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.

Objective: The increasing concern over adolescent suicide necessitates suicide prevention training for school teachers, as students spend a significant portion of their time at school. This study’s objective is to develop a suicide prevention program tailored for teachers. Methods: The program was developed by a multidisciplinary research team, drawing on a review of both domestic and international suicide prevention programs, related scholarly articles, and Korean psychological autopsy interviews of adolescents. This was complemented by a survey of teachers to assess the program’s practicality and usability. Results: The developed program comprises three parts, consistent with other versions: Careful Observation, Active Listening, and Risk Evaluation and Expert Referral. Careful Observation focuses on training teachers to recognize verbal, behavioral, and situational warning signs of suicidal ideation in students; Active Listening involves strategies for encouraging students to express their suicidal thoughts and techniques for being an empathetic and attentive listener; Risk Evaluation and Expert Referral provides instruction on how to assess suicide risk and assist students safely. Conclusion It is anticipated that this program will equip teachers with valuable knowledge and skills, contributing to a reduction in adolescents suicide rates.

Objective: To address the gap in timely diagnosis of dementia due to limited screening tools, we investigated the validity and reliability of the Hellocog, computerized neuropsychological test based on tablets for screening dementia. The higher the probability score on the Hellocog, the higher the likelihood of dementia. Methods: This study included 100 patients with dementia and 100 individuals with normal cognition who were aged 60 years or older and free of other major psychiatric, neurological, or medical conditions. They administered the Hellocog on a tablet under the supervision of a neuropsychologist. To determine test-retest reliability, 20 took the Hellocog again after 4 weeks. Diagnostic performance was assessed using the receiver operator characteristics (ROC) analysis. Results: The Hellocog showed adequate internal consistency (Cronbach’s alpha=0.69) and good test-retest reliability (intraclass correlation coefficient=0.86, p<0.001). Participants with dementia scored higher on the Hellocog than those with normal cognition (p<0.001), confirming its high criterion validity. Strong correlations with the Mini-Mental Status Examination (MMSE) score and the total score of the Consortium to Establish a Registry for Alzheimer’s Disease Neuropsychological Assessment Battery (CERAD-TS) highlight the concurrent validity of the Hellocog. The area under the ROC curve for dementia of the Hellocog was excellent (0.971) and comparable to that of the MMSE and CERAD-TS. The sensitivity and specificity for dementia were 0.945 and 0.872%, respectively, which were slightly better than those of the MMSE and CERAD-TS. Conclusion Hellocog stands out as a valid and reliable tool for self-administered dementia screening, with promise for improving early detection of dementia.

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.