Keywords: Machine Learning/Artificial Intelligence, Brain, GliomaTo contribute to better tumour classification and thus enhancing patient outcomes, we statistically analysed metabolic maps of 37 glioma patients obtained using high resolution 7T MRSI. We tested and optimised different semi-supervised learning based classification approaches. Random forest classification of IDH mutation status and tumour grade in clinical imaging based segmented tumour regions yielded high diagnostic accuracy with AUC of 86% and 99% respectively. We found Glu, Gln, GSH, tCho, Ins, Gly and tCr as important determining features. These are similar to comparable SVS studies while providing the advantage of whole-brain coverage.
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