| Kernel conversion improves correlation between emphysema extent and clinical parameters in COPD: a multicenter cohort study |
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Tai Joon An1, Youlim Kim2, Hyun Lee3, Hyeon-Kyoung Koo4, Naoya Tanabe5, Kum Ju Chae6, Kwang Ha Yoo2 |
1Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
2Division of Pulmonary and Allergy, Department of Internal Medicine, Konkuk University Hospital, School of Medicine, Konkuk University, Seoul, Republic of Korea
3Department of Internal Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea
4Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Inje University Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Republic of Korea
5Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
6Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea |
Correspondence:
Kum Ju Chae,
Email: para2727@gmail.com
Kwang Ha Yoo,
Email: 20010025@kuh.ac.kr
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Received: 3 November 2024 • Revised: 30 December 2024 • Accepted: 23 January 2025
*Tai Joon An and Youlim Kim contributed equally to this study as co-first authors. |
| 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. |
| Key Words:
chronic obstructive pulmonary disease, computed tomography, kernel conversion |
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