Quantification of lung function on CT images based on pulmonary radiomic filtering.

Purpose: To develop a radiomics filtering technique for characterizing spatial‐encoded regional pulmonary ventilation information on lung computed tomography (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 fourth‐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 the Spearman correlation (ρ) analysis. Results: The radiomic feature maps GLRLM‐based Run‐Length Non‐Uniformity and GLCOM‐based Sum Average are found to be highly correlated with the functional imaging. The achieved ρ (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. [ABSTRACT FROM AUTHOR]