Performance Analysis of Classification of DCE -MRI Using SVM

Dynamic contrast enhanced MRI provides insight into the vascular properties of tissue. Pharmacokinetic models may be fitted to DCE-MRI uptake patterns, enabling biologically relevant interpretations. The aim of our study was to determine whether treatment outcome for patients with locally advanced cervical cancer could be predicted from Brix parameters. First order statistical features of the Brix parameters were used. In addition, texture analysis of Brix parameter maps was done by constructing gray level co-occurrence matrix (GLCM) from the maps. Clinical factors such as first and second order features were used as explanatory variables for support vector machine (SVM) classification, with treatment outcome as response. Features derived from first order statistics could not discriminate between cured and relapsed patients. However, second order GLCM features could significantly predict treatment outcome with more accuracies. The result indicates the spatial relation with in tumor, quantified by texture features, were more suitable for outcome prediction than first order features.