  Purpose: To develop a radiomics filtering technique for characterizing
spatial-encoded regional pulmonary ventilation information on lung CT. Methods:
The lung volume was segmented on 46 CT images, and a 3D sliding window kernel
was implemented across the lung volume to capture the spatial-encoded image
information. Fifty-three radiomic features were extracted within the kernel,
resulting in a 4th order tensor object. As such, each voxel coordinate of the
original lung was represented as a 53-dimensional feature vector, such that
radiomic features could be viewed as feature maps within the lungs. To test the
technique as a potential pulmonary ventilation biomarker, the radiomic feature
maps were compared to paired functional images (Galligas PET or DTPA-SPECT)
based on Spearman correlation (r) analysis. Results: The radiomic feature map
GLRLM-based Run-Length Non-Uniformity and GLCOM-based Sum Average are found to
be highly correlated with the functional imaging. The achieved r (median
[range]) for the two features are 0.46 [0.05, 0.67] and 0.45 [0.21, 0.65]
across 46 patients and 2 functional imaging modalities, respectively.
Conclusions: The results provide evidence that local regions of sparsely
encoded heterogeneous lung parenchyma on CT are associated with diminished
radiotracer uptake and measured lung ventilation defects on PET/SPECT imaging.
These findings demonstrate the potential of radiomics to serve as a
complementary tool to the current lung quantification techniques and provide
hypothesis-generating data for future studies.
