Tracing the Evolution of Diagnostic Imaging in Glioma Management

Adult-type diffuse gliomas are malignant primary brain tumors, posing significant challenges in the fields of neuro-oncology and neuroradiology. The aggressiveness and prognosis of this disease depend on several factors, such as the patient's age, the histopathological grade, and the molecular profile. According to the World Health Organization (WHO) Classification of Tumors of the Central Nervous System (CNS), published in the 2021 grading system, gliomas are classified as grade 1 to grade 4 tumors based on their histopathological and molecular characteristics. Grade 4 gliomas include both glioblastoma (IDH-wildtype) and astrocytoma (IDH-mutant, grade 4), as categorized by the WHO 2021 classification. Both are considered the most aggressive forms of glioma. Lower-grade gliomas have 5-year survival rates higher than high-grade gliomas (80% versus 5%). The evolution of diagnostic imaging techniques has played an essential role in improving the clinical management of gliomas, enabling better tumor characterization, treatment response assessment, and prognostication.

Magnetic resonance imaging (MRI) is considered the gold standard in neuroradiology practice for brain tumor examination to examine tumor lesion location, size, and extent. Functional and molecular imaging, such as magnetic resonance spectroscopy, perfusion-weighted imaging, and positron emission tomography (PET), can provide additional information on tumor metabolism, microstructure, and treatment-induced changes. The integration of artificial intelligence (AI) and radiomics has further enhanced imaging-based decision-making. This Research Topic explores the evolution of imaging technologies in glioma management, highlighting their impact on diagnosis, therapy planning, and personalized treatment strategies.

Regarding diagnostic imaging, challenges remain in analyzing multimodal data and cross-institutional standardizations. Several clinics follow recommendations from expert consensus to adapt their imaging protocols to acquire state-of-the-art imaging contrast necessary for diagnostic and post-surgical follow-up. Acquiring molecular profiles from gliomas may not be offered by several clinical sites, or at least they need more tailoring and investigations. On the other hand, the use of AI in neuro-oncology is progressing, whereas the application of these approaches is not yet clear in the glioma imaging domain. AI may contribute to workflow and standardization after being carefully examined in several multi-center investigations to achieve precise tumor characterization, treatment response assessment, and early recurrence detection. Specific imaging biomarker discovery or validation in distinguishing tumor progression from treatment-related changes, such as pseudo-progression or radiation necrosis, remains to be investigated.

Finally, and probably the most challenging task, is related to the diffuse nature of gliomas and the disease's heterogeneous nature, which complicates precise prognosis and therapy planning. To tackle the above-mentioned and several more grade-specific challenges, studies on AI-driven solutions, analysis, multi-parametric MRI, hybrid PET/MRI scanning, and novel molecular tracers need to be investigated. Synergistic and novel solutions involving computational models and integrated imaging may enable more personalized treatment strategies. By bringing together multidisciplinary research, this research aims to bridge gaps in knowledge and foster innovative approaches to glioma imaging.

For this specific issue, we welcome original research, reviews, mini-reviews, case reports, expert insight, and perspectives on the following themes:
• Advances in MRI techniques (e.g., functional MRI, perfusion imaging, spectroscopy) for glioma assessment
• PET imaging and novel radiotracers for glioma characterization
• AI and radiomics in glioma imaging for diagnosis, prognostication, and reporting
• Multimodal imaging approaches and their impact on treatment planning
• Challenges in differentiating true progression from treatment-related changes
• Imaging biomarkers for predicting glioma recurrence and survival
• Integration of imaging with computational models for personalized therapy

We encourage contributions from researchers in neuroimaging, oncology, radiology, and AI to foster a comprehensive understanding of imaging advancements in glioma management.

Please note that manuscripts consisting solely of bioinformatics or computational analysis of public omics databases that are not supplemented by relevant functional validation (clinical cohort or biological validation in vitro or in vivo) are out of scope for this Research Topic.