Background/Aims: Early colorectal cancer (ECC) is commonly resected endoscopically. Perforation is a devastating complication of endoscopic resection. We aimed to identify the characteristics and predictive risk factors for perforation related to endoscopic resection of ECC. Methods: This nationwide retrospective multicenter study included patients with ECC who underwent endoscopic resection. We investigated the demographics, endoscopic findings at the time of treatment, and histopathological characteristics of the resected specimens. Logistic regression analysis was used to investigate the clinical factors associated with procedure-related perforations. Survival analysis was conducted to assess the impact of perforation on the overall survival of patients with ECC. Results: This study included 965 participants with a mean age of 63.4 years. The most common endoscopic treatment was conventional endoscopic mucosal resection (n=573, 59.4%), followed by conventional endoscopic submucosal dissection (n=259, 26.8%). Thirty-three patients (3.4%) experienced perforations, most of which were managed endoscopically (n=23/33, 69.7%). Patients who undergo endoscopic submucosal dissection-hybrid and precut endoscopic mucosal resection have a higher risk of perforation than those who undergo conventional endoscopic mucosal resection (odds ratio, 78.65 and 39.72, p<0.05). Procedure-related perforations were not associated with patient survival. Conclusions Perforation after endoscopic resection had no significant impact on the prognosis of ECC. The type of endoscopic resection was a crucial predictor of perforation. Large-scale prospective studies are needed to further investigate endoscopic resection of ECC.

Heart failure (HF) is a major cause of mortality and morbidity in South Korea, imposing substantial physical, emotional, and financial burdens on patients and society. Despite the high burden of symptom and complex care needs of HF patients, palliative care and hospice services remain underutilized in South Korea due to cultural, institutional, and knowledge-related barriers. This position statement from the Korean Society of Heart Failure emphasizes the need for integrating palliative and hospice care into HF management to improve quality of life and support holistic care for patients and their families. By clarifying the role of palliative care in HF and proposing practical referral criteria, this position statement aims to bridge the gap between HF and palliative care services in South Korea, ultimately improving patient-centered outcomes and aligning treatment with the goals and values of HF patients.

Background/Aims: Early colorectal cancer (ECC) is commonly resected endoscopically. Perforation is a devastating complication of endoscopic resection. We aimed to identify the characteristics and predictive risk factors for perforation related to endoscopic resection of ECC. Methods: This nationwide retrospective multicenter study included patients with ECC who underwent endoscopic resection. We investigated the demographics, endoscopic findings at the time of treatment, and histopathological characteristics of the resected specimens. Logistic regression analysis was used to investigate the clinical factors associated with procedure-related perforations. Survival analysis was conducted to assess the impact of perforation on the overall survival of patients with ECC. Results: This study included 965 participants with a mean age of 63.4 years. The most common endoscopic treatment was conventional endoscopic mucosal resection (n=573, 59.4%), followed by conventional endoscopic submucosal dissection (n=259, 26.8%). Thirty-three patients (3.4%) experienced perforations, most of which were managed endoscopically (n=23/33, 69.7%). Patients who undergo endoscopic submucosal dissection-hybrid and precut endoscopic mucosal resection have a higher risk of perforation than those who undergo conventional endoscopic mucosal resection (odds ratio, 78.65 and 39.72, p<0.05). Procedure-related perforations were not associated with patient survival. Conclusions Perforation after endoscopic resection had no significant impact on the prognosis of ECC. The type of endoscopic resection was a crucial predictor of perforation. Large-scale prospective studies are needed to further investigate endoscopic resection of ECC.

Background/Aims: Early colorectal cancer (ECC) is commonly resected endoscopically. Perforation is a devastating complication of endoscopic resection. We aimed to identify the characteristics and predictive risk factors for perforation related to endoscopic resection of ECC. Methods: This nationwide retrospective multicenter study included patients with ECC who underwent endoscopic resection. We investigated the demographics, endoscopic findings at the time of treatment, and histopathological characteristics of the resected specimens. Logistic regression analysis was used to investigate the clinical factors associated with procedure-related perforations. Survival analysis was conducted to assess the impact of perforation on the overall survival of patients with ECC. Results: This study included 965 participants with a mean age of 63.4 years. The most common endoscopic treatment was conventional endoscopic mucosal resection (n=573, 59.4%), followed by conventional endoscopic submucosal dissection (n=259, 26.8%). Thirty-three patients (3.4%) experienced perforations, most of which were managed endoscopically (n=23/33, 69.7%). Patients who undergo endoscopic submucosal dissection-hybrid and precut endoscopic mucosal resection have a higher risk of perforation than those who undergo conventional endoscopic mucosal resection (odds ratio, 78.65 and 39.72, p<0.05). Procedure-related perforations were not associated with patient survival. Conclusions Perforation after endoscopic resection had no significant impact on the prognosis of ECC. The type of endoscopic resection was a crucial predictor of perforation. Large-scale prospective studies are needed to further investigate endoscopic resection of ECC.

Background/Aims: Early colorectal cancer (ECC) is commonly resected endoscopically. Perforation is a devastating complication of endoscopic resection. We aimed to identify the characteristics and predictive risk factors for perforation related to endoscopic resection of ECC. Methods: This nationwide retrospective multicenter study included patients with ECC who underwent endoscopic resection. We investigated the demographics, endoscopic findings at the time of treatment, and histopathological characteristics of the resected specimens. Logistic regression analysis was used to investigate the clinical factors associated with procedure-related perforations. Survival analysis was conducted to assess the impact of perforation on the overall survival of patients with ECC. Results: This study included 965 participants with a mean age of 63.4 years. The most common endoscopic treatment was conventional endoscopic mucosal resection (n=573, 59.4%), followed by conventional endoscopic submucosal dissection (n=259, 26.8%). Thirty-three patients (3.4%) experienced perforations, most of which were managed endoscopically (n=23/33, 69.7%). Patients who undergo endoscopic submucosal dissection-hybrid and precut endoscopic mucosal resection have a higher risk of perforation than those who undergo conventional endoscopic mucosal resection (odds ratio, 78.65 and 39.72, p<0.05). Procedure-related perforations were not associated with patient survival. Conclusions Perforation after endoscopic resection had no significant impact on the prognosis of ECC. The type of endoscopic resection was a crucial predictor of perforation. Large-scale prospective studies are needed to further investigate endoscopic resection of ECC.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Background/Aims: Synchronous multiple gastric cancer (SMGC) accounts for approximately 6% to 14% of gastric cancer (GC) cases. This study aimed to identify risk factors for SMGC. Methods: A total of 14,603 patients diagnosed with GC were prospectively enrolled. Data including age, sex, body mass index, smoking, alcohol consumption, family history, p53 expression, microsatellite instability, cancer classification, lymph node metastasis, and treatment were collected. Risk factors were analyzed using logistic regression analysis between a single GC and SMGC. Results: The incidence of SMGC was 4.04%, and that of early GC (EGC) and advanced GC (AGC) was 5.43% and 3.11%, respectively. Patients with SMGC were older (65.33 years vs 61.75 years, p<0.001) and more likely to be male. Lymph node metastasis was found in 27% of patients with SMGC and 32% of patients with single GC. Multivariate analysis showed that SMGC was associated with sex (male odds ratio [OR], 1.669; 95% confidence interval [CI], 1.223 to 2.278; p=0.001), age (≥65 years OR, 1.532; 95% CI, 1.169 to 2.008; p=0.002), and EGC (OR, 1.929; 95% CI, 1.432 to 2.600; p<0.001). Survival rates were affected by Lauren classification, sex, tumor size, cancer type, distant metastasis, and venous invasion but were not related to the number of GCs. However, the survival rate of AGC with SMGC was very high. Conclusions SMGC had unique characteristics such as male sex, older age, and EGC, and the survival rate of AGC, in which the intestinal type was much more frequent, was very good (Trial registration number: NCT04973631).

The prevalence of thyroid cancer in pregnant women is unknown; however, given that thyroid cancer commonly develops in women, especially young women of childbearing age, new cases are often diagnosed during pregnancy. This recommendation summarizes the follow-up and treatment when thyroid cancer is diagnosed during pregnancy and when a woman with thyroid cancer becomes pregnant. If diagnosed in the first trimester, surgery should be postponed until after delivery, and the patient should be monitored with ultrasound. If follow-up before 24–26 weeks of gestation shows that thyroid cancer has progressed, surgery should be considered. If it has not progressed at 24–26 weeks of gestation or if papillary thyroid cancer is diagnosed after 20 weeks of pregnancy, surgery should be considered after delivery.