Early pseudoprogression and progression lesions in glioblastoma patients are both metabolically heterogeneous.

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.

A comparison of non-negative matrix underapproximation methods for the decomposition of magnetic resonance spectroscopy data from human brain tumors.

Magnetic resonance spectroscopy (MRS) is an MR technique that provides information about the biochemistry of tissues in a noninvasive way. MRS has been widely used for the study of brain tumors, both preoperatively and during follow-up. In this study, we investigated the performance of a range of variants of unsupervised matrix factorization methods of the non-negative matrix underapproximation (NMU) family, namely, sparse NMU, global NMU, and recursive NMU, and compared them with convex non-negative matrix factorization (C-NMF), which has previously shown a good performance on brain tumor diagnostic support problems using MRS data. The purpose of the investigation was 2-fold: first, to ascertain the differences among the sources extracted by these methods; and second, to compare the influence of each method in the diagnostic accuracy of the classification of brain tumors, using them as feature extractors. We discovered that, first, NMU variants found meaningful sources in terms of biological interpretability, but representing parts of the spectrum, in contrast to C-NMF; and second, that NMU methods achieved better classification accuracy than C-NMF for the classification tasks when one class was not meningioma.

Correlation of MR-Based Metabolomics and Molecular Profiling in the Tumor Microenvironment of Temozolomide-Treated Orthotopic GL261 Glioblastoma in Mice.

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.

Extraction of artefactual MRS patterns from a large database using non-negative matrix factorization.

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 1 H-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.

Successful Partnerships: Exploring the Potential of Immunogenic Signals Triggered by TMZ, CX-4945, and Combined Treatment in GL261 Glioblastoma Cells.

BACKGROUND: The relevance of the cancer immune cycle in therapy response implies that successful treatment may trigger the exposure or the release of immunogenic signals. Previous results with the preclinical GL261 glioblastoma (GB) showed that combination treatment of temozolomide (TMZ) + CX-4945 (protein kinase CK2 inhibitor) outperformed single treatments, provided an immune-friendly schedule was followed. Our purpose was to study possible immunogenic signals released in vitro by GB cells. METHODS: GL261 GB cells were treated with TMZ and CX-4945 at different concentrations (25 µM-4 mM) and time frames (12-72 h). Cell viability was measured with Trypan Blue and propidium iodide. Calreticulin exposure was assessed with immunofluorescence, and ATP release was measured with bioluminescence. RESULTS: TMZ showed cytostatic rather than cytotoxic effects, while CX-4945 showed remarkable cytotoxic effects already at low concentrations. Calreticulin exposure after 24 h was detected with TMZ treatment, as well as TMZ/CX-4945 low concentration combined treatment. ATP release was significantly higher with CX-4945, especially at high concentrations, as well as with TMZ/CX-4945. CONCLUSIONS: combined treatment may produce the simultaneous release of two potent immunogenic signals, which can explain the outperformance over single treatments in vivo. A word of caution may be raised since in vitro conditions are not able to mimic pharmacokinetics observed in vivo fully.

Anti-tumour immune response in GL261 glioblastoma generated by Temozolomide Immune-Enhancing Metronomic Schedule monitored with MRSI-based nosological images.

Glioblastomas (GB) are brain tumours with poor prognosis even after aggressive therapy. Improvements in both therapeutic and follow-up strategies are urgently needed. In previous work we described an oscillatory pattern of response to Temozolomide (TMZ) using a standard administration protocol, detected through MRSI-based machine learning approaches. In the present work, we have introduced the Immune-Enhancing Metronomic Schedule (IMS) with an every 6-d TMZ administration at 60 mg/kg and investigated the consistence of such oscillatory behaviour. A total of n = 17 GL261 GB tumour-bearing C57BL/6j mice were studied with MRI/MRSI every 2 d, and the oscillatory behaviour (6.2 ± 1.5 d period from the TMZ administration day) was confirmed during response. Furthermore, IMS-TMZ produced significant improvement in mice survival (22.5 ± 3.0 d for controls vs 135.8 ± 78.2 for TMZ-treated), outperforming standard TMZ treatment. Histopathological correlation was investigated in selected tumour samples (n = 6) analyzing control and responding fields. Significant differences were found for CD3+ cells (lymphocytes, 3.3 ± 2.5 vs 4.8 ± 2.9, respectively) and Iba-1 immunostained area (microglia/macrophages, 16.8% ± 9.7% and 21.9% ± 11.4%, respectively). Unexpectedly, during IMS-TMZ treatment, tumours from some mice (n = 6) fully regressed and remained undetectable without further treatment for 1 mo. These animals were considered "cured" and a GL261 re-challenge experiment performed, with no tumour reappearance in five out of six cases. Heterogeneous therapy response outcomes were detected in tumour-bearing mice, and a selected group was investigated (n = 3 non-responders, n = 6 relapsing tumours, n = 3 controls). PD-L1 content was found ca. 3-fold increased in the relapsing group when comparing with control and non-responding groups, suggesting that increased lymphocyte inhibition could be associated to IMS-TMZ failure. Overall, data suggest that host immune response has a relevant role in therapy response/escape in GL261 tumours under IMS-TMZ therapy. This is associated to changes in the metabolomics pattern, oscillating every 6 d, in agreement with immune cycle length, which is being sampled by MRSI-derived nosological images.

Anti-PD-1 Immunotherapy in Preclinical GL261 Glioblastoma: Influence of Therapeutic Parameters and Non-Invasive Response Biomarker Assessment with MRSI-Based Approaches.

Glioblastomas (GBs) are malignant brain tumours with poor prognosis even after aggressive therapy. Programmed cell death-1 (PD-1) immune checkpoint blockade is a promising strategy in many types of cancer, but its therapeutic effects in GB remain low and associated with immune infiltration. Previous work suggests that oscillations of magnetic resonance spectroscopic imaging (MRSI)-based response pattern with chemotherapy could act as a biomarker of efficient immune system attack onto GBs. The presence of such oscillations with other monotherapies such as anti-PD-1 would reinforce its monitoring potential. Here, we confirm that the oscillatory behaviour of the response biomarker is also detected in mice treated with anti PD-1 immunotherapy both in combination with temozolomide and as monotherapy. This indicates that the spectral pattern changes observed during therapy response are shared by different therapeutic strategies, provided the host immune system is elicited and able to productively attack tumour cells. Moreover, the participation of the immune system in response is also supported by the rate of cured animals observed with different therapeutic strategies (in the range of 50-100% depending on the treatment), which also held long-term immune memory against tumour cells re-challenge. Taken together, our findings open the way for a translational use of the MRSI-based biomarker in patient-tailored GB therapy, including immunotherapy, for which reliable non-invasive biomarkers are still missing.

Cancer metabolism in a snapshot: MRS(I).

The contribution of MRS(I) to the in vivo evaluation of cancer-metabolism-derived metrics, mostly since 2016, is reviewed here. Increased carbon consumption by tumour cells, which are highly glycolytic, is now being sampled by 13 C magnetic resonance spectroscopic imaging (MRSI) following the injection of hyperpolarized [1-13 C] pyruvate (Pyr). Hot-spots of, mostly, increased lactate dehydrogenase activity or flow between Pyr and lactate (Lac) have been seen with cancer progression in prostate (preclinical and in humans), brain and pancreas (both preclinical) tumours. Therapy response is usually signalled by decreased Lac/Pyr 13 C-labelled ratio with respect to untreated or non-responding tumour. For therapeutic agents inducing tumour hypoxia, the 13 C-labelled Lac/bicarbonate ratio may be a better metric than the Lac/Pyr ratio. 31 P MRSI may sample intracellular pH changes from brain tumours (acidification upon antiangiogenic treatment, basification at fast proliferation and relapse). The steady state tumour metabolome pattern is still in use for cancer evaluation. Metrics used for this range from quantification of single oncometabolites (such as 2-hydroxyglutarate in mutant IDH1 glial brain tumours) to selected metabolite ratios (such as total choline to N-acetylaspartate (plain ratio or CNI index)) or the whole 1 H MRSI(I) pattern through pattern recognition analysis. These approaches have been applied to address different questions such as tumour subtype definition, following/predicting the response to therapy or defining better resection or radiosurgery limits.

Magnetic resonance spectroscopy in posterior fossa tumours: the tumour spectroscopic signature may improve discrimination in adults among haemangioblastoma, ependymal tumours, medulloblastoma, and metastasis.

OBJECTIVES: Assessing a posterior fossa tumour in an adult can be challenging. Metastasis, haemangioblastoma, ependymal tumours, and medulloblastoma are the most common diagnostic possibilities. Our aim was to evaluate the contribution of magnetic resonance spectroscopy (MRS) in the diagnosis of these entities. METHODS: We retrospectively evaluated 56 consecutive patients with a posterior fossa tumour and histological diagnosis of ependymal tumour, medulloblastoma, haemangioblastoma, and metastasis in which good-quality spectra at short (TE 30 ms) or/and intermediate (TE, 136 ms) TE were available. Spectra were compared using the Mann-Whitney U non-parametric test in order to select the spectral datapoints and the intensity ratios that showed significant differences between groups of lesions. Performance of these datapoints and their ratios were assessed with ROC curves. RESULTS: The most characteristic signatures on spectroscopy were high choline (Cho) in medulloblastoma (p < 0.001), high myoinositol (mIns) in ependymal tumours (p < 0.05), and high lipids (LIP) in haemangioblastoma (p < 0.01) and metastasis (p < 0.01). Selected ratios between normalised intensity signals of resonances provided accuracy values between 79 and 95% for pairwise comparisons. Intensity ratio NI3.21ppm/3.55ppm provided satisfactory discrimination between medulloblastoma and ependymal tumours (accuracy, 92%), ratio NI2.11ppm/1.10ppm discriminated ependymal tumours from haemangioblastoma (accuracy, 94%), ratio NI3.21ppm/1.13ppm discriminated haemangioblastoma from medulloblastoma (accuracy, 95%), and ratio NI1.28ppm/2.02pmm discriminated haemangioblastoma from metastasis (accuracy, 83%). CONCLUSIONS: MRS may improve the non-invasive diagnosis of posterior fossa tumours in adults. KEY POINTS: • High choline suggests a medulloblastoma in a posterior fossa tumour. • High myoinositol suggests an ependymal lesion in a posterior fossa tumour. • High lipids suggest a metastasis or a haemangioblastoma in a posterior fossa tumour.

Quality of clinical brain tumor MR spectra judged by humans and machine learning tools.

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.

Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke.

BACKGROUND: Magnetic resonance spectroscopy (MRS) provides non-invasive information about the metabolic pattern of the brain parenchyma in vivo. The SpectraClassifier software performs MRS pattern-recognition by determining the spectral features (metabolites) which can be used objectively to classify spectra. Our aim was to develop an Infarct Evolution Classifier and a Brain Regions Classifier in a rat model of focal ischemic stroke using SpectraClassifier. RESULTS: A total of 164 single-voxel proton spectra obtained with a 7 Tesla magnet at an echo time of 12 ms from non-infarcted parenchyma, subventricular zones and infarcted parenchyma were analyzed with SpectraClassifier ( http://gabrmn.uab.es/?q=sc ). The spectra corresponded to Sprague-Dawley rats (healthy rats, n = 7) and stroke rats at day 1 post-stroke (acute phase, n = 6 rats) and at days 7 ± 1 post-stroke (subacute phase, n = 14). In the Infarct Evolution Classifier, spectral features contributed by lactate + mobile lipids (1.33 ppm), total creatine (3.05 ppm) and mobile lipids (0.85 ppm) distinguished among non-infarcted parenchyma (100% sensitivity and 100% specificity), acute phase of infarct (100% sensitivity and 95% specificity) and subacute phase of infarct (78% sensitivity and 100% specificity). In the Brain Regions Classifier, spectral features contributed by myoinositol (3.62 ppm) and total creatine (3.04/3.05 ppm) distinguished among infarcted parenchyma (100% sensitivity and 98% specificity), non-infarcted parenchyma (84% sensitivity and 84% specificity) and subventricular zones (76% sensitivity and 93% specificity). CONCLUSION: SpectraClassifier identified candidate biomarkers for infarct evolution (mobile lipids accumulation) and different brain regions (myoinositol content).

Metronomic treatment in immunocompetent preclinical GL261 glioblastoma: effects of cyclophosphamide and temozolomide.

Glioblastoma (GBM) causes poor survival in patients even when applying aggressive treatment. Temozolomide (TMZ) is the standard chemotherapeutic choice for GBM treatment, but resistance always ensues. In previous years, efforts have focused on new therapeutic regimens with conventional drugs to activate immune responses that may enhance tumor regression and prevent regrowth, for example the "metronomic" approaches. In metronomic scheduling studies, cyclophosphamide (CPA) in GL261 GBM growing subcutaneously in C57BL/6 mice was shown not only to activate antitumor CD8+ T-cell response, but also to induce long-term specific T-cell tumor memory. Accordingly, we have evaluated whether metronomic CPA or TMZ administration could increase survival in orthotopic GL261 in C57BL/6 mice, an immunocompetent model. Longitudinal in vivo studies with CPA (140 mg/kg) or TMZ (range 140-240 mg/kg) metronomic administration (every 6 days) were performed in tumor-bearing mice. Tumor evolution was monitored at 7 T with MRI (T2 -weighted, diffusion-weighted imaging) and MRSI-based nosological images of response to therapy. Obtained results demonstrated that both treatments resulted in increased survival (38.6 ± 21.0 days, n = 30) compared with control (19.4 ± 2.4 days, n = 18). Best results were obtained with 140 mg/kg TMZ (treated, 44.9 ± 29.0 days, n = 12, versus control, 19.3 ± 2.3 days, n = 12), achieving a longer survival rate than previous group work using three cycles of TMZ therapy at 60 mg/kg (33.9 ± 11.7 days, n = 38). Additional interesting findings were, first, clear edema appearance during chemotherapeutic treatment, second, the ability to apply the semi-supervised source analysis previously developed in our group for non-invasive TMZ therapy response monitoring to detect CPA-induced response, and third, the necropsy findings in mice cured from GBM after high TMZ cumulative dosage (980-1400 mg/kg), which demonstrated lymphoma incidence. In summary, every 6 day administration schedule of TMZ or CPA improves survival in orthotopic GL261 GBM with respect to controls or non-metronomic therapy, in partial agreement with previous work on subcutaneous GL261.

Development of a transplantable glioma tumour model from genetically engineered mice: MRI/MRS/MRSI characterisation.

The initial aim of this study was to generate a transplantable glial tumour model of low-intermediate grade by disaggregation of a spontaneous tumour mass from genetically engineered models (GEM). This should result in an increased tumour incidence in comparison to GEM animals. An anaplastic oligoastrocytoma (OA) tumour of World Health Organization (WHO) grade III was obtained from a female GEM mouse with the S100ß-v-erbB/inK4a-Arf (+/-) genotype maintained in the C57BL/6 background. The tumour tissue was disaggregated; tumour cells from it were grown in aggregates and stereotactically injected into C57BL/6 mice. Tumour development was followed using Magnetic Resonance Imaging (MRI), while changes in the metabolomics pattern of the masses were evaluated by Magnetic Resonance Spectroscopy/Spectroscopic Imaging (MRS/MRSI). Final tumour grade was evaluated by histopathological analysis. The total number of tumours generated from GEM cells from disaggregated tumour (CDT) was 67 with up to 100 % penetrance, as compared to 16 % in the local GEM model, with an average survival time of 66 ± 55 days, up to 4.3-fold significantly higher than the standard GL261 glioblastoma (GBM) tumour model. Tumours produced by transplantation of cells freshly obtained from disaggregated GEM tumour were diagnosed as WHO grade III anaplastic oligodendroglioma (ODG) and OA, while tumours produced from a previously frozen sample were diagnosed as WHO grade IV GBM. We successfully grew CDT and generated tumours from a grade III GEM glial tumour. Freezing and cell culture protocols produced progression to grade IV GBM, which makes the developed transplantable model qualify as potential secondary GBM model in mice.

From raw data to data-analysis for magnetic resonance spectroscopy--the missing link: jMRUI2XML.

BACKGROUND: Magnetic resonance spectroscopy provides metabolic information about living tissues in a non-invasive way. However, there are only few multi-centre clinical studies, mostly performed on a single scanner model or data format, as there is no flexible way of documenting and exchanging processed magnetic resonance spectroscopy data in digital format. This is because the DICOM standard for spectroscopy deals with unprocessed data. This paper proposes a plugin tool developed for jMRUI, namely jMRUI2XML, to tackle the latter limitation. jMRUI is a software tool for magnetic resonance spectroscopy data processing that is widely used in the magnetic resonance spectroscopy community and has evolved into a plugin platform allowing for implementation of novel features. RESULTS: jMRUI2XML is a Java solution that facilitates common preprocessing of magnetic resonance spectroscopy data across multiple scanners. Its main characteristics are: 1) it automates magnetic resonance spectroscopy preprocessing, and 2) it can be a platform for outputting exchangeable magnetic resonance spectroscopy data. The plugin works with any kind of data that can be opened by jMRUI and outputs in extensible markup language format. Data processing templates can be generated and saved for later use. The output format opens the way for easy data sharing- due to the documentation of the preprocessing parameters and the intrinsic anonymization--for example for performing pattern recognition analysis on multicentre/multi-manufacturer magnetic resonance spectroscopy data. CONCLUSIONS: jMRUI2XML provides a self-contained and self-descriptive format accounting for the most relevant information needed for exchanging magnetic resonance spectroscopy data in digital form, as well as for automating its processing. This allows for tracking the procedures the data has undergone, which makes the proposed tool especially useful when performing pattern recognition analysis. Moreover, this work constitutes a first proposal for a minimum amount of information that should accompany any magnetic resonance processed spectrum, towards the goal of achieving better transferability of magnetic resonance spectroscopy studies.

Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes.

The INTERPRET project was a multicentre European collaboration, carried out from 2000 to 2002, which developed a decision-support system (DSS) for helping neuroradiologists with no experience of MRS to utilize spectroscopic data for the diagnosis and grading of human brain tumours. INTERPRET gathered a large collection of MR spectra of brain tumours and pseudo-tumoural lesions from seven centres. Consensus acquisition protocols, a standard processing pipeline and strict methods for quality control of the aquired data were put in place. Particular emphasis was placed on ensuring the diagnostic certainty of each case, for which all cases were evaluated by a clinical data validation committee. One outcome of the project is a database of 304 fully validated spectra from brain tumours, pseudotumoural lesions and normal brains, along with their associated images and clinical data, which remains available to the scientific and medical community. The second is the INTERPRET DSS, which has continued to be developed and clinically evaluated since the project ended. We also review here the results of the post-INTERPRET period. We evaluate the results of the studies with the INTERPRET database by other consortia or research groups. A summary of the clinical evaluations that have been performed on the post-INTERPRET DSS versions is also presented. Several have shown that diagnostic certainty can be improved for certain tumour types when the INTERPRET DSS is used in conjunction with conventional radiological image interpretation. About 30 papers concerned with the INTERPRET single-voxel dataset have so far been published. We discuss stengths and weaknesses of the DSS and the lessons learned. Finally we speculate on how the INTERPRET concept might be carried into the future.

Effect of acute hyperglycemia on moderately hypothermic GL261 mouse glioma monitored by T1-weighted DCE MRI.

OBJECTIVE: We sought to evaluate the effects of acute hyperglycemia induced by intraperitoneal injection of glucose (2.7 g/kg) on vascular delivery to GL261 mouse gliomas kept at moderate hypothermia (~30 °C). MATERIALS AND METHODS: Seven GL261 glioma-bearing mice were studied by T1-weighted DCE MRI before and after an injection of glucose (n = 4) or saline (n = 3). Maximum relative contrast enhancement (RCE) and initial area under the enhancement curve (IAUC) were determined in each pixel. RESULTS: The mean tumor parameter values showed no significant changes after injecting either saline (RCE -5.9 ± 5.0 %; IAUC -3.7 ± 3.6 %) or glucose (RCE -1.6 ± 9.0 %; IAUC +0.6 ± 6.4 %). Pixel-by-pixel analysis revealed small post-injection changes in RCE and IAUC between the glucose and saline groups, all within 13 % range of their baseline values. CONCLUSION: Perturbing the metabolism of GL261 tumors kept at moderate hypothermia with hyperglycemia did not induce significant changes in the permeability/perfusion of these tumors. This is relevant for future studies with this model since regional differences in glucose accumulation could thus reflect basal heterogeneities in vasculature and/or metabolism of GL261 tumors.

Molecular imaging coupled to pattern recognition distinguishes response to temozolomide in preclinical glioblastoma.

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.

Multicentre evaluation of the INTERPRET decision support system 2.0 for brain tumour classification.

In a previous study, we have shown the added value of (1) H MRS for the neuroradiological characterisation of adult human brain tumours. In that study, several methods of MRS analysis were used, and a software program, the International Network for Pattern Recognition of Tumours Using Magnetic Resonance Decision Support System 1.0 (INTERPRET DSS 1.0), with a short-TE classifier, provided the best results. Since then, the DSS evolved into a version 2.0 that contains an additional long-TE classifier. This study has two objectives. First, to determine whether clinicians with no experience of spectroscopy are comparable with spectroscopists in the use of the system, when only minimum training in the use of the system was given. Second, to assess whether or not a version with another TE is better than the initial version. We undertook a second study with the same cases and nine evaluators to assess whether the diagnostic accuracy of DSS 2.0 was comparable with the values obtained with DSS 1.0. In the second study, the analysis protocol was flexible in comparison with the first one to mimic a clinical environment. In the present study, on average, each case required 5.4 min by neuroradiologists and 9 min by spectroscopists for evaluation. Most classes and superclasses of tumours gave the same results as with DSS 1.0, except for astrocytomas of World Health Organization (WHO) grade III, in which performance measured as the area under the curve (AUC) decreased: AUC = 0.87 (0.72-1.02) with DSS 1.0 and AUC = 0.62 (0.55-0.70) with DSS 2.0. When analysing the performance of radiologists and spectroscopists with respect to DSS 1.0, the results were the same for most classes. Having data with two TEs instead of one did not affect the results of the evaluation.

(1)H-MRS is useful to reinforce the suspicion of primary central nervous system lymphoma prior to surgery.

OBJECTIVES: To assess whether (1)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: (1)H-MRS is useful to reinforce diagnostic suspicion of PCNSL on MRI. KEY POINTS: • (1) 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.

A new ex vivo method to evaluate the performance of candidate MRI contrast agents: a proof-of-concept study.

BACKGROUND: Magnetic resonance imaging (MRI) plays an important role in tumor detection/diagnosis. The use of exogenous contrast agents (CAs) helps to improve the discrimination between lesion and neighbouring tissue, but most of the currently available CAs are non-specific. Assessing the performance of new, selective CAs requires exhaustive assays and large amounts of material. Accordingly, in a preliminary screening of new CAs, it is important to choose candidate compounds with good potential for in vivo efficiency. This screening method should reproduce as close as possible the in vivo environment. In this sense, a fast and reliable method to select the best candidate CAs for in vivo studies would minimize time and investment cost, and would benefit the development of better CAs. RESULTS: The post-mortem ex vivo relative contrast enhancement (RCE) was evaluated as a method to screen different types of CAs, including paramagnetic and superparamagnetic agents. In detail, sugar/gadolinium-loaded gold nanoparticles (Gd-GNPs) and iron nanoparticles (SPIONs) were tested. Our results indicate that the post-mortem ex vivo RCE of evaluated CAs, did not correlate well with their respective in vitro relaxivities. The results obtained with different Gd-GNPs suggest that the linker length of the sugar conjugate could modulate the interactions with cellular receptors and therefore the relaxivity value. A paramagnetic CA (GNP (E_2)), which performed best among a series of Gd-GNPs, was evaluated both ex vivo and in vivo. The ex vivo RCE was slightly worst than gadoterate meglumine (201.9 ± 9.3% versus 237 ± 14%, respectively), while the in vivo RCE, measured at the time-to-maximum enhancement for both compounds, pointed to GNP E_2 being a better CA in vivo than gadoterate meglumine. This is suggested to be related to the nanoparticule characteristics of the evaluated GNP. CONCLUSION: We have developed a simple, cost-effective relatively high-throughput method for selecting CAs for in vivo experiments. This method requires approximately 800 times less quantity of material than the amount used for in vivo administrations.