Spatially coherent modeling of 3D FDG-PET data for assessment of intratumoral heterogeneity and uptake gradients.

Radiomics have become invaluable for non-invasive cancer patient risk prediction, and the community now turns to exogenous assessment, e.g., from genomics, for interpretability of these agnostic analyses. Yet, some opportunities for clinically interpretable modeling of positron emission tomography (PET) imaging data remain unexplored, that could facilitate insightful characterization at voxel level. Here, we present a novel deformable tubular representation of the distribution of tracer uptake within a volume of interest, and derive interpretable prognostic summaries from it. This data-adaptive strategy yields a 3D-coherent and smooth model fit, and a profile curve describing tracer uptake as a function of voxel location within the volume. Local trends in uptake rates are assessed at each voxel via the calculation of gradients derived from this curve. Intratumoral heterogeneity can also be assessed directly from it. We illustrate the added value of this approach over previous strategies, in terms of volume rendering and coherence of the structural representation of the data. We further demonstrate consistency of the implementation via simulations, and prognostic potential of heterogeneity and statistical summaries of the uptake gradients derived from the model on a clinical cohort of 158 sarcoma patients imaged with -fluorodeoxyglucose-PET, in multivariate prognostic models of patient survival. The proposed approach captures uptake characteristics consistently at any location, and yields a description of variations in uptake that holds prognostic value complementarily to structural heterogeneity. This creates opportunities for monitoring of local areas of greater interest within a tumor, e.g., to assess therapeutic response in avid locations.