The purpose of the study was to investigate the discriminative potential of metabolites obtained from 3T scanners in classifying paediatric posterior fossa brain tumours by comparing performance of three different pattern recognition techniques on a multicentre data set. A total of 52 paediatric patients with cerebellar tumours (16 Medulloblastomas, 31 Pilocytic Astrocytomas and 5 Ependymomas) were scanned using PRESS, TE 30-46 ms, across 4 different hospitals. Achieved balanced classification accuracy were 88% with random-forest, 84 % for the support-vector-machine and 81% for naïve-bays classifier. The achieved accuracy was better than the balanced accuracy previously reported for multi-centre datasets at 1.5T.
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