Gastric cancer is one of the most common cancers in Korea and the world. Since 2004, this is the 4th gastric cancer guideline published in Korea which is the revised version of previous evidence-based approach in 2018. Current guideline is a collaborative work of the interdisciplinary working group including experts in the field of gastric surgery, gastroenterology, endoscopy, medical oncology, abdominal radiology, pathology, nuclear medicine, radiation oncology and guideline development methodology. Total of 33 key questions were updated or proposed after a collaborative review by the working group and 40 statements were developed according to the systematic review using the MEDLINE, Embase, Cochrane Library and KoreaMed database. The level of evidence and the grading of recommendations were categorized according to the Grading of Recommendations, Assessment, Development and Evaluation proposition. Evidence level, benefit, harm, and clinical applicability was considered as the significant factors for recommendation. The working group reviewed recommendations and discussed for consensus. In the earlier part, general consideration discusses screening, diagnosis and staging of endoscopy, pathology, radiology, and nuclear medicine. Flowchart is depicted with statements which is supported by meta-analysis and references. Since clinical trial and systematic review was not suitable for postoperative oncologic and nutritional follow-up, working group agreed to conduct a nationwide survey investigating the clinical practice of all tertiary or general hospitals in Korea. The purpose of this survey was to provide baseline information on follow up. Herein we present a multidisciplinary-evidence based gastric cancer guideline.

PURPOSE: . This study evaluated the reliability of the chair-side CAD-CAM surgical guide (CSG) in the anterior maxilla by comparing its accuracy with the laboratory 3D-printed surgical guide (3DSG) and manual surgical guide (MSG) concerning different levels of dentists' surgical experience. MATERIALS AND METHODS: . Ten surgical guides of each type (MSG, 3DSG, and CSG) were fabricated on a control study model with missing right and left central incisors. Sixty implants were placed in 30 study models by two dentists (one inexperienced and one experienced) using three different types of surgical guides. Horizontal deviations at shoulder and at apex, vertical, and angular deviations were measured after superimposing the planned and placed implant positions in the software. Kruskal-Wallis and Mann-Whitney U tests were used to compare the accuracy of three types of surgical guides in each dentist group and the accuracy of each surgical guide between two dentists (α = .05). RESULTS: . There were no significant differences in any deviations between CSG and 3DSG, apart from angular deviation, for both dentists’ groups. Moreover, both CSG and 3DSG showed no significant differences in accuracy between the two dentists (P > .05). In contrast, MSG demonstrated significant differences from CSG and 3DSG and a significant difference in accuracy between the two dentists (P < .05). CONCLUSION . CSG provides superior accuracy to MSG in implant placement in the maxillary anterior region and is comparable to 3DSG at different levels of surgical experience, while offering the benefits of shorter manufacturing time and reduced patient visits.

Objective: We aimed to develop and test a deep learning algorithm (DLA) for fully automated measurement of the volume and signal intensity (SI) of the liver and spleen using gadoxetic acid-enhanced hepatobiliary phase (HBP)-magnetic resonance imaging (MRI) and to evaluate the clinical utility of DLA-assisted assessment of functional liver capacity. Materials and Methods: The DLA was developed using HBP-MRI data from 1014 patients. Using an independent test dataset (110 internal and 90 external MRI data), the segmentation performance of the DLA was measured using the Dice similarity score (DSS), and the agreement between the DLA and the ground truth for the volume and SI measurements was assessed with a Bland-Altman 95% limit of agreement (LOA). In 276 separate patients (male:female, 191:85; mean age ± standard deviation, 40 ± 15 years) who underwent hepatic resection, we evaluated the correlations between various DLA-based MRI indices, including liver volume normalized by body surface area (LV BSA), liver-to-spleen SI ratio (LSSR), MRI parameter-adjusted LSSR (aLSSR), LSSR x LV BSA, and aLSSR x LV BSA, and the indocyanine green retention rate at 15 minutes (ICG-R15), and determined the diagnostic performance of the DLA-based MRI indices to detect ICG-R15 ≥ 20%. Results: In the test dataset, the mean DSS was 0.977 for liver segmentation and 0.946 for spleen segmentation. The BlandAltman 95% LOAs were 0.08% ± 3.70% for the liver volume, 0.20% ± 7.89% for the spleen volume, -0.02% ± 1.28% for the liver SI, and -0.01% ± 1.70% for the spleen SI. Among DLA-based MRI indices, aLSSR x LV BSA showed the strongest correlation with ICG-R15 (r = -0.54, p < 0.001), with area under receiver operating characteristic curve of 0.932 (95% confidence interval, 0.895–0.959) to diagnose ICG-R15 ≥ 20%. Conclusion Our DLA can accurately measure the volume and SI of the liver and spleen and may be useful for assessing functional liver capacity using gadoxetic acid-enhanced HBP-MRI.

Objective: This study aimed to evaluate the usefulness of quantitative indices obtained from deep learning analysis of gadoxetic acid-enhanced hepatobiliary phase (HBP) MRI and their longitudinal changes in predicting decompensation and death in patients with advanced chronic liver disease (ACLD). Materials and Methods: We included patients who underwent baseline and 1-year follow-up MRI from a prospective cohort that underwent gadoxetic acid-enhanced MRI for hepatocellular carcinoma surveillance between November 2011 and August 2012 at a tertiary medical center. Baseline liver condition was categorized as non-ACLD, compensated ACLD, and decompensated ACLD. The liver-to-spleen signal intensity ratio (LS-SIR) and liver-to-spleen volume ratio (LS-VR) were automatically measured on the HBP images using a deep learning algorithm, and their percentage changes at the 1-year follow-up (ΔLS-SIR and ΔLS-VR) were calculated. The associations of the MRI indices with hepatic decompensation and a composite endpoint of liver-related death or transplantation were evaluated using a competing risk analysis with multivariable Fine and Gray regression models, including baseline parameters alone and both baseline and follow-up parameters. Results: Our study included 280 patients (153 male; mean age ± standard deviation, 57 ± 7.95 years) with non-ACLD, compensated ACLD, and decompensated ACLD in 32, 186, and 62 patients, respectively. Patients were followed for 11–117 months (median, 104 months). In patients with compensated ACLD, baseline LS-SIR (sub-distribution hazard ratio [sHR], 0.81; p = 0.034) and LS-VR (sHR, 0.71; p = 0.01) were independently associated with hepatic decompensation. The ∆LS-VR (sHR, 0.54; p = 0.002) was predictive of hepatic decompensation after adjusting for baseline variables. ∆LS-VR was an independent predictor of liver-related death or transplantation in patients with compensated ACLD (sHR, 0.46; p = 0.026) and decompensated ACLD (sHR, 0.61; p = 0.023). Conclusion MRI indices automatically derived from the deep learning analysis of gadoxetic acid-enhanced HBP MRI can be used as prognostic markers in patients with ACLD.

Objective: Resuscitation-related gastric inflation is associated with inadequate ventilation and the risk of gastric regurgitation in out-of-hospital cardiac arrest (OHCA) patients. This study aims to estimate resuscitation-related gastric inflation values by using multi-detector computed tomography (MDCT) scanning. Methods: MDCT imaging data were obtained from OHCA patients undergoing resuscitation from January 2014 to December 2020. Thirty age- and sex-matched healthy controls that underwent an MDCT scan were included. Gastric air volume (GAV), total gastric volume (TGV), and GAV/gastric content volume (GCV) ratio values were estimated. Results: In healthy controls (n=30), GAV and TGV values were in the range 5.0-35.0 mL, and 202.0-1,002.0 mL, respectively. The mean GAV and TGV values of OHCA patients (n=97) were 251.0 mL (range, 55.5-896.0) and 878.0 mL (range, 430.5-1,696.0), respectively. Significant between-group differences were determined in the mean GCV, GAV, and GAV/GCV ratio values. In OHCA patients, the cut-off value for abnormal GAV was defined as 56.5 mL (mean value plus two times standard deviation). Patients with abnormal GAV findings on MDCT scans had a longer duration from arrest to the return of spontaneous circulation, low body mass index, and increased rates of lactic acidosis. Conclusion Our results indicate an association between gastric air accumulation after resuscitation with longer recovery from arrest to return of spontaneous circulation, low body mass index, and increased lactic acidosis.

Objective: Although the liver-to-spleen volume ratio (LSVR) based on CT reflects portal hypertension, its prognostic role in cirrhotic patients has not been proven. We evaluated the utility of LSVR, automatically measured from CT images using a deep learning algorithm, as a predictor of hepatic decompensation and transplantation-free survival in patients with hepatitis B viral (HBV)-compensated cirrhosis. Materials and Methods: A deep learning algorithm was used to measure the LSVR in a cohort of 1027 consecutive patients (mean age, 50.5 years; 675 male and 352 female) with HBV-compensated cirrhosis who underwent liver CT (2007–2010).Associations of LSVR with hepatic decompensation and transplantation-free survival were evaluated using multivariable Cox proportional hazards and competing risk analyses, accounting for either the Child-Pugh score (CPS) or Model for End Stage Liver Disease (MELD) score and other variables. The risk of the liver-related events was estimated using Kaplan-Meier analysis and the Aalen-Johansen estimator. Results: After adjustment for either CPS or MELD and other variables, LSVR was identified as a significant independent predictor of hepatic decompensation (hazard ratio for LSVR increase by 1, 0.71 and 0.68 for CPS and MELD models, respectively; p < 0.001) and transplantation-free survival (hazard ratio for LSVR increase by 1, 0.8 and 0.77, respectively; p < 0.001). Patients with an LSVR of < 2.9 (n = 381) had significantly higher 3-year risks of hepatic decompensation (16.7% vs. 2.5%, p < 0.001) and liver-related death or transplantation (10.0% vs. 1.1%, p < 0.001) than those with an LSVR ≥ 2.9 (n = 646). When patients were stratified according to CPS (Child-Pugh A vs. B–C) and MELD (< 10 vs. ≥ 10), an LSVR of < 2.9 was still associated with a higher risk of liver-related events than an LSVR of ≥ 2.9 for all Child-Pugh (p ≤ 0.045) and MELD (p ≤ 0.009) stratifications. Conclusion The LSVR measured on CT can predict hepatic decompensation and transplantation-free survival in patients with HBV-compensated cirrhosis.

Purpose: With changing fungal epidemiology and azole resistance in Aspergillus species, identifying fungal species and susceptibility patterns is crucial to the management of aspergillosis and mucormycosis. The objectives of this study were to evaluate performance of panfungal polymerase chain reaction (PCR) assays on formalin-fixed paraffin embedded (FFPE) samples in the identification of fungal species and in the detection of azole-resistance mutations in the Aspergillus fumigatus cyp51A gene at a South Korean hospital. Materials and Methods: A total of 75 FFPE specimens with a histopathological diagnosis of aspergillosis or mucormycosis were identified during the 10-year study period (2006–2015). After deparaffinization and DNA extraction, panfungal PCR assays were conducted on FFPE samples for fungal species identification. The identified fungal species were compared with histopathological diagnosis. On samples identified as A. fumigatus, sequencing to identify frequent mutations in the cyp51A gene [tandem repeat 46 (TR46), L98H, and M220 alterations] that confer azole resistance was performed. Results: Specific fungal DNA was identified in 31 (41.3%) FFPE samples, and of these, 16 samples of specific fungal DNA were in accord with a histopathological diagnosis of aspergillosis or mucormycosis; 15 samples had discordant histopathology and PCR results. No azole-mediating cyp51A gene mutation was noted among nine cases of aspergillosis. Moreover, no cyp51A mutations were identified among three cases with history of prior azole use. Conclusion Panfungal PCR assay with FFPE samples may provide additional information of use to fungal species identification. No azole-resistance mediating mutations in the A. fumigatus cyp51A gene were identified among FFPE samples during study period.

Objective: Measurement of the liver and spleen volumes has clinical implications. Although computed tomography (CT)volumetry is considered to be the most reliable noninvasive method for liver and spleen volume measurement, it has limitedapplication in clinical practice due to its time-consuming segmentation process. We aimed to develop and validate a deeplearning algorithm (DLA) for fully automated liver and spleen segmentation using portal venous phase CT images in variousliver conditions. Materials and Methods: A DLA for liver and spleen segmentation was trained using a development dataset of portal venousCT images from 813 patients. Performance of the DLA was evaluated in two separate test datasets: dataset-1 which included150 CT examinations in patients with various liver conditions (i.e., healthy liver, fatty liver, chronic liver disease, cirrhosis,and post-hepatectomy) and dataset-2 which included 50 pairs of CT examinations performed at ours and other institutions.The performance of the DLA was evaluated using the dice similarity score (DSS) for segmentation and Bland-Altman 95%limits of agreement (LOA) for measurement of the volumetric indices, which was compared with that of ground truth manualsegmentation. Results: In test dataset-1, the DLA achieved a mean DSS of 0.973 and 0.974 for liver and spleen segmentation, respectively,with no significant difference in DSS across different liver conditions (p = 0.60 and 0.26 for the liver and spleen, respectively).For the measurement of volumetric indices, the Bland-Altman 95% LOA was -0.17 ± 3.07% for liver volume and -0.56 ± 3.78%for spleen volume. In test dataset-2, DLA performance using CT images obtained at outside institutions and our institutionwas comparable for liver (DSS, 0.982 vs. 0.983; p = 0.28) and spleen (DSS, 0.969 vs. 0.968; p = 0.41) segmentation. Conclusion The DLA enabled highly accurate segmentation and volume measurement of the liver and spleen using portalvenous phase CT images of patients with various liver conditions.