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Simulating the Impact of Risk-Based Screening and Treatment on Breast Cancer Outcomes with MISCAN-Fadia

Author

Listed:
  • Jeroen J. van den Broek

    (Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands)

  • Nicolien T. van Ravesteyn

    (Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands)

  • Eveline A. Heijnsdijk

    (Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands)

  • Harry J. de Koning

    (Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands)

Abstract

The MISCAN-Fadia microsimulation model uses continuous tumor growth to simulate the natural history of breast cancer and has been used extensively to estimate the impact of screening and adjuvant treatment on breast cancer incidence and mortality trends. The model simulates individual life histories from birth to death, with and without breast cancer, in the presence and in the absence of screening and treatment. Life histories are simulated according to discrete events such as birth, tumor inception, the tumor’s clinical diagnosis diameter in the absence of screening, and death from breast cancer or death from other causes. MISCAN-Fadia consists of 4 main components: demography, natural history of breast cancer, screening, and treatment. Screening impact on the natural history of breast cancer is assessed by simulating continuous tumor growth and the “fatal diameter†concept. This concept implies that tumors diagnosed at a size that is between the screen detection threshold and the fatal diameter are cured, while tumors diagnosed at a diameter larger than the fatal tumor diameter metastasize and lead to breast cancer death. MISCAN-Fadia has been extended by including a different natural history for molecular subtypes based on a tumor’s estrogen receptor (ER) status and human epidermal growth factor receptor 2 (HER2) status. In addition, personalized screening strategies that target women based on their risk such as breast density have been incorporated into the model. This personalized approach to screening will continue to develop in light of potential polygenic risk stratification possibilities and new screening modalities.

Suggested Citation

  • Jeroen J. van den Broek & Nicolien T. van Ravesteyn & Eveline A. Heijnsdijk & Harry J. de Koning, 2018. "Simulating the Impact of Risk-Based Screening and Treatment on Breast Cancer Outcomes with MISCAN-Fadia," Medical Decision Making, , vol. 38(1_suppl), pages 54-65, April.
    • Handle: RePEc:sae:medema:v:38:y:2018:i:1_suppl:p:54s-65s
    DOI: 10.1177/0272989X17711928
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    References listed on IDEAS

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    1. Sita Tan & Gerrit van Oortmarssen & Nanda Piersma, 2003. "Estimating Parameters of a Microsimulation Model for Breast Cancer Screening Using the Score Function Method," Annals of Operations Research, Springer, vol. 119(1), pages 43-61, March.
    2. Wenjin Yin & Yiwei Jiang & Zhenzhou Shen & Zhimin Shao & Jinsong Lu, 2011. "Trastuzumab in the Adjuvant Treatment of HER2-Positive Early Breast Cancer Patients: A Meta-Analysis of Published Randomized Controlled Trials," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-9, June.
    3. Russell R. Barton & John S. Ivey, Jr., 1996. "Nelder-Mead Simplex Modifications for Simulation Optimization," Management Science, INFORMS, vol. 42(7), pages 954-973, July.
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