Enderling Heiko, Alfonso Juan Carlos López, Moros Eduardo, Caudell Jimmy J, Harrison Louis B
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.
Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, 38106 Braunschweig, Germany.
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).
在当前的放射肿瘤学实践中，治疗方案是根据大型临床试验的平均结果制定的，个性化程度有限，且不会根据个体患者的临床反应对剂量或剂量分割进行调整。预测肿瘤对放疗的反应并将预测结果与观察到的反应进行比较，为新型治疗评估提供了机会。这些分析可导致方案调整，旨在以更好的治疗比改善患者预后。我们预见将数学模型整合到放射肿瘤学中，以模拟个体患者的肿瘤生长，并预测作为个性化自适应放射治疗（RT）动态生物标志物的治疗反应。
