Cgal 5.2.1 – 3d mesh generation. https://doc.cgal.org/latest/Mesh_3/index.html#Chapter_3D_Mesh_Generation. Accessed: 19 Apr 2021.

C. Hogea, C. Davatzikos, and G. Biros. An image-driven parameter estimation problem for a reaction–diffusion glioma growth model with mass effects. Journal of Mathematical Biology, 3(56):793–825, Jun 2008.

S. Jbabdi, E. Mandonnet, H. Duffau, L. Capelle, K. R. Swanson, M. Pélégrini-Issac, R. Guillevin, and H. Benali. Simulation of anisotropic growth of low-grade gliomas using diffusion tensor imaging. Magnetic Resonance in Medicine, 54(3):616–624, Sep 2005.

E. Konukoglu, M. Sermesant, O. Clatz, J.-M. Peyrat, H. Delingette, and N. Ayache. A recursive anisotropic fast marching approach to reaction diffusion equation: Application to tumor growth modeling. In Information Processing in Medical Imaging, pages 687–699. Springer, 2007.

P. Mosayebi, Da. Cobzas, M. Jagersand, and A. Murtha. Stability effects of finite difference methods on mathematical tumor growth model. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops (CVPRW), pages 125–132, Jul 2010.

R. Rockne, J. K. Rockhill, M. Mrugala, A. M. Spence, I. Kalet, K. Hendrickson, A. Lai, T. Cloughesy, E. C. Alvord, and K. R. Swanson. Predicting efficacy of radiotherapy in individual glioblastoma patients in vivo: A mathematical modeling approach. Physics in Medicine and Biology, 55(12):3271–3285, Jun 2010.

K. R. Swanson, R. C. Rostomily, and E. C. Alvord. A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: A proof of principle. British Journal of Cancer, 98(1):113–119, Jan 2008.

J. Unkelbach, B. H. Menze, E. Konukoglu, F. Dittmann, M. Le, N. Ayache, and H. A. Shih. Radiotherapy planning for glioblastoma based on a tumor growth model: Improving target volume delineation. Physics in Medicine and Biology, 59(3):747–770, Feb 2014.