RESEARCH ARTICLE
Relationship of Predicted Risk of Developing Invasive Breast Cancer, as Assessed with Three Models, and Breast Cancer Mortality among Breast Cancer Patients Mark E. Sherman1☯*, Laura Ichikawa2☯, Ruth M. Pfeiffer3, Diana L. Miglioretti4,5, Karla Kerlikowske6, Jeffery Tice7, Pamela M. Vacek8, Gretchen L. Gierach3
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OPEN ACCESS Citation: Sherman ME, Ichikawa L, Pfeiffer RM, Miglioretti DL, Kerlikowske K, Tice J, et al. (2016) Relationship of Predicted Risk of Developing Invasive Breast Cancer, as Assessed with Three Models, and Breast Cancer Mortality among Breast Cancer Patients. PLoS ONE 11(8): e0160966. doi:10.1371/ journal.pone.0160966 Editor: Eugenio Paci, Centro per lo Studio e la Prevenzione Oncologica, ITALY Received: February 12, 2016 Accepted: July 27, 2016 Published: August 25, 2016 Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability Statement: We are restricted by our consent processes and IRB¹s from sharing certain data publicly. Data are from the Breast Cancer Surveillance Consortium whose authors may be contacted at National Cancer Institute. The data used in the analysis have been archived at the Statistical Coordinating Center at Group Health Research Institute. Requests for access to these data can be made to [email protected]. Funding: This was funded in part by the Intramural Research Program of the National Cancer Institute
1 National Cancer Institute, Division of Cancer Prevention and Division of Cancer Epidemiology and Genetics, Bethesda, Maryland, United States of America, 2 Group Health Research Institute, Group Health Cooperative, Seattle, Washington, United States of America, 3 Division of Cancer Epidemiology and Genetics, Bethesda, United States of America, 4 Group Health Research Institute, Group Health Cooperative, Seattle, Washington, United States of America, 5 University of California Davis, Department of Public Health, Davis, California, United States of America, 6 University of California San Francisco, Department of Medicine and Department of Epidemiology/Biostatistics, San Francisco, California, United States of America, 7 University of California at San Francisco, Department of Medicine, San Francisco, United States of America, 8 University of Vermont School of Medicine, Department of Medical Biostatistics, Burlington, Vermont, United States of America ☯ These authors contributed equally to this work. * [email protected]
Abstract Purpose Breast cancer risk prediction models are used to plan clinical trials and counsel women; however, relationships of predicted risks of breast cancer incidence and prognosis after breast cancer diagnosis are unknown.
Methods Using largely pre-diagnostic information from the Breast Cancer Surveillance Consortium (BCSC) for 37,939 invasive breast cancers (1996–2007), we estimated 5-year breast cancer risk (
Relationship of Predicted Risk of Developing Invasive Breast Cancer, as Assessed with Three Models, and Breast Cancer Mortality among Breast Cancer Patients Mark E. Sherman1☯*, Laura Ichikawa2☯, Ruth M. Pfeiffer3, Diana L. Miglioretti4,5, Karla Kerlikowske6, Jeffery Tice7, Pamela M. Vacek8, Gretchen L. Gierach3
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OPEN ACCESS Citation: Sherman ME, Ichikawa L, Pfeiffer RM, Miglioretti DL, Kerlikowske K, Tice J, et al. (2016) Relationship of Predicted Risk of Developing Invasive Breast Cancer, as Assessed with Three Models, and Breast Cancer Mortality among Breast Cancer Patients. PLoS ONE 11(8): e0160966. doi:10.1371/ journal.pone.0160966 Editor: Eugenio Paci, Centro per lo Studio e la Prevenzione Oncologica, ITALY Received: February 12, 2016 Accepted: July 27, 2016 Published: August 25, 2016 Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability Statement: We are restricted by our consent processes and IRB¹s from sharing certain data publicly. Data are from the Breast Cancer Surveillance Consortium whose authors may be contacted at National Cancer Institute. The data used in the analysis have been archived at the Statistical Coordinating Center at Group Health Research Institute. Requests for access to these data can be made to [email protected]. Funding: This was funded in part by the Intramural Research Program of the National Cancer Institute
1 National Cancer Institute, Division of Cancer Prevention and Division of Cancer Epidemiology and Genetics, Bethesda, Maryland, United States of America, 2 Group Health Research Institute, Group Health Cooperative, Seattle, Washington, United States of America, 3 Division of Cancer Epidemiology and Genetics, Bethesda, United States of America, 4 Group Health Research Institute, Group Health Cooperative, Seattle, Washington, United States of America, 5 University of California Davis, Department of Public Health, Davis, California, United States of America, 6 University of California San Francisco, Department of Medicine and Department of Epidemiology/Biostatistics, San Francisco, California, United States of America, 7 University of California at San Francisco, Department of Medicine, San Francisco, United States of America, 8 University of Vermont School of Medicine, Department of Medical Biostatistics, Burlington, Vermont, United States of America ☯ These authors contributed equally to this work. * [email protected]
Abstract Purpose Breast cancer risk prediction models are used to plan clinical trials and counsel women; however, relationships of predicted risks of breast cancer incidence and prognosis after breast cancer diagnosis are unknown.
Methods Using largely pre-diagnostic information from the Breast Cancer Surveillance Consortium (BCSC) for 37,939 invasive breast cancers (1996–2007), we estimated 5-year breast cancer risk (
