1. Integrating Mathematical Modeling into the Roadmap for Personalized Adaptive Radiation Therapy
- Author
-
Enderling, H., Enderling, Heiko, Alfonso, Juan Carlos López, Moros, Eduardo, Caudell, Jimmy J., Harrison, Louis B., and BRICS, Braunschweiger Zentrum für Systembiologie, Rebenring 56,38106 Braunschweig, Germany.
- Subjects
adaptive therapy ,0301 basic medicine ,Cancer Research ,medicine.medical_specialty ,medicine.medical_treatment ,mathematical oncology ,Radiation Tolerance ,Personalization ,03 medical and health sciences ,0302 clinical medicine ,Neoplasms ,Biomarkers, Tumor ,Tumor Microenvironment ,medicine ,Humans ,Medical physics ,Precision Medicine ,systems medicine ,Adaptation (computer science) ,radiotherapy ,Protocol (science) ,business.industry ,Radiotherapy Planning, Computer-Assisted ,Dose fractionation ,Dose-Response Relationship, Radiation ,Models, Theoretical ,Magnetic Resonance Imaging ,radiation ,Radiation therapy ,Systems medicine ,Clinical trial ,Treatment Outcome ,030104 developmental biology ,Oncology ,030220 oncology & carcinogenesis ,Radiation Oncology ,Dose Fractionation, Radiation ,Tomography, X-Ray Computed ,business ,Adaptive radiation therapy - 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).
- Published
- 2019