Radiophysiomics: Brain Tumors Classification by Machine Learning and Physiological MRI Data.

ABSTRACT: The precise initial characterization of contrast-enhancing brain tumors has significant consequences for clinical outcomes. Various novel neuroimaging methods have been developed to increase the specificity of conventional magnetic resonance imaging (cMRI) but also the increased complexity of data analysis. Artificial intelligence offers new options to manage this challenge in clinical settings. Here, we investigated whether multiclass machine learning (ML) algorithms applied to a high-dimensional panel of radiomic features from advanced MRI (advMRI) and physiological MRI (phyMRI; thus, radiophysiomics) could reliably classify contrast-enhancing brain tumors. The recently developed phyMRI technique enables the quantitative assessment of microvascular architecture, neovascularization, oxyge

SUBMITTER: Stadlbauer A 

PROVIDER: S-EPMC9139355 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature