## 3 Results  
Analysis of oxygenation and perfusion maps in both groups revealed OEF to be lower and CBF to be generally higher than in normal-appearing brain, leading to a net positive effect on CMRO\({}_{2}\), meaning a higher oxygen metabolism in tumor tissue than in an unaffected contralateral brain, both in GBM and in cMET patients. The OEF map of a 54-year-old female patient with left occipito-temporal GBM supplied in Figure 3 gives an illustrative example of the oxygenation values emulated by the artificial neural network. The patient's brain shows largely uniform OEF values across \"healthy\" brain matter. The solid-appearing region of the tumor displays a strong OEF signal compared to the surrounding tissue and the rest of the brain while OEF in the peritumoral edematous area appears to be lower than in contralateral white matter. When comparing oxygenation and perfusion parameters between GBM and cMET, OEF in CET was found to be significantly (\(p\) = 0.03) lower in GBM than in cMET. No significant differences were found between CET of GBM and cMET in terms of CBF (\(p\) = 0.33) and CMRO\({}_{2}\) (\(p\) = 0.15). For cMET patients, all parameters, i.e., OEF, CBF and CMRO\({}_{2}\), were significantly (\(p\) = 0.01) higher in the CET region than in the NET2 region. Meanwhile, for the GBM group, neither the difference between CET and NET2 in OEF (\(p\) = 0.11), nor CBF(\(p\) = 0.15), nor CMRO\({}_{2}\) (\(p\) = 0.08) was significant. A visual representation of this can be found in the boxplots supplied in Figure 4a. For an overview of oxygenation and perfusion parameters on a patient-by-patient basis, please consult Table S1 (supplement). The ratio of CET divided by NET2 was demonstrated to be another useful metric for differentiation of GBM and cMET. While OEF for CET/NET2 was not significantly different between GBM and cMET patients (\(p\) = 0.12), the ratio for CBF was significantly higher for cMET (\(p\) = 0.04), as was the ratio for CMRO\({}_{2}\) (\(p\) = 0.01). Boxplots depicting these quotients are displayed in Figure 4b. After fitting a classifier support-vector machine to the oxygenation and perfusion features, different metrics for binary classification of GBM and cMET were assessed. The receiver operating characteristic curves of the ratios of OEF, CBF and CMRO\({}_{2}\) in CET divided by NET2 as well as the best overall feature combination in terms of AUC (OEF in CET and CMRO\({}_{2}\) in CET/NET2) are exhibited in Figure 5.  
Figure 4: Boxplots (**a**) comparing OEF, CBF and CMRO\({}_{2}\) between CET and NET2 for GBM and cMET and (**b**) displaying the ratios of OEF, CBF and CMRO\({}_{2}\) in CET divided by NET2 for GBM and cMET patients. Box: first to third quartile; whiskers: 1.5 times the interquartile distance or the maximum/minimum value, if contained therein; red line: median. Outliers are displayed as red crosses. Significant differences (\(p\) < 0.05) are marked with an asterisk, the highly significant difference (\(p\) = 0.01) is marked with two asterisks. OEF: oxygen extraction fraction; CBF: cerebral blood flow; CMRO\({}_{2}\): cerebral metabolic rate of oxygen; CET: contrast-enhancing tumor; NET2: peritumoral non-enhancing T2 FLAIR hyperintensity. For each parameter, values for accuracy, optimal sensitivity and specificity and AUC are listed in Table 1. CMRO2 \(\text{CET/NET2}\) emerged as the best single feature for differentiation of GBM from cMET. The resulting model had an AUC of 0.85 with an accuracy of 83% at an optimal sensitivity and specificity of 85% and 82%, respectively. The next best single classification features in terms of AUC were the ratio \(\text{CBF}_{\text{CET/NET2}}\) (0.80) and OEF in CET (0.79). The highest discriminative power with the best diagnostic accuracy was achieved by combining \(\text{OEF}_{\text{CET}}\) and \(\text{CMRO}_{\text{2 CET/NET2}}\) for fitting a support-vector machine (AUC = 0.94). This allowed for an accurate classification of the tumors in 93% of cases, with a sensitivity of 99% and a specificity of 88%.