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Physical Biology, 2019

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Research Scientist | Biomedical Engineering + Imaging Science > > Computational Oncology

R. Rockne, A. Hawkins-Daarud, K. Swanson, J. Sluka, J. Glazier, P. Macklin, D. Hormuth, Angela M. Jarrett, E. Lima, J. Tinsley Oden, G. Biros, T. Yankeelov, K. Curtius, I. Al Bakir, D. Wodarz, N. Komarova, Luis Aparicio, M. Bordyuh, R. Rabadán, S. Finley, H. Enderling, J. Caudell, E. Moros, A. Anderson, R. Gatenby, Artem Kaznatcheev, P. Jeavons, Nikhil P. Krishnan, J. Pelesko, Raoul R. Wadhwa, N. Yoon, D. Nichol, A. Marusyk, M. Hinczewski, Jacob G. Scott

Physical Biology, 2019

Physical Biology, 2019

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**APA**
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Rockne, R., Hawkins-Daarud, A., Swanson, K., Sluka, J., Glazier, J., Macklin, P., … Scott, J. G. (2019). The 2019 mathematical oncology roadmap. *Physical Biology*.

**Chicago/Turabian**
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Rockne, R., A. Hawkins-Daarud, K. Swanson, J. Sluka, J. Glazier, P. Macklin, D. Hormuth, et al. “The 2019 Mathematical Oncology Roadmap.” *Physical Biology* (2019).

**MLA**
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Rockne, R., et al. “The 2019 Mathematical Oncology Roadmap.” *Physical Biology*, 2019.

**BibTeX**
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```
@article{r2019a,
title = {The 2019 mathematical oncology roadmap},
year = {2019},
journal = {Physical Biology},
author = {Rockne, R. and Hawkins-Daarud, A. and Swanson, K. and Sluka, J. and Glazier, J. and Macklin, P. and Hormuth, D. and Jarrett, Angela M. and Lima, E. and Oden, J. Tinsley and Biros, G. and Yankeelov, T. and Curtius, K. and Bakir, I. Al and Wodarz, D. and Komarova, N. and Aparicio, Luis and Bordyuh, M. and Rabadán, R. and Finley, S. and Enderling, H. and Caudell, J. and Moros, E. and Anderson, A. and Gatenby, R. and Kaznatcheev, Artem and Jeavons, P. and Krishnan, Nikhil P. and Pelesko, J. and Wadhwa, Raoul R. and Yoon, N. and Nichol, D. and Marusyk, A. and Hinczewski, M. and Scott, Jacob G.}
}
```

Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology—defined here simply as the use of mathematics in cancer research—complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.