Predictive modeling in glioma grading from MR perfusion images using support vector machines

Abstract

The advantages of predictive modeling in glioma grading from MR perfusion images have not yet been explored. The aim of the current study was to implement a predictive model based on support vector machines (SVM) for glioma grading using tumor blood volume histogram signatures derived from MR perfusion images and to assess the diagnostic accuracy of the model and the sensitivity to sample size. A total of 86 patients with histologically-confirmed gliomas were imaged using dynamic susceptibility contrast (DSC) MRI at 1.5T. Histogram signatures from 53 of the 86 patients were analyzed independently by four neuroradiologists and used as a basis for the predictive SVM model. The resulting SVM model was tested on the remaining 33 patients and analyzed by a fifth neuroradiologist. At optimal SVM parameters, the true positive rate (TPR) and true negative rate (TNR) of the SVM model on the 33 patients was 0.76 and 0.82, respectively. The interobserver agreement and the TPR increased significantly when the SVM model was based on an increasing sample size (P < 0.001). This result suggests that a predictive SVM model can aid in the diagnosis of glioma grade from MR perfusion images and that the model improves with increasing sample size.