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Preoperative clinical MRI protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this second part, we review magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), susceptibility-weighted imaging (SWI), MRI-PET, MR elastography (MRE), and MR-based radiomics applications. The first part of this review addresses dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) MRI, arterial spin labeling (ASL), diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting (MRF). Evidence Level: 3. Technical Efficacy: Stage 2.

Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications.Evidence Level: 3Technical Efficacy: Stage 2

Within the next twenty years, the number of cancer patients is expected to rise by 70%. Current cancer treatments still face several limitations, such as severe side effects and a high incidence of disease recurrence. Drug combination therapies are a promising strategy to achieve higher therapeutic effects while reducing side effects. This new direction in cancer drug research has led to data-driven medicine. To predict whether certain drugs would have a synergistic effect when combined, the DREAM Challenge coordinators released data for thousands of experimentally tested drug combinations. The DREAM Challenge served as inspiration for selecting drug combinations that have the potential to be synergistic. We here describe an approach using biological pathway knowledge and applying this to the selected combinations with a previously described mathematical model, the Loewe-Additivity approach. The calculated interaction index (II) served to distinguish between synergistic (II < 1), additive (II = 1) and antagonistic (II > 1). Pre-selection of putative drug combinations was performed prior to synergy prediction based on four case scenarios: 1) two drugs share the same target protein, 2) two drugs share the same pathway, 3) drugs are separated by one degree from two targets or 4) drugs are separated by more than one degree from two targets but act upon the same biological pathway. Results: The first method tried was using a drug synergy prediction method called the Loewe-Additivity model, in which two drugs share the same target and form the initial findings for this paper. The Loewe model acts as a baseline estimation to see if more combinations can be identified using the other methods tested. The remaining methods used were able to find additional drug combinations that were not proposed by the standard Loewe model. Although the additional methods did find additional combinations that would be predicted to be synergistic, a prediction is not a guarantee of success, so validation of the new or novel combinations would be needed to verify their effectiveness. This could be done by comparing our results to known data or against biological assays.

Purpose Current challenges of in vivo CEST imaging include overlapping signals from different pools. The overlap arises from closely resonating pools and/or the broad magnetization transfer contrast (MTC) from macromolecules. This study aimed to evaluate the feasibility of variable delay multipulse (VDMP) CEST to separately assess solute pools with different chemical exchange rates in the human brain in vivo, while mitigating the MTC. Methods VDMP saturation buildup curves were simulated for amines, amides, and relayed nuclear Overhauser effect. VDMP data were acquired from glutamate and bovine serum albumin phantoms, and from six healthy volunteers at 7T. For the in vivo data, MTC removal was performed via a three-pool Lorentzian fitting. Different B-1 amplitudes and mixing times were used to evaluate CEST pools with different exchange rates. Results The results show the importance of removing MTC when applying VDMP in vivo and the influence of B-1 for distinguishing different pools. Finally, the optimal B-1 and mixing times to effectively saturate slow- and fast-exchanging components are also reported. Slow-exchanging amides and rNOE components could be distinguished when using B-1 = 1 mu T and t(mix) = 10 ms and 40 ms, respectively. Fast-exchanging components reached the highest saturation when using a B-1 = 2.8 mu T and t(mix) = 0 ms. Conclusion VDMP is a powerful CEST-editing tool, exploiting chemical exchange-rate differences. After MTC removal, it allows separate assessment of slow- and fast-exchanging solute pools in in vivo human brain.