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Sarcoidosis is a rare, multi-systemic, inflammatory disease, primarilyaffecting the lungs. High-resolution computed tomography (CT) scans are used toclinically characterize pulmonary sarcoidosis. In the medical imaging field,there is growing recognition to switch from visual examination of CT images tomore rapid, objective assessments of the abnormalities. In this work, weexplore the usefulness of various objective measures of spatialheterogeneity---fractal dimension, Moran's I, and Geary's C ---fordistinguishing between abnormal sarcoidosis and normal lung parenchyma.  CT data for N=58 sarcoidosis subjects enrolled at National Jewish Health wereobtained from the GRADS study. CT data for N=101 control patients were obtainedfrom the COPDGene study. Radiomic measures were computed for eachtwo-dimensional slice of a given scan, in the axial, coronal, and sagittalplanes. Functional regression was applied to identify lung regions where CTnodules tend to proliferate.  Moran's I, Geary's C and fractal dimension significantly differentiatebetween subjects with and without sarcoidosis throughout the majority of thelung, with disease abnormalities most apparent in the top axial, middlecoronal, and outer sagittal regions. A trend appeared across Scadding stages,with CT scans from patients with Scadding stages I and III appearing thehealthiest, and Scadding stage IV appearing the least healthy.  The radiomic measures and techniques presented herein successfullycharacterize CT images in sarcoidosis by objectively and efficientlyapproximating what we know about the pathology of sarcoidosis.

An Exploration of Spatial Radiomic Features in Pulmonary Sarcoidosis