dc.contributor.author |
Wolsztynski, Eric |
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dc.contributor.author |
O'Sullivan, Finbarr |
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dc.contributor.author |
Keyes, Eimear |
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dc.contributor.author |
O'Sullivan, Janet |
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dc.contributor.author |
Eary, Janet F. |
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dc.date.accessioned |
2018-09-24T12:37:00Z |
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dc.date.available |
2018-09-24T12:37:00Z |
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dc.date.issued |
2018 |
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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 |
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dc.identifier.endpage |
16 |
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dc.identifier.issn |
2329-4302 |
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dc.identifier.issn |
2329-4310 |
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dc.identifier.uri |
http://hdl.handle.net/10468/6883 |
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dc.identifier.doi |
10.1117/1.JMI.5.2.024502 |
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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 |
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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 |
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dc.subject |
Statistical analysis |
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dc.subject |
Neural networks |
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dc.subject |
Positron emission tomography |
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dc.subject |
Model-based design |
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dc.subject |
3D modeling |
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dc.subject |
Feature selection |
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dc.subject |
Principal component analysis |
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dc.subject |
Tumors |
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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
|
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dc.contributor.funder |
National Institutes of Health
|
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dc.contributor.funder |
Science Foundation Ireland
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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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