Histogram analysis of mono-exponential, bi-exponential and stretched-exponential diffusion-weighted MR imaging in predicting consistency of meningiomas.

BACKGROUND: The consistency of meningiomas is critical to determine surgical planning and has a significant impact on surgical outcomes. Our aim was to compare mono-exponential, bi-exponential and stretched exponential MR diffusion-weighted imaging in predicting the consistency of meningiomas before surgery. METHODS: Forty-seven consecutive patients with pathologically confirmed meningiomas were prospectively enrolled in this study. Two senior neurosurgeons independently evaluated tumour consistency and classified them into soft and hard groups. A volume of interest was placed on the preoperative MR diffusion images to outline the whole tumour area. Histogram parameters (mean, median, 10th percentile, 90th percentile, kurtosis, skewness) were extracted from 6 different diffusion maps including ADC (DWI), D*, D, f (IVIM), alpha and DDC (SEM). Comparisons between two groups were made using Student's t-Test or Mann-Whitney U test. Parameters with significant differences between the two groups were included for Receiver operating characteristic analysis. The DeLong test was used to compare AUCs. RESULTS: DDC, D* and ADC 10th percentile were significantly lower in hard tumours than in soft tumours (P ≤ 0.05). The alpha 90th percentile was significantly higher in hard tumours than in soft tumours (P < 0.02). For all histogram parameters, the alpha 90th percentile yielded the highest AUC of 0.88, with an accuracy of 85.10%. The D* 10th percentile had a relatively higher AUC value, followed by the DDC and ADC 10th percentile. The alpha 90th percentile had a significantly greater AUC value than the ADC 10th percentile (P ≤ 0.05). The D* 10th percentile had a significantly greater AUC value than the ADC 10th percentile and DDC 10th percentile (P ≤ 0.03). CONCLUSION: Histogram parameters of Alpha and D* may serve as better imaging biomarkers to aid in predicting the consistency of meningioma.

A noval pulmonary function evaluation method based on ResNet50 + SVR model and cough.

Traditionally, the clinical evaluation of respiratory diseases was pulmonary function testing, which can be used for the detection of severity and prognosis through pulmonary function parameters. However, this method is limited by the complex process, which is impossible for patients to monitor daily. In order to evaluate pulmonary function parameters conveniently with less time and location restrictions, cough sound is the substitute parameter. In this paper, 371 cough sounds segments from 150 individuals were separated into 309 and 62 as the training and test samples. Short-time Fourier transform (STFT) was applied to transform cough sound into spectrogram, and ResNet50 model was used to extract 2048-dimensional features. Through support vector regression (SVR) model with biological attributes, the data were regressed with pulmonary function parameters, FEV1, FEV1%, FEV1/FVC, FVC, FVC%, and the performance of this models was evaluated with fivefold cross-validation. Combines with deep learning and machine learning technologies, the better results in the case of small samples were achieved. Using the coefficient of determination (R2), the ResNet50 + SVR model shows best performance in five basic pulmonary function parameters evaluation as FEV1(0.94), FEV1%(0.84), FEV1/FVC(0.68), FVC(0.92), and FVC%(0.72). This ResNet50 + SVR hybrid model shows excellent evaluation of pulmonary function parameters during coughing, making it possible to realize a simple and rapid evaluation for pneumonia patients. The technology implemented in this paper is beneficial in judge the patient's condition, realize early screening of respiratory diseases, evaluate postoperative disease changes and detect respiratory infectious diseases without time and location restrictions.

T1 and ADC histogram parameters may be an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma.

OBJECTIVE: To investigate the value of histogram analysis of T1 mapping and diffusion-weighted imaging (DWI) in predicting the grade, subtype, and proliferative activity of meningioma. METHODS: This prospective study comprised 69 meningioma patients who underwent preoperative MRI including T1 mapping and DWI. The histogram metrics, including mean, median, maximum, minimum, 10th percentiles (C10), 90th percentiles (C90), kurtosis, skewness, and variance, of T1 and apparent diffusion coefficient (ADC) values were extracted from the whole tumour and peritumoural oedema using FeAture Explorer. The Mann-Whitney U test was used for comparison between low- and high-grade tumours. Receiver operating characteristic (ROC) curve and logistic regression analyses were performed to identify the differential diagnostic performance. The Kruskal-Wallis test was used to further classify meningioma subtypes. Spearman's rank correlation coefficients were calculated to analyse the correlations between histogram parameters and Ki-67 expression. RESULTS: High-grade meningiomas showed significantly higher mean, maximum, C90, and variance of T1 (p = 0.001-0.009), lower minimum, and C10 of ADC (p = 0.013-0.028), compared to low-grade meningiomas. For all histogram parameters, the highest individual distinctive power was T1 C90 with an AUC of 0.805. The best diagnostic accuracy was obtained by combining the T1 C90 and ADC C10 with an AUC of 0.864. The histogram parameters differentiated 4/6 pairs of subtype pairs. Significant correlations were identified between Ki-67 and histogram parameters of T1 (C90, mean) and ADC (C10, kurtosis, variance). CONCLUSION: T1 and ADC histogram parameters may represent an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma. KEY POINTS: • The histogram parameter based on T1 mapping and DWI is useful to preoperatively evaluate the grade, subtype, and proliferative activity of meningioma. • The combination of T1 C90 and ADC C10 showed the best performance for differentiating low- and high-grade meningiomas.

A forced cough sound based pulmonary function assessment method by using machine learning.

Pulmonary function testing (PFT) has important clinical value for the early detection of lung diseases, assessment of the disease severity, causes identification of dyspnea, and monitoring of critical patients. However, traditional PFT can only be carried out in a hospital environment, and it is challenging to meet the needs for daily and frequent evaluation of chronic respiratory diseases. In this study, we propose a novel method for accurately assessing pulmonary function by analyzing recorded forced cough sounds by mobile device without time and location restrictions. In the experiment, 309 clips of cough sound segments were separated from 133 patients who underwent PFT by using Audacity software. There are 247 clips of training samples and 62 clips of testing samples. Totally 52 features were extracted from the dataset, and principal component analysis (PCA) was used for feature reduction. Combined with biological attributes, the normalized features were regressed by using machine learning models with pulmonary function parameters (i.e., FEV1, FVC, FEV1/FVC, FEV1%, and FVC%). And a 5-fold cross-validation was applied to evaluate the performance of the regression models. As described in the experimental result, the result of coefficient of determination (R2) indicates that the support vector regression (SVR) model performed best in assessing FVC (0.84), FEV1% (0.61), and FVC% (0.62) among these models. The gradient boosting regression (GBR) model performs best in evaluating FEV1 (0.86) and FEV1/FVC (0.54). The result confirmed that the proposed method was capable of accurately assessing pulmonary function with forced cough sound. Besides, the cough sound sampling by a smartphone made it possible to conduct sampling and assess pulmonary function frequently in the home environment.

A Novel MRI-Based Risk Stratification Algorithm for Predicting Postoperative Recurrence of Meningioma: More Benefits to Patients.

Pathological grading of meningioma is insufficient to predict recurrence after resection and to guide individualized treatment strategies. One hundred and thirty-three patients with meningiomas who underwent total resection were enrolled in this retrospective study. Univariate analyses were conducted to evaluate the association between factors and recurrence. Least absolute shrinkage and selection operator (Lasso) was used to further select variables to build a logistic model. The predictive efficiency of the model and WHO grade was compared by using receiver operating characteristic curve (ROC), decision curve analysis (DCA), and net reclassification improvement (NRI). Patients were given a new risk layer based on a nomogram. The recurrence of meningioma in different groups was observed through the Kaplan-Meier curve. Univariate analysis demonstrated that 11 risk factors were associated with prognosis (P < 0.05). The result of ROC proved that the quantified risk-scoring system (AUC = 0.853) had a higher benefit than pathological grade (AUC = 0.689, P = 0.011). The incidence of recurrence of the high risk cohort (69%) was significantly higher than that of the low risk cohort (9%) by Kaplan-Meier analysis (P < 0.001). And all patients who did not relapse in the high risk group received adjuvant radiotherapy. The novel risk stratification algorithm has a significant value for the recurrence of meningioma and can help in optimizing the individualized design of clinical therapy.

Association Between Carotid Bifurcation Geometry and Atherosclerotic Plaque Vulnerability: A Chinese Atherosclerosis Risk Evaluation Study.

OBJECTIVE: Carotid bifurcation geometry has been believed to be a risk factor for the initiation of atherosclerosis because of its influence on hemodynamics. However, the relationships between carotid bifurcation geometry and plaque vulnerability are not fully understood. This study aimed to determine the association between carotid bifurcation geometry and plaque vulnerability using magnetic resonance vessel wall imaging. Approach and Results: A total of 501 carotid arteries with nonstenotic atherosclerosis were included from the cross-sectional, multicenter CARE II study (Chinese Atherosclerosis Risk Evaluation). Four standardized carotid bifurcation geometric parameters (bifurcation angle, internal carotid artery planarity, luminal expansion FlareA, and tortuosity Tort2D) were derived from time-of-flight magnetic resonance angiography. Presence of vulnerable plaque, which was characterized by intraplaque hemorrhage, large lipid-rich necrotic core, or disrupted luminal surface, was determined based on multicontrast carotid magnetic resonance vessel wall images. Vulnerable plaques (N=43) were found to occur at more distal locations (ie, near the level of flow divider) than stable plaques (N=458). Multivariable logistic regression showed that the luminal expansion FlareA (odds ratio, 0.45 [95% CI, 0.25-0.81]; P=0.008) was associated with plaque vulnerability after adjustment for age, sex, maximum wall thickness, plaque location, and other geometric parameters. CONCLUSIONS: Smaller luminal expansion at carotid bifurcation is associated with vulnerable plaque. The finding needs to be verified with longitudinal studies and the underlying mechanism should be further explored with hemodynamics measurement in the future.

Carotid artery segmentation using level set method with double adaptive threshold (DATLS) on TOF-MRA images.

PURPOSE: This study aimed to propose a method to semi-automatically segment lumen of carotid artery on TOF-MRA images with well performance on images with weak boundary. METHODS: The proposed method modified traditional level set method with double adaptive threshold (DATLS). Two thresholds were calculated from probability density function of initial region of interest. Threshold Th1 was used to generate initial contour and threshold Th2 was used to control evolution by modifying energy function of the level set. The performance of the proposed method was tested on TOF-MRA images from CARE II study via using manual delineation as reference. Sixty cases were randomly selected to compare the performance of traditional level set with DATLS, all 283 cases were used to test the robustness of DATLS and 20 cases were used to compare the intra- and inter-operator reproducibility of manual delineation with DATLS. The Dice Similarity Coefficient, Mean Contour Distance and Hausdorff Distance between the proposed method and the manual segmentation were reported. RESULTS: Traditional level set failed to segment carotid artery on 13 cases because of over convergence. Compared to traditional level set, DATLS showed a higher DSC (0.88 ±â€¯0.07 vs. 0.75 ±â€¯0.15), lower MCD (0.48 ±â€¯0.37 mm vs. 1.89 ±â€¯2.24 mm) and HD (1.41 ±â€¯1.11 mm vs. 4.59 ±â€¯4.59 mm) in the remaining 47 cases. The average DSC, MCD and HD of DATLS on 283 cases were 0.87 ±â€¯0.09, 0.64 ±â€¯0.87 mm and 1.76 ±â€¯2.23 mm, respectively. Compared to manual delineation, DATLS had better reproducibility on DSC (intra-operator: 0.97 ±â€¯0.09 and inter-operator: 0.97 ±â€¯0.09 vs. intra-operator: 0.91 ±â€¯0.04 and inter-operator: 0.91 ±â€¯0.04), as well as MCD (intra-operator: 0.08 ±â€¯0.18 mm and inter-operator: 0.13 ±â€¯0.52 mm vs. intra-operator: 0.35 ±â€¯0.20 mm and inter-operator: 0.37 ±â€¯0.19 mm) and HD (intra-operator: 0.30 ±â€¯0.66 mm and inter-operator: 0.48 ±â€¯1.38 mm vs. intra-operator: 0.99 ±â€¯0.79 mm and inter-operator: 0.90 ±â€¯0.85 mm). CONCLUSION: Compared to traditional level set, DATLS out-performed on computing time, robustness and accuracy of segmentation on TOF-MRA. The proposed method using modified level set with double adaptive threshold might be a promising tool for lumen segmentation of carotid artery on TOF-MRA.

Adjuvant effect of liposome in chicken result from induction of nitric oxide.

Intranasal delivery of liposome-encapsulated inactivated Newcastle Disease virus (NDV) is known to be an effective vaccine for inducing immunity in the respiratory tract from our previous reports. Four-week-old specific pathogen-free chickens were intranasally immunized with NDV entrapped in phosphatidylcholine-liposomes (PC-Lip). The mucosal levels of anti-NDV s-immunoglobulin A (IgA), serum IgG, a high hemagglutination inhibition titer (1:640), and the high survival rate with the PC-Lip vaccine were comparable to those of our previous report. The immune mechanisms of the PC-Lip adjuvant were determined by in vitro cellular experiments using the NO production of chicken spleen macrophages. The most important finding of this study was proving that macrophages were stimulated by PC-Lip via the extracellular regulated kinase (ERK) 1/2 and nuclear factor (NF)-κB activation pathways. This finding may be useful for developing potent mucosal vaccine delivery systems in the future.

Mucoadhesive liposomes for intranasal immunization with an avian influenza virus vaccine in chickens.

The aim of this study was to characterize a nasally delivered bioadhesive liposome using an inactivated H5N3 virus as a model antigen. Bioadhesive liposomes were developed using tremella (T) or xanthan gum (XG) as the bioadhesive polysaccharide. Using chickens as the target animal, we evaluated whether delivery of a bioadhesive liposomal influenza vaccine via a mucosal site of infection could improve vaccine effectiveness. 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) cytotoxicity assays demonstrated that T, XG and liposomes were non toxic to chicken spleen macrophages. Enzyme-linked immunosorbent assay (ELISA) was used to determine the adjuvant effect of the bioadhesive liposomal-vaccines. Chickens immunized with a low dose (200 microL) of bioadhesive liposomal influenza vaccine had significantly higher mucosal and serum antibody levels (P<0.05). In addition, liposomes mixed with a low-viscosity bioadhesive gel used for nasal delivery resulted in superior antibody responses compared with liposomes mixed with a high-viscosity gel (P<0.05). This suggest that a low-viscosity gel mixed with liposomes is more suitable for nasal delivery, and that chickens elicit higher mucosal secretory immunoglobulin A (s-IgA) and serum IgG after two vaccinations.