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The standard treatment in glioblastoma includes maximal safe resection followed by concomitant radiotherapy plus chemotherapy and adjuvant temozolomide. The first follow‐up study to evaluate treatment response is performed 1 month after concomitant treatment, when contrast‐enhancing regions may appear that can correspond to true progression or pseudoprogression. We retrospectively evaluated 31 consecutive patients at the first follow‐up after concomitant treatment to check whether the metabolic pattern assessed with multivoxel MRS was predictive of treatment response 2 months later. We extracted the underlying metabolic patterns of the contrast‐enhancing regions with a blind‐source separation method and mapped them over the reference images. Pattern heterogeneity was calculated using entropy, and association between patterns and outcomes was measured with Cramér's V. We identified three distinct metabolic patterns—proliferative, necrotic, and responsive, which were associated with status 2 months later. Individually, 70% of the patients showed metabolically heterogeneous patterns in the contrast‐enhancing regions. Metabolic heterogeneity was not related to the regions' size and only stable patients were less heterogeneous than the rest. Contrast‐enhancing regions are also metabolically heterogeneous 1 month after concomitant treatment. This could explain the reported difficulty in finding robust pseudoprogression biomarkers. [ABSTRACT FROM AUTHOR]

The tumor microenvironment in glioblastoma (GB) is considered to be "cold", i.e., the fraction of cytotoxic T cells, for instance, is low. Instead, macrophages are the major immune cell population in GB, which stem either from tissue response (resident microglia) or recruitment of macrophages from the periphery, thereby undergoing tumor-dependent "imprinting" mechanisms by which macrophages can adapt a tumor-supportive phenotype. In this regard, it is important to describe the nature of macrophages associated with GB, in particular under therapy conditions using the gold standard chemotherapy drug temozolomide (TMZ). Here, we explored the suitability of combining information from in vivo magnetic resonance spectroscopic (MRS) approaches (metabolomics) with in vitro molecular analyses to assess therapy response and characterize macrophage populations in mouse GB using an isogenic GL261 model. For macrophage profiling, expression levels of matrix metalloproteinases (MMPs) and A disintegrin and metalloproteinases (ADAMs) were determined, since their gene products affect macrophage–tumor cell communication by extensive cleavage of immunomodulatory membrane proteins, such as PD-L1. In tumor mice with an overall therapy response, expression of genes encoding the proteases ADAM8, ADAM10, and ADAM17 was increased and might contribute to the immunosuppressive phenotype of GB and immune cells. In tumors responding to therapy, expression levels of ADAM8 were upregulated by TMZ, and higher levels of PD-L1 were correlated significantly. Using a CRISPR/Cas9 knockout of ADAM8 in GL261 cells, we demonstrated that soluble PD-L1 (sPD-L1) is only generated in the presence of ADAM8. Moreover, primary macrophages from WT and ADAM8-deficient mice showed ADAM8-dependent release of sPD-L1, independent of the macrophage polarization state. Since ADAM8 expression is induced in responding tumors and PD-L1 shedding is likely to decrease the anti-tumor activities of T-cells, we conclude that immunotherapy resistance is caused, at least in part, by the increased presence of proteases, such as ADAM8. [ABSTRACT FROM AUTHOR]

Despite the success of automated pattern recognition methods in problems of human brain tumor diagnostic classification, limited attention has been paid to the issue of automated data quality assessment in the field of MRS for neuro‐oncology. Beyond some early attempts to address this issue, the current standard in practice is MRS quality control through human (expert‐based) assessment. One aspect of automatic quality control is the problem of detecting artefacts in MRS data. Artefacts, whose variety has already been reviewed in some detail and some of which may even escape human quality control, have a negative influence in pattern recognition methods attempting to assist tumor characterization. The automatic detection of MRS artefacts should be beneficial for radiology as it guarantees more reliable tumor characterizations, as well as the development of more robust pattern recognition‐based tumor classifiers and more trustable MRS data processing and analysis pipelines. Feature extraction methods have previously been used to help distinguishing between good and bad quality spectra to apply subsequent supervised pattern recognition techniques. In this study, we apply feature extraction differently and use a variant of a method for blind source separation, namely Convex Non‐Negative Matrix Factorization, to unveil MRS signal sources in a completely unsupervised way. We hypothesize that, while most sources will correspond to the different tumor patterns, some of them will reflect signal artefacts. The experimental work reported in this paper, analyzing a combined short and long echo time 1H‐MRS database of more than 2000 spectra acquired at 1.5T and corresponding to different tumor types and other anomalous masses, provides a first proof of concept that points to the possible validity of this approach. [ABSTRACT FROM AUTHOR]

Purpose: To investigate and compare human judgment and machine learning tools for quality assessment of clinical MR spectra of brain tumors. Methods: A very large set of 2574 single voxel spectra with short and long echo time from the eTUMOUR and INTERPRET databases were used for this analysis. Original human quality ratings from these studies as well as new human guidelines were used to train different machine learning algorithms for automatic quality control (AQC) based on various feature extraction methods and classification tools. The performance was compared with variance in human judgment. Results: AQC built using the RUSBoost classifier that combats imbalanced training data performed best. When furnished with a large range of spectral and derived features where the most crucial ones had been selected by the TreeBagger algorithm it showed better specificity (98%) in judging spectra from an independent test‐set than previously published methods. Optimal performance was reached with a virtual three‐class ranking system. Conclusion: Our results suggest that feature space should be relatively large for the case of MR tumor spectra and that three‐class labels may be beneficial for AQC. The best AQC algorithm showed a performance in rejecting spectra that was comparable to that of a panel of human expert spectroscopists. Magn Reson Med 79:2500–2510, 2018. © 2017 International Society for Magnetic Resonance in Medicine. [ABSTRACT FROM AUTHOR]

In magnetic resonance imaging, contrast agents are employed to enhance the signal intensity. However, current commercial contrast agents are hindered by a low relaxivity constant. Dendrimers can be employed to create higher molecular weight contrast agents which have an increased relaxivity due to a lower molecular rotation. In this study, dendrimers containing DTPA derivatives as cores and/or branching units were used to chelate gadolinium ions. Locating the gadolinium ions inside the dendrimers results in higher relaxivity constants, possibly because the paramagnetic center is closer to the rotational axis of the macromolecule. The highest gain in relaxivity was produced by decorating the dendron surface with peptide sequences, which could be explained by the presence of more second-sphere water molecules attracted by the peptides. These findings could contribute to the development of more effective contrast agents, either by placing the paramagnetic gadolinium ion in a strategic position or through functionalization of the dendron surface. [ABSTRACT FROM AUTHOR]

Non-invasive monitoring of response to treatment of glioblastoma (GB) is nowadays carried out using MRI. MRS and MR spectroscopic imaging (MRSI) constitute promising tools for this undertaking. A temozolomide (TMZ) protocol was optimized for GL261 GB. Sixty-three mice were studied by MRI/MRS/MRSI. The spectroscopic information was used for the classification of control brain and untreated and responding GB, and validated against post-mortem immunostainings in selected animals. A classification system was developed, based on the MRSI-sampled metabolome of normal brain parenchyma, untreated and responding GB, with a 93% accuracy. Classification of an independent test set yielded a balanced error rate of 6% or less. Classifications correlated well both with tumor volume changes detected by MRI after two TMZ cycles and with the histopathological data: a significant decrease ( p < 0.05) in the proliferation and mitotic rates and a 4.6-fold increase in the apoptotic rate. A surrogate response biomarker based on the linear combination of 12 spectral features has been found in the MRS/MRSI pattern of treated tumors, allowing the non-invasive classification of growing and responding GL261 GB. The methodology described can be applied to preclinical treatment efficacy studies to test new antitumoral drugs, and begets translational potential for early response detection in clinical studies. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objectives: To assess whether H-MRS may be useful to reinforce the radiological suspicion of PCNSL. Methods: In this retrospective study, we included 546 patients with untreated brain tumours in which single-voxel spectroscopy at TE 30 ms and 136 ms had been performed. The patients were split into two subgroups: 'training set' and 'test set.' Differences between PCNSL and five other types of intracranial tumours were assessed in the test set of patients using the Mann-Whitney U nonparametric test and cut-off values for pair-wise comparisons defined by constructing receiver operating characteristic curves. These thresholds were used to construct classifiers for binary comparison between PCNSL and non-PCNSL. The performance of the obtained classifiers was assessed in the independent test set of patients. Results: Significant differences were found between PCNSL and the other groups evaluated. All bilateral comparisons performed in the test set obtained accuracy values above 70 % (71-89 %). Lipids were found to be useful to discriminate between PCNSL and glioblastoma/metastasis at short TE. Myo-inositol resonance was found to be very consistent for discriminating between PCNSL and astrocytomas at short TE. Conclusions: H-MRS is useful to reinforce diagnostic suspicion of PCNSL on MRI. Key Points: • H-MRS can be used to reinforce the diagnostic suspicion of PCNSL. • Lipids can be used to discriminate between PCNSL and GB/MET. • Myo-inositol resonance can be used to discriminate between PCNSL and astrocytomas. [ABSTRACT FROM AUTHOR]