwards; Brooke L Fridley; Ellen L Goode; Galina Lurie; Valerie McGuire; Francesmary Modugno; Kirsten B Moysich; Sara H Olson; Celeste Leigh Pearce; Malcolm C Pike; Joseph H Rothstein; Thomas A Sellers; Weiva Sieh; Daniel Stram; Pamela J Thompson; Robert A Vierkant; Kristine G Wicklund; Anna H Wu; Argyrios Ziogas; Shelley S Tworoger; Joellen M Schildkraut Journal: Am J Epidemiol Date: 2016-10-03 Impact factor: 4.897
Authors: Brian L Sprague; Natasha K Stout; Clyde Schechter; Nicolien T van Ravesteyn; Mucahit Cevik; Oguzhan Alagoz; Christoph I Lee; Jeroen J van den Broek; Diana L Miglioretti; Jeanne S Mandelblatt; Harry J de Koning; Karla Kerlikowske; Constance D Lehman; Anna N A Tosteson Journal: Ann Intern Med Date: 2015-02-03 Impact factor: 25.391