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To enable machines to understand human-centric images and videos, they need the capability to detect human–object interactions. This capability has been studied using various approaches, but previous research has mainly focused only on recognition accuracy using widely used open datasets. Given the need for advanced machine-learning systems that provide spatial analysis and services, the recognition model should be robust to various changes, have high extensibility, and provide sufficient recognition speed even with minimal computational overhead. Therefore, we propose a novel method that combines the skeletal method with object detection to accurately predict a set of $ \langle $ human, verb, object $ \rangle $ triplets in a video frame considering the robustness, extensibility, and lightweight of the model. Training a model with similar perceptual elements to those of humans produces sufficient accuracy for advanced social systems, even with only a small training dataset. The proposed model is trained using only the coordinates of the object and human landmarks, making it robust to various situations and lightweight compared with deep-learning methods. In the experiment, a scenario in which a human is working on a desk is simulated and an algorithm is trained on object-specific interactions. The accuracy of the proposed model was evaluated using various types of datasets.

In contemporary scenarios, privacy is paramount, especially in applications such as video interviews. This study introduces a privacy-focused real-time face and hair swapping method designed to conceal identity while retaining essential facial attributes of the original subject. Unlike conventional face swapping methods that rely on a reference image of a realistic person, our proposed method eliminates this need for enhanced privacy. Instead, this work introduces the use of a synthetic face image generated by a Generative Adversarial Networks (GANs), offering a secure solution that addresses the heightened importance of privacy in face and hair swapping applications, particularly in sensitive contexts like interviews. The proposed method in this study is an efficient 3-phase pipeline capable of performing these operations in real-time from a standard camera. This novel approach ensures a seamless integration of anonymity and attribute preservation, paving the way for more discreet and privacy-centric real-time face and hair swapping technologies, not only in video interviews but also in entertainment and communication applications such as video teleconferencing and streaming media.

In recent years, deep learning methodologies have been increasingly applied to the intricate challenges of visual-inertial odometry (VIO), especially in scenarios with rapid movements and scenes lacking clear structure. This paper introduces a novel hybrid approach that leverages the inherent strengths of traditional VIO techniques, while harnessing the potential of advanced machine learning technologies. By seamlessly integrating an iterated extended Kalman filter with deep learning techniques, our approach systematically takes into account uncertainties, thereby enhancing the overall reliability and robustness of the system. The proposed algorithm has been rigorously evaluated on the KITTI and EuroC datasets, outperforming other deep learning VIO methods. It achieved a translation error of 2.28% and a rotation error of 0.226 degrees per 100 meters on the KITTI odometry dataset.

Purpose To investigate the effect of patient positioning on tendinosis grade, visible range, and infraspinatus tendon (IST) thickness, and to determine the feasibility of internal rotation (IR) position to assess IST on ultrasound (US). Materials and Methods This study included 52 shoulders of 48 subjects who were evaluated for IST in three different positions: neutral position (N), IR, and position with the ipsilateral hand on the contralateral shoulder (HC). Two radiologists retrospectively graded IST tendinosis from grade 0 to grade 3 and the visible range from grade 1 to grade 4. The thickness of the IST was measured by another radiologist with a short-axis view. A generalized estimating equation was used for statistical analysis. Results The tendinosis grades were higher in the HC position than in the IR position, with a cumulative odds ratio of 2.087 (p = 0.004, 95% confidence interval [CI]: 1.268–3.433). The tendinosis grades in the HC position (p = 0.370) and IR position (p = 0.146) were not significantly different from those in the N position. The overall difference in IST thickness was significant (p < 0.001), but the visible range (p = 0.530) was not significantly different according to position. Conclusion Patient positioning significantly affected the grade of tendinosis and thickness but not the visible range of the IST. The IR position is a feasible position for assessing the IST on US.

3D object detection and localization based on only a monocular camera always faces its fundamental ill-posed issue to estimate 3D information. In combination with deep neural networks, recent researches have shown encouraging results to tackle this issue. However, most of them are only applied to street-view cameras based on several available small-size datasets and the 3D prediction accuracy of these methods is still low in comparison to traditional estimation methods using stereo-cameras. With the development of drone delivery applications in city spaces, it is also necessary to have a similar method to detect objects and estimate their 3D position from an aerial view. We proposed a novel Simulation to Reality approach to predict the object’s 3D position from an aerial view. An instance segmentation of an object is used as an intermediate representation not only to create a very large dataset for training by simulation but also to minimize the gap between simulation and reality. We designed a feed-forward neural network to predict the 3D position from instance segmentation and integrated it with a range-attention classification to improve accuracy, especially for 3D object detection at far distances. To evaluate our methods, we created two simulation datasets: one for cross-validation with other state-of-the-art methods and the other one for practical experiments on a real drone with a monocular camera. The experiment’s results demonstrate that we not only achieve better accuracy than the state-of-the-art methods using the monocular camera by testing on the same KITTI-3D dataset but also reach close to the accuracy of a stereo-based technique. Since our model is lightweight, we successfully deployed it on a companion computer on the real drone and the results of practical experiences are promising.

Graph Convolutional Networks (GCNs) have gained widespread adoption in modeling human skeleton sequences for two-person interaction recognition. Most GCN-based models achieve state-of-the-art results by leveraging either intra-body or inter-body connections. However, using only intra-body relations may ignore important interactive features between two individuals, whereas relying on inter-body relations may weaken the specific motion dynamics of each skeleton. To address these shortcomings, we propose a Distinct Motion-Preserving GCN (DMP-GCN) that utilizes intra-body and inter-body graphs to extract interactive features from two human bodies while preserving the distinct motion characteristics of each skeleton. Specifically, two motion-specific streams are adopted to capture specific motion features of each human skeleton and an interactive stream is applied to model the interactive dynamics of two bodies. In addition, we introduce a new graph labeling strategy, namely Distance Variation Labeling, which is a data-driven approach for defining the edge strength in the skeleton graph. Extensive experiments show that our proposed approaches outperform state-of-the-art methods on two large-scale human interaction datasets, NTU RGB+D (mutual) and NTU RGB+D 120 (mutual).

This paper explores the distribution and features of residual stresses formed by super duplex stainless steel pipe welding. Experimental investigations, which encompass an elevated temperature tensile test and metallographic observation along with a hardness test and residual stress measurement, were first conducted to obtain the mechanical properties at high base metal temperatures and to confirm whether or not the duplex stainless steel undergoes martensitic phase development during the welding process. Finally, experiments were performed to scrutinize the residual stress evolution through the metallurgical phase transformation in the weld region and its vicinity. A sequentially coupled 3D thermal, mechanical and metallurgical finite element (FE) model capable of incorporating the experimental consequences was established next. A 3D FE simulation of the girth welding process was conducted, and the axial and hoop residual stress profiles along the girth were evaluated. The results substantiate that martensitic phase evolution occurs in the process of cooling during the welding of super duplex stainless steel, and they also highlight the significance of taking the metallurgical phase transformation into account in the numerical reproduction of the girth welding process for the accurate expression of weld-induced residual stresses, which is especially important for precisely predicting hoop residual stresses.

Traditional models for various machine learning problems such as image classification perform well only under the assumption of a closed set. This implies that inputs must belong to the classes for which the models were trained. Data collected in the real world may not belong to any finite set of classes, and training a model with an infinite number of classes would obviously be impossible. Rather than incorrectly classifying outlier inputs that belong to unknown classes as members of one of the classes on which the model was trained, learning models must recognize and reject such data samples, or request human assistance in labeling them. For example, when a self-driving vehicle detects unfamiliar scenes or objects, it must notify the driver and hand over control. Various models have been proposed to address the open-set problem. However, the existing models are generally complex or use complicated techniques such as generative adversarial networks and auto-encoders, and their efficiency is not proportional to their complexity. In this study, we propose Triple-Sigmoid as a simple activation function comprised of three Sigmoid functions. Using Triple-Sigmoid in the last activation layer of any deep neural network model enables the model to recognize outliers. Although Triple-Sigmoid can be applied to a variety of machine learning problems, including semi-supervised learning, active learning, and incremental learning, we only investigated the open-set recognition problem in this work. The results of numerous experiments are presented to validate that substituting Triple-Sigmoid for conventional activations such as Softmax and Sigmoid not only retained performance in closed-set settings, but also significantly improved performance in open-set configurations. Furthermore, these results demonstrate that Triple-Sigmoid significantly outperformed existing state-of-the-art methods. Source code for the proposed model is available at https://github.com/dinhtuantran/triple-sigmoid.

PurposeThe discrepancy between preoperative and final pathological staging has been a long-standing challenge for the application of clinical trials or appropriate treatment options. This study aimed to demonstrate the accuracy of preoperative staging of locally advanced gastric cancer using data from a large-scale randomized clinical trial.Materials and methodsOf the 1050 patients enrolled in the clinical trial, 26 were excluded due to withdrawal of consent (n = 20) or non-surgery (n = 6). The clinical and pathological staging was compared. Risk factor analysis for underestimation was performed using univariate and multivariate analyses.ResultsRegarding T staging by computed tomography, accuracy rates were 74.48, 61.62, 58.56, and 85.16% for T1, T2, T3 and T4a, respectively. Multivariate analysis for underestimation of T staging revealed that younger age, ulcerative gross type, circular location, larger tumor size, and undifferentiated histology were independent risk factors. Regarding nodal status estimation, 54.9% of patients with clinical N0 disease were pathologic N0, and 36.4% of patients were revealed to have pathologic N0 among clinical node-positive patients. The percentage of metastasis involvement at the D1, D1+, and D2 lymph node stations significantly increased with the advanced clinical N stage. Among all patients, 29 (2.8%), including 26 with peritoneal seeding, exhibited distant metastases.ConclusionsEstimating the exact pathologic staging remains challenging. A thorough evaluation is mandatory before treatment selection or trial enrollment. Moreover, we need to set a sufficient case number when we design the clinical trial considering the stage migration.

Abstract As the geriatric population expands, caregivers require more accurate training to handle and care for the elderly. However, students lack methods for acquiring the necessary skill and experience, as well as sufficient opportunities to practice on real human beings. To investigate the necessity and feasibility of care training assistant robots in care education, we developed a simulated robot as a shoulder complex joint with multi-DOF. In this study, five experts with years of experience in elderly care participated in the data-acquisition process, to acquire information on aspects such as the glenohumeral angle, as well as the sterno-clavicular joint and its torque. The experts performed three types of range-of-motion exercises: (i) elevation–depression of sterno-clavicular joint; (ii) extension and flexion of glenohumeral joint; (iii) lateral and medial rotation of glenohumeral joint. The experimental results showed that the quantitative results for all the exercises were significantly different between the experts. Moreover, we observed that even experienced professionals need consistent care education based on quantitative data and feedback. Thus, we confirmed the necessity and feasibility of the care training assistant robot for improving the skills required for elderly care.

Night security is known for its long hours and heavy tasks. In Japan, a labor shortage of security guards has become an issue in recent years. To solve these problems, an increasing number of robotic security methods are being used. However, several problems exist with existing security robots. For example, wheeled robots traveling on the ground have difficulty in dealing with obstacles such as steps, while most drones are only for monitoring and do not have a function to help people. In this study, an aerial ubiquitous display (AUD) night security drone has been developed to solve the problems of existing security robots. The AUD is equipped with an infrared camera and a projector to detect human behavior at night and present information to people in need. In this paper, an experiment was conducted with the AUD to evaluate whether it can provide adequate nighttime security. In the experiment, real-time monitoring and information projection from the air were achieved. In addition, new security methods using the AUD were shown to be effective. Replacing security guards with the AUD to provide security at night will improve labor shortages in the future, and better security methods will be developed.

Background/Aims Endoscopic ultrasonography (EUS) is the most efficient imaging modality for gastric subepithelial tumors (SETs). However, abdominopelvic computed tomography (APCT) has other advantages in evaluating the characteristics, local extension, or invasion of SETs to adjacent organs. This study aimed to compare the diagnostic ability of EUS and APCT based on surgical histopathology results. Methods We retrospectively reviewed data from 53 patients who underwent both EUS and APCT before laparoscopic wedge resection for gastric SETs from January 2010 to December 2017 at a single institution. On the basis of histopathology results, we assessed the diagnostic ability of the 2 tests. Results The overall accuracy of EUS and APCT was 64.2% and 50.9%, respectively. In particular, the accuracy of EUS vs. APCT for the diagnosis of gastrointestinal stromal tumors (GISTs), leiomyomas, and ectopic pancreas was 83.9% vs. 74.2%, 37.5% vs. 0.0%, and 57.1% vs. 14.3%, respectively. Most of the incorrect diagnoses with EUS involved hypoechoic lesions originating in the fourth echolayer, with the most common misdiagnosed lesions being GISTs mistaken for leiomyomas and vice versa. Conclusions APCT showed a lower overall accuracy than EUS; however, APCT remains a useful modality for malignant/potentially malignant gastric SETs.

As the elderly population increases, the importance of the caregiver’s role in the quality of life of the elderly has increased. To achieve effective feedback in terms of care and nursing education, it is important to design a robot that can express emotions or feel pain like an actual human through visual-based feedback. This study proposes a care training assistant robot (CaTARo) system with 3D facial pain expression that simulates an elderly person for improving the skills of workers in elderly care. First, in order to develop an accurate and efficient system for elderly care training, this study introduces a fuzzy logic–based care training evaluation method that can calculate the pain level of a robot for giving the feedback. Elderly caregivers and trainees performed the range of motion exercise using the proposed CaTARo. We obtained quantitative data from CaTARo, and the pain level was calculated by combining four key parameters using the fuzzy logic method. Second, we developed a 3D facial avatar for use in CaTARo that is capable of expressing pain based on the UNBC-McMaster Pain Shoulder Archive, and we then generated four pain groups with respect to the pain level. To mimic the conditions for care training with actual humans, we designed the system to provide pain feedback based on the opinions of experts. The pain feedback was expressed in real time by using a projector and a 3D facial mask during care training. The results of the study confirmed the feasibility of utilizing a care training robot with pain expression for elderly care training, and it is concluded that the proposed approach may be used to improve caregiving and nursing skills upon further research.

In a wearable robot arm, the minimum joint configuration and link length must be considered to avoid increasing the burden on the user. This work investigated how the joint configuration, length of arm links, and mounting position of a wearable robot arm affect the cooperative and invasive workspaces of the overall workspace. We considered the joint configurations and link lengths of passive and active joints in our proposed wearable robot arm, which is called the Assist Oriented Arm (AOA). In addition, we comprehensively studied the position of the arm on the user. As a result, three locations around the shoulders and two around the waist were chosen as potential mounting sites. Furthermore, we evaluated the weight burden when the user mounted the wearable robot arm at those positions.

In this paper we propose a new method to evaluate the effects of nationwide conservation voltage reduction (CVR) on peak-load shaving, using substation operating results management system (SOMAS) data. Its evaluation is based on a national CVR factor, which is defined as the weighted average of CVR factors associated with all transformer banks and weighting coefficients are determined by the reconstructed loads corresponding to each transformer bank. To make use of the data resulting from nationwide CVR without installing additional measuring devices, we adopt a linearized static-load model with a linearizing parameter. SOMAS data are used to evaluate the effects of nationwide CVR on peak-load shaving in the Korean power system. Evaluation results show that the national CVR factor of the Korean power system has small values in the summer season and large values in the winter season. This means that the effect of nationwide CVR on peak-load shaving in the Korean power system presents stronger benefits during winter months.

This paper proposes a linearized load model to evaluate the effect of conservation voltage reduction at a national level. In this model, the respective active and reactive linearizing parameters for active and reactive loads in a power system are estimated using energy management system (EMS) data resulting from conservation voltage reductions. To verify the validity of the linearized load model, PSS/E simulations were conducted for a test power system. Given that conservation voltage reductions are usually executed in the range of 2.0%–5.0%, the proposed model was found to be sufficient to accurately evaluate the effect of conservation voltage reduction. Additionally, Korean EMS data were used to estimate the linearizing parameters for aggregated loads in an actual power system.

Laparoscopic gastrectomy is evolving. With the increasing expertise and experience of oncologic surgeons in the minimally invasive surgery for gastric cancer, the indication for laparoscopic gastrectomy is expanding to advanced cases. Many studies have demonstrated the benefits of minimally invasive surgery, including reduced risk of surgery-related injury, reduced blood loss, less pain, and earlier recovery. In order to establish concrete evidence for the suitability of minimal invasive surgery for gastric cancer, many multicenter RCTs, comparing the short- and long-term outcomes of laparoscopic versus open surgery, are in progress. Advances in laparoscopic gastrectomy are moving toward increasingly minimally invasive approaches that enable the improvement of the quality of life of patients, without compromising on oncologic safety.

In this paper, we present robust methods for automatically segmenting phases in a specified surgical workflow by using latent Dirichlet allocation (LDA) and hidden Markov model (HMM) approaches. More specifically, our goal is to output an appropriate phase label for each given time point of a surgical workflow in an operating room. The fundamental idea behind our work lies in constructing an HMM based on observed values obtained via an LDA topic model covering optical flow motion features of general working contexts, including medical staff, equipment, and materials. We have an awareness of such working contexts by using multiple synchronized cameras to capture the surgical workflow. Further, we validate the robustness of our methods by conducting experiments involving up to 12 phases of surgical workflows with the average length of each surgical workflow being 12.8 minutes. The maximum average accuracy achieved after applying leave-one-out cross-validation was 84.4%, which we found to be a very promising result.

Glucosylation of the 21-hydroxyl group of glucocorticoid changes its solubility into hydrophilicity from hydrophobicity and, as with glucocorticoid glucuronides as a moving object in vivo, it is conceivable that it exhibits the same behavior. Therefore, glucosylation to the 21-hydroxyl group while maintaining the 11β-hydroxyl group is particularly important, and glucosylation of corticosterone was confirmed by high-resolution mass spectrometry and 1D (1H and 13C) and 2D (COSY, ROESY, HSQC-DEPT and HMBC) NMR. Moreover, the difference in bioactivity between corticosterone and corticosterone 21-glucoside was investigated in vitro. Corticosterone 21-glucoside showed greater neuroprotective effects against H2O2-induced cell death and reactive oxygen species (ROS) compared with corticosterone. These results for the first time demonstrate that bioconversion of corticosterone through the region-selective glucosylation of a novel compound can present structural potential for developing new neuroprotective agents.

In this paper, we propose a novel interactive multi-resolution display system. The proposed system is based on a projector-mounted mobile robot in an Intelligent Space. The Intelligent Space can calculate the location of the region of interest (ROI) by recognizing the user's pointing gesture. The steerable projector mounted on the mobile robot can improve the brightness and resolution of the ROI of a large image projected by a stationary projector installed in the Intelligent Space. In the proposed system, the user is not required to hold any apparatuses for interacting with the display. Additionally, the proposed system is easy to use because it is designed with the natural and intuitive hand movement of user in mind. In the experiments, we demonstrate the feasibility of the proposed system.

Background/Aims Atezolizumab plus bevacizumab (ATE+BEV) therapy has become the recommended first-line therapy for patients with unresectable hepatocellular carcinoma (HCC) because of favorable treatment responses. However, there is a lack of data on sequential regimens after ATE+BEV treatment failure. We aimed to investigate the clinical outcomes of patients with advanced HCC who received subsequent systemic therapy for disease progression after ATE+BEV. Methods This multicenter, retrospective study included patients who started second-line systemic treatment with sorafenib or lenvatinib after HCC progressed on ATE+BEV between August 2019 and December 2022. Treatment response was assessed using the Response Evaluation Criteria in Solid Tumors (version 1.1.). Clinical features of the two groups were balanced through propensity score (PS) matching. Results This study enrolled 126 patients, 40 (31.7%) in the lenvatinib group, and 86 (68.3%) in the sorafenib group. The median age was 63 years, and males were predominant (88.1%). In PS-matched cohorts (36 patients in each group), the objective response rate was similar between the lenvatinib- and sorafenib-treated groups (5.6% vs. 8.3%; P=0.643), but the disease control rate was superior in the lenvatinib group (66.7% vs. 22.2%; P

Abstract Background Neurotrophic tropomyosin receptor kinase (NTRK) gene fusions are found in 1% of gliomas across children and adults. TRK inhibitors are promising therapeutic agents for NTRK-fused gliomas because they are tissue agnostic and cross the blood–brain barrier (BBB). Methods We investigated twelve NGS-verified NTRK-fused gliomas from a single institute, Seoul National University Hospital. Results The patient cohort included six children (aged 1–15 years) and six adults (aged 27–72 years). NTRK2 fusions were found in ten cerebral diffuse low-grade and high-grade gliomas (DLGGs and DHGGs, respectively), and NTRK1 fusions were found in one cerebral desmoplastic infantile ganglioglioma and one spinal DHGG. In this series, the fusion partners of NTRK2 were HOOK3, KIF5A, GKAP1, LHFPL3, SLMAP, ZBTB43, SPECC1L, FKBP15, KANK1, and BCR, while the NTRK1 fusion partners were TPR and TPM3. DLGGs tended to harbour only an NTRK fusion, while DHGGs exhibited further genetic alterations, such as TERT promoter/TP53/PTEN mutation, CDKN2A/2B homozygous deletion, PDGFRA/KIT/MDM4/AKT3 amplification, or multiple chromosomal copy number aberrations. Four patients received adjuvant TRK inhibitor therapy (larotrectinib, repotrectinib, or entrectinib), among which three also received chemotherapy (n = 2) or proton therapy (n = 1). The treatment outcomes for patients receiving TRK inhibitors varied: one child who received larotrectinib for residual DLGG maintained stable disease. In contrast, another child with DHGG in the spinal cord experienced multiple instances of tumour recurrence. Despite treatment with larotrectinib, ultimately, the child died as a result of tumour progression. An adult patient with glioblastoma (GBM) treated with entrectinib also experienced tumour progression and eventually died. However, there was a successful outcome for a paediatric patient with DHGG who, after a second gross total tumour removal followed by repotrectinib treatment, showed no evidence of disease. This patient had previously experienced relapse after the initial surgery and underwent autologous peripheral blood stem cell therapy with carboplatin/thiotepa and proton therapy. Conclusions Our study clarifies the distinct differences in the pathology and TRK inhibitor response between LGG and HGG with NTRK fusions.

Virtual reality(VR) applications are typically developed within their own immersive digital worlds; therefore, virtual spaces are usually treated as discrete from the physical space where augmented and mixed reality users exist, which makes it difficult to combine these heterogeneous realities into an integrated extended reality(XR) environment. Along these lines, we propose a method that enables a user to geometrically register the virtual space within a VR application to a real space using a commodity camera in a workspace as an anchor point. We first investigate the mathematical aspect of the computational model for connecting the virtual space to the physical world. Then, we present a computational procedure that implements our proposed method with numerical accuracy and stability. As an application, we demonstrate that users of two VR systems from different vendors may collaborate within a shared real workspace while interacting with each other physically. The presented method provides a key mechanism for enabling XR users to leverage these immersive technologies by effectively using different realities within an integrated environment.

Abstract Local recurrences in patients with grade 4 adult-type diffuse gliomas mostly occur within residual non-enhancing T2 hyperintensity areas after surgical resection. Unfortunately, it is challenging to distinguish non-enhancing tumors from edema in the non-enhancing T2 hyperintensity areas using conventional MRI alone. Quantitative DCE MRI parameters such as Ktrans and Ve convey permeability information of glioblastomas that cannot be provided by conventional MRI. We used the publicly available nnU-Net to train a deep learning model that incorporated both conventional and DCE MRI to detect the subtle difference in vessel leakiness due to neoangiogenesis between the non-recurrence area and the local recurrence area, which contains a higher proportion of high-grade glioma cells. We found that the addition of Ve doubled the sensitivity while nonsignificantly decreasing the specificity for prediction of local recurrence in glioblastomas, which implies that the combined model may result in fewer missed cases of local recurrence. The deep learning model predictive of local recurrence may enable risk-adapted radiotherapy planning in patients with grade 4 adult-type diffuse gliomas.

Traditional 3D projection models, such as perspective and orthographic projection, are limited to two types of projective ray fields: rays passing through a single point and parallel rays. In this paper, we introduce an ellipsoidal-based 3D projection model to overcome the sparsity of 3D projections. Our ellipsoidal 3D projection model comprises an ellipsoid and an axis-aligned geometry such as a line or a plane. By linearly mapping these two geometries along their principal axes, our model enables us to explore the continuous domain of projective ray fields while taking advantage of the anisotropy in ellipsoids. We introduce the intrinsic characteristic of our projection field, called the ellipse property, that enables testing isomorphism with other projection models. We prove the difference between ours and the catadioptric projection model employing an elliptic mirror. Besides, we propose a perspectivity metric for the intuitive control over the parameter space. We present both forward and backward projections of our model, demonstrating its applicability across several visual applications, ranging from image synthesis to scene reconstruction.

Abstract Background We examined the incidence and predictors of clinical outcomes in metabolic dysfunction‐associated fatty liver disease (MAFLD), focusing on anthropometric parameters. Methods Adult patients with MAFLD were identified in nationwide databases and a hospital cohort. Primary endpoints were atherosclerotic cardiovascular disease (ASCVD) and advanced fibrosis. Logistic and Cox regression analyses were used to analyse the association between anthropometric parameters and endpoints. Results In total, 4407 of 15 256 (28.9%) and 6274 of 25 784 subjects (24.3%) had MAFLD in the nationwide database; of these, 403 (9.2%) and 437 (7.0%) subjects were of lean/normal weight, respectively. Compared to the overweight/obese group, the lean/normal weight group had a significantly lower muscle mass (15.0 vs. 18.9 kg) and handgrip strength (31.9 vs. 35.1 kg) and had a higher ASCVD risk (9.0% vs. 6.3% and 15.9% vs. 8.5%; Ps

Abstract Adult-type diffuse glioma (grade 4) has infiltrating nature, and therefore local progression is likely to occur within surrounding non-enhancing T2 hyperintense areas even after gross total resection of contrast-enhancing lesions. Cerebral blood volume (CBV) obtained from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) is a parameter that is well-known to be a surrogate marker of both histologic and angiographic vascularity in tumors. We built two nnU-Net deep learning models for prediction of early local progression in adult-type diffuse glioma (grade 4), one using conventional MRI alone and one using multiparametric MRI, including conventional MRI and DSC-PWI. Local progression areas were annotated in a non-enhancing T2 hyperintense lesion on preoperative T2 FLAIR images, using the follow-up contrast-enhanced (CE) T1-weighted (T1W) images as the reference standard. The sensitivity was doubled with the addition of nCBV (80% vs. 40%, P = 0.02) while the specificity was decreased nonsignificantly (29% vs. 48%, P = 0.39), suggesting that fewer cases of early local progression would be missed with the addition of nCBV. While the diagnostic performance of CBV model is still poor and needs improving, the multiparametric deep learning model, which presumably learned from the subtle difference in vascularity between early local progression and non-progression voxels within perilesional T2 hyperintensity, may facilitate risk-adapted radiotherapy planning in adult-type diffuse glioma (grade 4) patients.

Abstract Background/Aims Regression of liver fibrosis during antiviral therapy in chronic hepatitis B (CHB) patients has been demonstrated, but data on the influence of long-term treatment with tenofovir disoproxil fumarate (TDF) on liver stiffness (LS) measured by transient elastography are scarce. We aimed to investigate the changes in LS values during the 144-week TDF therapy in treatment-naïve CHB patients. Methods This prospective observational study was conducted from April 2015 to July 2020 at CHA Bundang Medical Center. Laboratory tests and LS measurements were performed at baseline and repeated at weeks 12, 24, 48, 96, and 144. A significant decline in LS was defined as ≥ 30% decrease in LS value at week 96 from baseline. Results A total of 48 treatment-naïve CHB patients initiating TDF therapy were screened, and 36 patients were included in the final analysis (median age, 46 [interquartile range, 34.5–55.8] years; 19 men [52.8%]). During TDF therapy, the median LS values decreased from 13.8 kPa at baseline to 8.7 kPa, 6.5 kPa, and 6.4 kPa at weeks 48, 96, and 144, respectively (all P

Abstract Background The activation of the telomere maintenance mechanism (TMM) is one of the critical drivers of cancer cell immortality. In gliomas, TERT expression and TERT promoter mutation are considered to reliably indicate telomerase activation, while ATRX mutation and/or loss indicates an alternative lengthening of telomeres (ALT). However, these relationships have not been extensively validated in tumor tissues. Methods Telomerase repeated amplification protocol (TRAP) and C-circle assays were used to profile and characterize the TMM cross-sectionally (n = 412) and temporally (n = 133) across glioma samples. WES, RNA-seq, and NanoString analyses were performed to identify and validate the genetic characteristics of the TMM groups. Results We show through the direct measurement of telomerase activity and ALT in a large set of glioma samples that the TMM in glioma cannot be defined solely by the combination of telomerase activity and ALT, regardless of TERT expression, TERT promoter mutation, and ATRX loss. Moreover, we observed that a considerable proportion of gliomas lacked both telomerase activity and ALT. This telomerase activation-negative and ALT negative group exhibited evidence of slow growth potential. By analyzing a set of longitudinal samples from a separate cohort of glioma patients, we discovered that the TMM is not fixed and can change with glioma progression. Conclusions This study suggests that the TMM is dynamic and reflects the plasticity and oncogenicity of tumor cells. Direct measurement of telomerase enzyme activity and evidence of ALT should be considered when defining TMM. An accurate understanding of the TMM in glioma is expected to provide important information for establishing cancer management strategies.

This study aimed to assess the performance of a tongue-positioning device in interfractional tongue position reproducibility by cone-beam computed tomography (CBCT). Fifty-two patients treated with radiation therapy (RT) while using a tongue positioning device were included in the study. All patients were treated with 28 or 30 fractions using the volumetric modulated arc therapy technique. CBCT images were acquired at the 1st, 7th, 11th, 15th, 19th, 23th, and 27th fractions. Tongues on planning computed tomography (pCT) and CBCT images were contoured in the treatment planning system. Geometric differences in the tongue between pCT and CBCT were assessed by the Dice similarity coefficient (DSC) and averaged Hausdorff distance (AHD). Two-dimensional in vivo measurements using radiochromic films were performed in 13 patients once a week during sessions. The planned dose distributions were compared with the measured dose distributions using gamma analysis with criteria of 3%/3 mm. In all patients, the mean DSC at the 1st fraction (pCT versus 1st CBCT) was 0.80 while the mean DSC at the 27th fraction (pCT versus 27th CBCT) was 0.77 with statistical significance (p-value = 0.015). There was no statistically significant difference in DSC between the 1st fraction and any other fraction, except for the 27th fraction. There was statistically significant difference in AHD between the 1st fraction and the 19th, 23th, and 27th fractions (p-value < 0.05). In vivo measurements showed an average gamma passing rate of 90.54%. There was no significant difference between measurements at the 1st week and those at other weeks. The tongue geometry during RT was compared between pCT and CBCT. In conclusion, the novel tongue-positioning device was found to minimize interfractional variations in position and shape of the tongue.

Abstract Background The purpose of this study was to compare the treatment efficacy and safety of re-irradiation (re-RT) using stereotactic ablative radiotherapy (SABR) and initial SABR for primary, recurrent lung cancer or metastatic lung tumor. Methods A retrospective review of the medical records of 336 patients who underwent lung SABR was performed. Re-RT was defined as the overlap of the 70% isodose line of second-course SABR with that of the initial radiotherapy, and 20 patients were classified as the re-RT group. The median dose of re-RT using SABR was 54 Gy (range 48–60 Gy), and the median fraction number was 4 (range 4–6). One-to-three case-matched analysis with propensity score matching was used, and 60 patients were included in the initial SABR group of the matched cohort. Results The 1- and 2-year local control rates for the re-RT group were 73.9% and 63.3% and those for the initial SABR group in the matched cohort were 92.9% and 87.7%, respectively (P = 0.013). There was no difference in distant metastasis-free, progression-free, and overall survival rates. The crude grade ≥ 2 toxicity rates were 40.0% for the re-RT group and 25.0% for the initial SABR group (P = 0.318). Re-RT group had higher acute grade ≥ 2 toxicity rates (25.0% vs 5.0%, P = 0.031). One incident of grade 3 toxicity (pulmonary) was reported in the re-RT group; there was no grade 4‒5 toxicity. Conclusions The local control rate of the in-field re-RT SABR was lower than that of the initial SABR without compromising the survival rates. The toxicity of re-RT using SABR was acceptable.

Abstract Glioblastoma remains the most devastating brain tumor despite optimal treatment, because of the high rate of recurrence. Distant recurrence has distinct genomic alterations compared to local recurrence, which requires different treatment planning both in clinical practice and trials. To date, perfusion-weighted MRI has revealed that perfusional characteristics of tumor are associated with prognosis. However, not much research has focused on recurrence patterns in glioblastoma: namely, local and distant recurrence. Here, we propose two different neural network models to predict the recurrence patterns in glioblastoma that utilizes high-dimensional radiomic profiles based on perfusion MRI: area under the curve (AUC) (95% confidence interval), 0.969 (0.903–1.000) for local recurrence; 0.864 (0.726–0.976) for distant recurrence for each patient in the validation set. This creates an opportunity to provide personalized medicine in contrast to studies investigating only group differences. Moreover, interpretable deep learning identified that salient radiomic features for each recurrence pattern are related to perfusional intratumoral heterogeneity. We also demonstrated that the combined salient radiomic features, or “radiomic risk score”, increased risk of recurrence/progression (hazard ratio, 1.61; p = 0.03) in multivariate Cox regression on progression-free survival.

IntroductionThe dosimetric factors of radiotherapy have an acute impact on the host immune system during chemoradiotherapy (CRT) in locally advanced non-small cell lung cancer (NSCLC). However, even after CRT, a substantial number of patients remain immunosuppressed with delayed lymphopenia. Therefore, we aimed to evaluate clinical and dose-volumetric predictors of delayed lymphopenia after CRT in locally advanced NSCLC.Materials and methodsWe retrospectively reviewed 272 patients with locally advanced NSCLC who received definitive CRT from January 2012 to August 2020. Differential blood count data, including serum albumin values, were obtained at baseline, during and at first follow up after CRT. Acute and delayed lymphopenia events were defined as grade III/IV lymphopenia developed during or 4-12 weeks after CRT completion, which accounted for 84% and 10% of cases, respectively. Dose-volume histogram parameters for planned target volume, whole body, heart, lung, great vessels, spleen, esophagus and thoracic vertebral bodies were evaluated.ResultsMultivariate analysis revealed that patients with delayed lymphopenia were associated with inferior overall survival (HR 2.53, P = 0.001) and progression-free survival (HR 1.98, P = 0.006). However, there was no significant survival difference between groups stratified by acute lymphopenia. On multivariable logistic regression models, lung V5, baseline ALC, during-CRT ALC, and albumin nadir were significant predictors for delayed lymphopenia. Furthermore, the nomogram for delayed lymphopenia based on these variables had good discrimination (area under the curve, 0.905).ConclusionsIn this study, we investigated the prognostic significance of delayed lymphopenia and identified clinico-dosimetric parameters to predict delayed lymphopenia.

PurposeWe aimed to compare the outcomes of adjuvant radiotherapy (ART) and surveillance in patients with grade 2 meningiomas (MNG2) who underwent surgical resection.Materials and MethodsData from four hospitals, in which patients aged ≥18 years underwent Simpson grade 1−4 surgical resection for newly diagnosed MNG2 between 1998 and 2018, were examined in this multicenter retrospective cohort study. Patients receiving ART with conventional fractionation were compared with those undergoing surveillance. Progression-free survival (PFS), progression/recurrence (P/R) were evaluated.ResultsThis study included 518 patients, 158 of whom received ART. The median follow-up duration was 64.9 months. In the total cohort, ART was independently associated with significantly improved PFS (HR, 0.35; 95% CI, 0.23–0.55; P

Summary: Background/Objective: The number of gastric cancer and type II diabetes mellitus is increasing in Korea. Metabolic surgery could be extended to gastric cancer patients with type II diabetes, especially those who are expected to achieve long-term survival. This study aimed to investigate change of diabetic status in patients undergoing long-limb Roux-en-Y bypass reconstruction compared with conventional Billroth II after curative gastrectomy. In total, 130 patients from five university hospital centers underwent long-limb Roux-en Y reconstruction after radical distal gastrectomy. Methods: In the long-limb group, the length of biliopancreatic limbs was more than 80 cm, and the length of the Roux limb was more than 80 cm. The control group comprised 96 patients who underwent conventional Billroth II reconstruction after distal gastrectomy. Follow-up data at three, six, nine, and 12 months were compared between the two groups. Results: Fasting blood sugar (FBS) and hemoglobin (Hb) A1c levels decreased more significantly in the long-limb Roux-en-Y group (FBS: 28.8 mg/dL; HbA1c: 0.72%). However, decreases in body mass index, albumin, and hemoglobin did not differ significantly between the two groups. Diabetes control significantly improved in the long-limb group. In multivariate analysis, long-limb bypass reconstruction was the significant factor for glycemic outcomes. Conclusion: Roux-en-Y bypass with increased length of limbs after gastrectomy shows a favorable glycemic control for gastric cancer patients with type II diabetes without nutritional deficit and anemia. To obtain future perspectives, large-scale prospective studies with long-term outcomes are needed. Keywords: Type II DM, Gastric cancer, Long limb Roux-en Y

Obesity increases the risks of diabetes, hypertension, and cardiovascular diseases, ultimately contributing to mortality. Korean Society for the Study of Obesity (KSSO) was established to improve the management of obesity through research and education; to that end, the Committee of Clinical Practice Guidelines of KSSO reviews systemic evidence using expert panels to develop clinical guidelines. The clinical practice guidelines for obesity were revised in 2018 using National Health Insurance Service Health checkup data from 2006 to 2015. Following these guidelines, we added a category, class III obesity, which includes individuals with body mass index (BMI) ≥35 kg/m². Agreeing with the International Federation for the Surgery of Obesity and Metabolic Disorders, Asian Pacific Chapter consensus, we determined that bariatric surgery is indicated for Korean patients with BMI ≥35 kg/m² and for Korean patients with BMI ≥30 kg/m² who have comorbidities. The new guidelines focus on guiding clinicians and patients to manage obesity more effectively. Our recommendations and treatment algorithms can serve as a guide for the evaluation, prevention, and management of overweight and obesity.

Background/Aims Nonalcoholic fatty liver disease (NAFLD) is becoming a worldwide epidemic, and is frequently found in patients with chronic hepatitis B (CHB). We investigated the impact of histologically proven hepatic steatosis on the risk for hepatocellular carcinoma (HCC) in CHB patients without excessive alcohol intake. Methods Consecutive CHB patients who underwent liver biopsy from January 2007 to December 2015 were included. The association between hepatic steatosis (≥ 5%) and subsequent HCC risk was analyzed. Inverse probability weighting (IPW) using the propensity score was applied to adjust for differences in patient characteristics, including metabolic factors. Results Fatty liver was histologically proven in 70 patients (21.8%) among a total of 321 patients. During the median (interquartile range) follow-up of 5.3 (2.9–8.3) years, 17 of 321 patients (5.3%) developed HCC: 8 of 70 patients (11.4%) with fatty liver and 9 of 251 patients (3.6%) without fatty liver. The five-year cumulative incidences of HCC among patients without and with fatty liver were 1.9% and 8.2%, respectively (P=0.004). Coexisting fatty liver was associated with a higher risk for HCC (adjusted hazards ratio [HR], 3.005; 95% confidence interval [CI], 1.122–8.051; P=0.03). After balancing with IPW, HCC incidences were not significantly different between the groups (P=0.19), and the association between fatty liver and HCC was not significant (adjusted HR, 1.709; 95% CI, 0.404–7.228; P=0.47). Conclusions Superimposed NAFLD was associated with a higher HCC risk in CHB patients. However, the association between steatosis per se and HCC risk was not evident after adjustment for metabolic factors.

Laparoscopic sleeve gastrectomy has become a standard procedure in bariatric surgery owing to its efficacy and simplicity. However, this procedure can cause life-threatening complications such as a gastric staple-line leak. A 24-year-old woman was transferred to the emergency department for evaluation of epigastric pain. Nine days prior to transfer, she underwent laparoscopic sleeve gastrectomy at another institution. Abdominal computed tomography (CT) revealed fluid collection with air density along the left subphrenic space and gastrosplenic ligament area. Intravenous antibiotics and total parenteral nutrition were initiated. She underwent percutaneous catheter drainage. On postoperative day 18, an esophagogastroduodenoscopy was performed to assess the site and size of the leak, and revealed a leak at the proximal staple line just below the gastroesophageal junction. A newly designed, fully covered antimigratory esophageal stent was placed to cover the leak from the distal esophagus to gastric midbody. Follow-up abdominal CT demonstrated improvement of the fluid collection at the location of the previous gastric leak. The stent was removed 3 weeks after insertion, and a barium study confirmed no more leakage. In this case, we experienced that the newly designed esophageal stent was safe and effective for preventing migration in the management of leak after laparoscopic sleeve gastrectomy.

Portal vein thrombosis (PVT) is a form of venous thrombosis that usually presents in chronic form without any sequalae in patients with hepatocellular carcinoma (HCC) or liver cirrhosis. Accurate differential diagnosis of bland PVT from neoplastic PVT is an important step for planning treatment options, but the acute form can be challenging. Here we present a case of acute hepatic infarction caused by acute bland PVT combined with pylephlebitis, which was misdiagnosed as infiltrative hepatic malignancy with neoplastic PVT owing to the perplexing imaging results and elevated tumor markers. (Korean J Gastroenterol 2016;68:156-160)

Entecavir (BaracludeⓇ) is an oral antiviral drug used for the treatment of HBV. Entecavir is a reverse transcriptase inhibitor which prevents the HBV from multiplying. Most common adverse reactions caused by entecavir are headache, fatigue, dizziness, and nausea. Until now, there has been no report of peripheral neuropathy as a side effect associated with entecavir treatment. Herein, we report a case of peripheral neuropathy which probably occurred after treatment with entecavir in a hepatitis B patient. The possibility of the occurrence of this side effect should be carefully taken into consideration when a patient takes a high dose of entecavir for a long period of time or has risk factors for neuropathy at the time of initiating entecavir therapy. (Korean J Gastroenterol 2016;67:216-219)

Radiation dermatitis can develop after fluoroscopy-guided interventional procedures. Cases of fluoroscopy-induced radiation dermatitis have been reported since 1996, mostly documented in the fields of radiology, cardiology and dermatology. Since diagnosis and treatment of fluoroscopy-induced radiation dermatitis can be difficult, high grade of suspicion is required. The extent of this reaction is determined by radiation dose, duration of exposure, type of procedure, and host factors and can be aggravated by concomitant use of photosensitizers. Follow-up is important after long and complicated procedures and efforts to minimize radiation exposure time will be necessary to prevent radiation dermatitis. Herein, we report a case of a 58-year-old man with hepatocellular carcinoma presenting with subacute radiation dermatitis after prolonged fluoroscopic exposure during transarterial chemoembolization and chemoport insertion. Physicians should be aware that fluoroscopy is a potential cause of radiation dermatitis. (Korean J Gastroenterol 2016;67:112-115)

Background/AimsApolipoprotein E (ApoE) plays an important role in regulating lipid and lipoprotein metabolism, and ApoE genotypes are known to affect plasma lipoprotein concentrations. We investigated whether ApoE genotype determines the disease outcome in hepatitis B virus (HBV)-infected individuals, and verified the association between ApoE genotype and the occurrence of hepatocellular carcinoma (HCC) in patients with chronic liver diseases of various etiologies.MethodsThis hospital-based, case-controlled study enrolled 156 subjects (47 healthy controls, 50 HBV-related liver cirrhosis patients, and 59 HCC patients). ApoE genotypes were determined using PCR-based ApoE genotyping kits. The biological significance of ApoE genotype was verified by measuring serum ApoE levels using an ELISA kits.ResultsThe ε3 allele was the most common allele, with allele frequencies among the entire cohort of 5.8%, 84.3%, and 9.9% for the ε2, ε3, and ε4 alleles, respectively. Significantly more of those patients carrying the ε3/3 genotype had developed liver cirrhosis compared to the control subjects. Being an ApoE4 carrier was associated with a lower probability of developing liver cirrhosis. The allele frequencies and genotype distribution of ApoE did not differ significantly between the liver cirrhosis and HCC patients. The serum level of ApoE was significantly higher in patients with liver cirrhosis than in the healthy controls, but did not differ significantly with the ApoE genotype.ConclusionsThe ApoE ε3/3 genotype frequency was higher in patients with HBV-associated liver cirrhosis than in the controls.

This paper concerns the use of synthetic data for protecting the confidentiality of business data during statistical analysis. Synthetic data sets are traditionally constructed by replacing sensitive values in a confidential data set with draws from statistical models estimated on the confidential data set. Unfortunately, the process of generating effective statistical models can be a difficult and labour-intensive task. Recently, it has been proposed to use easily-implemented methods from machine learning instead of statistical model estimation in the data synthesis task. J. Drechsler and J.P. Reiter (2011) have conducted an evaluation of four such methods, and have found that regression trees could give rise to synthetic data sets which provide reliable analysis results as well as low disclosure risks. Their conclusion was based on simulations using a subset of the 2002 Uganda census public use file. It is an interesting question whether the same conclusion applies to other types of data with different characteristics, for example business data which have quite different characteristics from population census and survey data. In particular, business data generally have few variables that are mostly categorical, and often have highly skewed distributions with outliers. In this paper we investigate the applicability of regression-tree-based methods for constructing synthetic business data. We give a detailed example comparing exploratory data analysis and linear regression results under two variants of a regression-tree-based synthetic data approach. We also include an evaluation of the analysis results with respect to the results of analysis of the original data. We further investigate the impact of different stopping criteria on performance. While it is certainly true that any method designed to protect confidentiality introduces error, and may indeed give misleading conclusions, our analysis of the results for synthesisers based on CART models has provided some evidence that this error is not random but is due to the particular characteristics of business data. We conclude that more careful analysis needs to be done in applying these methods and end users certainly need aware of possible discrepancies.