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: Smoking may have a protective role in developing ulcerative colitis (UC) but have the opposite effect on Crohn’s disease (CD). This study aimed to determine the risk of developing inflammatory bowel disease (IBD) according to smoking status and onset age of smoking. Materials and Methods: We collected data on the smoking experiences of participants aged 20–39 years who underwent biannual examinations provided by the Korean National Health Screening Program from 2009 to 2012. IBD diagnosis was identified using the National Health Insurance Service. The risk of IBD according to smoking status and onset age of smoking was analyzed after adjusting for major clinical variables. Results: During a median 10.59-year follow-up, the risk of UC in ex-smokers was significantly higher than that in non-smokers, and the earlier ex-smokers started smoking, the higher risk of UC [ex-smokers whose onset age of smoking was <20 years, adjusted hazard ratio (aHR) 1.928, 95% confidence interval (CI)=1.649–2.255; 20–24 years, aHR 1.728, 95% CI=1.541–1.939; 25–29 years, aHR 1.676, 95% CI=1.489–1.887; ≥30 years, aHR 1.226, 95% CI=1.010–1.486]. The risk of UC was significantly lower in current smokers whose onset age of smoking was 25–29 years than in non-smokers (aHR 0.825, 95% CI=0.709–0.959). The risk of CD did not differ according to smoking status and onset age of smoking. Conclusion Ex-smokers who started smoking at a young age have a high risk of UC, even after adjusting for the smoking amount.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

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: Smoking may have a protective role in developing ulcerative colitis (UC) but have the opposite effect on Crohn’s disease (CD). This study aimed to determine the risk of developing inflammatory bowel disease (IBD) according to smoking status and onset age of smoking. Materials and Methods: We collected data on the smoking experiences of participants aged 20–39 years who underwent biannual examinations provided by the Korean National Health Screening Program from 2009 to 2012. IBD diagnosis was identified using the National Health Insurance Service. The risk of IBD according to smoking status and onset age of smoking was analyzed after adjusting for major clinical variables. Results: During a median 10.59-year follow-up, the risk of UC in ex-smokers was significantly higher than that in non-smokers, and the earlier ex-smokers started smoking, the higher risk of UC [ex-smokers whose onset age of smoking was <20 years, adjusted hazard ratio (aHR) 1.928, 95% confidence interval (CI)=1.649–2.255; 20–24 years, aHR 1.728, 95% CI=1.541–1.939; 25–29 years, aHR 1.676, 95% CI=1.489–1.887; ≥30 years, aHR 1.226, 95% CI=1.010–1.486]. The risk of UC was significantly lower in current smokers whose onset age of smoking was 25–29 years than in non-smokers (aHR 0.825, 95% CI=0.709–0.959). The risk of CD did not differ according to smoking status and onset age of smoking. Conclusion Ex-smokers who started smoking at a young age have a high risk of UC, even after adjusting for the smoking amount.

Background: and Purpose Previous research on patients with acute ischemic stroke (AIS) has shown a 0.5% incidence of major gastrointestinal bleeding (GIB) requiring blood transfusion during hospitalization. The existing literature has insufficiently explored the long-term incidence in this population despite the decremental impact of GIB on stroke outcomes. Methods: We analyzed the data from a cohort of patients with AIS admitted to 14 hospitals as part of a nationwide multicenter prospective stroke registry between 2011 and 2013. These patients were followed up for up to 6 years. The occurrence of major GIB events, defined as GIB necessitating at least two units of blood transfusion, was tracked using the National Health Insurance Service claims data. Results: Among 10,818 patients with AIS (male, 59%; mean age, 68±13 years), 947 (8.8%) experienced 1,224 episodes of major GIB over a median follow-up duration of 3.1 years. Remarkably, 20% of 947 patients experienced multiple episodes of major GIB. The incidence peaked in the first month after AIS, reaching 19.2 per 100 person-years, and gradually decreased to approximately one-sixth of this rate by the 2nd year with subsequent stabilization. Multivariable analysis identified the following predictors of major GIB: anemia, estimated glomerular filtration rate <60 mL/min/1.73 m2 , and a 3-month modified Rankin Scale score of ≥4. Conclusion Patients with AIS are susceptible to major GIB, particularly in the first month after the onset of AIS, with the risk decreasing thereafter. Implementing preventive strategies may be important, especially for patients with anemia and impaired renal function at stroke onset and those with a disabling stroke.

Background and Objectives: Evidence remains limited on the real-world prescription of very low-dose oral anticoagulation among frail patients with atrial fibrillation (AF). We described the practice patterns, effectiveness, and safety of very low-dose edoxaban (15 mg once daily). Methods: Patients with AF prescribed edoxaban 15 mg once daily in 2 tertiary hospitals between 2016 and September 2022 were included. Baseline clinical characteristics and clinical outcomes of interest were thromboembolic and bleeding events. Results: A total of 674 patients were included (mean age 78.3±9.1, 49.7% aged ≥80 years, 49.3% women, median follow-up 1.0±1.2 years). Mean CHA 2 DS 2 -VASc score was 3.9±1.6, and the modified HAS-BLED score was 2.0±1.1. Between 2016 and 2022, the number of very lowdose edoxaban prescriptions increased. The main reasons for the prescription of very lowdose were low body weight (55.5% below 60 kg), anaemia (62.8%), chronic kidney disease (40.2%), active cancer (15.3%), concomitant anti-platelet use (26.7%), and prior major bleeding (19.7%). During a median follow-up duration of 8 (interquartile range 3–16) months, overall thromboembolic and bleeding events occurred in 16 (2.3%) and 88 (13.1%) patients, respectively. Compared to the expected event rates on the established risk scoring systems, patients receiving very low-dose edoxaban demonstrated a 61% reduction in ischemic stroke, a 68% reduction of ischemic stroke/transient ischemic attack/systemic embolism, whereas a 49% increase in major bleeding. Conclusions The prescription of very low-dose edoxaban was increased over time, attributable to various clinical factors. The use of very low-dose edoxaban reduced the expected risk of thromboembolic events.