A Bayesian Nonparametric Approach for Functional Data Classification with Application to Hepatic Tissue Characterization

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dc.citation.firstpage151en_US
dc.citation.issueNumberSuppl 5en_US
dc.citation.journalTitleCancer Informaticsen_US
dc.citation.lastpage162en_US
dc.citation.volumeNumber14en_US
dc.contributor.authorFronczyk, Kassandra M.en_US
dc.contributor.authorGuindani, Micheleen_US
dc.contributor.authorHobbs, Brian P.en_US
dc.contributor.authorNg, Chaan S.en_US
dc.contributor.authorVannucci, Marinaen_US
dc.date.accessioned2016-11-10T22:23:40Zen_US
dc.date.available2016-11-10T22:23:40Zen_US
dc.date.issued2015en_US
dc.description.abstractComputed tomography perfusion (CTp) is an emerging functional imaging technology that provides a quantitative assessment of the passage of fluid through blood vessels. Tissue perfusion plays a critical role in oncology due to the proliferation of networks of new blood vessels typical of cancer angiogenesis, which triggers modifications to the vasculature of the surrounding host tissue. In this article, we consider a Bayesian semiparametric model for the analysis of functional data. This method is applied to a study of four interdependent hepatic perfusion CT characteristics that were acquired under the administration of contrast using a sequence of repeated scans over a period of 590 seconds. More specifically, our modeling framework facilitates borrowing of information across patients and tissues. Additionally, the approach enables flexible estimation of temporal correlation structures exhibited by mappings of the correlated perfusion biomarkers and thus accounts for the heteroskedasticity typically observed in those measurements, by incorporating change-points in the covariance estimation. This method is applied to measurements obtained from regions of liver surrounding malignant and benign tissues, for each perfusion biomarker. We demonstrate how to cluster the liver regions on the basis of their CTp profiles, which can be used in a prediction context to classify regions of interest provided by future patients, and thereby assist in discriminating malignant from healthy tissue regions in diagnostic settings.en_US
dc.identifier.citationFronczyk, Kassandra M., Guindani, Michele, Hobbs, Brian P., et al.. "A Bayesian Nonparametric Approach for Functional Data Classification with Application to Hepatic Tissue Characterization." <i>Cancer Informatics,</i> 14, no. Suppl 5 (2015) Libertas Academica: 151-162. http://dx.doi.org/10.4137/CIN.S31933.en_US
dc.identifier.doihttp://dx.doi.org/10.4137/CIN.S31933en_US
dc.identifier.urihttps://hdl.handle.net/1911/92703en_US
dc.language.isoengen_US
dc.publisherLibertas Academicaen_US
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/en_US
dc.subject.keywordfunctional data analysisen_US
dc.subject.keywordBayesian analysisen_US
dc.subject.keywordBayesian nonparametricsen_US
dc.subject.keywordcomputed tomography perfusionen_US
dc.titleA Bayesian Nonparametric Approach for Functional Data Classification with Application to Hepatic Tissue Characterizationen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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