A Statistical Diagnostic Decision Support Tool Using Magnetic Resonance Spectroscopy Data

The aim of this study was to develop a practical postprocessing statistical tool for spectroscopic data analysis to successively create an effective noninvasive tool for spectra evaluation and cerebral disease classification. Spectroscopic data were obtained from a total of 112 patients suffering from several brain lesions. The evaluation was based on histological diagnosis, and/or radiological diagnoses and/or medical physicists’ observation. First, calculation of metabolite ratio (NAA/Cr, Cho/Cr, mI/Cr, LL/Cr) means was conducted for each pathological case, and results were compared with the corresponding published data. A Matlab-based algorithm called FA.S.M.A (Fast Spectroscopic Multiple Analysis) with a Graphical User Interface (GUI) was developed, performing nearest mean classification. It is a fast and user-friendly radiological tool which provides fundamental functionality in estimating mean metabolite ratios values during spectroscopy examination. The user can insert the metabolite ratios and obtain the most probable diagnostic class and the corresponding mean spectrum based on published prior knowledge. In future, FA.S.M.A will be extended to enrich more advanced Pattern Recognition techniques and additional machine learning (ML) methods will be implemented in order to provide a more accurate mapping of the input data to facilitate brain tumor classification according to histological subtype. From a clinical point of view, FA.S.M.A will be extended to incorporate quantitative data from other advanced MR-based techniques such as DWI, DTI, and perfusion measurements not only for supporting primary diagnosis of tumor type but also for determining the extent of glioma infiltration with a high degree of spatial resolution.