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.

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.

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.

Colorectal cancer is the third most common cancer in Korea and the third leading cause of death from cancer. Treatment outcomes for colon cancer are steadily improving due to national health screening programs with advances in diagnostic methods, surgical techniques, and therapeutic agents.. The Korea Colon Cancer Multidisciplinary (KCCM) Committee intends to provide professionals who treat colon cancer with the most up-to-date, evidence-based practice guidelines to improve outcomes and help them make decisions that reflect their patients’ values and preferences. These guidelines have been established by consensus reached by the KCCM Guideline Committee based on a systematic literature review and evidence synthesis and by considering the national health insurance system in real clinical practice settings. Each recommendation is presented with a recommendation strength and level of evidence based on the consensus of the committee.

Purpose: Even though pazopanib, a multitargeted tyrosine kinase inhibitor, has been approved for refractory soft tissue sarcoma (STS), little is known about the molecular determinants of the response to pazopanib. We performed integrative molecular characterization to identify potential predictors of pazopanib efficacy. Materials and Methods: We obtained fresh pre-treatment tumor tissue from 35 patients with advanced STS receiving pazopanib-based treatment. Among those, 18 (51.4%) received pazopanib monotherapy, and the remaining 17 (48.6%) received pazopanib in combination with durvalumab, programmed death-ligand 1 blockade. Whole-exome and transcriptome sequencing were performed for each tumor and patient germline DNA. Results: Of the 35 patients receiving pazopanib-based treatment, nine achieved a partial response (PR), resulting in an objective response rate (ORR) of 27.3%, and the median progression-free survival (PFS) was 6.0 months. Patients with CDK4 amplification (copy ratio tumor to normal > 2) exhibited shorter PFS (3.7 vs. 7.9 months, p=2.09×10–4) and a poorer response (ORR; 0% vs. 33.3%) compared to those without a gene amplification (copy ratio ≤ 2). Moreover, non-responders demonstrated transcriptional activation of CDK4 via DNA amplification, resulting in cell cycle activation. In the durvalumab combination cohort, seven of the 17 patients (41.2%) achieved a PR, and gene expression analysis revealed that durvalumab responders exhibited high immune/stromal cell infiltration, mainly comprising natural killer cells, compared to non-responders as well as increased expression of CD19, a B-cell marker. Conclusion Despite the limitation of heterogeneity in the study population and treatment, we identified possible molecular predictors of pazopanib efficacy that can be employed in future clinical trials aimed at evaluating therapeutic strategies.

Purpose: Desmoid tumor, also known as aggressive fibromatosis, is well-characterized by abnormal Wnt/β-catenin signaling. Various therapeutic options, including imatinib, are available to treat desmoid tumor. However, the molecular mechanism of why imatinib works remains unclear. Here, we describe potential roles of NOTCH2 and HES1 in clinical response to imatinib at genome and transcriptome levels. Materials and Methods: We identified somatic mutations in coding and noncoding regions via whole-genome sequencing. To validate the genetic interaction with expression level in desmoid-tumor condition, we utilized large-scale whole-genome sequencing and transcriptome datasets from the Pan-Cancer Analysis of Whole Genomes project. RNA-sequencing was performed using prospective and retrospective cohort samples to evaluate the expressional relevance with clinical response. Results: Among 20 patients, four (20%) had a partial response and 14 (66.7%) had stable disease, 11 of which continued for ≥ 1 year. With gene-wise functional analyses, we detected a significant correlation between recurrent NOTCH2 noncoding mutations and clinical response to imatinib. Based on Pan-Cancer Analysis of Whole Genomes data analyses, NOTCH2 mutations affect expression levels particularly in the presence of CTNNB1 missense mutations. By analyzing RNA-sequencing with additional desmoid tumor samples, we found that NOTCH2 expression was significantly correlated with HES1 expression. Interestingly, NOTCH2 had no statistical power to discriminate between responders and non-responders. Instead, HES1 was differentially expressed with statistical significance between responders and non-responders. Conclusion Imatinib was effective and well tolerated for advanced desmoid tumor treatment. Our results show that HES1, regulated by NOTCH2, as an indicator of sensitivity to imatinib, and an important therapeutic consideration for desmoid tumor.

Purpose: Microbiota manipulation through selected probiotics may be a promising tool to prevent cancer development as well as onset, to improve clinical efficacy for cancer treatments. The purpose of this study was to evaluate change in microbiota composition after-probiotics supplementation and assessed the efficacy of probiotics in improving quality of life (QOL) in postoperative cancer patients. Methods: Stool samples were collected from 30 cancer patients from February to October 2020 before (group I) and after (group II) 8 weeks of probiotics supplementation. We performed 16S ribosomal RNA gene sequencing to evaluate differences in gut microbiota between groups by comparing gut microbiota diversity, overall composition, and taxonomic signature abundance. The health-related QOL was evaluated through the EORTC Quality of life Questionnaire Core 30 questionnaire. Results: Statistically significant differences were noted in group II; increase of Shannon and Simpson index (P = 0.004 and P = 0.001), decrease of Bacteroidetes and Fusobacteria at the phylum level (P = 0.032 and P = 0.014, retrospectively), increased of beneficial bacteria such as Weissella (0.096% vs. 0.361%, P < 0.004), Lactococcus (0.023% vs. 0.16%, P < 0.001), and Catenibacterium (0.0% vs. 0.005%, P < 0.042) at the genus level. There was a significant improvement in sleep disturbance (P = 0.039) in group II. Conclusion Gut microbiota in cancer patients can be manipulated by specific probiotic strains, result in an altered microbiota. Microbiota modulation by probiotics can be considered as part of a supplement that helps to increase gut microbiota diversity and improve QOL in cancer patients after surgery.