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.

Purpose: We aim to determine whether preoperative percutaneous needle aspiration or biopsy (PCNA/Bx) increases recurrence risk and reduces survival in stage I lung cancer patients, using a nationwide lung cancer registry. Materials and Methods: We retrospectively included 3,452 patients diagnosed with stage I lung cancer who underwent curative surgery between 2014 and 2019, as recorded in the Korean Association of Lung Cancer Registry. To balance the characteristics of patients with and without PCNA/Bx, we applied inverse probability of treatment weighting. We used cumulative incidence plots and a weighted subdistribution hazard model to analyze time to recurrence. Recurrence-free survival and overall survival were analyzed using Kaplan-Meier curves and weighted Cox proportional hazard ratio models. Results: In patients with adenocarcinoma, the use of PCNA/Bx was associated with a 1.9-fold increase (95% confidence interval [CI], 1.5 to 2.4) in the risk of recurrence and a 1.7-fold decrease (95% CI, 1.3 to 2.2) in recurrence-free survival. Subgroup analysis based on pathologic pleural invasion revealed that the risk of recurrence increased when PCNA/Bx was performed, with 2.1-fold (95% CI, 1.5 to 2.8) in patients without pleural invasion and 1.6-fold (95% CI, 1.0 to 2.4) in those with pleural invasion. No association was found between the use of PCNA/Bx and overall survival. Conclusion Preoperative PCNA/Bx was associated with increased recurrence risks in stage I adenocarcinoma, regardless of pathologic pleural invasion status. In early lung cancer cases where adenocarcinoma is strongly suspected and curative surgery is feasible, the use of transthoracic biopsy should be approached with caution.

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.

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.

Purpose: We aim to determine whether preoperative percutaneous needle aspiration or biopsy (PCNA/Bx) increases recurrence risk and reduces survival in stage I lung cancer patients, using a nationwide lung cancer registry. Materials and Methods: We retrospectively included 3,452 patients diagnosed with stage I lung cancer who underwent curative surgery between 2014 and 2019, as recorded in the Korean Association of Lung Cancer Registry. To balance the characteristics of patients with and without PCNA/Bx, we applied inverse probability of treatment weighting. We used cumulative incidence plots and a weighted subdistribution hazard model to analyze time to recurrence. Recurrence-free survival and overall survival were analyzed using Kaplan-Meier curves and weighted Cox proportional hazard ratio models. Results: In patients with adenocarcinoma, the use of PCNA/Bx was associated with a 1.9-fold increase (95% confidence interval [CI], 1.5 to 2.4) in the risk of recurrence and a 1.7-fold decrease (95% CI, 1.3 to 2.2) in recurrence-free survival. Subgroup analysis based on pathologic pleural invasion revealed that the risk of recurrence increased when PCNA/Bx was performed, with 2.1-fold (95% CI, 1.5 to 2.8) in patients without pleural invasion and 1.6-fold (95% CI, 1.0 to 2.4) in those with pleural invasion. No association was found between the use of PCNA/Bx and overall survival. Conclusion Preoperative PCNA/Bx was associated with increased recurrence risks in stage I adenocarcinoma, regardless of pathologic pleural invasion status. In early lung cancer cases where adenocarcinoma is strongly suspected and curative surgery is feasible, the use of transthoracic biopsy should be approached with caution.

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.