Journal article
T. Yankeelov, N. Atuegwu, D. Hormuth, J. Weis, Stephanie L. Barnes, M. Miga, E. Rericha, V. Quaranta
Science Translational Medicine, 2013
Science Translational Medicine, 2013
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APA
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Yankeelov, T., Atuegwu, N., Hormuth, D., Weis, J., Barnes, S. L., Miga, M., … Quaranta, V. (2013). Clinically Relevant Modeling of Tumor Growth and Treatment Response. Science Translational Medicine.
Chicago/Turabian
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Yankeelov, T., N. Atuegwu, D. Hormuth, J. Weis, Stephanie L. Barnes, M. Miga, E. Rericha, and V. Quaranta. “Clinically Relevant Modeling of Tumor Growth and Treatment Response.” Science Translational Medicine (2013).
MLA
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Yankeelov, T., et al. “Clinically Relevant Modeling of Tumor Growth and Treatment Response.” Science Translational Medicine, 2013.
BibTeX Click to copy
@article{t2013a,
title = {Clinically Relevant Modeling of Tumor Growth and Treatment Response},
year = {2013},
journal = {Science Translational Medicine},
author = {Yankeelov, T. and Atuegwu, N. and Hormuth, D. and Weis, J. and Barnes, Stephanie L. and Miga, M. and Rericha, E. and Quaranta, V.}
}
Abstract
Noninvasive imaging technologies can help create patient-specific mathematical models to predict tumor growth. 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.
