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Background Previous research has shown promising results for treating intracranial aneurysms (IAs) with a flow redirection endoluminal device (FRED). In this systematic review and meta-analysis, we aimed to assess the safety and efficacy of this device by providing pooled estimates using the data from previous studies.Methods A systematic literature search of Web of Sciences, PubMed, Scopus, and Embase was performed until October 8th, 2023. After selecting the final articles, relevant data were extracted. Parameters relating to safety and efficacy were pooled using STATA software. Heterogeneity was assessed using I-squared and Cochran’s Q. Funnel plots and Egger’s regression methods were used to evaluate publication bias. Sensitivity analysis was also performed using the leave-one-out method.Results The data of 37 studies were used for meta-analysis. The rates of immediate adequate occlusion and complete occlusion were 0.51 (95% CI: 0.31–0.71) and 0.34 (95% CI: 0.16–0.53), respectively, while the rates of the adequate and complete occlusion at the latest follow-up were 0.90 (95% CI: 0.84–0.94) and 0.75 (95% CI: 0.65–0.84), respectively. The periprocedural complications rate was 0.04 (95% CI: 0.03–0.06), and the overall complications rate was 0.12 (95% CI: 0.09–0.15). The rate of good functional outcome was 0.99 (95% CI: 0.99–1.00) and the successful implantation rate was 1.00 (95% CI: 1.00–1.00). There was substantial heterogeneity among the reports for most of the evaluated parameters.Conclusion FRED had high safety and efficacy in treating IAs, as evidenced by its high occlusion and low complication rates.

Background Glioma is one of the most common primary brain tumors. The presence of the telomerase reverse transcriptase promoter (pTERT) mutation is associated with a better prognosis. This study aims to investigate the TERT mutation in patients with glioma using machine learning (ML) algorithms on radiographic imaging.Method This study was prepared according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The electronic databases of PubMed, Embase, Scopus, and Web of Science were searched from inception to August 1, 2023. The statistical analysis was performed using the MIDAS package of STATA v.17.Results A total of 22 studies involving 5371 patients were included for data extraction, with data synthesis based on 11 reports. The analysis revealed a pooled sensitivity of 0.86 (95% CI: 0.78–0.92) and a specificity of 0.80 (95% CI 0.72–0.86). The positive and negative likelihood ratios were 4.23 (95% CI: 2.99–5.99) and 0.18 (95% CI: 0.11–0.29), respectively. The pooled diagnostic score was 3.18 (95% CI: 2.45–3.91), with a diagnostic odds ratio 24.08 (95% CI: 11.63–49.87). The Summary Receiver Operating Characteristic (SROC) curve had an area under the curve (AUC) of 0.89 (95% CI: 0.86–0.91).Conclusion The study suggests that ML can predict TERT mutation status in glioma patients. ML models showed high sensitivity (0.86) and moderate specificity (0.80), aiding disease prognosis and treatment planning. However, further development and improvement of ML models are necessary for better performance metrics and increased reliability in clinical practice.