For survival prediction of brain tumor patients based on MRI scans, radiomic features have been a major research focus in the last years. However, radiomic features do not take the location of the lesion into account, which, in relation to the functional regions of the brain, could be a significant factor in predicting survival. An automatic and exact localization of the tumor in relation to specific functional areas is not straightforward, as typical brain parcellation methods fail in presence of large lesions. Here, we propose a model that replaces the tumorous region in 3D brain MRI scans with healthy tissue in order to improve the registration process towards a brain template. Further, we assemble a set of features for quantitative description of brain tumor location. On an openly available dataset, registration is strongly improved. The extracted location features also have better predictive performance when used after the proposed registration step and reach accuracies in survival prediction comparable to radiomic features.Clinical relevance- This work improves the quantification of the location of brain tumors in the human brain and proposes an extension of radiomic features to include the location, resulting in a refined prediction of patient survival.