Artificial intelligence-driven radiological biomarkers: A narrative review of artificial intelligence in meningioma diagnosis

Abstract

Meningiomas, the most common primary intracranial tumors, present unique challenges in diagnosis and management due to their diverse behaviors and potential for recurrence. Recent advances in artificial intelligence, particularly machine learning and deep learning, are transforming the way radiologists detect, classify, and predict outcomes for these tumors. Artificial intelligence-driven techniques enable precise extraction of imaging biomarkers from magnetic resonance imaging and computed tomography scans, enhancing diagnostic accuracy by distinguishing subtle tumor characteristics not readily visible to the human eye. Hybrid models that integrate multiple imaging modalities, such as T1-weighted and contrast-enhanced magnetic resonance imaging, further improve assessment accuracy by providing a comprehensive view of tumor heterogeneity and invasiveness. Moreover, radiomics, an artificial intelligence application that analyzes quantitative imaging features, has shown promise in predicting meningioma grade and recurrence risk, supporting personalized treatment strategies. Another area of progress involves multi-habitat analysis, which examines tumor heterogeneity within various “habitats” in a single tumor. This approach, combined with unsupervised machine learning algorithms, enhances the ability to differentiate low-grade from high-grade meningiomas by capturing differences in cellular structure. Artificial intelligence models incorporating clinical and molecular data, such as the Ki-67 proliferation index, are advancing recurrence prediction, which is critical for optimizing postoperative follow-up. Predictive models based on artificial intelligence are also being developed to assess quality of life outcomes, guiding supportive care in ways that optimize resources while addressing specific patient needs. Despite these advancements, several challenges remain, including variability in imaging protocols and the need for large, annotated datasets to ensure model generalizability. Future research should focus on developing robust algorithms capable of handling diverse data sources, improving the integration of functional and anatomical imaging modalities, and enhancing tumor heterogeneity analysis. These steps will bring artificial intelligence closer to routine clinical implementation, ultimately contributing to better patient outcomes through tailored, data-driven meningioma management. In summary, artificial intelligence-driven radiological biomarkers, machine learning models for tumor grading, multi-modality imaging, and the ability to predict recurrence and quality of life represent transformative developments in the field of meningioma diagnostics.

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