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World J Radiol. Apr 28, 2014; 6(4): 72-81
Published online Apr 28, 2014. doi: 10.4329/wjr.v6.i4.72
Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques
Evangelia Tsolaki, Evanthia Kousi, Patricia Svolos, Efthychia Kapsalaki, Kyriaki Theodorou, Constastine Kappas, Ioannis Tsougos
Evangelia Tsolaki, Evanthia Kousi, Patricia Svolos, Kyriaki Theodorou, Constastine Kappas, Ioannis Tsougos, Medical Physics Department, University of Thessaly, Biopolis, 41110 Larissa, Greece
Efthychia Kapsalaki, Department of Radiology, University Hospital of Larissa, Biopolis, 41110 Larissa, Greece
Author contributions: Tsolaki E and Tsougos I designed the research; Tsolaki E, Kousi E and Svolos P performed the research; Tsolaki E, Kousi E and Tsougos I wrote the paper; Kapsalaki E, Theodorou K and Kappas C performed a critical review and data analysis.
Correspondence to: Ioannis Tsougos, MSc, PhD, Assistant Professor in Medical Physics Department, University of Thessaly, Panepistimiou 2, Biopolis, 41110 Larissa, Greece.
Telephone: +30-241-3501863 Fax: +30-241-3501863
Received: November 12, 2013
Revised: January 23, 2014
Accepted: March 17, 2014
Published online: April 28, 2014

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.

Keywords: Decision support systems, Magnetic resonance imaging, Magnetic resonance spectroscopy, Diffusion weighted imaging, Diffusion tensor imaging, Perfusion weighted imaging, Pattern recognition

Core tip: The quantification of the imaging profile of brain neoplasms by combining conventional magnetic resonance imaging and advanced imaging techniques introduces critical underlying pathophysiological information which seems to be the key to success. Thus, it is evident that the pursuit of this goal should be oriented towards the development of decision support software that will utilize large amounts of clinical data with extremely significant diagnostic value which often remain unexploited, hence resulting in a more valid and precise method of differential diagnosis and the selection of the most successful treatment scheme.