Assessment of the prognostic value of radiomic features in 18F-FMISO PET imaging of hypoxia in postsurgery brain cancer patients: secondary analysis of imaging data from a single-center study and the multicenter ACRIN 6684 trial

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dc.contributor.author Muzi, Mark
dc.contributor.author Wolsztynski, Eric
dc.contributor.author Fink, James R.
dc.contributor.author O'Sullivan, Janet N.
dc.contributor.author O'Sullivan, Finbarr
dc.contributor.author Krohn, Kenneth A.
dc.contributor.author Mankoff, David A.
dc.date.accessioned 2021-02-22T14:06:18Z
dc.date.available 2021-02-22T14:06:18Z
dc.date.issued 2020-03-01
dc.identifier.citation Muzi, M., Wolsztynski, E., Fink, J. R., O’Sullivan, J. N., O’Sullivan, F., Krohn, K. A. and Mankoff, D. A. (2020) 'Assessment of the Prognostic Value of Radiomic Features in 18F-FMISO PET Imaging of Hypoxia in Postsurgery Brain Cancer Patients: Secondary Analysis of Imaging Data from a Single-Center Study and the Multicenter ACRIN 6684 Trial', Tomography, 6(1), pp. 14-22. doi: 10.18383/j.tom.2019.00023 en
dc.identifier.volume 6 en
dc.identifier.issued 1 en
dc.identifier.startpage 14 en
dc.identifier.endpage 22 en
dc.identifier.issn 2379-139X
dc.identifier.uri http://hdl.handle.net/10468/11085
dc.identifier.doi 10.18383/j.tom.2019.00023 en
dc.description.abstract Hypoxia is associated with resistance to radiotherapy and chemotherapy in malignant gliomas, and it can be imaged by positron emission tomography with 18F-fluoromisonidazole (18F-FMISO). Previous results for patients with brain cancer imaged with 18F-FMISO at a single center before conventional chemoradiotherapy showed that tumor uptake via T/Bmax (tissue SUVmax/blood SUV) and hypoxic volume (HV) was associated with poor survival. However, in a multicenter clinical trial (ACRIN 6684), traditional uptake parameters were not found to be prognostically significant, but tumor SUVpeak did predict survival at 1 year. The present analysis considered both study cohorts to reconcile key differences and examine the potential utility of adding radiomic features as prognostic variables for outcome prediction on the combined cohort of 72 patients with brain cancer (30 University of Washington and 42 ACRIN 6684). We used both 18F-FMISO intensity metrics (T/Bmax, HV, SUV, SUVmax, SUVpeak) and assessed radiomic measures that determined first-order (histogram), second-order, and higher-order radiomic features of 18F-FMISO uptake distributions. A multivariate model was developed that included age, HV, and the intensity of 18F-FMISO uptake. HV and SUVpeak were both independent predictors of outcome for the combined data set (P < .001) and were also found significant in multivariate prognostic models (P < .002 and P < .001, respectively). Further model selection that included radiomic features showed the additional prognostic value for overall survival of specific higher order texture features, leading to an increase in relative risk prediction performance by a further 5%, when added to the multivariate clinical model. en
dc.description.sponsorship National Institutes of Health/National Cancer Institute (NIH/NCI R50-CA211270, P01-CA042045, U01-CA079778, U01-CA080098); Science Foundation Ireland (SFI Grants PI 11/1027 and 12/RC/2289-P2) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher MDPI en
dc.relation.uri https://www.mdpi.com/2379-1381/6/1/14
dc.rights © 2020 The Authors. Published by Grapho Publications, LLC This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) en
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ en
dc.subject Fluoromisonidazole en
dc.subject ACRIN 6684 en
dc.subject Brain cancer en
dc.subject PET imaging en
dc.subject Radiomics en
dc.title Assessment of the prognostic value of radiomic features in 18F-FMISO PET imaging of hypoxia in postsurgery brain cancer patients: secondary analysis of imaging data from a single-center study and the multicenter ACRIN 6684 trial 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: e.wolsztynski@ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2021-02-22T13:51:52Z
dc.description.version Published Version en
dc.internal.rssid 511577258
dc.contributor.funder National Institutes of Health en
dc.contributor.funder National Cancer Institute en
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Tomography en
dc.internal.copyrightchecked Yes
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress e.wolsztynski@ucc.ie en
dc.internal.IRISemailaddress f.osullivan@ucc.ie en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/ 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/ en


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© 2020 The Authors. Published by Grapho Publications, LLC This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Except where otherwise noted, this item's license is described as © 2020 The Authors. Published by Grapho Publications, LLC This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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