Background/Aims: The aim of this study was to investigate the effect of a surgical treatment algorithm recently proposed by the American Association for the Study of Liver Diseases (AASLD) on survival outcomes in patients with early-stage hepatocellular carcinoma (HCC) and identify effective alternative treatment modalities when liver transplantation (LT) is not available. Methods: We studied the clinical data of 1,442 patients who were diagnosed with early-stage HCC (a single lesion measuring 2–5 cm in size or 2 to 3 lesions measuring ≤3 cm in size) be-tween 2013 and 2018 and classified as Child-Turcotte-Pugh (CTP) A or B. Analyses were separately performed for individuals recommended for resection (single lesion, CTP A and no clinically significant portal hypertension) and those recommended for LT (single lesion with impaired liver function such as CTP B or clinically significant portal hypertension or multiple lesions). Results: Of 791 patients recommended for surgical resection, 85.8% underwent resection. The 5-year survival rate was higher for patients who underwent surgical resection than for those who received other treatments (89.4% vs 72.3%). Among 651 patients recommended for LT, only 3.4% underwent the procedure. The most common alternative treatment modalities were transarterial therapy (39.3%) followed by resection (28.9%) and ablation (27.8%). The overall survival rate associated with transarterial therapy was lower than that for resection and ablation, whereas that of the latter two treatments were comparable. Conclusions The survival outcomes of treatment strategies that most closely aligned with the algorithm proposed by the AASLD were superior to those of alternative treatment approaches.However, LT in patients with early-stage HCC can be challenging. When LT is not feasible, resection and ablation can be considered first-line alternative options.

Background/Aims: The aim of this study was to investigate the effect of a surgical treatment algorithm recently proposed by the American Association for the Study of Liver Diseases (AASLD) on survival outcomes in patients with early-stage hepatocellular carcinoma (HCC) and identify effective alternative treatment modalities when liver transplantation (LT) is not available. Methods: We studied the clinical data of 1,442 patients who were diagnosed with early-stage HCC (a single lesion measuring 2–5 cm in size or 2 to 3 lesions measuring ≤3 cm in size) be-tween 2013 and 2018 and classified as Child-Turcotte-Pugh (CTP) A or B. Analyses were separately performed for individuals recommended for resection (single lesion, CTP A and no clinically significant portal hypertension) and those recommended for LT (single lesion with impaired liver function such as CTP B or clinically significant portal hypertension or multiple lesions). Results: Of 791 patients recommended for surgical resection, 85.8% underwent resection. The 5-year survival rate was higher for patients who underwent surgical resection than for those who received other treatments (89.4% vs 72.3%). Among 651 patients recommended for LT, only 3.4% underwent the procedure. The most common alternative treatment modalities were transarterial therapy (39.3%) followed by resection (28.9%) and ablation (27.8%). The overall survival rate associated with transarterial therapy was lower than that for resection and ablation, whereas that of the latter two treatments were comparable. Conclusions The survival outcomes of treatment strategies that most closely aligned with the algorithm proposed by the AASLD were superior to those of alternative treatment approaches.However, LT in patients with early-stage HCC can be challenging. When LT is not feasible, resection and ablation can be considered first-line alternative options.

Background/Aims: The aim of this study was to investigate the effect of a surgical treatment algorithm recently proposed by the American Association for the Study of Liver Diseases (AASLD) on survival outcomes in patients with early-stage hepatocellular carcinoma (HCC) and identify effective alternative treatment modalities when liver transplantation (LT) is not available. Methods: We studied the clinical data of 1,442 patients who were diagnosed with early-stage HCC (a single lesion measuring 2–5 cm in size or 2 to 3 lesions measuring ≤3 cm in size) be-tween 2013 and 2018 and classified as Child-Turcotte-Pugh (CTP) A or B. Analyses were separately performed for individuals recommended for resection (single lesion, CTP A and no clinically significant portal hypertension) and those recommended for LT (single lesion with impaired liver function such as CTP B or clinically significant portal hypertension or multiple lesions). Results: Of 791 patients recommended for surgical resection, 85.8% underwent resection. The 5-year survival rate was higher for patients who underwent surgical resection than for those who received other treatments (89.4% vs 72.3%). Among 651 patients recommended for LT, only 3.4% underwent the procedure. The most common alternative treatment modalities were transarterial therapy (39.3%) followed by resection (28.9%) and ablation (27.8%). The overall survival rate associated with transarterial therapy was lower than that for resection and ablation, whereas that of the latter two treatments were comparable. Conclusions The survival outcomes of treatment strategies that most closely aligned with the algorithm proposed by the AASLD were superior to those of alternative treatment approaches.However, LT in patients with early-stage HCC can be challenging. When LT is not feasible, resection and ablation can be considered first-line alternative options.

Background/Aims: The aim of this study was to investigate the effect of a surgical treatment algorithm recently proposed by the American Association for the Study of Liver Diseases (AASLD) on survival outcomes in patients with early-stage hepatocellular carcinoma (HCC) and identify effective alternative treatment modalities when liver transplantation (LT) is not available. Methods: We studied the clinical data of 1,442 patients who were diagnosed with early-stage HCC (a single lesion measuring 2–5 cm in size or 2 to 3 lesions measuring ≤3 cm in size) be-tween 2013 and 2018 and classified as Child-Turcotte-Pugh (CTP) A or B. Analyses were separately performed for individuals recommended for resection (single lesion, CTP A and no clinically significant portal hypertension) and those recommended for LT (single lesion with impaired liver function such as CTP B or clinically significant portal hypertension or multiple lesions). Results: Of 791 patients recommended for surgical resection, 85.8% underwent resection. The 5-year survival rate was higher for patients who underwent surgical resection than for those who received other treatments (89.4% vs 72.3%). Among 651 patients recommended for LT, only 3.4% underwent the procedure. The most common alternative treatment modalities were transarterial therapy (39.3%) followed by resection (28.9%) and ablation (27.8%). The overall survival rate associated with transarterial therapy was lower than that for resection and ablation, whereas that of the latter two treatments were comparable. Conclusions The survival outcomes of treatment strategies that most closely aligned with the algorithm proposed by the AASLD were superior to those of alternative treatment approaches.However, LT in patients with early-stage HCC can be challenging. When LT is not feasible, resection and ablation can be considered first-line alternative options.

Immunoglobulin A nephropathy (IgAN) is a major cause of end-stage kidney disease (ESKD). The International IgA Nephropathy Prediction Tool (IIgAN-PT) predicts IgAN prognosis, but improvement in the prediction performance using machine learning (ML)-based methods is needed. Methods: We analyzed 4,425 biopsy-confirmed patients with IgAN and ≥6 months of follow-up from nine tertiary university hospitals in Korea. The study population was divided into development and validation cohorts. Using the collected 87 clinicodemographic and pathological variables, ML-based prediction models for ESKD or estimated glomerular filtration rate decline (50% reduction or <15 mL/min/1.73 m2 ) were constructed: 1) the conventional CatBoost model, 2) the optimized CatBoost model with Cox proportional hazards, 3) the deep Cox proportional hazards model, and 4) the deep Cox mixture model. The area under the curve (AUC) and calibration plots were used to investigate the discriminative and calibration performance of the models, which were then compared with those of the IIgAN-PT full model. Results: The full model showed excellent performance (AUC [95% confidence interval] for 5-year outcome, 0.896 [0.853–0.940]), with acceptable calibration results. The ML-based models showed good performance in predicting adverse kidney outcomes and revealed acceptable discrimination performance in the external validation (AUC [95% confidence interval] for the 5-year outcome: 1) 0.829 [0.791–0.866]; 2) 0.847 [0.804–0.890]; 3) 0.823 [0.784–0.862]; and 4) 0.832 [0.794–0.870]), although the models showed underestimation in calibration analysis of the external validation cohort. With the validation data, the overall performance of the IIgAN-PT was non-inferior to that of the ML-based model. Conclusions: Our ML-based models showed good performance in predicting adverse kidney outcomes in patients with IgAN but they did not outperform the IIgAN-PT.

This study aimed to investigate differences in intrinsic prefrontal functional connectivity according to the presence of cognitive impairment in patients with end-stage renal disease (ESRD) using functional near-infrared spectroscopy (fNIRS). Methods: We prospectively enrolled 37 patients with ESRD who had been undergoing hemodialysis for more than 6 months and had no history of neurological or psychiatric disorders. All patients with ESRD underwent the Korean version of the Montreal Cognitive Assessment (MoCA-K) to assess cognitive function. The NIRSIT Lite device (OBELAB Inc.) was used to acquire fNIRS data, and the NIRSIT Lite Analysis Tool program was used to process the data and generate a functional connectivity matrix. We obtained functional connectivity measures by applying graph theory to the connectivity matrix using the BRAPH (brain analysis using graph theory) program. Results: Of the 37 patients with ESRD, 23 had cognitive impairment, whereas 14 patients showed no cognitive impairment. Intrinsic prefrontal functional connectivity was significantly different between groups. Network measures of strength, global efficiency, and mean clustering coefficient were lower in ESRD patients with cognitive impairment than in those without cognitive impairment (4.458 vs. 5.129, p = 0.02; 0.397 vs. 0.437, p = 0.03; and 0.316 vs. 0.421, p = 0.003; respectively). There were no significant correlations between MoCA-K scores and clinical characteristics. Conclusion: We demonstrated a significant association between cognitive function and intrinsic prefrontal functional connectivity in patients with ESRD. ESRD patients with cognitive impairment have reduced connectivity and segregation in the prefrontal brain network compared to those without cognitive impairment.

Background/Aims: Improved knowledge of local epidemiology and predicting risk factors of multidrug-resistant (MDR) bacteria are required to optimize the management of infections. This study examined local epidemiology and antibiotic resistance patterns of liver cirrhosis (LC) patients and evaluated the predictors of MDR bacteremia in Korea. Methods: This was a retrospective study including 140 LC patients diagnosed with bacteremia between January 2017 and December 2022. Local epidemiology and antibiotic resistance patterns and the determinants of MDR bacteremia were analyzed using logistic regression analysis. Results: The most frequently isolated bacteria, from the bloodstream, were Escherichia coli (n = 45, 31.7%) and Klebsiella spp. (n = 35, 24.6%). Thirty-four isolates (23.9%) were MDR, and extended-spectrum beta-lactamase E. coli (52.9%) and methicillin-resistant Staphylococcus aureus (17.6%) were the most commonly isolated MDR bacteria. When Enterococcus spp. were cultured, the majority were MDR (MDR 83.3% vs. 16.7%, p = 0.003), particularly vancomycin-susceptible Enterococcus faecium. Antibiotics administration within 30 days and/or nosocomial infection was a significant predictor of MDR bacteremia (OR: 3.40, 95% CI: 1.24–9.27, p = 0.02). MDR bacteremia was not predicted by sepsis predictors, such as positive systemic inflammatory response syndrome (SIRS) or quick Sequential Organ Failure Assessment (qSOFA). Conclusions More than 70% of strains that can be treated with a third-generation cephalosporin have been cultured. In cirrhotic patients, antibiotic administration within 30 days and/or nosocomial infection are predictors of MDR bacteremia; therefore, empirical administration of broad-spectrum antibiotics should be considered when these risk factors are present.

This study aimed to investigate differences in intrinsic prefrontal functional connectivity according to the presence of cognitive impairment in patients with end-stage renal disease (ESRD) using functional near-infrared spectroscopy (fNIRS). Methods: We prospectively enrolled 37 patients with ESRD who had been undergoing hemodialysis for more than 6 months and had no history of neurological or psychiatric disorders. All patients with ESRD underwent the Korean version of the Montreal Cognitive Assessment (MoCA-K) to assess cognitive function. The NIRSIT Lite device (OBELAB Inc.) was used to acquire fNIRS data, and the NIRSIT Lite Analysis Tool program was used to process the data and generate a functional connectivity matrix. We obtained functional connectivity measures by applying graph theory to the connectivity matrix using the BRAPH (brain analysis using graph theory) program. Results: Of the 37 patients with ESRD, 23 had cognitive impairment, whereas 14 patients showed no cognitive impairment. Intrinsic prefrontal functional connectivity was significantly different between groups. Network measures of strength, global efficiency, and mean clustering coefficient were lower in ESRD patients with cognitive impairment than in those without cognitive impairment (4.458 vs. 5.129, p = 0.02; 0.397 vs. 0.437, p = 0.03; and 0.316 vs. 0.421, p = 0.003; respectively). There were no significant correlations between MoCA-K scores and clinical characteristics. Conclusion: We demonstrated a significant association between cognitive function and intrinsic prefrontal functional connectivity in patients with ESRD. ESRD patients with cognitive impairment have reduced connectivity and segregation in the prefrontal brain network compared to those without cognitive impairment.

Objective: This study aimed to assess the degree of interest in robot-assisted spine surgery (RASS) among residents and to investigate the learning curve for beginners performing robotic surgery. Methods: We conducted a survey to assess awareness and interest in RASS among young neurosurgery residents. Subsequently, we offered a hands-on training program using a dummy to educate one resident. After completing the program, the trained resident performed spinal fusion surgery with robotic assistance under the supervision of a mentor. The clinical outcomes and learning curve associated with robotic surgery were then analyzed. Results: Neurosurgical residents had limited opportunities to participate in spinal surgery during their training. Despite this, there was a significant interest in the emerging field of robotic surgery. A trained resident performed RASS under the supervision of a senior surgeon. A total of 166 screw insertions were attempted in 28 patients, with 2 screws failing due to skiving. According to the Gertzbein-Robbins classification, 85.54% of the screws were rated as grade A, 11.58% as grade B, 0.6% as grade C, and 1.2% as grade D. The clinical acceptance rate was approximately 96.99%, which is comparable to the results reported by senior experts and time per screw statistically significantly decreased as experience was gained. Conclusion RASS can be performed with high accuracy within a relatively short timeframe, if residents receive adequate training.

Purpose: This study aims to develop a comprehensive preprocessing workflow for Digital Imaging and Communications in Medicine (DICOM) files to facilitate their effective use in AI-driven medical applications. With the increasing utilization of DICOM data for AI learning, analysis, Metaverse platform integration, and 3D printing of anatomical structures, the need for streamlined preprocessing is essential. The workflow is designed to optimize DICOM files for diverse applications, improving their usability and accessibility for advanced medical technologies. Methods: The proposed workflow employs a systematic approach to preprocess DICOM files for AI applications, focusing on noise reduction, normalization, segmentation, and conversion to 3D-renderable formats. These steps are integrated into a unified process to address challenges such as data variability, format incompatibilities, and high computational demands. The studyincorporates real-world medical imaging datasets to evaluate the workflow’s effectiveness and adaptability for AI analysis and 3D visualization. Additionally, the workflow’s compatibility withvirtual environments, such as Metaverse platforms, is assessed to ensure seamless integration. Results: The implementation of the workflow demonstrated significant improvements in the preprocessing of DICOM files. The processed files were optimized for AI analysis, yielding enhanced model performance and accuracy in learning tasks. Furthermore, the workflow enabled the successful conversion of DICOM data into 3D-printable formats and virtual environments, supporting applications like anatomical visualization and simulation. The study highlights the workflow's ability to reduce preprocessing time and errors, making advanced medical imaging technologies more accessible. Conclusions This study emphasizes the critical role of effective preprocessing in maximizing the potential of DICOM data for AI-driven applications and innovative medical solutions. The proposed workflow simplifies the preprocessing of DICOM files, facilitating their integration into AI models, Metaverse platforms, and 3D printing processes. By enhancing usability and accessibility, the workflow fosters broader adoption of advanced imaging technologies in the medical field.