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24 May 2018 Positron emission tomography-based assessment of metabolic gradient and other prognostic features in sarcoma
Eric Wolsztynski, Finbarr O'Sullivan, Eimear Keyes, Janet O'Sullivan, Janet F. Eary
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Journal of Medical Imaging, Vol. 5, Issue 2, 024502 (May 2018). https://doi.org/10.1117/1.JMI.5.2.024502
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.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Eric Wolsztynski, Finbarr O'Sullivan, Eimear Keyes, Janet O'Sullivan, and Janet F. Eary "Positron emission tomography-based assessment of metabolic gradient and other prognostic features in sarcoma," Journal of Medical Imaging 5(2), 024502 (24 May 2018). https://doi.org/10.1117/1.JMI.5.2.024502
Received: 11 January 2018; Accepted: 30 April 2018; Published: 24 May 2018
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Cited by 17 scholarly publications.
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KEYWORDS
Tumors
Feature selection
Principal component analysis
Neural networks
Statistical analysis
Machine learning
Statistical modeling
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