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.

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.

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.

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.

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.