Non-invasive, imaging-based examination of glioma biology has received increasing attention in the past couple of years. To this end, the development and refinement of novel MRI techniques, reflecting underlying oncogenic processes such as hypoxia or angiogenesis, has greatly benefitted this research area. We have recently established a novel BOLD (blood oxygenation level dependent) based MRI method for the measurement of relative oxygen extraction fraction (rOEF) in glioma patients. In a set of 37 patients with newly diagnosed glioma, we assessed the performance of a machine learning model based on multiple MRI modalities including rOEF and perfusion imaging to predict WHO grade. An oblique random forest machine learning classifier using the entire feature vector as input yielded a five-fold cross-validated area under the curve of 0.944, with 34/37 patients correctly classified (accuracy 91.8%). The most important features in this classifier as per bootstrapped feature importance scores consisted of standard deviation of T1-weighted contrast enhanced signal, maximum rOEF value and cerebral blood volume (CBV) standard deviation. This study suggests that multimodal MRI information reflects underlying tumor biology, which is non-invasively detectable through integrative data analysis, and thus highlights the potential of such integrative approaches in the field of radiogenomics.

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Hypoxia plays an important role for the prognosis and therapy response of cancer. Thus, hypoxia imaging would be a valuable tool for pre-therapeutic assessment of tumor malignancy. However, there is no standard validated technique for clinical application available yet. Therefore, we performed a study in 12 patients with high-grade glioma, where we directly compared the two currently most promising techniques, namely the MR-based relative oxygen extraction fraction (MR-rOEF) and the PET hypoxia marker H-1-(3-[ F]-fluoro-2-hydroxypropyl)-2-nitroimidazole ([ F]-FMISO). MR-rOEF was determined from separate measurements of T , T * and relative cerebral blood volume (rCBV) employing a multi-parametric approach for quantification of the blood-oxygenation-level-dependent (BOLD) effect. With respect to [ F]-FMISO-PET, besides the commonly used late uptake between 120 and 130 min ([ F]-FMISO ), we also analyzed the hypoxia specific uptake rate [ F]-FMISO-k , as obtained by pharmacokinetic modeling of dynamic uptake data. Since pharmacokinetic modeling of partially acquired dynamic [ F]-FMISO data was sensitive to a low signal-to-noise-ratio, analysis was restricted to high-uptake tumor regions. Individual spatial analyses of deoxygenation and hypoxia-related parameter maps revealed that high MR-rOEF values clustered in (edematous) peritumoral tissue, while areas with high [ F]-FMISO concentrated in and around active tumor with disrupted blood-brain barrier, i.e. contrast enhancement in T -weighted MRI. Volume-of-interest-based correlations between MR-rOEF and [ F]-FMISO as well as [ F]-FMISO-k , and voxel-wise analyses in individual patients, yielded limited correlations, supporting the notion that [ F]-FMISO uptake, even after 2 h, might still be influenced by perfusion while [ F]-FMISO-k was severely hampered by noise. According to these results, vascular deoxygenation, as measured by MR-rOEF, and severe tissue hypoxia, as measured by [ F]-FMISO, show a poor spatial correspondence. Overall, the two methods appear to rather provide complementary than redundant information about high-grade glioma biology.

Our results show that the relation between peak values of MR-based rCBV and static FET-uptake can also be observed intra-individually on a voxel basis and also applies to a dynamic FET parameter, possibly determining hotspots of higher biological malignancy. However, just a small part of the FET-PET signal variance is explained by rCBV and tumour volumes determined by the two modalities showed only moderate overlap. These findings indicate that FET-PET and MR-based rCBV provide both congruent and complimentary information on glioma biology.

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