scholarly journals Semi-Automated Glioblastoma Tumor Detection based on Different Classifiers using Magnetic Resonance Spectroscopy

2021 ◽  
Author(s):  
Ayob Faramarzi ◽  
Nazila Loghmani ◽  
Roqaie Moghadam ◽  
Armin Allahverdy ◽  
Meysam Siyah Mansoory
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Fuzzy System ◽  
Reference Signal ◽  
Primary Treatment ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Resonance Spectroscopy ◽  
Mri Imaging ◽  
Water Signal

Purpose: Glioblastoma Multiform (GBM) is one of the most common and deadly malignant brain tumors. Surgery is the primary treatment, and careful surgery can minimize recurrence odds. Magnetic Resonance Imaging (MRI) imaging with Magnetic Resonance Spectroscopy (MRS) is used to diagnose various types of tumors in the Central Nervous System (CNS). In this study, several classification methods were used to separate tumor and healthy tissue. Materials and Methods: This study examined the MRI and MRS results of seven people enrolled in this study in 2018. The data was obtained with a prescription from a neurologist and neurosurgeon. Choline (Cho) and N-Acetylaspartate (NAA) metabolite signals were selected as the reference signal after preprocessing and removing the water signal. With the support of 3 radiologists, each tumor and healthy vesicles were identified for every patient. Then, tumor and healthy voxels were separated based on Multilayer Perceptron (MLP), linear Support Vector Machine (SVM), Gaussian SVM, and Fuzzy system using the obtained values and four different methods. Results: Data extracted from Cho and NAA metabolites were fed into MLP, linear SVM, Gaussian and Fuzzy SVM as input, and the amounts of accuracy, sensitivity, and specificity were determined for each method. The maximum accuracy for training mode and test mode was equal to 89.7% and 87%, respectively, specific to classification using Gaussian SVM. The results also showed that the classification accuracy can be significantly increased by increasing the number of fuzzy membership functions from 2 to 6. Conclusion: The results of this study suggested that a more complex classification system, such as SVM with a Gaussian kernel and fuzzy system can be more efficient and reliable when it comes to separating tumor tissue from healthy tissues from MRS data.

Early Diagnosis of Neuropsychiatric Systemic Lupus Erythematosus by Deep Learning Enhanced Magnetic Resonance Spectroscopy

2021 ◽  
Vol 11 (5) ◽  
pp. 1341-1347
Author(s):  
Xin Li ◽  
Lu Bai ◽  
Zuhao Ge ◽  
Zhizhe Lin ◽  
Xi Yang ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Magnetic Resonance ◽  
Early Diagnosis ◽  
Magnetic Resonance Spectroscopy ◽  
Lupus Erythematosus ◽  
Support Vector ◽  
Resonance Spectroscopy ◽  
Systemic Lupus ◽  
Proton Mrs ◽  
Neuropsychiatric Systemic Lupus Erythematosus

The neuropsychiatric systemic lupus erythematosus (NPSLE) has higher disability and mortality rates, which is one of the main causes of death in systemic lupus erythematosus (SLE) patients. Magnetic resonance spectroscopy (MRS) can detect the changes of metabolites in different intracranial areas in vivo in patients with SLE, so as to provide evidence for the early diagnosis of NPSLE. Different from the conventional single-voxel MRS, which can only screen one brain region with one metabolic change, we simultaneously detect 13 kinds of intracranial metabolic changes in nine brain regions by multivoxel proton MRS (MVS). We use a recursive feature elimination algorithm to select the most related metabolites for better identifying NPSLE. To accurately diagnosis NPSLE by these intracranial metabolites, we train a support vector machine deep stacked network (SVM-DSN) for quantitative analysis of these metabolites. Comparing with the conventional statistic method, which is about 70% of accuracy, the proposed model achieves 97.5% of accuracy for NPSLE diagnosis. We conclude the trained SVM-DSN can effectively analyze the metabolites obtained by multivoxel proton MRS for NPSLE diagnosis, which may help to early diagnosis and intervention of NPSLE, and alleviate the bias of manual screening.

scholarly journals Non-Invasive Prediction of IDH Mutation in Patients with Glioma WHO II/III/IV Based on F-18-FET PET-Guided In Vivo 1H-Magnetic Resonance Spectroscopy and Machine Learning

Cancers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3406
Author(s):  
Elisabeth Bumes ◽  
Fro-Philip Wirtz ◽  
Claudia Fellner ◽  
Jirka Grosse ◽  
Dirk Hellwig ◽  
...  
Keyword(s):  
Machine Learning ◽  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Support Vector ◽  
Resonance Spectroscopy ◽  
Idh Mutation ◽  
1H Mrs ◽  
1H Magnetic Resonance Spectroscopy ◽  
Non Invasive

Isocitrate dehydrogenase (IDH)-1 mutation is an important prognostic factor and a potential therapeutic target in glioma. Immunohistological and molecular diagnosis of IDH mutation status is invasive. To avoid tumor biopsy, dedicated spectroscopic techniques have been proposed to detect D-2-hydroxyglutarate (2-HG), the main metabolite of IDH, directly in vivo. However, these methods are technically challenging and not broadly available. Therefore, we explored the use of machine learning for the non-invasive, inexpensive and fast diagnosis of IDH status in standard 1H-magnetic resonance spectroscopy (1H-MRS). To this end, 30 of 34 consecutive patients with known or suspected glioma WHO grade II-IV were subjected to metabolic positron emission tomography (PET) imaging with O-(2-18F-fluoroethyl)-L-tyrosine (18F-FET) for optimized voxel placement in 1H-MRS. Routine 1H-magnetic resonance (1H-MR) spectra of tumor and contralateral healthy brain regions were acquired on a 3 Tesla magnetic resonance (3T-MR) scanner, prior to surgical tumor resection and molecular analysis of IDH status. Since 2-HG spectral signals were too overlapped for reliable discrimination of IDH mutated (IDHmut) and IDH wild-type (IDHwt) glioma, we used a nested cross-validation approach, whereby we trained a linear support vector machine (SVM) on the complete spectral information of the 1H-MRS data to predict IDH status. Using this approach, we predicted IDH status with an accuracy of 88.2%, a sensitivity of 95.5% (95% CI, 77.2–99.9%) and a specificity of 75.0% (95% CI, 42.9–94.5%), respectively. The area under the curve (AUC) amounted to 0.83. Subsequent ex vivo 1H-nuclear magnetic resonance (1H-NMR) measurements performed on metabolite extracts of resected tumor material (eight specimens) revealed myo-inositol (M-ins) and glycine (Gly) to be the major discriminators of IDH status. We conclude that our approach allows a reliable, non-invasive, fast and cost-effective prediction of IDH status in a standard clinical setting.

Retrospective Research: Analysis of Liver 31P Magnetic Resonance Spectroscopy Combined with Support Vector Machine

2012 ◽  
Vol 220-223 ◽  
pp. 2936-2940
Author(s):  
Qiang Liu ◽  
Shao Qing Wang ◽  
Dong Yue Yu ◽  
Guang Ju Liang
Keyword(s):  
Support Vector Machine ◽  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Kernel Function ◽  
Recognition Rate ◽  
Support Vector ◽  
Resonance Spectroscopy ◽  
Spectroscopy Data ◽  
Magnetic Resonance Spectroscopy Data ◽  
Wide Range

Abstract. Support vector machine is in the statistical learning theory developed on the basis of a new kind of machine learning method, field in pattern recognition in a wide range of applications. Artificial intelligence technology has been widely used in medical field. Among them, the support vector machine (SVM) technology can mass of data for feature vector extraction.31P(Phosphorus-31) magnetic resonance imaging in clinical spectrum analysis, facing mass data, can use the support vector machine (SVM) of31P magnetic resonance spectroscopy data modeling, used in liver disease classification of common nodules, this experiment set up three research object: hepatocellular carcinoma (HCC), liver cirrhosis and normal liver tissue. Through the kernel function based on polynomial and RBF kernel function of support vector machine classifier carries on the comparison, and get three liver classification recognition rate. Experiments show that based on31P magnetic resonance spectroscopy data of support vector machine (SVM) model can be classified to living liver diagnostic forecast, so as to improve the31P magnetic resonance spectroscopy on HCC diagnosis accuracy rate.

scholarly journals Brain glutamate concentration in men with early psychosis: a magnetic resonance spectroscopy case–control study at 7 T

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Beata R. Godlewska ◽  
Amedeo Minichino ◽  
Uzay Emir ◽  
Ilinca Angelescu ◽  
Belinda Lennox ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
High Field ◽  
Brain Regions ◽  
Early Psychosis ◽  
Anterior Cingulate ◽  
Psychotic Symptoms ◽  
Resonance Spectroscopy ◽  
Glutamate Concentration ◽  
Water Signal

AbstractAbnormalities in glutamate neurotransmission are linked to psychotic symptoms and cognitive dysfunction in schizophrenia. magnetic resonance spectroscopy (MRS) provides an acceptable means of measuring glutamate in the human brain but findings from patient studies at conventional magnetic field strength show considerable heterogeneity. Ultra-high-field MRS offers greater precision in glutamate measurement, particularly in delineation of glutamate from its precursor and metabolite, glutamine. This study aimed to use high-field (7 T) MRS to measure concentrations of glutamate and glutamine in three brain regions, anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC) and putamen (PUT), in young men with early psychosis. MRS was performed in 17 male participants with early psychosis and 18 healthy age-matched controls. Neurometabolite levels were calculated with unsuppressed water signal as the reference and corrected for individual grey matter, white matter and cerebrospinal fluid concentration. Cognitive function was measured with the Brief Assessment of Cognition in Schizophrenia (BACS). Compared to controls, patients with early psychosis had lower concentrations of glutamate and glutamine in ACC. No differences were apparent in the DLPFC and PUT. In patients with early psychosis, there was a highly significant correlation between glutamate concentration in ACC and performance on the BACS, though the numbers available for this analysis were small. Our finding of lower glutamate levels in ACC in patients with schizophrenia is consistent with a recent meta-analysis of 7 T studies and suggests that this abnormality is present in both patients with early psychosis and those with longer-established illness. The possible link between ACC glutamate and cognitive performance requires replication in larger studies.

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