title: Predicting the growth probability function of tumors in medical images.
abstract: The dynamics of a tumor can be studied using a feature-based stochastic method to predict the temporal and spatial growth of the tumor. A posterior probability of growth function is incorporated into the interacting particle model definition, and the probability influences growth direction at each location. Using features derived from images and data-mining, the growth probability function is predicted and tested to investigate the ability of the derived feature values to explain the tumor evolution.
authors.affiliation: Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, USA.
authors.forename: Kwangsik
authors.initials: K
authors.lastname: Nho
authors.forename: Donald E
authors.initials: DE
authors.lastname: Brown
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009361:Neoplasm Invasiveness / Q000473:pathology*; D018570:Risk Assessment; D012307:Risk Factors