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.
Highlights
The importance of quantitative assessment beyond semiquantitative SUV-based summaries is firmly established in a number of contexts
0 20 40 60 80 100 Time the metabolic activity in sarcoma tumors based on FDG-Positron emission tomography (PET) imaging data
This paper demonstrates that the proposed model-based structural variables complement characteristics captured by radiomic features, in that each quantitation methodology captures distinct parts of the information space
Summary
The importance of quantitative assessment beyond semiquantitative SUV-based summaries is firmly established in a number of contexts (diseases and modalities). Positron emission tomography (PET) has been found useful in the evaluation of intratumoral heterogeneity at the macroscopic level[1] and calls for more elaborate algorithmic methodologies to capture prognostic information. This assessment can be achieved using spatial mathematical modeling of the metabolic tracer uptake information observed within the volume of interest (VoI). We develop an approach, involving a nonparametric analysis of the 3-D elliptical contour profile, for evaluating the “metabolic gradient” of the tumor at each voxel These model-based volumetric gradients can be combined with structural heterogeneity in multivariate prognostic analyses.
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