Risk prediction models of breast cancer: a systematic review of model performances
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The number of risk prediction models has been increasingly developed, for estimating about breast cancer in individual women. However, those model performances are questionable. We therefore have conducted a study with the aim to systematically review previous risk prediction models. The results from this review help to identify the most reliable model and indicate the strengths and weaknesses of each model for guiding future model development. We searched MEDLINE (PubMed) from 1949 and EMBASE (Ovid) from 1974 until October 2010. Observational studies which constructed models using regression methods were selected. Information about model development and performance were extracted. Twenty-five out of 453 studies were eligible. Of these, 18 developed prediction models and 7 validated existing prediction models. Up to 13 variables were included in the models and sample sizes for each study ranged from 550 to 2,404,636. Internal validation was performed in four models, while five models had external validation. Gail and Rosner and Colditz models were the significant models which were subsequently modified by other scholars. Calibration performance of most models was fair to good (expected/observe ratio: 0.87–1.12), but discriminatory accuracy was poor to fair both in internal validation (concordance statistics: 0.53–0.66) and in external validation (concordance statistics: 0.56–0.63). Most models yielded relatively poor discrimination in both internal and external validation. This poor discriminatory accuracy of existing models might be because of a lack of knowledge about risk factors, heterogeneous subtypes of breast cancer, and different distributions of risk factors across populations. In addition the concordance statistic itself is insensitive to measure the improvement of discrimination. Therefore, the new method such as net reclassification index should be considered to evaluate the improvement of the performance of a new develop model.
KeywordsBreast cancer Risk prediction model Systematic review
This study was supported by the Health Intervention and Technology Assessment Program, the Thai Health Promotion Foundation, the Health Systems Research Institute, the Bureau of Policy and Strategy of the Ministry of Public Health, and Thai Health-Global Link Initiative Project.
Conflict of interest
TA: received honorarium from the Health Intervention and Technology Assessment Program, Ministry of Public Health, Thailand; received travel grants for conference from the Health Intervention and Technology Assessment Program and Faculty of Graduate Studies, Mahidol University, Thailand. YT, CW, VK, and AT have no conflicts of interest to declare.
- 2.Ferlay J, Bray F, Parkin DM, Pisani P eds (2001) Globalcan 2000. IARC Press, LyonGoogle Scholar
- 3.VH BF (2002) IARC handbooks of cancer prevention. IARC Press, LyonGoogle Scholar
- 4.Ries LAG EM, Kosary CL, et al. (2004) SEER cancer statistics review, 1975–2001. National Cancer Institute. http://seer.cancer.gov/csr/1975_2001/. Accessed Dec 22 2004
- 5.Steyerberg EW (ed) (2009) Clinical prediction models a practical approach to development, validation, and updating. Springer Science + Business Media LLC, New YorkGoogle Scholar
- 10.Decarli A, Calza S, Masala G, Specchia C, Palli D, Gail MH (2006) Gail model for prediction of absolute risk of invasive breast cancer: independent evaluation in the Florence-European prospective investigation into cancer and nutrition cohort. J Natl Cancer Inst 98(23):1686–1693PubMedCrossRefGoogle Scholar
- 14.Barlow WE, White E, Ballard-Barbash R, Vacek PM, Titus-Ernstoff L, Carney PA, Tice JA, Buist DS, Geller BM, Rosenberg R, Yankaskas BC, Kerlikowske K (2006) Prospective breast cancer risk prediction model for women undergoing screening mammography. J Natl Cancer Inst 98(17):1204–1214. doi:10.1093/jnci/djj331 PubMedCrossRefGoogle Scholar
- 16.Gail MH, Costantino JP, Pee D, Bondy M, Newman L, Selvan M, Anderson GL, Malone KE, Marchbanks PA, McCaskill-Stevens W, Norman SA, Simon MS, Spirtas R, Ursin G, Bernstein L (2007) Projecting individualized absolute invasive breast cancer risk in African American women. J Natl Cancer Inst 99(23):1782–1792PubMedCrossRefGoogle Scholar
- 19.Tamimi RM, Rosner B, Colditz GA Evaluation of a breast cancer risk prediction model expanded to include category of prior benign breast disease lesion. Cancer. doi: 10.1002/cncr.25386
- 26.McKian KP, Reynolds CA, Visscher DW, Nassar A, Radisky DC, Vierkant RA, Degnim AC, Boughey JC, Ghosh K, Anderson SS, Minot D, Caudill JL, Vachon CM, Frost MH, Pankratz VS, Hartmann LC (2009) Novel breast tissue feature strongly associated with risk of breast cancer. J Clin Oncol 27(35):5893–5898PubMedCrossRefGoogle Scholar
- 38.Haynes RB, Sackett DL, Guyatt GH (eds) (2006) Clinical epidemiology: how to do clinical practice research, 3rd edn. Lippincott Williams and Wilkins, PhiladelphiaGoogle Scholar
- 39.Breast cancer and hormone replacement therapy: collaborative reanalysis of data from 51 epidemiological studies of 52,705 women with breast cancer and 108,411 women without breast cancer. Collaborative Group on Hormonal Factors in Breast Cancer (1997). Lancet 350(9084):1047–1059Google Scholar
- 42.North RA, McCowan LME, Dekker GA, Poston L, Chan EHY, Stewart AW, Black MA, Taylor RS, Walker JJ, Baker PN, Kenny LC (2011) Clinical risk prediction for pre-eclampsia in nulliparous women: development of model in international prospective cohort. BMJ (Clinical research ed) 342:d1875s. doi: 10.1136/bmj.d1875