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Trends Cancer. 2019 Aug;5(8):467-474. doi: 10.1016/j.trecan.2019.06.006. Epub 2019 Jul 10.

Integrating Mathematical Modeling into the Roadmap for Personalized Adaptive Radiation Therapy.

Author information

1
Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA. Electronic address: heiko.enderling@moffitt.org.
2
Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, 38106 Braunschweig, Germany.
3
Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.

Abstract

In current radiation oncology practice, treatment protocols are prescribed based on the average outcomes of large clinical trials, with limited personalization and without adaptations of dose or dose fractionation to individual patients based on their individual clinical responses. Predicting tumor responses to radiation and comparing predictions against observed responses offers an opportunity for novel treatment evaluation. These analyses can lead to protocol adaptation aimed at the improvement of patient outcomes with better therapeutic ratios. We foresee the integration of mathematical models into radiation oncology to simulate individual patient tumor growth and predict treatment response as dynamic biomarkers for personalized adaptive radiation therapy (RT).

KEYWORDS:

adaptive therapy; mathematical oncology; radiation; radiotherapy; systems medicine

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