Cargando…

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

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 t...

Descripción completa

Detalles Bibliográficos
Autores principales: Wolsztynski, Eric, O’Sullivan, Finbarr, Keyes, Eimear, O’Sullivan, Janet, Eary, Janet F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society of Photo-Optical Instrumentation Engineers 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5967597/
https://www.ncbi.nlm.nih.gov/pubmed/29845091
http://dx.doi.org/10.1117/1.JMI.5.2.024502
_version_ 1783325632142245888
author Wolsztynski, Eric
O’Sullivan, Finbarr
Keyes, Eimear
O’Sullivan, Janet
Eary, Janet F.
author_facet Wolsztynski, Eric
O’Sullivan, Finbarr
Keyes, Eimear
O’Sullivan, Janet
Eary, Janet F.
author_sort Wolsztynski, Eric
collection PubMed
description 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.
format Online
Article
Text
id pubmed-5967597
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Society of Photo-Optical Instrumentation Engineers
record_format MEDLINE/PubMed
spelling pubmed-59675972019-05-24 Positron emission tomography-based assessment of metabolic gradient and other prognostic features in sarcoma Wolsztynski, Eric O’Sullivan, Finbarr Keyes, Eimear O’Sullivan, Janet Eary, Janet F. J Med Imaging (Bellingham) Computer-Aided Diagnosis 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. Society of Photo-Optical Instrumentation Engineers 2018-05-24 2018-04 /pmc/articles/PMC5967597/ /pubmed/29845091 http://dx.doi.org/10.1117/1.JMI.5.2.024502 Text en © The Authors. https://creativecommons.org/licenses/by/3.0/ 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.
spellingShingle Computer-Aided Diagnosis
Wolsztynski, Eric
O’Sullivan, Finbarr
Keyes, Eimear
O’Sullivan, Janet
Eary, Janet F.
Positron emission tomography-based assessment of metabolic gradient and other prognostic features in sarcoma
title Positron emission tomography-based assessment of metabolic gradient and other prognostic features in sarcoma
title_full Positron emission tomography-based assessment of metabolic gradient and other prognostic features in sarcoma
title_fullStr Positron emission tomography-based assessment of metabolic gradient and other prognostic features in sarcoma
title_full_unstemmed Positron emission tomography-based assessment of metabolic gradient and other prognostic features in sarcoma
title_short Positron emission tomography-based assessment of metabolic gradient and other prognostic features in sarcoma
title_sort positron emission tomography-based assessment of metabolic gradient and other prognostic features in sarcoma
topic Computer-Aided Diagnosis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5967597/
https://www.ncbi.nlm.nih.gov/pubmed/29845091
http://dx.doi.org/10.1117/1.JMI.5.2.024502
work_keys_str_mv AT wolsztynskieric positronemissiontomographybasedassessmentofmetabolicgradientandotherprognosticfeaturesinsarcoma
AT osullivanfinbarr positronemissiontomographybasedassessmentofmetabolicgradientandotherprognosticfeaturesinsarcoma
AT keyeseimear positronemissiontomographybasedassessmentofmetabolicgradientandotherprognosticfeaturesinsarcoma
AT osullivanjanet positronemissiontomographybasedassessmentofmetabolicgradientandotherprognosticfeaturesinsarcoma
AT earyjanetf positronemissiontomographybasedassessmentofmetabolicgradientandotherprognosticfeaturesinsarcoma