Mathematical Modeling to Address Questions in Breast Cancer Screening: An Overview of the Breast Cancer Models of the Cancer Intervention and Surveillance Modeling Network

Oguzhan Alagoz¹, Jennifer L Caswell-Jin², Harry J de Koning³, Hui Huang⁴, Xuelin Huang⁵, Sandra J Lee⁴, Yisheng Li⁵, Sylvia K Plevritis^{6

7}, Swarnavo Sarkar⁸, Clyde B Schechter⁹, Natasha K Stout¹⁰, Amy Trentham-Dietz^{11

12}, Nicolien van Ravesteyn³, Kathryn P Lowry¹³; from the CISNET Breast Working Group

Affiliations

¹ Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA.
² Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
³ Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands.
⁴ Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.
⁵ Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
⁶ Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.
⁷ Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
⁸ Georgetown Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA.
⁹ Department of Family & Social Medicine, Albert Einstein College of Medicine, The Bronx, NY, USA.
¹⁰ Division of Cancer Control and Population Sciences, National Cancer Institute, National Institute of Health, Bethesda, MD, USA.
¹¹ Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA.
¹² Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA.
¹³ Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, WA, USA.

PMID: 40036318
PMCID: PMC11920616 (available on 2026-02-26)
DOI: 10.1093/jbi/wbaf003

Review

Mathematical Modeling to Address Questions in Breast Cancer Screening: An Overview of the Breast Cancer Models of the Cancer Intervention and Surveillance Modeling Network

Oguzhan Alagoz et al. J Breast Imaging. 2025.

. 2025 Mar 18;7(2):141-154.

doi: 10.1093/jbi/wbaf003.

Authors

Affiliations

¹ Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA.
² Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
³ Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands.
⁴ Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.
⁵ Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
⁶ Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.
⁷ Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
⁸ Georgetown Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA.
⁹ Department of Family & Social Medicine, Albert Einstein College of Medicine, The Bronx, NY, USA.
¹⁰ Division of Cancer Control and Population Sciences, National Cancer Institute, National Institute of Health, Bethesda, MD, USA.
¹¹ Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA.
¹² Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA.
¹³ Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, WA, USA.

PMID: 40036318
PMCID: PMC11920616 (available on 2026-02-26)
DOI: 10.1093/jbi/wbaf003

Abstract

The National Cancer Institute-funded Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer mathematical models have been increasingly utilized by policymakers to address breast cancer screening policy decisions and influence clinical practice. These well-established and validated models have a successful track record of use in collaborations spanning over 2 decades. While mathematical modeling is a valuable approach to translate short-term screening performance data into long-term breast cancer outcomes, it is inherently complex and requires numerous inputs to approximate the impacts of breast cancer screening. This review article describes the 6 independently developed CISNET breast cancer models, with a particular focus on how they represent breast cancer screening and estimate the contribution of screening to breast cancer mortality reduction and improvements in life expectancy. We also describe differences in structures and assumptions across the models and how variation in model results can highlight areas of uncertainty. Finally, we offer insight into how the results generated by the models can be used to aid decision-making regarding breast cancer screening policy.

Keywords: breast cancer screening; mammography; mammography screening; simulation modeling.

© Society of Breast Imaging 2025. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.

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Conflict of interest statement

J.L.C.-J. reports previous funding to her institution from Novartis and Effector Therapeutics (both now ended) outside of submitted work. O.A. reports receiving consulting fees from Bristol Myers Squibb and Exact Sciences and owning stock in Innovo Analytics LLC outside of submitted work.

References

1. Berry DA, Cronin KA, Plevritis SK, et al.; Cancer Intervention and Surveillance Modeling Network (CISNET) Collaborators. Effect of screening and adjuvant therapy on mortality from breast cancer. N Engl J Med. 2005;353(17):1784-1792. doi: https://doi.org/10.1056/NEJMoa050518 - DOI - PubMed
1. Plevritis SK, Munoz D, Kurian AW, et al.Association of screening and treatment with breast cancer mortality by molecular subtype in US women, 2000-2012. JAMA. 2018;319(2):154-164. doi: https://doi.org/10.1001/jama.2017.19130 - DOI - PMC - PubMed
1. Gao Y, Moy L, Heller SL.. Digital breast tomosynthesis: update on technology, evidence, and clinical practice. Radiographics. 2021;41(2):321-337. doi: https://doi.org/10.1148/rg.2021200101 - DOI - PMC - PubMed
1. Lee C, McCaskill-Stevens W.. Tomosynthesis mammographic Imaging Screening Trial (TMIST): an invitation and opportunity for the National Medical Association community to shape the future of precision screening for breast cancer. J Natl Med Assoc. 2020;112(6):613-618. doi: https://doi.org/10.1016/j.jnma.2020.05.021 - DOI - PubMed
1. Cancer Intervention and Surveillance Modeling Network (CISNET). Modeling to guide public health research and priorities. National Cancer Institute. Accessed July 24, 2024. https://cisnet.cancer.gov/

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Mathematical Modeling to Address Questions in Breast Cancer Screening: An Overview of the Breast Cancer Models of the Cancer Intervention and Surveillance Modeling Network

Affiliations

Mathematical Modeling to Address Questions in Breast Cancer Screening: An Overview of the Breast Cancer Models of the Cancer Intervention and Surveillance Modeling Network

Authors

Affiliations

Abstract

Conflict of interest statement

References

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Grants and funding

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Medical