We demonstrated how to comprehensively translate the existing and updated scientific evidence on genomic discovery, tumour phenotype, clinical features, and conventional risk factors in association with breast cancer to facilitate individually tailored screening for breast cancer. We proposed an individual-risk-score-based approach that translates state-of-the-art scientific evidence into the initiators and promoters affecting onset and subsequent progression of breast tumour underpinning a novel multi-variable three-state temporal natural history model. We applied such a quantitative approach to a population-based Taiwanese women periodical screening cohort. Risk prediction for pre-clinical detectable and clinical-detected breast cancer was made by the two risk scores to stratify the underlying population to assess the optimal age to begin screening and the inter-screening interval for each category and to ascertain which high-risk group requires an alternative image technique. The risk-score-based approach significantly reduced the interval cancer rate as a percentage of the expected rate in the absence of screening by 30% and also reduced 8.2% false positive cases compared with triennial universal screening. We developed a novel quantitative approach following the principle of translational research to provide a roadmap with state-of-the-art genomic discovery and clinical parameters to facilitate individually tailored breast cancer screening.
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