Structural and functional abnormalities and cognitive profiles in older adults with early-onset and late-onset focal epilepsy.

This study aimed to determine the patterns of changes in structure, function, and cognitive ability in early-onset and late-onset older adults with focal epilepsy (OFE). This study first utilized the deformation-based morphometry analysis to identify structural abnormalities, which were used as the seed region to investigate the functional connectivity with the whole brain. Next, a correlation analysis was performed between the altered imaging findings and neuropsychiatry assessments. Finally, the potential role of structural-functional abnormalities in the diagnosis of epilepsy was further explored by using mediation analysis. Compared with healthy controls (n = 28), the area of reduced structural volume was concentrated in the bilateral cerebellum, right thalamus, and right middle cingulate cortex, with frontal, temporal, and occipital lobes also affected in early-onset focal epilepsy (n = 26), while late-onset patients (n = 31) displayed cerebellar, thalamic, and cingulate atrophy. Furthermore, correlation analyses suggest an association between structural abnormalities and cognitive assessments. Dysfunctional connectivity in the cerebellum, cingulate cortex, and frontal gyrus partially mediates the relationship between structural abnormalities and the diagnosis of early-onset focal epilepsy. This study identified structural and functional abnormalities in early-onset and late-onset focal epilepsy and explored characters in cognitive performance. Structural-functional coupling may play a potential role in the diagnosis of epilepsy.

A causal effect study of cortical morphology and related covariate networks in classical trigeminal neuralgia patients.

Structural covariance networks and causal effects within can provide critical information on gray matter reorganization and disease-related hierarchical changes. Based on the T1WI data of 43 classical trigeminal neuralgia patients and 45 controls, we constructed morphological similarity networks of cortical thickness, sulcal depth, fractal dimension, and gyrification index. Moreover, causal structural covariance network analyses were conducted in regions with morphological abnormalities or altered nodal properties, respectively. We found that patients showed reduced sulcal depth, gyrification index, and fractal dimension, especially in the salience network and the default mode network. Additionally, the integration of the fractal dimension and sulcal depth networks was significantly reduced, accompanied by decreased nodal efficiency of the bilateral temporal poles, and right pericalcarine cortex within the sulcal depth network. Negative causal effects existed from the left insula to the right caudal anterior cingulate cortex in the gyrification index map, also from bilateral temporal poles to right pericalcarine cortex within the sulcal depth network. Collectively, patients exhibited impaired integrity of the covariance networks in addition to the abnormal gray matter morphology in the salience network and default mode network. Furthermore, the patients may experience progressive impairment in the salience network and from the limbic system to the sensory system in network topology, respectively.

Progressive structural damage in sleep-related hypermotor epilepsy.

This study aimed to explore the alterations in gray matter volume (GMV) based on high-resolution structural data and the temporal precedence of structural alterations in patients with sleep-related hypermotor epilepsy (SHE). After preprocessing of T1 structural images, the voxel-based morphometry and source-based morphometry (SBM) methods were applied in 60 SHE patients and 56 healthy controls to analyze the gray matter volumetric alterations. Furthermore, a causal network of structural covariance (CaSCN) was constructed using Granger causality analysis based on structural data of illness duration ordering to assess the causal impact of structural changes in abnormal gray matter regions. The GMVs of SHE patients were widely reduced, mainly in the bilateral cerebellums, fusiform gyri, the right angular gyrus, the right postcentral gyrus, and the left parahippocampal gyrus. In addition to those regions, the results of the SBM analysis also found decreased GMV in the bilateral frontal lobes, precuneus, and supramarginal gyri. The analysis of CaSCN showed that along with disease progression, the cerebellum was the prominent node that tended to affect other brain regions in SHE patients, while the frontal lobe was the transition node and the supramarginal gyrus was the prominent node that may be easily affected by other brain regions. Our study found widely affected regions of decreased GMVs in SHE patients; these regions underlie the morphological basis of epileptic networks, and there is a temporal precedence relationship between them.

An MRI Study Combining Virtual Brain Grafting and Surface-Based Morphometry Analysis to Investigate Contralateral Alterations in Cortical Morphology in Patients With Diffuse Low-Grade Glioma.

BACKGROUND: The human brain has ability to reorganize itself in response to glioma. However, the mechanism of cortical reorganization remains unclear. PURPOSE: To investigate alterations in cortical thickness and local gyration index (LGI) in patients with unilateral frontal lobe diffuse low-grade glioma (DLGG). STUDY TYPE: Retrospective. SUBJECTS: Ninety-nine patients with histopathologically proven DLGG invading the left frontal lobe (LF; N = 56) or the right frontal lobe (RF; N = 43), and healthy controls (HC; N = 53). FIELD STRENGTH/SEQUENCE: 3.0 T, 3D T1-weighted images and gadolinium enhanced T1-weighted images using magnetization-prepared rapid gradient echo sequence, T2-weighted images, and fluid-attenuated inversion recovery using turbo spin echo sequence. ASSESSMENT: In patients with DLGG, virtual brain grafting combined with Freesurfer was utilized to enable automated cortical thickness and LGI calculation. In HC, standard FreeSurfer pipeline was applied to calculate these measures. Radiomic features were extracted from glioma using Pyradiomic software. STATISTICAL TESTS: General linear model and Pearson's correlation analysis. A P value <0.05 was considered statistically significant. RESULTS: For LF patients, there was significantly increased cortical thickness in the rostral middle frontal gyrus, significantly reduced cortical thickness in the precentral gyrus and hypogyrification in the lingual and medial orbitofrontal (MOF) gyrus in contralateral hemisphere. For RF patients, there was significantly increased cortical thickness in the middle temporal, lateral occipital extending to isthmus cingulate gyrus, significantly reduced cortical thickness in the precentral gyrus and hypogyrification in the lingual gyrus in the contralateral hemisphere. A negative association between four textural features of DLGG and LGI in the right MOF gyrus of LF group was found (r = -0.609, -0.442, -0.545, and -0.417, respectively). DATA CONCLUSION: Cortical thickness compensation was shown in contralateral homotopic location and some distant contralateral regions. Additionally, there was decreased cortical thickness in the contralateral precentral gyrus and hypogyrification in contralateral lingual gyrus. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

1H-MRS reveals metabolic alterations in generalized tonic-clonic seizures before and after treatment.

AIMS: To explore the possible metabolic alterations of bilateral dorsolateral prefrontal cortices (DLPFC) of generalized tonic-clonic seizures (GTCS) patients before and after antiepileptic drugs treatment as compared with healthy controls (HCs) using proton magnetic resonance spectroscopy (1H-MRS). METHODS: We included 23 newly diagnosed and unmedicated GTCS patients and 23 sex- and age-matched HCs. Metabolites including N-acetyl aspartate (NAA), myo-inositol (Ins), choline (Cho), creatine (Cr), and glutamate + glutamine (Glu + Gln, Glx) concentrations were quantified by using LCModel software and then corrected for the partial volume effect of cerebrospinal fluid. RESULTS: The results demonstrated that metabolite concentrations were not equal between the left and the right DLPFC. Compared with HC, NAA of the left DLPFC and Cr of the right DLPFC were significantly lower in pre-treatment patients. Self-controlled study revealed that the patients' NAA of the left DLPFC increased while their Cr of the right DLPFC decreased after treatment. Correlation analysis showed a negative correlation between the duration of medication and the pre- and post-treatment difference of Cr. CONCLUSION: These findings may shed a light on the metabolic mechanism of GTCS and the neurobiochemical mechanisms of AEDs.

Automated machine learning to predict the co-occurrence of isocitrate dehydrogenase mutations and O6 -methylguanine-DNA methyltransferase promoter methylation in patients with gliomas.

Combining isocitrate dehydrogenase mutation (IDHmut) with O6 -methylguanine-DNA methyltransferase promoter methylation (MGMTmet) has been identified as a critical prognostic molecular marker for gliomas. The aim of this study was to determine the ability of glioma radiomics features from magnetic resonance imaging (MRI) to predict the co-occurrence of IDHmut and MGMTmet by applying the tree-based pipeline optimization tool (TPOT), an automated machine learning (autoML) approach. This was a retrospective study, in which 162 patients with gliomas were evaluated, including 58 patients with co-occurrence of IDHmut and MGMTmet and 104 patients with other status comprising: IDH wildtype and MGMT unmethylated (n = 67), IDH wildtype and MGMTmet (n = 36), and IDHmut and MGMT unmethylated (n = 1). Three-dimensional (3D) T1-weighted images, gadolinium-enhanced 3D T1-weighted images (Gd-3DT1WI), T2-weighted images, and fluid-attenuated inversion recovery (FLAIR) images acquired at 3.0 T were used. Radiomics features were extracted from FLAIR and Gd-3DT1WI images. The TPOT was employed to generate the best machine learning pipeline, which contains both feature selector and classifier, based on input feature sets. A 4-fold cross-validation was used to evaluate the performance of automatically generated models. For each iteration, the training set included 121 subjects, while the test set included 41 subjects. Student's t-test or a chi-square test was applied on different clinical characteristics between two groups. Sensitivity, specificity, accuracy, kappa score, and AUC were used to evaluate the performance of TPOT-generated models. Finally, we compared the above metrics of TPOT-generated models to identify the best-performing model. Patients' ages and grades between two groups were significantly different (p = 0.002 and p = 0.000, respectively). The 4-fold cross-validation showed that gradient boosting classifier trained on shape and textual features from the Laplacian-of-Gaussian-filtered Gd-3DT1 achieved the best performance (average sensitivity = 81.1%, average specificity = 94%, average accuracy = 89.4%, average kappa score = 0.76, average AUC = 0.951). Using autoML based on radiomics features from MRI, a high discriminatory accuracy was achieved for predicting co-occurrence of IDHmut and MGMTmet in gliomas. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 3.

Intelligent difficulty scoring and assistance system for endoscopic extraction of common bile duct stones based on deep learning: multicenter study.

BACKGROUND: The study aimed to construct an intelligent difficulty scoring and assistance system (DSAS) for endoscopic retrograde cholangiopancreatography (ERCP) treatment of common bile duct (CBD) stones. METHODS: 1954 cholangiograms were collected from three hospitals for training and testing the DSAS. The D-LinkNet34 and U-Net were adopted to segment the CBD, stones, and duodenoscope. Based on the segmentation results, the stone size, distal CBD diameter, distal CBD arm, and distal CBD angulation were estimated. The performance of segmentation and estimation was assessed by mean intersection over union (mIoU) and average relative error. A technical difficulty scoring scale, which was used for assessing the technical difficulty of CBD stone removal, was developed and validated. We also analyzed the relationship between scores evaluated by the DSAS and clinical indicators including stone clearance rate and need for endoscopic papillary large-balloon dilation (EPLBD) and lithotripsy. RESULTS: The mIoU values of the stone, CBD, and duodenoscope segmentation were 68.35 %, 86.42 %, and 95.85 %, respectively. The estimation performance of the DSAS was superior to nonexpert endoscopists. In addition, the technical difficulty scoring performance of the DSAS was more consistent with expert endoscopists than two nonexpert endoscopists. A DSAS assessment score ≥ 2 was correlated with lower stone clearance rates and more frequent EPLBD. CONCLUSIONS: An intelligent DSAS based on deep learning was developed. The DSAS could assist endoscopists by automatically scoring the technical difficulty of CBD stone extraction, and guiding the choice of therapeutic approach and appropriate accessories during ERCP.

Evaluation of the effects of an artificial intelligence system on endoscopy quality and preliminary testing of its performance in detecting early gastric cancer: a randomized controlled trial.

BACKGROUND: Esophagogastroduodenoscopy (EGD) is a prerequisite for detecting upper gastrointestinal lesions especially early gastric cancer (EGC). An artificial intelligence system has been shown to monitor blind spots during EGD. In this study, we updated the system (ENDOANGEL), verified its effectiveness in improving endoscopy quality, and pretested its performance in detecting EGC in a multicenter randomized controlled trial. METHODS: ENDOANGEL was developed using deep convolutional neural networks and deep reinforcement learning. Patients undergoing EGD in five hospitals were randomly assigned to the ENDOANGEL-assisted group or to a control group without use of ENDOANGEL. The primary outcome was the number of blind spots. Secondary outcomes included performance of ENDOANGEL in predicting EGC in a clinical setting. RESULTS: 1050 patients were randomized, and 498 and 504 patients in the ENDOANGEL and control groups, respectively, were analyzed. Compared with the control group, the ENDOANGEL group had fewer blind spots (mean 5.38 [standard deviation (SD) 4.32] vs. 9.82 [SD 4.98]; P < 0.001) and longer inspection time (5.40 [SD 3.82] vs. 4.38 [SD 3.91] minutes; P < 0.001). In the ENDOANGEL group, 196 gastric lesions with pathological results were identified. ENDOANGEL correctly predicted all three EGCs (one mucosal carcinoma and two high grade neoplasias) and two advanced gastric cancers, with a per-lesion accuracy of 84.7 %, sensitivity of 100 %, and specificity of 84.3 % for detecting gastric cancer. CONCLUSIONS: In this multicenter study, ENDOANGEL was an effective and robust system to improve the quality of EGD and has the potential to detect EGC in real time.

Quantitative 1H-MRS reveals metabolic difference between subcategories of malformations of cortical development.

PURPOSE: To figure out the spectra features of malformations of cortical development (MCDs) and the differences between MCDs subcategories. METHODS: Twenty patients and 18 controls were studied. The patients included two subcategories: disorders of migration (DOM) and postmigration (DOPM). Spectra of patients were acquired from both the lesion and the normal-appearing contralateral side (NACS), and they were compared to those of the controls obtained from the frontal lobe. RESULTS: Compared to the controls, a decreased NAA (P = 0.002) was identified in MCDs. After dividing the MCDs into the DOM and DOPM, we found that NAA reduction was only notable in the DOM (P = 0.007). Moreover, Ins and Cr of the DOPM were higher than those of the controls (P = 0.017 and 0.013) and the DOM (P = 0.027 and 0.001). Compared to the NACS, a decreased NAA (P = 0.042) and an increased Ins (P = 0.039) were identified in the lesion of MCDs. After dividing the MCDs into the DOM and DOPM, we found no significant differences in the DOM, but Ins, Cr, and Glx of the lesion were higher than those of the NACS (P = 0.007, 0.005 and 0.047) in the DOPM. In addition, we found that Cr and Glx correlated positively to the seizure frequency (P = 0.003 and 0.016). CONCLUSION: Decreased NAA was the prominent abnormality confirmed in MCDs. Spectra of different MCDs subcategories were different: the DOM was characterized by decreased NAA, while the DOPM was characterized by increased Ins.

Analysis of Risk Factors in Endoscopic Retrograde Cholangiopancreatography-Related Immediate and Delayed Hemorrhage.

BACKGROUND/AIMS: Hemorrhage is a serious complication of endoscopic retrograde cholangiopancreatography (ERCP). However, there is a lack of comparative studies on immediate and delayed hemorrhage. The present study aims to explore the relevant risk factors of immediate and delayed hemorrhage of ERCP and compare the similarities and differences. METHODS: ERCP cases conducted by our hospital between January 2017 and January 2020 were selected for retrospective analysis. Then age, gender, basic disease, laboratory examinations, and other relevant clinical information were collected for the analysis. RESULTS: A total of 1009 ERCP cases were included in the present study. Among these cases, 76 patients were in the immediate hemorrhage group, 28 patients were in the delayed hemorrhage group, and 905 patients were in the non-hemorrhage group. The univariate analysis revealed that choledocholithiasis, pre-cut, and endoscopic papillary sphincterotomy (EST) were risk factors for immediate hemorrhage, while cholangitis, jaundice, coronary heart disease, pre-cut, high postoperative lipase at four hours and amylase at 24 h, high postoperative leukocyte, urea, bilirubin, low postoperative platelet counts and fibrinogen, and prolonged prothrombin time (PT) and thrombin time (TT) were risk factors for delayed hemorrhage. The logistic regression analysis revealed that EST, pre-cut, and activated partial thromboplastin time (APTT) were independent risk factors for immediate hemorrhage, while high amylase at 24 h after ERCP, high postoperative urea, prolonged TT, and coronary heart disease were independent risk factors for delayed hemorrhage. CONCLUSIONS: Pre-cut was a common risk factor for immediate and delayed hemorrhage, while other risk factors were different.

A novel artificial intelligence system for the assessment of bowel preparation (with video).

BACKGROUND AND AIMS: The quality of bowel preparation is an important factor that can affect the effectiveness of a colonoscopy. Several tools, such as the Boston Bowel Preparation Scale (BBPS) and Ottawa Bowel Preparation Scale, have been developed to evaluate bowel preparation. However, understanding the differences between evaluation methods and consistently applying them can be challenging for endoscopists. There are also subjective biases and differences among endoscopists. Therefore, this study aimed to develop a novel, objective, and stable method for the assessment of bowel preparation through artificial intelligence. METHODS: We used a deep convolutional neural network to develop this novel system. First, we retrospectively collected colonoscopy images to train the system and then compared its performance with endoscopists via a human-machine contest. Then, we applied this model to colonoscopy videos and developed a system named ENDOANGEL to provide bowel preparation scores every 30 seconds and to show the cumulative ratio of frames for each score during the withdrawal phase of the colonoscopy. RESULTS: ENDOANGEL achieved 93.33% accuracy in the human-machine contest with 120 images, which was better than that of all endoscopists. Moreover, ENDOANGEL achieved 80.00% accuracy among 100 images with bubbles. In 20 colonoscopy videos, accuracy was 89.04%, and ENDOANGEL continuously showed the accumulated percentage of the images for different BBPS scores during the withdrawal phase and prompted us for bowel preparation scores every 30 seconds. CONCLUSIONS: We provided a novel and more accurate evaluation method for bowel preparation and developed an objective and stable system-ENDOANGEL-that could be applied reliably and steadily in clinical settings.

Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy.

OBJECTIVE: Esophagogastroduodenoscopy (EGD) is the pivotal procedure in the diagnosis of upper gastrointestinal lesions. However, there are significant variations in EGD performance among endoscopists, impairing the discovery rate of gastric cancers and precursor lesions. The aim of this study was to construct a real-time quality improving system, WISENSE, to monitor blind spots, time the procedure and automatically generate photodocumentation during EGD and thus raise the quality of everyday endoscopy. DESIGN: WISENSE system was developed using the methods of deep convolutional neural networks and deep reinforcement learning. Patients referred because of health examination, symptoms, surveillance were recruited from Renmin hospital of Wuhan University. Enrolled patients were randomly assigned to groups that underwent EGD with or without the assistance of WISENSE. The primary end point was to ascertain if there was a difference in the rate of blind spots between WISENSE-assisted group and the control group. RESULTS: WISENSE monitored blind spots with an accuracy of 90.40% in real EGD videos. A total of 324 patients were recruited and randomised. 153 and 150 patients were analysed in the WISENSE and control group, respectively. Blind spot rate was lower in WISENSE group compared with the control (5.86% vs 22.46%, p<0.001), and the mean difference was -15.39% (95% CI -19.23 to -11.54). There was no significant adverse event. CONCLUSIONS: WISENSE significantly reduced blind spot rate of EGD procedure and could be used to improve the quality of everyday endoscopy. TRIAL REGISTRATION NUMBER: ChiCTR1800014809; Results.

A deep neural network improves endoscopic detection of early gastric cancer without blind spots.

BACKGROUND: Gastric cancer is the third most lethal malignancy worldwide. A novel deep convolution neural network (DCNN) to perform visual tasks has been recently developed. The aim of this study was to build a system using the DCNN to detect early gastric cancer (EGC) without blind spots during esophagogastroduodenoscopy (EGD). METHODS: 3170 gastric cancer and 5981 benign images were collected to train the DCNN to detect EGC. A total of 24549 images from different parts of stomach were collected to train the DCNN to monitor blind spots. Class activation maps were developed to automatically cover suspicious cancerous regions. A grid model for the stomach was used to indicate the existence of blind spots in unprocessed EGD videos. RESULTS: The DCNN identified EGC from non-malignancy with an accuracy of 92.5 %, a sensitivity of 94.0 %, a specificity of 91.0 %, a positive predictive value of 91.3 %, and a negative predictive value of 93.8 %, outperforming all levels of endoscopists. In the task of classifying gastric locations into 10 or 26 parts, the DCNN achieved an accuracy of 90 % or 65.9 %, on a par with the performance of experts. In real-time unprocessed EGD videos, the DCNN achieved automated performance for detecting EGC and monitoring blind spots. CONCLUSIONS: We developed a system based on a DCNN to accurately detect EGC and recognize gastric locations better than endoscopists, and proactively track suspicious cancerous lesions and monitor blind spots during EGD.

Characteristics of Intestinal Myoelectrical and Motor Activities in Diet-Induced Obese Rats: Obesity and Motility.

BACKGROUND: Gastrointestinal motility has been reported to be altered in obesity. However, it is unknown whether intestinal myoelectrical activity (IMA) is also changed in obesity. AIMS: The aim of this study was to characterize intestinal myoelectrical and motility activities in the fasting state, during feeding, and postprandial state after various test meals in diet-induced obese (DIO) rats in comparison with regular rats. METHODS: IMA was recorded in the fasting, feeding, and postprandial states in DIO and regular rats. Regular laboratory chow, high-fat solid food, and high-fat liquid food were used to test IMA responses to different meals. RESULTS: (1) The intestinal slow waves in the DIO rats were not different from those in normal rats in the fasting or postprandial state. Neither intestinal transit nor the number of intestinal contractions per minute was altered in DIO rats although gastric emptying was accelerated. (2) Both DIO rats and normal rats showed altered IMA during the first minute of feeding (cephalic stimulation). (3) The intestinal slow waves in both DIO rats and regular rats were impaired slightly but significantly after intake of a high-fat meal. CONCLUSIONS: Our study demonstrates that intestinal myoelectrical activity is not altered in DIO rats and its postprandial responses to various meals are not altered either. High-fat meals induce intestinal dysrhythmia but do not have a chronic impact on intestinal slow waves in DIO rats.

An Augmented Reality Serious Game for Children's Optical Science Education: Randomized Controlled Trial.

Background: Knowledge construction in the context of children's science education is an important part of fostering the development of early scientific literacy. Nevertheless, children sometimes struggle to comprehend scientific knowledge due to the presence of abstract notions. Objective: This study aimed to evaluate the efficacy of augmented reality (AR) games as a teaching tool for enhancing children's understanding of optical science education. Methods: A total of 36 healthy Chinese children aged 6-8 years were included in this study. The children were randomly divided into an intervention group (n=18, 50%) and a control group (n=18, 50%). The intervention group received 20 minutes of AR science education using 3 game-based learning modules, whereas the control group was asked to learn the same knowledge for 20 minutes with a non-AR science learning app. Predict observe explain tests for 3 topics (animal vision, light transmission, and color-light mixing) were conducted for all participants before and after the experiment. Additionally, the Intrinsic Motivation Inventory, which measures levels of interest-enjoyment, perceived competence, effort-importance, and tension-pressure, was conducted for children after the experiment. Results: There was a statistically significant difference in light transmission (z=-2.696; P=.008), color-light mixing (z=-2.508; P=.01), and total predict observe explain test scores (z=2.458; P=.01) between the 2 groups. There were also variations between the groups in terms of levels of interest-enjoyment (z=-2.440; P=.02) and perceived competence (z=-2.170; P=.03) as measured by the Intrinsic Motivation Inventory. Conclusions: The randomized controlled trial confirmed that the AR-based science education game we designed can correct children's misconceptions about science and enhance the effectiveness of science education.

The efficient of application of a fine magnetic traction system simplifies colorectal endoscopic submucosal dissection: A porcine study.

BACKGROUND: Sometimes it is difficult to maintain good visualization of the submucosal layer during colorectal endoscopic submucosal dissection (ESD). This study aimed to evaluate the feasibility and efficacy of a novel traction method, the fine magnetic traction system (FMTS), in colorectal ESD. METHODS: ESD was performed 10, 15, or 30 cm from the anus in the colorectums of 10 Bama miniature pigs with or without FMTS. The circumcision and dissection per unit time (cm2/min), en bloc resection, perforation and bleeding rates, size and integrity of the specimen and submucosal injection times were analysed. RESULTS: A total of 60 ESD procedures were performed with or without FMTS assistance. The en bloc resection rates were 100% at 10 and 15 cm from the anus in both the control group (conventional ESD) and the FMTS group. However, at 30 cm from the anus, these rates were only 10% and 70% (p = 0.006). The resection speeds (control vs. FMTS) at the 10, 15, and 30 cm points were 0.35 ± 0.07 cm2/min vs. 0.39 ± 0.19 cm2/min (p = 0.56), 0.30 ± 0.09 cm2/min vs. 0.38 ± 0.02 cm2/min (p = 0.04), and 0.11 cm2/min vs. 0.26 ± 0.10 cm2/min, respectively. CONCLUSIONS: The FMTS provides effective counter-traction and efficiently reduces the risks and difficulties of difficult colonic ESD in the porcine model.

Abnormal brain functional network dynamics in sleep-related hypermotor epilepsy.

AIMS: This study aimed to use resting-state functional magnetic resonance imaging (rs-fMRI) to determine the temporal features of functional connectivity states and changes in connectivity strength in sleep-related hypermotor epilepsy (SHE). METHODS: High-resolution T1 and rs-fMRI scanning were performed on all the subjects. We used a sliding-window approach to construct a dynamic functional connectivity (dFC) network. The k-means clustering method was performed to analyze specific FC states and related temporal properties. Finally, the connectivity strength between the components was analyzed using network-based statistics (NBS) analysis. The correlations between the abovementioned measures and disease duration were analyzed. RESULTS: After k-means clustering, the SHE patients mainly exhibited two dFC states. The frequency of state 1 was higher, which was characterized by stronger connections within the networks; state 2 occurred at a relatively low frequency, characterized by stronger connections between networks. SHE patients had greater fractional time and a mean dwell time in state 2 and had a larger number of state transitions. The NBS results showed that SHE patients had increased connectivity strength between networks. None of the properties was correlated with illness duration among patients with SHE. CONCLUSION: The patterns of dFC patterns may represent an adaptive and protective mode of the brain to deal with epileptic seizures.

CT based intratumor and peritumoral radiomics for differentiating complete from incomplete capsular characteristics of parotid pleomorphic adenoma: a two-center study.

OBJECTIVE: Capsular characteristics of pleomorphic adenoma (PA) has various forms. Patients without complete capsule has a higher risk of recurrence than patients with complete capsule. We aimed to develop and validate CT-based intratumoral and peritumoral radiomics models to make a differential diagnosis between parotid PA with and without complete capsule. METHODS: Data of 260 patients (166 patients with PA from institution 1 (training set) and 94 patients (test set) from institution 2) were retrospectively analyzed. Three Volume of interest (VOIs) were defined in the CT images of each patient: tumor volume of interest (VOItumor), VOIperitumor, and VOIintra-plus peritumor. Radiomics features were extracted from each VOI and used to train nine different machine learning algorithms. Model performance was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC). RESULTS: The results showed that the radiomics models based on features from VOIintra-plus peritumor achieved higher AUCs compared to models based on features from VOItumor. The best performing model was Linear discriminant analysis, which achieved an AUC of 0.86 in the tenfold cross-validation and 0.869 in the test set. The model was based on 15 features, including shape-based features and texture features. CONCLUSIONS: We demonstrated the feasibility of combining artificial intelligence with CT-based peritumoral radiomics features can be used to accurately predict capsular characteristics of parotid PA. This may assist in clinical decision-making by preoperative identification of capsular characteristics of parotid PA.

Magnetic Resonance Imaging Features of Zinc Finger Translocation Associated-RELA Fusion Ependymoma Compared to Its Wild-Type Counterpart.

OBJECTIVE: To explore the predictive value of quantitative features extracted from conventional magnetic resonance imaging (MRI) in distinguishing Zinc Finger Translocation Associated (ZFTA)-RELA fusion-positive and wild-type ependymomas. METHODS: Twenty-seven patients with pathologically confirmed ependymomas (17 patients with ZFTA-RELA fusions and 10 ZFTA-RELA fusion-negative patients) who underwent conventional MRI were enrolled in this retrospective study. Two experienced neuroradiologists who were blinded to the histopathological subtypes independently extracted imaging features using Visually Accessible Rembrandt Images annotations. The consistency between the readers was evaluated with the Kappa test. The imaging features with significant differences between the 2 groups were obtained using the least absolute shrinkage and selection operator regression model. Logistic regression analysis and receiver operating characteristic analysis were performed to analyze the diagnostic performance of the imaging features in predicting the ZFTA-RELA fusion status in ependymoma. RESULTS: There was a good interevaluator agreement on the imaging features (kappa value range 0.601-1.000). Enhancement quality, thickness of the enhancing margin, and edema crossing the midline have high predictive performance in identifying ZFTA-RELA fusion-positive and ZFTA-RELA fusion-negative ependymomas (C-index = 0.862 and area under the curve= 0.8618). CONCLUSIONS: Quantitative features extracted from preoperative conventional MRI by Visually Accessible Rembrandt Images provide high discriminatory accuracy in predicting the ZFTA-RELA fusion status of ependymoma.