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.

Objectives: Presenteeism is known to be a much more economically damaging social cost than disease rest while going to work despite physical pain. Since COVID-19, social discussions on the sickness benefit have been taking place as a countermeasure against presenteeism, and in particular, farmers and fishermen do not have an institutional mechanism for livelihood support when a disease other than work occurs. This study attempted to examine the relationship between agricultural, fishing, and forestry workers and presenteeism using the 6th Korean Work Conditions Survey. Methods: From October 2020 to January 2021, data from the 6th working conditions survey conducted on 17 cities and provinces in Korea were used, and a total of 34,981 people were studied. Control variables were gender, age, self-health assessment, education level, night work, shift work, monthly income, occupation, working hours per week, and employment status. Results: As a result of the analysis, farmers and fishermen showed the characteristics of the self-employed and the elderly, and as a result of the regression analysis, when farmers and fishermen analyzed the relationship with presenteeism tendency compared to other industry workers, farmers and fishermen increased by 23% compared to other industry groups. Conclusion This study is significant in that it has representation by utilizing the 6th working conditions survey and objectively suggests the need for a sickness benefit for farmers and fishermen who may be overlooked in the sickness benefit.

Objectives: Presenteeism is known to be a much more economically damaging social cost than disease rest while going to work despite physical pain. Since COVID-19, social discussions on the sickness benefit have been taking place as a countermeasure against presenteeism, and in particular, farmers and fishermen do not have an institutional mechanism for livelihood support when a disease other than work occurs. This study attempted to examine the relationship between agricultural, fishing, and forestry workers and presenteeism using the 6th Korean Work Conditions Survey. Methods: From October 2020 to January 2021, data from the 6th working conditions survey conducted on 17 cities and provinces in Korea were used, and a total of 34,981 people were studied. Control variables were gender, age, self-health assessment, education level, night work, shift work, monthly income, occupation, working hours per week, and employment status. Results: As a result of the analysis, farmers and fishermen showed the characteristics of the self-employed and the elderly, and as a result of the regression analysis, when farmers and fishermen analyzed the relationship with presenteeism tendency compared to other industry workers, farmers and fishermen increased by 23% compared to other industry groups. Conclusion This study is significant in that it has representation by utilizing the 6th working conditions survey and objectively suggests the need for a sickness benefit for farmers and fishermen who may be overlooked in the sickness benefit.

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.

Objectives: Presenteeism is known to be a much more economically damaging social cost than disease rest while going to work despite physical pain. Since COVID-19, social discussions on the sickness benefit have been taking place as a countermeasure against presenteeism, and in particular, farmers and fishermen do not have an institutional mechanism for livelihood support when a disease other than work occurs. This study attempted to examine the relationship between agricultural, fishing, and forestry workers and presenteeism using the 6th Korean Work Conditions Survey. Methods: From October 2020 to January 2021, data from the 6th working conditions survey conducted on 17 cities and provinces in Korea were used, and a total of 34,981 people were studied. Control variables were gender, age, self-health assessment, education level, night work, shift work, monthly income, occupation, working hours per week, and employment status. Results: As a result of the analysis, farmers and fishermen showed the characteristics of the self-employed and the elderly, and as a result of the regression analysis, when farmers and fishermen analyzed the relationship with presenteeism tendency compared to other industry workers, farmers and fishermen increased by 23% compared to other industry groups. Conclusion This study is significant in that it has representation by utilizing the 6th working conditions survey and objectively suggests the need for a sickness benefit for farmers and fishermen who may be overlooked in the sickness benefit.

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: This study aimed to assess the real-world clinical outcomes of consolidative durvalumab in patients with unresectable locally advanced non–small cell lung cancer (LA-NSCLC) and to explore the role of radiotherapy in the era of immunotherapy. Materials and Methods: This retrospective study assessed 171 patients with unresectable LA-NSCLC who underwent concurrent chemoradiotherapy (CCRT) with or without consolidative durvalumab at Asan Medical Center between May 2018 and May 2021. Primary outcomes included freedom from locoregional failure (FFLRF), distant metastasis-free survival (DMFS), progression-free survival (PFS), and overall survival (OS). Results: Durvalumab following CCRT demonstrated a prolonged median PFS of 20.9 months (p=0.048) and a 3-year FFLRF rate of 57.3% (p=0.008), compared to 13.7 months and 38.8%, respectively, with CCRT alone. Furthermore, the incidence of in-field recurrence was significantly greater in the CCRT-alone group compared to the durvalumab group (26.8% vs. 12.4%, p=0.027). While median OS was not reached with durvalumab, it was 35.4 months in patients receiving CCRT alone (p=0.010). Patients positive for programmed cell death ligand 1 (PD-L1) expression showed notably better outcomes, including FFLRF, DMFS, PFS, and OS. Adherence to PACIFIC trial eligibility criteria identified 100 patients (58.5%) as ineligible. The use of durvalumab demonstrated better survival regardless of eligibility criteria. Conclusion The use of durvalumab consolidation following CCRT significantly enhanced locoregional control and OS in patients with unresectable LA-NSCLC, especially in those with PD-L1–positive tumors, thereby validating the role of durvalumab in standard care.

Purpose: Hypoxaemia is a significant adverse event during endoscopic retrograde cholangiopancreatography (ERCP) under monitored anaesthesia care (MAC); however, no model has been developed to predict hypoxaemia. We aimed to develop and compare logistic regression (LR) and machine learning (ML) models to predict hypoxaemia during ERCP under MAC. Materials and Methods: We collected patient data from our institutional ERCP database. The study population was randomly divided into training and test sets (7:3). Models were fit to training data and evaluated on unseen test data. The training set was further split into k-fold (k=5) for tuning hyperparameters, such as feature selection and early stopping. Models were trained over k loops; the i-th fold was set aside as a validation set in the i-th loop. Model performance was measured using area under the curve (AUC). Results: We identified 6114 cases of ERCP under MAC, with a total hypoxaemia rate of 5.9%. The LR model was established by combining eight variables and had a test AUC of 0.693. The ML and LR models were evaluated on 30 independent data splits. The average test AUC for LR was 0.7230, which improved to 0.7336 by adding eight more variables with an l 1 regularisation-based selection technique and ensembling the LRs and gradient boosting algorithm (GBM). The high-risk group was discriminated using the GBM ensemble model, with a sensitivity and specificity of 63.6% and 72.2%, respectively. Conclusion We established GBM ensemble model and LR model for risk prediction, which demonstrated good potential for preventing hypoxaemia during ERCP under MAC.

Purpose: The detection rate of early-stage lung cancer with ground-glass opacity (GGO) has increased, and stereotactic body radiotherapy (SBRT) has been suggested as an alternative to surgery in inoperable patients. However, reports on treatment results are limited. Therefore, we performed a retrospective study to investigate the clinical outcome after SBRT in patients with early-stage lung cancer with GGO-predominant tumor lesions at a single institution. Materials and Methods: This study included 89 patients with 99 lesions who were treated with SBRT for lung cancer with GGO-predominant lesions that had a consolidation-to-tumor ratio of ≤0.5 at Asan Medical Center between July 2016 and July 2021. A median total dose of 56.0 Gy (range, 48.0–60.0) was delivered using 10.0–15.0 Gy per fraction. Results: The overall follow-up period for the study was median 33.0 months (range, 9.9 to 65.9 months). There was 100% local control with no recurrences in any of the 99 treated lesions. Three patients had regional recurrences outside of the radiation field, and three had distant metastasis. The 1-year, 3-year, and 5-year overall survival rates were 100.0%, 91.6%, and 82.8%, respectively. Univariate analysis revealed that advanced age and a low level of diffusing capacity of the lungs for carbon monoxide were significantly associated with overall survival. There were no patients with grade ≥3 toxicity. Conclusion SBRT is a safe and effective treatment for patients with GGO-predominant lung cancer lesions and is likely to be considered as an alternative to surgery.

Purpose: An optimal once-daily radiotherapy (RT) regimen is under investigation for definitive concurrent chemoradiotherapy (CCRT) in limited disease small cell lung cancer (LD-SCLC). We compared the efficacy and safety of dose escalation with intensity-modulated radiotherapy (IMRT). Materials and Methods: Between January 2016 and March 2021, patients treated with definitive CCRT for LD-SCLC with IMRT were retrospectively reviewed. Patients who received a total dose <50 Gy or those with a history of thoracic RT or surgery were excluded. The patients were divided into two groups (standard and dose-escalated) based on the total biologically effective dose (BED, α/β = 10) of 70 Gy. The chemotherapeutic regimen comprised four cycles of etoposide and cisplatin. Results: One hundred and twenty-two patients were analyzed and the median follow-up was 27.8 months (range, 4.4 to 76.9 months). The median age of the patients was 63 years (range, 35 to 78 years) and the majority had a history of smoking (86.0%). The 1- and 3-year overall survival rates of the escalated dose group were significantly higher than those of the standard group (93.5% and 50.5% vs. 76.7% and 33.3%, respectively; p = 0.008), as were the 1- and 3-year freedom from in-field failure rates (91.4% and 66.5% vs. 73.8% and 46.9%, respectively; p = 0.018). The incidence of grade 2 or higher acute and late pneumonitis was not significantly different between the two groups (p = 0.062, 0.185). Conclusion Dose-escalated once-daily CCRT with IMRT led to improved locoregional control and survival, with no increase in toxicity.