Article open access publication

Risk prediction for estrogen receptor-specific breast cancers in two large prospective cohorts

Breast Cancer Research, Springer Nature, ISSN 1465-5411

Volume 20, 1, 2018

DOI:10.1186/s13058-018-1073-0, Dimensions: pub.1110351834, PMC: PMC6276150, PMID: 30509329,



  1. (1) International Agency For Research On Cancer, grid.17703.32
  2. (2) Fred Hutchinson Cancer Research Center, grid.270240.3
  3. (3) Breast and Gynaecologic Cancer Registry of Côte d’Or, Georges-François Leclerc Comprehensive Cancer Care Centre, Dijon, France
  4. (4) University of Burgundy, grid.5613.1
  5. (5) Centre for research in epidemiology and population health, grid.463845.8
  6. (6) Institut Gustave Roussy, grid.14925.3b
  7. (7) Fondazione IRCCS Istituto Nazionale dei Tumori, grid.417893.0
  8. (8) Cancer Registry and Histopathology Department, “Civic-M. P.Arezzo” Hospital, ASP, Ragusa, Italy
  9. (9) Andalusian School of Public Health, grid.413740.5
  10. (10) Institute of Health Carlos III, grid.413448.e
  11. (11) IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
  12. (12) Navarra Public Health Institute, Pamplona, Spain
  13. (13) Instituto Murciano de Investigación Biosanitaria, grid.452553.0
  14. (14) University of Murcia, grid.10586.3a
  15. (15) L'Hospitalet de Llobregat, grid.417656.7
  16. (16) Imperial College London, grid.7445.2
  17. (17) University of Oxford, grid.4991.5
  18. (18) National Institute for Public Health and the Environment, grid.31147.30
  19. (19) University Medical Center Utrecht, grid.7692.a
  20. (20) University of Malaya, grid.10347.31
  21. (21) Hellenic Health Foundation, grid.424637.0
  22. (22) National and Kapodistrian University of Athens, grid.5216.0
  23. (23) German Cancer Research Center, grid.7497.d
  24. (24) New York University, grid.137628.9
  25. (25) Umeå University, grid.12650.30
  26. (26) Aarhus University, grid.7048.b, AU
  27. (27) Aalborg Hospital, grid.27530.33, North Denmark Region
  28. (28) Department of Nutrition, Bjørknes University College, Oslo, Norway
  29. (29) Cancer Registry of Norway, grid.418941.1
  30. (30) Folkhälsans Forskningscentrum, grid.428673.c
  31. (31) Karolinska Institute, grid.4714.6
  32. (32) The Arctic University of Norway, grid.10919.30


BACKGROUND: Few published breast cancer (BC) risk prediction models consider the heterogeneity of predictor variables between estrogen-receptor positive (ER+) and negative (ER-) tumors. Using data from two large cohorts, we examined whether modeling this heterogeneity could improve prediction. METHODS: We built two models, for ER+ (ModelER+) and ER- tumors (ModelER-), respectively, in 281,330 women (51% postmenopausal at recruitment) from the European Prospective Investigation into Cancer and Nutrition cohort. Discrimination (C-statistic) and calibration (the agreement between predicted and observed tumor risks) were assessed both internally and externally in 82,319 postmenopausal women from the Women's Health Initiative study. We performed decision curve analysis to compare ModelER+ and the Gail model (ModelGail) regarding their applicability in risk assessment for chemoprevention. RESULTS: Parity, number of full-term pregnancies, age at first full-term pregnancy and body height were only associated with ER+ tumors. Menopausal status, age at menarche and at menopause, hormone replacement therapy, postmenopausal body mass index, and alcohol intake were homogeneously associated with ER+ and ER- tumors. Internal validation yielded a C-statistic of 0.64 for ModelER+ and 0.59 for ModelER-. External validation reduced the C-statistic of ModelER+ (0.59) and ModelGail (0.57). In external evaluation of calibration, ModelER+ outperformed the ModelGail: the former led to a 9% overestimation of the risk of ER+ tumors, while the latter yielded a 22% underestimation of the overall BC risk. Compared with the treat-all strategy, ModelER+ produced equal or higher net benefits irrespective of the benefit-to-harm ratio of chemoprevention, while ModelGail did not produce higher net benefits unless the benefit-to-harm ratio was below 50. The clinical applicability, i.e. the area defined by the net benefit curve and the treat-all and treat-none strategies, was 12.7 × 10- 6 for ModelER+ and 3.0 × 10- 6 for ModelGail. CONCLUSIONS: Modeling heterogeneous epidemiological risk factors might yield little improvement in BC risk prediction. Nevertheless, a model specifically predictive of ER+ tumor risk could be more applicable than an omnibus model in risk assessment for chemoprevention.


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