Kernel Conversion Improves the Correlation between the Extent of Emphysema and Clinical Parameters in Chronic Obstructive Pulmonary Disease: A Multicenter Cohort Study

Background: Computed tomography (CT) scans are utilized to assess emphysema, a prominent phenotype of chronic obstructive pulmonary disease (COPD). Variability in CT protocols and equipment across hospitals can impact accuracy. This study aims to implement kernel conversion across different CT settings and evaluate changes in the correlation between the emphysema index pre- and post-kernel conversion, along with clinical measures in COPD patients. Methods: Data were extracted from the Korea COPD Subgroup Study database, which included CT scan images from 484 COPD patients. These images underwent kernel conversion. Emphysema extent was quantified using the percentage of low-attenuation areas (%LAA-950) determined by a deep learning-based program. The correlation between %LAA-950 and clinical parameters, including lung function tests, the modified Medical Research Council (mMRC), 6-minute walking distance (6MWD), COPD assessment test (CAT), and the St. George’s Respiratory Questionnaire for COPD (SGRQ-c), was analyzed. Subsequently, these values were compared across various CT settings. Results: A total of 484 participants were included. Kernel conversion significantly reduced the variance in %LAA-950 values (before vs. after: 12.6±11.0 vs. 8.8±11.9). Post-kernel conversion, %LAA-950 demonstrated moderate correlations with forced expiratory volume in 1 second (r=–0.41), residual volume/total lung capacity (r=0.42), mMRC (r=0.25), CAT score (r=0.12), SGRQ-c (r=0.21), and 6MWD (r=0.15), all of which were improved compared to the unconverted dataset (all p<0.01). Conclusion: CT images processed through kernel conversion enhance the correlation between the extent of emphysema and clinical parameters in COPD.