Stereotactic radiosurgery versus combined stereotactic radiosurgery and bevacizumab for recurrent glioblastoma; a systematic review and meta-analysis of survival.

Recurrent glioblastoma (rGBM) is a brain tumor that is resistant to standard treatments. Although stereotactic radiosurgery (SRS) is a non-invasive radiation technique, it cannot fully prevent tumor recurrence and progression. Bevacizumab blocks tumor blood supply and has been approved for rGBM. However, the best way to combine SRS and bevacizumab is still unclear. We did a systematic review and meta-analysis of studies comparing SRS alone and SRS plus bevacizumab for rGBM. We searched three databases for articles published until June 2023. All statistical analysis was performed by STATA v.17. Our meta-analysis included 20 studies with 926 patients. We found that the combination therapy had a significantly lower rate of overall survival (OS) than SRS alone at 6-month 0.77[95%CI:0.74-0.85] for SRS alone and (100%) for SRS plus bevacizumab. At 1-year OS, 0.39 [95%CI: 0.32-0.47] for SRS alone and 0.61 [95%CI:0.44-0.77] for SRS plus bevacizumab (P-value:0.02). However, this advantage was not seen in the long term (18 months and two years). Additionally, the combination therapy had lower chances of progression-free survival (PFS) than SRS alone at the 6-month and 1-year time points, but the differences were insignificant. Our study indicates that incorporating bevacizumab with SRS may lead to a short-term increase in OS for rGBM patients but not long-term. Additionally, the PFS rate did not show significant improvement in the group receiving combination therapy. Further clinical trials are necessary to validate the enhanced overall survival with combination therapy for rGBM.

Frameless stereotactic radiosurgery for brain metastasis: a systematic review and meta-analysis.

Stereotactic Radiosurgery (SRS) delivers a high dose of radiation to a specific brain area while limiting radiation to nearby healthy tissue. While most SRS has traditionally been performed with a stereotactic frame-based approach, this study aims to investigate the safety and efficacy of frameless radiosurgery in patients with brain metastases. Our study followed the recommended guidelines summarized in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. The electronic databases of PubMed/Medline, Scopus, Embase, and Web of Science (WOS) were searched from inception to 10 October 2023. The pooled rate of outcomes was calculated using random effect model and Restricted maximum-likelihood (REML) method. All statistical analysis was performed by STATA V.17. A total of 499 studies were recruited from the electronic databases. After removing duplicates (n = 117), 382 studies were used for title/abstract, and 329 were removed from the study selection process. A total of 53 articles were used for full-text assessment, and 35 studies were included for data extraction. Our analysis revealed a significant increase across all pooled survival rates and local control rates by initiating the radiosurgery for patients, estimating the pooled 6-month OSR of 75% (95% CI: 68-81%), 1-year overall survival rate (OSR) of 60% (95% CI: 51-69%), 18-month OSR of 48% (95% CI: 10-85%), 2-year OSR of 39% (95% CI: 19-58%), 1-year progression-free survival rate (PFSR) of 68% (95% CI: 39-98%), 2-year PFSR of 75% (95% CI: 58-91%), 6-month local control rate (LCR) of 93% (95% CI: 90-96%), and 12-month LCR of 86% (95% CI: 82-90%). Our meta-analysis findings confirm the efficacy of frameless radiosurgery in treating brain metastases. Using data from several trials, we were able to demonstrate stereotactic radiosurgery's effectiveness as a therapy option for brain metastasis patients, demonstrating local control and reasonable overall survival.

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.

Anti-PD-1/PD-L1 inhibitor therapy for melanoma brain metastases: a systematic review and meta-analysis.

Melanoma brain metastases present a major challenge in cancer treatment and reduce overall survival despite advances in managing primary melanoma. Immune checkpoint inhibitors (ICIs) that target PD-1/PD-L1 pathways have shown promise in treating advanced melanoma, but their efficacy for melanoma brain metastases is debated. This systematic review and meta-analysis summarize evidence on anti-PD-1/PD-L1 inhibitors for melanoma brain metastases. This systematic review and meta-analysis followed PRISMA guidelines. PICO criteria targeted melanoma brain metastasis patients treated with PD-1/PD-L1 inhibitors, assessing overall survival, progression-free survival, and complications. Inclusion criteria were English studies on humans using PD-1/PD-L1 inhibitors for melanoma brain metastases with > 10 patients. A total of 22 trials involving 1523 melanoma brain metastase patients treated with anti-PD-1/PD-L1 inhibitors were thoroughly analyzed. Our findings show the 6-month OS rate of 0.75 [95%CI:0.67-0.84], the 6-months PFS rate of 0.42 [95%CI:0.31-0.52], the 1-year OS rate of 0.63 [95%CI:0.52-0.74], the 1-year PFS rate was 0.45 [95%CI:0.32-0.58], the 18-months OS rate of 0.52 [95%CI:0.37-0.67], the 2-year OS rate of 50% [95% CI: (34%-65%)], the 2 year PFS rate of 0.36 (95%CI:0.23-0.50), the 3-year OS rate of 0.42 (95%CI:0.17-0.67), the 4-year PFS rate of 0.35 [95%CI:0.08-0.61], the 4-year OS rate of 0.29 [95%CI:0.01-0.56], the 5-year OS rate of 0.29 (95%CI:0.09-0.50), and the 5-year PFS rate of 0.11 (95%CI:0.03-0.19). The combined disease stability rate was 0.13 [95%CI:0.05-0.20], the progressive disease rate was 0.49 [95%CI:0.37-0.62], the partial response rate was 0.14 [95%CI:0.07-0.20], the object response rate was 0.35 [95%CI:0.24-0.46], and the complete response rate was 0.22 [95%CI:0.12-0.32]. In conclusion, our meta-analysis provides compelling evidence supporting the efficacy of PD-1/PD-L1 inhibitors in patients with melanoma brain tumors, as evidenced by favorable survival outcomes and disease control rates.

The clinical utility of autologous tumor lysate-loaded dendritic cell vaccination for patients with glioma: A systematic review and meta-analysis.

BACKGROUND: Dendritic cell (DC) vaccines show promise for glioma treatment, but optimal use remains uncertain. This meta-analysis examined DC vaccine efficacy and safety for gliomas. METHODS: This systematic review and meta-analysis study was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. From the date of inception to October 23, 2023, electronic databases PubMed, Embase, Web of Science, and Scopus have been thoroughly evaluated. RESULTS: A total of 12 studies with 998 patients and a mean age ranging from 40.2 to 56 years were included. Across 12 articles, DC vaccine 6-month overall survival (OS) was 100% [95% confidence interval {95%CI}: 100%-100%]. Respectively, 12-month OS reported 75% [95%CI: 65%-85%] but declined to 32% [95%CI: 20%-43%] for 24-month OS. 6- and 12-month progression-free survival reached 49% [95%CI: 21%-77%] and 19% [95%CI:8%-30%]. Studying radiological outcomes shows that complete response and partial response rates were 13% [95%CI: 17%-42%], and 26% [95%CI: 10%-42%], though stable disease reached 33% [95%CI: 15%-51%], suggesting predominant antineoplastic effects. The progressive disease rate also was 24% [95%CI: 9%-57%]. CONCLUSIONS: In gliomas, DC vaccinations show a temporary efficacy; stability is more prevalent than regression. Impacts favor decreased resistance to early disease. Enhancing efficacy remains critical. Early therapy can be enhanced by appropriate supplementary therapy integration.

Predicting telomerase reverse transcriptase promoter mutation in glioma: A systematic review and diagnostic meta-analysis on machine learning algorithms.

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.

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.