Jingjing Cui

Introduction: Endovascular therapy is the standard of care for severe acute ischemic stroke caused by large‐vessel occlusion in the anterior circulation, but there is a debate on the optimal anesthetic approach during this therapy. Meta‐analyses of observational studies suggest that general anesthesia increases disability and death compared with conscious sedation However, their results are conflicting. This meta‐analysis study was performed to assess the relationship between the effects of general anesthesia compared to conscious sedation during endovascular therapy for acute ischemic stroke.Methods: Through a systematic literature search up to August 2020, 18 studies included 4,802 subjects at baseline with endovascular therapy for acute ischemic stroke and reported a total of 1,711 subjects using general anesthesia and 1,961 subjects using conscious sedation were found. They recorded relationships between the effects of general anesthesia compared to conscious sedation during endovascular therapy for acute ischemic stroke. Odds ratio (OR) or Mean differences (MD) with 95% confidence intervals (CIs) were calculated between the effect of general anesthesia compared to conscious sedation during endovascular therapy for acute ischemic stroke using the dichotomous or contentious methods with a random or fixed‐effect model.Results: No significant difference were found between general anesthesia and conscious sedation during the endovascular therapy for acute ischemic stroke in functional independence at 90 days (OR, 0.78; 95% CI, 0.44–1.40, p = 40); successful recanalization at 24 hr (OR, 1.23; 95% CI, 0.62–2.41, p = 55); mortality at 90 days (OR, 1.36; 95% CI, 0.83–2.24, p = .22); interventional complication (OR, 1.24; 95% CI, 0.76–2.02, p = .40); symptomatic intracranial hemorrhage (OR, 0.64; 95% CI, 0.41–0.99, p = .05); aspiration pneumonia (OR, 0.96; 95% CI, 0.58–1.58, p = .87); and National Institute of Health Stroke Scale score after 24 hr (MD, 0.38; 95% CI, −1.15–1.91, p = .62); with relative relationship favoring general anesthesia only in decreasing the symptomatic intracranial hemorrhage.Conclusions: General anesthesia has no independent relationship compared to conscious sedation during the endovascular therapy for acute ischemic stroke with a relative relationship favoring general anesthesia only in decreasing the symptomatic intracranial hemorrhage.This relationship encouraged us to recommend either anesthetic strategy during the endovascular therapy for acute ischemic stroke with no possible fear of higher complication.

Here we present a protocol to visualize spatial correlation of calcitonin gene-related peptide (CGRP)-immunoreactive nerve fibers and blood vessels in the cranial dura mater using immunofluorescence and fluorescent histochemistry with CGRP and phalloidin, respectively. In addition, the origin of these nerve fibers was retrograde traced with a fluorescent neural tracer.

OBJECTIVE: To evaluate the utility of radiomics analysis for differentiating benign and malignant epithelial salivary gland tumors on diffusion-weighted imaging (DWI). METHODS: A retrospective dataset involving 218 and 51 patients with histology-confirmed benign and malignant epithelial salivary gland tumors was used in this study. A total of 396 radiomic features were extracted from the DW images. Analysis of variance (ANOVA) and least-absolute shrinkage and selection operator regression (LASSO) were used to select optimal radiomic features. The selected features were used to build three classification models namely, logistic regression method (LR), support vector machine (SVM), and K-nearest neighbor (KNN) by using a five-fold cross validation strategy on the training dataset. The diagnostic performance of each classification model was quantified by receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) in the training and validation datasets. RESULTS: Eight most valuable features were selected by LASSO. LR and SVM models yielded optimally diagnostic performance. In the training dataset, LR and SVM yielded AUC values of 0.886 and 0.893 via five-fold cross validation, respectively, while KNN model showed relatively lower AUC (0.796). In the testing dataset, a similar result was found, where AUC values for LR, SVM, and KNN were 0.876, 0.870, and 0.791, respectively. CONCLUSIONS: Classification models based on optimally selected radiomics features computed from DW images present a promising predictive value in distinguishing benign and malignant epithelial salivary gland tumors and thus have potential to be used for preoperative auxiliary diagnosis.

Purpose Lymphovascular invasion (LVI) impairs surgical outcomes in lung adenocarcinoma (LAC) patients. Preoperative prediction of LVI is challenging by using traditional clinical and imaging parameters. The purpose of this study was to investigate the value of the radiomics nomogram integrating clinical factors, CT features, and maximum standardized uptake value (SUVmax) to predict LVI and outcome in LAC and to evaluate the additional value of the SUVmax to the PET/CT-based radiomics nomogram. Methods A total of 272 LAC patients (87 LVI-present LACs and 185 LVI-absent LACs) with PET/CT scans were retrospectively enrolled, and 160 patients with SUVmax ≥ 2.5 of them were used for PET radiomics analysis. Clinical data and CT features were analyzed to select independent LVI predictors. The performance of the independent LVI predictors and SUVmax was evaluated. Two-dimensional (2D) and three-dimensional (3D) CT radiomics signatures (RSs) and PET-RS were constructed with the least absolute shrinkage and selection operator algorithm and radiomics scores (Rad-scores) were calculated. The radiomics nomograms, incorporating Rad-score and independent clinical and CT factors, with SUVmax (RNWS) or without SUVmax (RNWOS) were built. The performance of the models was assessed with respect to calibration, discrimination, and clinical usefulness. All the clinical, PET/CT, pathologic, therapeutic, and radiomics parameters were assessed to identify independent predictors of progression-free survival (PFS). Results CT morphology was the independent LVI predictor. SUVmax provided better discrimination capability compared with CT morphology in the training set (P < 0.001) and test set (P = 0.042). A total of 1409 CT and PET radiomics features were extracted and reduced to 8, 8, and 10 features to build the 2D CT-RS, 3D CT-RS, and the PET-RS, respectively. There was no significant difference in AUC between the 2D-RS and 3D-RS (P > 0.05), and 2D CT-RS showed a relatively higher AUC than 3D CT-RS. The CT-RS, the CT-RNWOS, and the CT-RNWS showed good discrimination in the training set (AUC [area under the curve], 0.799, 0.796, and 0.851, respectively) and the test set (AUC, 0.818, 0.822, and 0.838, respectively). There was significant difference in AUC between the CT-RNWS and CT-RNWOS (P = 0.044) in the training set. Decision curve analysis (DCA) demonstrated the CT-RNWS outperformed the CT-RS and the CT-RNWOS in terms of clinical usefulness. Furthermore, DCA showed the PETCT-RNWS provided the highest net benefit compared with the PET-RNWS and CT-RNWS. PFS was significantly different between the pathologic and RNWS-predicted LVI-present and LVI-absent patients (P < 0.001). Carbohydrate antigen 125 (CA125), carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), pathologic LVI, histologic subtype, and SUVmax were independent predictors of PFS in the 244 CT-RNWS-predicted cohort; and CA125, NSE, pathologic LVI, and SUVmax were the independent predictors of PFS in the 141 PETCT-RNWS-predicted cohort. Conclusions The radiomics nomogram, incorporating Rad-score, clinical and PET/CT parameters, shows favorable predictive efficacy for LVI status in LAC. Pathologic LVI and SUVmax are associated with LAC prognosis.