Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Background: s/Aims: The hepatocellular carcinoma (HCC) with portal vein tumor thrombosis (PVTT) is classified as the advanced stage (BCLC stage C) with extremely poor prognosis, and in current guidelines is recommended for systemic therapy. This study aimed to evaluate the surgical outcomes and long-term prognosis after hepatic resection (HR) for patients who have HCC combined with PVTT. Methods: We retrospectively analyzed 332 patients who underwent HR for HCC with PVTT at ten tertiary referral hospitals in South Korea. Results: The median overall and recurrence-free survival after HR were 32.4 and 8.6 months, while the 1-, 3-, and 5-year overall survival rates were 75%, 48%, and 39%, respectively. In multivariate analysis, tumor number, tumor size, AFP, PIVKA−II, neutrophil-to-lymphocyte ratio, and albumin–bilirubin (ALBI) grade were significant prognostic factors. The risk scoring was developed using these seven factors–tumor, inflammation and hepatic function (TIF), to predict patient prognosis. The prognosis of the patients was well stratified according to the scores (log-rank test, p < 0.001). Conclusions HR for patients who have HCC combined with PVTT provided favorable survival outcomes. The risk scoring was useful in predicting prognosis, and determining the appropriate treatment strategy for those patients who have HCC with PVTT.

Background: Enlarged perivascular space (ePVS) is recently reported to be associated with cerebral small vessel disease (SVD) and Alzheimer’s disease (AD). The topographical location of ePVS may relate to the underlying pathology; basal ganglia (BG)-ePVS has been associated with cerebral vascular diseases and centrum semi-ovale (CSO)-ePVS associated with cerebral amyloid angiopathy (CAA). However, the effects of ePVS on various neurological conditions remain still controversial. To investigate the clinical relevance of ePVS in neurodegenerative diseases, we tested relationships between ePVS and cognition, markers of SVD, vascular risk factors, or amyloid pathology. Methods: We retrospectively reviewed 292 patients (133 AD dementia, 106 mild cognitive impairment, 39 other neurodegenerative diseases, 14 subjective cognitive decline) who underwent both amyloid positron emission tomography and brain magnetic resonance imaging. Vascular risk factors and cognitive tests results were collected. The ePVS in the BG and CSO, SVD markers and the volume of white matter hyperintensities were measured. Results: There were no significant differences in the severity and distribution of ePVS among clinical syndromes. Both BG- and CSO-ePVS were not related to cognitive function. Patients with lacunes were more likely to have high-degree BG-ePVS. High degree CSO-ePVS had an odds ratio (OR) for amyloid positive of 2.351, while BG-ePVS was a negative predictor for amyloid pathology (OR, 0.336). Conclusions Our findings support that ePVS has different underlying pathologies according to the cerebral topography. BG-ePVS would be attributed to hypertensive angiopathy considering the relation with SVD markers, whereas and CSO-ePVS would be attributed to CAA considering the association with amyloid pathology.

The phosphorylated 43-kDa transactive response DNA-binding protein (TDP-43) was identified as a major disease protein in sporadic amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration. We present a case with progressive muscle weakness who was diagnosed with sporadic ALS. On postmortem examination, TDP-43 immunoreactive neuronal cytoplasmic inclusions were noted in motor cortex, hippocampus and anterior horns of spinal cord, which was compatible with ALS-TDP, stage 4. This is the first documented autopsy-confirmed ALS case with ALS-TDP pathology in Korea.

Purpose: The clinical implications of tumor-infiltrating T cell subsets and their spatial distribution in biliary tract cancer (BTC) patients treated with gemcitabine plus cisplatin were investigated. Materials and Methods: A total of 52 BTC patients treated with palliative gemcitabine plus cisplatin were included. Multiplexed immunohistochemistry was performed on tumor tissues, and immune infiltrates were separately analyzed for the stroma, tumor margin, and tumor core. Results: The density of CD8+ T cells, FoxP3- CD4+ helper T cells, and FoxP3+ CD4+ regulatory T cells was significantly higher in the tumor margin than in the stroma and tumor core. The density of LAG3- or TIM3-expressing CD8+ T cell and FoxP3- CD4+ helper T cell infiltrates was also higher in the tumor margin. In extrahepatic cholangiocarcinoma, there was a higher density of T cell subsets in the tumor core and regulatory T cells in all regions. A high density of FoxP3- CD4+ helper T cells in the tumor margin showed a trend toward better progression-free survival (PFS) (p=0.092) and significantly better overall survival (OS) (p=0.012). In multivariate analyses, a high density of FoxP3- CD4+ helper T cells in the tumor margin was independently associated with favorable PFS and OS. Conclusion The tumor margin is the major site for the active infiltration of T cell subsets with higher levels of LAG3 and TIM3 expression in BTC. The density of tumor margin-infiltrating FoxP3- CD4+ helper T cells may be associated with clinical outcomes in BTC patients treated with gemcitabine plus cisplatin.

Kawasaki disease (KD) is an acute pediatric vasculitis that affects genetically susceptible infants and children. To identify coding variants that influence susceptibility to KD, we conducted whole exome sequencing of 159 patients with KD and 902 controls, and performed a replication study in an independent 586 cases and 732 controls. We identified five rare coding variants in five genes (FCRLA, PTGER4, IL17F, CARD11, and SIGLEC10) associated with KD (odds ratio [OR], 1.18–4.41; p = 0.0027–0.031). We also performed association analysis in 26 KD patients with coronary artery aneurysms (CAAs; diameter > 5 mm) and 124 patients without CAAs (diameter < 3 mm), and identified another five rare coding variants in five genes (FGFR4, IL31RA, FNDC1, MMP8, and FOXN1), which may be associated with CAA (OR, 3.89–37.3; p = 0.0058–0.0261). These results provide insights into new candidate genes and genetic variants potentially involved in the development of KD and CAA.

PURPOSE: Unplanned conversion is sometimes necessary during minimally invasive liver resection (MILR) of hepatocellular carcinoma (HCC). The aims of this study were to compare surgical outcomes of planned MILR and unplanned conversion and to investigate the risk factors after unplanned conversion. METHODS: We retrospectively analyzed 286 patients who underwent MILR with HCC from January 2006 to December 2017. All patients were divided into a MILR group and an unplanned conversion group. The clinicopathologic characteristics and outcomes were compared between the 2 groups. In addition, surgical outcomes in the conversion group were compared with the planned open surgery group (n = 505). Risk factors for unplanned conversion were analyzed. RESULTS: Of the 286 patients who underwent MILR, 18 patients (6.7%) had unplanned conversion during surgery. The unplanned conversion group showed statistically more blood loss, higher transfusion rate and postoperative complication rate, and longer hospital stay compared to the MILR group, whereas no such difference was observed in comparison with the planned open surgery group. There were no significant differences in overall and disease-free survival among 3 groups. The right-sided sectionectomy (right anterior and posterior sectionectomy), central bisectionectomy and tumor size were risk factors of unplanned conversion. CONCLUSION Unplanned conversion during MILR for HCC was associated with poor perioperative outcomes, but it did not affect long-term oncologic outcomes in our study. In addition, when planning right-sided sectionectomy or central bisectionectomy for a large tumor (more than 5 cm), we should recommend open surgery or MILR with an informed consent for unplanned open conversions.

Objective: Recently, microRNA (miRNA) has been identified both as a powerful regulator involved in various biological processes through the regulation of numerous genes and as an effective biomarker for the prediction and diagnosis of various disease states. The objective of this study was to identify and validate miRNAs and their target genes involved in inflammation in placental tissue. Methods: Microarrays were utilized to obtain miRNA and gene expression profiles from placentas with or without inflammation obtained from nine normal pregnant women and 10 preterm labor patients. Quantitative real-time polymerase chain reaction and Western blots were performed to validate the miRNAs and differentially-expressed genes in the placentas with inflammation. Correlations between miRNA and target gene expression were confirmed by luciferase assays in HTR-8/SVneo cells. Results: We identified and validated miRNAs and their target genes that were differentially expressed in placentas with inflammation. We also demonstrated that several miRNAs (miR-371a-5p, miR-3065-3p, miR-519b-3p, and miR-373-3p) directly targeted their target genes (LEF1, LOX, ITGB4, and CD44). However, some miRNAs and their direct target genes showed no correlation in tissue samples. Interestingly, miR-373-3p and miR-3065-3p were markedly regulated by lipopolysaccharide (LPS) treatment, although the expression of their direct targets CD44 and LOX was not altered by LPS treatment. Conclusion These results provide candidate miRNAs and their target genes that could be used as placental biomarkers of inflammation. These candidates may be useful for further miRNA-based biomarker development.