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A quantitative SVM approach potentially improves the accuracy of magnetic resonance spectroscopy in the preoperative evaluation of the grades of diffuse gliomasDOI: 10.1016/j.nicl.2019.101835 Keywords: Proton magnetic resonance spectroscopy, Machine learning, Glioma grading, Support vector machine Abstract: To investigate the association between proton magnetic resonance spectroscopy (1H-MRS) metabolic features and the grade of gliomas, and to establish a machine-learning model to predict the glioma grade
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