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dc.contributor.authorRomanov, S.en
dc.contributor.authorHowell, Sachaen
dc.contributor.authorHarkness, E.en
dc.contributor.authorBydder, M.en
dc.contributor.authorEvans, D. G.en
dc.contributor.authorSquires, S.en
dc.contributor.authorFergie, M.en
dc.contributor.authorAstley, S.en
dc.date.accessioned2024-01-29T13:19:47Z
dc.date.available2024-01-29T13:19:47Z
dc.date.issued2023en
dc.identifier.citationRomanov S, Howell S, Harkness E, Bydder M, Evans DG, Squires S, et al. Artificial Intelligence for Image-Based Breast Cancer Risk Prediction Using Attention. Tomography (Ann Arbor, Mich). 2023 Nov 24;9(6):2103-15. PubMed PMID: 38133069. Pubmed Central PMCID: PMC10747439. Epub 2023/12/22. eng.en
dc.identifier.pmid38133069en
dc.identifier.doi10.3390/tomography9060165en
dc.identifier.urihttp://hdl.handle.net/10541/626846
dc.description.abstractAccurate prediction of individual breast cancer risk paves the way for personalised prevention and early detection. The incorporation of genetic information and breast density has been shown to improve predictions for existing models, but detailed image-based features are yet to be included despite correlating with risk. Complex information can be extracted from mammograms using deep-learning algorithms, however, this is a challenging area of research, partly due to the lack of data within the field, and partly due to the computational burden. We propose an attention-based Multiple Instance Learning (MIL) model that can make accurate, short-term risk predictions from mammograms taken prior to the detection of cancer at full resolution. Current screen-detected cancers are mixed in with priors during model development to promote the detection of features associated with risk specifically and features associated with cancer formation, in addition to alleviating data scarcity issues. MAI-risk achieves an AUC of 0.747 [0.711, 0.783] in cancer-free screening mammograms of women who went on to develop a screen-detected or interval cancer between 5 and 55 months, outperforming both IBIS (AUC 0.594 [0.557, 0.633]) and VAS (AUC 0.649 [0.614, 0.683]) alone when accounting for established clinical risk factors.en
dc.language.isoenen
dc.relation.urlhttps://dx.doi.org/10.3390/tomography9060165en
dc.titleArtificial Intelligence for image-based breast cancer risk prediction using attentionen
dc.typeArticleen
dc.contributor.departmentDivision of Cancer Sciences, University of Manchester, Manchester M20 4GJ, UK. Department of Medical Oncology, The Christie NHS Foundation Trust, Manchester M20 4BX, UK.en
dc.identifier.journalTomographyen
dc.description.noteen]
refterms.dateFOA2024-01-31T18:59:55Z


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