Exploratory personalized radiobiological modeling of bystander and immune effects to inform SFRT‐SBRT scheduling
Jiaxin Li, Fen Wang, Wangyao Li, Chayu Yang, Jufri Setianegara, Shahed Badiyan, Kenneth Westover, Sean Domal, Yuting Lin, Hao GaoAbstract
Background
Spatially fractionated radiation therapy (SFRT) demonstrates clinical efficacy against bulky tumors, but optimal treatment scheduling remains empirical. The technique's heterogeneous dose distribution triggers complex biological effects—including bystander signaling and immune activation—that are not captured by conventional dose‐response models.
Purpose
This study aims to develop a proof‐of‐concept computational framework to simulate tumor and immune responses during combined Lattice radiotherapy and SBRT) as an initial step toward patient‐specific modeling.
Methods
A four‐compartment ordinary differential equation (ODE) model was established to simulate tumor and lymphocyte dynamics, integrating Gompertz tumor growth kinetics, direct radiation‐induced cell killing, and indirect biological effects mediated by intercellular signaling and immune activation. Bystander signaling was described by reaction–diffusion equations modeling spatial propagation from high‐ to low‐dose regions. Immune responses were modeled with coupled lymphocyte‐tumor equations, with lymphocyte dose exposure estimated using the HEDOS model. Model parameters were derived from literature and fitted to tumor volume and absolute lymphocyte count (ALC) data from four non‐small cell lung cancer (NSCLC) patients treated with SFRT and SBRT.
Results
The model demonstrated feasibility in reproducing tumor volume (normalized root‐mean‐square error [NRMSE]: 0.071–0.197) and ALC dynamics (NRMSE: 0.017–0.385). Simulations revealed substantial inter‐patient heterogeneity in the estimated contributions of direct radiation and immune‐mediated effects, with immune‐mediated killing exceeding direct radiation in 2 patients. Combined SFRT‐SBRT achieved superior tumor control over either modality alone in our simulations. Notably, the optimal SFRT‐SBRT interval appeared patient‐specific: extending the interval to 2 months improved outcomes in some patients, while others showed limited benefit, depending on the balance between treatment‐induced cell kill and tumor regrowth.
Conclusions
We developed a radiobiological model that simulates tumor and lymphocyte dynamics under combined SFRT–SBRT regimens, providing a preliminary framework for exploring personalized SFRT‐SBRT scheduling. These findings warrant further prospective validation in larger patient cohorts before clinical translation.