Learning the Cell-Graphs: Macroscopic Modeling of Brain Tumors. Çigdem Gündüz Bülent Yener S. Humayun Gultekin Diffuse gliomas are brain tumors that invade the surrounding normal tissue by an aggressive diffusion process. This diffuse invasive behavior affects the prognosis adversely, and renders radical treatment impossible. Current mathematical models to quantify and analyze a cancer tumor are not scalable due to their enormous complexity. We developed a scalable,graph theoretical model, based on the spatial relationship between the cells, to quantify the properties of the invasion. The graph theoretical model is used by a machine learning algorithm. The learning algorithm uses graph metrics to distinguish(1) gliomas from surrounding normal tissue, and(ii) gliomas from inflammation. We tested the algorithms on real data to validate the proposed approach. Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12/03/2003 cd-03-12
Learning the Cell-Graphs: Macroscopic Modeling of Brain Tumors.
Çigdem Gündüz
Bülent Yener
S. Humayun Gultekin
Diffuse gliomas are brain tumors that invade the surrounding normal tissue by an aggressive diffusion process. This diffuse invasive behavior affects the prognosis adversely, and renders radical treatment impossible. Current mathematical models to quantify and analyze a cancer tumor are not scalable due to their enormous complexity. We developed a scalable,graph theoretical model, based on the spatial relationship between the cells, to quantify the properties of the invasion. The graph theoretical model is used by a machine learning algorithm. The learning algorithm uses graph metrics to distinguish(1) gliomas from surrounding normal tissue, and(ii) gliomas from inflammation. We tested the algorithms on real data to validate the proposed approach.
Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY
12/03/2003
cd-03-12