A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer's disease classification

PurposeAlzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. In recent years, machine learning methods have been widely used on analysis of neuroimage for quantitative evaluation and computer-aided diagnosis of AD or prediction on the conversion from mild cognitive impairment (MCI) to AD. In this study, we aimed to develop a new deep learning method to detect or predict AD in an efficient way.Materials and methodsWe proposed a densely connected convolution neural network with connection-wise attention mechanism to learn the multi-level features of brain MR images for AD classification. We used the densely connected neural network to extract multi-scale features from pre-processed images, and connection-wise attention mechanism was applied to combine connections among features from different layers to hierarchically transform the MR images into more compact high-level features. Furthermore, we extended the convolution operation to 3D to capture the spatial information of MRI. The features extracted from each 3D convolution layer were integrated with features from all preceding layers with different attention, and were finally used for classification. Our method was evaluated on the baseline MRI of 968 subjects from ADNI database to discriminate (1) AD versus healthy subjects, (2) MCI converters versus healthy subjects, and (3) MCI converters versus non-converters.ResultsThe proposed method achieved 97.35% accuracy for distinguishing AD patients from healthy control, 87.82% for MCI converters against healthy control, and 78.79% for MCI converters against non-converters. Compared with some neural networks and methods reported in recent studies, the classification performance of our proposed algorithm was among the top ranks and improved in discriminating MCI subjects who were in high risks of conversion to AD.ConclusionsDeep learning techniques provide a powerful tool to explore minute but intricate characteristics in MR images which may facilitate early diagnosis and prediction of AD.     

Brain Dynamics Altered by Photic Stimulation in Patients with Alzheimer’s Disease and Mild Cognitive Impairment

Individuals with mild cognitive impairment (MCI) are at high risk of developing Alzheimer’s disease (AD). Repetitive photic stimulation (PS) is commonly used in routine electroencephalogram (EEG) examinations for rapid assessment of perceptual functioning. This study aimed to evaluate neural oscillatory responses and nonlinear brain dynamics under the effects of PS in patients with mild AD, moderate AD, severe AD, and MCI, as well as healthy elderly controls (HC). EEG power ratios during PS were estimated as an index of oscillatory responses. Multiscale sample entropy (MSE) was estimated as an index of brain dynamics before, during, and after PS. During PS, EEG harmonic responses were lower and MSE values were higher in the AD subgroups than in HC and MCI groups. PS-induced changes in EEG complexity were less pronounced in the AD subgroups than in HC and MCI groups. Brain dynamics revealed a “transitional change” between MCI and Mild AD. Our findings suggest a deficiency in brain adaptability in AD patients, which hinders their ability to adapt to repetitive perceptual stimulation. This study highlights the importance of combining spectral and nonlinear dynamical analysis when seeking to unravel perceptual functioning and brain adaptability in the various stages of neurodegenerative diseases.     

Usefulness of event-related potentials in the assessment of mild cognitive impairment

Background  

The aim of this study was to determine if changes in latencies and amplitudes of the major waves of Auditory Event-Related Potentials (AERP), correlate with memory status of patients with mild cognitive impairment (MCI) and conversion to Alzheimer's disease (AD).     

Catastrophe Theory Applied to Neuropsychological Data: Nonlinear Effects of Depression on Financial Capacity in Amnestic Mild Cognitive Impairment and Dementia

Financial incapacity is one of the cognitive deficits observed in amnestic mild cognitive impairment and dementia, while the combined interference of depression remains unexplored. The objective of this research is to investigate and propose a nonlinear model that explains empirical data better than ordinary linear ones and elucidates the role of depression. Four hundred eighteen (418) participants with a diagnosis of amnestic MCI with varying levels of depression were examined with the Geriatric Depression Scale (GDS-15), the Functional Rating Scale for Symptoms of Dementia (FRSSD), and the Legal Capacity for Property Law Transactions Assessment Scale (LCPLTAS). Cusp catastrophe analysis was applied to the data, which suggested that the nonlinear model was superior to the linear and logistic alternatives, demonstrating depression contributes to a bifurcation effect. Depressive symptomatology induces nonlinear effects, that is, beyond a threshold value sudden decline in financial capacity is observed. Implications for theory and practice are discussed.     

A preliminary study of functional abnormalities in aMCI subjects during different episodic memory tasks

Functional magnetic resonance imaging (fMRI) is an important imaging modality to understand the neurodegenerative course of mild cognitive impairment (MCI) and early Alzheimer's disease (AD), because the memory dysfunction may occur before structural degeneration is obvious. In this research, we investigated the functional abnormalities of subjects with amnestic MCI (aMCI) using three episodic memory paradigms that are relevant to different memory domains in both encoding and recognition phases. Both whole-brain analysis and region-of-interest (ROI) analysis of the medial temporal lobes (MTL), which are central to the memory formation and retrieval, were used to compare the efficiency of the different memory paradigms and the functional difference between aMCI subjects and normal control subjects. We also investigated the impact of using different functional activation measurements in ROI analysis. This pilot study could facilitate the use of fMRI activations in the MTL as a marker for early detection and monitoring progression of AD.     

Individual identification using multi-metric of DTI in Alzheimer's disease and mild cognitive impairment

Accurate identification of Alzheimer's disease(AD) and mild cognitive impairment(MCI) is crucial so as to improve diagnosis techniques and to better understand the neurodegenerative process. In this work, we aim to apply the machine learning method to individual identification and identify the discriminate features associated with AD and MCI. Diffusion tensor imaging scans of 48 patients with AD, 39 patients with late MCI, 75 patients with early MCI, and 51 age-matched healthy controls(HCs) are acquired from the Alzheimer's Disease Neuroimaging Initiative database. In addition to the common fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity metrics, there are two novel metrics,named local diffusion homogeneity that used Spearman's rank correlation coefficient and Kendall's coefficient concordance,which are taken as classification metrics. The recursive feature elimination method for support vector machine(SVM)and logistic regression(LR) combined with leave-one-out cross validation are applied to determine the optimal feature dimensions. Then the SVM and LR methods perform the classification process and compare the classification performance.The results show that not only can the multi-type combined metrics obtain higher accuracy than the single metric, but also the SVM classifier with multi-type combined metrics has better classification performance than the LR classifier.Statistically, the average accuracy of the combined metric is more than 92% for all between-group comparisons of SVM classifier. In addition to the high recognition rate, significant differences are found in the statistical analysis of cognitive scores between groups. We further execute the permutation test, receiver operating characteristic curves, and area under the curve to validate the robustness of the classifiers, and indicate that the SVM classifier is more stable and efficient than the LR classifier. Finally, the uncinated fasciculus, cingulum, corpus callosum, corona radiate, external capsule, and internal capsule have been regarded as the most important white matter tracts to identify AD, MCI, and HC. Our findings reveal a guidance role for machine-learning based image analysis on clinical diagnosis.     

基于脑部磁共振图像三维局部模式变换特征提取进行阿尔茨海默病病程预测分类

本文提出一种三维局部模式变换提取进行纹理特征并与常规特征相融合的方法,基于脑部磁共振图像,对认知功能正常的健康人体（CN）、轻度认知障碍（MCI）患者和阿尔茨海默病（AD）患者进行预测分类.首先对46例CN对照组、61例MCI患者和25例AD患者的脑部磁共振图像提取感兴趣区域,然后提取双侧海马体组织、灰质和白质的三维局部模式变换纹理特征和常规特征,并将两类特征融合,使用支持向量机分类算法进行分类.结果显示利用本方法,基于双侧海马体组织对AD组和CN组进行分类的准确率为88.73%、敏感度为78.00%、特异度为95.7%、受试者工作特征（ROC）曲线下面积（AUC）为0.886 5;基于灰质的准确率为85.92%、敏感度为80.00%、特异度为86.6%、AUC为0.854 3.这证明基于海马体磁共振图像,利用本文提出的改进三维局部模式变换提取的纹理特征进行阿尔茨海默病病程分类效果较好,融合常规特征后更可提高分类预测的精度.     

Non-Gaussian diffusion MRI assessment of brain microstructure in mild cognitive impairment and Alzheimer’s disease

We report the first application of a novel diffusion-based MRI method, called diffusional kurtosis imaging (DKI), to investigate changes in brain tissue microstructure in patients with mild cognitive impairment (MCI) and AD and in cognitively intact controls. The subject groups were characterized and compared in terms of DKI-derived metrics for selected brain regions using analysis of covariance with a Tukey multiple comparison correction. Receiver operating characteristic (ROC) and binary logistic regression analyses were used to assess the utility of regional diffusion measures, alone and in combination, to discriminate each pair of subject groups. ROC analyses identified mean and radial kurtoses in the anterior corona radiata as the best individual discriminators of MCI from controls, with the measures having an area under the ROC curve (AUC) of 0.80 and 0.82, respectively. The next best discriminators of MCI from controls were diffusivity and kurtosis (both mean and radial) in the prefrontal white matter (WM), with each measure having an AUC between 0.77 and 0.79. Finally, the axial diffusivity in the hippocampus was the best overall discriminator of MCI from AD, having an AUC of 0.90. These preliminary results suggest that non-Gaussian diffusion MRI may be beneficial in the assessment of microstructural tissue damage at the early stage of MCI and may be useful in developing biomarkers for the clinical staging of AD.     

Increased glucose concentration in the hippocampus in early Alzheimer's disease following oral glucose ingestion

Glucose is the primary source of energy for brain cells. Because energy storage in the brain is limited, an uninterrupted supply of glucose and its rapid metabolism are essential for normal cognitive function. This study utilized an oral glucose load to examine hippocampal glucose metabolism in early Alzheimer's disease (AD) - a disease characterized by progressive deterioration of cognitive function and glucose hypometabolism. Short echo time 1H MR spectra (20 ms) from the right hippocampus of 8 patients with probable AD, 14 healthy elderly and 14 healthy young adults were compared pre- and post-glucose loading. In contrast to the healthy adults, the AD patients exhibited significantly elevated hippocampal glucose concentrations post-glucose ingestion relative to baseline (P < .01). These results suggest that cerebral glucose hypometabolism in AD leads to an increased steady-state concentration of cerebral glucose. This research demonstrates the feasibility of studying cerebral glucose metabolism in AD with 1H MR spectroscopy.     

1H-MRS of the macaque monkey primary visual cortex at 7 T: strategies and pitfalls of shimming at the brain surface

Magnetic resonance spectroscopy (MRS) is ideally suited for physiology-neurochemistry experiments with the living brain and particularly for studies on the primary visual cortex (striate cortex or area V1). Yet, the highly convoluted form of the human V1 has thus far prevented the performance of MRS investigations that are spatially confined within the gray matter of this area. Typically, these studies are compromised by partial volume contaminations originating from white matter tissue, cerebrospinal fluid and other cortical areas. In this study, was exploited the relative flatness of V1 in macaques to enable single-voxel 1H-MRS from a small volume (5 x 1.6 x 5 mm3, 40 microl) that was entirely confined within the V1 gray matter of anesthetized monkeys. Linewidths of 13.5+/-1.9 Hz and 13.0+/-1.3 Hz for water and creatine, respectively, were achieved with a two-step shimming strategy for voxels at the brain surface. The quality of the obtained results paves the way for further neuroscientific research, including studies on the cortical microcircuits and the dynamic longitudinal changes occurring during cortical reorganization and plasticity.     

Diagnostic Value of Short and Long Echo Time in <Superscript>1</Superscript>H-MRS for Patients with Multiple Sclerosis

The quality of the signal received from metabolites in ¹H-magnetic resonance spectroscopy (MRS) directly depends on physical parameters of the impulse sequence used, namely on Time of Echo (TE). We compare MRS (Achieva 3T PRESS ¹H-MRS (TE = 53 and 144 ms, TR = 2000 ms) data acquired in supraventricular white matter and medial cortex at two various TE (53 and 144 ms) for patients with the multiple sclerosis (25 patients with the confirmed diagnosis of relapsing-remitting multiple sclerosis and 20 patients with the diagnosis of secondary progressive multiple sclerosis) and control group (21 healthy volunteers, comparable on age), to evaluate advantages and disadvantages of these two Echo Time in clinical practice.     

In vivo1H-magnetic resonance spectroscopy can detect metabolic changes in APP/PS1 mice after donepezil treatment

Background  

Donepezil improves cognitive functions in AD patients. Effects on the brain metabolites N-acetyl-L-aspartate, choline and myo-inositol levels have been reported in clinical studies using this drug. The APP/PS1 mouse coexpresses the mutated forms of human β-amyloid precursor protein (APP) and mutated human presenilin 1 (PS1). Consequently, the APP/PS1 mouse model reflects important features of the neurochemical profile in humans. In vivo magnetic resonance spectroscopy (¹H-MRS) was performed in fronto-parietal cortex and hippocampus (ctx/hipp) and in striatum (str). Metabolites were quantified using the LCModel and the final analysis was done using multivariate data analysis. The aim of this study was to investigate if multivariate data analysis could detect changes in the pattern of the metabolic profile after donepezil treatment.     

Correlation of proton MR spectroscopy and diffusion tensor imaging

Proton magnetic resonance spectroscopy ((1)H-MRS) provides indices of neuronal damage. Diffusion tensor imaging (DTI) relates to water diffusivity and fiber tract orientation. A method to compare (1)H-MRS and DTI findings was developed, tested on phantom and applied on normal brain. Point-resolved spectroscopy (T(R)/T(E)=1500/135) was used for chemical shift imaging of a supraventricular volume of interest of 8 x 8 x 2 cm(3) (64 voxels). In DTI, a segmental spin-echo sequence (T(R)/T(E)=5500/91) was used and slices were stacked to reproduce the slab used in MRS. The spatial distributions of choline and N-acetylaspartate (NAA) correlated to mean fractional anisotropy and apparent diffusion coefficient (ADC) for the inner 6 x 6=36 voxels defined in MRS, most notably NAA and ADC value (r=-.70, P<.00001; correlation across four subjects, 144 data pairs). This is the first association of neuron metabolite contents in volunteers with structure as indicated by DTI.     

Hypoxia-induced transcription of dopamine D3 and D4 receptors in human neuroblastoma and astrocytoma cells

Background

The aim of this study is to examine the influence of the catechol-O-methyltranferase (COMT) gene (polymorphism Val158 Met) as a risk factor for Alzheimer's disease (AD) and mild cognitive impairment of amnesic type (MCI), and its synergistic effect with the apolipoprotein E gene (APOE). A total of 223 MCI patients, 345 AD and 253 healthy controls were analyzed. Clinical criteria and neuropsychological tests were used to establish diagnostic groups. The DNA Bank of the University of the Basque Country (UPV-EHU) (Spain) determined COMT Val158 Met and APOE genotypes using real time polymerase chain reaction (rtPCR) and polymerase chain reaction (PCR), and restriction fragment length polymorphism (RFLPs), respectively. Multinomial logistic regression models were used to determine the risk of AD and MCI.

Results

Neither COMT alleles nor genotypes were independent risk factors for AD or MCI. The high activity genotypes (GG and AG) showed a synergistic effect with APOE ε4 allele, increasing the risk of AD (OR = 5.96, 95%CI 2.74-12.94, p < 0.001 and OR = 6.71, 95%CI 3.36-13.41, p < 0.001 respectivily). In AD patients this effect was greater in women. In MCI patients such as synergistic effect was only found between AG and APOE ε4 allele (OR = 3.21 95%CI 1.56-6.63, p = 0.02) and was greater in men (OR = 5.88 95%CI 1.69-20.42, p < 0.01).

Conclusion

COMT (Val158 Met) polymorphism is not an independent risk factor for AD or MCI, but shows a synergistic effect with APOE ε4 allele that proves greater in women with AD.     

Alzheimer''s disease histologically proven studied by MRI and MRS: Two cases

Two patients affected by severe Alzheimer's disease (AD) were investigated by MRI and image-guided ³¹P MRS. In one case, ¹H MRS was additionally performed. In both cases the diagnosis of AD was confirmed, post mortem, by the pathologist. The spectral parameters of the ³¹P MR spectra were estimated by fitting the ³¹P MR signals in the time domain. Our ³¹P MRS results suggest that it is possible to detect the membrane catabolism, as indexed with the level of PDE resonances visible in in vivo ³¹P MRS, at least in severe AD cases. The ¹H spectrum from AD brain showed a marked decrease of NAA signal respect to choline.     

Longitudinal stability of resting-state networks in normal aging,mild cognitive impairment,and Alzheimer's disease

Test–retest reliability is essential for using resting-state functional magnetic resonance imaging (rs-fMRI) as a potential biomarker for Alzheimer's disease (AD), especially when monitoring longitudinal changes and treatment effects. In addition, test–retest variability itself might represent a feature of AD. Using 3.0 T rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we examined the long-term (1-year) test–retest reliability of resting-state networks (RSNs) in 31 healthy elderly subjects, 63 patients with mild cognitive impairment (MCI), and 17 patients with AD by applying temporal concatenation group independent component analysis and dual regression. The intraclass correlation coefficient estimates of RSN amplitudes ranged from 0.44 to 0.77 in healthy elderly subjects, from 0.31 to 0.62 in patients with MCI, and from −0.06 to 0.44 in patients with AD. The overall test–retest reliability of RSNs was lower in patients with MCI than in healthy elderly subjects, and was lower in patients with AD than in patients with MCI. The differences in the test–retest reliabilities were due to the RSN amplitudes rather than the RSN shapes. Head motion was not significantly different among the three groups of subjects. The results indicate that the test–retest stability of RSNs generally declines with progression to MCI and AD, mainly due to the RSN amplitudes rather than the RSN shapes. The test–retest instability in MCI and AD may reflect progressive neurofunctional alterations related to the pathology of AD.     

Brain metabolism and cognitive impairment in HIV infection: a 3-T magnetic resonance spectroscopy study

Background and Purpose

Human immunodeficiency virus (HIV)-associated dementia (HAD) has been extensively studied using magnetic resonance spectroscopy (MRS) at field strengths of 1.5 T. Higher magnetic field strengths (such as 3 T) allow for more reliable determination of certain compounds, such as glutamate (Glu) and glutamine (Gln). The current study was undertaken to investigate the utility of 3-T MRS for evaluating HIV+ patients with different levels of cognitive impairment with emphasis on the measurement of Glu and Glx (the sum of Glu and Gln).

Methods

Eighty-six HIV+ subjects were evaluated at 3 T using quantitative short echo time single-voxel MRS of frontal white matter (FWM) and basal ganglia (BG). Subjects were divided into three groups according to the Memorial Sloan Kettering (MSK) HIV dementia stage: 21 had normal cognition (NC) (MSK 0), 31 had mild cognitive impairment (MCI) without dementia (clinical MSK stage=0.5), and 34 had dementia (HAD) (MSK≥1). HIV+ subjects had also undergone standardized cognitive testing covering the domains of executive function, verbal memory, attention, information processing speed and motor and psychomotor speed. Between-group differences in metabolite levels in FWM and BG were evaluated using ANOVA. Pearson correlation coefficients were used to explore the associations between the Glu and Glx metabolites and neurocognitive results.

Results

FWM Glx was lower in HAD (8.1±2.1 mM) compared to both the MCI (9.17±2.1 mM) and NC groups (10.0±1.6 mM) (P=.006). FWM myo-inositol (mI) was higher in HAD (4.15±0.75 mM) compared to both MCI (3.86±0.85 mM) and NC status (3.4±0.67 mM) (P=.006). FWM Glx/creatine (Cr) was lower and FWM mI/Cr was significantly higher in the HAD compared to the MCI and NC groups (P=.01 and P=.004, respectively). BG N-acetyl aspartate (NAA) was lower in the HAD group (6.79±1.53 mM), compared to the MCI (7.5±1.06 mM) and NC (7.6±1.01 mM) groups (P=.036). Significant negative correlations were observed between Glu, Glx and NAA concentrations with Trail-Making Test B (P=.006, P=.0001 and P=.007, respectively), and significant positive correlation was found with the Digit symbol test (P=.02, P=.002 and P=.008, respectively). FWM Glx and NAA concentrations showed negative correlation with Grooved Pegboard nondominant hand (P=.02 and P=.04, respectively).

Conclusion

Patients with HAD have lower levels of Glx concentrations and Glx/Cr ratio in FWM, which was associated with impaired performance in specific cognitive domains, including executive functioning, fine motor, attention and working memory performance. Three-Tesla MRS measurements of Glx may be a useful indicator of neuronal loss/dysfunction in patients with HIV infection.     

Brain metabolite alterations demonstrated by proton magnetic resonance spectroscopy in diabetic patients with retinopathy

Due to the homology between retinal and cerebral microvasculatures, retinopathy is a putative indicator of cerebrovascular dysfunction. This study aimed to detect metabolite changes of brain tissue in type 2 diabetes mellitus (T2DM) patients with diabetic retinopathy (DR) using proton magnetic resonance spectroscopy (¹H-MRS). Twenty-nine T2DM patients with DR (DR group), thirty T2DM patients without DR (DM group) and thirty normal controls (NC group) were involved in this study. Single-voxel ¹H-MRS (TR: 2000 ms, TE: 30 ms) was performed at 3.0 T MRI/MRS imager in cerebral left frontal white matter, left lenticular nucleus, and left optic radiation. Our data showed that NAA/Cr ratios of the DR group were significantly lower than those of the DM group in the frontal white matter and optic radiation. In the lenticular nucleus, MI/Cr ratios were significantly higher in the DM group than those in the NC group, while MI/Cr ratios were significantly lower in the DR group than those in the DM group. In the frontal white matter, NAA/Cho ratios were found to be decreased in the DR group as compared to the NC group. Additionally, our finding indicated that NAA/Cr ratios were negatively associated with DR severity in both the frontal white matter and optic radiation. A decrease in NAA indicated neuronal loss and the likely explanation for a decrease in MI was glial loss. In conclusion, we inferred that cerebral neurons and glia cells were damaged in patients with DR. Our data support that DR is associated with brain tissue damage.     

Machine learning-based radiomic,clinical and semantic feature analysis for predicting overall survival and MGMT promoter methylation status in patients with glioblastoma

IntroductionSurvival varies in patients with glioblastoma due to intratumoral heterogeneity and radiomics/imaging biomarkers have potential to demonstrate heterogeneity. The objective was to combine radiomic, semantic and clinical features to improve prediction of overall survival (OS) and O⁶-methylguanine-DNA methyltransferase (MGMT) promoter methylation status from pre-operative MRI in patients with glioblastoma.MethodsA retrospective study of 181 MRI studies (mean age 58 ± 13 years, mean OS 497 ± 354 days) performed in patients with histopathology-proven glioblastoma. Tumour mass, contrast-enhancement and necrosis were segmented from volumetric contrast-enhanced T1-weighted imaging (CE-T1WI). 333 radiomic features were extracted and 16 Visually Accessible Rembrandt Images (VASARI) features were evaluated by two experienced neuroradiologists. Top radiomic, VASARI and clinical features were used to build machine learning models to predict MGMT status, and all features including MGMT status were used to build Cox proportional hazards regression (Cox) and random survival forest (RSF) models for OS prediction.ResultsThe optimal cut-off value for MGMT promoter methylation index was 12.75%; 42 radiomic features exhibited significant differences between high and low-methylation groups. However, model performance accuracy combining radiomic, VASARI and clinical features for MGMT status prediction varied between 45 and 67%. For OS predication, the RSF model based on clinical, VASARI and CE radiomic features achieved the best performance with an average iAUC of 96.2 ± 1.7 and C-index of 90.0 ± 0.3.ConclusionsVASARI features in combination with clinical and radiomic features from the enhancing tumour show promise for predicting OS with a high accuracy in patients with glioblastoma from pre-operative volumetric CE-T1WI.     

Noninvasive evaluation of cerebral glioma grade by using multivoxel 3D proton MR spectroscopy

Objective

To determine whether metabolite ratios in multivoxel 3D proton MR spectroscopy (¹H MRS) is different between low-grade and high-grade gliomas and may be useful for glioma grading.

Materials and Methods

Thirty-nine patients (23 male and 16 female; 22-75 years old; mean age, 44.92±12.65 years) suspected of having gliomas underwent 3D ¹H MRS examinations. Metabolite ratios [choline (Cho)/creatine (Cr), N-acetylaspartate (NAA)/Cr and Cho/NAA] were measured. Tumor grade was determined by using the histopathologic grading. Receiver operating characteristic analysis of metabolite ratios was performed, and optimum thresholds for tumor grading were determined. The resulting sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for identifying high-grade gliomas were calculated.

Results

Diagnostic-quality 3D ¹H MRS with readily quantifiable Cho, Cr and NAA peaks was obtained in 94.87% of the cases. The Cho/Cr and Cho/NAA ratios were significantly higher in high-grade than in low-grade glioma (P<.001), whereas the NAA/Cr ratios were significantly lower in high-grade than in low-grade glioma (P<.001). Receiver operating characteristic analysis demonstrated a threshold value of 2.04 for Cho/Cr ratio to provide sensitivity, specificity, PPV and NPV of 84.00%, 83.33%, 91.30% and 71.43%, respectively. Threshold value of 2.20 for Cho/NAA ratio resulted in sensitivity, specificity, PPV and NPV of 88.00%, 66.67%, 84.62% and 72.73%, respectively. Overall diagnostic accuracy was not statistically significantly different between Cho/Cr and Cho/NAA ratios (χ²=0.093, P=.76).

Conclusion

Metabolite ratios of low-grade gliomas were significantly different from high-grade gliomas. Cho/Cr and Cho/NAA ratios could have the superior diagnostic performance in predicting the glioma grade.