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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.

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


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.


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

[Available on 2019-05-24]

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