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Related Experiment Video

Updated: Feb 13, 2026

Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer
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Common Model Inputs Used in CISNET Collaborative Breast Cancer Modeling.

Jeanne S Mandelblatt1, Aimee M Near1, Diana L Miglioretti2

  • 1Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|March 20, 2018
PubMed
Summary

The Cancer Intervention and Surveillance Network (CISNET) breast cancer models utilize common input parameters for consistent analysis. This approach enhances comparability and transparency in breast cancer control research.

Keywords:
breast cancer epidemiologycancer simulationsimulation models

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Area of Science:

  • Oncology
  • Biostatistics
  • Public Health

Background:

  • The Cancer Intervention and Surveillance Network (CISNET) has utilized standardized breast cancer models since 2000.
  • Common input parameters ensure national representativeness and facilitate model output comparison.
  • Regular updates to common input data reflect current breast cancer knowledge and practices.

Purpose of the Study:

  • To summarize the methods and results of common input values used in CISNET breast cancer models.
  • To document assumptions made due to data limitations.
  • To outline future parameter development plans.

Main Methods:

  • Utilizing a nationally representative core of common input parameters across CISNET breast cancer models.
  • Updating common input data for each analysis to reflect current practices.
  • Incorporating parameters such as birth/death rates, incidence, risk factors, mammography, screening, tumor characteristics, ER/HER2 distribution, survival, therapy, and competing mortality.

Main Results:

  • The use of common inputs allows for greater comparison of model outputs.
  • Common inputs enhance the inference of results and provide a range of reasonable outcomes.
  • The common parameter core includes diverse data from population rates to therapy effectiveness.

Conclusions:

  • The presented data aim to increase the transparency of CISNET breast cancer models.
  • Standardized inputs improve the reliability and interpretability of breast cancer control models.
  • Future development plans for parameters are highlighted to further enhance model utility.