BackgroundThe peritumoral region (PTR) of glioblastoma (GBM) appears as a T2W-hyperintensity and is characterized by microscopic tumor and edema. In ltrative low grade glioma (LGG) comprises tumor cells that seem similar to GBM PTR on MRI. The work here explored if a radiomics-based approach can distinguish between LGG and GBM PTR, which can have future implications on existing treatment paradigms.
MethodsPatients with GBM and LGG imaged using a 1.5 T MRI were included in the study. Image data from cases of GBM PTR, and LGG were manually segmented guided by T2W hyperintensity. A set of 91 rst-order and texture features were determined from each of T1W-contrast, and T2W-FLAIR, diffusion-weighted imaging sequences. Applying ltration techniques, a total of 3822 features were obtained. Different feature reduction techniques were employed, and a subsequent model was constructed using four machine learning classi ers. Leave-one-out cross-validation was used to assess classi er performance.
ResultsThe analysis included 42 GBM and 36 LGG. The best performance was obtained using AdaBoost classi er using all the features with a sensitivity, speci city, accuracy, and area of curve (AUC) of 91%, 86%, 89%, and 0.96, respectively. Amongst the feature selection techniques, the recursive feature elimination technique had the best results, with an AUC ranging from 0.87 to 0.92. Evaluation with the Ftest resulted in the most consistent feature selection with 3 T1W-contrast texture features chosen in over 90% of instances.
ConclusionsQuantitative analysis of conventional MRI sequences can effectively demarcate GBM PTR from LGG, which is otherwise indistinguishable on visual estimation.