Grouping gliomas using the telomerase reverse transcriptase (TERT) gene and IDH mutations, and 1p/19q co-deletion status was demonstrated to be useful previously for clinical decisions. MR based radiogenomics might potentially be advantageous.
The aim of this study was to determine for the first time whether full distributions of the fractional anisotropy, relative anisotropy and ADC in normal appearing white matter were adequate predictors for machine learning algorithms to classify molecular subgroups based on TERT, IDH and 1p/19q co-deletion information.
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