Background/Aims: Hepatocellular carcinoma (HCC) exhibits high de novo recurrence rates post-resection. Current post-surgery recurrence prediction methods are limited, emphasizing the need for reliable biomarkers to assess recurrence risk. We aimed to develop methylation-based markers for classifying HCC patients and predicting their risk of de novo recurrence post-surgery. Methods: In this retrospective cohort study, we analyzed data from HCC patients who underwent surgical resection in Korea, excluding those with recurrence within one year post-surgery. Using the Infinium Methylation EPIC array on 140 samples in the discovery cohort, we classified patients into low- and high-risk groups based on methylation profiles. Distinctive markers were identified through random forest analysis. These markers were validated in the cancer genome atlas (n=217), Validation cohort 1 (n=63) and experimental Validation using a methylation-sensitive high-resolution melting (MS-HRM) assay in Validation cohort 1 and Validation cohort 2 (n=63). Results: The low-risk recurrence group (methylation group 1; MG1) showed a methylation average of 0.73 (95% confidence interval [CI] 0.69–0.77) with a 23.5% recurrence rate, while the high-risk group (MG2) had an average of 0.17 (95% CI 0.14–0.20) with a 44.1% recurrence rate (P<0.03). Validation confirmed the applicability of methylation markers across diverse populations, showing high accuracy in predicting the probability of HCC recurrence risk (area under the curve 96.8%). The MS-HRM assay confirmed its effectiveness in predicting de novo recurrence with 95.5% sensitivity, 89.7% specificity, and 92.2% accuracy. Conclusions Methylation markers effectively classified HCC patients by de novo recurrence risk, enhancing prediction accuracy and potentially offering personalized management strategies.

Background/Aims: Hepatocellular carcinoma (HCC) exhibits high de novo recurrence rates post-resection. Current post-surgery recurrence prediction methods are limited, emphasizing the need for reliable biomarkers to assess recurrence risk. We aimed to develop methylation-based markers for classifying HCC patients and predicting their risk of de novo recurrence post-surgery. Methods: In this retrospective cohort study, we analyzed data from HCC patients who underwent surgical resection in Korea, excluding those with recurrence within one year post-surgery. Using the Infinium Methylation EPIC array on 140 samples in the discovery cohort, we classified patients into low- and high-risk groups based on methylation profiles. Distinctive markers were identified through random forest analysis. These markers were validated in the cancer genome atlas (n=217), Validation cohort 1 (n=63) and experimental Validation using a methylation-sensitive high-resolution melting (MS-HRM) assay in Validation cohort 1 and Validation cohort 2 (n=63). Results: The low-risk recurrence group (methylation group 1; MG1) showed a methylation average of 0.73 (95% confidence interval [CI] 0.69–0.77) with a 23.5% recurrence rate, while the high-risk group (MG2) had an average of 0.17 (95% CI 0.14–0.20) with a 44.1% recurrence rate (P<0.03). Validation confirmed the applicability of methylation markers across diverse populations, showing high accuracy in predicting the probability of HCC recurrence risk (area under the curve 96.8%). The MS-HRM assay confirmed its effectiveness in predicting de novo recurrence with 95.5% sensitivity, 89.7% specificity, and 92.2% accuracy. Conclusions Methylation markers effectively classified HCC patients by de novo recurrence risk, enhancing prediction accuracy and potentially offering personalized management strategies.

Background/Aims: Hepatocellular carcinoma (HCC) exhibits high de novo recurrence rates post-resection. Current post-surgery recurrence prediction methods are limited, emphasizing the need for reliable biomarkers to assess recurrence risk. We aimed to develop methylation-based markers for classifying HCC patients and predicting their risk of de novo recurrence post-surgery. Methods: In this retrospective cohort study, we analyzed data from HCC patients who underwent surgical resection in Korea, excluding those with recurrence within one year post-surgery. Using the Infinium Methylation EPIC array on 140 samples in the discovery cohort, we classified patients into low- and high-risk groups based on methylation profiles. Distinctive markers were identified through random forest analysis. These markers were validated in the cancer genome atlas (n=217), Validation cohort 1 (n=63) and experimental Validation using a methylation-sensitive high-resolution melting (MS-HRM) assay in Validation cohort 1 and Validation cohort 2 (n=63). Results: The low-risk recurrence group (methylation group 1; MG1) showed a methylation average of 0.73 (95% confidence interval [CI] 0.69–0.77) with a 23.5% recurrence rate, while the high-risk group (MG2) had an average of 0.17 (95% CI 0.14–0.20) with a 44.1% recurrence rate (P<0.03). Validation confirmed the applicability of methylation markers across diverse populations, showing high accuracy in predicting the probability of HCC recurrence risk (area under the curve 96.8%). The MS-HRM assay confirmed its effectiveness in predicting de novo recurrence with 95.5% sensitivity, 89.7% specificity, and 92.2% accuracy. Conclusions Methylation markers effectively classified HCC patients by de novo recurrence risk, enhancing prediction accuracy and potentially offering personalized management strategies.

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 aims to prospectively analyze the effects of anticholinergic therapy using imidafenacin on detrusor overactivity occurring after robot-assisted radical prostatectomy (RARP). Materials and Methods: Patients were followed-up at outpatient visits 2–4 weeks post-surgery (visit 2) to confirm the presence of urinary incontinence. Those confirmed with urinary incontinence were randomly assigned in a 1:1 ratio to the anticholinergic medication group (imidafenacin 0.1 mg twice daily) or the control group. Patients were followed-up at 1, 3, and 6 months post-surgery for observational assessments, including the International Prostate Symptom Score (IPSS) and Overactive Bladder Symptom Score (OABSS). Results: A total of 49 patients (25 in the treatment group and 24 in the control group) were randomized for the study. There were no differences observed between the groups in terms of age, comorbidities, prostate size, or pathological staging. According to the IPSS questionnaire results, there was no statistically significant difference between the medication and control groups (p=0.161).However, when comparing storage and voiding symptoms separately, there was a statistically significant improvement in storage symptom scores (p=0.012). OABSS also revealed statistically significant improvement in symptoms from 3 months post-surgery (p=0.005), which persisted until 6 months post-surgery (IPSS storage: p=0.023, OABSS: p=0.013). Conclusions In the case of urinary incontinence that occurs after RARP, even if the function of the intrinsic sphincter is sufficiently preserved, if urinary incontinence persists due to changes in the bladder, pharmacological therapy using imidafenacin can be beneficial in managing urinary incontinence.

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: In the Rivaroxaban Once-daily oral direct factor Xa inhibition Compared with vitamin K antagonism for prevention of stroke and Embolism Trial in Atrial Fibrillation (ROCKET AF) trial, rivaroxaban 20 mg was the on-label dose, and the dose-reduction criterion for rivaroxaban was a creatinine clearance of < 50 mL/min. Some Asian countries are using reduced doses label according to the J-ROCKET AF trial. The aim of this study was to assess the safety and efficacy of a high-dose rivaroxaban regimen (HDRR, 20/15 mg) and low-dose rivaroxaban regimen (LDRR, 15/10 mg) among elderly East Asian patients with atrial fibrillation (AF) in real-world practice. Methods: This study was a multicenter, prospective, non-interventional observational study designed to evaluate the efficacy and safety of rivaroxaban in AF patients > 65 years of age with or without renal impairment. Results: A total of 1,093 patients (mean age, 72.8 ± 5.8 years; 686 [62.9%] men) were included in the analysis, with 493 patients allocated to the HDRR group and 598 patients allocated to the LDRR group. A total of 765 patients received 15 mg of rivaroxaban (203 in the HDRR group and 562 in the LDRR group). There were no significant differences in the incidence rates of major bleeding (adjusted hazard ratio [HR], 0.64; 95% confidential interval [CI], 0.21–1.93), stroke (adjusted HR, 3.21; 95% CI, 0.54–19.03), and composite outcomes (adjusted HR, 1.13;95% CI, 0.47–2.69) between the HDRR and LDRR groups. Conclusion This study revealed the safety and effectiveness of either dose regimen of rivaroxaban in an Asian population for stroke prevention of AF. Considerable numbers of patients are receiving LDRR therapy in real-world practice in Asia. Both regimens were safe and effective for these patients.

Purpose: This phase II, open-label, multicenter study aimed to investigate the efficacy and safety of a rituximab intensification for the 1st cycle with every 21-day of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone (R-CHOP-21) among patients with previously untreated advanced-stage or bulky diffuse large B-cell lymphoma (DLBCL). Materials and Methods: Ninety-two patients with stage III/IV or bulky DLBCL from 21 institutions were administered 8 cycles of R-CHOP-21 with an additional one dose of rituximab intensification on day 0 of the 1st cycle (RR-CHOP). The primary endpoint was a complete response (CR) rate after 3 cycles of chemotherapy. Results: Among the 92 DLBCL patients assessed herein, the response rate after 3 cycles of chemotherapy was 88.0% (38.0% CR+50.0% partial response [PR]). After the completion of 8 cycles of chemotherapy, the overall response rate was observed for 68.4% (58.7% CR+9.8% PR). The 3-year progression-free survival rate was 64.0%, and the 3-year overall survival rate was 70.4%. Febrile neutropenia was one of the most frequent grade 3 adverse events (40.0%) and 5 treatment-related deaths occurred. Compared with the clinical outcomes of patients who received R-CHOP chemotherapy as a historical control, the interim CR rate was higher in male patients with RR-CHOP (20.5% vs. 48.8%, p=0.016). Conclusion Rituximab intensification on days 0 to the 1st cycle of the standard 8 cycles R-CHOP-21 for advanced DLBCL yielded favorable response rates after the 3 cycles of chemotherapy and acceptable toxicities, especially for male patients. ClinicalTrials.gov ID: NCT01054781.

Background/Aims: The ursodeoxycholic acid (UDCA) response score (URS) was developed to identify poor responders to UDCA before treatment, in order to offer timely and proactive intervention. However, validation of the URS in Asian population is warranted. Methods: A total of 173 Asian patients diagnosed with primary biliary cholangitis (PBC) between 2007 and 2016 at seven academic institutions in Korea who started UDCA treatment were analyzed to validate the performance of URS. UDCA response was defined as an alkaline phosphatase level less than 1.67 times the upper limit of normal after 1-year of UDCA treatment. In addition, prognostic performance of URS for liver-related events, defined as newly developed hepatic decompensation or hepatocellular carcinoma was evaluated. Results: After 1 year of UDCA treatment, 133 patients (76.9%) achieved UDCA response. UDCAresponse rate was 98.7% for those with URS ≥1.41 (n=76) and 58.8% for those with URS <1.41(n=97). The area under the receiver operating characteristic curve of URS in predicting UDCAresponse was 0.84 (95% confidence interval, 0.78 to 0.88). During a median follow-up of 6.5years, liver-related events developed in 18 patients (10.4%). Among 117 patients with PBC stage I-III by histological evaluation, the 5-year liver-related event-free survival rate differed accordingto the URS; 100% for URS ≥1.41 and 86.5% for URS <1.41 (p=0.005). Conclusions URS demonstrated good performance in predicting a UDCA treatment response in Asian PBC patients. In addition, the risk of liver-related events differed according to the URS for the PBC stage. Thus, URS can be used to predict the response and clinical outcome in patients with PBC.