Radiomic features computed from multiparametric MRI were found to be relevant as early in-vivo biomarkers for pseudoprogression evaluation in recurrent glioblastoma patients. At baseline, predictive biomarker for pseudoprogression outcome was related to kurtosis parameter of FLAIR histogram plotted from abnormal hyper-intense signal area. When considering variation between baseline and first event (either pseudoprogression or true progression), four early biomarkers were found for entropy of T1-weighted, T1-weighted-post-contrast morphological MRI and Apparent Diffusion Coefficient maps derived from diffusion-weighted MRI.
Such early in-vivo biomarkers easily computed from automatic segmentation and first order radiomics analysis could be useful for the assessment of treatment response.
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