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

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dc.contributor.author Wolsztynski, Eric
dc.contributor.author O'Sullivan, Finbarr
dc.contributor.author Keyes, Eimear
dc.contributor.author O'Sullivan, Janet
dc.contributor.author Eary, Janet F.
dc.date.accessioned 2018-09-24T12:37:00Z
dc.date.available 2018-09-24T12:37:00Z
dc.date.issued 2018
dc.identifier.citation Wolsztynski, 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.5.2.024502 en
dc.identifier.volume 5
dc.identifier.issued 2
dc.identifier.startpage 1
dc.identifier.endpage 16
dc.identifier.issn 2329-4302
dc.identifier.issn 2329-4310
dc.identifier.uri http://hdl.handle.net/10468/6883
dc.identifier.doi 10.1117/1.JMI.5.2.024502
dc.description.abstract Intratumoral 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.sponsorship National Institutes of Health /National Cancer Institute (ROI-CA-65537) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Society of Photo-optical Instrumentation Engineers (SPIE) en
dc.relation.uri https://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.subject Statistical modeling
dc.subject Statistical analysis
dc.subject Neural networks
dc.subject Positron emission tomography
dc.subject Model-based design
dc.subject 3D modeling
dc.subject Feature selection
dc.subject Principal component analysis
dc.subject Tumors
dc.title Positron emission tomography-based assessment of metabolic gradient and other prognostic features in sarcoma en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Eric Wolsztynski, School Of Mathematical Sciences, University College Cork, Cork, Ireland. +353-21-490-3000 Email: eric.w@ucc.ie en
dc.internal.availability Full text available en
dc.description.version Published Version en
dc.contributor.funder National Cancer Institute
dc.contributor.funder National Institutes of Health
dc.contributor.funder Science Foundation Ireland
dc.description.status Peer reviewed en
dc.identifier.journaltitle Journal of Medical Imaging en
dc.internal.IRISemailaddress eric.w@ucc.ie en
dc.identifier.articleid 24502
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Principal Investigator Programme (PI)/11/PI/1027/IE/Statistical Methods for Molecular Imaging of Cancer with PET/

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