  Radiomic models have been shown to outperform clinical data for outcome
prediction in glioblastoma (GBM). However, clinical implementation is limited
by lack of parameters standardization. We aimed to compare nine machine
learning classifiers, with different optimization parameters, to predict
overall survival (OS), isocitrate dehydrogenase (IDH) mutation,
O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal
growth factor receptor (EGFR) VII amplification and Ki-67 expression in GBM
patients, based on radiomic features from conventional and advanced MR. 156
adult patients with pathologic diagnosis of GBM were included. Three tumoral
regions were analyzed: contrast-enhancing tumor, necrosis and non-enhancing
tumor, selected by manual segmentation. Radiomic features were extracted with a
custom version of Pyradiomics, and selected through Boruta algorithm. A Grid
Search algorithm was applied when computing 4 times K-fold cross validation
(K=10) to get the highest mean and lowest spread of accuracy. Once optimal
parameters were identified, model performances were assessed in terms of Area
Under The Curve-Receiver Operating Characteristics (AUC-ROC). Metaheuristic and
ensemble classifiers showed the best performance across tasks. xGB obtained
maximum accuracy for OS (74.5%), AB for IDH mutation (88%), MGMT methylation
(71,7%), Ki-67 expression (86,6%), and EGFR amplification (81,6%). Best
performing features shed light on possible correlations between MR and tumor
histology.
