A Predictive Prognostic Model for Brain Cancer
Each year, almost half of all diagnosed primary brain tumors in the United States are Grade IV glioblastoma multiforme (GBMs). While recent efforts have begun to uncover the genetic pathways involved in this cancer's etiology – and potential methods for treatment – arguably, no specific prognostic model has arisen (and been sufficiently validated) to provide widespread usability and individually tailored predictions about a patient's prognosis, let alone suggest optimal treatment. This work focuses on the development of a Bayesian belief network for predicting outcomes for GBM patients. This research effort looks to validate a BBN developed using two NIH datasets: the National Cancer Institute (NCI) Rembrandt Project; and the Cancer Genome Atlas (TCGA), and its application to local datasets. Model variables encompass the full spectrum of available observations (demographics, initial presentation, histopathology, treatment, imaging, performance scores, end outcomes, etc.). The ultimate result of this endeavor will be a set of tools and well-validated disease model to provide more tailored information and guidance to physicians about GBM patient outcomes.