ARTIFICIAL INTELLIGENCE IN NEUROIMAGING OF GLIOMAS: CURRENT APPLICATIONS, CLINICAL VALUE AND CHALLENGES
DOI:
https://doi.org/10.31435/ijitss.1(49).2026.4738Keywords:
Artificial Intelligence, Glioma, Magnetic Resonance Imaging, Radiomics, Deep Learning, Neuro-OncologyAbstract
Introduction and purpose: Gliomas represent a heterogeneous group of primary central nervous system tumors that pose substantial diagnostic challenges and are associated with highly variable clinical outcomes. Magnetic resonance imaging (MRI) plays a central role in the evaluation of gliomas, however, conventional image interpretation provides limited insight into tumor biology and prognostic stratification.Recent methodological advances in artificial intelligence, including machine learning and deep learning techniques, have enabled more advanced analysis of complex neuroimaging data and the extraction of clinically relevant information. This review summarizes and critically analyzes current AI applications in glioma neuroimaging, focusing on image enhancement, tumor segmentation, grading and classification, molecular characterization, and outcome prediction.
Methodology: This narrative review synthesizes recent literature on AI-based neuroimaging analysis in glioma, focusing on machine learning and deep learning approaches applied to MRI and advanced imaging modalities, including diffusion MRI, sodium MRI, and multimodal imaging. Selected clinical contexts, such as pediatric gliomas and diffuse midline gliomas, are also discussed.
Results: Across multiple retrospective cohorts and benchmark datasets, AI-based methods have consistently achieved high diagnostic and predictive performance.These methods show potential to improve tumor characterization, noninvasive molecular assessment, and prognostic modeling, although clinical translation remains limited.
Conclusions: Artificial intelligence is increasingly recognized as a complementary approach to conventional neuro-oncological imaging. However, major challenges persist, including data heterogeneity, limited external and prospective validation, insufficient model interpretability, and difficulties in clinical workflow integration. Future research should emphasize standardized reporting, multi-center validation, and explainable AI to enable safe and clinically meaningful implementation.
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Copyright (c) 2026 Adrian Morawiec, Paweł Słoma, Paulina Dybiak, Maciej Zachara, Mateusz Bartoszek, Patryk Harnicki, Mikołaj Grodzki, Jakub Minas, Erwin Grzegorzak, Rafał Pelczar, Oliwia Krawczyk
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