Author: Yan Chu, Madison Emily Grayson, Radhe Mohan, Pablo P. Yepes 👨🔬
Affiliation: UT Health Science Center at Houston, Rice University, The University of Texas MD Anderson Cancer Center 🌍
Purpose: Radiation-induced lymphopenia (RIL) is a common adverse effect of radiation therapy, negatively impacting overall survival and anti-PD1 immunotherapy efficacy. Our lab previously developed a RIL risk model incorporating dosimetric and non-dosimetric parameters but without considering LET distributions. However, higher LET in low-dose regions surrounding the treatment site may kill more lymphocytes than previously estimated. We hypothesize that incorporating LET-weighted dose-volume histograms (DVHs) may improve RIL risk prediction.
Methods: The Monte Carlo dose distribution was calculated for 8 proton therapy esophageal cancer patients using a fast Monte Carlo dose calculator. The dose-volume histograms for these patients were then weighted using a fixed relative biological effectiveness (RBE) of 1.1 and several variable RBE models (McNamara, Wedenberg, and repair-misrepair-fixation). These RBE-weighted DVHs were then input into the RIL risk prediction model. The resulting absolute lymphocyte count (ALC) nadir, the patients’ lowest lymphocyte count during and immediately after treatment, was compared to the measured clinical values.
Results: For 4 of the 8 patients in this study, the ALC nadir predictions using the variable RBE-weighted DVHs were closer to the clinical ALC value than the original model predictions. Between the three variable RBE weighting models, McNamara performs the best in this context. Therefore, in future studies, we will focus on using McNamara as the variable RBE model. Although the RIL risk prediction model was trained with unweighted DVHs, this indicates that for some patients, RBE and LET may be important dosimetric factors.
Conclusion: This preliminary work suggests RBE-weighted DVHs may improve the RIL risk model. The original model has not been trained with these DVHs, so retraining is needed to fully assess their impact. We are currently calculating variable-weighted DVHs for a cohort of 477 esophageal cancer proton patients. Future studies will investigate LET’s role in refining lymphopenia prediction.