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An optimization framework to guide the choice of thresholds for risk-based cancer screening.
- Source :
- NPJ Digital Medicine; 11/28/2023, Vol. 6 Issue 1, p1-8, 8p
- Publication Year :
- 2023
-
Abstract
- It is uncommon for risk groups defined by statistical or artificial intelligence (AI) models to be chosen by jointly considering model performance and potential interventions available. We develop a framework to rapidly guide choice of risk groups in this manner, and apply it to guide breast cancer screening intervals using an AI model. Linear programming is used to define risk groups that minimize expected advanced cancer incidence subject to resource constraints. In the application risk stratification performance is estimated from a case–control study (2044 cases, 1:1 matching), and other parameters are taken from screening trials and the screening programme in England. Under the model, re-screening in 1 year for the highest 4% AI model risk, in 3 years for the middle 64%, and in 4 years for 32% of the population at lowest risk, was expected to reduce the number of advanced cancers diagnosed by approximately 18 advanced cancers per 1000 diagnosed with triennial screening, for the same average number of screens in the population as triennial screening for all. Sensitivity analyses found the choice of thresholds was robust to model parameters, but the estimated reduction in advanced cancers was not precise and requires further evaluation. Our framework helps define thresholds with the greatest chance of success for reducing the population health burden of cancer when used in risk-adapted screening, which should be further evaluated such as in health-economic modelling based on computer simulation models, and real-world evaluations. [ABSTRACT FROM AUTHOR]
- Subjects :
- BREAST tumor prevention
BREAST tumor risk factors
COMPUTER simulation
STATISTICS
EARLY detection of cancer
ARTIFICIAL intelligence
MAMMOGRAMS
CONCEPTUAL structures
RISK assessment
QUALITY assurance
RESEARCH funding
DESCRIPTIVE statistics
STATISTICAL models
DATA analysis
SECONDARY analysis
ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 23986352
- Volume :
- 6
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- NPJ Digital Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 173893496
- Full Text :
- https://doi.org/10.1038/s41746-023-00967-9