RESUMO
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.
RESUMO
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.