Repeat single-session stereotactic radiosurgery for arteriovenous malformation: a systematic review and meta-analysis.

BACKGROUND: Stereotactic radiosurgery is the preferred option for treating brain arteriovenous malformation (AVM) when the risks associated with surgery outweigh the potential benefits. However, some patients require repeat radiosurgery due to residual AVM after the first procedure. This systematic review and meta-analysis aimed to investigate the safety and efficacy of repeated procedure of radiosurgery for AVM. METHOD: A systematic review was conducted according to the PRISMA guideline. The search was conducted on PubMed, Scopus, Embase, and Web of Science, using a pre-designed search string. Studies investigating the efficacy of repeat radiosurgery for residual AVM following initial single session radiosurgery were included. The risk of bias was assessed using the JBI tool. Meta-analysis and met-regression were performed to pool and inspect data. RESULTS: Our meta-analysis, with a mean follow-up of 45.57 months, reveals repeat radiosurgery as a viable option for arteriovenous malformations (AVMs), achieving a 60.82% obliteration rate with a mean time to obliteration of 33.18 months. Meta-regression identifies AVM volume and Spetzler-Martin (SM) grade as factors influencing obliteration, with smaller volume and lower SM grades associated with higher rates. Complications include 10.33% radiation-induced changes, 5.26% post-radiosurgery hemorrhage, 2.56% neurologic deficits, and 0.67% cyst formation. Heterogeneity in complications is primarily attributed to male proportion and SM grade, while factors influencing post-radiosurgery hemorrhage remain unclear. The type of radiosurgery, whether Gamma Knife Radiosurgery (GKRS) or LINAC, does not significantly impact outcomes. CONCLUSION: Repeat radiosurgery is a feasible, effective, and safe treatment for AVMs following failure of initial radiosurgery. When utilized in appropriate patient subgroups, it provides an acceptable risk-to-benefit profile. Feature studies are required to clarify its clear indications.

Prediction of the treatment response and local failure of patients with brain metastasis treated with stereotactic radiosurgery using machine learning: A systematic review and meta-analysis.

BACKGROUND: Stereotactic radiosurgery (SRS) effectively treats brain metastases. It can provide local control, symptom relief, and improved survival rates, but it poses challenges in selecting optimal candidates, determining dose and fractionation, monitoring for toxicity, and integrating with other modalities. Practical tools to predict patient outcomes are also needed. Machine learning (ML) is currently used to predict treatment outcomes. We aim to investigate the accuracy of ML in predicting treatment response and local failure of brain metastasis treated with SRS. METHODS: PubMed, Scopus, Web of Science (WoS), and Embase were searched until April 16th, which was repeated on October 17th, 2023 to find possible relevant papers. The study preparation adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. The statistical analysis was performed by the MIDAS package of STATA v.17. RESULTS: A total of 17 articles were reviewed, of which seven and eleven were related to the clinical use of ML in predicting local failure and treatment response. The ML algorithms showed sensitivity and specificity of 0.89 (95% CI: 0.84-0.93) and 0.87 (95% CI: 0.81-0.92) for predicting treatment response. The positive likelihood ratio was 7.1 (95% CI: 4.5-11.1), the negative likelihood ratio was 0.13 (95% CI: 0.08-0.19), and the diagnostic odds ratio was 56 (95% CI: 25-125). Moreover, the pooled estimates for sensitivity and specificity of ML algorithms for predicting local failure were 0.93 (95% CI: 0.76-0.98) and 0.80 (95% CI: 0.53-0.94). The positive likelihood ratio was 4.7 (95% CI: 1.6-14.0), the negative likelihood ratio was 0.09 (95% CI: 0.02-0.39), and the diagnostic odds ratio was 53 (95% CI: 5-606). CONCLUSION: ML holds promise in predicting treatment response and local failure in brain metastasis patients receiving SRS. However, further studies and improvements in the treatment process can refine the models and effectively integrate them into clinical practice.

The safety and efficacy of dabrafenib and trametinib in patients with glioma: A systematic review and meta-analysis.

BACKGROUND: Dabrafenib and trametinib represent targeted therapy options under investigation for treatment of gliomas harboring BRAF V600 mutations. We systematically reviewed the literature and conducted meta-analyses to assess the efficacy and safety of these agents. METHODS: PubMed, Embase, and Scopus were searched from inception to September 2023 for studies examining dabrafenib and/or trametinib for gliomas. Outcomes included response rates (ORR, CR, PR), progression rates (PD), 6- and 12-month PFS, adverse events, and dosing modifications. Meta-analyses were conducted using random effect models. RESULTS: Nine studies met the inclusion criteria. Meta-analysis demonstrated overall response rates (ORR) of 50% (95% confidence interval (CI): 35-65%) for low-grade gliomas (LGG) and 40% (95% CI: 29-51%) for high-grade gliomas (HGG). Pooled ORR was 45% (95% CI: 36-54%) for both glioma grades. The complete response rate was 13% (95% CI: 05-27%) for HGG and 5% (95% CI: 1-10%) for both LGG and HGG. Six-month progression-free survival (PFS) rates reached 87% in LGG and 67% in HGG and a pooled 6-month PFS 78% (95% CI: 58-98%), declining at 12 months to 67% and 44%, respectively, with a pooled 12-month PFS 56% (95% CI: 34-79%). Grade 1-4 adverse events occurred in 100% of LGG and 63% of HGG patients. CONCLUSIONS: Dabrafenib and trametinib demonstrate promising anti-tumor efficacy in gliomas, particularly low-grade tumors, achieving durable disease stabilization in many patients. However, toxicity significantly limited tolerability. Additional research should further examine efficacy and refine safe administration protocols across glioma subtypes.

Prediction of cerebral aneurysm rupture risk by machine learning algorithms: a systematic review and meta-analysis of 18,670 participants.

It is possible to identify unruptured intracranial aneurysms (UIA) using machine learning (ML) algorithms, which can be a life-saving strategy, especially in high-risk populations. To better understand the importance and effectiveness of ML algorithms in practice, a systematic review and meta-analysis were conducted to predict cerebral aneurysm rupture risk. PubMed, Scopus, Web of Science, and Embase were searched without restrictions until March 20, 2023. Eligibility criteria included studies that used ML approaches in patients with cerebral aneurysms confirmed by DSA, CTA, or MRI. Out of 35 studies included, 33 were cohort, and 11 used digital subtraction angiography (DSA) as their reference imaging modality. Middle cerebral artery (MCA) and anterior cerebral artery (ACA) were the commonest locations of aneurysmal vascular involvement-51% and 40%, respectively. The aneurysm morphology was saccular in 48% of studies. Ten of 37 studies (27%) used deep learning techniques such as CNNs and ANNs. Meta-analysis was performed on 17 studies: sensitivity of 0.83 (95% confidence interval (CI), 0.77-0.88); specificity of 0.83 (95% CI, 0.75-0.88); positive DLR of 4.81 (95% CI, 3.29-7.02) and the negative DLR of 0.20 (95% CI, 0.14-0.29); a diagnostic score of 3.17 (95% CI, 2.55-3.78); odds ratio of 23.69 (95% CI, 12.75-44.01). ML algorithms can effectively predict the risk of rupture in cerebral aneurysms with good levels of accuracy, sensitivity, and specificity. However, further research is needed to enhance their diagnostic performance in predicting the rupture status of IA.

The performance of Machine Learning for prediction of H3K27M Mutation in Midline Gliomas; A Systematic Review and Meta-Analysis.

BACKGROUND: Diffuse midline gliomas (DMGs) encompass a set of tumors, and those tumors with H3K27M mutation carry a poor prognosis. In recent years, machine learning (ML)-based radiomics have shown promising results in predicting gene mutation status non-invasively. Therefore, this study aims to comprehensively evaluate the diagnostic performance of ML-based magnetic resonance imaging (MRI) radiomics in predicting H3K27M mutation status in DMG patients. METHODS: A systematic search was conducted using relevant keywords in PubMed/Medline, Scopus, Embase, and Web of Science from inception to May 2023. Original studies evaluating the diagnostic performance of ML models in predicting H3K27M mutation status in DMGs were enrolled. Quality assessment of the enrolled studies was conducted using QUADAS-2. Data were analyzed using STATA version 17.0 to calculate pooled sensitivity, specificity, positive (PLR) and negative likelihood ratio (NLR), diagnostic score, and diagnostic odds ratio (DOR). RESULTS: A total of 13 studies, including 12 retrospectives and one both retrospective and prospective study, enrolled 1510 (male=777) DMG patients. Six studies underwent meta-analysis which showed a pooled sensitivity, specificity, PLR, NLR, diagnostic score, and DOR of 0.91 (95% CI 0.77-0.97), 0.81 (95% CI 0.73-0.88), 4.86 (95% CI 3.25-7.24), 0.11 (95% CI 0.04-0.29), 3.75 (95% CI 2.62-4.88), and 42.61 (95% CI 13.77-131.87), respectively. CONCLUSION: Non-invasive prediction of H3K27M mutation status in patients with DMGs using MRI radiomics is a promising tool with good diagnostic performance. However, the pooled metrics had a wide confidence interval, which required further studies to enhance ML algorithms' accuracy and facilitate their integration into daily clinical practice.

The Safety and Efficacy of eCLIPs for Treatment of Wide-Necked Bifurcation Aneurysms: A Systematic Review and Meta-Analysis.

BACKGROUND: Wide-necked bifurcation aneurysms (WNBAs) are challenging intracranial aneurysms. Several device and treatment approaches have been proposed for the treatment of WNBAs. The endovascular clip system (eCLIPs) is a newly developed endovascular device with flow diverter and flow disruptor features. This study aims to investigate the safety and efficacy of the eCLIPs for treatment of patients with WNBAs. METHODS: This is a systematic review and meta-analysis study conducted in accordance with the PRISMA (preferred reporting items for systematic reviews and meta-analyses) guidelines. The electronic databases of PubMed, Embase, Scopus, and Web of Science were systematically reviewed from inception to June 19, 2023. The rate of complete and near-complete occlusion, successful device implantation, and serious adverse events were pooled using STATA, version 17. RESULTS: A total of 5 studies with 110 patients with WNBAs were systematically investigated. Our findings show that the immediate successful implantation rate of eCLIPs was 0.93 (95% confidence interval [CI], 0.88-0.97). Moreover, the immediate postoperative complete occlusion rate was 0.34 (95% CI, 0.10-0.58), and the immediate postoperative near-complete occlusion rate was 0.35 (95% CI, 0.24-0.45). Also, the near-complete occlusion rate at the latest follow-up was 0.3 (95% CI, 0.16-0.44). The serious adverse event rate was 0.14 (95% CI, 0.05-0.22). Stroke was also reported in 2 studies, with 1 study reporting 1 patient who experienced stroke within the first 24 hours and 1 study reporting no patients with stroke. CONCLUSIONS: Our findings document that the eCLIPs is a safe and effective device for treating patients with WNBAs and associated with favorable outcomes.