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Published on: June 7, 2020
Advances of MR imaging in glioma: what the neurosurgeon needs to know
1Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden. anna.falk.delgado@ki.se.
Abstract:
Glial tumors and especially glioblastoma present a major challenge in neuro-oncology due to their infiltrative growth, resistance to therapy, and poor overall survival-despite aggressive treatments such as maximal safe resection and chemoradiotherapy. These tumors typically manifest through neurological symptoms such as seizures, headaches, and signs of increased intracranial pressure, prompting urgent neuroimaging. At initial diagnosis, MRI plays a central role in differentiating true neoplasms from tumor mimics, including inflammatory or infectious conditions. Advanced techniques such as perfusion-weighted imaging (PWI) and diffusion-weighted imaging (DWI) enhance diagnostic specificity and may prevent unnecessary surgical intervention. In the preoperative phase, MRI contributes to surgical planning through the use of functional MRI (fMRI) and diffusion tensor imaging (DTI), enabling localization of eloquent cortex and white matter tracts. These modalities support safer resections by informing trajectory planning and risk assessment. Emerging MR techniques, including magnetic resonance spectroscopy, amide proton transfer imaging, and 2HG quantification, offer further potential in delineating tumor infiltration beyond contrast-enhancing margins. Postoperatively, MRI is important for evaluating residual tumor, detecting surgical complications, and guiding radiotherapy planning. During treatment surveillance, MRI assists in distinguishing true progression from pseudoprogression or radiation necrosis, thereby guiding decisions on additional surgery, changes in systemic therapy, or inclusion into clinical trials. The continued evolution of MRI hardware, software, and image analysis-particularly with the integration of machine learning-will be critical for supporting precision neurosurgical oncology. This review highlights how advanced MRI techniques can inform clinical decision-making at each stage of care in patients with high-grade gliomas.
Insights
Advanced MRI techniques are crucial for diagnosing and managing high-grade gliomas. These magnetic resonance imaging (MRI) methods improve surgical planning, treatment monitoring, and distinguishing tumor progression from other conditions.
Area of Science:
- Neuro-oncology
- Radiology
- Neurosurgery
Background:
- High-grade gliomas, particularly glioblastoma, pose significant challenges in neuro-oncology due to infiltrative growth, therapeutic resistance, and poor survival rates.
- Neurological symptoms often necessitate urgent neuroimaging for diagnosis and differentiation from other conditions.
Purpose of the Study:
- To review the role of advanced magnetic resonance imaging (MRI) techniques in the comprehensive management of high-grade gliomas.
- To highlight how various MRI modalities inform clinical decision-making throughout the patient care pathway.
Main Methods:
- Review of current and emerging advanced MRI techniques, including perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI), functional MRI (fMRI), diffusion tensor imaging (DTI), magnetic resonance spectroscopy, amide proton transfer imaging, and 2HG quantification.
- Discussion of MRI's application in initial diagnosis, preoperative planning, postoperative evaluation, and treatment surveillance.
Main Results:
- Advanced MRI techniques enhance diagnostic specificity, aid in surgical planning by mapping critical brain structures, and improve the assessment of tumor extent.
- Postoperative MRI is vital for evaluating treatment response, detecting complications, and guiding radiotherapy.
- During surveillance, MRI helps differentiate tumor progression from pseudoprogression or radiation necrosis, influencing subsequent treatment strategies.
Conclusions:
- Advanced MRI techniques are indispensable tools in precision neurosurgical oncology for high-grade gliomas.
- Continued advancements in MRI technology, including machine learning integration, will further enhance clinical decision-making and patient outcomes.
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