Martin J Yaffe1, Nicole Mittmann2, Pablo Lee3, Anna N A Tosteson4, Amy Trentham-Dietz5, Oguzhan Alagoz6, Natasha K Stout7. 1. Physical Sciences Program, Sunnybrook Research Institute; Departments of Medical Biophysics and Medical Imaging, University of Toronto. 2. Cancer Care Ontario. 3. Institute for Technology Assessment, Massachusetts General Hospital. 4. Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth. 5. Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin. 6. Department of Population Health Sciences and Carbone Cancer Center and the Department of Industrial and Systems Engineering, University of Wisconsin. 7. Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute.
Abstract
BACKGROUND: Modelling is a flexible and efficient approach to gaining insight into the trade-offs surrounding a complex process like breast screening, which involves more variables than can be controlled in an experimental study. DATA AND METHODS: The University of Wisconsin Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer microsimulation model was adapted to simulate breast cancer incidence and screening performance in Canada. The model considered effects of breast density on the sensitivity and specificity of screening. The model's ability to predict age-specific incidence of breast cancer was assessed. RESULTS: Predictions of age-adjusted incidence over calendar years and age-specific incidence of breast cancer in Canadian women are presented. Based on standard screening strategies, ratios of in situ to invasive disease and stage distribution of disease at diagnosis are compared with data from the British Columbia provincial screening program. INTERPRETATION: The adapted model performs well in predicting age-specific incidence and cross-sectional incidence in the absence of screening. The ratios of detection of in situ to invasive cancers and the overall stage distribution of detected cancers are in reasonable agreement with empirical data from British Columbia.
BACKGROUND: Modelling is a flexible and efficient approach to gaining insight into the trade-offs surrounding a complex process like breast screening, which involves more variables than can be controlled in an experimental study. DATA AND METHODS: The University of Wisconsin Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer microsimulation model was adapted to simulate breast cancer incidence and screening performance in Canada. The model considered effects of breast density on the sensitivity and specificity of screening. The model's ability to predict age-specific incidence of breast cancer was assessed. RESULTS: Predictions of age-adjusted incidence over calendar years and age-specific incidence of breast cancer in Canadian women are presented. Based on standard screening strategies, ratios of in situ to invasive disease and stage distribution of disease at diagnosis are compared with data from the British Columbia provincial screening program. INTERPRETATION: The adapted model performs well in predicting age-specific incidence and cross-sectional incidence in the absence of screening. The ratios of detection of in situ to invasive cancers and the overall stage distribution of detected cancers are in reasonable agreement with empirical data from British Columbia.
Entities:
Keywords:
Breast screening; incidence; microsimulation model; preventive health; sensitivity; specificity
Authors: Dennis G Fryback; Natasha K Stout; Marjorie A Rosenberg; Amy Trentham-Dietz; Vipat Kuruchittham; Patrick L Remington Journal: J Natl Cancer Inst Monogr Date: 2006
Authors: Natasha K Stout; Sandra J Lee; Clyde B Schechter; Karla Kerlikowske; Oguzhan Alagoz; Donald Berry; Diana S M Buist; Mucahit Cevik; Gary Chisholm; Harry J de Koning; Hui Huang; Rebecca A Hubbard; Diana L Miglioretti; Mark F Munsell; Amy Trentham-Dietz; Nicolien T van Ravesteyn; Anna N A Tosteson; Jeanne S Mandelblatt Journal: J Natl Cancer Inst Date: 2014-05-28 Impact factor: 13.506
Authors: Martin J Yaffe; Nicole Mittmann; Pablo Lee; Anna N A Tosteson; Amy Trentham-Dietz; Oguzhan Alagoz; Natasha K Stout Journal: Health Rep Date: 2015-12 Impact factor: 4.796