In vivo molecular imaging for planning radiation therapy of gliomas: an application of 1H MRSI

Tracking Therapy Response in Glioblastoma Using 1D Convolutional Neural Networks

BACKGROUND

Glioblastoma (GB) is a malignant brain tumour that is challenging to treat, often relapsing even after aggressive therapy. Evaluating therapy response relies on magnetic resonance imaging (MRI) following the Response Assessment in Neuro-Oncology (RANO) criteria. However, early assessment is hindered by phenomena such as pseudoprogression and pseudoresponse. Magnetic resonance spectroscopy (MRS/MRSI) provides metabolomics information but is underutilised due to a lack of familiarity and standardisation.

METHODS

This study explores the potential of spectroscopic imaging (MRSI) in combination with several machine learning approaches, including one-dimensional convolutional neural networks (1D-CNNs), to improve therapy response assessment. Preclinical GB (GL261-bearing mice) were studied for method optimisation and validation.

RESULTS

The proposed 1D-CNN models successfully identify different regions of tumours sampled by MRSI, i.e., normal brain (N), control/unresponsive tumour (T), and tumour responding to treatment (R). Class activation maps using Grad-CAM enabled the study of the key areas relevant to the models, providing model explainability. The generated colour-coded maps showing the N, T and R regions were highly accurate (according to Dice scores) when compared against ground truth and outperformed our previous method.

CONCLUSIONS

The proposed methodology may provide new and better opportunities for therapy response assessment, potentially providing earlier hints of tumour relapsing stages.

Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T: A Proof-of-Concept Study

In vivo magnetic resonance spectroscopy (MRS) has two modalities, single-voxel (SV) and multivoxel (MV), in which one or more contiguous grids of SVs are acquired.

PURPOSE

To test whether MV grids can be classified with models trained with SV.

METHODS

Retrospective study. Training dataset: Multicenter multiformat SV INTERPRET, 1.5T. Testing dataset: MV eTumour, 3T. Two classification tasks were completed: 3-class (meningioma vs. aggressive vs. normal) and 4-class (meningioma vs. low-grade glioma vs. aggressive vs. normal). Five different methods were tested for feature selection. The classification was implemented using linear discriminant analysis (LDA), random forest, and support vector machines. The evaluation was completed with balanced error rate (BER) and area under the curve (AUC) on both sets. The accuracy in class prediction was calculated by developing a solid tumor index (STI) and segmentation accuracy with the Dice score.

RESULTS

The best method was sequential forward feature selection combined with LDA, with AUCs = 0.95 (meningioma), 0.89 (aggressive), 0.82 (low-grade glioma), and 0.82 (normal). STI was 66% (4-class task) and 71% (3-class task) because two cases failed completely and two more had suboptimal STI as defined by us.

DISCUSSION

The reasons for failure in the classification of the MV test set were related to the presence of artifacts.

A multi-institutional pilot clinical trial of spectroscopic MRI-guided radiation dose escalation for newly diagnosed glioblastoma

Background

Glioblastomas (GBMs) are aggressive brain tumors despite radiation therapy (RT) to 60 Gy and temozolomide (TMZ). Spectroscopic magnetic resonance imaging (sMRI), which measures levels of specific brain metabolites, can delineate regions at high risk for GBM recurrence not visualized on contrast-enhanced (CE) MRI. We conducted a clinical trial to assess the feasibility, safety, and efficacy of sMRI-guided RT dose escalation to 75 Gy for newly diagnosed GBMs.

Methods

Our pilot trial (NCT03137888) enrolled patients at 3 institutions (Emory University, University of Miami, Johns Hopkins University) from September 2017 to June 2019. For RT, standard tumor volumes based on T2-FLAIR and T1w-CE MRIs with margins were treated in 30 fractions to 50.1 and 60 Gy, respectively. An additional high-risk volume based on residual CE tumor and Cho/NAA (on sMRI) ≥2× normal was treated to 75 Gy. Survival curves were generated by the Kaplan-Meier method. Toxicities were assessed according to CTCAE v4.0.

Results

Thirty patients were treated in the study. The median age was 59 years. 30% were MGMT promoter hypermethylated; 7% harbored IDH1 mutation. With a median follow-up of 21.4 months for censored patients, median overall survival (OS) and progression-free survival were 23.0 and 16.6 months, respectively. This regimen appeared well-tolerated with 70% of grade 3 or greater toxicity ascribed to TMZ and 23% occurring at least 1 year after RT.

Conclusion

Dose-escalated RT to 75 Gy guided by sMRI appears feasible and safe for patients with newly diagnosed GBMs. OS outcome is promising and warrants additional testing. Based on these results, a randomized phase II trial is in development.

Clinical Applications of Magnetic Resonance Spectroscopy in Brain Tumors: From Diagnosis to Treatment

Magnetic resonance spectroscopy (MRS) is a valuable tool for imaging brain tumors, primarily as an adjunct to conventional imaging and clinical presentation. MRS is useful in initial diagnosis of brain tumors, helping differentiate tumors from possible mimics such as metastatic disease, lymphoma, demyelination, and infection, as well as in the subsequent follow-up of patients after resection and chemoradiation. Unfortunately, the spectroscopic appearance of many pathologies can overlap, and ultimately follow-up or biopsy may be required to make a definitive diagnosis. Future developments may continue to increase the value of MRS for initial diagnosis, treatment planning, and early detection of recurrence.

Magnetic resonance spectroscopic imaging in gliomas: clinical diagnosis and radiotherapy planning

The reprogramming of cellular metabolism is a hallmark of cancer diagnosis and prognosis. Proton magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic technique for investigating brain metabolism to establish cancer diagnosis and IDH gene mutation diagnosis as well as facilitate pre-operative planning and treatment response monitoring. By allowing tissue metabolism to be quantified, MRSI provides added value to conventional MRI. MRSI can generate metabolite maps from a single volume or multiple volume elements within the whole brain. Metabolites such as NAA, Cho and Cr, as well as their ratios Cho:NAA ratio and Cho:Cr ratio, have been used to provide tumor diagnosis and aid in radiation therapy planning as well as treatment assessment. In addition to these common metabolites, 2-hydroxygluterate (2HG) has also been quantified using MRSI following the recent discovery of IDH mutations in gliomas. This has opened up targeted drug development to inhibit the mutant IDH pathway. This review provides guidance on MRSI in brain gliomas, including its acquisition, analysis methods, and evolving clinical applications.

Translating preclinical MRI methods to clinical oncology

The complexity of modern in vivo magnetic resonance imaging (MRI) methods in oncology has dramatically changed in the last 10 years. The field has long since moved passed its (unparalleled) ability to form images with exquisite soft-tissue contrast and morphology, allowing for the enhanced identification of primary tumors and metastatic disease. Currently, it is not uncommon to acquire images related to blood flow, cellularity, and macromolecular content in the clinical setting. The acquisition of images related to metabolism, hypoxia, pH, and tissue stiffness are also becoming common. All of these techniques have had some component of their invention, development, refinement, validation, and initial applications in the preclinical setting using in vivo animal models of cancer. In this review, we discuss the genesis of quantitative MRI methods that have been successfully translated from preclinical research and developed into clinical applications. These include methods that interrogate perfusion, diffusion, pH, hypoxia, macromolecular content, and tissue mechanical properties for improving detection, staging, and response monitoring of cancer. For each of these techniques, we summarize the 1) underlying biological mechanism(s); 2) preclinical applications; 3) available repeatability and reproducibility data; 4) clinical applications; and 5) limitations of the technique. We conclude with a discussion of lessons learned from translating MRI methods from the preclinical to clinical setting, and a presentation of four fundamental problems in cancer imaging that, if solved, would result in a profound improvement in the lives of oncology patients. Level of Evidence: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1377-1392.

Incorporation of a spectral model in a convolutional neural network for accelerated spectral fitting

PURPOSE

MRSI has shown great promise in the detection and monitoring of neurologic pathologies such as tumor. A necessary component of data processing includes the quantitation of each metabolite, typically done through fitting a model of the spectrum to the data. For high-resolution volumetric MRSI of the brain, which may have ~10,000 spectra, significant processing time is required for spectral analysis and generation of metabolite maps.

METHODS

A novel unsupervised deep learning architecture that combines a convolutional neural network with a priori models of the spectrum is presented. This architecture, a convolutional encoder-model decoder (CEMD), combines the strengths of adaptive and unbiased convolutional networks with models of magnetic resonance and is readily interpretable.

RESULTS

The CEMD architecture performs accurate spectral fitting for volumetric MRSI in patients with glioblastoma, provides whole-brain fitting in 1 min on a standard computer, and handles a variety of spectral artifacts.

CONCLUSION

A new architecture combining physics domain knowledge with convolutional neural networks has been developed and is able to perform rapid spectral fitting of whole-brain data. Rapid processing is a critical step toward routine clinical practice.

Clinical applications of in vivo magnetic resonance spectroscopy in oncology

Magnetic resonance spectroscopy (MRS) can be performed in vivo using commercial MRI systems to obtain biochemical information about tissues and cancers. Applications in brain, prostate and breast aid lesion detection and characterisation (differential diagnosis), treatment planning and response assessment. Multi-centre clinical trials have been performed in all these tissues. Single centre studies have been performed in many other tissues including cervix, uterus, musculoskeletal and liver. While generally MRS is used to study endogenous metabolites it has also been used in drug studies, for example those that include 19F as part of their structure. Recently the hyperpolarisation of compounds enriched with 13C such as [1-13C] pyruvate has been demonstrated in animal models and now in preliminary clinical studies, permitting the monitoring of biochemical processes with unprecedented sensitivity. This review briefly introduces the underlying methods and then discusses the current status of these applications.

Elliptical localization with pulsed second-order fields (ECLIPSE) for robust lipid suppression in proton MRSI

Proton MRSI has great clinical potential for metabolic mapping of the healthy and pathological human brain. Unfortunately, the promise has not yet been fully achieved due to numerous technical challenges related to insufficient spectral quality caused by magnetic field inhomogeneity, insufficient RF transmit power and incomplete lipid suppression. Here a robust, novel method for lipid suppression in 1 H MRSI is presented. The method is based on 2D spatial localization of an elliptical region of interest using pulsed second-order spherical harmonic (SH) magnetic fields. A dedicated, high-amplitude second-order SH gradient setup was designed and constructed, containing coils to generate Z2, X2Y2 and XY magnetic fields. Simulations and phantom MRI results are used to demonstrate the principles of the method and illustrate the manifestation of chemical shift displacement. 1 H MRSI on human brain in vivo demonstrates high quality, robust suppression of extracranial lipids. The method allows a wide range of inner or outer volume selection or suppression and should find application in MRSI, reduced-field-of-view MRI and single-volume MRS.

Imaging Cancer Metabolism

It is widely accepted that altered metabolism contributes to cancer growth and has been described as a hallmark of cancer. Our view and understanding of cancer metabolism has expanded at a rapid pace, however, there remains a need to study metabolic dependencies of human cancer in vivo. Recent studies have sought to utilize multi-modality imaging (MMI) techniques in order to build a more detailed and comprehensive understanding of cancer metabolism. MMI combines several in vivo techniques that can provide complementary information related to cancer metabolism. We describe several non-invasive imaging techniques that provide both anatomical and functional information related to tumor metabolism. These imaging modalities include: positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS) that uses hyperpolarized probes and optical imaging utilizing bioluminescence and quantification of light emitted. We describe how these imaging modalities can be combined with mass spectrometry and quantitative immunochemistry to obtain more complete picture of cancer metabolism. In vivo studies of tumor metabolism are emerging in the field and represent an important component to our understanding of how metabolism shapes and defines cancer initiation, progression and response to treatment. In this review we describe in vivo based studies of cancer metabolism that have taken advantage of MMI in both pre-clinical and clinical studies. MMI promises to advance our understanding of cancer metabolism in both basic research and clinical settings with the ultimate goal of improving detection, diagnosis and treatment of cancer patients.

A Metabolic Therapy for Malignant Glioma Requires a Clinical Measure

Cancers are "reprogrammed" to use a much higher rate of glycolysis (GLY) relative to oxidative phosphorylation (OXPHOS), even in the presence of adequate amounts of oxygenation. Originally identified by Nobel Laureate Otto Warburg, this hallmark of cancer has recently been termed metabolic reprogramming and represents a way for the cancer tissue to divert carbon skeletons to produce biomass. Understanding the mechanisms that underlie this metabolic shift should lead to better strategies for cancer treatments. Malignant gliomas, cancers that are very resistant to conventional treatments, are highly glycolytic and seem particularly suited to approaches that can subvert this phenotype.

Adult Brain Tumors: Clinical Applications of Magnetic Resonance Spectroscopy

Proton magnetic resonance spectroscopy (H-MRS) may be helpful in suggesting tumor histology and tumor grade and may better define tumor extension and the ideal site for biopsy compared with conventional magnetic resonance (MR) imaging. A multifunctional approach with diffusion-weighted imaging, perfusion-weighted imaging, and permeability maps, along with H-MRS, may enhance the accuracy of the diagnosis and characterization of brain tumors and estimation of therapeutic response. Integration of advanced imaging techniques with conventional MR imaging and the clinical history help to improve the accuracy, sensitivity, and specificity in differentiating tumors and nonneoplastic lesions.

The use of hyperpolarized carbon-13 magnetic resonance for molecular imaging

Until recently, molecular imaging using magnetic resonance (MR) has been limited by the modality's low sensitivity, especially with non-proton nuclei. The advent of hyperpolarized (HP) MR overcomes this limitation by substantially enhancing the signal of certain biologically important probes through a process known as external nuclear polarization, enabling real-time assessment of tissue function and metabolism. The metabolic information obtained by HP MR imaging holds significant promise in the clinic, where it could play a critical role in disease diagnosis and therapeutic monitoring. This review will provide a comprehensive overview of the developments made in the field of hyperpolarized MR, including advancements in polarization techniques and delivery, probe development, pulse sequence optimization, characterization of healthy and diseased tissues, and the steps made towards clinical translation.

Simulating the Effect of Spectroscopic MRI as a Metric for Radiation Therapy Planning in Patients with Glioblastoma

Due to glioblastoma's infiltrative nature, an optimal radiation therapy (RT) plan requires targeting infiltration not identified by anatomical magnetic resonance imaging (MRI). Here, high-resolution, whole-brain spectroscopic MRI (sMRI) is used to describe tumor infiltration alongside anatomical MRI and simulate the degree to which it modifies RT target planning. In 11 patients with glioblastoma, data from preRT sMRI scans were processed to give high-resolution, whole-brain metabolite maps normalized by contralateral white matter. Maps depicting choline to N-Acetylaspartate (Cho/NAA) ratios were registered to contrast-enhanced T1-weighted RT planning MRI for each patient. Volumes depicting metabolic abnormalities (1.5-, 1.75-, and 2.0-fold increases in Cho/NAA ratios) were compared with conventional target volumes and contrast-enhancing tumor at recurrence. sMRI-modified RT plans were generated to evaluate target volume coverage and organ-at-risk dose constraints. Conventional clinical target volumes and Cho/NAA abnormalities identified significantly different regions of microscopic infiltration with substantial Cho/NAA abnormalities falling outside of the conventional 60 Gy isodose line (41.1, 22.2, and 12.7 cm³, respectively). Clinical target volumes using Cho/NAA thresholds exhibited significantly higher coverage of contrast enhancement at recurrence on average (92.4%, 90.5%, and 88.6%, respectively) than conventional plans (82.5%). sMRI-based plans targeting tumor infiltration met planning objectives in all cases with no significant change in target coverage. In 2 cases, the sMRI-modified plan exhibited better coverage of contrast-enhancing tumor at recurrence than the original plan. Integration of the high-resolution, whole-brain sMRI into RT planning is feasible, resulting in RT target volumes that can effectively target tumor infiltration while adhering to conventional constraints.

Non-invasive metabolic imaging of brain tumours in the era of precision medicine

The revolution in cancer genomics has uncovered a variety of clinically relevant mutations in primary brain tumours, creating an urgent need to develop non-invasive imaging biomarkers to assess and integrate this genetic information into the clinical management of patients. Metabolic reprogramming is a central hallmark of cancer, including brain tumours; indeed, many of the molecular pathways implicated in the pathogenesis of brain tumours result in reprogramming of metabolism. This relationship provides the opportunity to devise in vivo metabolic imaging modalities to improve diagnosis, patient stratification, and monitoring of treatment response. Metabolic phenomena, such as the Warburg effect and altered mitochondrial metabolism, can be leveraged to image brain tumours using techniques including PET and MRI. Moreover, genetic alterations, such as mutations affecting isocitrate dehydrogenase, are associated with unique metabolic signatures that can be detected using magnetic resonance spectroscopy. The need to translate our understanding of the molecular features of brain tumours into imaging modalities with clinical utility is growing; metabolic imaging provides a unique platform to achieve this objective. In this Review, we examine the molecular basis for metabolic reprogramming in brain tumours, and examine current non-invasive metabolic imaging strategies that can be used to interrogate these molecular characteristics with the ultimate goal of guiding and improving patient care.

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 S100β-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.

Radiotherapy planning using MRI

The use of magnetic resonance imaging (MRI) in radiotherapy (RT) planning is rapidly expanding. We review the wide range of image contrast mechanisms available to MRI and the way they are exploited for RT planning. However a number of challenges are also considered: the requirements that MR images are acquired in the RT treatment position, that they are geometrically accurate, that effects of patient motion during the scan are minimized, that tissue markers are clearly demonstrated, that an estimate of electron density can be obtained. These issues are discussed in detail, prior to the consideration of a number of specific clinical applications. This is followed by a brief discussion on the development of real-time MRI-guided RT.

Functional MRI and CT biomarkers in oncology

Imaging biomarkers derived from MRI or CT describe functional properties of tumours and normal tissues. They are finding increasing numbers of applications in diagnosis, monitoring of response to treatment and assessment of progression or recurrence. Imaging biomarkers also provide scope for assessment of heterogeneity within and between lesions. A wide variety of functional parameters have been investigated for use as biomarkers in oncology. Some imaging techniques are used routinely in clinical applications while others are currently restricted to clinical trials or preclinical studies. Apparent diffusion coefficient, magnetization transfer ratio and native T1 relaxation time provide information about structure and organization of tissues. Vascular properties may be described using parameters derived from dynamic contrast-enhanced MRI, dynamic contrast-enhanced CT, transverse relaxation rate (R2*), vessel size index and relative blood volume, while magnetic resonance spectroscopy may be used to probe the metabolic profile of tumours. This review describes the mechanisms of contrast underpinning each technique and the technical requirements for robust and reproducible imaging. The current status of each biomarker is described in terms of its validation, qualification and clinical applications, followed by a discussion of the current limitations and future perspectives.

Hypercellularity Components of Glioblastoma Identified by High b-Value Diffusion-Weighted Imaging

PURPOSE

Use of conventional magnetic resonance imaging (MRI) for target definition may expose glioblastomas (GB) to inadequate radiation dose coverage of the nonenhanced hypercellular subvolume. This study aimed to develop a technique to identify the hypercellular components of GB by using high b-value diffusion-weighted imaging (DWI) and to investigate its relationship with the prescribed 95% isodose volume (PDV) and progression-free survival (PFS).

METHODS AND MATERIALS

Twenty-one patients with GB underwent chemoradiation therapy post-resection and biopsy. Radiation therapy (RT) treatment planning was based upon conventional MRI. Pre-RT DWIs were acquired in 3 orthogonal directions with b-values of 0, 1000, and 3000 s/mm(2). Hypercellularity volume (HCV) was defined on the high b-value (3000 s/mm(2)) DWI by a threshold method. Nonenhanced signified regions not covered by the Gd-enhanced gross tumor volume (GTV-Gd) on T1-weighted images. The PDV was used to evaluate spatial coverage of the HCV by the dose plan. Association between HCV and PFS or other clinical covariates were assessed using univariate proportional hazards regression models.

RESULTS

HCVs and nonenhanced HCVs varied from 0.58 to 67 cm(3) (median: 9.8 cm(3)) and 0.15 to 60 cm(3) (median: 2.5 cm(3)), respectively. Fourteen patients had incomplete dose coverage of the HCV, 6 of whom had >1 cm(3) HCV missed by the 95% PDV (range: 1.01-25.4 cm(3)). Of the 15 patients who progressed, 5 progressed earlier, within 6 months post-RT, and 10 patients afterward. Pre-RT HCVs within recurrent GTVs-Gd were 78% (range: 65%-89%) for the 5 earliest progressions but lower, 53% (range: 0%-85%), for the later progressions. HCV and nonenhanced HCV were significant negative prognostic indicators for PFS (P<.002 and P<.01, respectively). The hypercellularity subvolume not covered by the 95% PDV was a significant negative predictor for PFS (P<.05).

CONCLUSIONS

High b-value DWI identifies the hypercellular components of GB and could aid in RT target volume definition. Future studies will allow us to investigate the role of high b-value DWI in identifying radiation boost volumes and diagnosing progression.

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.

Advanced magnetic resonance imaging methods for planning and monitoring radiation therapy in patients with high-grade glioma

This review explores how the integration of advanced imaging methods with high-quality anatomical images significantly improves the characterization, target definition, assessment of response to therapy, and overall management of patients with high-grade glioma. Metrics derived from diffusion-, perfusion-, and susceptibility-weighted magnetic resonance imaging in conjunction with magnetic resonance spectroscopic imaging, allows us to characterize regions of edema, hypoxia, increased cellularity, and necrosis within heterogeneous tumor and surrounding brain tissue. Quantification of such measures may provide a more reliable initial representation of tumor delineation and response to therapy than changes in the contrast-enhancing or T2 lesion alone and have a significant effect on targeting resection, planning radiation, and assessing treatment effectiveness. In the long term, implementation of these imaging methodologies can also aid in the identification of recurrent tumor and its differentiation from treatment-related confounds and facilitate the detection of radiationinduced vascular injury in otherwise normal-appearing brain tissue.

Volumetric spectroscopic imaging of glioblastoma multiforme radiation treatment volumes

PURPOSE

Magnetic resonance (MR) imaging and computed tomography (CT) are used almost exclusively in radiation therapy planning of glioblastoma multiforme (GBM), despite their well-recognized limitations. MR spectroscopic imaging (MRSI) can identify biochemical patterns associated with normal brain and tumor, predominantly by observation of choline (Cho) and N-acetylaspartate (NAA) distributions. In this study, volumetric 3-dimensional MRSI was used to map these compounds over a wide region of the brain and to evaluate metabolite-defined treatment targets (metabolic tumor volumes [MTV]).

METHODS AND MATERIALS

Volumetric MRSI with effective voxel size of ∼1.0 mL and standard clinical MR images were obtained from 19 GBM patients. Gross tumor volumes and edema were manually outlined, and clinical target volumes (CTVs) receiving 46 and 60 Gy were defined (CTV46 and CTV60, respectively). MTVCho and MTVNAA were constructed based on volumes with high Cho and low NAA relative to values estimated from normal-appearing tissue.

RESULTS

The MRSI coverage of the brain was between 70% and 76%. The MTVNAA were almost entirely contained within the edema, and the correlation between the 2 volumes was significant (r=0.68, P=.001). In contrast, a considerable fraction of MTVCho was outside of the edema (median, 33%) and for some patients it was also outside of the CTV46 and CTV60. These untreated volumes were greater than 10% for 7 patients (37%) in the study, and on average more than one-third (34.3%) of the MTVCho for these patients were outside of CTV60.

CONCLUSIONS

This study demonstrates the potential usefulness of whole-brain MRSI for radiation therapy planning of GBM and revealed that areas of metabolically active tumor are not covered by standard RT volumes. The described integration of MTV into the RT system will pave the way to future clinical trials investigating outcomes in patients treated based on metabolic information.

Measuring mitochondrial metabolism in rat brain in vivo using MR Spectroscopy of hyperpolarized [2-¹³C]pyruvate

Hyperpolarized [1-(13) C]pyruvate ([1-(13) C]Pyr) has been used to assess metabolism in healthy and diseased states, focusing on the downstream labeling of lactate (Lac), bicarbonate and alanine. Although hyperpolarized [2-(13) C]Pyr, which retains the labeled carbon when Pyr is converted to acetyl-coenzyme A, has been used successfully to assess mitochondrial metabolism in the heart, the application of [2-(13) C]Pyr in the study of brain metabolism has been limited to date, with Lac being the only downstream metabolic product reported previously. In this study, single-time-point chemical shift imaging data were acquired from rat brain in vivo. [5-(13) C]Glutamate, [1-(13) C]acetylcarnitine and [1-(13) C]citrate were detected in addition to resonances from [2-(13) C]Pyr and [2-(13) C]Lac. Brain metabolism was further investigated by infusing dichloroacetate, which upregulates Pyr flux to acetyl-coenzyme A. After dichloroacetate administration, a 40% increase in [5-(13) C]glutamate from 0.014 ± 0.004 to 0.020 ± 0.006 (p = 0.02), primarily from brain, and a trend to higher citrate (0.002 ± 0.001 to 0.004 ± 0.002) were detected, whereas [1-(13) C]acetylcarnitine was increased in peripheral tissues. This study demonstrates, for the first time, that hyperpolarized [2-(13) C]Pyr can be used for the in vivo investigation of mitochondrial function and tricarboxylic acid cycle metabolism in brain.

Magnetic resonance spectroscopy of the breast: current status

In vivo magnetic resonance spectroscopy (MRS) of the breast can be used to measure the level of choline-containing compounds, which is a biomarker of malignancy. In the diagnostic setting, MRS can provide high specificity for distinguishing benign from malignant lesions. MRS also can be used as an early response indicator in patients undergoing neoadjuvant chemotherapy. This article describes the acquisition and analysis methods used for measuring total choline levels in the breast using MRS, reviews the findings from clinical studies of diagnosis and treatment response, and discusses problems, limitations, and future developments for this promising clinical technology.

Three-dimensional amide proton transfer MR imaging of gliomas: Initial experience and comparison with gadolinium enhancement

PURPOSE

To investigate the feasibility of a three-dimensional amide-proton-transfer (APT) imaging sequence with gradient- and spin-echo readouts at 3 Tesla in patients with high- or low-grade gliomas.

MATERIALS AND METHODS

Fourteen patients with newly diagnosed gliomas were recruited. After B0 inhomogeneity correction on a voxel-by-voxel basis, APT-weighted images were reconstructed using a magnetization-transfer-ratio asymmetry at offsets of ±3.5 ppm with respect to the water resonance. Analysis of variance post hoc tests were used for statistical evaluations, and results were validated with pathology.

RESULTS

In six patients with gadolinium-enhancing high-grade gliomas, enhancing tumors on the postcontrast T1 -weighted images were consistently hyperintense on the APT-weighted images. Increased APT-weighted signal intensity was also clearly visible in two pathologically proven, high-grade gliomas without gadolinium enhancement. The average APT-weighted signal was significantly higher in the lesions than in the contralateral normal-appearing brain tissue (P < 0.001). In six low-grade gliomas, including two with gadolinium enhancement, APT-weighted imaging showed iso-intensity or mild punctate hyperintensity within all the lesions, which was significantly lower than that seen in the high-grade gliomas (P < 0.001).

CONCLUSION

The proposed three-dimensional APT imaging sequence can be incorporated into standard brain MRI protocols for patients with malignant gliomas.

Survival analysis in patients with newly diagnosed glioblastoma using pre- and postradiotherapy MR spectroscopic imaging

BACKGROUND

The objective of this study was to examine the predictive value of parameters of 3D (1)H magnetic resonance spectroscopic imaging (MRSI) prior to treatment with radiation/chemotherapy (baseline) and at a postradiation 2-month follow-up (F2mo) in relationship to 6-month progression-free survival (PFS6) and overall survival (OS).

METHODS

Sixty-four patients with newly diagnosed glioblastoma multiforme (GBM) being treated with radiation and concurrent chemotherapy were involved in this study. Evaluated were metabolite indices and metabolite ratios. Logistic linear regression and Cox proportional hazards models were utilized to evaluate PFS6 and OS, respectively. These analyses were adjusted by age and MR scanner field strength (1.5 T or 3 T). Stepwise regression was performed to determine a subset of the most relevant variables.

RESULTS

Associated with shorter PFS6 were a decrease in the ratio of N-acetyl aspartate to choline-containing compounds (NAA/Cho) in the region with a Cho-to-NAA index (CNI) >3 at baseline and an increase of the CNI within elevated CNI regions (>2) at F2mo. Patients with higher normalized lipid and lactate at either time point had significantly worse OS. Patients who had larger volumes with abnormal CNI at F2mo had worse PFS6 and OS.

CONCLUSIONS

Our study found more 3D MRSI parameters that predicted PFS6 and OS for patients with GBM than did anatomic, diffusion, or perfusion imaging, which were previously evaluated in the same population of patients.

Convex non-negative matrix factorization for brain tumor delimitation from MRSI data

Background

Pattern Recognition techniques can provide invaluable insights in the field of neuro-oncology. This is because the clinical analysis of brain tumors requires the use of non-invasive methods that generate complex data in electronic format. Magnetic Resonance (MR), in the modalities of spectroscopy (MRS) and spectroscopic imaging (MRSI), has been widely applied to this purpose. The heterogeneity of the tissue in the brain volumes analyzed by MR remains a challenge in terms of pathological area delimitation.

Methodology/Principal Findings

A pre-clinical study was carried out using seven brain tumor-bearing mice. Imaging and spectroscopy information was acquired from the brain tissue. A methodology is proposed to extract tissue type-specific sources from these signals by applying Convex Non-negative Matrix Factorization (Convex-NMF). Its suitability for the delimitation of pathological brain area from MRSI is experimentally confirmed by comparing the images obtained with its application to selected target regions, and to the gold standard of registered histopathology data. The former showed good accuracy for the solid tumor region (proliferation index (PI)>30%). The latter yielded (i) high sensitivity and specificity in most cases, (ii) acquisition conditions for safe thresholds in tumor and non-tumor regions (PI>30% for solid tumoral region; ≤5% for non-tumor), and (iii) fairly good results when borderline pixels were considered.

Conclusions/Significance

The unsupervised nature of Convex-NMF, which does not use prior information regarding the tumor area for its delimitation, places this approach one step ahead of classical label-requiring supervised methods for discrimination between tissue types, minimizing the negative effect of using mislabeled voxels. Convex-NMF also relaxes the non-negativity constraints on the observed data, which allows for a natural representation of the MRSI signal. This should help radiologists to accurately tackle one of the main sources of uncertainty in the clinical management of brain tumors, which is the difficulty of appropriately delimiting the pathological area.

Growth properties of SF188/V+ human glioma in rats in vivo observed by magnetic resonance imaging

SF188/V+ is a highly vascular human glioma model that is based on transfection of vascular endothelial growth factor (VEGF) cDNA into SF188/V- cells. This study aims to assess its growth and vascularity properties in vivo in a rat model. Thirty-two adult rats were inoculated with SF188/V+ tumor cells, and, for comparison, five were inoculated with SF188/V- tumor cells. Several conventional magnetic resonance imaging (MRI) sequences were acquired, and several quantitative structural (T(2) and T(1)), functional [isotropic apparent diffusion coefficient (ADC) and blood flow], and molecular [protein and peptide-based amide proton transfer (APT)] MRI parameters were mapped on a 4.7 T animal scanner. In rats inoculated with SF188/V+ tumor cells, conventional T(2)-weighted images showed a highly heterogeneous tumor mass, and post-contrast T(1)-weighted images showed a heterogeneous, strong enhancement of the mass. There were moderate increases in T(2), T(1), and ADC, and large increases in blood flow and APT in the tumor, compared to contralateral brain tissue. Microscopic examination revealed prominent vascularity and hemorrhage in the VEGF-secreting xenografts as compared to controls, and immunohistochemical staining confirmed increased expression of VEGF in tumor xenografts. Our results indicate that the SF188/V+ glioma model exhibits some MRI and histopathology features that closely resemble human glioblastoma.

Spatial characteristics of newly diagnosed grade 3 glioma assessed by magnetic resonance metabolic and diffusion tensor imaging

The spatial heterogeneity in magnetic resonance (MR) metabolic and diffusion parameters and their relationship were studied for patients with treatment-naive grade 3 gliomas. MR data were evaluated from 51 patients with newly diagnosed grade 3 gliomas. Anatomic, diffusion, and metabolic imaging data were considered. Variations in metabolite levels, apparent diffusion coefficient (ADC), and fractional anisotropy (FA) were evaluated in regions of gadolinium enhancement and T2 hyperintensity as well as regions with abnormal metabolic signatures. Contrast enhancement was present in only 21 of the 51 patients. When present, the enhancing component of the lesion had higher choline-to-N-acetylaspartate index (CNI), higher choline, lower N-acetylaspartate, similar creatine, similar ADC and FA, and higher lactate/lipid than the nonenhancing lesion. Regions with CNI ≥ 4 had higher choline, lower N-acetylaspartate, higher lactate/lipid, higher ADC, and lower FA than normal-appearing white matter and regions with intermediate CNI values. For lesions that exhibited gadolinium enhancement, the metabolite levels and diffusion parameters in the region of enhancement were consistent with it corresponding to the most abnormal portion of the tumor. For nonenhancing lesions, areas with CNI ≥ 4 were the most abnormal in metabolic and diffusion parameters. This suggests that the region with the highest CNI might provide a good target for biopsies for nonenhancing lesions to obtain a representative histologic diagnosis of its degree of malignancy. Metabolic and diffusion parameter levels may be of interest not only for directing tissue sampling but also for defining the targets for focal therapy and assessing response to therapy.

Multimodality assessment of brain tumors and tumor recurrence

Neuroimaging plays a significant role in the diagnosis of intracranial tumors, especially brain gliomas, and must consist of an assessment of location and extent of the tumor and of its biologic activity. Therefore, morphologic imaging modalities and functional, metabolic, or molecular imaging modalities should be combined for primary diagnosis and for following the course and evaluating therapeutic effects. MRI is the gold standard for providing detailed morphologic information and can supply some additional insights into metabolism (MR spectroscopy) and perfusion (perfusion-weighted imaging) but still has limitations in identifying tumor grade, invasive growth into neighboring tissue, and treatment-induced changes, as well as recurrences. These insights can be obtained by various PET modalities, including imaging of glucose metabolism, amino acid uptake, nucleoside uptake, and hypoxia. Diagnostic accuracy can benefit from coregistration of PET results and MRI, combining the high-resolution morphologic images with the biologic information. These procedures are optimized by the newly developed combination of PET and MRI modalities, permitting the simultaneous assessment of morphologic, functional, metabolic, and molecular information on the human brain.

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.

Five-Year Longitudinal MRI Follow-up and (1)H Single Voxel MRS in 14 patients with Gliomatosis Treated with Temodal, Radiotherapy and Antiangiogenic Therapy

Gliomatosis cerebri (GC) is a challenging tumor, considered to have a poor prognosis and poor response to treatments. The purpose of this study is to better understand glial tumor metabolism and post chemotherapy, radiotherapy and antiangiogenic variations in a longitudinal study to determine cerebral variation in MRS area, amplitude, and ratios of metabolites and spectral profiles during a five year longitudinal follow-up in 14 patients with gliomatosis without initial hyperperfusion and treated with chemotherapy (Temozolomide (Temodal(®))), radiotherapy and subsequent antiangiogenic therapy. The study also aimed to detect changes in infiltration, proliferation, lipids or glycolytic metabolism, as these changes could be monitored longitudinally in humans with glial brain tumors (low and high grade) after therapy, using conventional magnetic resonance imaging (MRI), spectroscopy (MRS) and MR perfusion. Most patients had first initial clinical and MRS improvement and stable MRI. After 12 to 24 chemotherapy treatment cycles MRS usually showed an increase in the Cho/Cr ratio (proliferation) and sometimes contrast enhancements. Later, the patients showed clinical deterioration and radiotherapy was started. There was an improvement with radiotherapy that lasted nine to 18 months. This was followed by a worsening that led to try antiangiogenic therapy. Later in the evolution for three patients with hyperperfusion this symptom disappeared, but proliferation, infiltration and glycolytic metabolism remained at a high level. Spectroscopic and metabolic changes often occur well before clinical deterioration and sometimes before improvement. Therefore, MRS could be more sensitive and could detect changes earlier than MRI and is sometimes predictive. Despite the difficulty, the variability and unknown factors, these repeated measurements give us a better insight into the nature of the different processes, tumor progression and could lead to better understanding of therapeutic response.

Analysis of cancer metabolism by imaging hyperpolarized nuclei: prospects for translation to clinical research

A major challenge in cancer biology is to monitor and understand cancer metabolism in vivo with the goal of improved diagnosis and perhaps therapy. Because of the complexity of biochemical pathways, tracer methods are required for detecting specific enzyme-catalyzed reactions. Stable isotopes such as (13)C or (15)N with detection by nuclear magnetic resonance provide the necessary information about tissue biochemistry, but the crucial metabolites are present in low concentration and therefore are beyond the detection threshold of traditional magnetic resonance methods. A solution is to improve sensitivity by a factor of 10,000 or more by temporarily redistributing the populations of nuclear spins in a magnetic field, a process termed hyperpolarization. Although this effect is short-lived, hyperpolarized molecules can be generated in an aqueous solution and infused in vivo where metabolism generates products that can be imaged. This discovery lifts the primary constraint on magnetic resonance imaging for monitoring metabolism-poor sensitivity-while preserving the advantage of biochemical information. The purpose of this report was to briefly summarize the known abnormalities in cancer metabolism, the value and limitations of current imaging methods for metabolism, and the principles of hyperpolarization. Recent preclinical applications are described. Hyperpolarization technology is still in its infancy, and current polarizer equipment and methods are suboptimal. Nevertheless, there are no fundamental barriers to rapid translation of this exciting technology to clinical research and perhaps clinical care.

Tumor imaging using hyperpolarized 13C magnetic resonance spectroscopy

Dynamic nuclear polarization is an emerging technique for increasing the sensitivity of magnetic resonance imaging and spectroscopy, particularly for low-γ nuclei. The technique has been applied recently to a number of 13C-labeled cell metabolites in biological systems: the increase in signal-to-noise allows the spatial distribution of an injected molecule to be imaged as well as its metabolic product or products. This review highlights the most significant molecules investigated to date in preclinical cancer models, either in terms of their demonstrated metabolism in vivo or the biological processes that they can probe. In particular, label exchange between hyperpolarized 13C-labeled pyruvate and lactate, catalyzed by lactate dehydrogenase, has been shown to have a number of potential applications. Finally, techniques to image these molecules are also discussed as well as methods that may extend the lifetime of the hyperpolarized signal. Hyperpolarized magnetic resonance imaging and magnetic resonance spectroscopic imaging have shown great promise for the imaging of cancer in preclinical work, both for diagnosis and for monitoring therapy response. If the challenges in translating this technique to human imaging can be overcome, then it has the potential to significantly alter the management of cancer patients.

Metabolic tumor imaging using magnetic resonance spectroscopy

The adaptability and the genomic plasticity of cancer cells, and the interaction between the tumor microenvironment and co-opted stromal cells, coupled with the ability of cancer cells to colonize distant organs, contribute to the frequent intractability of cancer. It is becoming increasingly evident that personalized molecular targeting is necessary for the successful treatment of this multifaceted and complex disease. Noninvasive imaging modalities such as magnetic resonance (MR), positron emission tomography (PET), and single-photon emission computed tomography (SPECT) are filling several important niches in this era of targeted molecular medicine, in applications that span from bench to bedside. In this review we focus on noninvasive magnetic resonance spectroscopy (MRS) and spectroscopic imaging (MRSI) and their roles in future personalized medicine in cancer. Diagnosis, the identification of the most effective treatment, monitoring treatment delivery, and response to treatment are some of the broad areas into which MRS techniques can be integrated to improve treatment outcomes. The development of novel probes for molecular imaging--in combination with a slew of functional imaging capabilities--makes MRS techniques, especially in combination with other imaging modalities, valuable in cancer drug discovery and basic cancer research.

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.

The INTERPRET Decision-Support System version 3.0 for evaluation of Magnetic Resonance Spectroscopy data from human brain tumours and other abnormal brain masses

BACKGROUND

Proton Magnetic Resonance (MR) Spectroscopy (MRS) is a widely available technique for those clinical centres equipped with MR scanners. Unlike the rest of MR-based techniques, MRS yields not images but spectra of metabolites in the tissues. In pathological situations, the MRS profile changes and this has been particularly described for brain tumours. However, radiologists are frequently not familiar to the interpretation of MRS data and for this reason, the usefulness of decision-support systems (DSS) in MRS data analysis has been explored.

RESULTS

This work presents the INTERPRET DSS version 3.0, analysing the improvements made from its first release in 2002. Version 3.0 is aimed to be a program that 1st, can be easily used with any new case from any MR scanner manufacturer and 2nd, improves the initial analysis capabilities of the first version. The main improvements are an embedded database, user accounts, more diagnostic discrimination capabilities and the possibility to analyse data acquired under additional data acquisition conditions. Other improvements include a customisable graphical user interface (GUI). Most diagnostic problems included have been addressed through a pattern-recognition based approach, in which classifiers based on linear discriminant analysis (LDA) were trained and tested.

CONCLUSIONS

The INTERPRET DSS 3.0 allows radiologists, medical physicists, biochemists or, generally speaking, any person with a minimum knowledge of what an MR spectrum is, to enter their own SV raw data, acquired at 1.5 T, and to analyse them. The system is expected to help in the categorisation of MR Spectra from abnormal brain masses.

Nanoplex delivery of siRNA and prodrug enzyme for multimodality image-guided molecular pathway targeted cancer therapy

The ability to destroy cancer cells while sparing normal tissue is highly sought after in cancer therapy. Small interfering RNA (siRNA)-mediated silencing of cancer-cell-specific targets and the use of a prodrug enzyme delivered to the tumor to convert a nontoxic prodrug to an active drug are two promising approaches in achieving this goal. Combining both approaches into a single treatment strategy can amplify selective targeting of cancer cells while sparing normal tissue. Noninvasive imaging can assist in optimizing such a strategy by determining effective tumor delivery of the siRNA and prodrug enzyme to time prodrug administration and detecting target down-regulation by siRNA and prodrug conversion by the enzyme. In proof-of-principle studies, we synthesized a nanoplex carrying magnetic resonance imaging (MRI) reporters for in vivo detection and optical reporters for microscopy to image the delivery of siRNA and a functional prodrug enzyme in breast tumors and achieve image-guided molecular targeted cancer therapy. siRNA targeting of choline kinase-α (Chk-α), an enzyme significantly up-regulated in aggressive breast cancer cells, was combined with the prodrug enzyme bacterial cytosine deaminase (bCD) that converts the nontoxic prodrug 5-fluorocytosine (5-FC) to cytotoxic 5-fluorouracil (5-FU). In vivo MRI and optical imaging showed efficient intratumoral nanoplex delivery. siRNA-mediated down-regulation of Chk-α and the conversion of 5-FC to 5-FU by bCD were detected noninvasively with (1)H MR spectroscopic imaging and (19)F MR spectroscopy. Combined siRNA and prodrug enzyme activated treatment achieved higher growth delay than either treatment alone. The strategy can be expanded to target multiple pathways with siRNA.

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

Gliomas are the most common primary brain tumors and the majority are highly malignant, with one of the worst prognoses for patients. Gliomas are characterized by invasive growth into normal brain tissue that makes complete surgical resection and accurate radiotherapy planning extremely difficult. We have performed independent component analysis of magnetic resonance spectroscopy imaging data from human gliomas to segment brain tissue into tumor core, tumor infiltration, and normal brain, with confirmation by diffusion tensor imaging analysis. Our data are consistent with previous studies that compared anomalies in isotropic and anisotropic diffusion images to determine regions of potential glioma infiltration. We show that coefficients of independent components can be used to create colored images for easy visual identification of regions of infiltrative tumor growth.