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 B1 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 B1 for distinguishing different pools. Finally, the optimal B1 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 B1 = 1 μT and tmix = 10 ms and 40 ms, respectively. Fast‐exchanging components reached the highest saturation when using a B1 = 2.8 μT and tmix = 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.
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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: 3 Technical Efficacy: Stage 2
Background Perfusion MRI by Arterial Spin Labeling (ASL) and Dynamic Susceptibility Contrast (DSC) has shown its potential for differentiating tumor progression from pseudo-progression in glioblastoma patients. The ASL scans can be affected by arterial transit time (ATT) delays, which could be caused by treatment effects due to concomitant radiochemotherapy. A prolonged ATT is present as apparent signal increase in the large arteries due to labeled spins still residing within the vasculature, leading to underestimation of tissue perfusion and thus potentially affecting clinical decision-making. The research questions were: 1) Do delayed ATTs lead to a difference in the visual assessment of ASL perfusion (normal/increased) maps compared to DSC-MRI?; 2) Does the radiological evaluation (progression vs. pseudo-progression) of ASL and DSC perfusion maps differ when ATT artifacts are present?; 3) Do delayed ATTs affect the predictive value of ASL-MRI scans 3 months post-radiotherapy for detecting true disease progression? Material and Methods This retrospective, single-center study included 68 adult patients with histologically confirmed glioblastoma who received postoperative radio(chemo)therapy. ASL and DSC scans were acquired 3 months post-radiotherapy as part of routine clinical follow-up. The perfusion data were visually scored by a neuroradiologist who determined presence/absence of ATT artifacts and their severity (%), perfusion of the enhancing tumor lesion and the radiological evaluation of tumor progression versus pseudo-progression. Presence of true disease progression was determined by follow-up of clinical data until 9 months post-radiotherapy available for 49/68 patients. Logistic regression was performed with gender, age, treatment type and tumor genetic status as covariates to assess the predictive value of ASL. Results In 78% of the patients ATT artifacts were present. No statistically significant association between the agreement of the perfusion maps and presence of ATT artifacts was found, but presence of ATT artifacts lowered the agreement between the DSC and ASL radiological evaluation. The logistic regression analysis showed that the ASL-based radiological score could not predict true disease progression, whereas higher age and unmethylated MGMT gene were associated with progression. Presence of ATT artifacts was not associated with tumor progression. Conclusion The presence of delayed ATT in ASL data seems to impact the radiological evaluation of ASL data, steering interpretation towards tumor progression (as compared to the DSC evaluation), whereas in patients without ATT artifacts ASL and DSC provide more similar radiological scores. Therefore, it is highly recommended to consider these artifacts when interpreting ASL perfusion MRI to differentiate between tumor progression and pseudo-progression in glioblastoma patients.
In the clinical follow-up of glioblastoma patients, presence of delayed arterial transit times (ATT) could affect the evaluation of ASL perfusion data. In this retrospective study the influence of the presence and severity of ATT-artifacts on perfusion assessment and differentiation between tumor progression and pseudo-progression were studied. The results show that the presence of ATT-artifacts lowers the agreement between radiological evaluation of DSC-MRI and ASL, although the severity of ATT-artifacts did not have significant influence. In conclusion, detection of ATT-artifacts is important as it could affect radiological evaluation of ASL-data. Future work aims to include additional quantitative perfusion measures.
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.
BACKGROUND Gliomas can be highly heterogeneous on brain MRI, especially at higher grades. These tumors often include a low-grade component and/or surrounding edema. Both these components appear as hyperintense regions on clinical T2-weighted MR images and cannot be accurately distinguished. Ultrahigh field 7T MR systems have the potential for a higher sensitivity due to its increase signal-to-noise ratio (SNR). This could allow for enhanced detailed visualization of gliomas. Therefore our goal was to assess the difference in extent of T2 hyperintense regions in glioma patients between clinical (1.5T/3T) and 7T MR images. MATERIAL AND METHODS We prospectively scanned 6 glioma patients (3 glioblastomas (GBM) WHO grade IV post-operative, 1 anaplastic diffuse glioma (ADG) WHO grade III, 1 biopsied oligodendroglioma (OD) WHO grade II and 1 suspected low-grade glioma (LGG); 4 Females, mean age: 56.8 years and 2 Males, mean age: 45.5 years) on a routine clinical MRI scanner (3T or 1.5T) and on a 7T MRI scanner (Philips Achieva). Resolution of the T2-weighted image at 7T, 3T and 1.5T: 0.75mm3, 3mm3 and 5mm3, respectively. T2-weighted scans were visually assessed by an independent researcher and experienced neuro-radiologist. The T2 hyperintense regions were measured in 3 directions and compared brain structures as reference points between the clinical and 7T scans (e.g. brain stem, gyri), also considering differences in slice thickness and spatial resolution. RESULTS T2 hyperintensity lesion measurements in the AP direction were as follows: GBM: 114.7mm (clinical field strength) vs 115.56mm (7T); ADG: 45mm vs 58.87mm and 24.7mm vs 25.55mm; OD: 45.1mm vs 48.5mm; GBM: 45.3mm vs 44.75mm; suspected LGG: 47.4mm vs 51.57mm; GBM: 105.8mm vs 114.5mm. We observed the largest difference between 3T and 7T T2 hyperintense region measurement to be 13.87mm (30%), belonging to a GBM WHO grade IV patient. For all patients the T2 hyperintense region was measured on average 8.3% larger at 7T MRI compared to the routine clinical MRI scan. Overall the T2 hyperintense regions at 7T appeared sharper and tumor boundaries were better defined. Likewise, the contrast between the tumor and healthy tissue was enhanced, most likely because of the increase in spatial resolution. CONCLUSION In gliomas the T2 hyperintense regions were more extensive at 7T MRI compared to the routine clinical MRI. These preliminary results might indicate that at clinical field strength the extent of the lesion is underestimated and that 7T MRI can potentially aid in detecting this more extensive disease process. Therefore, 7T MRI scan in gliomas might have a future role in treatment decisions regarding the extent of tumor resection and radiotherapy planning.
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