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Abstract Background Epicardial adipose tissue (EAT) is known as an important imaging indicator for cardiovascular risk stratification. The present study aimed to determine whether the EAT volume (EV) and mean EAT attenuation (mEA) measured by non-contrast routine chest CT (RCCT) could be more consistent with those measured by coronary CT angiography (CCTA) by adjusting the threshold of fatty attenuation. Methods In total, 83 subjects who simultaneously underwent CCTA and RCCT were enrolled. EV and mEA were quantified by CCTA using a threshold of (N30) (− 190 HU, − 30 HU) as a reference and measured by RCCT using thresholds of N30, N40 (− 190 HU, − 40 HU), and N45 (− 190 HU, − 45 HU). The correlation and agreement of EAT metrics between the two imaging modalities and differences between patients with coronary plaques (plaque ( +)) and without plaques (plaque ( −)) were analyzed. Results EV obtained from RCCT showed very strong correlation with the reference (r = 0.974, 0.976, 0.972 (N30, N40, N45), P

Background and objectivesThe glymphatic system is a recently discovered cerebrospinal fluid transport system and little is known about its dynamic changes after stroke. This study aimed to dynamically observe the structural and functional changes of the impaired glymphatic system in the thalamus after ischemic stroke by pathology and MRI.Materials and methodsIschemic stroke was induced by the middle cerebral artery occlusion (MCAO) model. A total of 20 Sprague-Dawley rats were randomly assigned into four groups: sham, MCAO 1 week, MCAO 2 week, and MCAO 2 month. All rats successively underwent neurological examination, dynamic contrast-enhanced MRI (DCE-MRI), and immunofluorescence staining. Immunofluorescence staining of glial fibrillary acidic protein (GFAP), aquaporin-4 (AQP4), ionized calcium-binding adaptor molecule 1 (Iba1), and beta-amyloid precursor protein (APP) were done in thalamus ventroposterior nucleus.ResultsThe astrocyte and microglial activation and the APP deposition in the MCAO 2 week group were the highest (P < 0.05 for all). The AQP4 polarization rates of the MCAO 2 week and 2 month groups were the lowest (P < 0.05 for all). Although there was no correlation between histological changes and MRI metrics in all four groups (P > 0.05 for all), the tendency of the APP deposition was nearly consistent with the one of the contrast agent retention in DCE-MRI.ConclusionThe glymphatic system in the thalamus was severely impaired at 2nd week after MCAO, and may be revealed by DCE-MRI. This study may provide a relevant theoretical basis for making a thorough inquiry of the mechanism of brain injury after stroke and clinical treatment of ischemic stroke and help readers appreciate the importance of DCE-MRI.

ObjectiveWe aimed to develop a Radiological-Radiomics (R-R) based model for predicting the high-grade pattern (HGP) of lung adenocarcinoma and evaluate its predictive performance.MethodsThe clinical, pathological, and imaging data of 374 patients pathologically confirmed with lung adenocarcinoma (374 lesions in total) were retrospectively analyzed. The 374 lesions were assigned to HGP (n = 81) and non-high-grade pattern (n-HGP, n = 293) groups depending on the presence or absence of high-grade components in pathological findings. The least absolute shrinkage and selection operator (LASSO) method was utilized to screen features on the United Imaging artificial intelligence scientific research platform, and logistic regression models for predicting HGP were constructed, namely, Radiological model, Radiomics model, and R-R model. Also, receiver operating curve (ROC) curves were plotted on the platform, generating corresponding area under the curve (AUC), sensitivity, specificity, and accuracy. Using the platform, nomograms for R-R models were also provided, and calibration curves and decision curves were drawn to evaluate the performance and clinical utility of the model. The statistical differences in the performance of the models were compared by the DeLong test.ResultsThe R-R model for HGP prediction achieved an AUC value of 0.923 (95% CI: 0.891-0.948), a sensitivity of 87.0%, a specificity of 83.4%, and an accuracy of 84.2% in the training set. In the validation set, this model exhibited an AUC value of 0.920 (95% CI: 0.887-0.945), a sensitivity of 87.5%, a specificity of 83.3%, and an accuracy of 84.2%. The DeLong test demonstrated optimal performance of the R-R model among the three models, and decision curves validated the clinical utility of the R-R model.ConclusionIn this study, we developed a fusion model using radiomic features combined with radiological features to predict the high-grade pattern of lung adenocarcinoma, and this model shows excellent diagnostic performance. The R-R model can provide certain guidance for clinical diagnosis and surgical treatment plans, contributing to improving the prognosis of patients.

Abstract. The purpose of the study is to investigate the correlation of computed tomography (CT) quantitative parameters with tumor invasion and Ki-67 expression in early lung adenocarcinoma. The study involved 141 lesions in 141 patients with early lung adenocarcinoma. According to the degree of tumor invasion, the lesions were assigned into (adenocarcinoma in situ + minimally invasive adenocarcinoma) group and invasive adenocarcinoma (IAC) group. Artificial intelligence-assisted diagnostic software was used to automatically outline the lesions and extract corresponding quantitative parameters on CT images. Statistical analysis was performed to explore the correlation of these parameters with tumor invasion and Ki-67 expression. The results of logistic regression analysis showed that the short diameter of the lesion and the average CT value were independent predictors of IAC. Receiver operating characteristic curve analysis identified the average CT value as an independent predictor of IAC with the best performance, with the area under the receiver operating characteristic curve of 0.893 (P

BackgroundIschemic stroke is one of the leading causes of human death and disability. Brain edema and peri-infarct astrocyte reactivity are crucial pathological changes, both involving aquaporin-4 (AQP4). Studies revealed that acute inhibition of AQP4 after stroke diminishes brain edema, however, its effect on peri-infarct astrocyte reactivity and the subacute outcome is unclear. And if diffusion-weighted imaging (DWI) could reflect the AQP4 expression patterns is uncertain.MethodsRats were subjected to middle cerebral artery occlusion (MCAO) and allocated randomly to TGN 020-treated and control groups. One day after stroke, brain swelling and lesion volumes of the rats were checked using T2-weighted imaging (T2-WI). Fourteen days after stroke, the rats successively underwent neurological examination, T2-WI and DWI with standard b-values and ultra-high b-values, apparent diffusion coefficient (ADC) was calculated correspondingly. Finally, the rats’ brains were acquired and used for glial fibrillary acidic protein (GFAP) and AQP4 immunoreactive analysis.ResultsAt 1 day after stroke, the TGN-020-treated animals exhibited reduced brain swelling and lesion volumes compared with those in the control group. At 14 days after stroke, the TGN-020-treated animals showed fewer neurological function deficits and smaller lesion volumes. In the peri-infarct region, the control group showed evident astrogliosis and AQP4 depolarization, which were reduced significantly in the TGN-020 group. In addition, the ultra-high b-values of ADC (ADCuh) in the peri-infarct region of the TGN-020 group was higher than that of the control group. Furthermore, correlation analysis revealed that peri-infarct AQP4 polarization correlated negatively with astrogliosis extent, and ADCuh correlated positively with AQP4 polarization.ConclusionWe found that acutely inhibiting AQP4 using TGN-020 promoted neurological recovery by diminishing brain edema at the early stage and attenuating peri-infarct astrogliosis and AQP4 depolarization at the subacute stage after stroke. Moreover, ADCuh could reflect the AQP4 polarization.

A computational hemodynamics method was employed to investigate how the morphotype and functional state of aortic valve would affect the characteristics of blood flow in aortas with pathological dilation, especially the intensity and distribution of flow turbulence. Two patient-specific aortas diagnosed to have pathological dilation of the ascending segment while differential aortic valve conditions (i.e., one with a stenotic and regurgitant RL bicuspid aortic valve (RL-BAV), whereas the other with a quasi-normal tricuspid aortic valve (TAV)) were studied. When building the computational models, in addition to in vivo data-based reconstruction of geometrical model and boundary condition setting, the large eddy simulation method was adopted to quantify potential flow turbulence in the aortas. Obtained results revealed the presence of complex flow patterns (denoted by time-varying changes in vortex structure), flow turbulence (indicated by high turbulent eddy viscosity (TEV)), and regional high wall shear stress (WSS) in the ascending segment of both aortas. Such hemodynamic characteristics were significantly augmented in the aorta with RL-BAV. For instance, the space-averaged TEV in late systole and the wall area exposed to high time-averaged WSS (judged by WSS> two times of the mean WSS in the entire aorta) in the ascending aortic segment were increased by 176% and 465%, respectively. Relatively, flow patterns in the descending aortic segment were less influenced by the aortic valve disease. These results indicate that aortic valve disease has profound impacts on flow characteristics in the ascending aorta, especially the distribution and degree of high WSS and flow turbulence.

Computational modeling methods have been increasingly employed to quantify aortic hemodynamic parameters that are challenging to in vivo measurements but important for the diagnosis/treatment of aortic disease. Although the presence of turbulence-like behaviors of blood flow in normal or diseased aorta has long been confirmed, the majority of existing computational model studies adopted the laminar flow assumption (LFA) in the treatment of sub-grid flow variables. So far, it remains unclear whether LFA would significantly compromise the reliability of hemodynamic simulation. In the present study, we addressed the issue in the context of a specific aortopathy, namely aortic dilation, which is usually accompanied by disturbed flow patterns. Three patient-specific aortas with treated/untreated dilation of the ascending segment were investigated, and their geometrical models were reconstructed from computed tomography angiographic images, with the boundary conditions being prescribed based on flow velocity information measured in vivo with the phase contrast magnetic resonance imaging technique. For the modeling of blood flow, apart from the traditional LFA-based method in which sub-grid flow dynamics is ignored, the large eddy simulation (LES) method capable of incorporating the dissipative energy loss induced by turbulent eddies at the sub-grid level, was adopted and taken as a reference for examining the performance of the LFA-based method. Obtained results showed that the simulated large-scale flow patterns with the two methods had high similarity, both agreeing well with in vivo measurements, although locally large between-method discrepancies in computed hemodynamic quantities existed in regions with high intensity of flow turbulence. Quantitatively, a switch from the LES to the LFAbased modeling method led to mild (<6%) changes in computed space-averaged wall shear stress metrics (i.e., SA-TAWSS, SA-OSI) in the ascending aortic segment where intensive vortex evolution accompanied by high statistical Reynolds stress was observed. In addition, comparisons among the three aortas revealed that the treatment status of aortic dilation or the concomitant presence of aortic valve disease, despite its remarkable influence on flow patterns in the ascending aortic segment, did not significantly affect the degrees of discrepancies between the two modeling methods in predicting SA-TAWSS and SA-OSI. These findings suggest that aortic dilation per se does not induce strong flow turbulence that substantially negates the validity of LFA-based modeling, especially in simulating macro-scale hemodynamic features. [ABSTRACT FROM AUTHOR]

Recent studies have revealed that aortic valve diseases are associated with the increased incidence of the aortopathy development. However, the influence of aortic valve diseases on aortic hemodynamics remains unclear. The purpose of this study was therefore to investigate the hemodynamic differences in patients with and without aortic valve disease through patient-specific simulations performed on two aorta models (BAV with severe stenosis vs. normal tricuspid aortic valve (TAV)). Realistic geometries and boundary conditions were obtained from computed tomography angiography (CTA) and phase-contrast magnetic resonance imaging (PC-MRI) measurements, respectively. In addition, 4D-MRI were performed to validate the numerical methods used to simulate transient flow characteristics. Obtained results shown that the 3D streamlines in the patient with normal TAV were relatively symmetric and evenly distributed. For the patient with BAV, concentrated and high-speed inflow jets were found to impinge on the ascending aorta accompanied by strong vortices. These results indicate that the aortic valve phenotype plays a crucial role in featuring the disturbed flows primarily in ascending aorta, which may relate to the development of aortic diseases. [ABSTRACT FROM AUTHOR]