Positron emission tomography-based assessment of metabolic gradient and other prognostic features in sarcoma

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dc.contributor.authorWolsztynski, Eric
dc.contributor.authorO'Sullivan, Finbarr
dc.contributor.authorKeyes, Eimear
dc.contributor.authorO'Sullivan, Janet
dc.contributor.authorEary, Janet F.
dc.contributor.funderNational Cancer Institute
dc.contributor.funderNational Institutes of Health
dc.contributor.funderScience Foundation Ireland
dc.date.accessioned2018-09-24T12:37:00Z
dc.date.available2018-09-24T12:37:00Z
dc.date.issued2018
dc.description.abstractIntratumoral heterogeneity biomarkers derived from positron emission tomography (PET) imaging with fluorodeoxyglucose (FDG) are of interest for a number of cancers, including sarcoma. A range of radiomic texture variables, adapted from general methodologies for image analysis, has shown promise in the setting. In the context of sarcoma, our group introduced an alternative model-based approach to the measurement of heterogeneity. In this approach, the heterogeneity of a tumor is characterized by the extent to which the 3-D FDG uptake pattern deviates from a simple elliptically contoured structure. By using a nonparametric analysis of the uptake profile obtained from this spatial model, a variable assessing the metabolic gradient of the tumor is developed. The work explores the prognostic potential of this new variable in the context of FDG-PET imaging of sarcoma. A mature clinical series involving 197 patients, 88 of whom have complete time-to-death information, is used. Texture variables based on the imaging data are also evaluated in this series and a range of appropriate machine learning methodologies are then used to explore the complementary prognostic roles for structure and texture variables. We conclude that both texture-based and model-based variables can be combined to achieve enhanced prognostic assessments of outcome for patients with sarcoma based on FDG-PET imaging information.en
dc.description.sponsorshipNational Institutes of Health /National Cancer Institute (ROI-CA-65537)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid24502
dc.identifier.citationWolsztynski, E., O’Sullivan, F., Keyes, E., O’Sullivan, J. and Eary, J. F. (2018) 'Positron emission tomography-based assessment of metabolic gradient and other prognostic features in sarcoma', Journal of Medical Imaging, 5(2), 024502 (16pp). doi: 10.1117/1.JMI.5.2.024502en
dc.identifier.doi10.1117/1.JMI.5.2.024502
dc.identifier.endpage16
dc.identifier.issn2329-4302
dc.identifier.issn2329-4310
dc.identifier.issued2
dc.identifier.journaltitleJournal of Medical Imagingen
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/10468/6883
dc.identifier.volume5
dc.language.isoenen
dc.publisherSociety of Photo-optical Instrumentation Engineers (SPIE)en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Principal Investigator Programme (PI)/11/PI/1027/IE/Statistical Methods for Molecular Imaging of Cancer with PET/
dc.relation.urihttps://www.spiedigitallibrary.org/journals/journal-of-medical-imaging/volume-5/issue-02/024502/Positron-emission-tomography-based-assessment-of-metabolic-gradient-and-other/10.1117/1.JMI.5.2.024502.full
dc.rights© The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.en
dc.subjectStatistical modeling
dc.subjectStatistical analysis
dc.subjectNeural networks
dc.subjectPositron emission tomography
dc.subjectModel-based design
dc.subject3D modeling
dc.subjectFeature selection
dc.subjectPrincipal component analysis
dc.subjectTumors
dc.titlePositron emission tomography-based assessment of metabolic gradient and other prognostic features in sarcomaen
dc.typeArticle (peer-reviewed)en
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