Abstract
Breast cancer screening with mammography has been shown to reduce breast cancer mortality. However the frequency and the age range for screening eligibility has been controversial. Individual risk based screening regimens have recently been proposed to overcome some of the weaknesses of screening mammography. However, it is not possible to evaluate the full impact of such risk based individualized screening strategies in Canadian context. Therefore a mathematical cancer control model for breast cancer using care paths and cancer control data from the province of BC is being developed to model different early detection strategies. The model will incorporate the incidence, detection, diagnosis, progression, and case fatality of breast cancer in BC as baseline to make projections of the population health and economic impacts of different early detection methods for breast cancer. Once the model is validated, it will be possible to test early detection pathways and strategies, frequencies and durations, as well as any health care costs associated with detection, diagnosis, treatment and on-going care of breast cancer patients.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kerlikowske, K., Grady, D., Rubin, S.M., Sandrock, C., Ernster, V.L.: Efficacy of screening mammography. A meta-analysis. JAMA 273, 149–154 (1995)
Nelson, H.D., Tyne, K., Naik, A., Bougatsos, C., Chan, B.K., Humphrey, L.: Screening for Breast Cancer: An Update for the U.S. Preventive Services Task Force. Annals of Internal Medicine 151, 727–737 (2009)
Humphrey, L.L., Helfand, M., Chan, B.K., Woolf, S.H.: Breast cancer screening: A summary of the evidence for the U.S. Preventive Services Task Force. Ann. Intern. Med. 137(5, pt. 1), 347–360 (2002)
Hellquist, B.N., Duffy, S.W., Abdsaleh, S., Bjorneld, L., Bordas, P., Tabar, L., et al.: Effectiveness of population-based service screening with mammography for women ages 40 to 49 years: Evaluation of the Swedish Mammography Screening in Young Women (SCRY) cohort. Cancer 117, 714–722 (2011)
Tice, J.A., Cummings, S.R., Smith-Bindman, R., Ichikawa, L., Barlow, W.E., Kerlikowske, K.: Using clinical factors and mammographic breast density to estimate breast cancer risk: Development and validation of a new predictive model. Ann. Intern. Med. 148, 337–347 (2008)
Gail, M., Rimer, B.: Risk-based recommendations for mammographic screening for women in their forties. J. Clin. Oncol. 16, 3105–3114 (1998)
Schousboe, J.T., Kerlikowske, K., Loh, A., Cummings, S.R.: Personalizing mammography by breast density and other risk factors for breast cancer: Analysis of health benefits and cost-effectiveness. Ann. Intern. Med. 155, 10–20 (2011)
Mandelblatt, J.S., Cronin, K.A., Berry, D.A., Chang, Y., de Koning, H.J., Lee, S.J., et al.: Modeling the impact of population screening on breast cancer mortality in the United States. Breast 20(suppl. 3), S75–S81 (2011)
Feuer, E.J.: Modeling the impact of adjuvant therapy and screening mammography on U.S. breast cancer mortality between 1975 and 2000: Introduction to the problem. J. Natl. Cancer Inst. Monogr. 36, 2–6 (2006)
Cancer Intervention and Surveillance Modeling Network. CISNET Publication, http://cisnet.cancer.gov/publications/ (accessed March 8, 2014)
Canadian Partnership Against Cancer. Cancer Risk Management Model Website, http://www.cancerview.ca (accessed March 8, 2014)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Rajapakshe, R. et al. (2014). Development of a Micro-Simulation Model for Breast Cancer to Evaluate the Impacts of Personalized Early Detection Strategies. In: Fujita, H., Hara, T., Muramatsu, C. (eds) Breast Imaging. IWDM 2014. Lecture Notes in Computer Science, vol 8539. Springer, Cham. https://doi.org/10.1007/978-3-319-07887-8_52
Download citation
DOI: https://doi.org/10.1007/978-3-319-07887-8_52
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07886-1
Online ISBN: 978-3-319-07887-8
eBook Packages: Computer ScienceComputer Science (R0)