The online version of this article (doi:10.1186/1471-2407-13-587) contains supplementary material, which is available to authorized users.
The authors declare that they have no competing interest in the research.
MB and MR conceived and coordinated the study, discussed the results and reviewed the manuscript. AA and CF performed the statistical analysis and drafted the manuscript. JM and NT discussed the results and reviewed the manuscript. All authors read and approved the final manuscript.
The aim of this study was to evaluate the calibration and discriminatory power of three predictive models of breast cancer risk.
We included 13,760 women who were first-time participants in the Sabadell-Cerdanyola Breast Cancer Screening Program, in Catalonia, Spain. Projections of risk were obtained at three and five years for invasive cancer using the Gail, Chen and Barlow models. Incidence and mortality data were obtained from the Catalan registries. The calibration and discrimination of the models were assessed using the Hosmer-Lemeshow C statistic, the area under the receiver operating characteristic curve (AUC) and the Harrell’s C statistic.
The Gail and Chen models showed good calibration while the Barlow model overestimated the number of cases: the ratio between estimated and observed values at 5 years ranged from 0.86 to 1.55 for the first two models and from 1.82 to 3.44 for the Barlow model. The 5-year projection for the Chen and Barlow models had the highest discrimination, with an AUC around 0.58. The Harrell’s C statistic showed very similar values in the 5-year projection for each of the models. Although they passed the calibration test, the Gail and Chen models overestimated the number of cases in some breast density categories.
These models cannot be used as a measure of individual risk in early detection programs to customize screening strategies. The inclusion of longitudinal measures of breast density or other risk factors in joint models of survival and longitudinal data may be a step towards personalized early detection of BC.
Additional file 1: Table S1: Incidence rates of breast cancer and mortality rates from other causes in Catalonia. (DOCX 13 KB)12885_2013_4193_MOESM1_ESM.docx
El cancer en España.com: Sociedad Española de Oncología Médica (SEOM). http://www.seom.org/es/prensa/el-cancer-en-espanyacom?format=pdf,
Cronin KA, Feuer EJ, Clarke LD, Plevritis SK: Impact of adjuvant therapy and mammography on U.S. mortality from, to 2000: comparison of mortality results from the CISNET breast cancer base case analysis. J Natl Cancer Inst Monogr. 1975, 2006 (36): 112-121. CrossRef
Feuer EJ: Modeling the Impact of Adjuvant Therapy and Screening Mammography on U.S. Breast Cancer Mortality Between 1975 and 2000: Introduction to the Problem. J Natl Cancer Inst Monogr. 2000, 2006 (36): 2-6. CrossRef
Baré M, Bonfill X, Andreu X: Relationship between the method of detection and prognostic factors for breast cancer in a community with a screening programme. J Med Screen. 2006, 13 (4): 183-191. PubMed
American College of Radiology: The American College of Radiology Breast Imaging Reporting and Data System (BI-RADS). 2003, Reston (VA): American College of Radiology, 3
Anderson SJ, Ahnn S, Duff K: NSABP Biostatistical Center Technical Report. 1992, Pittsburgh (PA): Department of Biostatistics, University of Pittsburgh
Wolfram Research, Inc.: Mathematica version 7. 2008, USA: Wolfram Research
Hosmer D, Lemeshow S: Applied logistic regression. 2000, New York: Wiley CrossRef
StataCorp: Stata Statistical Software: Release 11. 2009, College Station, TX: StataCorp LP
Decarli A, Calza S, Masala G, Specchia C, Palli D, Gail MH: 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. 2006, 98 (23): 1686-1693. 10.1093/jnci/djj463. CrossRefPubMed
Pastor-Barriuso R, Ascunce N, Ederra M, Erdozáin N, Murillo A, Alés-Martínez JE, et al: Recalibration of the Gail model for predicting invasive breast cancer risk in Spanish women: a population-based cohort study. Breast Cancer Res Treat. 2013, 138 (1): 249-259. 10.1007/s10549-013-2428-y. CrossRefPubMedPubMedCentral
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: development and validation of a new predictive model. Ann Intern Med. 2008, 148 (5): 337-347. 10.7326/0003-4819-148-5-200803040-00004. CrossRefPubMedPubMedCentral
National Cancer Institute: Breast Cancer Risk Assessment Tool, SAS codes for Gail model prediction. http:\\www.cancer.gov\bcrisktool\.
Buron A, Vernet M, Roman M, Checa MA, Pérez JM, Sala M, et al: Can the Gail model increase the predictive value of a positive mammogram in a European population screening setting? Results from a Spanish cohort. Breast Cancer Res. 2013, 22 (1): 83-88.
Chiu SY-H, Duffy S, Yen AM-F, Tabár L, Smith RA, Chen H-H: Effect of baseline breast density on breast cancer incidence, stage, mortality, and screening parameters: 25-year follow-up of a Swedish mammographic screening. Cancer Epidemiol Biomarkers Prev. 2010, 19 (5): 1219-1228. 10.1158/1055-9965.EPI-09-1028. CrossRefPubMed
Pérez Lacasta M, Gregori A, Carles M, Gispert R, Martinez-Alonso M, Vilaprinyo E, et al: The evolution of breast cancer mortality and the dissemination of mammography in Catalonia: an analysis by health region. Rev Esp Salud Publica. 2010, 84 (6): 691-703. 10.1590/S1135-57272010000600002. CrossRefPubMed
- An assessment of existing models for individualized breast cancer risk estimation in a screening program in Spain
- BioMed Central
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