Prospective machine learning CT quantitative evaluation of idiopathic pulmonary fibrosis in patients undergoing anti-fibrotic treatment using low- and ultra-low-dose CT

Abstract

To compare the machine learning computed tomography (CT) quantification tool, Computer-Aided Lung Informatics for Pathology Evaluation and Ratings (CALIPER) to pulmonary function testing (PFT) in assessing idiopathic pulmonary fibrosis (IPF) for patients undergoing treatment and determine the effects of limited (LD) and ultra-low dose (ULD) CT on CALIPER performance. Thirty-eight IPF patients underwent PFT and standard, LD, and ULD CT. CALIPER classified each CT voxel into either vessel-related structures (VRS), normal, reticular (R), honeycomb (HC) or ground-glass (GG) features. CALIPER-derived interstitial lung disease (ILD) extent represented the sum of GG, R and HC values. Repeated-measures correlation coefficient (ρrm) and 95% confidence interval (CI) evaluated CALIPER features correlation with PFT. Lin's concordance correlation coefficient (CCC) assessed concordance of CALIPER parameters across different CT dosages. Twenty patients completed 12 months of follow-up. CALIPER ILD correlated significantly with percent predicted (%) forced vital capacity (FVC) and forced expiratory volume in 1 second (%FEV1; p=0.004, ρrm -0.343, 95% CI [-0.547, -0.108] and 0.008, -0.321, [-0.518, -0.07], respectively). VRS significantly correlated with %FVC and %FEV1 (p=0.000, ρrm -0.491, 95% CI [-0.685, -0.251] and -0.478, 0.000, [-0.653, -0.231], respectively). There was near perfect LD and moderate ULD concordance with standard dose CT for both ILD (CCC 0.995, 95% CI 0.988-0.999 and 0.9, 0.795-0.983, respectively) and VRS (CCC 0.989, 95% CI 0.963-0.997 and 0.915, 0.806-0.956, respectively). CALIPER parameters correlate well with PFTs for evaluation of IPF in patients undergoing anti-fibrotic treatment without being influenced by dose variation. CALIPER may serve as a robust, objective adjunct to PFTs in assessing anti-fibrotic treatment related changes.