Integrating Mathematical Modeling into the Roadmap for Personalized Adaptive Radiation Therapy

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


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.

Publication types

  • Review

MeSH terms

  • Biomarkers, Tumor / genetics
  • Dose Fractionation, Radiation
  • Dose-Response Relationship, Radiation
  • Humans
  • Magnetic Resonance Imaging
  • Models, Theoretical*
  • Neoplasms / diagnostic imaging
  • Neoplasms / genetics
  • Neoplasms / radiotherapy*
  • Precision Medicine / methods*
  • Radiation Oncology / methods*
  • Radiation Tolerance / genetics
  • Radiotherapy Planning, Computer-Assisted / methods*
  • Tomography, X-Ray Computed
  • Treatment Outcome
  • Tumor Microenvironment / genetics
  • Tumor Microenvironment / radiation effects


  • Biomarkers, Tumor