Positron emission tomography-based assessment of metabolic gradient and other prognostic features in sarcoma
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.contributor.funder | National Cancer Institute | |
dc.contributor.funder | National Institutes of Health | |
dc.contributor.funder | Science Foundation Ireland | |
dc.date.accessioned | 2018-09-24T12:37:00Z | |
dc.date.available | 2018-09-24T12:37:00Z | |
dc.date.issued | 2018 | |
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.description.status | Peer reviewed | en |
dc.description.version | Published Version | en |
dc.format.mimetype | application/pdf | en |
dc.identifier.articleid | 24502 | |
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.doi | 10.1117/1.JMI.5.2.024502 | |
dc.identifier.endpage | 16 | |
dc.identifier.issn | 2329-4302 | |
dc.identifier.issn | 2329-4310 | |
dc.identifier.issued | 2 | |
dc.identifier.journaltitle | Journal of Medical Imaging | en |
dc.identifier.startpage | 1 | |
dc.identifier.uri | https://hdl.handle.net/10468/6883 | |
dc.identifier.volume | 5 | |
dc.language.iso | en | en |
dc.publisher | Society of Photo-optical Instrumentation Engineers (SPIE) | en |
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/ | |
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 |
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