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

Aim: This 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. Methods: Kaplan-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). Results: Our 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). Conclusion: The 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.

Prediction of the mean transit time using machine learning models based on radiomics features from digital subtraction angiography in moyamoya disease or moyamoya syndrome-a development and validation model study.

Background: Digital subtraction angiography (DSA) is an important technique for diagnosis of moyamoya disease (MMD) or moyamoya syndrome (MMS), and computed tomography perfusion (CTP) is essential for assessing intracranial blood supply. The aim of this study was to assess whether radiomics features based on images of DSA could predict the mean transit time (MTT; outcome of CTP) using machine learning models. Methods: The DSA images and MTT values of adult patients with MMD or MMS, according to the diagnostic guidelines for MMD, as well as control cases, were retrospectively collected in the Guangdong Provincial People's Hospital between January 2018 and December 2020. A total of 93 features were extracted from the images of each case through 3-dimensional (3D) slicer. After features preprocessing and filtering, 3-4 features were selected by the least absolute shrinkage and selection operator (LASSO) regression algorithm. Prediction models were established using random forest (RF) and support vector machine (SVM) for MTT values. Single-factor receiver operating characteristic (ROC) curve analysis and partial-dependence (PD) profiles were conducted to investigate selected features and prediction models. Results: Our results showed that prediction models based on RF models had the best performance in frontal lobe {area under the curve (AUC) [95% confidence interval (CI)] =1.000 (1.000-1.000)], parietal lobe [AUC (95% CI) =1.000 (1.000-1.000)], and basal ganglia/thalamus [AUC (95% CI) =0.922 (0.797-1.000)] in the test set, whereas the SVM model performed the best in the temporal lobe [AUC (95% CI) =0.962 (0.876-1.000)] in the test set. The AUC values in the test set were greater than 0.9. The PD profiles showed good robustness and consistency. Conclusions: Prediction models based on radiomics features extracted from DSA images demonstrate excellent performance in predicting MTT in patients with MMD or MMS, which may provide guidance for future clinical practice.

Radiomic Features of the Edema Region May Contribute to Grading Meningiomas With Peritumoral Edema.

BACKGROUND: Meningiomas are frequently accompanied by peritumoral edema (PTE). The potential value of radiomic features of edema region in meningioma grading has not been investigated. PURPOSE: To investigate whether radiomic features of edema region contribute to grading meningiomas with PTE. STUDY TYPE: Retrospective. POPULATION: A total of 444 patients including 196 grade II and 248 WHO grade I meningiomas: 356 patients for training, 88 for validation. FIELD STRENGTH/SEQUENCE: A 1.5-T/3.0-T, noncontrast T1-weighted (T1WI), T2-weighted (T2WI), contrast-enhanced T1-weighted (T1CE) spin echo sequences. ASSESSMENT: A total of 851 radiomic features were extracted from each sequence on each region (tumor and edema region). These features were integrated by region respectively. Three subsets of clinical-radiomic features were constructed by joining clinical information (sex, age, tumor volume, and edema volume) and radiomic features of three regions: tumor, edema, and combined subsets. For each subset, features were filtered by the least absolute shrinkage and selection operator (LASSO) and Random Forest algorithm. Top 20 features of each subset were finally selected. STATISTICAL TESTS: Stochastic Gradient Boosting, Random Forest, and Bagged AdaBoost predictive models were built based on each subset. Discriminative abilities of models were quantified using receiver operating characteristics (ROC) and the area under the curve (AUC). A P value < 0.05 was considered statistically significant. RESULTS: Random Forest model based on combined subset (AUC [95% CI] = 0.880 [0.807-0.953]) had the best discriminative ability in grading meningiomas among the final models. The best model of edema subset and tumor subset were Random Forest model (AUC [95% CI] = 0.864 [0.791-0.938]) and Stochastic Gradient Boosting model (AUC [95% CI] = 0.844 [0.760-0.928]), respectively. DATA CONCLUSION: Radiomic features of edema region may contribute to grading meningiomas with PTE. The Random Forest model based on combined subset surpasses the best model based on tumor or edema subset regarding grading meningiomas with PTE. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 3.

A Predictive Nomogram for Postoperative Hydrocephalus After Intra- and Paraventricular Tumor Resection: A Retrospective Study of 196 Patients.

BACKGROUND: Hydrocephalus is a common complication of intra- and paraventricular tumors both before and after tumor resection. We investigated the risk factors for postoperative hydrocephalus and established a predictive nomogram to improve surgical planning and adjust the frequency of postoperative clinical and radiographic monitoring. METHODS: A retrospective study of 196 intra- and paraventricular tumor patients with follow-up data was conducted to investigate the risk factors for postoperative hydrocephalus via univariate and multivariate Cox regression analyses. We developed a nomogram incorporating these factors based on multivariate analysis and according to scores calculated by the predictive model; a hazard function curve was used to compared risk of hydrocephalus. RESULTS: Among the 196 patients, 33 had postoperative hydrocephalus. Intraventricular tumor (P = 0.005), glioblastoma (P = 0.010), preoperative hydrocephalus (P = 0.007), and radiotherapy (P = 0.033) were independent risk factors for postoperative hydrocephalus. The nomogram including these independent risk factors had moderate predictive accuracy, with a concordance index of 0.716 (95% confidence interval [CI]: 0.605-0.828), while the area under the curve values at 6, 12, and 24 months were 0.708 (95% CI: 0.563-0.853), 0.763 (95% CI: 0.656-0.870) and 0.861 (95% CI: 0.779-0.943), respectively. The hazard function showed differences between the lower and higher nomogram score groups. The higher the nomogram score, the higher the risk of postoperative hydrocephalus (P < 0.001). CONCLUSIONS: The established nomogram performs well for predicting postoperative hydrocephalus. Clinicians can use this nomogram to review their practice regarding hydrocephalus associated with intra- and paraventricular tumors, plan surgical treatment and adjust the frequency of postoperative clinical and radiographic monitoring.

Effectiveness of Postoperative Radiotherapy in Patients with Atypical Meningiomas After Gross Total Resection: Analysis of 260 Cases.

OBJECTIVE: It remains controversial whether patients with atypical meningiomas can benefit from postoperative radiotherapy (PORT) after gross total resection (GTR). This study aimed to explore the effectiveness of PORT in patients with atypical meningiomas after GTR based on our single-center data with a relatively large sample size. METHODS: Patients with atypical meningiomas who underwent GTR in our center were reviewed. Univariable and multivariable Cox proportional hazard models were conducted for survival analyses. Kaplan-Meier survival curves were generated, and 5-year progression-free survival (PFS) rates were calculated. RESULTS: This study enrolled 260 patients. PORT was not associated with PFS (P = 0.507). Sex (P = 0.006, hazard ratio 0.418, 95% confidence interval 0.224-0.781), age (P = 0.032, hazard ratio 1.032, 95% confidence interval 1.003-1.061), and tumor location (P = 0.026, hazard ratio 0.199, 95% confidence interval 0.048-0.824) were independent predictors of PFS. The 5-year PFS rate of patients receiving PORT (85.6%) was similar to that of patients not receiving PORT (84.8%). The 5-year PFS rate was 100% in patients with convexity atypical meningiomas regardless of whether or not they received PORT. CONCLUSIONS: PORT after GTR may not prolong PFS in patients with atypical meningiomas. Patients with convexity atypical meningiomas had favorable outcomes after GTR regardless of receipt of PORT.

Effectiveness of Postoperative Adjuvant Radiotherapy in Atypical Meningioma Patients After Gross Total Resection: A Meta-Analysis Study.

BACKGROUND: It still remains unclear whether patients with atypical meningioma (AM) could benefit from postoperative adjuvant radiotherapy (PORT) after gross-total resection (GTR). OBJECTIVE: Exploring the effectiveness of PORT on AM patients after GTR. METHODS: Literatures on PubMed, Embase, Web of science, and Scopus databases published between January 2000 and January 2019 were searched. After the selection based on the certain exclusion criteria, the Newcastle-Ottawa evaluation scale was used to evaluate the quality of the included literatures. Finally, a meta-analysis was conducted to analyze the effectiveness of PORT on local control (LC), progression-free survival (PFS) and overall survival (OS) in atypical meningioma patients after GTR. RESULTS: A total of 17 articles with 2,008 AM patients were included in the meta-analysis. The 5-year LC, 5-year PFS, and 5-year OS rates were 82.2, 84.1, and 79.0%, respectively, for AM patients receiving PORT after GTR, and they were 71.0, 71.9, and 81.5%, respectively, for those not receiving PORT after GTR. PORT could significantly improve 5-year LC rate (OR [95% Cl] = 2.59 [1.40-4.81], P = 0.002) and 5-year PFS rate (OR [95% Cl] = 1.99 [1.35-2.95], P = 0.001), but did not significantly improve 5-year OS rate (OR [95% Cl] = 1.07 [0.60-1.91], P = 0.828). CONCLUSION: PORT could improve the 5-year LC rate and 5-year PFS rate in AM patients after GTR. AM patients might benefit from PORT after GTR.

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.

CD73-derived adenosine controls inflammation and neurodegeneration by modulating dopamine signalling.

Ectonucleotidase-mediated ATP catabolism provides a powerful mechanism to control the levels of extracellular adenosine. While increased adenosine A2A receptor (A2AR) signaling has been well-documented in both Parkinson's disease models and patients, the source of this enhanced adenosine signalling remains unclear. Here, we show that the ecto-5'-nucleotidase (CD73)-mediated adenosine formation provides an important input to activate A2AR, and upregulated CD73 and A2AR in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced Parkinson's disease models coordinatively contribute to the elevated adenosine signalling. Importantly, we demonstrate that CD73-derived adenosine-A2AR signalling modulates microglial immunoresponses and morphological dynamics. CD73 inactivation significantly attenuated lipopolysaccharide-induced pro-inflammatory responses in microglia, but enhanced microglia process extension, movement and morphological transformation in the laser injury and acute MPTP-induced Parkinson's disease models. Limiting CD73-derived adenosine substantially suppressed microglia-mediated neuroinflammation and improved the viability of dopaminergic neurons and motor behaviours in Parkinson's disease models. Moreover, CD73 inactivation suppressed A2AR induction and A2AR-mediated pro-inflammatory responses, whereas replenishment of adenosine analogues restored these effects, suggesting that CD73 produces a self-regulating feed-forward adenosine formation to activate A2AR and promote neuroinflammation. We further provide the first evidence that A2A enhanced inflammation by antagonizing dopamine-mediated anti-inflammation, suggesting that the homeostatic balance between adenosine and dopamine signalling is key to microglia immunoresponses. Our study thus reveals a novel role for CD73-mediated nucleotide metabolism in regulating neuroinflammation and provides the proof-of-principle that targeting nucleotide metabolic pathways to limit adenosine production and neuroinflammation in Parkinson's disease might be a promising therapeutic strategy.