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Al-Rahbi A, Al-Mahrouqi O, Al-Saadi T.
Uses of artificial intelligence in glioma: A systematic review.
Med Int (Lond). 2024 May 20;4(4):40. doi: 10.3892/mi.2024.164. PMID: 38827949. Review. ˍ

Hajikarimloo B, Tos SM, Kooshki A, Alvani MS, Eftekhar MS, Hasanzade A, Tavanaei R, Akhlaghpasand M, Hashemi R, Ghaffarzadeh-Esfahani M, Mohammadzadeh I, Habibi MA.
Machine learning radiomics for H3K27M mutation prediction in gliomas: A systematic review and meta-analysis.
Neuroradiology. 2025 Mar 31. doi: 10.1007/s00234-025-03597-y. PMID: 40163098. Review; Meta-analysis˰ ˍ

Ying YZ, Cai XH, Yang H, Huang HW, Zheng D, Li HY, Dong GH, Wang YG, Jiang ZL, An ZL, Zhang GB.
Development and validation of a deep learning algorithm for discriminating glioma recurrence from radiation necrosis on MRI.
Front Oncol. 2025 Jun 6;15:1573700. doi: 10.3389/fonc.2025.1573700. PMID: 40548110. Observational study. ˍ

Fu FX, Li G, Hong L, Chen WS.
Preliminary investigation on predicting postoperative glioma recurrences based on a multiparametric radiomics model.
Front Oncol. 2025 Jun 19;15:1592881. doi: 10.3389/fonc.2025.1592881. PMID: 40612337. Observational study˰ ˍ

Shao D, Lou S, Liu Y, Kou Z.
Artificial intelligence in glioma research: a bibliometric analysis of global trends, hotspots, and future directions.
Front Neurol. 2026 Jan 12;16:1701499. doi: 10.3389/fneur.2025.1701499. PMID: 41603012. Bibliometric analysis. ˍ