Author: Rachel B. Ger, Xun Jia, Youfang Lai, Todd R. McNutt, Xingyi Zhao 👨🔬
Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Johns Hopkins University 🌍
Purpose: Radiotherapy (RT) plays an essential role for head and neck (HN) cancer treatment but often results in xerostomia due to salivary gland damage. It is important to mechanistically model the radiation response of parotid, which can facilitate the understanding of damage mechanism, and develop novel approaches to reduce toxicity. This study develops a mathematical model to predict parotid gland normal tissue complication probability (NTCP) by simulating cellular dynamics and survival behaviors during RT.
Methods: We segmented the parotid gland of an HN cancer patient and assigned each voxel with 5% stem cells and 95% ductal cells in ductal region and 20% myoepithelial and 80% acinar cells for the rest region. Using cell survival models, we computed cell number variations over a 70 Gy treatment in 35 fractions, incorporating cell transitions and migration. Parameters included alpha/beta = 3 Gy, doubling times of 40 h for stem and acinar cells, and 70 h for myoepithelial and ductal cells. A 75% reduction in acinar cells indicated xerostomia. NTCP was computed by sampling biological parameters from a Gaussian distribution to simulate responses among patient population and fitting NTCP versus mean dose using logistic regression.
Results: The NTCP curve showed a D50 value of 41.4 Gy, consistent with published clinical data. The acinar cell distribution after RT revealed that high-dose regions significantly reduce cell numbers and suppress cell proliferation, contributing to NTCP, suggesting the importance of reducing dose variation in treatment planning, in addition to the mean dose to the parotid gland.
Conclusion: This model offers a mechanistic framework for predicting parotid gland NTCP, which can be potentially used to enhance treatment planning and optimizing patient outcomes.