Advances in MRI Radiomics for Non-Invasive Glioma Assessment

Glioma research has rapidly evolved in recent years, particularly in the context of diagnostic imaging and personalized medicine. Gliomas, representing the most prevalent malignant primary brain tumors, are remarkably heterogeneous, posing significant challenges for classification, treatment, and prognosis. While magnetic resonance imaging (MRI) remains indispensable for diagnosing and monitoring gliomas, traditional visual assessments are often insufficient to uncover the full spectrum of tumor heterogeneity and correlate with underlying molecular features. With advances in computational methods, MRI radiomics has emerged as a transformative approach by extracting large sets of quantitative features from routine imaging, promising to bridge the gap between imaging phenotypes and the molecular landscape of brain tumors.

This Research Topic aims to evaluate the current and emerging applications of MRI radiomics in glioma characterization, focusing on its capacity to support non-invasive diagnosis and evidence-based clinical decision-making. The main objectives are to assess radiomics in distinguishing between benign and malignant lesions, predicting tumor grade, and inferring critical molecular alterations such as IDH mutation status and 1p/19q codeletion. Further aims include the integration of radiomics data with clinical, genomic, and histopathological parameters to refine prognostic models, as well as investigating the use of radiomic features in monitoring therapeutic response and early detection of disease progression.

The scope of this article collection encompasses both methodological advancements and clinical investigations in MRI radiomics for glioma management, with an emphasis on practical implementation in neuro-oncology. Submissions are expected to address, but are not limited to, the following themes:

- Automated and manual tumor segmentation methods
- Strategies for MRI acquisition standardization and data harmonization
- Machine learning and artificial intelligence algorithms for radiomics analysis
- Predictive modeling for molecular profiles and treatment outcomes
- Validation protocols and compliance with radiomics quality standards
- Multi-modal data integration: combining radiomics with genomics, histology, and clinical data
- Translation of radiomics findings into routine neuro-oncological practice

This Research Topic focuses exclusively on adult gliomas and does not encompass pediatric gliomas, which are biologically and clinically distinct entities.

Please note: manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this Research Topic.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Case Report
• Clinical Trial
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Case Report
• Clinical Trial
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Glioma, MRI radiomics, molecular profiling, machine learning, neuro-oncology

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.