Background: Late-onset hypogonadism (LOH) is associated with reduced testosterone levels and an increase in various physical, mental, and emotional changes in men with age. Several lifestyle factors, including smoking, are reported to be related to LOH; however, very few studies have sufficiently investigated the relationships between smoking and the symptoms of LOH. This study aimed to use the Androgen Deficiency in Aging Males (ADAM) questionnaire to assess the associations between smoking and LOH symptoms in Korean men. Methods: Men who underwent medical check-ups and transrectal ultrasonography at a university hospital between January 1, 2018 and March 31, 2021 (n=793) were included in this study. Multiple logistic regression was used to assess the risk of LOH symptoms among non-smokers, exsmokers, and current smokers, with adjustments for age, body mass index, alcohol consumption, and exercise and education levels. Results: There were significant correlations between LOH symptoms, as assessed using the ADAM questionnaire, and smoking status. The results of the multivariate logistic regression analysis adjusted for confounding factors indicated that the risk of LOH symptoms was higher in the ex-smokers (odds ratio, 2.446; 95% confidential interval, 1.511–3.962) and current smokers (odds ratio, 6.664; 95% confidential interval, 3.485–12.74) groups. Conclusion These results indicate a positive correlation between smoking and LOH symptoms in Korean men. Nevertheless, large-scale studies are required to further validate these findings.

Background: Late-onset hypogonadism (LOH) is associated with reduced testosterone levels and an increase in various physical, mental, and emotional changes in men with age. Several lifestyle factors, including smoking, are reported to be related to LOH; however, very few studies have sufficiently investigated the relationships between smoking and the symptoms of LOH. This study aimed to use the Androgen Deficiency in Aging Males (ADAM) questionnaire to assess the associations between smoking and LOH symptoms in Korean men. Methods: Men who underwent medical check-ups and transrectal ultrasonography at a university hospital between January 1, 2018 and March 31, 2021 (n=793) were included in this study. Multiple logistic regression was used to assess the risk of LOH symptoms among non-smokers, exsmokers, and current smokers, with adjustments for age, body mass index, alcohol consumption, and exercise and education levels. Results: There were significant correlations between LOH symptoms, as assessed using the ADAM questionnaire, and smoking status. The results of the multivariate logistic regression analysis adjusted for confounding factors indicated that the risk of LOH symptoms was higher in the ex-smokers (odds ratio, 2.446; 95% confidential interval, 1.511–3.962) and current smokers (odds ratio, 6.664; 95% confidential interval, 3.485–12.74) groups. Conclusion These results indicate a positive correlation between smoking and LOH symptoms in Korean men. Nevertheless, large-scale studies are required to further validate these findings.

In the rapidly evolving healthcare environment, radiologists strive to establish their rightful place.Thus, there is a need for enhanced outpatient and clinical education within the Department of Radiology and exploration of its methodologies. Accordingly, the Korean Society of Radiology established a task force to investigate the clinical and outpatient practice status of radiologists overseas, current state of related education, involvement of other specialties in radiologic practices and education in Korea, and clinical and outpatient practice status among Korean radiologists. Furthermore, a survey on clinical competency enhancement was conducted among the members of the Korean Society of Radiology. These findings suggest the need for visibility and clinical competency enhancement in radiologists and methodologies for strengthening clinical competencies.

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: We sought to evaluate the time-dependent efficacy and safety of eplerenone for central serous chorioretinopathy. Methods: A systematic search was performed from inception to May 2023 in the Medline, EMBASE, and Cochrane literature databases to find randomized controlled trials that have administered oral eplerenone therapy to central serous chorioretinopathy patients. Results: Five randomized controlled trials were included in the final analysis. Among a total of 252 central serous chorioretinopathy patients, 134 were included in the eplerenone group and 118 were included in the control group. The best-corrected visual acuity was statistically significantly improved in the eplerenone group compared to the control group (95% confidence interval [CI], -0.08 to -0.02; p = 0.001). After meta-analysis was performed at each follow-up point, it was found that eplerenone statistically significantly improved the best-corrected visual acuity compared to the control group at 2 and 3 months after starting oral eplerenone therapy, but there was no statistically significant difference at 6 months. Subretinal fluid, chorioretinal thickness, central macular thickness, and complications showed no statistically significant differences between the eplerenone and control groups (p = 0.43, 0.67, 0.64, and 0.12, respectively). The difference in the risk of complications occurring between the eplerenone and control groups also didn’t show statistical significance (p = 0.12). Conclusions Although eplerenone is not superior to a control protocol when considering anatomical improvements, the best-corrected visual acuity seems to improve up to 3 months superiorly compared to in the control group when oral eplerenone therapy is administered for central serous chorioretinopathy. In addition, the complications of eplerenone are tolerable. Therefore, clinically short-term use of eplerenone up to 3 months in central serous chorioretinopathy patients can be considered.

In the rapidly evolving healthcare environment, radiologists strive to establish their rightful place.Thus, there is a need for enhanced outpatient and clinical education within the Department of Radiology and exploration of its methodologies. Accordingly, the Korean Society of Radiology established a task force to investigate the clinical and outpatient practice status of radiologists overseas, current state of related education, involvement of other specialties in radiologic practices and education in Korea, and clinical and outpatient practice status among Korean radiologists. Furthermore, a survey on clinical competency enhancement was conducted among the members of the Korean Society of Radiology. These findings suggest the need for visibility and clinical competency enhancement in radiologists and methodologies for strengthening clinical competencies.

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.