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. Author manuscript; available in PMC: 2019 Apr 1.
Published in final edited form as: Med Decis Making. 2018 Apr;38(1 Suppl):3S–8S. doi: 10.1177/0272989X17737507

Table 1.

Major changes in CISNET breast cancer models since 2006 6,11,2934

Component Description
Breast cancer molecular subtype portrayal of four distinct molecular subtypes based on estrogen receptor (ER) and human epidermal growth factor-2 receptor (HER2) status, each with its own underlying natural history (survival, sojourn times, screen detectability, and impact of therapeutic advances on survival)
Incidence incidence of breast cancer to reflect the current trends in underlying risk and by molecular subtype
Non-breast cancer mortality non-breast cancer mortality inputs to reflect changes in medical care and competing causes of death
Screening dissemination the use and dissemination of digital mammography
Accuracy of mammography sensitivity and specificity of film and digital mammography with recent data reflecting the improvements in the accuracy of mammography over time
Treatment dissemination dissemination of the most current therapies including anthracyclines, taxanes, and herceptin
Treatment effectiveness treatment effectiveness using data from more recent trials
Risk factors risk factors such as breast density, postmenopausal hormone use and body mass index are added to some models
Ductal carcinoma in situ (DCIS) DCIS representations in the models have been improved
Comorbidities Some models have been updated to account for comorbidities

CISNET, Cancer Intervention and Surveillance Modeling Network.

6

Feuer EJ. Modeling the impact of adjuvant therapy and screening mammography on US breast cancer mortality between 1975 and 2000: introduction to the problem. Journal of the National Cancer Institute. Monographs. 2006(36):2–6.

11

Mandelblatt JS, Near AM, Miglioretti DL, et al. Common Model Inputs in Collaborative Breast Cancer Modeling Medical Decision Making. 2017;In Press.

29

Lee SJ, Li X, Huang H. Models for Breast Cancer Screening Strategies Updated for Ductal Carcinoma In Situ and Subgroups Medical Decision Making. 2017;In Press.

30

van den Broek JJ, van Ravesteyn NT, Heijnsdijk EA, de Koning H. Estimating the effects of risk-based screening and adjuvant treatment using the MISCAN-Fadia continuous tumor growth model for breast cancer Medical Decision Making. 2017;In Press.

31

Schechter CB, Near AM, Jayasekera J, Chang Y, Mandelblatt JS. Structure, Function, and Applications of the Georgetown-Einstein (GE) Breast Cancer Simulation Model Medical Decision Making. 2017;In Press.

32

Huang, Xuelin, Li Y, Song J, Berry D. A Bayesian Simulation Model for Breast Cancer Screening, Incidence, Treatment, and Mortality Medical Decision Making. 2017;In Press.

33

Alagoz, Oguzhan, Ergun MA, Cevik M, et al. The University Of Wisconsin Breast Cancer Epidemiology Simulation Model: An Update Medical Decision Making. 2017;In Press.

34

Munoz D, Xu C, Plevritis SK. A Molecular Subtype-Specific Stochastic Simulation Model of US Breast Cancer Incidence and Mortality Trends from 1975 to 2010. Medical Decision Making. 2017;In Press.