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Annual Review of Biomedical Data Science

Volume 6, 2023

Combining Molecular and Radiomic Features for Risk Assessment in Breast Cancer

Abstract

Breast cancer risk is highly variable within the population and current research is leading the shift toward personalized medicine. By accurately assessing an individual woman's risk, we can reduce the risk of over/undertreatment by preventing unnecessary procedures or by elevating screening procedures. Breast density measured from conventional mammography has been established as one of the most dominant risk factors for breast cancer; however, it is currently limited by its ability to characterize more complex breast parenchymal patterns that have been shown to provide additional information to strengthen cancer risk models. Molecular factors ranging from high penetrance, or high likelihood that a mutation will show signs and symptoms of the disease, to combinations of gene mutations with low penetrance have shown promise for augmenting risk assessment. Although imaging biomarkers and molecular biomarkers have both individually demonstrated improved performance in risk assessment, few studies have evaluated them together. This review aims to highlight the current state of the art in breast cancer risk assessment using imaging and genetic biomarkers.

Keyword(s): breast cancer, polygenic risk score, radiomics, risk assessment, single-nucleotide polymorphisms
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/content/journals/10.1146/annurev-biodatasci-020722-092748
2023-08-10
2024-04-28
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Combining Molecular and Radiomic Features for Risk Assessment in Breast Cancer
Annual Review of Biomedical Data Science 6, 299 (2023); https://doi.org/10.1146/annurev-biodatasci-020722-092748
/content/journals/10.1146/annurev-biodatasci-020722-092748
/content/journals/10.1146/annurev-biodatasci-020722-092748
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