Multivariate Pattern Analysis of in vivo MR Imaging Parameters for Detecting Transformations to a Higher Grade in Patients with Recurrent Low Grade Gliomas

Alexandra Constantin¹, Llewellyn Jalbert¹, Adam Elkhaled², Rupa Parvataneni², Annette Molinaro², Joanna Phillips², Soonmee Cha², Susan M. Chang², Sarah J. Nelson²

A multivariate diagnostic model was built to estimate the probability that a recurrent low grade glioma had progressed to a higher grade based on in vivo MR imaging parameters. The model was able to discriminate between recurrent low grade gliomas that upgraded versus those that remained grade 2 with 93% cross-validation accuracy and 84% bootstrapping accuracy, based on the 75th percentile normalized choline height, the 25th percentile recovery to baseline of the perfusion susceptibility curve, the 75th percentile ratio of choline to n-acetylaspartate height, and the maximum choline height inside the T2 lesion.