Effect of perioperative intravenous lidocaine for patients undergoing spine surgery: A meta-analysis and systematic review.

BACKGROUND: Perioperative intravenous lidocaine has been reported to have analgesic and opioid-sparing effects in many kinds of surgery. Several studies have evaluated its use in the settings of spine surgery. The aim of the study is to examine the effect of intravenous lidocaine in patients undergoing spine surgery. METHODS: We performed a quantitative systematic review. Databases of PubMed, Medline, Embase database and Cochrane library were investigated for eligible literatures from their establishments to June, 2019. Articles of randomized controlled trials that compared intravenous lidocaine to a control group in patients undergoing spine surgery were included. The primary outcome was postoperative pain intensity. Secondary outcomes included postoperative opioid consumption and the length of hospital stay. RESULT: Four randomized controlled trials with 275 patients were included in the study. postoperative pain compared with control was reduced at 6 hours after surgery (WMD -0.50, 95%CI, -0.76 to -0.25, P < .001), at 24 hours after surgery (WMD -0.50, 95%CI, -0.70 to -0.29, P < .001) and at 48 hours after surgery (WMD -0.57, 95%CI, -0.96 to -0.17, P = .005). The effect of intravenous lidocaine on postoperative opioid consumption compared with control revealed a significant effect (WMD -15.36, 95%CI, -21.40 to -9.33 mg intravenous morphine equivalents, P < .001). CONCLUSION: This quantitative analysis of randomized controlled trials demonstrated that the perioperative intravenous lidocaine was effective for reducing postoperative opioid consumption and pain in patients undergoing spine surgery. The intravenous lidocaine should be considered as an effective adjunct to improve analgesic outcomes in patients undergoing spine surgery. However, the quantity of the studies was very low, more research is needed.

Expression and clinical significance of PD-L1, B7-H3, B7-H4 and VISTA in craniopharyngioma.

BACKGROUND: Craniopharyngioma (CP) is a common refractory tumor of the central nervous system. However, little is known about the expression and clinical significance of B7 family ligands/receptors in CP patients. Thus, we conducted the present study to address this issue in a cohort of 132 CP cases. METHODS: We mapped and quantified the expression of B7 family ligands/receptors molecules programmed cell death ligand 1 (PD-L1), B7-H3, B7-H4 and V-domain Ig-containing suppressor of T cell activation (VISTA) in 89 adamantinomatous-type CP and 43 papillary-type CP samples using immunohistochemistry and immunofluorescence. Associations between the marker levels, clinicopathological variables and survival were evaluated. RESULTS: The positive rates of PD-L1, B7-H3, B7-H4 and VISTA in the cohort of 132 CP cases were 76.5%, 100%, 40.2% and 80.3%, respectively. The cut-off values of PD-L1, B7-H3, B7-H4 and PD-L1 expression were determined by survival receiver operating characteristic (ROC) package, which was 70, 182, 0 and 20, respectively. Elevated expressions of PD-L1, B7-H3, B7-H4 and VISTA were significantly associated with some clinicopathological characteristics. Kaplan-Meier analysis indicated that higher VISTA expressions correlated with better overall survival (OS) and progression-free survival (PFS) (p=0.0053 and p=0.0066, respectively). Multivariate Cox regression analysis indicated that VISTA was an independent prognostic factor for OS (p=0.018) but not for PFS (p=0.898). CONCLUSIONS: We found variable expression of PD-L1, B7-H3, B7-H4 and VISTA proteins in CPs. The results suggest that the expression level of VISTA may be used as an important indicator to predict the OS and PFS of CPs. B7 family ligands/receptors could be potential immunotherapeutic targets when treating CPs.

Discrimination between pituitary adenoma and craniopharyngioma using MRI-based image features and texture features.

PURPOSE: To investigate differences between pituitary adenoma and craniopharyngioma on magnetic resonance imaging (MRI) with image features and three-dimensional texture features. MATERIALS AND METHODS: A total of 126 patients diagnosed with pituitary adenoma (N = 63) or craniopharyngioma (N = 63) were enrolled. Qualitative magnetic resonance (MR) image features and texture features of tumors were extracted from preoperative MRI and evaluated using chi-square test or Mann-Whitney U test. Binary logistic regression analyses were performed to assess their abilities as independent diagnostic predictors, and ROC analyses were conducted to evaluate the diagnostic value of significant features. Mann-Whitney U test and ROC analyses were performed to explore the relationship between MR image features and texture features. RESULTS: Five MR image features were suggested to be significantly different between pituitary adenoma and craniopharyngioma. Three texture features from contrast-enhanced T1WI (HISTO-Skewness, GLCM-Contrast and GLCM-Energy), two texture features from T2WI (HISTO-Skewness and GLCM-Contrast) showed significant differences between two types of tumors. Logistic regression analyses suggested GLCM-Energy from contrast-enhanced T1WI, HISTO-Skewness and GLCM-Contrast from T2WI could be taken as independent predictors. Moreover, HISTO-Skewness and GLCM-Contrast from T2WI were found to be significantly related to cystic change. CONCLUSION: MR image features and texture features were associated with each other, and both types of features represented feasible diagnostic value in discrimination between pituitary adenoma and craniopharyngioma.

Radiomic Analysis of Craniopharyngioma and Meningioma in the Sellar/Parasellar Area with MR Images Features and Texture Features: A Feasible Study.

Purpose: To investigate the ability of qualitative Magnetic Resonance (MR) images features and quantitative Magnetic Resonance Imaging (MRI) texture features in the contrastive analysis between craniopharyngioma and meningioma. Method: A total number of 127 patients were included in this study (craniopharyngioma = 63; meningioma = 64). All the features analyzed in this study were acquired from preoperative MRI images. Qualitative MR images features were evaluated with chi-square tests or Fisher exact test, while MRI texture features were evaluated with the Mann-Whitney U test with the Benjamini-Hochberg method. Then binary logistic regression analysis for texture features was performed to evaluate their ability as independent predictors, and the diagnostic accuracy was calculated next for these texture features with high abilities as independent predictors using receiver operating characteristic (ROC) curves. Results: Four qualitative MR images features showed significant difference between craniopharyngioma and meningioma, but only cystic alteration could be considered as diagnostic independent predictors. Meanwhile, three quantitative parameters, histogram-based matrix- (HISTO-) Skewness, Grey-level co-occurrence matrix- (GLCM-) Contrast on contrast-enhanced images, and HISTO-Skewness on images of T2-weighted imaging (T2WI), showed promising abilities in the contrastive analysis. Besides, these texture features were found significantly to be relative to cystic alteration. Conclusion: MR images features and texture features were useful in the contrastive analysis of craniopharyngioma and meningioma. Furthermore, qualitative MR images features and MRI texture features could be related to each other.

The Diagnostic Value of MRI-Based Texture Analysis in Discrimination of Tumors Located in Posterior Fossa: A Preliminary Study.

OBJECTIVES: To investigate the diagnostic value of MRI-based texture analysis in discriminating common posterior fossa tumors, including medulloblastoma, brain metastatic tumor, and hemangioblastoma. METHODS: A total number of 185 patients were enrolled in the current study: 63 of them were diagnosed with medulloblastoma, 56 were diagnosed with brain metastatic tumor, and 66 were diagnosed with hemangioblastoma. Texture features were extracted from contrast-enhanced T1-weighted (T1C) images and fluid-attenuation inversion recovery (FLAIR) images within two matrixes. Mann-Whitney U test was conducted to identify whether texture features were significantly different among subtypes of tumors. Logistic regression analysis was performed to assess if they could be taken as independent predictors and to establish the integrated models. Receiver operating characteristic analysis was conducted to evaluate their performances in discrimination. RESULTS: There were texture features from both T1C images and FLAIR images found to be significantly different among the three types of tumors. The integrated model represented that the promising diagnostic performance of texture analysis depended on a series of features rather than a single feature. Moreover, the predictive model that combined texture features and clinical feature implied feasible performance in prediction with an accuracy of 0.80. CONCLUSION: MRI-based texture analysis could potentially be served as a radiological method in discrimination of common tumors located in posterior fossa.

Differentiation of Pituitary Adenoma from Rathke Cleft Cyst: Combining MR Image Features with Texture Features.

Objectives: To differentiate pituitary adenoma from Rathke cleft cyst in magnetic resonance (MR) scan by combing MR image features with texture features. Methods: A total number of 133 patients were included in this study, 83 with pituitary adenoma and 50 with Rathke cleft cyst. Qualitative MR image features and quantitative texture features were evaluated by using the chi-square tests or Mann-Whitney U test. Binary logistic regression analysis was conducted to investigate their ability as independent predictors. ROC analysis was conducted subsequently on the independent predictors to assess their practical value in discrimination and was used to investigate the association between two types of features. Results: Signal intensity on the contrast-enhanced image was found to be the only significantly different MR image feature between the two lesions. Two texture features from the contrast-enhanced images (Histo-Skewness and GLCM-Correlation) were found to be the independent predictors in discrimination, of which AUC values were 0.80 and 0.75, respectively. Besides, the above two texture features (Histo-Skewness and GLCM-Contrast) were suggested to be associated with signal intensity on the contrast-enhanced image. Conclusion: Signal intensity on the contrast-enhanced image was the most significant MR image feature in differentiation between pituitary adenoma and Rathke cleft cyst, and texture features also showed promising and practical ability in discrimination. Moreover, two types of features could be coordinated with each other.

Radiomics-Based Machine Learning Technology Enables Better Differentiation Between Glioblastoma and Anaplastic Oligodendroglioma.

Purpose: The aim of this study was to test whether radiomics-based machine learning can enable the better differentiation between glioblastoma (GBM) and anaplastic oligodendroglioma (AO). Methods: This retrospective study involved 126 patients histologically diagnosed as GBM (n = 76) or AO (n = 50) in our institution from January 2015 to December 2018. A total number of 40 three-dimensional texture features were extracted from contrast-enhanced T1-weighted images using LIFEx package. Six diagnostic models were established with selection methods and classifiers. The optimal radiomics features were separately selected into three datasets with three feature selection methods [distance correlation, least absolute shrinkage and selection operator (LASSO), and gradient boosting decision tree (GBDT)]. Then datasets were separately adopted into linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Specificity, sensitivity, accuracy, and area under curve (AUC) of each model were calculated to evaluate their diagnostic performances. Results: The diagnostic performance of machine learning models was superior to human readers. Both classifiers showed promising ability in discrimination with AUC more than 0.900 when combined with suitable feature selection method. For LDA-based models, the AUC of models were 0.986, 0.994, and 0.970 in the testing group, respectively. For the SVM-based models, the AUC of models were 0.923, 0.817, and 0.500 in the testing group, respectively. The over-fitting model was GBDT + SVM, suggesting that this model was too volatile that unsuitable for classification. Conclusion: This study indicates radiomics-based machine learning has the potential to be utilized in clinically discriminating GBM from AO.

Intrasellar symptomatic salivary gland: case series and literature review.

PURPOSE: Ectopic salivary glands have been found in both extracranial and intracranial locations, however, intrasellar symptomatic salivary gland is extremely rare and its clinical manifestation, radiological characteristics and outcome have not been systematically studied. Here we present a case series of intrasellar symptomatic salivary gland and perform a literature review to better characterize this disease. METHODS: We retrospectively reviewed the data of three patients with intrasellar symptomatic salivary gland from our institutional and other cases available in literatures. Information for sex, age at diagnosis, clinical symptoms, radiological features, treatment strategy and prognosis were recorded. RESULTS: A total of 11 cases (including our own) were identified. There were three men and eight women, with an average age at diagnosis of 28.3 years. The peak incidence was in the second and the third decade (72.7% of all cases). The most common symptom was headache (81.8% of all patients). About 63.6% patients had one or more abnormal hormone levels, and prolactin was likely the most vulnerable hormone. The radiological appearances of intrasellar salivary gland were various, and four cases mimicked pituitary adenoma radiologically. All patients underwent transsphenoidal surgery with no mortality. CONCLUSION: Although intrasellar symptomatic salivary gland is rare, it should be considered in the differential diagnosis of intrasellar lesions. Preoperative diagnosis is challenging since it mimics pituitary neoplasm in clinical and radiological manifestations, and confirmation for this disease could only be conducted through pathological examination. Transsphenoidal surgical resection is the preferred therapy and the patient prognosis is usually good.

Glioblastoma and Anaplastic Astrocytoma: Differentiation Using MRI Texture Analysis.

Introduction: Glioblastoma and anaplastic astrocytoma (ANA) are two of the most common primary brain tumors in adults. The differential diagnosis is important for treatment recommendations and prognosis assessment. This study aimed to assess the discriminative ability of texture analysis using machine learning to distinguish glioblastoma from ANA. Methods: A total of 123 patients with glioblastoma (n = 76) or ANA (n = 47) were enrolled in this study. Texture features were extracted from contrast-enhanced Magnetic Resonance (MR) images using LifeX package. Three independent feature-selection methods were performed to select the most discriminating parameters:Distance Correlation, least absolute shrinkage and selection operator (LASSO), and gradient correlation decision tree (GBDT). These selected features (datasets) were then randomly split into the training and the validation group at the ratio of 4:1 and were fed into linear discriminant analysis (LDA), respectively, and independently. The standard sensitivity, specificity, the areas under receiver operating characteristic curve (AUC) and accuracy were calculated for both training and validation group. Results: All three models (Distance Correlation + LDA, LASSO + LDA and GBDT + LDA) showed promising ability to discriminate glioblastoma from ANA, with AUCs ≥0.95 for both the training and the validation group using LDA algorithm and no overfitting was observed. LASSO + LDA showed the best discriminative ability in horizontal comparison among three models. Conclusion: Our study shows that MRI texture analysis using LDA algorithm had promising ability to discriminate glioblastoma from ANA. Multi-center studies with greater number of patients are warranted in future studies to confirm the preliminary result.