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.</p

Differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation

The purpose of this study was to establish a high-performing radiomics strategy with machine learning from conventional and diffusion MRI to differentiate recurrent glioblastoma (GBM) from radiation necrosis (RN) after concurrent chemoradiotherapy (CCRT) or radiotherapy. Eighty-six patients with GBM were enrolled in the training set after they underwent CCRT or radiotherapy and presented with new or enlarging contrast enhancement within the radiation field on follow-up MRI. A diagnosis was established either pathologically or clinicoradiologically (63 recurrent GBM and 23 RN). Another 41 patients (23 recurrent GBM and 18 RN) from a different institution were enrolled in the test set. Conventional MRI sequences (T2-weighted and postcontrast T1-weighted images) and ADC were analyzed to extract 263 radiomic features. After feature selection, various machine learning models with oversampling methods were trained with combinations of MRI sequences and subsequently validated in the test set. In the independent test set, the model using ADC sequence showed the best diagnostic performance, with an AUC, accuracy, sensitivity, specificity of 0.80, 78%, 66.7%, and 87%, respectively. In conclusion, the radiomics models models using other MRI sequences showed AUCs ranging from 0.65 to 0.66 in the test set. The diffusion radiomics may be helpful in differentiating recurrent GBM from RN..ope