Determination of histopathological tumor grade in neuroepithelial brain tumors by using spectral pattern analysis of in vivo spectroscopic data

Advances in imaging modalities for spinal tumors

The spinal cord occupies a narrow region and is tightly surrounded by osseous and ligamentous structures; spinal tumors can damage this structure and deprive patients of their ability to independently perform activities of daily living. Hence, imaging is vital for the prompt detection and accurate diagnosis of spinal tumors, as well as determining the optimal treatment and follow-up plan. However, many clinicians may not be familiar with the imaging characteristics of spinal tumors due to their rarity. In addition, spinal surgeons might not fully utilize imaging for the surgical planning and management of spinal tumors because of the complex heterogeneity of these lesions. In the present review, we focus on conventional and advanced spinal tumor imaging techniques. These imaging modalities include computed tomography, positron emission tomography, digital subtraction angiography, conventional and microstructural magnetic resonance imaging, and high-resolution ultrasound. We discuss the advantages and disadvantages of conventional and emerging imaging modalities, followed by an examination of cutting-edge medical technology to complement current needs in the field of spinal tumors. Moreover, machine learning and artificial intelligence are anticipated to impact the application of spinal imaging techniques. Through this review, we discuss the importance of conventional and advanced spinal tumor imaging, and the opportunity to combine advanced technologies with conventional modalities to better manage patients with these lesions.

Preclinical and Clinical Use of Indigenously Developed 99mTc-Diethylenetriaminepentaacetic Acid-Bis-Methionine: l-Type Amino Acid Transporter 1-Targeted Single Photon Emission Computed Tomography Radiotracer for Glioma Management

A new era in tumor classification, diagnosis, and prognostic evaluation has begun as a consequence of recent developments in the molecular and genetic characterization of central nervous system tumors. In this newly emerging era, molecular imaging modalities are essential for preoperative diagnosis, surgical planning, targeted treatment, and post-therapy evaluation of gliomas. The radiotracers are able to identify brain tumors, distinguish between low- and high-grade lesions, confirm a patient's eligibility for theranostics, and assess post-radiation alterations. We previously synthesized and reported the novel l-type amino acid transporter 1 (LAT-1)-targeted amino acid derivative in light of the use of amino acid derivatives in imaging technologies. Further, we have developed a single vial ready to label Tc-lyophilized kit preparations of diethylenetriaminepentaacetic acid-bis-methionine [DTPA-bis(Met)], also referred to as methionine-diethylenetriaminepentaacetic acid-methionine (MDM) and evaluated its imaging potential in numerous clinical studies. This review summarizes our previous publications on 99mTc-DTPA-bis(Met) in different clinical studies such as detection of breast cancer, as a prognostic marker, in detection of recurrent/residual gliomas, for differentiation of recurrent/residual gliomas from radiation necrosis, and for comparison of 99mTc-DTPA-bis(Met) with 11C-L-methionine (11C-MET), with relevant literature on imaging modalities in glioma management.

Convolutional neural networks to predict brain tumor grades and Alzheimer’s disease with MR spectroscopic imaging data

Purpose

To evaluate the value of convolutional neural network (CNN) in the diagnosis of human brain tumor or Alzheimer’s disease by MR spectroscopic imaging (MRSI) and to compare its Matthews correlation coefficient (MCC) score against that of other machine learning methods and previous evaluation of the same data. We address two challenges: 1) limited number of cases in MRSI datasets and 2) interpretability of results in the form of relevant spectral regions.

Methods

A shallow CNN with only one hidden layer and an ad-hoc loss function was constructed involving two branches for processing spectral and image features of a brain voxel respectively. Each branch consists of a single convolutional hidden layer. The output of the two convolutional layers is merged and fed to a classification layer that outputs class predictions for the given brain voxel.

Results

Our CNN method separated glioma grades 3 and 4 and identified Alzheimer’s disease patients using MRSI and complementary MRI data with high MCC score (Area Under the Curve were 0.87 and 0.91 respectively). The results demonstrated superior effectiveness over other popular methods as Partial Least Squares or Support Vector Machines. Also, our method automatically identified the spectral regions most important in the diagnosis process and we show that these are in good agreement with existing biomarkers from the literature.

Conclusion

Shallow CNNs models integrating image and spectral features improved quantitative and exploration and diagnosis of brain diseases for research and clinical purposes. Software is available at https://bitbucket.org/TeslaH2O/cnn_mrsi.

In vivo Human MR Spectroscopy Using a Clinical Scanner: Development, Applications, and Future Prospects

MR spectroscopy (MRS) is a unique and useful method for noninvasively evaluating biochemical metabolism in human organs and tissues, but its clinical dissemination has been slow and often limited to specialized institutions or hospitals with experts in MRS technology. The number of 3-T clinical MR scanners is now increasing, representing a major opportunity to promote the use of clinical MRS. In this review, we summarize the theoretical background and basic knowledge required to understand the results obtained with MRS and introduce the general consensus on the clinical utility of proton MRS in routine clinical practice. In addition, we present updates to the consensus guidelines on proton MRS published by the members of a working committee of the Japan Society of Magnetic Resonance in Medicine in 2013. Recent research into multinuclear MRS equipped in clinical MR scanners is explained with an eye toward future development. This article seeks to provide an overview of the current status of clinical MRS and to promote the understanding of when it can be useful. In the coming years, MRS-mediated biochemical evaluation is expected to become available for even routine clinical practice.

Imaging biomarkers for evaluating tumor response: RECIST and beyond

Response Evaluation Criteria in Solid Tumors (RECIST) is the gold standard for assessment of treatment response in solid tumors. Morphologic change of tumor size evaluated by RECIST is often correlated with survival length and has been considered as a surrogate endpoint of therapeutic efficacy. However, the detection of morphologic change alone may not be sufficient for assessing response to new anti-cancer medication in all solid tumors. During the past fifteen years, several molecular-targeted therapies and immunotherapies have emerged in cancer treatment which work by disrupting signaling pathways and inhibited cell growth. Tumor necrosis or lack of tumor progression is associated with a good therapeutic response even in the absence of tumor shrinkage. Therefore, the use of unmodified RECIST criteria to estimate morphological changes of tumor alone may not be sufficient to estimate tumor response for these new anti-cancer drugs. Several studies have reported the low reliability of RECIST in evaluating treatment response in different tumors such as hepatocellular carcinoma, lung cancer, prostate cancer, brain glioma, bone metastasis, and lymphoma. There is an increased need for new medical imaging biomarkers, considering the changes in tumor viability, metabolic activity, and attenuation, which are related to early tumor response. Promising imaging techniques, beyond RECIST, include dynamic contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI), diffusion-weight imaging (DWI), magnetic resonance spectroscopy (MRS), and ¹⁸ F-fluorodeoxyglucose (FDG) positron emission tomography (PET). This review outlines the current RECIST with their limitations and the new emerging concepts of imaging biomarkers in oncology.

Incidental Finding of Bilateral Thalamic Glioma on 99mTc TRODAT-1 SPECT/CT

ABSTRACT

99mTc-TRODAT-1 SPECT/CT has been used to evaluate parkinsonian disorders. We present an interesting case of a 63-year-old woman with progressive tremulousness over the left side of the body for 6 months. Bilateral thalamic glioma with reduced uptake was shown on 99mTc-TRODAT-1 SPECT/CT. Secondary parkinsonism was impressed in this case.

Magnetic Resonance Spectroscopy and its Clinical Applications: A Review

In vivo NMR spectroscopy is known as magnetic resonance spectroscopy (MRS). MRS has been applied as both a research and a clinical tool in order to detect visible or nonvisible abnormalities. The adaptability of MRS allows a technique that can probe a wide variety of metabolic uses across different tissues. Although MRS is mostly applied for brain tissue, it can be used for detection, localization, staging, tumour aggressiveness evaluation, and tumour response assessment of breast, prostate, hepatic, and other cancers. In this article, the medical applications of MRS in the brain, including tumours, neural and psychiatric disorder studies, breast, prostate, hepatic, gastrointestinal, and genitourinary investigations have been reviewed.

The diagnostic value of high-frequency power-based diffusion-weighted imaging in prediction of neuroepithelial tumour grading

OBJECTIVES

To retrospectively evaluate the diagnostic value of high-frequency power (HFP) compared with the minimum apparent diffusion coefficient (MinADC) in the prediction of neuroepithelial tumour grading.

METHODS

Diffusion-weighted imaging (DWI) data were acquired on 115 patients by a 3.0-T MRI system, which included b0 images and b1000 images over the whole brain in each patient. The HFP values and MinADC values were calculated by an in-house script written on the MATLAB platform.

RESULTS

There was a significant difference among each group excluding grade I (G1) vs. grade II (G2) (P = 0.309) for HFP and among each group for MinADC. ROC analysis showed a higher discriminative accuracy between low-grade glioma (LGG) and high-grade glioma (HGG) for HFP with area under the curve (AUC) value 1 compared with that for MinADC with AUC 0.83 ± 0.04 and also demonstrated a higher discriminative ability among the G1-grade IV (G4) group for HFP compared with that for MinADC except G1 vs. G2.

CONCLUSIONS

HFP could provide a simple and effective optimal tool for the prediction of neuroepithelial tumour grading based on diffusion-weighted images in routine clinical practice.

KEY POINTS

• HFP shows positive correlation with neuroepithelial tumour grading. • HFP presents a good diagnostic efficacy for LGG and HGG. • HFP is helpful in the selection of brain tumour boundary.

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

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

The diagnostic performance of magnetic resonance spectroscopy in differentiating high-from low-grade gliomas: A systematic review and meta-analysis

OBJECTIVE

Magnetic resonance spectroscopy (MRS) is a powerful tool for preoperative grading of gliomas. We performed a meta-analysis to evaluate the diagnostic performance of MRS in differentiating high-grade gliomas (HGGs) from low-grade gliomas (LGGs).

METHODS

PubMed and Embase databases were systematically searched for relevant studies of glioma grading assessed by MRS through 27 March 2015. Based on the data from eligible studies, pooled sensitivity, specificity, diagnostic odds ratio and areas under summary receiver operating characteristic curve (SROC) of different metabolite ratios were obtained.

RESULTS

Thirty articles comprising a total sample size of 1228 patients were included in our meta-analysis. Quantitative synthesis of studies showed that the pooled sensitivity/specificity of Cho/Cr, Cho/NAA and NAA/Cr ratios was 0.75/0.60, 0.80/0.76 and 0.71/0.70, respectively. The area under the curve (AUC) of the SROC was 0.83, 0.87 and 0.78, respectively.

CONCLUSIONS

MRS demonstrated moderate diagnostic performance in distinguishing HGGs from LGGs using tumoural metabolite ratios including Cho/Cr, Cho/NAA and NAA/Cr. Although there was no significant difference in AUC between Cho/Cr and Cho/NAA groups, Cho/NAA ratio showed higher sensitivity and specificity than Cho/Cr ratio and NAA/Cr ratio. We suggest that MRS should combine other advanced imaging techniques to improve diagnostic accuracy in differentiating HGGs from LGGs.

KEY POINTS

• MRS has moderate diagnostic performance in distinguishing HGGs from LGGs. • There is no significant difference in AUC between Cho/Cr and Cho/NAA ratios. • Cho/NAA ratio is superior to NAA/Cr ratio. • Cho/NAA ratio shows higher sensitivity and specificity than Cho/Cr and NAA/Cr ratios. • MRS should combine other advanced imaging techniques to improve diagnostic accuracy.

Gamma Knife radiosurgery for low-grade tectal gliomas

BACKGROUND

Tectal gliomas are present in a critical location that makes their surgical treatment difficult. Stereotactic radiosurgery presents an attractive noninvasive treatment option. However, tectal gliomas are also commonly associated with aqueductal obstruction and consequently hydrocephalus. This necessitates some form of CSF diversion procedure before radiosurgery. The aim of the study was to assess the efficacy and safety of Gamma Knife radiosurgery for tectal gliomas.

PATIENTS AND METHODS

Between October 2002 and May 2011, 11 patients with tectal gliomas were treated with Gamma Knife radiosurgery. Five patients had pilocytic astrocytomas and six nonpilocytic astrocytomas. Ten patients presented with hydrocephalus and underwent a CSF diversion procedure [7 V-P shunt and 3 endoscopic third ventriculostomy (ETV)]. The tumor volume ranged between 1.2-14.7 cc (median 4.5 cc). The prescription dose was 11-14 Gy (median 12 Gy).

RESULTS

Patients were followed for a median of 40 months (13-114 months). Tumor control after radiosurgery was seen in all cases. In 6/11 cases, the tumors eventually disappeared after treatment. Peritumoral edema developed in 5/11 cases at an onset of 3-6 months after treatment. Transient tumor swelling was observed in four cases. Four patients developed cysts after treatment. One of these cases required aspiration and eventually disappeared, one became smaller spontaneously, and two remained stable.

CONCLUSION

Gamma Knife radiosurgery is an effective and safe technique for treatment of tectal gliomas. Tumor shrinkage or disappearance after Gamma Knife radiosurgery may preclude the need for a shunt later on.

Added value of advanced over conventional magnetic resonance imaging in grading gliomas and other primary brain tumors

BACKGROUND

Although conventional MR imaging (MRI) is the most widely used non-invasive technique for brain tumor grading, its accuracy has been reported to be relatively low. Advanced MR techniques, such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI), and magnetic resonance spectroscopy (MRS), could predict neoplastic histology, but their added value over conventional MRI is still open to debate.

METHODS

We prospectively analyzed 129 patients diagnosed with primary brain tumors (118 gliomas) classified as low-grade in 30 cases and high-grade in 99 cases.

RESULTS

Significant differences were obtained in high-grade tumors for conventional MRI variables (necrosis, enhancement, edema, hemorrhage, and neovascularization); high relative cerebral blood volume values (rCBV), low relative apparent diffusion coefficients (rADC), high ratio of N-acetyl-aspartate/creatine at short echo time (TE) and high choline/creatine at long TE. Among conventional MRI variables, the presence of enhancement and necrosis were demonstrated to be the best predictors of high grade in primary brain tumors (sensitivity 95.9%; specificity 70%). The best results in primary brain tumors were obtained for enhancement, necrosis, and rADC (sensitivity 98.9%; specificity 75.9%). Necrosis and enhancement were the only predictors of high grade in gliomas (sensitivity 97.6%; specificity 76%) when all the magnetic resonance variables were combined.

CONCLUSIONS

MRI is highly accurate in the assessment of tumor grade. The combination of conventional MRI features with advanced MR variables showed only improved tumor grading by adding rADC to conventional MRI variables in primary brain tumors.

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

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

Magnetic resonance spectroscopy in intracranial tumours of glial origin

BACKGROUND AND PURPOSE

To determine in vivo magnetic resonance spectroscopy (MRS) characteristics of intracranial glial tumours and to assess MRS reliability in glioma grading and discrimination between different histopathological types of tumours.

MATERIAL AND METHODS

Analysis of spectra of 26 patients with glioblastomas, 6 with fibrillary astrocytomas, 4 with anaplastic astrocytomas, 2 with pilocytic astrocytoma, 3 with oligodendrogliomas, 3 with anaplastic oligodendrogliomas and 17 control spectra taken from healthy hemispheres.

RESULTS

All tumours' metabolite ratios, except for Cho/Cr in fibrillary astrocytomas (p = 0.06), were statistically significantly different from the control. The tumours showed decreased Naa and Cr contents and a high Cho signal. The Lac-Lip signal was high in grade III astrocytomas and glioblastomas. Reports that Cho/Cr ratio increases with glioma's grade whereas Naa/Cr decreases were not confirmed. Anaplastic astrocytomas compared to grade II astrocytomas had a statistically significantly greater mI/Cr ratio (p = 0.02). In pilocytic astrocytomas the Naa/Cr value (2.58 ± 0.39) was greater, whilst the Cho/Naa ratio was lower (2.14 ± 0.64) than in the other astrocytomas. The specific feature of oligodendrogliomas was the presence of glutamate/glutamine peak Glx. However, this peak was absent in two out of three anaplastic oligodendrogliomas. Characteristically, the latter tumours had a high Lac-Lip signal.

CONCLUSIONS

MRS in vivo cannot be used as a reliable method for glioma grading. The method is useful in discrimination between WHO grade I and WHO grade II astrocytomas as well as oligodendrogliomas from other gliomas.

Classification of single-voxel 1H spectra of brain tumours using LCModel

This study presents a novel method for the direct classification of (1)H single-voxel MR brain tumour spectra using the widespread analysis tool LCModel. LCModel is designed to estimate individual metabolite proportions by fitting a linear combination of in vitro metabolite spectra to an in vivo MR spectrum. In this study, it is used to fit representations of complete tumour spectra and to perform a classification according to the highest estimated tissue proportion. Each tumour type is represented by two spectra, a mean component and a variability term, as calculated using a principal component analysis of a training dataset. In the same manner, a mean component and a variability term for normal white matter are also added into the analysis to allow a mixed tissue approach. An unbiased evaluation of the method is carried out through the automatic selection of training and test sets using the Kennard and Stone algorithm, and a comparison of LCModel classification results with those of the INTERPRET Decision Support System (IDSS) which incorporates an advanced pattern recognition method. In a test set of 46 spectra comprising glioblastoma multiforme, low-grade gliomas and meningiomas, LCModel gives a classification accuracy of 90% compared with an accuracy of 95% by IDSS.

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.

MR spectroscopy differentiation between high and low grade astrocytomas: a comparison between paediatric and adult tumours

OBJECTIVE

To investigate whether pathologically similar astrocytomas in adults and children may also show metabolic similarities in proton magnetic resonance spectroscopy ((1)H-MRS) and whether the MRS data could help to differentiate between low and high grade gliomas for the different groups.

MATERIAL AND METHODS

Twelve children (5 WHO II astrocytomas, 7 WHO III astrocytomas) and 37 adults (21 WHO II astrocytomas, 16 WHO III astrocytomas) were included in this study. MR spectroscopic data were evaluated retrospectively using normalized measures of total choline (tCho), N-acetyl-aspartate (NAA) and total creatine (tCr). These metabolites were used to differentiate between WHO II and WHO III astrocytomas in children and adults. Histopathological grading was performed using WHO criteria. (1)H-MRS was carried out prior to the commencement of any treatment. Signal intensities of tCho, NAA and tCr were normalized to their values in contralateral brain tissue. The resulting concentration ratios were then used to calculate the change in the intratumoural ratio of NAA to tCho. A Mann-Whitney U-Test was performed to evaluate differences within the respective groups.

RESULTS

In both groups, loss of NAA and increase of tCho were more pronounced in WHO III than in WHO II astrocytoma. The best discriminator to differentiate between low and high grade gliomas was found to be the ratio of NAA/tCho (p < 0.01).

CONCLUSION

The normalized metabolite signal intensities ratio NAA to tCho is the most accurate in differentiating between low and high grade astrocytomas in both children and adults.

Molecular imaging of cancer: MR spectroscopy and beyond

Proton magnetic resonance spectroscopic imaging is a non-invasive diagnostic tool for the investigation of cancer metabolism. As an adjunct to morphologic and dynamic magnetic resonance imaging, it is routinely used for the staging, assessment of treatment response, and therapy monitoring in brain, breast, and prostate cancer. Recently, its application was extended to other cancerous diseases, such as malignant soft-tissue tumours, gastrointestinal and gynecological cancers, as well as nodal metastasis. In this review, we discuss the current and evolving clinical applications of proton magnetic resonance spectroscopic imaging. In addition, we will briefly discuss other evolving techniques, such as phosphorus magnetic resonance spectroscopic imaging, sodium imaging and diffusion-weighted imaging in cancer assessment.

Advances in imaging low-grade gliomas

Imaging plays a key role in the management of low-grade gliomas. The traditional view of these tumours as non-enhancing areas of increased signal on T2-weighted imaging is now accepted as being incorrect. Using new MR and PET techniques that can probe the pathological changes with in these tumours by assessing vascularity (perfusion MR), cellularity and infiltration (diffusion weighted and diffusion tensor MR), metabolism (MR spectroscopy and FDG PET) and proliferation (MR spectroscopy, methionine PET and 18F-fluorothymidine FLT PET). These tools will allow improvements in tumour grading, biopsy/therapy guidance and earlier assessment of the response to therapy.

Assessment of 31P-NMR analysis of phospholipid profiles for potential differential diagnosis of human cerebral tumors

We describe a novel protocol for the non-histological diagnosis of human brain tumors in vitro combining high-resolution (31)P magnetic resonance spectroscopy ((31)P-MRS) of their phospholipid profile and statistical multivariate analysis. Chloroform/methanol extracts from 40 biopsies of human intracranial tumors obtained during neurosurgical procedures were prepared and analyzed by high-resolution (31)P-MRS. The samples were grouped in the following seven major classes: normal brain (n = 3), low-grade astrocytomas (n = 4), high-grade astrocytomas (n = 7), meningiomas (n = 9), schwannomas (n = 3), pituitary adenomas (n = 4), and metastatic tumors (n = 4). The phospholipid profile of every biopsy was determined by (31)P-NMR analysis of its chloroform/methanol extract and characterized by 19 variables including 10 individual phospholipid contributions and 9 phospholipid ratios. Most tumors depicted a decrease in phosphatidylethanolamine (PtdEtn) and phosphatidylserine (PtdSer), the former mainly in neuroepithelial neoplasms and the latter in metastases. An increase in phosphatidylcholine (PtdCho) and phosphatidylinositol (PtdIns) appeared predominantly in primary non-neuroepithelial tumors. Linear discriminant analysis (LDA) revealed the optimal combination of variables that could classify each biopsy between every pair of classes. The resultant discriminant functions were used to calculate the probability of correct classifications for each individual biopsy within the seven classes considered. Multilateral analysis classified correctly 100% of the normal brain samples, 89% of the meningiomas, 75% of the metastases, and 57% of the high-grade astrocytomas. The use of phospholipid profiles may complement appropriately previously proposed methods of intelligent diagnosis of human cerebral tumors.

Short-echo spectroscopic imaging combined with lactate editing in a single scan

A short-echo spectroscopic imaging sequence extended with a frequency-selective multiple-quantum- coherence technique (Sel-MQC) is presented. The method enables acquisition of a complete water-suppressed proton spectrum with a short echo time and filtering of the J-coupling metabolite, lactate, from co-resonant lipids in one scan. The purpose of the study was to validate this combined pulse sequence in vitro and in vivo. Measurements on phantoms confirmed the feasibility of the method, and, for a practical in vivo application, experiments were carried out on eight tumors from two different tumor models [UT-SCC-8 (n = 4) and SAS (n = 4)]. T(1)- and T(2)-weighted metabolite and lipid ratios were calculated, and the tumors showed different values in the central and outer regions. The ratio of the lipid methylene peak area (1.30 ppm) to choline peak area (3.20 ppm) was significantly (p < 0.01) different in the central tumor area between the two models, and lactate was detected in only three out of four tumors in the SAS tumor line. The present approach of combining short-echo spectroscopic imaging and lactate editing allows the characterization of tumor-specific metabolites such as choline, lipid methylene and methyl resonances as well as lactate in a single scan.

Imaging of the cerebrum

The history of the development of cerebral imaging is a complex combination of the forces of innovation at both the individual and industrial levels. Principal paradigms of neuroimaging shifted as a result of technological breakthroughs, beginning with the discovery of x-rays and continuing with the development of computerized imaging to the latest imaging paradigm, nuclear magnetic resonance imaging. We discuss these landmarks in neuroimaging in historical context, with emphasis on the particularly rapid development of imaging technology during the past 30 to 40 years, including the most recent emerging technologies.

Low Grade Gliomas. Can We Predict Tumour Behaviour from Imaging Features?

Low grade gliomas (WHO grade II) are diffuse, infiltrative glial tumours of the brain. The low grade glioma group includes a number of entities, namely diffuse astrocytoma, oligodendroglioma and oligoastrocytoma. This group of the low-grade gliomas share certain common issues of behaviour, clinical assessment and management. Even though these tumours are termed low grade they are not to be considered “benign”: untreated they are invariably fatal. They may remain “stable” for many years and hence a “watch and wait” treatment policy is often adopted. Unfortunately some tumours progress more rapidly than others with dedifferentiation into high grade tumours which become rapidly fatal. Based on standard imaging criteria it has been difficult to predict which of these low grade gliomas will progress more rapidly. Treatment decisions would benefit from some prediction as to which tumours are likely to progress more rapidly than others. This review will discuss some of the imaging features that may help to predict which low grade gliomas will progress more rapidly than others. Such imaging features include the rate of growth on serial imaging; the morphological features that parallel genetic markers; the assessment and change of tumour vascular status as assessed by MR perfusion imaging and tumour characteristics on PET and MR spectroscopy.

Differentiation of High-Grade and Low-Grade Gliomas Using Pattern Analysis of Long-Echo Single-Voxel Proton Magnetic Resonance Spectroscopy ((1)H-MRS)

The usefulness of proton magnetic resonance spectroscopy ((1)H-MRS) for glioma grading is not clear, particularly due to the absence of standard criteria for data analysis. Previously we had developed an original classification of the pathological (1)H-MRS spectra based on the identification of the predominant metabolite peak, N-acetylaspartate (NAA) for Type I, choline-containing compounds (Cho) for Type II, and mobile lipids (Lip) for Type III, and presence or absence of other metabolite peaks: lactate (Lac), Lip, or Cho. The present study evaluated the effectiveness of this classification in grading of previously non-treated gliomas. A total of 38 low-grade and 33 high-grade neoplasms were investigated. Four tumors had (1)H-MRS spectra Type I, and all of those were low-grade. Three tumors had (1)H-MRS spectra Type III, and all those were glioblastomas. Fifteen tumors with (1)H-MRS spectra Type II had a Lip/NAA ratio more than 1 (Type II C with moderate elevation of lipids), and 12 of those neoplasms were high-grade. The differences in distribution of high-grade and low-grade gliomas among another 49 gliomas with (1)H-MRS spectra Type II did not depend on the presence of Lac and/or Lip peaks, and in this subgroup NAA/Cho ratio was also evaluated. Inclusion of both characteristics (type of the (1)H-MRS spectrum and NAA/Cho ratio with defined cut-off level of 0.6) into the diagnostic algorithm yielded 72% diagnostic accuracy (95% confidence interval: 62%-82%) in discriminating high-grade and low-grade neoplasms. In conclusion, pattern analysis of the pathological (1)H-MRS spectra using the proposed classification along with evaluation of NAA/Cho ratio might be helpful for non-invasive glioma grading.

Proton MR spectroscopy in the evaluation of cerebral metabolism in patients with fibromyalgia: comparison with healthy controls and correlation with symptom severity

BACKGROUND AND PURPOSE

Widespread pain sensitivity in patients with fibromyalgia (FM) suggests a central nervous system (CNS)-processing problem. Therefore, it is conceivable that metabolic alterations exist in pain-processing brain regions of people with FM compared with healthy controls (HC) and that such metabolic data could correlate with clinical symptoms. The purpose of this study was to test these hypotheses using proton MR spectroscopy ((1)H-MR spectroscopy).

MATERIALS AND METHODS

There were 21 patients with FM and 27 HC who underwent conventional structural MR imaging and additional 2D-chemical shift imaging (CSI) MR-spectroscopy sequences. For the 2D-CSI spectroscopy, larger volumes of interest (VOIs) were centered at the level of the basal ganglia and the supraventricular white matter. Within these larger areas, 16 smaller voxels were placed in a number of regions previously implicated in pain processing. N-acetylaspartate (NAA)/creatine(Cr), choline (Cho)/Cr and NAA/Cho ratios were calculated for each voxel. Subjects underwent clinical and experimental pain assessment.

RESULTS

Mean metabolite ratios and ratio variability for each region were analyzed by using repeated-measures analysis of variance (ANOVA). Correlations between clinical symptoms and metabolite ratios were assessed. Cho/Cr variability in the right dorsolateral prefrontal cortex (DLPFC) was significantly different in the 2 groups; a significant correlation between Cho/Cr in this location and clinical pain was present in the FM group. Evoked pain threshold correlated significantly with NAA/Cho ratios in the left insula and left basal ganglia.

CONCLUSION

Our data suggest that there are baseline differences in the variability of brain metabolite relative concentrations between patients with FM and HC, especially in the right DLPFC. Furthermore, there are significant correlations between metabolite ratios and clinical and experimental pain parameters in patients with FM.

(1)H MR spectroscopy of human brain tumours: a practical approach

Magnetic resonance spectroscopy (MRS) is proposed in addition to magnetic resonance imaging (MRI) to help in the characterization of brain tumours by detecting metabolic alterations that may be indicative of the tumour class. MRS can be routinely performed on clinical magnets, within a reasonable acquisition time and if performed under adequate conditions, MRS is reproducible and thus can be used for longitudinal follow-up of treatment. MRS can also be performed in clinical practice to guide the neurosurgeon into the most aggressive part of the lesions or to avoid unnecessary surgery, which may furthermore decrease the risk of surgical morbidity.

Apparent T(2) relaxation times of lipid and macromolecules: a study of high-grade tumor spectra

PURPOSE

To determine T(2) relaxation times of lipid and macromolecules (Lip/MMs) observed by (1)H magnetic resonance spectroscopy ((1)H MRS) of metastases (MET) and glioblastomas (GBM), so that they may be better characterized.

MATERIALS AND METHODS

(1)H spectra were acquired at multiple echo times from brain lesions using point-resolved spectroscopy sequence (PRESS) at TE = 30 msec either with metabolite-nulling (six GBM and 11 MET), or without metabolite-nulling (four MET and one mucocele). All lesions were previously untreated and had subsequent histopathological classification.

RESULTS

The T(2) of the 1.3-ppm Lip/MM peak was concentration-dependent, but at high concentrations it was significantly different (P = 0.015) between GBM (42 +/- 6 msec) and MET (63 +/- 18 msec). The broad 2.05-ppm and 0.09-ppm Lip/MM peaks had similar T(2)s in MET and GBM. The T(2) of the narrow 2.05-ppm Lip/MM peak sometimes observed had a T(2) of 100 +/- 17 msec in MET and 75 msec in the mucocele.

CONCLUSION

We observed significantly higher T(2) of the 1.3-ppm Lip/MM peak in MET compared with GBM at high 1.3-ppm proton concentrations, in agreement with a higher 1.3/0.9-ppm peak ratio found in MET. The relatively long T(2) of the 2.05-ppm Lip/MM peak sometimes observed in MET may cause it to be confused with N-acetyl aspartate (NAA).

Cerebral ependymoma in a patient with multiple sclerosis case report and critical review of the literature

BACKGROUND

The concurrence of multiple sclerosis (MS) and brain tumors is a rare but well-recognized condition. The radiologic evidence of the progressive evolution of a mega-plaque in a tumor has never been described. We report the first case of such an occurrence.

METHODS

A 27-year-old woman with a diagnosis of MS was referred to us for an intense frontal headache. Magnetic resonance imaging showed a mass lesion in correspondence of a black hole lesion previously diagnosed. The patient was operated on, with complete removal of the tumor documented by an intraoperative MRI. The histologic examination evidenced an ependymoma. Postoperative radiotherapy was performed.

RESULTS

The patient is well and recurrence-free at 2 years follow-up.

CONCLUSIONS

The present case, documenting the transformation of a mega-plaque into a tumor, suggests a cause-effect relationship between MS and brain tumors.

Linear discriminant analysis of brain tumour (1)H MR spectra: a comparison of classification using whole spectra versus metabolite quantification

(1)H MRS is an attractive choice for non-invasively diagnosing brain tumours. Many studies have been performed to create an objective decision support system, but there is not yet a consensus as to the best techniques of MRS acquisition or data processing to be used for optimum classification. In this study, we investigate whether LCModel analysis of short-TE (30 ms), single-voxel tumour spectra provide a better input for classification than the use of the original spectra. A total of 145 histologically diagnosed brain tumour spectra were acquired [14 astrocytoma grade II (AS2), 15 astrocytoma grade III (AS3), 42 glioblastoma (GBM), 41 metastases (MET) and 33 meningioma (MNG)], and linear discriminant analyses (LDA) were performed on the LCModel analysis of the spectra and the original spectra. The results consistently suggest improvement in classification when the LCModel concentrations are used. LDA of AS2, MNG and high-grade tumours (HG, comprising GBM and MET) correctly classified 94% using the LCModel dataset compared with 93% using the spectral dataset. The inclusion of AS3 reduced the accuracy to 82% and 78% for LCModel analysis and the original spectra, respectively, and further separating HG into GBM and MET gave 70% compared with 60%. Generally MNG spectra have profiles that are visually distinct from those of the other tumour types, but the classification accuracy was typically about 80%, with MNG with substantial lipid/macromolecule signals being classified as HG. Omission of the lipid/macromolecule concentrations in the LCModel dataset provided an improvement in classification of MNG (91% compared with 76%). In conclusion, there appears to be an advantage to performing pattern recognition on the quantitative analysis of tumour spectra rather than using the whole spectra. However, the results suggest that a two-step LDA process may help in classifying the five tumour groups to provide optimum classification of MNG with high lipid/macromolecule contributions which maybe misclassified as HG.

Correlations between MR and endothelial hyperplasia in low-grade gliomas

PURPOSE

To investigate the capacity of multimodal MR to detect endothelial hyperplasia (EH), which has been linked to the aggressiveness of gliomas and which is so far only detected by biopsy, an invasive technique that prevents repeated measurements and early detection.

MATERIALS AND METHODS

A total of 26 patients with low-grade gliomas participated in the study. All underwent a histopathological analysis and a multimodal MR examination (spectroscopic, anatomic, diffusion, perfusion, and postcontrast imaging).

RESULTS

EH was present (EH+) in 15 patients and absent (EH-) in 11. No differences were observed between EH- and EH+ groups when comparing spectroscopic and diffusion parameters. Perfusion measurements, however, allowed us to distinguish EH+ from EH-: the relative regional cerebral blood flow (rCBF) was found equal to 3.23 +/- 2.05 for EH+ and 1.33 +/- 0.46 for EH- (P = 0.006).

CONCLUSION

We have observed a strong correlation between the presence of EH and the increase of rCBF. Compared to conventional imaging, MR perfusion provides additional and complementary information that may be used for biopsy guidance, early detection of tumor aggressiveness, and noninvasive follow-ups.

Arterial spin-labeling and MR spectroscopy in the differentiation of gliomas

BACKGROUND AND PURPOSE

Noninvasive grading of gliomas remains a challenge despite its important role in the prognosis and management of patients with intracranial neoplasms. In this study, we evaluated the ability of cerebral blood flow (CBF)-guided voxel-by-voxel analysis of multivoxel proton MR spectroscopic imaging ((1)H-MRSI) to differentiate low-grade from high-grade gliomas.

MATERIALS AND METHODS

A total of 35 patients with primary gliomas (22 high grade and 13 low grade) underwent continuous arterial spin-labeling perfusion-weighted imaging (PWI) and (1)H-MRSI. Different regions of the gliomas were categorized as "hypoperfused," "isoperfused," and "hyperperfused" on the basis of the average CBF obtained from contralateral healthy white matter. (1)H-MRSI indices were computed from these regions and compared between low- and high-grade gliomas. Using a similar approach, we applied a subgroup analysis to differentiate low- from high-grade oligodendrogliomas because they show different physiologic and genetic characteristics.

RESULTS

Cho(glioma (G)/white matter (WM)), Glx(G/WM), and Lip+Lac(G)/Cr(WM) were significantly higher in the "hyperperfused" regions of high-grade gliomas compared with low-grade gliomas. Cho(G/WM) and Lip+Lac(G)/Cr(WM) were also significantly higher in the "hyperperfused" regions of high-grade oligodendrogliomas. However, metabolite ratios from the "hypoperfused" or "isoperfused" regions did not exhibit any significant differences between high-grade and low-grade gliomas.

CONCLUSION

The results suggest that (1)H-MRSI indices from the "hyperperfused" regions of gliomas, on the basis of PWI, may be helpful in distinguishing high-grade from low-grade gliomas including oligodendrogliomas.

[Brain magnetic resonance spectroscopy]

MR spectroscopy (MRS) sequences allow noninvasive exploration of brain metabolism during a MRI examination. Their day-to-day use in a clinical setting has recently been improved by simple programming of sequences and automated quantification of metabolites. However, a few simple rules should be observed in the choice of sequences and the location of the voxels so as to obtain an informative, high-quality examination. The research applications of MR spectroscopy, where use of this examination seeks to better understand the pathophysiology of the disease, must be distinguished from its clinical indications, where MRS provides information that can be used directly in patient management. The most significant of the clinical uses are imaging intracranial tumors (positive and differential diagnosis, extension, treatment follow-up), diffuse brain injury, encephalopathies (especially hepatic and HIV-related), and the diagnosis of metabolic disorders.

MR-Spektroskopie bei Hirntumoren

MRT allows the anatomical visualization of intracerebral space-occupying lesions, and when magnetic resonance spectroscopy (MRS) is used in routine clinical practice it can give more information and be helpful in the diagnosis of such lesions. In MRS with long echo times for nerve tissue there are five metabolites that are particularly significant: N-acetyl aspartate (NAA), creatine, choline, lactate, and lipids. NAA levels are lowered in the presence of intracerebral tumors. Creatine is lowered in situations of hypermetabolic metabolism and elevated in hypometabolic conditions, but remains constant in many pathologic states and can be used as a reliable reference value. With malignant tumors there are usually elevated choline concentrations, reflecting increased membrane synthesis and a higher cell turnover. The lactate level rises following a switch in metabolism from aerobic to anaerobic glycolysis, and this is frequently observed in the presence of malignant tumors. The occurrence of lipid peaks in a tumor spectrum suggests the presence of tissue necroses or metastases. There are typical constellations that are seen on MRS for individual tumors, which are discussed in detail in the present paper.

[Indications for cerebral MR proton spectroscopy in 2007]

Magnetic resonance spectroscopy (MRS) is being increasingly performed alongside the more conventional MRI sequences in the exploration of neurological disorders. It is however important to clearly differentiate its clinical applications aiming at improving the differential diagnosis or the prognostic evaluation of the patient, from the research protocols, when MRS can contribute to a better understanding of the pathophysiology of the disease or to the evaluation of new treatments. The most important applications in clinical practice are intracranial space occupying lesions (especially the positive diagnosis of intracranial abscesses and gliomatosis cerebri and the differential diagnosis between edema and tumor infiltration), alcoholic, hepatic, and HIV-related encephalopathies and the exploration of metabolic diseases. Among the research applications, MRS is widely used in multiple sclerosis, ischemia and brain injury, epilepsy and neuro degenerative diseases.

Diagnostic impact of proton MR-spectroscopy versus image-guided stereotactic biopsy

BACKGROUND

The aim of this study was to compare the diagnostic accuracy of (1)H MR-spectroscopy versus image-guided stereotactic biopsy.

METHOD

A cohort of 83 consecutive patients with a broad spectrum of brain lesions were examined. Prior to stereotactic biopsy, the patients were subjected to (1)H MR-spectroscopy examination. Diagnostic accuracy of (1)H MR-spectroscopy and image guided stereotactic biopsy was determined for the largest diagnostic subgroups. Each diagnostic procedure was tested for concordance in every subgroup.

FINDINGS

The subgroups of patients comprised: low grade glioma, high grade glioma (grades III and IV), lymphoma and metastasis. For the sensitivity of (1)H MR-spectroscopy ranged from 87.7 in high grade glioma to 92.3% in metastasis and for specificity from 93.3% for high grade glioma to 100% in low grade glioma. The highest positive predictive value of 100% was reached in the subgroup of low grade glioma. The highest negative predictive value was reached in lymphoma and metastasis, 100%. The kappa values were highly significant for all comparisons (p<0.001). The co-efficient ranged from 0.68 to 0.84. It was lowest in assessing high grade glioma and highest in lymphoma.

CONCLUSION

Compared with each other (1)H MR-spectroscopy and image-guided stereotactic biopsy showed a moderate to good, statistically highly significant concordance. In patients in whom operation is at an increased risk e.g., due to severe medical illness, (1)H MR-spectroscopy as a noninvasive procedure may be sufficient to assess the diagnosis.

The clinical value of proton magnetic resonance spectroscopy in adult brain tumours

Proton magnetic resonance spectroscopy (1H MRS) non-invasively provides information on the biochemical profile (typically including up to nine metabolites and mobile lipids) of brain tissue, which varies according to the underlying disease process. A number of studies have assessed its accuracy in the diagnosis of adult brain tumours. This article describes the basic principles of 1H MRS, the metabolic profiles of different brain tumours, and practical points to aid interpretation of spectra. The literature is reviewed regarding the role of 1H MRS in the diagnosis of brain tumours and more specifically where it has proven to be of additional benefit over conventional magnetic resonance imaging.

Spectroscopie 1H, perfusion, diffusion : quelle place pour ces techniques lors du diagnostic et du suivi des principales tumeurs cérébrales sus-tentorielles de l’adulte ?

INTRODUCTION

In a few years, magnetic resonance imaging (MRI) has evolved from a morphology-based examination to one that encompasses metabolism and function.

STATE OF ART

MRI is a well-established tool for the initial evaluation of brain tumors, but conventional MR sequences have some limitations. Conventional MRI is unable to distinguish high-grade glioma from metastasis and abscess, to define precisely the histopathological grade of gliomas, to determine exactly the limits of tumor extension, to characterize meningeal tumors. Differentiation of tumor recurrence from treatment-related changes may be difficult with standard MR imaging because the interpretation is essentially based on volume analysis.

PERSPECTIVES

1H Spectroscopy, diffusion and perfusion imaging become possible with the development of MR imagers and can be routinely performed in clinical settings. They give complementary information about tumor metabolism and vascularity and allow a better analysis of post-treatment modifications. Functional and metabolic explorations should be used to characterize brain tumors.

Optimal classification of long echo time in vivo magnetic resonance spectra in the detection of recurrent brain tumors

We describe the optimal high-level postprocessing of single-voxel (1)H magnetic resonance spectra and assess the benefits and limitations of automated methods as diagnostic aids in the detection of recurrent brain tumor. In a previous clinical study, 90 long-echo-time single-voxel spectra were obtained from 52 patients and classified during follow-up (30/28/32 normal/non-progressive tumor/tumor). Based on these data, a large number of evaluation strategies, including both standard resonance line quantification and algorithms from pattern recognition and machine learning, were compared in a quantitative evaluation. Results from linear and non-linear feature extraction, including ICA, PCA and wavelet transformations, and also the data from resonance line quantification were combined systematically with different classifiers such as LDA, chemometric methods (PLS, PCR), support vector machines and ensemble methods. Classification accuracy was assessed using a leave-one-out cross-validation scheme and the area under the curve (AUC) of the receiver operator characteristic (ROC). A regularized linear regression on spectra with binned channels reached 91% classification accuracy compared with 83% from quantification. Interpreting the loadings of these regressions, we find that lipid and lactate signals are too unreliable to be used in a simple machine rule. Choline and NAA are the main source of relevant information. Overall, we find that fully automated pattern recognition algorithms perform as well as, or slightly better than, a manually controlled and optimized resonance line quantification.

Interrater reliability in assessing quality of diagnostic accuracy studies using the QUADAS tool. A preliminary assessment

RATIONALE AND OBJECTIVES

Quality Assessment of Diagnostic Accuracy Studies (QUADAS) is a new tool to measure the methodological quality of diagnostic accuracy studies in systematic reviews. We used data from a systematic review of magnetic resonance spectroscopy (MRS) in the characterization of suspected brain tumors to provide a preliminary evaluation of the inter-rater reliability of QUADAS.

MATERIALS AND METHODS

A structured literature search identified 19 diagnostic accuracy studies. These publications were distributed randomly to primary and secondary reviewers for dual independent assessment. Reviewers recorded methodological quality by using QUADAS on a custom-designed spreadsheet. We calculated correlation, percentage of agreement, and kappa statistic to assess inter-rater reliability.

RESULTS

Most studies in our review were judged to have used an accurate reference standard. Conversely, the MRS literature frequently failed to specify the length of time between index and reference tests or that the clinicians were unaware of the index test findings when reporting the reference standard. There was good correlation (rho = 0.78) between reviewers in assessment of the overall number of quality criteria met. However, mean agreement for individual QUADAS questions was only fair (kappa = 0.22) and ranged from no agreement beyond chance (kappa < 0) to moderate agreement (kappa = 0.58).

CONCLUSION

Inter-rater reliability in our study was relatively low. Nevertheless, we believe that QUADAS potentially is a useful tool for highlighting the strengths and weaknesses of existing diagnostic accuracy studies. Low reliability suggests that different reviewers will reach different conclusions if QUADAS is used to exclude "low-quality" articles from meta-analyses. We discuss methods for improving the validity and reliability of QUADAS.

Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra

A computer-based decision support system to assist radiologists in diagnosing and grading brain tumours has been developed by the multi-centre INTERPRET project. Spectra from a database of 1H single-voxel spectra of different types of brain tumours, acquired in vivo from 334 patients at four different centres, are clustered according to their pathology, using automated pattern recognition techniques and the results are presented as a two-dimensional scatterplot using an intuitive graphical user interface (GUI). Formal quality control procedures were performed to standardize the performance of the instruments and check each spectrum, and teams of expert neuroradiologists, neurosurgeons, neurologists and neuropathologists clinically validated each case. The prototype decision support system (DSS) successfully classified 89% of the cases in an independent test set of 91 cases of the most frequent tumour types (meningiomas, low-grade gliomas and high-grade malignant tumours--glioblastomas and metastases). It also helps to resolve diagnostic difficulty in borderline cases. When the prototype was tested by radiologists and other clinicians it was favourably received. Results of the preliminary clinical analysis of the added value of using the DSS for brain tumour diagnosis with MRS showed a small but significant improvement over MRI used alone. In the comparison of individual pathologies, PNETs were significantly better diagnosed with the DSS than with MRI alone.

Spectroscopie par résonance magnétique des tumeurs cérébrales

MR spectroscopy (MRS) can complement MRI in the evaluation of intracranial tumors. Before treatment, MRS can contribute to the differential diagnosis between tumor and non tumoral lesion (especially intracranial abscesses), to assess the aggressiveness of a glial tumor or to determine its extension to better delineate the surgical removal or the target volume of radiotherapy. During treatment follow-up, MRS helps differentiate recurrent tumor from radionecrosis or physiological post-surgical contrast enhancement. The current studies are trying to determine if the indications of MRS, alone or in association with other MR sequences can further be extended in the study of brain tumors, in particular the follow-up of lesions undergoing chemo or radiotherapy.

Preoperative Grading of Gliomas by Using Metabolite Quantification with High-Spatial-Resolution Proton MR Spectroscopic Imaging

PURPOSE

To evaluate proton magnetic resonance (MR) spectroscopic imaging with high spatial resolution for preoperative grading of suspected World Health Organization grades II and III gliomas.

MATERIALS AND METHODS

Institutional ethics committee approval and informed consent were obtained for control subjects but were not required for the retrospective component involving patients. Twenty-six patients (10 women, 16 men; mean age, 37.5 years) suspected of having gliomas and 26 age- and sex-matched control subjects underwent proton MR spectroscopy. Absolute metabolite concentrations for choline-containing compounds (Cho), creatine (Cr), and N-acetylaspartate (NAA)-N-acetylaspartylglutamate (total NAA [tNAA]) were calculated by using a user-independent spectral fit program. Metabolic maps of Cho/tNAA ratios were calculated, segmented, and used for MR spectroszpcopy-guided stereotactic brain biopsy. Two-sided paired Student t tests were used to test for statistical significance.

RESULTS

Significantly lower Cho levels (P = .002) and higher tNAA levels (P = .010) were found in grade II tumors (n = 9) compared with grade III tumors (n = 17). The average Cho/tNAA ratio over the voxels in the tumor center showed a distinct difference (P < .001) between grade II and III gliomas at a threshold of 0.8 (with ratios <0.8 for grade II). The maximum Cr concentration in the tumor showed a clear-cut threshold between grade III oligodendrogliomas and oligoastrocytomas (Cr level, <7 mmol/L) and grade III astrocytomas (Cr level, >7 mmol/L; P = .020). Comparison between the histopathologic findings from the MR spectroscopy-guided biopsy samples (76 biopsies from 26 patients) and molar metabolite values in corresponding voxels located at the biopsy sampling points showed a negative linear correlation for tNAA (r = -0.905) and a positive exponential correlation for Cho (r = 0.769) and Cho/tNAA (r = 0.885).

CONCLUSION

Proton MR spectroscopic imaging with high spatial resolution allows preoperative grading of gliomas.

In vivomagnetic resonance spectroscopy by the fast Padé transform

The fast Padé transform (FPT) is thoroughly illustrated on two in vivo time signals encoded at 4 T and 7 T via magnetic resonance spectroscopy (MRS). The exact quantum-mechanical spectrum as the Green function series truncated at any partial sum reduces to the unique quotient of two polynomials, which is the FPT. In this Green function as a Maclaurin series in powers of the harmonic variable, the expansion coefficients are the time signal values as damped complex-exponentials with stationary and non-stationary amplitudes for non-degenerate (Lorentzian) and degenerate (non-Lorentzian) spectra. This is automatically shared by the FPT to represent an enormous advantage over the Hankel-Lanczos singular value decomposition (HLSVD) which works only for Lorentzian spectra. Moreover, the resonance amplitudes in the FPT are obtained analytically, rather than solving a system of linear equations as done in the HLSVD. We use two variants of the FPT, initially defined inside and outside the unit circle, but extended automatically to the whole complex frequency plane by the Cauchy analytical continuation. The converged spectra from these two variants of the FPT are found to give the same results, within the experimental background noise level, and this represents an intrinsic cross-validation of the findings and the error analysis.

Noninvasive diagnostic assessment of brain tumors using combined in vivo MR imaging and spectroscopy

To determine the potential value of multimodal MRI for the presurgical management of patients with brain tumors, we performed combined magnetic resonance imaging (MRI) and proton MR spectroscopy (MRS) in 164 patients who presented with tumors of various histological subtypes confirmed by surgical biopsy. Univariate statistical analysis of metabolic ratios carried out on the first 121 patients demonstrated significant differences in between-group comparisons, but failed to provide sufficiently robust classification of individual cases. However, a multivariate statistical approach correctly classified the tumors using linear discriminant analysis (LDA) of combined MRI and MRS data. After initial separation of contrast-enhancing and non-contrast-enhancing lesions, 91% of the former and 87% of the latter were correctly classified. The results were stable when this diagnostic strategy was tested on the additional 43 patients included for validation after the initial statistical analysis, with over 90% of correct classification. Combined MRI and MRS had superior diagnostic value compared to MRS alone, especially in the contrast-enhancing group. This study shows the clinical value of a multivariate statistical analysis based on multimodal MRI and MRS for the noninvasive evaluation of intracranial tumors.

[Proton magnetic resonance spectroscopy (1H-MRS) for the diagnosis of brain tumors and the evaluation of treatment]

MR spectroscopy (MRS) is a technique used to study a few metabolites in the brain or tumors in situ. This technique can provide information on tumor histological type and grade, and is helpful to identify tumor-like lesions, particularly abscesses. MRS can be used for treatment monitoring.