title: Clinically relevant modeling of tumor growth and treatment response.
abstract: Current mathematical models of tumor growth are limited in their clinical application because they require input data that are nearly impossible to obtain with sufficient spatial resolution in patients even at a single time point--for example, extent of vascularization, immune infiltrate, ratio of tumor-to-normal cells, or extracellular matrix status. Here we propose the use of emerging, quantitative tumor imaging methods to initialize a new generation of predictive models. In the near future, these models could be able to forecast clinical outputs, such as overall response to treatment and time to progression, which will provide opportunities for guided intervention and improved patient care.
authors.affiliation: Institute of Imaging Science, Vanderbilt University, Nashville, TN 37212, USA. thomas.yankeelov@vanderbilt.edu
authors.forename: Thomas E
authors.initials: TE
authors.lastname: Yankeelov
authors.forename: Nkiruka
authors.initials: N
authors.lastname: Atuegwu
authors.forename: David
authors.initials: D
authors.lastname: Hormuth
authors.forename: Jared A
authors.initials: JA
authors.lastname: Weis
authors.forename: Stephanie L
authors.initials: SL
authors.lastname: Barnes
authors.forename: Michael I
authors.initials: MI
authors.lastname: Miga
authors.forename: Erin C
authors.initials: EC
authors.lastname: Rericha
authors.forename: Vito
authors.initials: V
authors.lastname: Quaranta
mesh_terms: D002455:Cell Division; D006801:Humans; D008954:Models, Biological; D009369:Neoplasms / Q000473:pathology* / Q000628:therapy*