Extending the Breast Cancer Surveillance Consortium Model of Invasive Breast Cancer

被引:5
作者
Gard, Charlotte C. [1 ]
Tice, Jeffrey A. [2 ]
Miglioretti, Diana L. [3 ,4 ]
Sprague, Brian L. [5 ,6 ]
Bissell, Michael C. S. [3 ]
Henderson, Louise M. [7 ]
Kerlikowske, Karla [8 ,9 ,10 ]
机构
[1] New Mexico State Univ, Dept Econ Appl Stat & Int Business, Las Cruces, NM USA
[2] Univ Calif San Francisco, Dept Med, Div Gen Internal Med, 1545 Divisadero St,Ste 309, San Francisco 94143, CA USA
[3] Univ Calif Davis, Davis, CA USA
[4] Kaiser Permanente Washington Hlth Res Inst, Seattle, WA USA
[5] Univ Vermont, Dept Surg, Canc Ctr, Burlington, VT USA
[6] Univ Vermont, Canc Ctr, Dept Radiol, Burlington, VT USA
[7] Univ N Carolina, Dept Radiol, Chapel Hill, NC USA
[8] Univ Calif San Francisco, Dept Vet Affairs, Gen Internal Med Sect, San Francisco, CA USA
[9] Univ Calif San Francisco, Dept Med, San Francisco, CA USA
[10] Univ Calif San Francisco, Dept Epidemiol & Biostat, San Francisco, CA USA
关键词
RISK PREDICTION; FAMILY-HISTORY; WOMEN; DENSITY; PERFORMANCE; VALIDATION;
D O I
10.1200/JCO.22.02470
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
PURPOSEWe extended the Breast Cancer Surveillance Consortium (BCSC) version 2 (v2) model of invasive breast cancer risk to include BMI, extended family history of breast cancer, and age at first live birth (version 3 [v3]) to better inform appropriate breast cancer prevention therapies and risk-based screening.METHODSWe used Cox proportional hazards regression to estimate the age- and race- and ethnicity-specific relative hazards for family history of breast cancer, breast density, history of benign breast biopsy, BMI, and age at first live birth for invasive breast cancer in the BCSC cohort. We evaluated calibration using the ratio of expected-to-observed (E/O) invasive breast cancers in the cohort and discrimination using the area under the receiver operating characteristic curve (AUROC).RESULTSWe analyzed data from 1,455,493 women age 35-79 years without a history of breast cancer. During a mean follow-up of 7.3 years, 30,266 women were diagnosed with invasive breast cancer. The BCSC v3 model had an E/O of 1.03 (95% CI, 1.01 to 1.04) and an AUROC of 0.646 for 5-year risk. Compared with the v2 model, discrimination of the v3 model improved most in Asian, White, and Black women. Among women with a BMI of 30.0-34.9 kg/m2, the true-positive rate in women with an estimated 5-year risk of 3% or higher increased from 10.0% (v2) to 19.8% (v3) and the improvement was greater among women with a BMI of >= 35 kg/m2 (7.6%-19.8%).CONCLUSIONThe BCSC v3 model updates an already well-calibrated and validated breast cancer risk assessment tool to include additional important risk factors. The inclusion of BMI was associated with the largest improvement in estimated risk for individual women. Adding BMI and extended family history to the BCSC model improved discrimination and the true positive rate.
引用
收藏
页码:779 / 789
页数:13
相关论文
共 44 条
  • [1] Family History of Breast Cancer, Breast Density, and Breast Cancer Risk in a US Breast Cancer Screening Population
    Ahern, Thomas P.
    Sprague, Brian L.
    Bissell, Michael C. S.
    Miglioretti, Diana L.
    Buist, Diana S. M.
    Braithwaite, Dejana
    Kerlikowske, Karla
    [J]. CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION, 2017, 26 (06) : 938 - 944
  • [2] Population-based relative risks for specific family history constellations of breast cancer
    Albright, Frederick S.
    Kohlmann, Wendy
    Neumayer, Leigh
    Buys, Saundra S.
    Matsen, Cindy B.
    Kaphingst, Kimberly A.
    Cannon-Albright, Lisa A.
    [J]. CANCER CAUSES & CONTROL, 2019, 30 (06) : 581 - 590
  • [3] [Anonymous], 2021, CDC WONDER Online Database
  • [4] Breast Cancer Population Attributable Risk Proportions Associated with Body Mass Index and Breast Density by Race/Ethnicity and Menopausal Status
    Bissell, Michael C. S.
    Kerlikowske, Karla
    Sprague, Brian L.
    Tice, Jeffery A.
    Gard, Charlotte C.
    Tossas, Katherine Y.
    Rauscher, Garth H.
    Trentham-Dietz, Amy
    Henderson, Louise M.
    Onega, Tracy
    Keegan, Theresa H. M.
    Miglioretti, Diana L.
    [J]. CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION, 2020, 29 (10) : 2048 - 2056
  • [5] Prospective Approach to Breast Cancer Risk Prediction in African American Women: The Black Women's Health Study Model
    Boggs, Deborah A.
    Rosenberg, Lynn
    Adams-Campbell, Lucile L.
    Palmer, Julie R.
    [J]. JOURNAL OF CLINICAL ONCOLOGY, 2015, 33 (09) : 1038 - +
  • [6] Evaluation of the Tyrer-Cuzick (International Breast Cancer Intervention Study) Model for Breast Cancer Risk Prediction in Women With Atypical Hyperplasia
    Boughey, Judy C.
    Hartmann, Lynn C.
    Anderson, Stephanie S.
    Degnim, Amy C.
    Vierkant, Robert A.
    Reynolds, Carol A.
    Frost, Marlene H.
    Pankratz, V. Shane
    [J]. JOURNAL OF CLINICAL ONCOLOGY, 2010, 28 (22) : 3591 - 3596
  • [7] Breast Cancer Surveillance Consortium, Breast Cancer Surveillance Consortium (BCSC)
  • [8] Breast Cancer Surveillance Consortium, BCSC Tools
  • [9] Long-term Accuracy of Breast Cancer Risk Assessment Combining Classic Risk Factors and Breast Density
    Brentnall, Adam R.
    Cuzick, Jack
    Buist, Diana S. M.
    Bowles, Erin J. Aiello
    [J]. JAMA ONCOLOGY, 2018, 4 (09)
  • [10] Estimation of time-dependent area under the ROC curve for long-term risk prediction
    Chambless, Lloyd E.
    Diao, Guoqing
    [J]. STATISTICS IN MEDICINE, 2006, 25 (20) : 3474 - 3486