Radiomics for diagnostic process and comprehensive treatment in glioblastoma: clinical case

The paper highlights diagnostic and therapeutic options for glioblastoma. Glioblastoma is known to be a neuroepithelial malignant with an aggressive clinical course and extremely adverse prognosis. It is pointed out that contrast-enhanced magnetic resonance imaging (MRI) is the “gold standard” in glioblastoma diagnostics. Special attention is paid to radiomics that presents a multi-stage process involving image acquisition and pre-processing, segmentation, feature extraction and selection, and advanced statistics using machine learning algorithms. The aim of the study is to investigate objective numerical control options of pathological process dynamics and monitoring of the comprehensive glioblastoma treatment effectiveness in a particular patient according to the informative parameters of MR-images. Primary confirmation of objectifying diagnostic and treatment process in patient with glioblastoma according to the indicated statistical parameters of T2-weighted images was obtained. Further research should be aimed at the use of radiomics for planning, monitoring treatment of glioblastoma, predicting clinical outcomes.