Abstract
The peritumoral region (PTR) of glioblastoma (GBM) appears as a T2W-hyperintensity and is composed of microscopic tumor and edema. Infiltrative 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 the two groups (tumor and edema versus tumor alone). Patients with GBM and LGG imaged using a 1.5T 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 first-order and texture features were determined from each of T1W-contrast, and T2W-FLAIR, diffusion-weighted imaging sequences. Applying filtration techniques, a total of 3822 features were obtained. Different feature reduction techniques were employed, and a subsequent model was constructed using four machine learning classifiers. Leave-one-out cross-validation was used to assess classifier performance. The analysis included 42 GBM and 36 LGG. The best performance was obtained using AdaBoost classifier using all the features with a sensitivity, specificity, 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 F-test resulted in the most consistent feature selection with 3 T1W-contrast texture features chosen in over 90% of instances. Quantitative analysis of conventional MRI sequences can effectively demarcate GBM PTR from LGG, which is otherwise indistinguishable on visual estimation.
Highlights
Radiomics is an emerging field in medicine and oncology involving quantitative feature analysis of highquality radiographic images [1, 2]
Infiltrative low grade glioma (LGG) comprises tumor cells that seem similar to GBM peritumoral region (PTR) on Magnetic resonance imaging (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
Summary
Radiomics is an emerging field in medicine and oncology involving quantitative feature analysis of highquality radiographic images [1, 2]. Radiomic analysis has been widely undertaken in various CNS tumors, including gliomas, aiding in the differentiation between tumor histologies, grading, genetic profiling, and prognostication [5, 6]. We refer to grade 2 infiltrative gliomas as low-grade glioma (LGG), which typically are identified on MRI as T2-W hyperintense lesions. On MRI, distinct anatomical compartments can be identified in GBM, including tumor enhancement appearing as contrast-enhancing regions on T1-W sequences, with a central non-enhancing necrotic core and the adjacent peritumoural region (PTR) appearing as a T2-Weighted hyperintense area. In LGG, the areas of T2-W hyperintensity represent tumor, whereas GBM PTR is an admixture of microscopic infiltrative tumor and vasogenic edema [8, 9]. Infiltrative 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
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