Discrimination between glioblastoma multiforme and solitary metastasis using morphological features derived from the p:q tensor decomposition of diffusion tensor imaging

The management and treatment of high‐grade glioblastoma multiforme (GBM) and solitary metastasis (MET) are very different and influence the prognosis and subsequent clinical outcomes. In the case of a solitary MET, diagnosis using conventional radiology can be equivocal. Currently, a definitive diagnosis is based on histopathological analysis on a biopsy sample. Here, we present a computerised decision support framework for discrimination between GBM and solitary MET using MRI, which includes: (i) a semi‐automatic segmentation method based on diffusion tensor imaging; (ii) two‐dimensional morphological feature extraction and selection; and (iii) a pattern recognition module for automated tumour classification. Ground truth was provided by histopathological analysis from pre‐treatment stereotactic biopsy or at surgical resection. Our two‐dimensional morphological analysis outperforms previous methods with high cross‐validation accuracy of 97.9% and area under the receiver operating characteristic curve of 0.975 using a neural networks‐based classifier. Copyright © 2014 John Wiley & Sons, Ltd. Pattern recognition of tumour shape was used to discriminate between high‐grade glioblastoma multiforme (GBM) and solitary metastasis (MET) with a cross‐validated accuracy of 97.9%. Diffusion tensor imaging was used to segment the brain tumour core (red arrows above) on the basis of isotropic (p) and anisotropic (q) diffusion characteristics. Morphological features were extracted and feature selection indicated that five parameters related to tumour shape variability optimally distinguished GBMs from METs. A neural network classifier applied to these shape features outperformed other classifiers.