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: This study used data from the Youth Risk Behavior Survey of Korean adolescents to determine the current trends insecondhand smoke (SHS) among adolescents and to identify the association between the level of exposure to SHS and oral symptoms. Methods: This study employed data that were extracted from the 17th Korea Youth Risk Behavior Web-based Survey in 2021 andfinally analyzed data from 54,848 adolescents. Oral symptoms were classified into three classes to analyze subjective oral symptoms: Class I for tooth pain when eating or drinking hot or cold foods; Class II for throbbing tooth pain; and Class III for sore or bleeding gums during the recent 12 months. The frequency and place of SHS per week were analyzed for SHS variables. The analysis plan file was created by reflecting weight, stratification variables, and cluster variables for analysis. Results: Among the adolescents, 53.2% were exposed to SHS at least once a week, of which 23.3%, 7.9%, and 40.0% wereexposed at home, school, and in public indoor places. Subjective oral symptom classes I, II, and III were associated with sex, grade, educational level of their fathers and mothers, subjective economic class, and subject academic class. Compared to a group not exposed to SHS, the risk of subjective oral symptom classes I, II, and III was significantly higher in those who were exposed to SHS at least once a week. Conclusion This study identified the association between the level of exposure to SHS and oral symptoms among adolescents.Factors for SHS should be considered to prevent oral symptoms among adolescents, and plans that can control SHS of adolescents should be established.

Recently, several attempts have been made to integrate AI into the field of dentistry. To overcome the limitations of traditionalfixed prosthetic fabrication methods such as CAD-CAM (computer-aided design-computer-aided manufacturing), AI programs arebeing developed for automated crown fabrication, and various studies are underway to applicate in clinical situation. In these casestudies, single-unit fixed prostheses were fabricated using an AI program (Dentbird Crown, Imagoworks Inc, Seoul, Korea) in boththe anterior and posterior regions and the fabrication time and accuracy were compared with previously used CAD-CAM method.The first case is a 44-year-old woman who presented for re-fabrication of a zirconia prosthesis due to a prosthesis fracture on thelingual side of the upper right lateral incisor. The second case is a 53-year-old male patient who presented for a crown restorationon an upper left first molar following root canal treatment, where he received a final zirconia restoration. In both cases, the firstprosthesis was designed manually using a CAD program, the second prosthesis was designed using AI alone, and the third prosthesis was designed using AI and then modified by CAD program, and the three designs were superimposed to compare suitability. When evaluated after temporary placement, the final prosthesis demonstrates adequate stability, retention and support, resulting in functional and esthetic satisfaction.

In the 2023 series, we summarized the major clinical research advances in gynecologic oncology based on communications at the conference of Asian Society of Gynecologic Oncology Review Course. The review consisted of 1) Endometrial cancer: immune checkpoint inhibitor, antibody drug conjugates (ADCs), selective inhibitor of nuclear export, CDK4/6 inhibitors WEE1 inhibitor, poly (ADP-ribose) polymerase (PARP) inhibitors. 2) Cervical cancer: surgery in low-risk early-stage cervical cancer, therapy for locally advanced stage and advanced, metastatic, or recurrent setting; and 3) Ovarian cancer: immunotherapy, triplet therapies using immune checkpoint inhibitors along with antiangiogenic agents and PARP inhibitors, and ADCs. In 2023, the field of endometrial cancer treatment witnessed a landmark year, marked by several practice-changing outcomes with immune checkpoint inhibitors and the reliable efficacy of PARP inhibitors and ADCs.

Background: This study used data from the Youth Risk Behavior Survey of Korean adolescents to determine the current trends insecondhand smoke (SHS) among adolescents and to identify the association between the level of exposure to SHS and oral symptoms. Methods: This study employed data that were extracted from the 17th Korea Youth Risk Behavior Web-based Survey in 2021 andfinally analyzed data from 54,848 adolescents. Oral symptoms were classified into three classes to analyze subjective oral symptoms: Class I for tooth pain when eating or drinking hot or cold foods; Class II for throbbing tooth pain; and Class III for sore or bleeding gums during the recent 12 months. The frequency and place of SHS per week were analyzed for SHS variables. The analysis plan file was created by reflecting weight, stratification variables, and cluster variables for analysis. Results: Among the adolescents, 53.2% were exposed to SHS at least once a week, of which 23.3%, 7.9%, and 40.0% wereexposed at home, school, and in public indoor places. Subjective oral symptom classes I, II, and III were associated with sex, grade, educational level of their fathers and mothers, subjective economic class, and subject academic class. Compared to a group not exposed to SHS, the risk of subjective oral symptom classes I, II, and III was significantly higher in those who were exposed to SHS at least once a week. Conclusion This study identified the association between the level of exposure to SHS and oral symptoms among adolescents.Factors for SHS should be considered to prevent oral symptoms among adolescents, and plans that can control SHS of adolescents should be established.

Recently, several attempts have been made to integrate AI into the field of dentistry. To overcome the limitations of traditionalfixed prosthetic fabrication methods such as CAD-CAM (computer-aided design-computer-aided manufacturing), AI programs arebeing developed for automated crown fabrication, and various studies are underway to applicate in clinical situation. In these casestudies, single-unit fixed prostheses were fabricated using an AI program (Dentbird Crown, Imagoworks Inc, Seoul, Korea) in boththe anterior and posterior regions and the fabrication time and accuracy were compared with previously used CAD-CAM method.The first case is a 44-year-old woman who presented for re-fabrication of a zirconia prosthesis due to a prosthesis fracture on thelingual side of the upper right lateral incisor. The second case is a 53-year-old male patient who presented for a crown restorationon an upper left first molar following root canal treatment, where he received a final zirconia restoration. In both cases, the firstprosthesis was designed manually using a CAD program, the second prosthesis was designed using AI alone, and the third prosthesis was designed using AI and then modified by CAD program, and the three designs were superimposed to compare suitability. When evaluated after temporary placement, the final prosthesis demonstrates adequate stability, retention and support, resulting in functional and esthetic satisfaction.

In the 2023 series, we summarized the major clinical research advances in gynecologic oncology based on communications at the conference of Asian Society of Gynecologic Oncology Review Course. The review consisted of 1) Endometrial cancer: immune checkpoint inhibitor, antibody drug conjugates (ADCs), selective inhibitor of nuclear export, CDK4/6 inhibitors WEE1 inhibitor, poly (ADP-ribose) polymerase (PARP) inhibitors. 2) Cervical cancer: surgery in low-risk early-stage cervical cancer, therapy for locally advanced stage and advanced, metastatic, or recurrent setting; and 3) Ovarian cancer: immunotherapy, triplet therapies using immune checkpoint inhibitors along with antiangiogenic agents and PARP inhibitors, and ADCs. In 2023, the field of endometrial cancer treatment witnessed a landmark year, marked by several practice-changing outcomes with immune checkpoint inhibitors and the reliable efficacy of PARP inhibitors and ADCs.

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: This study used data from the Youth Risk Behavior Survey of Korean adolescents to determine the current trends insecondhand smoke (SHS) among adolescents and to identify the association between the level of exposure to SHS and oral symptoms. Methods: This study employed data that were extracted from the 17th Korea Youth Risk Behavior Web-based Survey in 2021 andfinally analyzed data from 54,848 adolescents. Oral symptoms were classified into three classes to analyze subjective oral symptoms: Class I for tooth pain when eating or drinking hot or cold foods; Class II for throbbing tooth pain; and Class III for sore or bleeding gums during the recent 12 months. The frequency and place of SHS per week were analyzed for SHS variables. The analysis plan file was created by reflecting weight, stratification variables, and cluster variables for analysis. Results: Among the adolescents, 53.2% were exposed to SHS at least once a week, of which 23.3%, 7.9%, and 40.0% wereexposed at home, school, and in public indoor places. Subjective oral symptom classes I, II, and III were associated with sex, grade, educational level of their fathers and mothers, subjective economic class, and subject academic class. Compared to a group not exposed to SHS, the risk of subjective oral symptom classes I, II, and III was significantly higher in those who were exposed to SHS at least once a week. Conclusion This study identified the association between the level of exposure to SHS and oral symptoms among adolescents.Factors for SHS should be considered to prevent oral symptoms among adolescents, and plans that can control SHS of adolescents should be established.

Recently, several attempts have been made to integrate AI into the field of dentistry. To overcome the limitations of traditionalfixed prosthetic fabrication methods such as CAD-CAM (computer-aided design-computer-aided manufacturing), AI programs arebeing developed for automated crown fabrication, and various studies are underway to applicate in clinical situation. In these casestudies, single-unit fixed prostheses were fabricated using an AI program (Dentbird Crown, Imagoworks Inc, Seoul, Korea) in boththe anterior and posterior regions and the fabrication time and accuracy were compared with previously used CAD-CAM method.The first case is a 44-year-old woman who presented for re-fabrication of a zirconia prosthesis due to a prosthesis fracture on thelingual side of the upper right lateral incisor. The second case is a 53-year-old male patient who presented for a crown restorationon an upper left first molar following root canal treatment, where he received a final zirconia restoration. In both cases, the firstprosthesis was designed manually using a CAD program, the second prosthesis was designed using AI alone, and the third prosthesis was designed using AI and then modified by CAD program, and the three designs were superimposed to compare suitability. When evaluated after temporary placement, the final prosthesis demonstrates adequate stability, retention and support, resulting in functional and esthetic satisfaction.