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Machine Learning in Meningioma MRI: Past to Present. A Narrative Review
Eleftherios Neromyliotis1, Theodosis Kalamatianos1, Athanasios Paschalis2
1Departent of Neurosurgery, University of Athens Medical School, National and Kapodistrian University of Athens, Athens, Greece.
Abstract:
Meningioma is one of the most frequent primary central nervous system tumors. While magnetic resonance imaging (MRI), is the standard radiologic technique for provisional diagnosis and surveillance of meningioma, it nevertheless lacks the prima facie capacity in determining meningioma biological aggressiveness, growth, and recurrence potential. An increasing body of evidence highlights the potential of machine learning and radiomics in improving the consistency and productivity and in providing novel diagnostic, treatment, and prognostic modalities in neuroncology imaging. The aim of the present article is to review the evolution and progress of approaches utilizing machine learning in meningioma MRI-based sementation, diagnosis, grading, and prognosis. We provide a historical perspective on original research on meningioma spanning over two decades and highlight recent studies indicating the feasibility of pertinent approaches, including deep learning in addressing several clinically challenging aspects. We indicate the limitations of previous research designs and resources and propose future directions by highlighting areas of research that remain largely unexplored. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2.
Insights
Machine learning and radiomics show promise in analyzing brain tumors like meningioma using MRI scans. These advanced techniques can improve diagnosis, grading, and prognosis, offering new tools for neuro-oncology.
Area of Science:
- Neuro-oncology
- Radiology
- Artificial Intelligence
Background:
- Meningioma is a common primary central nervous system tumor.
- Magnetic resonance imaging (MRI) is standard for diagnosis and surveillance but cannot reliably assess tumor aggressiveness or recurrence.
- Machine learning (ML) and radiomics offer potential for enhanced diagnostic and prognostic capabilities in neuroncology.
Purpose of the Study:
- To review the evolution and progress of ML approaches in meningioma MRI analysis.
- To highlight recent advancements, including deep learning, in segmentation, diagnosis, grading, and prognosis.
- To identify limitations and propose future research directions in ML for meningioma.
Main Methods:
- Review of historical and recent research on ML applications in meningioma.
- Focus on MRI-based segmentation, diagnosis, grading, and prognosis.
- Inclusion of deep learning methodologies.
Main Results:
- ML and radiomics show feasibility in improving consistency and productivity in meningioma imaging analysis.
- Deep learning approaches are emerging as powerful tools for addressing clinical challenges.
- Significant progress has been made over the past two decades.
Conclusions:
- ML and radiomics hold significant potential to advance meningioma diagnosis, grading, and prognosis.
- Further research is needed to explore under-researched areas and overcome current limitations.
- These technologies promise novel diagnostic and prognostic modalities in neuroncology.
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