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J Med Imaging (Bellingham). 2018 Apr;5(2):024502. doi: 10.1117/1.JMI.5.2.024502. Epub 2018 May 24.

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

Author information

1
University College Cork, Statistics Department, Cork, Ireland.
2
National Cancer Institute, Bethesda, Maryland, United States.

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.

KEYWORDS:

FDG-positron emission tomography; heterogeneity; machine learning; metabolic gradient; prognosis; radiomics; sarcoma; spatial modeling; texture

PMID:
29845091
PMCID:
PMC5967597
[Available on 2019-05-24]
DOI:
10.1117/1.JMI.5.2.024502

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