Javier Louro1,2,3,4, Marta Román1,2,3, Margarita Posso1,2,3, Ivonne Vázquez5, Francina Saladié6, Ana Rodriguez-Arana7, M Jesús Quintana8,9, Laia Domingo1,2,3, Marisa Baré2,10, Rafael Marcos-Gragera9,11, María Vernet-Tomas12, Maria Sala1,2,3, Xavier Castells1,2,3. 1. IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain. 2. Research Network on Health Services in Chronic Diseases (REDISSEC), Barcelona, Spain. 3. Servei d'Epidemiologia i Avaluació, Hospital del Mar, Barcelona, Spain. 4. European Higher Education Area (EHEA) Doctoral Programme in Methodology of Biomedical Research and Public Health in Department of Pediatrics, Obstetrics and Gynecology, Preventive Medicine and Public Health, Universitat Autónoma de Barcelona (UAB), Bellaterra, Barcelona, Spain. 5. Servei de Patologia, Hospital del Mar, Barcelona, Spain. 6. Cancer Epidemiology and Prevention Service, Hospital Universitari Sant Joan de Reus, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Reus, Spain. 7. Servei de Diagnòstic per la imatge, Hospital del Mar, Barcelona, Spain. 8. Department of Clinical Epidemiology and Public Health, University Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Barcelona, Spain. 9. CIBER of Epidemiology and Public Health (CIBERESP), Barcelona, Spain. 10. Clinical Epidemiology and Cancer Screening, Parc Taulí University Hospital, Sabadell, Spain. 11. Department of Health, Epidemiology Unit and Girona Cancer Registry, Oncology Coordination Plan, Autonomous Government of Catalonia, Catalan Institute of Oncology, Girona, Spain. 12. Servei d'Obstetricia i Ginecologia, Hospital del Mar, Barcelona, Spain.
Abstract
BACKGROUND: Several studies have proposed personalized strategies based on women's individual breast cancer risk to improve the effectiveness of breast cancer screening. We designed and internally validated an individualized risk prediction model for women eligible for mammography screening. METHODS: Retrospective cohort study of 121,969 women aged 50 to 69 years, screened at the long-standing population-based screening program in Spain between 1995 and 2015 and followed up until 2017. We used partly conditional Cox proportional hazards regression to estimate the adjusted hazard ratios (aHR) and individual risks for age, family history of breast cancer, previous benign breast disease, and previous mammographic features. We internally validated our model with the expected-to-observed ratio and the area under the receiver operating characteristic curve. RESULTS: During a mean follow-up of 7.5 years, 2,058 women were diagnosed with breast cancer. All three risk factors were strongly associated with breast cancer risk, with the highest risk being found among women with family history of breast cancer (aHR: 1.67), a proliferative benign breast disease (aHR: 3.02) and previous calcifications (aHR: 2.52). The model was well calibrated overall (expected-to-observed ratio ranging from 0.99 at 2 years to 1.02 at 20 years) but slightly overestimated the risk in women with proliferative benign breast disease. The area under the receiver operating characteristic curve ranged from 58.7% to 64.7%, depending of the time horizon selected. CONCLUSIONS: We developed a risk prediction model to estimate the short- and long-term risk of breast cancer in women eligible for mammography screening using information routinely reported at screening participation. The model could help to guiding individualized screening strategies aimed at improving the risk-benefit balance of mammography screening programs.
BACKGROUND: Several studies have proposed personalized strategies based on women's individual breast cancer risk to improve the effectiveness of breast cancer screening. We designed and internally validated an individualized risk prediction model for women eligible for mammography screening. METHODS: Retrospective cohort study of 121,969 women aged 50 to 69 years, screened at the long-standing population-based screening program in Spain between 1995 and 2015 and followed up until 2017. We used partly conditional Cox proportional hazards regression to estimate the adjusted hazard ratios (aHR) and individual risks for age, family history of breast cancer, previous benign breast disease, and previous mammographic features. We internally validated our model with the expected-to-observed ratio and the area under the receiver operating characteristic curve. RESULTS: During a mean follow-up of 7.5 years, 2,058 women were diagnosed with breast cancer. All three risk factors were strongly associated with breast cancer risk, with the highest risk being found among women with family history of breast cancer (aHR: 1.67), a proliferative benign breast disease (aHR: 3.02) and previous calcifications (aHR: 2.52). The model was well calibrated overall (expected-to-observed ratio ranging from 0.99 at 2 years to 1.02 at 20 years) but slightly overestimated the risk in women with proliferative benign breast disease. The area under the receiver operating characteristic curve ranged from 58.7% to 64.7%, depending of the time horizon selected. CONCLUSIONS: We developed a risk prediction model to estimate the short- and long-term risk of breast cancer in women eligible for mammography screening using information routinely reported at screening participation. The model could help to guiding individualized screening strategies aimed at improving the risk-benefit balance of mammography screening programs.
Authors: E W Steyerberg; F E Harrell; G J Borsboom; M J Eijkemans; Y Vergouwe; J D Habbema Journal: J Clin Epidemiol Date: 2001-08 Impact factor: 6.437
Authors: William E Barlow; Emily White; Rachel Ballard-Barbash; Pamela M Vacek; Linda Titus-Ernstoff; Patricia A Carney; Jeffrey A Tice; Diana S M Buist; Berta M Geller; Robert Rosenberg; Bonnie C Yankaskas; Karla Kerlikowske Journal: J Natl Cancer Inst Date: 2006-09-06 Impact factor: 13.506
Authors: M H Gail; L A Brinton; D P Byar; D K Corle; S B Green; C Schairer; J J Mulvihill Journal: J Natl Cancer Inst Date: 1989-12-20 Impact factor: 13.506
Authors: Lynn C Hartmann; Thomas A Sellers; Marlene H Frost; Wilma L Lingle; Amy C Degnim; Karthik Ghosh; Robert A Vierkant; Shaun D Maloney; V Shane Pankratz; David W Hillman; Vera J Suman; Jo Johnson; Cassann Blake; Thea Tlsty; Celine M Vachon; L Joseph Melton; Daniel W Visscher Journal: N Engl J Med Date: 2005-07-21 Impact factor: 91.245
Authors: Yiwey Shieh; Donglei Hu; Lin Ma; Scott Huntsman; Charlotte C Gard; Jessica W T Leung; Jeffrey A Tice; Celine M Vachon; Steven R Cummings; Karla Kerlikowske; Elad Ziv Journal: Breast Cancer Res Treat Date: 2016-08-26 Impact factor: 4.872
Authors: Ester Vilaprinyo; Carles Forné; Misericordia Carles; Maria Sala; Roger Pla; Xavier Castells; Laia Domingo; Montserrat Rue Journal: PLoS One Date: 2014-02-03 Impact factor: 3.240
Authors: Nora Pashayan; Antonis C Antoniou; Urska Ivanus; Laura J Esserman; Douglas F Easton; David French; Gaby Sroczynski; Per Hall; Jack Cuzick; D Gareth Evans; Jacques Simard; Montserrat Garcia-Closas; Rita Schmutzler; Odette Wegwarth; Paul Pharoah; Sowmiya Moorthie; Sandrine De Montgolfier; Camille Baron; Zdenko Herceg; Clare Turnbull; Corinne Balleyguier; Paolo Giorgi Rossi; Jelle Wesseling; David Ritchie; Marc Tischkowitz; Mireille Broeders; Dan Reisel; Andres Metspalu; Thomas Callender; Harry de Koning; Peter Devilee; Suzette Delaloge; Marjanka K Schmidt; Martin Widschwendter Journal: Nat Rev Clin Oncol Date: 2020-06-18 Impact factor: 65.011
Authors: Marta Román; Javier Louro; Margarita Posso; Carmen Vidal; Xavier Bargalló; Ivonne Vázquez; María Jesús Quintana; Rodrigo Alcántara; Francina Saladié; Javier Del Riego; Lupe Peñalva; Maria Sala; Xavier Castells Journal: Int J Environ Res Public Health Date: 2022-02-24 Impact factor: 3.390