A precision tumor growth model integrating time-resolved flow cytometry: predicting fractionation efficacy and...

Objective.Current mathematical models of tumor growth are often limited by a scarcity of quantitative immune data, leading to imprecise characterizations of tumor progression. To address this limitation, we developed a novel coupled tumor-immune-radiotherapy dynamics model by integrating time-resolved flow cytometry data.Approach.An MC38 tumor-bearing mouse model was established to evaluate distinct radiotherapy fractionation regimens (control, 5 Gy × 5, and 8 Gy × 3). Flow cytometry was employed to dynamically quantify the temporal evolution of tumor-infiltrating lymphocytes. By integrating the linear-quadratic model and immune cytotoxicity into the Gompertz equation, a dynamic coupled mathematical model was formulated. Least squares fitting was applied to experimental data to calibrate parameters governing immune-mediated tumor suppression and promotion.Main results.The proposed immune-growth coupled framework demonstrated superior goodness-of-fit compared to conventional models for both hypofractionated (8 Gy × 3) and medium-dose (5 Gy × 5) regimens. Notably, the incorporation of a dynamic temporal dimension facilitated the estimation of immune intervention timing, revealing that the onset of immune synergy is distinct and critically dictated by the selected dose fractionation strategy.Significance. The coupled model established herein not only accurately predicts tumor growth dynamics but also serves as a robust, biologically validated computational tool for predicting the efficacy of fractionation schemes and providing strategic recommendations for immunotherapy scheduling.