Neurosurgery 96:1181–1192, 2025
This review highlights how advanced neuroimaging and machine learning, especially radiomics and deep learning models, are transforming the noninvasive diagnosis, molecular characterization, and prognosis prediction in IDH-wildtype glioblastoma, offering improved patient stratification and personalized treatment strategies while emphasizing the need for further clinical integration.
• Neuroimaging and machine learning have greatly improved diagnosis, classification, and prognosis of IDH-wildtype glioblastoma, a highly heterogeneous and aggressive brain tumor.
• Advanced MRI techniques, including diffusion tensor imaging (DTI) and radiomics, provide noninvasive insights into tumor infiltration, metabolic profiles, and microstructural changes.
• Machine learning algorithms, especially CNNs, enhance glioblastoma characterization, enabling accurate prediction of genetic mutations, IDH status, tumor subtypes, and survival outcomes.
• Radiomics extracts quantitative features from neuroimages, serving as potential biomarkers for tumor classification, prognosis, and guiding treatment strategies.
• Integration of radiomics and machine learning helps differentiate pseudoprogression from true tumor progression and predicts patterns of tumor invasion and recurrence.
• Imaging biomarkers and machine learning models are promising but remain complementary to molecular diagnostics and are not yet standard in clinical practice.
• Ongoing research aims to refine models, integrate emerging imaging techniques, and better link imaging features to underlying molecular processes for personalized therapy.
• The synergy of neuroimaging and AI is expected to enable noninvasive, precision management and better outcomes for glioblastoma patients.
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