Kernel conversion improves correlation between emphysema extent and clinical parameters in COPD: a multicenter cohort study.

Abstract

Background: Computed tomography (CT) scans are used to assess emphysema, a significant phenotype of chronic obstructive pulmonary disease (COPD), but variability in CT protocols and devices across the hospitals may affect accuracy. This study aims to perform kernel conversion among different CT settings and to evaluate differences in the correlation between emphysema index before and after kernel conversion, as well as clinical measures in COPD patients.

Methods: The data were extracted from the Korea COPD Subgroup Study database, involving 484 COPD patients with CT scan images. These were processed with kernel conversion. Emphysema extent was quantified as the percentage of low-attenuation areas (%LAA-950) by deep learning-based program. The correlation between %LAA-950 and clinical parameters, such as lung function tests, the modified Medical Research Council (mMRC), six-minute walking distance (6MWD), COPD assessment test (CAT), and the St. George's Respiratory Questionnaire for COPD (SGRQ-c), were analyzed. These values were then compared across different CT settings.

Results: A total of 484 participants were included. Compared to before, kernel conversion reduced the variance in %LAA-950 values (before vs. after: 12.6±11.0 vs. 8.8±11.9). After kernel conversion, %LAA-950 showed moderate correlations with forced expiratory volume in one 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 improved compared to the unconverted dataset (all, P<0.01).

Conclusion: CT images processed with kernel conversion improve the correlation between emphysema extent and clinical parameters in COPD.