Comparing CISNET Breast Cancer Models Using the Maximum Clinical Incidence Reduction Methodology
| Autor: | Sylvia K. Plevritis, Mucahit Cevik, Natasha K. Stout, Jeanne S. Mandelblatt, Marjolein van Ballegooijen, Hui Huang, Diego F. Munoz, Harry J. de Koning, Yisheng Li, Sandra J. Lee, Nicolien T. van Ravesteyn, Jeroen J. van den Broek, Clyde B. Schechter |
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| Přispěvatelé: | Public Health |
| Rok vydání: | 2018 |
| Předmět: |
Oncology
medicine.medical_specialty Breast cancer mortality Breast Neoplasms Risk Assessment Article 03 medical and health sciences 0302 clinical medicine Breast cancer SDG 3 - Good Health and Well-being Disease Screening Internal medicine Cancer screening medicine Humans Mammography Surveillance Modeling Computer Simulation 030212 general & internal medicine Early Detection of Cancer Aged medicine.diagnostic_test business.industry Incidence 030503 health policy & services Health Policy Incidence (epidemiology) Cancer Middle Aged medicine.disease United States Disease Progression Female 0305 other medical science business SEER Program |
| Zdroj: | Medical Decision Making, 38, 112S-125S. SAGE Publishing |
| ISSN: | 1552-681X 0272-989X |
| DOI: | 10.1177/0272989x17743244 |
| Popis: | Background. Collaborative modeling has been used to estimate the impact of potential cancer screening strategies worldwide. A necessary step in the interpretation of collaborative cancer screening model results is to understand how model structure and model assumptions influence cancer incidence and mortality predictions. In this study, we examined the relative contributions of the pre-clinical duration of breast cancer, the sensitivity of screening, and the improvement in prognosis associated with treatment of screen-detected cases to the breast cancer incidence and mortality predictions of 5 Cancer Intervention and Surveillance Modeling Network (CISNET) models. Methods. To tease out the impact of model structure and assumptions on model predictions, the Maximum Clinical Incidence Reduction (MCLIR) method compares changes in the number of breast cancers diagnosed due to clinical symptoms and cancer mortality between 4 simplified scenarios: 1) no-screening; 2) one-time perfect screening exam, which detects all existing cancers and perfect treatment (i.e., cure) of all screen-detected cancers; 3) one-time digital mammogram and perfect treatment of all screen-detected cancers; and 4) one-time digital mammogram and current guideline-concordant treatment of all screen-detected cancers. Results. The 5 models predicted a large range in maximum clinical incidence (19% to 71%) and in breast cancer mortality reduction (33% to 67%) from a one-time perfect screening test and perfect treatment. In this perfect scenario, the models with assumptions of tumor inception before it is first detectable by mammography predicted substantially higher incidence and mortality reductions than models with assumptions of tumor onset at the start of a cancer’s screen-detectable phase. The range across models in breast cancer clinical incidence (11% to 24%) and mortality reduction (8% to 18%) from a one-time digital mammogram at age 62 y with observed sensitivity and current guideline-concordant treatment was considerably smaller than achievable under perfect conditions. Conclusions. The timing of tumor inception and its effect on the length of the pre-clinical phase of breast cancer had a substantial impact on the grouping of models based on their predictions for clinical incidence and breast cancer mortality reduction. This key finding about the timing of tumor inception will be included in future CISNET breast analyses to enhance model transparency. The MCLIR approach should aid in the interpretation of variations in model results and could be adopted in other disease screening settings to enhance model transparency. |
| Databáze: | OpenAIRE |
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