Effectiveness of Postoperative Radiotherapy on Atypical Meningioma Patients: A Population-Based Study

Purpose: It is controversial whether atypical meningioma patients undergoing gross-total resection (GTR) can benefit from postoperative radiotherapy (PORT). This study aimed to investigate the effectiveness of PORT on atypical meningioma patients.Methods: Patients diagnosed with atypical meningioma from 2008 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The Kaplan–Meier survival curves were generated, and the log-rank test was used to compare the differences among groups. Univariable and multivariable COX regressions were conducted for survival analyses.Results: A total of 1,014 patients were enrolled. The 5-years survival rate of the overall patients was 79.0%. PORT was performed in 315 (31.1%) patients. The utilization rates of PORT in patients undergoing GTR and undergoing subtotal resection (STR) were 26.7% and 42.2%, respectively. For patients undergoing STR, log-rank test showed that overall survival (OS) time was significantly longer in patients receiving PORT than those not (p = 0.026). For patients undergoing GTR, OS time did not show significant association with PORT (p = 0.339). In addition, patients undergoing STR with PORT had no significantly different OS time compared with those undergoing GTR with PORT (p = 0.398). Multivariable Cox regression analysis showed that receipt of PORT (p = 0.187) was not an independent predictor of OS after adjustment.Conclusion: PORT may not prolong the OS in atypical meningioma patients undergoing GTR. However, patients undergoing STR may benefit from PORT and achieve similar OS to those undergoing GTR

Novel Prehospital Prediction Model of Large Vessel Occlusion Using Artificial Neural Network

Background: Identifying large vessel occlusion (LVO) patients in the prehospital triage stage to avoid unnecessary and costly delays is important but still challenging. We aim to develop an artificial neural network (ANN) algorithm to predict LVO using prehospital accessible data including demographics, National Institutes of Health Stroke Scale (NIHSS) items and vascular risk factors.Methods: Consecutive acute ischemic stroke patients who underwent CT angiography (CTA) or time of flight MR angiography (TOF-MRA) and received reperfusion therapy within 8 h from symptom onset were included. The diagnosis of LVO was defined as occlusion of the intracranial internal carotid artery (ICA), M1 and M2 segments of the middle cerebral artery (MCA) and basilar artery on CTA or TOF-MRA before treatment. Patients with and without LVO were randomly selected at a 1:1 ratio. The ANN model was developed using backpropagation algorithm, and 10-fold cross-validation was used to validate the model. The comparison of diagnostic parameters between the ANN model and previously established prehospital prediction scales were performed.Results: Finally, 300 LVO and 300 non-LVO patients were randomly selected for the training and validation of the ANN model. The mean Youden index, sensitivity, specificity and accuracy of the ANN model based on the 10-fold cross-validation analysis were 0.640, 0.807, 0.833 and 0.820, respectively. The area under the curve (AUC), Youden index and accuracy of the ANN model were all higher than other prehospital prediction scales.Conclusions: The ANN can be an effective tool for the recognition of LVO in the prehospital triage stage

Conveniently-Grasped Field Assessment Stroke Triage (CG-FAST): A Modified Scale to Detect Large Vessel Occlusion Stroke

Background and Purpose: Patients with large vessel occlusion stroke (LVOS) need to be rapidly identified and transferred to comprehensive stroke centers (CSC). However, previous pre-hospital strategy remains challenging. We aimed to develop a modified scale to better predict LVOS.Methods: We retrospectively reviewed our prospectively collected database for acute ischemic stroke (AIS) patients who underwent CT angiography (CTA) or time of flight MR angiography (TOF-MRA) and had a detailed National Institutes of Health Stroke Scale (NIHSS) score at admission. Large vessel occlusion (LVO) was defined as the complete occlusion of large vessels, including the intracranial internal carotid artery (ICA), M1, and M2 segments of the middle cerebral artery (MCA), and basilar artery (BA). The Conveniently-Grasped Field Assessment Stroke Triage (CG-FAST) scale consisted of Level of Consciousness (LOC) questions, Gaze deviation, Facial palsy, Arm weakness, and Speech changes. Receiver Operating Characteristic (ROC) analysis was used to obtain the Area Under the Curve (AUC) of CG-FAST and previously established pre-hospital prediction scales.Results: Finally, 1,355 patients were included in the analysis. LVOS was detected in 664 (49.0%) patients. The sensitivity, specificity, positive predictive value, and negative predictive value of CG-FAST were 0.617, 0.810, 0.785, and 0.692 respectively, at the optimal cutoff (≥4). The AUC, Youden index and accuracy of the CG-FAST scale (0.758, 0.428, and 0.728) were all higher than other pre-hospital prediction scales.Conclusions: CG-FAST scale could be an effective and simple scale for accurate identification of LVOS among AIS patients

Patients With Ischemic Core ≥70 ml Within 6 h of Symptom Onset May Still Benefit From Endovascular Treatment

Background: Large core is associated with poor outcome in acute ischemic stroke (AIS) patients. It is unclear whether endovascular treatment (EVT) could bring benefits to patients with core volume ≥70 ml before treatment. We aimed to compare the impact of EVT with intravenous thrombolysis (IVT) on the outcome in patients with core volume ≥70 ml.Methods: We included consecutive anterior circulation AIS patients who underwent MR or CT perfusion within 6 h post stroke onset, which revealed a core ≥70 ml before reperfusion therapy. Good outcome was defined by modified Rankin Scale of 0 to 2 at 90-day. Reperfusion was defined as a reduction in hypoperfusion volume of ≥70% between baseline and 24 h.Results: One hundred four patients were included. Among them, 76 received IVT only, and 28 received EVT. After adjusting for age, NIHSS score, baseline core volume and onset to imaging time, patients in EVT group were more likely to achieve good outcome compared to IVT patients (OR, 3.875; 95% Cl 1.068–14.055, p = 0.039). More patients in EVT group achieved recanalization (84.0 vs. 58.5%, p = 0.027) and reperfusion (66.7 vs. 33.3%, p = 0.010) than in IVT group. Reperfusion also independently predicted good outcome (OR, 7.718; 95% Cl 1.713-34.772, p = 0.008). All patients with good outcome achieved recanalization at 24 h.Conclusions: Our data indicated that patients with core volume ≥70 ml might still benefit from EVT, which was related to its high reperfusion rate

Multi-parameter MRI radiomic features may contribute to predict progression-free survival in patients with WHO grade II meningiomas

AimThis study aims to investigate the potential value of radiomic features from multi-parameter MRI in predicting progression-free survival (PFS) of patients with WHO grade II meningiomas.MethodsKaplan–Meier survival curves were used for survival analysis of clinical features. A total of 851 radiomic features were extracted based on tumor region segmentation from each sequence, and Max-Relevance and Min-Redundancy (mRMR) algorithm was applied to filter and select radiomic features. Bagged AdaBoost, Stochastic Gradient Boosting, Random Forest, and Neural Network models were built based on selected features. Discriminative abilities of models were evaluated using receiver operating characteristics (ROC) and area under the curve (AUC).ResultsOur study enrolled 164 patients with WHO grade II meningiomas. Female gender (p=0.023), gross total resection (GTR) (p<0.001), age <68 years old (p=0.023), and edema index <2.3 (p=0.006) are protective factors for PFS in these patients. Both the Bagged AdaBoost model and the Neural Network model achieved the best performance on test set with an AUC of 0.927 (95% CI, Bagged AdaBoost: 0.834–1.000; Neural Network: 0.836–1.000).ConclusionThe Bagged AdaBoost model and the Neural Network model based on radiomic features demonstrated decent predictive ability for PFS in patients with WHO grade II meningiomas who underwent operation using preoperative multi-parameter MR images, thus bringing benefit for patient prognosis prediction in clinical practice. Our study emphasizes the importance of utilizing advanced imaging techniques such as radiomics to improve personalized treatment strategies for meningiomas by providing more accurate prognostic information that can guide clinicians toward better decision-making processes when treating their patients’ conditions effectively while minimizing risks associated with unnecessary interventions or treatments that may not be beneficial

Severe Blood–Brain Barrier Disruption in Cardioembolic Stroke

BackgroundPrevious studies demonstrated that cardioembolism (CE) was prone to develop hemorrhagic transformation (HT), whereas hyper-permeability of blood–brain barrier (BBB) might be one reason for the development of HT. We, thus, aimed to investigate whether the BBB permeability (BBBP) was higher in CE stroke than other stroke subtypes in acute ischemic stroke (AIS) patients.MethodsThis study was a retrospective review of prospectively collected clinical and imaging database of AIS patients who underwent CT perfusion. Hypoperfusion was defined as Tmax >6 s. The average relative permeability-surface area product (rPS), reflecting the BBBP, was calculated within the hypoperfusion region (rPShypo). CE was diagnosed according to the international Trial of Org 10172 in Acute Stroke Treatment criteria. Receiver operating characteristics (ROC) curve analysis was used to determine predictive value of rPShypo for CE. Logistic regression was used to identify independent predictors for CE.ResultsA total of 187 patients were included in the final analysis [median age, 73 (61–80) years; 75 (40.1%) females; median baseline National Institutes of Health Stroke Scale score, 12 (7–16)]. Median rPShypo was 65.5 (35.8–110.1)%. Ninety-seven (51.9%) patients were diagnosed as CE. ROC analysis revealed that the optimal rPShypo threshold for CE was 86.71%. The value of rPShypo and the rate of rPShypo>86.71% were significantly higher in patients with CE than other stroke subtypes (p < 0.05), after adjusting for the potential confounds.ConclusionThe extent of BBB disruption is more severe in CE stroke than other stroke subtypes during the hyperacute stage

A Modified Tri-Exponential Model for Multi-b-value Diffusion-Weighted Imaging: A Method to Detect the Strictly Diffusion-Limited Compartment in Brain

Purpose: To present a new modified tri-exponential model for diffusion-weighted imaging (DWI) to detect the strictly diffusion-limited compartment, and to compare it with the conventional bi- and tri-exponential models.Methods: Multi-b-value diffusion-weighted imaging (DWI) with 17 b-values up to 8,000 s/mm2 were performed on six volunteers. The corrected Akaike information criterions (AICc) and squared predicted errors (SPE) were calculated to compare these three models.Results: The mean f0 values were ranging 11.9–18.7% in white matter ROIs and 1.2–2.7% in gray matter ROIs. In all white matter ROIs: the AICcs of the modified tri-exponential model were the lowest (p < 0.05 for five ROIs), indicating the new model has the best fit among these models; the SPEs of the bi-exponential model were the highest (p < 0.05), suggesting the bi-exponential model is unable to predict the signal intensity at ultra-high b-value. The mean ADCvery−slow values were extremely low in white matter (1–7 × 10−6 mm2/s), but not in gray matter (251–445 × 10−6 mm2/s), indicating that the conventional tri-exponential model fails to represent a special compartment.Conclusions: The strictly diffusion-limited compartment may be an important component in white matter. The new model fits better than the other two models, and may provide additional information

DataSheet1.PDF

<p>Purpose: To present a new modified tri-exponential model for diffusion-weighted imaging (DWI) to detect the strictly diffusion-limited compartment, and to compare it with the conventional bi- and tri-exponential models.</p><p>Methods: Multi-b-value diffusion-weighted imaging (DWI) with 17 b-values up to 8,000 s/mm² were performed on six volunteers. The corrected Akaike information criterions (AICc) and squared predicted errors (SPE) were calculated to compare these three models.</p><p>Results: The mean f₀ values were ranging 11.9–18.7% in white matter ROIs and 1.2–2.7% in gray matter ROIs. In all white matter ROIs: the AICcs of the modified tri-exponential model were the lowest (p < 0.05 for five ROIs), indicating the new model has the best fit among these models; the SPEs of the bi-exponential model were the highest (p < 0.05), suggesting the bi-exponential model is unable to predict the signal intensity at ultra-high b-value. The mean ADC_very−slow values were extremely low in white matter (1–7 × 10⁻⁶ mm²/s), but not in gray matter (251–445 × 10⁻⁶ mm²/s), indicating that the conventional tri-exponential model fails to represent a special compartment.</p><p>Conclusions: The strictly diffusion-limited compartment may be an important component in white matter. The new model fits better than the other two models, and may provide additional information.</p

Table_1_Novel Prehospital Prediction Model of Large Vessel Occlusion Using Artificial Neural Network.DOCX

<p>Background: Identifying large vessel occlusion (LVO) patients in the prehospital triage stage to avoid unnecessary and costly delays is important but still challenging. We aim to develop an artificial neural network (ANN) algorithm to predict LVO using prehospital accessible data including demographics, National Institutes of Health Stroke Scale (NIHSS) items and vascular risk factors.</p><p>Methods: Consecutive acute ischemic stroke patients who underwent CT angiography (CTA) or time of flight MR angiography (TOF-MRA) and received reperfusion therapy within 8 h from symptom onset were included. The diagnosis of LVO was defined as occlusion of the intracranial internal carotid artery (ICA), M1 and M2 segments of the middle cerebral artery (MCA) and basilar artery on CTA or TOF-MRA before treatment. Patients with and without LVO were randomly selected at a 1:1 ratio. The ANN model was developed using backpropagation algorithm, and 10-fold cross-validation was used to validate the model. The comparison of diagnostic parameters between the ANN model and previously established prehospital prediction scales were performed.</p><p>Results: Finally, 300 LVO and 300 non-LVO patients were randomly selected for the training and validation of the ANN model. The mean Youden index, sensitivity, specificity and accuracy of the ANN model based on the 10-fold cross-validation analysis were 0.640, 0.807, 0.833 and 0.820, respectively. The area under the curve (AUC), Youden index and accuracy of the ANN model were all higher than other prehospital prediction scales.</p><p>Conclusions: The ANN can be an effective tool for the recognition of LVO in the prehospital triage stage.</p