Combining Structural and Textural Assessments of Volumetric FDG-PET Uptake in NSCLC

Abstract—Numerous studies have reported the prognostic utility
of texture analyses and the effectiveness of radiomics in PET
and PET/CT assessment of nonsmall cell lung cancer (NSCLC).
Here we explore the potential, relative to this methodology, of an
alternative model-based approach to tumour characterization,
which was successfully applied to sarcoma in previous works.
The spatial distribution of 3-D FDG-PET uptake is evaluated in
the spatial referential determined by the best-fitting ellipsoidal
pattern, which provides a univariate uptake profile function of
the radial position of intratumoral voxels. A group of structural
features is extracted from this fit that include two heterogeneity
variables and statistical summaries of local metabolic gradients.
We demonstrate that these variables capture aspects of tumour
metabolism that are separate to those described by conventional
texture features. Prognostic model selection is performed in terms
of a number of classifiers, including stepwise selection of logistic
models, LASSO, random forests and neural networks with
respect to two-year survival status. Our results for a cohort of 93
NSCLC patients show that structural variables have significant
prognostic potential, and that they may be used in conjunction
with texture features in a traditional radiomics sense, toward
improved baseline multivariate models of patient overall survival.
The statistical significance of these models also demonstrates
the relevance of these machine learning classifiers for prognostic
variable selection.
Index Terms—FDG-PET, heterogeneity, machine learning,
metabolic gradient, nonsmall cell lung cancer (NSCLC), prognosis,
radiomics, spatial modeling, texture.