Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Background/Aims: The genetic expression in the active inflammatory regions is increased in ulcerative colitis (UC) with endoscopic activity. The aim of this study was to investigate the molecular activity of inflammation and tissue remodeling markers in endoscopically inflamed and uninflamed regions of UC. Methods: Patients with UC (n=47) and controls (n=20) were prospectively enrolled at the Seoul National University Bundang Hospital. Inflamed tissue was obtained at the most active lesion, and uninflamed tissue was collected from approximately 15 cm above the upper end of the active lesion via colonoscopic biopsies. The messenger RNA expression levels of transforming growth factor β (TGF-β), interleukin (IL)-1β, IL-6, IL-17A, E-cadherin, olfactomedin-4 (OLFM4), leucine-rich repeat-containing G protein-coupled receptor 5 (LGR5), vimentin, fibroblast-specific protein-1 (FSP1), and α-smooth muscle actin (SMA) were evaluated. Mucosal healing (MH) was defined according to a Mayo endoscopic score of 0, 1 or non-MH (Mayo endoscopic score of 2 or 3). Results: The messenger RNA expressions of TGF-β, IL-1β, OLFM4, FSP1, vimentin, and α-SMA were significantly higher, and that of E-cadherin was significantly lower in inflamed and uninflamed regions of patients with UC than those in controls. In the inflamed regions, patients in the non-MH group had significantly increased genetic expression of TGF-β, FSP1, vimentin, and α-SMA compared to patients in the MH group. Similarly, the non-MH group had significantly higher genetic expression of TGF-β, IL-1β, IL-6, vimentin, and α-SMA than the MH group in the uninflamed regions. Conclusions Endoscopic activity in UC suggests inflammation and tissue remodeling of uninflamed regions similar to inflamed regions.

Background/Aims: The genetic expression in the active inflammatory regions is increased in ulcerative colitis (UC) with endoscopic activity. The aim of this study was to investigate the molecular activity of inflammation and tissue remodeling markers in endoscopically inflamed and uninflamed regions of UC. Methods: Patients with UC (n=47) and controls (n=20) were prospectively enrolled at the Seoul National University Bundang Hospital. Inflamed tissue was obtained at the most active lesion, and uninflamed tissue was collected from approximately 15 cm above the upper end of the active lesion via colonoscopic biopsies. The messenger RNA expression levels of transforming growth factor β (TGF-β), interleukin (IL)-1β, IL-6, IL-17A, E-cadherin, olfactomedin-4 (OLFM4), leucine-rich repeat-containing G protein-coupled receptor 5 (LGR5), vimentin, fibroblast-specific protein-1 (FSP1), and α-smooth muscle actin (SMA) were evaluated. Mucosal healing (MH) was defined according to a Mayo endoscopic score of 0, 1 or non-MH (Mayo endoscopic score of 2 or 3). Results: The messenger RNA expressions of TGF-β, IL-1β, OLFM4, FSP1, vimentin, and α-SMA were significantly higher, and that of E-cadherin was significantly lower in inflamed and uninflamed regions of patients with UC than those in controls. In the inflamed regions, patients in the non-MH group had significantly increased genetic expression of TGF-β, FSP1, vimentin, and α-SMA compared to patients in the MH group. Similarly, the non-MH group had significantly higher genetic expression of TGF-β, IL-1β, IL-6, vimentin, and α-SMA than the MH group in the uninflamed regions. Conclusions Endoscopic activity in UC suggests inflammation and tissue remodeling of uninflamed regions similar to inflamed regions.

Background/Aims: The genetic expression in the active inflammatory regions is increased in ulcerative colitis (UC) with endoscopic activity. The aim of this study was to investigate the molecular activity of inflammation and tissue remodeling markers in endoscopically inflamed and uninflamed regions of UC. Methods: Patients with UC (n=47) and controls (n=20) were prospectively enrolled at the Seoul National University Bundang Hospital. Inflamed tissue was obtained at the most active lesion, and uninflamed tissue was collected from approximately 15 cm above the upper end of the active lesion via colonoscopic biopsies. The messenger RNA expression levels of transforming growth factor β (TGF-β), interleukin (IL)-1β, IL-6, IL-17A, E-cadherin, olfactomedin-4 (OLFM4), leucine-rich repeat-containing G protein-coupled receptor 5 (LGR5), vimentin, fibroblast-specific protein-1 (FSP1), and α-smooth muscle actin (SMA) were evaluated. Mucosal healing (MH) was defined according to a Mayo endoscopic score of 0, 1 or non-MH (Mayo endoscopic score of 2 or 3). Results: The messenger RNA expressions of TGF-β, IL-1β, OLFM4, FSP1, vimentin, and α-SMA were significantly higher, and that of E-cadherin was significantly lower in inflamed and uninflamed regions of patients with UC than those in controls. In the inflamed regions, patients in the non-MH group had significantly increased genetic expression of TGF-β, FSP1, vimentin, and α-SMA compared to patients in the MH group. Similarly, the non-MH group had significantly higher genetic expression of TGF-β, IL-1β, IL-6, vimentin, and α-SMA than the MH group in the uninflamed regions. Conclusions Endoscopic activity in UC suggests inflammation and tissue remodeling of uninflamed regions similar to inflamed regions.

Background/Aims: The genetic expression in the active inflammatory regions is increased in ulcerative colitis (UC) with endoscopic activity. The aim of this study was to investigate the molecular activity of inflammation and tissue remodeling markers in endoscopically inflamed and uninflamed regions of UC. Methods: Patients with UC (n=47) and controls (n=20) were prospectively enrolled at the Seoul National University Bundang Hospital. Inflamed tissue was obtained at the most active lesion, and uninflamed tissue was collected from approximately 15 cm above the upper end of the active lesion via colonoscopic biopsies. The messenger RNA expression levels of transforming growth factor β (TGF-β), interleukin (IL)-1β, IL-6, IL-17A, E-cadherin, olfactomedin-4 (OLFM4), leucine-rich repeat-containing G protein-coupled receptor 5 (LGR5), vimentin, fibroblast-specific protein-1 (FSP1), and α-smooth muscle actin (SMA) were evaluated. Mucosal healing (MH) was defined according to a Mayo endoscopic score of 0, 1 or non-MH (Mayo endoscopic score of 2 or 3). Results: The messenger RNA expressions of TGF-β, IL-1β, OLFM4, FSP1, vimentin, and α-SMA were significantly higher, and that of E-cadherin was significantly lower in inflamed and uninflamed regions of patients with UC than those in controls. In the inflamed regions, patients in the non-MH group had significantly increased genetic expression of TGF-β, FSP1, vimentin, and α-SMA compared to patients in the MH group. Similarly, the non-MH group had significantly higher genetic expression of TGF-β, IL-1β, IL-6, vimentin, and α-SMA than the MH group in the uninflamed regions. Conclusions Endoscopic activity in UC suggests inflammation and tissue remodeling of uninflamed regions similar to inflamed regions.

Purpose: Notable effectiveness of trastuzumab deruxtecan in patients with human epidermal growth factor receptor 2 (HER2)–low advanced breast cancer (BC) has focused pathologists’ attention. We studied the incidence and clinicopathologic characteristics of HER2-low BC, and the effects of immunohistochemistry (IHC) associated factors on HER2 IHC results. Materials and Methods: The Breast Pathology Study Group of the Korean Society of Pathologists conducted a nationwide study using real-world data on HER2 status generated between January 2022 and December 2022. Information on HER2 IHC protocols at each participating institution was also collected. Results: Total 11,416 patients from 25 institutions included in this study. Of these patients, 40.7% (range, 6.0% to 76.3%) were classified as HER2-zero, 41.7% (range, 10.5% to 69.1%) as HER2-low, and 17.5% (range, 6.7% to 34.0%) as HER2-positive. HER2-low tumors were associated with positive estrogen receptor and progesterone receptor statuses (p < 0.001 and p < 0.001, respectively). Antigen retrieval times (≥ 36 minutes vs. < 36 minutes) and antibody incubation times (≥ 12 minutes vs. < 12 minutes) affected on the frequency of HER2 IHC 1+ BC at institutions using the PATHWAY HER2 (4B5) IHC assay and BenchMark XT or Ultra staining instruments. Furthermore, discordant results between core needle biopsy and subsequent resection specimen HER2 statuses were observed in 24.1% (787/3,259) of the patients. Conclusion The overall incidence of HER2-low BC in South Korea concurs with those reported in previously published studies. Significant inter-institutional differences in HER2 IHC protocols were observed, and it may have impact on HER2-low status. Thus, we recommend standardizing HER2 IHC conditions to ensure precise patient selection for targeted therapy.