Long-Term accuracy of breast cancer risk assessment combining classic risk factors and breast density

JAMA Oncology

Volumn 4, Issue 9, 2018, Pages

  Brentnall, Adam R ^a   Cuzick, Jack ^a   Buist, Diana S M ^b   Bowles, Erin J Aiello ^b  

^a QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

^b KAISER PERMANENTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ADULT; AGED; ARTICLE; BREAST CANCER; BREAST DENSITY; CALIBRATION; CANCER INCIDENCE; CANCER RISK; FEMALE; FOLLOW UP; HAZARD RATIO; HUMAN; INTRADUCTAL CARCINOMA; MAJOR CLINICAL STUDY; MAMMOGRAPHY; QUESTIONNAIRE; RISK ASSESSMENT; RISK FACTOR; BREAST; BREAST TUMOR; COHORT ANALYSIS; DIAGNOSTIC IMAGING; EARLY CANCER DIAGNOSIS; MIDDLE AGED; PATHOLOGY; PROCEDURES; REPRODUCIBILITY; TIME FACTOR;

ADULT; AGED; BREAST; BREAST DENSITY; BREAST NEOPLASMS; COHORT STUDIES; EARLY DETECTION OF CANCER; FEMALE; HUMANS; MAMMOGRAPHY; MIDDLE AGED; REPRODUCIBILITY OF RESULTS; RISK ASSESSMENT; RISK FACTORS; TIME FACTORS;

EID: 85048295777     PISSN: 23742437     EISSN: 23742445     Source Type: Journal    
DOI: 10.1001/jamaoncol.2018.0174     Document Type: Article

References (37)

1. Ferlay J, Soerjomataram I, Ervik M, et al, eds. GLOBOCAN 2012: Estimated Cancer Incidence and MortalityWorldwide in 2012, version 1.0. IARC Cancer Base 11. Lyon, France: International Agency for Research on Cancer; 2013. http://globocan.iarc.fr. Accessed July 23, 2014.
2. Marmot MG, Altman DG, Cameron DA, Dewar JA, Thompson SG, Wilcox M. The benefits and harms of breast cancer screening. Br J Cancer. 2013; 108(11):2205-2240.
3. Massat NJ, Dibden A, Parmar D, Cuzick J, Sasieni PD, Duffy SW. Impact of screening on breast cancer mortality. Cancer Epidemiol Biomarkers Prev. 2016; 25(3):455-462.
4. Cuzick J, Sestak I, Bonanni B, et al; SERM Chemoprevention of Breast Cancer Overview Group. Selective oestrogen receptor modulators in prevention of breast cancer. Lancet. 2013;381 (9880):1827-1834.
5. Gail MH, Brinton LA, Byar DP, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst. 1989;81(24):1879-1886.
6. Tyrer J, Duffy SW, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med. 2004;23(7):1111-1130.
7. Saslow D, Boetes C, BurkeW, et al; American Cancer Society Breast Cancer Advisory Group. American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J Clin. 2007;57(2):75-89.
8. Visvanathan K, Hurley P, Bantug E, et al. Use of pharmacologic interventions for breast cancer risk reduction. J Clin Oncol. 2013;31(23):2942-2962.
9. Smith RA, Manassaram-Baptiste D, Brooks D, et al. Cancer screening in the United States, 2015. CA Cancer J Clin. 2015;65(1):30-54.
10. Howell A, Anderson AS, Clarke RB, et al. Risk determination and prevention of breast cancer. Breast Cancer Res. 2014;16(5):446.
11. Brentnall AR, Harkness EF, Astley SM, et al. Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort. Breast Cancer Res. 2015;17(1):147.
12. Warwick J, Birke H, Stone J, et al. Mammographic breast density refines Tyrer-Cuzick estimates of breast cancer risk in high-risk women. Breast Cancer Res. 2014;16(5):451.
13. Tice JA, Cummings SR, Smith-Bindman R, Ichikawa L, Barlow WE, Kerlikowske K. Using clinical factors and mammographic breast density to estimate breast cancer risk. Ann Intern Med. 2008; 148(5):337-347.
14. Tice JA, Miglioretti DL, Li CS, Vachon CM, Gard CC, Kerlikowske K. Breast density and benign breast disease: risk assessment to identify women at high risk of breast cancer. J Clin Oncol. 2015;33(28):3137-3143.
15. Vachon CM, Pankratz VS, Scott CG, et al. The contributions of breast density and common genetic variation to breast cancer risk. J Natl Cancer Inst. 2015;107(5):dju397.
16. Amir E, Evans DG, Shenton A, et al. Evaluation of breast cancer risk assessment packages in the Family History Evaluation and Screening Programme. J Med Genet. 2003;40(11):807-814.
17. Quante AS, Whittemore AS, Shriver T, Strauch K, Terry MB. Breast cancer risk assessment across the risk continuum. Breast Cancer Res. 2012;14(6): R144.
18. Gail MH, Mai PL. Comparing breast cancer risk assessment models. J Natl Cancer Inst. 2010;102 (10):665-668.
19. Dorsi C, Sickles E, Mendelson E, et al; American College of Radiology BI-RADS Committee. ACR BI-RADS Atlas: Breast Imaging Reporting and Data System. Reston, VA: American College of Radiology; 2013.
20. Ballard-Barbash R, Taplin SH, Yankaskas BC, et al. Breast Cancer Surveillance Consortium. AJR Am J Roentgenol. 1997;169(4):1001-1008.
21. Taplin SH, Ichikawa L, Buist DS, Seger D, White E. Evaluating organized breast cancer screening implementation: The prevention of late-stage disease? Cancer Epidemiol Biomarkers Prev. 2004; 13(2):225-234.
22. Buist DS, Walker R, Bowles EJ, et al. Screening mammography use among current, former, and never hormone therapy users may not explain recent declines in breast cancer incidence. Cancer Epidemiol Biomarkers Prev. 2012;21(5):720-727.
23. Group Health Cooperative. Risk Factor Questionnaire 2.2. http://www.bcsc-research.org/data/questionnaires/ssq-ghc-pat.pdf. Updated June 2016. Accessed September 19, 2017.
24. Fred Hutchinson Cancer Research Center. Cancer Surveillance System. http://www.fredhutch.org/en/labs/phs/projects/cancer-surveillance-system.html. Updated 2018. Accessed January 5, 2018.
25. Kaiser PermanenteWashington Health Research Institute. Kasier PermenanteWashington Breast Cancer Surveillance Registry. http://www.kpwashingtonresearch.org/our-research/research-Areas/cancer/breast-cancer-surveillance. Updated 2015. Accessed January 5, 2018.
26. Wieneke AE, Bowles EJ, Cronkite D, et al. Validation of natural language processing to extract breast cancer pathology procedures and results. J Pathol Inform. 2015;6:38.
27. US Department of Agriculture. Rural-urban commuting area codes. http://www.ers.usda.gov/data-products/rural-urban-commuting-Area-codes. Updated October 12, 2016. Accessed September 19, 2017.
28. Grambsch PM, Therneau TM. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika. 1994;81(3):515-526.
29. Wood SN. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc Series B Stat Methodol. 2011;73(1):3-36.
30. R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2012.
31. Kassambara A, Kosinski M. survminer: Survival analysis and visualization. 2017. R package version 0.4.0. https://cran.r-project.org/web/packages/survminer. Accessed February 13, 2018.
32. Therneau TM. A Package for Survival Analysis in S, version 2.38. 2015. https://cran.r-project.org/web/packages/survival. Accessed February 13, 2018.
33. Kerlikowske K, Gard CC, Sprague BL, Tice JA, Miglioretti DL; Breast Cancer Surveillance Consortium. One versus two breast density measures to predict 5-And 10-year breast cancer risk. Cancer Epidemiol Biomarkers Prev. 2015;24(6): 889-897.
34. Kerlikowske K, ZhuW, Tosteson ANA, et al; Breast Cancer Surveillance Consortium. Identifying women with dense breasts at high risk for interval cancer. Ann Intern Med. 2015;162(10):673-681.
35. Kerlikowske K, Gard CC, Tice JA, Ziv E, Cummings SR, Miglioretti DL; Breast Cancer Surveillance Consortium. Risk factors that increase risk of estrogen receptor-positive and-negative breast cancer. J Natl Cancer Inst. 2016;109(5):djw276.
36. IBIS Breast Cancer Risk Evaluation Tool. http://www.ems-Trials.org/riskevaluator. Updated September 17, 2017. Accessed February 13, 2018.
37. Howlader N, Noone AM, Krapcho M, et al, eds. SEER Cancer Statistics Review (CSR) 1975-2014. Bethesda, MD: National Cancer Institute; April 2017. https://seer.cancer.gov/csr/1975-2014/. Updated June 28, 2017. Accessed November 1, 2017.