Introduction
Meningiomas represent the most common primary intracranial tumors, and recent advances in large-scale genomic and epigenomic profiling have reshaped their biological classification frameworks. The cIMPACT-NOW update 8 has highlighted the importance of integrating molecular signatures—such as DNA methylation classes and chromosomal alterations—into clinical decision-making. As precision oncology grows, one key question is whether non-invasive imaging can reflect these complex molecular landscapes. Magnetic Resonance Imaging (MRI), with its rich structural and textural information, offers a promising avenue. By incorporating radiomics and machine-learning models, researchers aim to capture molecular heterogeneity directly from imaging, potentially reducing dependency on invasive tissue sampling. This introduction sets the foundation for exploring how radiomics-based MRI analysis may transform risk stratification and treatment planning in meningioma management.
Advances in Molecular Profiling of Meningiomas
Large-scale epigenomic studies have uncovered significant molecular diversity within meningiomas, moving beyond conventional WHO grading. DNA methylation profiling and copy-number variation analyses have identified biologically distinct tumor subgroups with different prognoses and therapeutic implications. These molecular systems provide a more accurate prediction of recurrence risk and treatment response compared to histopathology alone. As molecular criteria increasingly supplement or supersede traditional grading, understanding how these patterns correlate with imaging characteristics becomes essential. The integration of these datasets underscores the evolution toward biologically driven classification frameworks.
Radiomics: A Non-Invasive Window Into Tumor Biology
Radiomics has emerged as a powerful tool that transforms MRI scans into quantitative datasets capturing tissue heterogeneity, morphology, and microenvironment features. By leveraging advanced segmentation frameworks such as BraTS pipelines, researchers extract high-dimensional features from tumor core and edema regions. These features can correlate with underlying molecular alterations, enabling models to infer biological risk signatures. Radiomics thereby offers a non-invasive “imaging biomarker” approach, potentially allowing earlier and more accessible prediction of aggressive behavior or specific genetic alterations before surgical sampling.
Machine Learning Models for Molecular Risk Prediction
Random Forest classifiers trained on radiomic signatures have shown strong diagnostic potential in predicting molecular risk categories within meningiomas. In particular, accuracy above 91% for integrated molecular risk groups demonstrates that radiomic patterns align closely with epigenetic and copy-number alterations. The ability to predict specific markers such as 1p loss with 87.5% accuracy and AUC 0.90 further supports the biological sensitivity of radiomics. These findings highlight how machine learning models can bridge imaging and molecular diagnostics, providing rapid, automated assessments that may guide preoperative planning.
Challenges in Predicting WHO Grade Using MRI
Despite promising results in molecular risk prediction, radiomics-based models show inferior performance in predicting WHO grade, with accuracy around 76.8%. This limitation emphasizes the imperfect alignment between histopathological grading criteria and MRI-based phenotypes. Tumor grade often relies on microscopic features such as mitotic count and necrotic foci that are not always reflected in gross radiographic appearance. This discrepancy suggests that while MRI captures molecular risk effectively, WHO grade remains more challenging due to its histology-centric foundation. Future studies should explore integrating radiomics with advanced MRI sequences or multimodal imaging to improve prediction accuracy.
Clinical Implications and Future Research Directions
The ability of MRI-radiomics models to approximate key molecular attributes offers significant clinical potential. Preoperative prediction of high-risk molecular signatures could help prioritize surgical strategies, biopsy planning, early referral for adjuvant therapy, or expedited confirmatory molecular testing. However, transitioning these models into routine clinical pathways requires robust external validation, harmonization across MRI scanners, and integration with prospective trials. Future research should broaden datasets across institutions, enhance model interpretability, and explore multimodal approaches combining radiomics, genomics, and liquid biopsy data. Ultimately, these advancements may redefine meningioma management through precision imaging.
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