Differentiation of Recurrent Glioblastoma Multiforme and Radiation Necrosis using Magnetic Resonance Imaging and Computerized Approaches: A Review



Glioblastoma Multiforme (GBM) is a highly aggressive brain tumor originating from glial cells that is a subset of higher-grade gliomas (HGG). Given the extreme malignancy of GBM and HGG, radiotherapy is often used to shrink tumor and inhibit tumor cell function. Despite the use of radiotherapy, GBM recurrence rates remain high, and complications, such as radiation necrosis, can arise. Recurrent GBM and radiation necrosis are nearly indistinguishable using current imaging techniques, which is a considerable challenge in management of GBM treatment. Radiation necrosis is treated conservatively using corticosteroids while recurrent GBM requires aggressive treatments given its markedly short prognosis. Currently, invasive biopsy is the only available method for accurate differentiation of recurrent GBM from radiation necrosis. Clearly, noninvasive differentiation techniques are imperative to effective clinical decision-making surrounding GBM treatment. Many studies have attempted to use conventional MRI, advanced MRI parameters, modalities, and techniques, and machine learning methods to solve this crucial problem. In this review, we attempt to overview the difficulty of differential diagnosis and analyze the current state of knowledge on image-based differentiation approaches utilizing MRI. We identify major gaps in the research and make suggestions to improve current tactics and direct future investigations.


Glioma; Glioblastoma; Radiotherapy; Necrosis; MRI; Machine Learning

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