Background: Acute deterioration of patients in general wards often leads to major adverse events (MAEs), including unplanned intensive care unit transfers, cardiac arrest, or death. Traditional early warning scores (EWSs) have shown limited predictive accuracy, with frequent false positives. We conducted a prospective observational external validation study of an artificial intelligence (AI)-based EWS, the VitalCare - Major Adverse Event Score (VC-MAES), at a tertiary medical center in the Republic of Korea. Methods: Adult patients from general wards, including internal medicine (IM) and obstetrics and gynecology (OBGYN)—the latter were rarely investigated in prior AI-based EWS studies—were included. The VC-MAES predictions were compared with National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS) predictions using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and logistic regression for baseline EWS values. False-positives per true positive (FPpTP) were assessed based on the power threshold. Results: Of 6,039 encounters, 217 (3.6%) had MAEs (IM: 9.5%, OBGYN: 0.26%). Six hours prior to MAEs, the VC-MAES achieved an AUROC of 0.918 and an AUPRC of 0.352, including the OBGYN subgroup (AUROC, 0.964; AUPRC, 0.388), outperforming the NEWS (0.797 and 0.124) and MEWS (0.722 and 0.079). The FPpTP was reduced by up to 71%. Baseline VC-MAES was strongly associated with MAEs (P<0.001). Conclusions The VC-MAES significantly outperformed traditional EWSs in predicting adverse events in general ward patients. The robust performance and lower FPpTP suggest that broader adoption of the VC-MAES may improve clinical efficiency and resource allocation in general wards.

Purpose: The partner and localizer of breast cancer 2 (PALB2) mutation is a predisposition to breast cancer development. However, limited clinical data are available for the Korean population. Therefore, this study aimed to compare the characteristics and oncological outcomes of patients with PALB2-mutated and non-mutated PALB2 in Korea. Methods: A total of 1,463 breast cancer (BRCA) 1/2 mutation-negative breast cancer underwent comprehensive multigene sequencing between 2016 and 2019 at Severance Hospital, Seoul, Korea. Clinicopathological data and oncological results of PALB2 mutated patients were prospectively collected and compared with those of the non-mutated group. Results: PALB2 mutations were identified in 1.2% (17/1,463) of the patients. The median age at diagnosis was 46 (30–73) years, and the median tumor size was 1.8 (0.05–3.5) cm. All patients with PALB2 mutations had histologic grades II–III, and a triple-negative subtype was found in 23.5% (4/17); however, there were no significant differences in clinicopathological data between the groups. During the median follow-up time of 38.5 months, locoregional recurrence occurred in 4.2% (44/1,043), distant recurrence was reported in 3.0% (31/1,043), and contralateral breast cancer was diagnosed in 0.8% (9/1,043) of patients, with no significant difference observed between the groups. All-cause mortality was observed in 1.8% (19/1,028) of the non-mutated group and none in the PALB2 mutation group. However, survival analyses demonstrated no significant differences in all-cause mortality (p = 0.524) and recurrence-free survival (p = 0.319). Conclusion Clinicopathological features and oncological outcomes of PALB2 mutated breast cancer were not significantly different from those of non-mutated breast cancer in the Korean population.

Purpose: The partner and localizer of breast cancer 2 (PALB2) mutation is a predisposition to breast cancer development. However, limited clinical data are available for the Korean population. Therefore, this study aimed to compare the characteristics and oncological outcomes of patients with PALB2-mutated and non-mutated PALB2 in Korea. Methods: A total of 1,463 breast cancer (BRCA) 1/2 mutation-negative breast cancer underwent comprehensive multigene sequencing between 2016 and 2019 at Severance Hospital, Seoul, Korea. Clinicopathological data and oncological results of PALB2 mutated patients were prospectively collected and compared with those of the non-mutated group. Results: PALB2 mutations were identified in 1.2% (17/1,463) of the patients. The median age at diagnosis was 46 (30–73) years, and the median tumor size was 1.8 (0.05–3.5) cm. All patients with PALB2 mutations had histologic grades II–III, and a triple-negative subtype was found in 23.5% (4/17); however, there were no significant differences in clinicopathological data between the groups. During the median follow-up time of 38.5 months, locoregional recurrence occurred in 4.2% (44/1,043), distant recurrence was reported in 3.0% (31/1,043), and contralateral breast cancer was diagnosed in 0.8% (9/1,043) of patients, with no significant difference observed between the groups. All-cause mortality was observed in 1.8% (19/1,028) of the non-mutated group and none in the PALB2 mutation group. However, survival analyses demonstrated no significant differences in all-cause mortality (p = 0.524) and recurrence-free survival (p = 0.319). Conclusion Clinicopathological features and oncological outcomes of PALB2 mutated breast cancer were not significantly different from those of non-mutated breast cancer in the Korean population.

Purpose: The partner and localizer of breast cancer 2 (PALB2) mutation is a predisposition to breast cancer development. However, limited clinical data are available for the Korean population. Therefore, this study aimed to compare the characteristics and oncological outcomes of patients with PALB2-mutated and non-mutated PALB2 in Korea. Methods: A total of 1,463 breast cancer (BRCA) 1/2 mutation-negative breast cancer underwent comprehensive multigene sequencing between 2016 and 2019 at Severance Hospital, Seoul, Korea. Clinicopathological data and oncological results of PALB2 mutated patients were prospectively collected and compared with those of the non-mutated group. Results: PALB2 mutations were identified in 1.2% (17/1,463) of the patients. The median age at diagnosis was 46 (30–73) years, and the median tumor size was 1.8 (0.05–3.5) cm. All patients with PALB2 mutations had histologic grades II–III, and a triple-negative subtype was found in 23.5% (4/17); however, there were no significant differences in clinicopathological data between the groups. During the median follow-up time of 38.5 months, locoregional recurrence occurred in 4.2% (44/1,043), distant recurrence was reported in 3.0% (31/1,043), and contralateral breast cancer was diagnosed in 0.8% (9/1,043) of patients, with no significant difference observed between the groups. All-cause mortality was observed in 1.8% (19/1,028) of the non-mutated group and none in the PALB2 mutation group. However, survival analyses demonstrated no significant differences in all-cause mortality (p = 0.524) and recurrence-free survival (p = 0.319). Conclusion Clinicopathological features and oncological outcomes of PALB2 mutated breast cancer were not significantly different from those of non-mutated breast cancer in the Korean population.

Purpose: The integration of artificial intelligence (AI) and magnetic resonance imaging in rectal cancer has the potential to enhance diagnostic accuracy by identifying subtle patterns and aiding tumor delineation and lymph node assessment. According to our systematic review focusing on convolutional neural networks, AI-driven tumor staging and the prediction of treatment response facilitate tailored treat­ment strategies for patients with rectal cancer. Methods: This paper summarizes the current landscape of AI in the imaging field of rectal cancer, emphasizing the performance reporting design based on the quality of the dataset, model performance, and external validation. Results: AI-driven tumor segmentation has demonstrated promising results using various convolutional neural network models. AI-based predictions of staging and treatment response have exhibited potential as auxiliary tools for personalized treatment strategies. Some studies have indicated superior performance than conventional models in predicting microsatellite instability and KRAS status, offer­ing noninvasive and cost-effective alternatives for identifying genetic mutations. Conclusion Image-based AI studies for rectal can­cer have shown acceptable diagnostic performance but face several challenges, including limited dataset sizes with standardized data, the need for multicenter studies, and the absence of oncologic relevance and external validation for clinical implantation. Overcoming these pitfalls and hurdles is essential for the feasible integration of AI models in clinical settings for rectal cancer, warranting further research.

Objectives: Intraoperative navigation reduces the risk of major complications and increases the likelihood of optimal surgical outcomes. This paper presents an augmented reality (AR)-based simulation technique for ventriculostomy that visualizes brain deformations caused by the movements of a surgical instrument in a three-dimensional brain model. This is achieved by utilizing a position-based dynamics (PBD) physical deformation method on a preoperative brain image. Methods: An infrared camera-based AR surgical environment aligns the real-world space with a virtual space and tracks the surgical instruments. For a realistic representation and reduced simulation computation load, a hybrid geometric model is employed, which combines a high-resolution mesh model and a multiresolution tetrahedron model. Collision handling is executed when a collision between the brain and surgical instrument is detected. Constraints are used to preserve the properties of the soft body and ensure stable deformation. Results: The experiment was conducted once in a phantom environment and once in an actual surgical environment. The tasks of inserting the surgical instrument into the ventricle using only the navigation information presented through the smart glasses and verifying the drainage of cerebrospinal fluid were evaluated. These tasks were successfully completed, as indicated by the drainage, and the deformation simulation speed averaged 18.78 fps. Conclusions This experiment confirmed that the AR-based method for external ventricular drain surgery was beneficial to clinicians.

Objective: The prognostic value of the volume and density of skeletal muscles in the abdominal waist of patients with colon cancer remains unclear. This study aimed to investigate the association between the automated computed tomography (CT)-based volume and density of the muscle in the abdominal waist and survival outcomes in patients with colon cancer. Materials and Methods: We retrospectively evaluated 474 patients with colon cancer who underwent surgery with curative intent between January 2010 and October 2017. Volumetric skeletal muscle index and muscular density were measured at the abdominal waist using artificial intelligence (AI)-based volumetric segmentation of body composition on preoperative pre-contrast CT images. Patients were grouped based on their skeletal muscle index (sarcopenia vs. not) and muscular density (myosteatosis vs. not) values and combinations (normal, sarcopenia alone, myosteatosis alone, and combined sarcopenia and myosteatosis). Postsurgical disease-free survival (DFS) and overall survival (OS) were analyzed using univariable and multivariable analyses, including multivariable Cox proportional hazard regression. Results: Univariable analysis showed that DFS and OS were significantly worse for the sarcopenia group than for the nonsarcopenia group (P = 0.044 and P = 0.003, respectively, by log-rank test) and for the myosteatosis group than for the nonmyosteatosis group (P < 0.001 by log-rank test for all). In the multivariable analysis, the myosteatotic muscle type was associated with worse DFS (adjusted hazard ratio [aHR], 1.89 [95% confidence interval, 1.25–2.86]; P = 0.003) and OS (aHR, 1.90 [95% confidence interval, 1.84–3.04]; P = 0.008) than the normal muscle type. The combined muscle type showed worse OS than the normal muscle type (aHR, 1.95 [95% confidence interval, 1.08–3.54]; P = 0.027). Conclusion Preoperative volumetric sarcopenia and myosteatosis, automatically assessed from pre-contrast CT scans using AI-based software, adversely affect survival outcomes in patients with colon cancer.

Purpose: Weight loss and deterioration in body composition are observed in patients with gastric cancer (GC) following gastrectomy. This study aimed to investigate the impact of residual stomach volume (RSV) on the nutritional status and body composition of patients with GC treated with distal gastrectomy. Materials and Methods: In total, 227 patients who underwent minimally invasive distal gastrectomy with Billroth 1 anastomosis for stage I GC between February 2015 and May 2018 were enrolled. Clinicodemographic and laboratory data were collected from the GC registry. The RSV, abdominal muscle area, and subcutaneous/visceral fat areas were measured using computed tomography data. Results: A larger RSV was associated with a lower decrease in the nutritional risk index (P=0.004) and hemoglobin level (P=0.003) during the first 3 months after surgery, and better recovery at 12 months. A larger RSV demonstrated an advantage in the preservation of abdominal muscle area (P=0.02) and visceral fat (P=0.04) after surgery, as well as less reduction in weight (P=0.02) and body mass index (P=0.03). Conclusions Larger RSV was associated with improved nutritional status and better preservation of muscle and fat after distal gastrectomy.

Objective: The clinical relevance of myosteatosis has not been well evaluated in patients with pancreatic ductal adenocarcinoma (PDAC), although sarcopenia has been extensively researched. Therefore, we evaluated the prognostic value of muscle quality, including myosteatosis, in patients with resectable PDAC treated surgically. Materials and Methods: We retrospectively evaluated 347 patients with resectable PDAC who underwent curative surgery (mean age ± standard deviation, 63.6 ± 9.6 years; 202 male). Automatic muscle segmentation was performed on preoperative computed tomography (CT) images using an artificial intelligence program. A single axial image of the portal phase at the inferior endplate level of the L3 vertebra was used for analysis in each patient. Sarcopenia was evaluated using the skeletal muscle index, calculated as the skeletal muscle area (SMA) divided by the height squared. The mean SMA attenuation was used to evaluate myosteatosis. Diagnostic cutoff values for sarcopenia and myosteatosis were devised using the Contal and O’Quigley methods, and patients were classified according to normal (nMT), sarcopenic (sMT), myosteatotic (mMT), or combined (cMT) muscle quality types. Multivariable Cox regression analyses were conducted to assess the effects of muscle type on the overall survival (OS) and recurrence-free survival (RFS) after surgery. Results: Eighty-four (24.2%), 73 (21.0%), 75 (21.6%), and 115 (33.1%) patients were classified as having nMT, sMT, mMT, and cMT, respectively. Compared to nMT, mMT and cMT were significantly associated with poorer OS, with hazard ratios (HRs) of 1.49 (95% confidence interval, 1.00–2.22) and 1.68 (1.16–2.43), respectively, while sMT was not (HR of 1.40 [0.94–2.10]). Only mMT was significantly associated with poorer RFS, with an HR of 1.59 (1.07–2.35), while sMT and cMT were not. Conclusion Myosteatosis was associated with poor OS and RFS in patients with resectable PDAC who underwent curative surgery.

Background: Sarcopenia is defined as the loss of skeletal muscle mass and is associated with negative clinical outcomes. This study aimed to establish sex-specific cutoff values for the skeletal muscle area (SMA) and skeletal muscle index (SMI) at the third lumbar vertebral (L3) level using computed tomography (CT) imaging to identify sarcopenia in healthy Korean liver donors. Methods: This retrospective study included 659 healthy liver donors (408 men and 251 women) aged 20 to 60 years who had undergone abdominal CT examinations between January 2017 and December 2018. Assessment of body composition was performed with an automated segmentation technique using a deep-learning system. Sex-specific SMA and SMI distributions were assessed, and cutoff values for determining sarcopenia were defined as values at either two standard deviations (SDs) below the mean reference value or below the fifth percentile. Results: Using the SD definition, cutoff values for SMA and SMI were 117.04 cm2 and 39.33 cm2/m2, respectively, in men and 71.39 cm2 and 27.77 cm2/m2, respectively, in women. Using the fifth percentile definition, cutoff values for SMA and SMI were 126.88 cm2 and 40.96 cm2/m2, respectively, in men and 78.85 cm2 and 30.60 cm2/m2, respectively, in women. Conclusion Our data provide sex-specific cutoff values for the SMA and SMI at the L3 level measured by CT imaging in a healthy Korean population, which may be applicable for identifying sarcopenia in this population.