Early Detection of Treatment Response for GBM Brain Tumor using ADC Map of DW-MRI

This preliminary study explores novel methods using diffusion weighted(DW) MR images as a biomarker to detect early GBM brain tumor response to treatment. Apparent diffusion coefficient(ADC) map, calculated from DW-MR images, can provide unique information of tumor response at cellular level. In this study, we investigate whether changes in ADC histograms between two scans, taken 5-7 weeks apart before and after treatment, could predict treatment effectiveness before lesion size changes are observed on later scans. The contribution of our work is to exploit quantitative pattern classification techniques for the prediction. For both preand post-treatment scans, we first compute the histogram from the ADC values covered within the tumor. Then we apply supervised learning on features extracted from the histogram for classification. We evaluated our approach with pool data of 86 patients with GBM under chemotherapy while 40 responded and 46 did not respond based on tumor size reduction. We compared Fisher’s linear discriminant analysis, AdaBoost and random forests classifier using leave one out cross validation(LOOCV), resulting in the best accuracy of 67.44%.