  The inherent characteristics of lung tissues, which are independent of
breathing manoeuvre, may provide fundamental information on lung function. This
paper attempted to study function-correlated lung textures and their spatial
distribution from CT. 21 lung cancer patients with thoracic 4DCT scans,
DTPA-SPECT ventilation images (V), and available pulmonary function test (PFT)
measurements were collected. 79 radiomic features were included for analysis,
and a sparse-to-fine strategy including subregional feature discovery and
voxel-wise feature distribution study was carried out to identify the
function-correlated radiomic features. At the subregion level, lung CT images
were partitioned and labeled as defected/non-defected patches according to
reference V. At the voxel-wise level, feature maps (FMs) of selected feature
candidates were generated for each 4DCT phase. Quantitative metrics, including
Spearman coefficient of correlation (SCC) and Dice similarity coefficient (DSC)
for FM-V spatial agreement assessments, intra-class coefficient of correlation
(ICC) for FM robustness evaluations, and FM-PFT comparisons, were applied to
validate the results. At the subregion level, eight function-correlated
features were filtered out with medium-to-large statistical strength (effect
size>0.330) to differentiate defected/non-defected lung regions. At the
voxel-wise level, FMs of candidates yielded moderate-to-strong voxel-wise
correlations with reference V. Among them, FMs of GLDM Dependence
Non-uniformity showed the highest robust (ICC=0.96) spatial correlation, with
median SCCs ranging from 0.54 to 0.59 throughout ten phases. Its phase-averaged
FM achieved a median SCC of 0.60, the median DSC of 0.60/0.65 for high/low
functional lung volumes, respectively, and the correlation of 0.646 between the
spatially averaged feature values and PFT measurements.
