Personalization of Patient Specific Radiation Dose and Dose Fractionation Using Volumetric Tumor Dynamics
H Enderling1*, M Zahid1, E Moros1, J Caudell1, A Mohamed2, C Fuller2, (1) H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL,(2) UT MD Anderson Cancer Center, Houston, TX, (6) UT MD Anderson Cancer Center, Houston, TX
Presentations
PO-GePV-M-22 (Sunday, 7/10/2022) [Eastern Time (GMT-4)]
ePoster Forums
Purpose: The standard radiotherapy protocol for head and neck cancer patients delivers a total of 66–70 Gy in 30–35 weekday fractions of 1.8–2 Gy each. One shortcoming of current clinical practice is that radiation protocols do not consider patient-specific factors that may influence outcome.
Methods: Mathematical modeling and computer simulations of tumor volume dynamics can reveal an emergent description of tumor radiosensitivity. We present a quantitative framework to estimate a personalized radiation dose for individual patients, based on pre- and early on-treatment tumor volume dynamics. We the discuss an in silico trial of this dose personalization using retrospective data from a combined cohort of n = 39 head and neck cancer patients from the Moffitt and MD Anderson Cancer Centers that received 66–70 Gy RT in 2–2.12 Gy weekday fractions.
Results: A mathematical model of tumor growth, radiation response, and patient-specific tumor carrying capacity can fit the clinical data with high accuracy. Selected ranges of patient-specific carrying capacity predicts superior responses to hyperfractionation protocols. Early treatment response dynamcis predict total dose to provide tumor control and disease free survival.
Conclusion: We demonstrate the feasibility of using tumor volume dynamics to inform dose personalization and stratification for dose escalation and de-escalation. The dose personalization methodology presented herein could potentially be applied in any treatment setting where fractionated RT is used with a potential for dynamics informed dose adaptation. The advantage of applying this methodology to fractionated delivery of RT is that this context allows for enough time to observe, calibrate the model, make forecasts, and then adjust the treatment course. While the focus of the presented work is on head and neck cancer, it is conceivable that the dynamics adapted radiation dose (DARD) framework is translatable to other cancer types.
Keywords
Simulation, Dose Response, Tumor Control
Taxonomy
IM/TH- Mathematical/Statistical Foundational Skills: Virtual clinical trials
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