Classification methods for the differentiation of atypical meningiomas using diffusion and perfusion techniques at 3-T MRI.

The purpose was to investigate the contribution of machine learning algorithms using diffusion and perfusion techniques in the differentiation of atypical meningiomas from glioblastomas and metastases. Apparent diffusion coefficient, fractional anisotropy, and relative cerebral blood volume were measured in different tumor regions. Naive Bayes, k-Nearest Neighbor, and Support Vector Machine classifiers were used in the classification procedure. The application of classification methods adds incremental differential diagnostic value. Differentiation is mainly achieved using diffusion metrics, while perfusion measurements may provide significant information for the peritumoral regions.