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Gliomas are the most common malignant tumors in the central nervous system and are known for their inherent diversity and propensity to invade surrounding tissue. These features pose significant challenges in diagnosing and treating these tumors. Magnetic resonance imaging (MRI) has not only remained at the forefront of glioma management but has also evolved significantly with the advent of multimodal MRI. The rise of multimodal MRI represents a pivotal leap forward, as it seamlessly integrates diverse MRI sequences and advanced techniques to offer an unprecedented, comprehensive, and multidimensional glimpse into the complexities of glioma pathology, including encompassing structural, functional, and even molecular imaging. This holistic approach empowers clinicians with a deeper understanding of tumor characteristics, enabling more precise diagnoses, tailored treatment strategies, and enhanced monitoring capabilities, ultimately improving patient outcomes. Looking ahead, the integration of artificial intelligence (AI) with MRI data heralds a new era of unparalleled precision in glioma diagnosis and therapy. This integration holds the promise to revolutionize the field, enabling more sophisticated analyses that fully leverage all aspects of multimodal MRI. In summary, with the continuous advancement of multimodal MRI techniques and future deep integrations with artificial intelligence, glioma care is poised to evolve toward increasingly personalized, precise, and efficacious strategies.
Yan et al. (Thu,) studied this question.