Imaging of brain tumors: MR spectroscopy and metabolic imaging.

Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Baltimore, MD 21287, USA.
Neuroimaging Clinics of North America (Impact Factor: 1.29). 08/2010; 20(3):293-310. DOI: 10.1016/j.nic.2010.04.003
Source: PubMed

ABSTRACT The utility of magnetic resonance spectroscopy (MRS) in diagnosis and evaluation of treatment response to human brain tumors has been widely documented. The role of MRS in tumor classification, tumors versus nonneoplastic lesions, prediction of survival, treatment planning, monitoring of therapy, and post-therapy evaluation is discussed. This article delineates the need for standardization and further study in order for MRS to become widely used as a routine clinical tool.

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    ABSTRACT: Advances in neuroimaging have modified diagnosis, treatment and clinical management of brain tumors. However, neuropathological study remains necessary in order to get the best clinical management. Surgery and radiotherapy planning are imaging-dependent procedures, and MRI is the standard imaging modality for determining precisely tumor location and its anatomical relationship with surrounding brain structures.In high-grade tumors it has been accepted that tumoral areas with contrast uptake in CT, or T1-weighted MRI contrast enhancement corresponds to solid tumor. However, relationship between MRI and invasive tumor areas remains less defined. Therefore, it is generally accepted that conventional MRI is not sufficient to delineate the real extension of brain tumors. In recent years, PET using 18FDG and amino acid radiotracers (11C-Methionine, 18FDOPA, 18FET) and SPECT with 201-Thallium, as well as advanced MRI sequences (Perfusion, Diffusion-weighted, Diffusion tensor imaging and tractography), and functional MRI, have added important complementary information in the characterization, therapy planning and recurrence differential diagnosis of brain tumors. In this continuing education review of neuroimaging in brain tumors, technical aspects and clinical applications of different imaging modalities are approached in a multidisciplinary way.
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    ABSTRACT: The systematic compilation of Magnetic Resonance Spectroscopy (MRS) has allowed the application of statistical and signal processing techniques to analyze the contribution of metabolites and other compounds in the brain tissues. The complex nature of the MR spectra and the intrinsic difficulty of the Brain Tumor (BT) classification has led researchers towards the Machine Learning discipline, as an objective, as well as practical, methodology for discovering common patterns in the MR spectra acquired from the tumor tissues. This chapter tries to introduce the reader in the classification of brain tumor using MRS. The classification of the most prevalent types of brain tumors using MRS has been largely studied by several authors. Recently, classifiers for the childhood and for a wider range of types of tumors have been also obtained. Furthermore, incremental learning is a promising solution for the dynamism of the clinical environments. During the text we will justify the necessity of agreed acquisition protocols and prospective evaluation of the automatic classifiers to improve the predictive power of the classifiers. The aim of this chapter is to give a practical perspective of the automatic classification of brain tumors using magnetic resonance spectroscopy through the development of Clinical Decision Support Systems (CDSSs) and multicenter studies. KeywordsMagnetic resonance spectroscopy-Pattern classification-Brain tumors-Decision support systems-Multicenter evaluation study
    08/2011: pages 5-19;
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    Diagnostic Techniques and Surgical Management of Brain Tumors, 09/2011; , ISBN: 978-953-307-589-1


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