Nasopharyngeal carcinoma (NPC) is an abnormal growth of tissue which arises from the back of the nose. At the time of diagnosis, detection of tumor features with prognostic significance, including patient demographics, imaging characteristics and molecular characteristics, can enable the treating clinician to select a treatment that is optimized for the individual patient. At present, the analysis of tumor imaging features is limited to size criteria and macroscopic textural semantic descriptors, but computerized quantification of intratumoral heterogeneity and their temporal evolution may provide another metric for predicting prognosis. We propose medical imaging feature analysis methods and radiomics machine learning methods to predict failure of treatment. NPC tumors on contrast-enhanced T1 (T1Gd) sequences of 25 NPC patients' diagnostic magnetic resonance images (MRI) were manually contoured. Otsu segmentation was applied to segment the tumor into highly enhancing vs. weakly enhancing signal intensity subregions. Within these subregions, texture features were extracted to numerically quantify the intraregional heterogeneity. Patients were divided into two prognostic groups; a progression-freesurvival group (those without locoregional recurrence or distant metastases), and the disease progression group (those with locoregional recurrence or distant metastases). We used Support Vector Machines (SVM) to perform classification (prediction of prognosis). The features from the highly enhancing subregion classify prognosis with 80% predictive accuracy with AUC=0.60, while the captured features from the weakly enhancing subregion classify prognosis with 76% accuracy with AUC= 0.76.