Patricia Svolos

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.

Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques.

The aim of this study was to evaluate the contribution of diffusion and perfusion MR metrics in the discrimination of intracranial brain lesions at 3T MRI, and to investigate the potential diagnostic and predictive value that pattern recognition techniques may provide in tumor characterization using these metrics as classification features. Conventional MRI, diffusion weighted imaging (DWI), diffusion tensor imaging (DTI) and dynamic-susceptibility contrast imaging (DSCI) were performed on 115 patients with newly diagnosed intracranial tumors (low-and- high grade gliomas, meningiomas, solitary metastases). The Mann-Whitney U test was employed in order to identify statistical differences of the diffusion and perfusion parameters for different tumor comparisons in the intra-and peritumoral region. To assess the diagnostic contribution of these parameters, two different methods were used; the commonly used receiver operating characteristic (ROC) analysis and the more sophisticated SVM classification, and accuracy, sensitivity and specificity levels were obtained for both cases. The combination of all metrics provided the optimum diagnostic outcome. The highest predictive outcome was obtained using the SVM classification, although ROC analysis yielded high accuracies as well. It is evident that DWI/DTI and DSCI are useful techniques for tumor grading. Nevertheless, cellularity and vascularity are factors closely correlated in a non-linear way and thus difficult to evaluate and interpret through conventional methods of analysis. Hence, the combination of diffusion and perfusion metrics into a sophisticated classification scheme may provide the optimum diagnostic outcome. In conclusion, machine learning techniques may be used as an adjunctive diagnostic tool, which can be implemented into the clinical routine to optimize decision making.

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.

Classification methods for the differentiation of atypical meningiomas using diffusion and perfusion techniques at 3-T MRI.

The purpose was to investigate the contribution of machine learning algorithms using diffusion and perfusion techniques in the differentiation of atypical meningiomas from glioblastomas and metastases. Apparent diffusion coefficient, fractional anisotropy, and relative cerebral blood volume were measured in different tumor regions. Naive Bayes, k-Nearest Neighbor, and Support Vector Machine classifiers were used in the classification procedure. The application of classification methods adds incremental differential diagnostic value. Differentiation is mainly achieved using diffusion metrics, while perfusion measurements may provide significant information for the peritumoral regions.

Quantification of Normal CSF Flow Through the Aqueduct Using PC-Cine MRI at 3T

INTRODUCTION Quantification of cerebrospinal fluid (CSF) flow through the cerebral aqueduct is of paramount importance in patients with hydrocephalus. The purpose of this study was to evaluate the normal CSF flow measurements at three different anatomical levels of the aqueduct utilizing 3-Tesla (3 T) magnetic resonance imaging. MATERIALS AND METHODS The CSF hydrodynamics in 22 healthy volunteers were evaluated. Phase-contrast cine MRI was performed on a 3 T General Electric MR system (GE Medical Systems, Milwaukee, WI, USA). A cardiac-gated, flow-compensated GRE sequence with flow encoding was used, and the aqueduct was visualized using a sagittal T1 FLAIR sequence. Velocity maps were acquired at three different anatomical levels. Region-of-interest (ROI) analysis was performed. RESULTS CSF flow velocities were slightly increased at the upper in comparison with the lower part of the aqueduct. The mean values for the peak positive and negative velocity and the mean average flow were calculated for both ROIs. DISCUSSION/CONCLUSIONS CSF peak positive velocity, peak negative velocity, and mean flow through the aqueduct were calculated in 22 young healthy volunteers performed at 3 T. Our measurements did not show significant difference compared with the reported measurements obtained at 1.5 T. Slight differences were observed in the CSF hydrodynamic measurements, depending on the anatomical level of the aqueduct; however, they did not vary significantly.

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.

T2 FLAIR artifacts at 3-T brain magnetic resonance imaging

The purpose of this retrospective clinical study was to identify and evaluate the presence and frequency of T2 FLAIR artifacts on brain MRI studies performed at 3 T. We reviewed axial T2 FLAIR images in 200 consecutive unremarkable brain MRI studies performed at 3 T. All studies were reviewed for the presence of artifacts caused by pulsatile CSF flow, magnetic susceptibility and no nulling of the CSF signal. T2 FLAIR images introduce several artifacts that may degrade image quality and mimic pathology. Knowledge of these artifacts and increased severity and frequency at 3 T is of particular importance in avoiding a misdiagnosis.

On the use of published radiobiological parameters and the evaluation of NTCP models regarding lung pneumonitis in clinical breast radiotherapy

In this study we sought to evaluate and accent the importance of radiobiological parameter selection and implementation to the normal tissue complication probability (NTCP) models. The relative seriality (RS) and the Lyman–Kutcher–Burman (LKB) models were studied. For each model, a minimum and maximum set of radiobiological parameter sets was selected from the overall published sets applied in literature and a theoretical mean parameter set was computed. In order to investigate the potential model weaknesses in NTCP estimation and to point out the correct use of model parameters, these sets were used as input to the RS and the LKB model, estimating radiation induced complications for a group of 36 breast cancer patients treated with radiotherapy. The clinical endpoint examined was Radiation Pneumonitis. Each model was represented by a certain dose–response range when the selected parameter sets were applied. Comparing the models with their ranges, a large area of coincidence was revealed. If the parameter uncertainties (standard deviation) are included in the models, their area of coincidence might be enlarged, constraining even greater their predictive ability. The selection of the proper radiobiological parameter set for a given clinical endpoint is crucial. Published parameter values are not definite but should be accompanied by uncertainties, and one should be very careful when applying them to the NTCP models. Correct selection and proper implementation of published parameters provides a quite accurate fit of the NTCP models to the considered endpoint.

The Role of Diffusion Weighted and Diffusion Tensor Imaging in Epilepsy

Epilepsy is a chronic neurologic disorder characterized by unpredictable, recurrent, unprovoked seizures. It is the fourth most common neurologic disorder and affects people of all ages. A substantial number of epilepsies are well controlled with the administration of suitable antiepileptic medication. However, approximately 20–30% of epilepsy cases can be medically intractable, and hence there is an increasing interest in surgical approaches for seizure abolition [1]. It follows that accurate lateralization and localization of the epileptogenic focus are significant prerequisites for determining surgical candidacy once the patient has been deemed medically intractable.