Integrating Imaging Data into Predictive Biomathematical and Biophysical Models of Cancer

摘要: While there is a mature literature on biomathematical and biophysical modeling in cancer, many of the existing approaches are not clinical utility, as they require input data that extremely difficult to obtain an intact organism, and/or large number assumptions free parameters included models. Thus, has only been very limited application such models solve problems import. More recently, however, increased activity at interface quantitative, noninvasive imaging data, tumor mathematical modeling. In addition reporting bulk morphology volume, emerging techniques can quantitatively report for example vascularity, glucose metabolism, cell density proliferation, hypoxia. this paper, we first motivate problem predicting therapy response by highlighting some (acknowledged) shortcomings methods. We then provide introductions representative quantitative methods describe how currently (and potentially be) used initialize constrain patient specific growth treatment response, thereby increasing utility approaches. conclude exciting research directions when one integrates