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: Palliative chemotherapy (PC) is not standardized for patients with advanced ampulla of Vater adenocarcinoma (AA). This multicenter, retrospective study evaluated first-line PC outcomes in patients with AA. Methods: Patients diagnosed with AA between January 2010 and December 2020 who underwent PC were enrolled from 10 institutions. Overall survival (OS) and progression-free survival (PFS) according to the chemotherapy regimen were analyzed. Results: Of 255 patients (mean age, 64.0±10.0 years; male, 57.6%), 14 (5.5%) had locally advanced AA and 241 (94.5%) had metastatic AA. Gemcitabine plus cisplatin (GP) was administered as first-line chemotherapy to 192 patients (75.3%), whereas capecitabine plus oxaliplatin (CAPOX) was administered to 39 patients (15.3%). The median OS of all patients was 19.8 months (95% confidence interval [CI], 17.3 to 22.3), and that of patients who received GP and CAPOX was 20.4 months (95% CI, 17.2 to 23.6) and 16.0 months (95% CI, 11.2 to 20.7), respectively. The median PFS of GP and CAPOX patients were 8.4 months (95% CI, 7.1 to 9.7) and 5.1 months (95% CI, 2.5 to 7.8), respectively. PC for AA demonstrated improved median outcomes in both OS and PFS compared to conventional bile duct cancers that included AA. Conclusions While previous studies have shown mixed prognostic outcomes when AA was analyzed together with other biliary tract cancers, our study unveils a distinct clinical prognosis specific to AA on a large scale with systemic anticancer therapy. These findings suggest that AA is a distinct type of tumor, different from other biliary tract cancers, and AA itself could be expected to have a favorable response to PC.

Objective: The Scales for Outcomes in Parkinson’s Disease–Cognition (SCOPA-Cog) was developed to assess cognition in patients with Parkinson’s disease (PD). In this study, we aimed to evaluate the validity and reliability of the Korean version of the SCOPACog (K-SCOPA-Cog). Methods: We enrolled 129 PD patients with movement disorders from 31 clinics in South Korea. The original version of the SCOPA-Cog was translated into Korean using the translation-retranslation method. The test–retest method with an intraclass correlation coefficient (ICC) and Cronbach’s alpha coefficient were used to assess reliability. Spearman’s rank correlation analysis with the Montreal Cognitive Assessment-Korean version (MOCA-K) and the Korean Mini-Mental State Examination (K-MMSE) were used to assess concurrent validity. Results: The Cronbach’s alpha coefficient was 0.797, and the ICC was 0.887. Spearman’s rank correlation analysis revealed a significant correlation with the K-MMSE and MOCA-K scores (r = 0.546 and r = 0.683, respectively). Conclusion Our results demonstrate that the K-SCOPA-Cog has good reliability and validity.

Purpose: Although osimertinib is the standard-of-care treatment of epidermal growth factor receptor (EGFR) T790M mutation–positive non–small cell lung cancer, real-world evidence on the efficacy of osimertinib is not enough to reflect the complexity of the entire course of treatment. Herein, we report on the use of osimertinib in patients with EGFR T790M mutation–positive non–small cell lung cancer who had previously received EGFR tyrosine kinase inhibitor (TKI) treatment in Korea. Materials and Methods: Patients with confirmed EGFR T790M after disease progression of prior EGFR-TKI were enrolled and administered osimertinib 80 mg daily. The primary effectiveness outcome was progression-free survival, with time-to-treatment discontinuation, treatment and adverse effects leading to treatment discontinuation, and overall survival being the secondary endpoints. Results: A total of 558 individuals were enrolled, and 55.2% had investigator-assessed responses. The median progression-free survival was 14.2 months (95% confidence interval [CI], 13.0 to 16.4), and the median time-to-treatment discontinuation was 15.0 months (95% CI, 14.1 to 15.9). The median overall survival was 36.7 months (95% CI, 30.9 to not reached). The benefit with osimertinib was consistent regardless of the age, sex, smoking history, and primary EGFR mutation subtype. However, hepatic metastases at the time of diagnosis, the presence of plasma EGFR T790M, and the shorter duration of prior EGFR-TKI treatment were poor predictors of osimertinib treatment. Ten patients (1.8%), including three with pneumonitis, had to discontinue osimertinib due to severe adverse effects. Conclusion Osimertinib demonstrated its clinical effectiveness and survival benefit for EGFR T790M mutation–positive in Korean patients with no new safety signals.