Bayesian feature selection for radiomics using reliability metrics

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dc.citation.articleNumber1112914en_US
dc.citation.journalTitleFrontiers in Geneticsen_US
dc.citation.volumeNumber14en_US
dc.contributor.authorShoemaker, Katherineen_US
dc.contributor.authorGer, Rachelen_US
dc.contributor.authorCourt, Laurence E.en_US
dc.contributor.authorAerts, Hugoen_US
dc.contributor.authorVannucci, Marinaen_US
dc.contributor.authorPeterson, Christine B.en_US
dc.date.accessioned2023-04-25T14:48:09Zen_US
dc.date.available2023-04-25T14:48:09Zen_US
dc.date.issued2023en_US
dc.description.abstractIntroduction: Imaging of tumors is a standard step in diagnosing cancer and making subsequent treatment decisions. The field of radiomics aims to develop imaging based biomarkers using methods rooted in artificial intelligence applied to medical imaging. However, a challenging aspect of developing predictive models for clinical use is that many quantitative features derived from image data exhibit instability or lack of reproducibility across different imaging systems or image-processing pipelines.Methods: To address this challenge, we propose a Bayesian sparse modeling approach for image classification based on radiomic features, where the inclusion of more reliable features is favored via a probit prior formulation.Results: We verify through simulation studies that this approach can improve feature selection and prediction given correct prior information. Finally, we illustrate the method with an application to the classification of head and neck cancer patients by human papillomavirus status, using as our prior information a reliability metric quantifying feature stability across different imaging systems.en_US
dc.identifier.citationShoemaker, Katherine, Ger, Rachel, Court, Laurence E., et al.. "Bayesian feature selection for radiomics using reliability metrics." <i>Frontiers in Genetics,</i> 14, (2023) Frontiers Media S.A.: https://doi.org/10.3389/fgene.2023.1112914.en_US
dc.identifier.digitalfgene-14-1112914en_US
dc.identifier.doihttps://doi.org/10.3389/fgene.2023.1112914en_US
dc.identifier.urihttps://hdl.handle.net/1911/114837en_US
dc.language.isoengen_US
dc.publisherFrontiers Media S.A.en_US
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleBayesian feature selection for radiomics using reliability metricsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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