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Predicting Telomerase Reverse Transcriptase Promoter Mutation in Glioma: A Systematic Review and Diagnostic Meta-Analysis on Machine Learning Algorithms Publisher Pubmed



Habibi MA1 ; Dinpazhouh A2 ; Aliasgary A2 ; Mirjani MS2 ; Mousavinasab M3 ; Ahmadi MR3 ; Minaee P2 ; Eazi S2 ; Shafizadeh M1 ; Gurses ME4 ; Lu VM4 ; Berke CN4 ; Ivan ME4 ; Komotar RJ4 Show All Authors
Authors
  1. Habibi MA1
  2. Dinpazhouh A2
  3. Aliasgary A2
  4. Mirjani MS2
  5. Mousavinasab M3
  6. Ahmadi MR3
  7. Minaee P2
  8. Eazi S2
  9. Shafizadeh M1
  10. Gurses ME4
  11. Lu VM4
  12. Berke CN4
  13. Ivan ME4
  14. Komotar RJ4
  15. Shah AH4

Source: Neuroradiology Journal Published:2025


Abstract

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 Author(s) 2024.
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