Abstract 230:In silicotrial to estimate personalized RT dose in head and neck cancer

As current radiotherapy (RT) treatment schedules are not personalized for individual patients, with the prescribed dose being uniform for particular subtypes and stages of cancer, despite highly variable responses between patients, we performed an in silico trial to determine optimal personalized RT dose for head and neck cancer patients in order to minimize excess dose with the objective of minimizing toxicity and improving QOL without sacrificing tumor control. Weekly tumor volume data were collected from Moffitt Cancer Center and MD Anderson Cancer Center for n=39 head and neck cancer patients that received 66-70 Gy in 2-2.12 Gy daily fractions or with accelerated fractionation. Clinical outcome data, i.e. locoregional control (LRC) and disease-free survival (DFS), were also collected. Tumor growth was modeled as logistic growth with one parameter (λ, volumetric growth rate), and the effect of each RT dose was modeled as an instantaneous reduction in the carrying capacity with one parameter (δ, carrying capacity reduction fraction). Using a leave-one-out study design we trained a prediction pipeline that used this math model calibrated on the data from N-1 patients in combination with 4 on-treatment measurements for the left-out patient to simulate forward tumor volume reduction for that patient in order to determine the minimum radiation dose required for LRC. Tumor volume reduction was connected to LRC by means of a volume reduction threshold associated with LRC calculated from the N-1 training cohort. We were able to calculate the optimal minimum radiation dose required for LRC for each patient. We found that 87% of the patients (34/39) received a higher total dose than estimated as necessary by our model with an average overdose of 37 Gy/patient, while the remaining patients were estimated to have received too little dose, with an average underdose of 47 Gy/patient. Notably, our results showed that the current one-size-fits-all approach results in no patient receiving their optimal RT dose. By means of this unique in silico trial, we demonstrate a potential method to use historical clinical observations in conjunction with patient-specific measurements from early on in a treatment course to determine a personalized minimum dose to achieve locoregional control. Such estimates early on during an RT treatment course may allow radiation oncologists to identify candidates for dose de-escalation and candidates who may be better suited for concurrent or alternative treatment options. Citation Format: Mohammad Usama Zahid, Nuverah Mohsin, Abdallah Mohamed, Jimmy J. Caudell, Louis B. Harrison, Clifton D. Fuller, Eduardo Moros, Heiko Enderling. In silico trial to estimate personalized RT dose in head and neck cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 230.