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dc.contributor.authorFronczyk, Kassandra M.
Guindani, Michele
Hobbs, Brian P.
Ng, Chaan S.
Vannucci, Marina
dc.date.accessioned 2016-11-10T22:23:40Z
dc.date.available 2016-11-10T22:23:40Z
dc.date.issued 2015
dc.identifier.citation Fronczyk, Kassandra M., Guindani, Michele, Hobbs, Brian P., et al.. "A Bayesian Nonparametric Approach for Functional Data Classification with Application to Hepatic Tissue Characterization." Cancer Informatics, 14, no. Suppl 5 (2015) Libertas Academica: 151-162. http://dx.doi.org/10.4137/CIN.S31933.
dc.identifier.urihttps://hdl.handle.net/1911/92703
dc.description.abstract Computed 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.
dc.language.iso eng
dc.publisher Libertas Academica
dc.rights This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License.
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/
dc.title A Bayesian Nonparametric Approach for Functional Data Classification with Application to Hepatic Tissue Characterization
dc.type Journal article
dc.citation.journalTitle Cancer Informatics
dc.subject.keywordfunctional data analysis
Bayesian analysis
Bayesian nonparametrics
computed tomography perfusion
dc.citation.volumeNumber 14
dc.citation.issueNumber Suppl 5
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.4137/CIN.S31933
dc.identifier.pmcid PMC4886897
dc.type.publication publisher version
dc.citation.firstpage 151
dc.citation.lastpage 162


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