Evanthia Kousi

Differentiation of glioblastoma multiforme from metastatic brain tumor using proton magnetic resonance spectroscopy, diffusion and perfusion metrics at 3 T

Abstract Purpose: To assess the contribution of 1H-magnetic resonance spectroscopy (1H-MRS), diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI) and dynamic susceptibility contrast-enhanced (DSCE) imaging metrics in the differentiation of glioblastomas from solitary metastasis, and particularly to clarify the controversial reports regarding the hypothesis that there should be a significant differentiation between the intratumoral and peritumoral areas. Methods: Conventional MR imaging, 1H-MRS, DWI, DTI and DSCE MRI was performed on 49 patients (35 glioblastomas multiforme, 14 metastases) using a 3.0-T MR unit. Metabolite ratios, apparent diffusion coefficient (ADC), fractional anisotropy (FA) and relative cerebral blood volume (rCBV) were measured in the intratumoral and peritumoral regions of the lesions. Receiver-operating characteristic analysis was used to obtain the cut-off values for the parameters presenting a statistical difference between the two tumor groups. Furthermore, we investigated the potential effect of the region of interest (ROI) size on the quantification of diffusion properties in the intratumoral region of the lesions, by applying two different ROI methods. Results: Peritumoral N-acetylaspartate (NAA)/creatine (Cr), choline (Cho)/Cr, Cho/NAA and rCBV significantly differentiated glioblastomas from intracranial metastases. ADC and FA presented no significant difference between the two tumor groups. Conclusions: 1H-MRS and dynamic susceptibility measurements in the peritumoral regions may definitely aid in the differentiation of glioblastomas and solitary metastases. The quantification of the diffusion properties in the intratumoral region is independent of the ROI size placed.

The role of diffusion and perfusion weighted imaging in the differential diagnosis of cerebral tumors: a review and future perspectives

The role of conventional Magnetic Resonance Imaging (MRI) in the detection of cerebral tumors has been well established. However its excellent soft tissue visualization and variety of imaging sequences are in many cases non-specific for the assessment of brain tumor grading. Hence, advanced MRI techniques, like Diffusion-Weighted Imaging (DWI), Diffusion Tensor Imaging (DTI) and Dynamic-Susceptibility Contrast Imaging (DSCI), which are based on different contrast principles, have been used in the clinical routine to improve diagnostic accuracy. The variety of quantitative information derived from these techniques provides significant structural and functional information in a cellular level, highlighting aspects of the underlying brain pathophysiology. The present work, reviews physical principles and recent results obtained using DWI/DTI and DSCI, in tumor characterization and grading of the most common cerebral neoplasms, and discusses how the available MR quantitative data can be utilized through advanced methods of analysis, in order to optimize clinical decision making.

The contribution of diffusion tensor imaging and magnetic resonance spectroscopy for the differentiation of breast lesions at 3T.

Background Conventional breast magnetic resonance imaging (MRI), including dynamic contrast-enhanced MR mammography (DCE-MRM), may lead to ambiguous diagnosis and unnecessary biopsies. Purpose To investigate the contribution of proton MR spectroscopy (1H-MRS) combined with diffusion tensor imaging (DTI) metrics in the discrimination between benign and malignant breast lesions. Material and Methods Fifty-one women with known breast abnormalities from conventional imaging were examined on a 3T MR scanner. DTI was performed during breast MRI, and fractional anisotropy (FA) and apparent diffusion coefficient (ADC) were measured in the breast lesions and the contralateral normal breast. FA and ADC were compared between malignant lesions, benign lesions, and normal tissue. 1H-MRS was performed after gadolinium administration and choline peak was qualitatively evaluated. Results In our study 1H-MRS showed a sensitivity of 93.5%, specificity 80%, and accuracy 88.2%. FA was significantly higher in breast carcinomas compared to benign lesions. However, no significant difference was observed in ADC between benign and malignant lesions. The combination of Cho presence and FA achieved higher levels of accuracy and specificity in discriminating malignant from benign lesions over Cho presence or FA alone. Conclusion In conclusion, applying DTI and 1H-MRS together, adds incremental diagnostic value in the characterization of breast lesions and may sufficiently improve the low specificity of conventional breast MRI.

Magnetic Resonance Spectroscopy of the Breast at 3T: Pre- and Post-Contrast Evaluation for Breast Lesion Characterization

Purpose. To determine whether in vivo proton magnetic resonance spectroscopy at 3T can provide accurate breast lesion characterization, and to determine the effect of gadolinium on the resonance of tCho. Methods. Twenty-four positive-mammogram patients were examined on a 3T MR scanner. 1H-MRS was performed before and after gadolinium administration. tCho peak was qualitatively evaluated before and after contrast injection. Results. Fourteen out of 27 lesions proved to be malignant after histopathological diagnosis. Using 1H-MRS, before contrast injection, 6/14 confirmed malignancies and 11/13 benign lesions were correctly classified; while, after contrast injection, 11/14 confirmed malignancies and 12/13 benign processes were correctly classified. Post gadolinium 1H-MRS proved useful in picking up tCho signal, improving the overall accuracy, sensitivity, and specificity by 35%, 83%, and 9%, respectively. Conclusion. 1H-MRS overall accuracy, sensitivity, and specificity in detecting breast lesions malignancy were increased after gadolinium administration. It is prudent to perform 1H-MRS before contrast injection in large breast lesions to avoid choline underestimation. In cases of small or non-mass lesions, it is recommended to perform 1H-MRS after contrast injection for better voxel prescription to enable a reliable preoperative diagnosis.

Spectroscopic evaluation of glioma grading at 3T: the combined role of short and long TE.

Purpose. To evaluate the diagnostic value of 3T 1H-MRS in grading cerebral gliomas using short and long echo times. Methods. 1H-MRS was performed on 71 patients with untreated cerebral gliomas. Metabolite ratios of NAA/Cr, Cho/Cr, Cho/NAA, and mI/Cr were calculated for short and long TE and compared between low and high grade gliomas. Lipids were qualitatively evaluated. ROC analysis was performed to obtain the cut-off values for the metabolic ratios presenting statistical difference between the two glioma grades. Results. Intratumoral Cho/Cr at both TEs and long TE Cho/NAA were significantly different between low and high grade gliomas. Peritumoral NAA/Cr of both TEs, as well as long TE Cho/Cr and Cho/NAA ratios, significantly differentiated the two tumor grades. Diagnostic sensitivity of peritumoral short TE NAA/Cr proved to be superior over the other metabolic ratios, whereas intratumoral short TE Cho/Cr reached the highest levels of specificity and accuracy. Overall, short TE 1H-MRS reached higher total sensitivity in predicting glioma grade, over long TE. Conclusion. An advantage was found in using short TE over long TE 1H-MRS in the discrimination of low versus high grade gliomas. Moreover, the results suggested that the peritumoral area of gliomas may be more valuable in predicting glioma grade than using only the intratumoral area.

Distinct peak at 3.8 ppm observed by 3T MR spectroscopy in meningiomas, while nearly absent in high-grade gliomas and cerebral metastases

The purpose of the present study was to evaluate distinct metabolic features of meningiomas to distinguish them from other brain lesions using proton magnetic resonance spectroscopy. The study was performed on 17 meningiomas, 24 high-grade gliomas and 9 metastases. Elevated signal intensity at 3.8 ppm observed in low TE spectra adequately differentiated meningioma from other brain tumors while alanine was not indicative of meningioma occurrence; the presence of lipids and lactate did not provide a strong index for meningioma malignancy.

Application value of 3T 1H-magnetic resonance spectroscopy in diagnosing breast tumors

Background Assessment of breast lesions with magnetic resonance imaging (MRI) provides a means for lesion detection and diagnosis. Proton (hydrogen-1) magnetic resonance spectroscopy (1H-MRS) has been proposed as a useful diagnostic technique in providing metabolic information of suspicious breast lesions. Purpose To determine the clinical significance of in-vivo single voxel 1H-MRS at 3T in the assessment of benign and malignant breast lesions in combination with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Material and Methods Twenty-four women with known breast abnormalities from conventional imaging (mammography, ultrasonography) underwent DCE-MRI at a 3T MR scanner and 26 breast lesions were detected. Breast lesions were assessed according BI-RADS classification. Single voxel 1H-MRS was performed after gadolinium administration and choline peak was qualitatively evaluated. All lesions were confirmed histologically from the surgically excised specimens. Sensitivity, specificity, and accuracy of the 1H-MRS, of the BI-RADS classification and of their combination (DCE-MRI + 1H-MRS) were calculated. Results Fifteen out of 26 lesions proved to be malignant and 11 proved to be benign. In our study 1H-MRS showed sensitivity 80%, specificity 81.8%, and accuracy 80.7%. DCE-MRI showed sensitivity 100%, specificity 63.6%, and accuracy 84.6%. The combination of DCE-MRI and 1H-MRS provided higher accuracy (96.4%), as well as higher specificity 81.8% compared to BI-RADS classification. Conclusion The combined use of 1H-MRS and DCE-MRI found to have improved diagnostic performance in the assessment of equivocal breast lesions. 1H-MRS can be used as a useful adjunct during lesion characterization in clinical routine in cases classified as BI-RADS 3 and 4.

Brain lesion classification using 3T MRS spectra and paired SVM kernels

Abstract The increased power and resolution capabilities of 3T Magnetic Resonance (MR) scanners have extended the reach of Magnetic Resonance Spectroscopy as a non-invasive diagnostic tool. Practical sensor calibration issues, magnetic field homogeneity effects and measurement noise introduce distortion into the obtained spectra. Therefore, a combination of robust preprocessing models and nonlinear pattern analysis algorithms is needed in order to evaluate and map the underlying relations of the measured metabolites. The aim of this work is threefold. Firstly we propose the use of a paired support vector machine kernel utilizing metabolic data from both affected and normal voxels in the patients brain for lesion classification problem. Secondly we quantify the performance of an optimal reduced feature set based on targeted CSI-144 scans in order to further reduce the data volume required for a reliable computed aided diagnosis. Thirdly we expand our previous formulation to full multiclass classification. The long term aim remains to provide the human expert with an easily interpretable system to assist clinicians with the time, volume and accuracy demanding diagnostic process.

Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques

In recent years, advanced magnetic resonance imaging (MRI) techniques, such as magnetic resonance spectroscopy, diffusion weighted imaging, diffusion tensor imaging and perfusion weighted imaging have been used in order to resolve demanding diagnostic problems such as brain tumor characterization and grading, as these techniques offer a more detailed and non-invasive evaluation of the area under study. In the last decade a great effort has been made to import and utilize intelligent systems in the so-called clinical decision support systems (CDSS) for automatic processing, classification, evaluation and representation of MRI data in order for advanced MRI techniques to become a part of the clinical routine, since the amount of data from the aforementioned techniques has gradually increased. Hence, the purpose of the current review article is two-fold. The first is to review and evaluate the progress that has been made towards the utilization of CDSS based on data from advanced MRI techniques. The second is to analyze and propose the future work that has to be done, based on the existing problems and challenges, especially taking into account the new imaging techniques and parameters that can be introduced into intelligent systems to significantly improve their diagnostic specificity and clinical application.

Proton Magnetic Resonance Spectroscopy of the Central Nervous System

Currently, conventional MR imaging (MRI) is considered to be an established and useful tool in brain disease detection and it is widely chosen as the initial examination step in patients suspected of brain lesions as it is effective in simultaneously characterizing the soft tissue, cerebrospinal fluid (CSF) spaces, and blood vessels. It is a flexible imaging modality for which contrast can be extensively manipulated without patient burdening by ionizing radiation. Nevertheless, the accurate characterization of brain lesions with MR imaging remains problematic in several cases as the sensitivity and specificity with which this modality defines several brain lesions remains limited [1].