Artificial intelligence in neuro-oncology imaging: Advancing brain tumor detection, grading, and treatment response evaluation

This narrative review synthesizes how artificial intelligence (AI) is reshaping neuro-oncology imaging through automated detection, segmentation, grading, and longitudinal monitoring of brain tumors using multiparametric magnetic resonance imaging (MRI) and hybrid positron emission tomography (PET) combined with MRI. Conventional interpretation remains constrained by interobserver variability, subjective estimation of tumor burden, and persistent difficulty in separating treatment-related changes, particularly pseudoprogression and radiation effects, from true recurrence, creating uncertainty in clinical decision-making. Deep learning segmentation pipelines now support fast, reproducible delineation of enhancing tumors, non-enhancing tumors, edema, and postoperative cavities, generating quantitative two-dimensional and three-dimensional metrics that better reflect complex, infiltrative diseases than manual measurements. Radiomics and multimodal machine learning models leveraging diffusion, perfusion, spectroscopy, and amino-acid PET features provide biologically informed characterization, support radiogenomic associations aligned with the 2021 World Health Organization Classification of Central Nervous System Tumors, fifth edition molecular taxonomy, and improve post-therapy response evaluation when morphology alone is equivocal. However, real-world translation remains limited by domain shifts across scanners and protocols, missing or inconsistent sequences, label noise, and uncertain ground truth in the post-treatment setting. Clinical adoption further depends on standards-based interoperability, human-in-the-loop quality assurance, usability and workflow fit, rigorous external validation including prospective and multi-reader studies, and clear lifecycle governance. Regulatory readiness requires alignment with United States Food and Drug Administration Software as a Medical Device guidance and the evolving European Union Medical Device Regulation and AI Act landscape, alongside transparent reporting and risk-of-bias appraisal using emerging guidelines that target prediction models incorporating AI. This review synthesizes end-to-end evidence and practical pathways for the safe and scalable deployment of AI in brain tumor neuroimaging, with an emphasis on segmentation-driven quantification, distinguishing pseudoprogression from true progression, probability calibration, and decision-curve evaluation of clinical utility.