Pattern recognition of MRSI data shows regions of glioma growth that agree with DTI markers of brain tumor infiltration

Is pre-radiotherapy metabolic heterogeneity of glioblastoma predictive of progression-free survival?

BACKGROUND AND PURPOSE

All glioblastoma subtypes share the hallmark of aggressive invasion, meaning that it is crucial to identify their different components if we are to ensure effective treatment and improve survival. Proton MR spectroscopic imaging (MRSI) is a noninvasive technique that yields metabolic information and is able to identify pathological tissue with high accuracy. The aim of the present study was to identify clusters of metabolic heterogeneity, using a large MRSI dataset, and determine which of these clusters are predictive of progression-free survival (PFS).

MATERIALS AND METHODS

MRSI data of 180 patients acquired in a pre-radiotherapy examination were included in the prospective SPECTRO-GLIO trial. Eight features were extracted for each spectrum: Cho/NAA, NAA/Cr, Cho/Cr, Lac/NAA, and the ratio of each metabolite to the sum of all the metabolites. Clustering of data was performed using a mini-batch k-means algorithm. The Cox model and logrank test were used for PFS analysis.

RESULTS

Five clusters were identified as sharing similar metabolic information and being predictive of PFS. Two clusters revealed metabolic abnormalities. PFS was lower when Cluster 2 was the dominant cluster in patients' MRSI data. Among the metabolites, lactate (present in this cluster and in Cluster 5) was the most statistically significant predictor of poor outcome.

CONCLUSION

Results showed that pre-radiotherapy MRSI can be used to reveal tumor heterogeneity. Groups of spectra, which have the same metabolic information, reflect the different tissue components representative of tumor burden proliferation and hypoxia. Clusters with metabolic abnormalities and high lactate are predictive of PFS.

Preliminary Study on the Therapeutic Effect of Doxorubicin-Loaded Targeting Nanoparticles on Glioma

Doxorubicin (DOX) is an anthracycline anticancer drug, which is often associated with drug resistance and cytotoxicity. More unfortunately, the biological barrier in the human environment can weaken the efficacy of DOX, such as the blood-brain barrier (BBB). This work attempts to make efforts to solve this problem. We used polyethylene glycol distearoylphosphatidylethanolamine (PEG-DSPE) as a nanocarrier and DOX as a model drug to construct a composite nanodrug (TF-PEG-DSPE/DOX NPs) by coupling transferrin (TF). The results of glioma experiments show that the nanodrug can effectively penetrate BBB to achieve an antitumor effect.

Establishing Imaging Biomarkers of Host Immune System Efficacy during Glioblastoma Therapy Response: Challenges, Obstacles and Future Perspectives

This hypothesis proposal addresses three major questions: (1) Why do we need imaging biomarkers for assessing the efficacy of immune system participation in glioblastoma therapy response? (2) Why are they not available yet? and (3) How can we produce them? We summarize the literature data supporting the claim that the immune system is behind the efficacy of most successful glioblastoma therapies but, unfortunately, there are no current short-term imaging biomarkers of its activity. We also discuss how using an immunocompetent murine model of glioblastoma, allowing the cure of mice and the generation of immune memory, provides a suitable framework for glioblastoma therapy response biomarker studies. Both magnetic resonance imaging and magnetic resonance-based metabolomic data (i.e., magnetic resonance spectroscopic imaging) can provide non-invasive assessments of such a system. A predictor based in nosological images, generated from magnetic resonance spectroscopic imaging analyses and their oscillatory patterns, should be translational to clinics. We also review hurdles that may explain why such an oscillatory biomarker was not reported in previous imaging glioblastoma work. Single shot explorations that neglect short-term oscillatory behavior derived from immune system attack on tumors may mislead actual response extent detection. Finally, we consider improvements required to properly predict immune system-mediated early response (1–2 weeks) to therapy. The sensible use of improved biomarkers may enable translatable evidence-based therapeutic protocols, with the possibility of extending preclinical results to human patients.

Differential Effects of a Left Frontal Glioma on the Cortical Thickness and Complexity of Both Hemispheres

Glioma is a type of brain tumor that infiltrates and compresses the brain as it grows. Focal gliomas affect functional connectivity both in the local region of the lesion and the global network of the brain. Any anatomical changes associated with a glioma should thus be clarified. We examined the cortical structures of 15 patients with a glioma in the left lateral frontal cortex and compared them with those of 15 healthy controls by surface-based morphometry. Two regional parameters were measured with 3D-MRI: the cortical thickness (CT) and cortical fractal dimension (FD). The FD serves as an index of the topological complexity of a local cortical surface. Our comparative analyses of these parameters revealed that the left frontal gliomas had global effects on the cortical structures of both hemispheres. The structural changes in the right hemisphere were mainly characterized by a decrease in CT and mild concomitant decrease in FD, whereas those in the peripheral regions of the glioma (left hemisphere) were mainly characterized by a decrease in FD with relative preservation of CT. These differences were found irrespective of tumor volume, location, or grade. These results elucidate the structural effects of gliomas, which extend to the distant contralateral regions.

Main genetic differences in high-grade gliomas may present different MR imaging and MR spectroscopy correlates

OBJECTIVE

To assess whether the main genetic differences observed in high-grade gliomas (HGG) will present different MR imaging and MR spectroscopy correlates that could be used to better characterize lesions in the clinical setting.

METHODS

Seventy-nine patients with histologically confirmed HGG were recruited. Immunohistochemistry analyses for isocitrate dehydrogenase gene 1 (IDH1), alpha thalassemia mental retardation X-linked gene (ATRX), Ki-67, and p53 protein expression were performed. Tumour radiological features were examined on MR images. Metabolic profile and infiltrative pattern were assessed with MR spectroscopy. MR features were analysed to identify imaging-molecular associations. The Kaplan-Meier method and the Cox regression model were used to identify survival prognostic factors.

RESULTS

In total, 17.7% of the lesions were IDH1-mutated, 8.9% presented ATRX-mutated, 70.9% presented p53 unexpressed, and 22.8% had Ki-67 > 5%. IDH1 wild-type tumours had higher levels of mobile lipids (p = 0.001). The tumour-infiltrative pattern was higher in HGG with unexpressed p53 (p = 0.009). Mutated ATRX tumours presented higher levels of glutamate and glutamine (Glx) (p = 0.001). An association was observed between Glx tumour levels (p = 0.038) and Ki-67 expression (p = 0.008) with the infiltrative pattern. Survival analyses identified IDH1 status, age, and tumour choline levels as independent predictors of prognostic significance.

CONCLUSIONS

Our results suggest that IDH1-wt tumours are more necrotic than IDH1-mut. And that the presence of an infiltrative pattern in HGG is associated with loss of p53 expression, Ki-67 index, and Glx levels. Finally, tumour choline levels could be used as a predictive factor in survival in addition to the IDH1 status to provide a more accurate prediction of survival in HGG patients.

KEY POINTS

• IDH1-wt tumours present higher levels of mobile lipids than IDH1-mut. • Mutated ATRX tumours exhibit higher levels of glutamate and glutamine. • Loss of p53 expression, Ki-67 expression, and glutamate and glutamine levels may contribute to the presence of an infiltrative pattern in HGG.

Pre- and post-surgery MRSI predictive value in adult oligodendroglioma prognosis

PURPOSE

The aim of this study was to study the relationship between MRSI, before and after surgery, and patient survival. The accuracy of pre-operative MRSI in differentiating low- from high-grade oligodendrogliomas (ODGs) was also studied.

METHODS

Two hundred patients with ODG were retrospectively included in this study between 2000 and 2016. All patients underwent MRSI before any treatment or biopsy and/or after surgery for an intra-axial brain tumour. The R software was used for statistical data analysis. p < 0.05 was considered statistically significant. Kaplan-Meier curves were calculated for patients with low-grade ODG and high-grade ODG pre- and post-operatively, to study survival (overall survival, OS). The best threshold of each MRSI metabolite ratio was obtained using receiver operating characteristic curves (ROCs).

RESULTS

One hundred patients underwent pre-operative MRSI and 170 post-operative MRSI. N-acetylaspartate (NAA), lactate (Lac), choline (Cho) and creatine (Cr) were measured. Kapan-Meier curves showed that survival was poorer for a nCho/Cr > 3.02 in the pre-operative and nCho/Cr > 2.04, Lac/Cr > 0.743 and nCho/NAA > 3.63 in the post-operative period. Post-operative MRSI predicts survival better than pre-operative MRSI. nCho/Cr and Lac/Cr distinguished low- from high-grade ODG with a good positive predictive value.

CONCLUSION

MRSI is associated with survival. It is a non-invasive tool which completes histopathology and can predict patients' prognosis, thus improving patient management.

A combined diffusion tensor imaging and Ki-67 labeling index study for evaluating the extent of tumor infiltration using the F98 rat glioma model

Diffusion tensor imaging (DTI) has been proven to be a sophisticated and useful tool for the delineation of tumors. In the present study, we investigated the predictive role of DTI compared to other magnetic resonance imaging (MRI) techniques in combination with Ki-67 labeling index in defining tumor cell infiltration in the peritumoral regions of F98 glioma-bearing rats. A total of 29 tumor-bearing Fischer rats underwent T2-weighted imaging, contrast-enhanced T1-weighted imaging, and DTI of their brain using a 7.0-T MRI scanner. The fractional anisotropy (FA) ratios were correlated to the Ki-67 labeling index using the Spearman correlation analysis. A receiver operating characteristic curve (ROC) analysis was established to evaluate parameters with sensitivity and specificity in order to identify the threshold values for predicting tumor infiltration. Significant correlations were observed between the FA ratios and Ki-67 labeling index (r = - 0.865, p < 0.001). The ROC analysis demonstrated that the apparent diffusion coefficient (ADC) and FA ratios could predict 50% of the proliferating cells in the regions of interest (ROI), with a sensitivity of 88.1 and 81.3%, and a specificity of 86.2 and 90.2%, respectively (p < 0.001). Meanwhile, the two ratios could also predict 10% of the proliferating cells in the ROI, with a sensitivity of 82.5 and 94.9%, and a specificity of 100 and 88.9%, respectively (p < 0.001). The present study demonstrated that the FA ratios are closely correlated with the Ki-67 labeling index. Furthermore, both ADC and FA ratios, derived from DTI, were useful for quantitatively predicting the Ki-67 labeling of glioma cells.

Association of early changes in 1H MRSI parameters with survival for patients with newly diagnosed glioblastoma receiving a multimodality treatment regimen

Background

The heterogeneous biology of glioblastoma (GBM) emphasizes the need for imaging methods to assess tumor burden and assist in evaluating individual patients. The purpose of this study was to investigate early changes in metrics from 3D 1H magnetic resonance spectroscopic imaging (MRSI) data, compare them with anatomic lesion volumes, and determine whether they were associated with survival for patients with newly diagnosed GBM receiving a multimodality treatment regimen.

Methods

Serial MRI and MRSI scans provided estimates of anatomic lesion volumes and levels of choline, creatine, N-acetylaspartate, lactate, and lipid. The association of metrics derived from these data with survival was assessed using Cox proportional hazards models with adjustments for age, Karnofsky performance score, and extent of resection. Temporal changes in parameters were evaluated using a Wilcoxon signed rank test.

Results

Anatomic lesion volumes at the post-radiotherapy (RT) scan, metabolic lesion volume at mid-RT and post-RT scans, as well as metrics describing levels of choline, lactate, and lipid were associated with overall survival. There was a significant reduction in the enhancing lesion volume, increase in T2 lesion volume from mid-RT to post-RT, and decrease in parameters describing metabolite levels during these early time points.

Conclusion

The MRSI data provided metrics that described the effects of treatment on the metabolic lesion burden and were associated with overall survival. This suggests that adding these parameters to standard assessments of changes in anatomic lesion volumes could contribute to making early decisions about the efficacy of such combination therapies.

Proton MRS imaging in pediatric brain tumors

Magnetic resonance (MR) techniques offer a noninvasive, non-irradiating yet sensitive approach to diagnosing and monitoring pediatric brain tumors. Proton MR spectroscopy (MRS), as an adjunct to MRI, is being more widely applied to monitor the metabolic aspects of brain cancer. In vivo MRS biomarkers represent a promising advance and may influence treatment choice at both initial diagnosis and follow-up, given the inherent difficulties of sequential biopsies to monitor therapeutic response. When combined with anatomical or other types of imaging, MRS provides unique information regarding biochemistry in inoperable brain tumors and can complement neuropathological data, guide biopsies and enhance insight into therapeutic options. The combination of noninvasively acquired prognostic information and the high-resolution anatomical imaging provided by conventional MRI is expected to surpass molecular analysis and DNA microarray gene profiling, both of which, although promising, depend on invasive biopsy. This review focuses on recent data in the field of MRS in children with brain tumors.

Magnetic resonance spectroscopy detection of high lipid levels in intraaxial tumors without central necrosis: a characteristic of malignant lymphoma

OBJECT

The differentiation of malignant lymphomas from gliomas or malignant gliomas by conventional MRI can be difficult. The authors studied Gd-enhanced MR images to obtain a differential diagnosis between malignant lymphomas and gliomas without central necrosis or cystic changes and investigated the diagnostic value of single-voxel proton MR spectroscopy ((1)H-MRS) using different parameters, including lipid levels.

METHODS

This was a retrospective study of patients with primary malignant CNS lymphoma (n = 17) and glioma (n = 122 [Grades I, II, III, and IV in 10, 30, 33, and 49 patients, respectively]) who were treated between 2007 and 2013. The authors focused on 15 patients with homogeneously enhanced primary malignant CNS lymphomas and 7 homogeneously enhanced gliomas. Images of all the included tumors were acquired with (1)H-MRS at 3 T, and the diagnoses were histologically confirmed.

RESULTS

Using a short echo time (1)H-MRS, large lipid peaks were observed in all 17 patients with a malignant lymphoma, in 39 patients (79.6%) with a Grade IV glioma, and in 10 patients (30.3%) with a Grade III glioma. A focus on homogeneously enhanced tumors revealed large lipid peaks in 15 malignant lymphomas that were free of central necrosis on Gd-enhanced T1-weighted images. Conversely, in the 7 homogeneously enhanced gliomas (glioblastoma and anaplastic astrocytoma, n = 2 each; anaplastic oligodendroglioma, diffuse astrocytoma, and pilomyxoid astrocytoma, n = 1 each), lipid peaks were small or absent.

CONCLUSIONS

Large lipid peaks on (1)H-MRS images of tumors without central necrosis were characteristic of malignant lymphomas. Conversely, small or absent lipid peaks in intraaxial tumors without central necrosis were strongly suggestive of glioma.

Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering

PURPOSE

To investigate whether nonlinear dimensionality reduction improves unsupervised classification of (1) H MRS brain tumor data compared with a linear method.

METHODS

In vivo single-voxel (1) H magnetic resonance spectroscopy (55 patients) and (1) H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data.

RESULTS

An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With (1) H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC.

CONCLUSION

The LE method is promising for unsupervised clustering to separate brain and tumor tissue with automated color-coding for visualization of (1) H MRSI data after cluster analysis.

Delineation of gliomas using radial metabolite indexing

(1) H MRSI has demonstrated the ability to characterise and delineate brain tumours, but robust data analysis methods are still needed. In this study, we present an objective analysis method for MRSI data to delineate tumour abnormality regions. The presented method is a development of the choline-to-N-acetylaspartate index (CNI), which uses perpendicular distances in a choline versus N-acetylaspartate plot as a measure of abnormality. We propose a radial CNI (rCNI) method that uses the choline to N-acetylaspartate ratio directly as an abnormality measure. To avoid problems with small or zero denominators, we perform an arctangent transformation. CNI abnormality contours were evaluated using a z-score threshold of 2 (CNI2) and 2.5 (CNI2.5) and compared with rCNI2. Simulations modelling low-grade (LGG) and high-grade (HGG) gliomas with different tissue compartments and partial volume effects suggest improved specificity of rCNI2 (LGG 92%/HGG 91%) over CNI2 (LGG 69%/HGG 69%) and CNI2.5 (LGG 74%/HGG 75%), whilst retaining a similar sensitivity to both CNI2 and CNI2.5. Our simulation results also confirm a previously reported increase in specificity of CNI2.5 over CNI2 with little penalty in sensitivity. The analysis of MRSI data acquired from 10 patients with low-grade glioma at 3 T suggests a more robust delineation of the lesions using rCNI with respect to conventional imaging compared with standard CNI. Further analysis of 29 glioma datasets acquired at 1.5 T, together with previously published estimated tumour proportions, suggests that rCNI has higher sensitivity and specificity for the identification of abnormal MRSI voxels.

(1) H 2D MRSI tissue type analysis of gliomas

PURPOSE

To decompose 1H MR spectra of glioma patients into normal and abnormal tissue proportions for tumor classification and delineation.

METHODS

Anatomical imaging and 1H magnetic resonance spectroscopic imaging data have been acquired from 11 grade II and 13 grade IV glioma patients. LCModel was used to decompose the magnetic resonance spectroscopic imaging data into normal brain, grade II, and grade IV tissue proportions using a tissue type basis set. Simulations were conducted to evaluate the accuracy of the methodology. Results were visualized using colormaps and abnormality contours showing tumor grade and extent.

RESULTS

Simulations suggest that infiltrative tumor proportions as low as 20% can be identified at the typical 1H magnetic resonance spectroscopy signal-to-noise found in vivo. Tumor grading according to the highest estimated tumor grade within a lesion gave a classification accuracy of 86% discriminating between grade II and grade IV glioma. Voxels with significant proportions of tumor type spectra were found beyond the margins of contrast enhancement for most grade IV cases consistent with infiltration whereas the abnormality contours show that some tumors are confined within the hyperintensities shown by both post contrast T1 weighted and T2 weighted imaging.

CONCLUSION

LCModel can be used to decompose 1H MR spectra into proportions of normal and abnormal tissue to identify tumor extent, infiltration, and overall grade.

Data analysis and tissue type assignment for glioblastoma multiforme

Glioblastoma multiforme (GBM) is characterized by high infiltration. The interpretation of MRSI data, especially for GBMs, is still challenging. Unsupervised methods based on NMF by Li et al. (2013, NMR in Biomedicine) and Li et al. (2013, IEEE Transactions on Biomedical Engineering) have been proposed for glioma recognition, but the tissue types is still not well interpreted. As an extension of the previous work, a tissue type assignment method is proposed for GBMs based on the analysis of MRSI data and tissue distribution information. The tissue type assignment method uses the values from the distribution maps of all three tissue types to interpret all the information in one new map and color encodes each voxel to indicate the tissue type. Experiments carried out on in vivo MRSI data show the feasibility of the proposed method. This method provides an efficient way for GBM tissue type assignment and helps to display information of MRSI data in a way that is easy to interpret.

Hierarchical non-negative matrix factorization (hNMF): a tissue pattern differentiation method for glioblastoma multiforme diagnosis using MRSI

MRSI has shown potential in the diagnosis and prognosis of glioblastoma multiforme (GBM) brain tumors, but its use is limited by difficult data interpretation. When the analyzed MRSI data present more than two tissue patterns, conventional non-negative matrix factorization (NMF) implementation may lead to a non-robust estimation. The aim of this article is to introduce an effective approach for the differentiation of GBM tissue patterns using MRSI data. A hierarchical non-negative matrix factorization (hNMF) method that can blindly separate the most important spectral sources in short-TE ¹H MRSI data is proposed. This algorithm consists of several levels of NMF, where only two tissue patterns are computed at each level. The method is demonstrated on both simulated and in vivo short-TE ¹H MRSI data in patients with GBM. For the in vivo study, the accuracy of the recovered spectral sources was validated using expert knowledge. Results show that hNMF is able to accurately estimate the three tissue patterns present in the tumoral and peritumoral area of a GBM, i.e. normal, tumor and necrosis, thus providing additional useful information that can help in the diagnosis of GBM. Moreover, the hNMF results can be displayed as easily interpretable maps showing the contribution of each tissue pattern to each voxel.

Quality control of prostate 1 H MRSI data

MRSI of prostate cancer provides a potential clinical tool to aid in the detection and characterisation of this disease, but its clinical use is limited by the need for the specialist training of radiologists to read these datasets. An essential part of this reading is the assessment of the usability and reliability of MRSI spectra because they can be affected by artefacts such as poor signal to noise, lipid signal contamination and broad resonances that could cause errors of interpretation. We have developed an automated quality control algorithm that classifies every voxel of an MRSI dataset as either acceptable or unacceptable for further analysis, based on the spectral profile alone. The method was trained and tested based on a gold standard of agreement of four experts. It was highly accurate: testing with a novel set of data from MRSI patients produced agreement with the experts' consensus decisions with a specificity of 0.95 and sensitivity of 0.95. This method provides fast quality control of three-dimensional MRSI datasets of the prostate, removing the need for radiologists to perform this time consuming, but necessary, task prior to further analysis.

A Monte Carlo based formalism to identify potential locations at high risk of tumor recurrence with a numerical model for glioblastoma multiforme

PURPOSE

The strategy currently used to treat glioblastoma multiforme (GBM) patients, which mostly relies on population-based failure patterns, does not consider the important variability in such patterns reported in the literature. As part of the multidisciplinary efforts being made to develop personalized therapeutic approaches, numerical models of tumor growth and treatment are increasingly being used by different groups around the world. In this study, a new formalism relying on the proliferation-invasion model is developed to identify potential locations of GBM recurrences. The authors assess the sensitivity of the location of potential tumor recurrences to the input parameter values predicted for a given patient by varying those values using a Monte-Carlo based approach. Our approach is designed to be prospective in the sense that it relies on patient-specific imaging data that can be gathered in one single preradiotherapy imaging session.

METHODS

The authors modeled the infiltration paths of glial cells using patient-specific diffusion tensor imaging (DTI) data. Nine GBM patients with preradiotherapy DTI data are considered in this study. The possible locations of tumor recurrences are determined by randomly selecting many ensembles of values for each of the growth and radiobiological parameters in the GBM growth model. A novel concept, the occurrence probability (OP), is introduced to assess the sensitivity of potential tumor recurrence locations to the input parameter values. For a given patient, the OP map is derived from a superposition of all potential tumor recurrence locations obtained with all sets of parameter values.

RESULTS

For eight out of nine of patients, the authors have identified a statistically significant region where the OP is above 50%. For two patients, these high risk regions are found to be located at a distance greater than 3.9 cm from the border of the gross tumor volume highlighting the inaccuracy of current margins for some patients. The exact location and size of these volumes with OP > 50 % are, however, sensitive to the number N of ensembles of parameter values for N ≲ 400. On the other hand, the authors have identified for each patient a threshold OP, the OP(T), which defines a volume that converges more rapidly with increasing N. The OP(T) for each patient varies between 20% and 40%. The volume defined by OP > OP(T) may be an adequate candidate to define a personalized margin for radiotherapy treatment planning of GBM patients.

CONCLUSIONS

A new Monte-Carlo based formalism was described and used to assess the variability of sites of potential recurrence predicted by the proliferation-invasion model to input parameter values. The authors have shown that high risk areas could be consistently identified with a limited number of sets (N ≲ 400) of randomly chosen parameter values. A major strength of this formalism is its potential prospective nature. Although a validation of the accuracy of the model-predicted tumor recurrence location still remains to be done, our method is potentially applicable to orient patient-specific definition of margins.

Functional imaging in adult and paediatric brain tumours

Imaging is a key component in the management of brain tumours, with MRI being the preferred modality for most clinical scenarios. However, although conventional MRI provides mainly structural information, such as tumour size and location, it leaves many important clinical questions, such as tumour type, aggressiveness and prognosis, unanswered. An increasing number of studies have shown that additional information can be obtained using functional imaging methods (which probe tissue properties), and that these techniques can give key information of clinical importance. These techniques include diffusion imaging, which can assess tissue structure, and perfusion imaging and magnetic resonance spectroscopy, which measures tissue metabolite profiles. Tumour metabolism can also be investigated using PET, with 18F-deoxyglucose being the most readily available tracer. This Review discusses these methods and the studies that have investigated their clinical use. A strong emphasis is placed on the measurement of quantitative parameters, which is a move away from the qualitative nature of conventional radiological reporting and presents major challenges, particularly for multicentre studies.

Quantitative MR imaging and spectroscopy of brain tumours: a step forward?

OBJECTIVES

A prospective quantitative MR study of brain tumours was performed to show the potential of combining different MR techniques to distinguish various disease processes in routine clinical practice.

METHODS

Twenty-three patients with various intracranial tumours before treatment (diagnosis confirmed by a biopsy) and 59 healthy subjects were examined on a 3-T system by conventional MR imaging, 1H spectroscopic imaging, diffusion tensor imaging and T2 relaxometry. Metabolic concentrations and their ratios, T2 relaxation times and mean diffusivities were calculated and correlated on a pixel-by-pixel basis and compared to control data.

RESULTS

Different tumour types and different localisations revealed specific patterns of correlations between metabolic concentrations and mean diffusivity or T2 relaxation times. The patterns distinguish given tissue states in the examined area: healthy tissue, tissue infiltrated by tumour, active tumour, oedema infiltrated by tumour, oedema, etc. This method is able to describe the complexity of a highly heterogeneous tissue in the tumour and its vicinity, and determines crucial parameters for tissue differentiation.

CONCLUSIONS

A combination of different MR parameters on a pixel-by-pixel basis in individual patients enables better identification of the tumour type, direction of proliferation and assessment of the tumour extension.

KEY POINTS

• Magnetic resonance offers many different methods of examining the brain. • A combination of quantitative MR parameters helps distinguish different brain lesions • Different tumour types revealed specific correlation patterns amongst different MR parameters • The correlation patterns reflect highly heterogeneous complex tissue within tumours.

Improving the classification of brain tumors in mice with perturbation enhanced (PE)-MRSI

Classifiers based on statistical pattern recognition analysis of MRSI data are becoming important tools for the non-invasive diagnosis of human brain tumors. Here we investigate the potential interest of perturbation-enhanced MRSI (PE-MRSI), in this case acute hyperglycemia, for improving the discrimination between mouse brain MRS patterns of glioblastoma multiforme (GBM), oligodendroglioma (ODG), and non-tumor brain parenchyma (NT). Six GBM-bearing mice and three ODG-bearing mice were scanned at 7 Tesla by PRESS-MRSI with 12 and 136 ms echo-time, during euglycemia (Eug) and also during induced acute hyperglycemia (Hyp), generating altogether four datasets per animal (echo time + glycemic condition): 12Eug, 136Eug, 12Hyp, and 136Hyp. For classifier development all spectral vectors (spv) selected from the MRSI matrix were unit length normalized (UL2) and used either as a training set (76 GBM spv, four mice; 70 ODG spv, two mice; 54 NT spv) or as an independent testing set (61 GBM spv, two mice; 31 ODG, one mouse; 23 NT spv). All Fisher's LDA classifiers obtained were evaluated as far as their descriptive performance-correctly classified cases of the training set (bootstrapping)-and predictive accuracy-balanced error rate of independent testing set classification. MRSI-based classifiers at 12Hyp were consistently more efficient in separating GBM, ODG, and NT regions, with overall accuracies always >80% and up to 95-96%; remaining classifiers were within the 48-85% range. This was also confirmed by user-independent selection of training and testing sets, using leave-one-out (LOO). This highlights the potential interest of perturbation-enhanced MRSI protocols for improving the non-invasive characterization of preclinical brain tumors.

Exploiting spatial information to estimate metabolite levels in two-dimensional MRSI of heterogeneous brain lesions

MRSI provides MR spectra from multiple adjacent voxels within a body volume represented as a two- or three-dimensional matrix, allowing the measurement of the distribution of metabolites over this volume. The spectra of these voxels are usually analyzed one by one, without exploiting their spatial context. In this article, we present an advanced metabolite quantification method for MRSI data, in which the available spatial information is considered. A nonlinear least-squares algorithm is proposed in which prior knowledge is included in the form of proximity constraints on the spectral parameters within a grid and optimized starting values. A penalty term that promotes a spatially smooth spectral parameter map is added to the fitting algorithm. This method is adaptive, in the sense that several sweeps through the grid are performed and each solution may tune some hyperparameters at run-time. Simulation studies of MRSI data showed significantly improved metabolite estimates after the inclusion of spatial information. Improved metabolite maps were also demonstrated by applying the method to in vivo MRSI data. Overlapping peaks or peaks of compounds present at low concentration can be better quantified with the proposed method than with single-voxel approaches. The new approach compares favorably against the multivoxel approach embedded in the well-known quantification software LCModel.

MR-visible lipids and the tumor microenvironment

MR-visible lipids or mobile lipids are defined as lipids that are observable using proton MRS in cells and tissues. These MR-visible lipids are composed of triglycerides and cholesterol esters that accumulate in neutral lipid droplets, where their MR visibility is conferred as a result of the increased molecular motion available in this unique physical environment. This review discusses the factors that lead to the biogenesis of MR-visible lipids in cancer cells and in other cell types, such as immune cells and fibroblasts. We focus on the accumulations of mobile lipids that are inducible in cultured cells by a number of stresses, including culture conditions, and in response to activating stimuli or apoptotic cell death induced by anticancer drugs. This is compared with animal tumor models, where increases in mobile lipids are observed in response to chemo- and radiotherapy, and to human tumors, where mobile lipids are observed predominantly in high-grade brain tumors and in regions of necrosis. Conducive conditions for mobile lipid formation in the tumor microenvironment are discussed, including low pH, oxygen availability and the presence of inflammatory cells. It is concluded that MR-visible lipids appear in cancer cells and human tumors as a stress response. Mobile lipids stored as neutral lipid droplets may play a role in the detoxification of the cell or act as an alternative energy source, especially in cancer cells, which often grow in ischemic/hypoxic environments. The role of MR-visible lipids in cancer diagnosis and the assessment of the treatment response in both animal models of cancer and human brain tumors is also discussed. Although technical limitations exist in the accurate detection of intratumoral mobile lipids, early increases in mobile lipids after therapeutic interventions may be useful as a potential biomarker for the assessment of treatment response in cancer.

Assessment of therapeutic response and treatment planning for brain tumors using metabolic and physiological MRI

MRI is routinely used for diagnosis, treatment planning and assessment of response to therapy for patients with glioma. Gliomas are spatially heterogeneous and infiltrative lesions that are quite variable in terms of their response to therapy. Patients classified as having low-grade histology have a median overall survival of 7 years or more, but need to be monitored carefully to make sure that their tumor does not upgrade to a more malignant phenotype. Patients with the most aggressive grade IV histology have a median overall survival of 12-15 months and often undergo multiple surgeries and adjuvant therapies in an attempt to control their disease. Despite improvements in the spatial resolution and sensitivity of anatomic images, there remain considerable ambiguities in the interpretation of changes in the size of the gadolinium-enhancing lesion on T(1) -weighted images as a measure of treatment response, and in differentiating between treatment effects and infiltrating tumor within the larger T(2) lesion. The planning of focal therapies, such as surgery, radiation and targeted drug delivery, as well as a more reliable assessment of the response to therapy, would benefit considerably from the integration of metabolic and physiological imaging techniques into routine clinical MR examinations. Advanced methods that have been shown to provide valuable data for patients with glioma are diffusion, perfusion and spectroscopic imaging. Multiparametric examinations that include the acquisition of such data are able to assess tumor cellularity, hypoxia, disruption of normal tissue architecture, changes in vascular density and vessel permeability, in addition to the standard measures of changes in the volume of enhancing and nonenhancing anatomic lesions. This is particularly critical for the interpretation of the results of Phase I and Phase II clinical trials of novel therapies, which are increasingly including agents that are designed to have anti-angiogenic and anti-proliferative properties as opposed to having a direct effect on tumor cell viability.

Imaging of brain tumors: MR spectroscopy and metabolic imaging

The utility of magnetic resonance spectroscopy (MRS) in diagnosis and evaluation of treatment response to human brain tumors has been widely documented. The role of MRS in tumor classification, tumors versus nonneoplastic lesions, prediction of survival, treatment planning, monitoring of therapy, and post-therapy evaluation is discussed. This article delineates the need for standardization and further study in order for MRS to become widely used as a routine clinical tool.