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Reflecting on 20 years of breast cancer modeling in CISNET: Recommendations for future cancer systems modeling efforts.
Trentham-Dietz, Amy; Alagoz, Oguzhan; Chapman, Christina; Huang, Xuelin; Jayasekera, Jinani; van Ravesteyn, Nicolien T; Lee, Sandra J; Schechter, Clyde B; Yeh, Jennifer M; Plevritis, Sylvia K; Mandelblatt, Jeanne S.
Afiliação
  • Trentham-Dietz A; Department of Population Health Sciences and Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
  • Alagoz O; Department of Population Health Sciences and Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
  • Chapman C; Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
  • Huang X; Department of Radiation Oncology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
  • Jayasekera J; Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
  • van Ravesteyn NT; Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown Lombardi Comprehensive Cancer Center, Washington, DC, United States of America.
  • Lee SJ; Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands.
  • Schechter CB; Department of Data Science, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts, United States of America.
  • Yeh JM; Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, New York, United States of America.
  • Plevritis SK; Department of Pediatrics, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.
  • Mandelblatt JS; Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, United States of America.
PLoS Comput Biol ; 17(6): e1009020, 2021 06.
Article em En | MEDLINE | ID: mdl-34138842
Since 2000, the National Cancer Institute's Cancer Intervention and Surveillance Modeling Network (CISNET) modeling teams have developed and applied microsimulation and statistical models of breast cancer. Here, we illustrate the use of collaborative breast cancer multilevel systems modeling in CISNET to demonstrate the flexibility of systems modeling to address important clinical and policy-relevant questions. Challenges and opportunities of future systems modeling are also summarized. The 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to affect the burden of breast cancer. Multidisciplinary modeling teams have explored alternative representations of breast cancer to reveal insights into breast cancer natural history, including the role of overdiagnosis and race differences in tumor characteristics. The models have been used to compare strategies for improving the balance of benefits and harms of breast cancer screening based on personal risk factors, including age, breast density, polygenic risk, and history of Down syndrome or a history of childhood cancer. The models have also provided evidence to support the delivery of care by simulating outcomes following clinical decisions about breast cancer treatment and estimating the relative impact of screening and treatment on the United States population. The insights provided by the CISNET breast cancer multilevel modeling efforts have informed policy and clinical guidelines. The 20 years of CISNET modeling experience has highlighted opportunities and challenges to expanding the impact of systems modeling. Moving forward, CISNET research will continue to use systems modeling to address cancer control issues, including modeling structural inequities affecting racial disparities in the burden of breast cancer. Future work will also leverage the lessons from team science, expand resource sharing, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Modelos Estatísticos Tipo de estudo: Diagnostic_studies / Etiology_studies / Guideline / Prognostic_studies / Risk_factors_studies / Screening_studies Limite: Female / Humans País como assunto: America do norte Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Modelos Estatísticos Tipo de estudo: Diagnostic_studies / Etiology_studies / Guideline / Prognostic_studies / Risk_factors_studies / Screening_studies Limite: Female / Humans País como assunto: America do norte Idioma: En Ano de publicação: 2021 Tipo de documento: Article