Stochastic Modeling of Lymphocyte Kinetics Reveals Mechanistic Biomarkers post Cancer Chemoradiation

Thomas M. Menino Convention & Exhibition Center 

Radiation therapy induces profound disruptions in lymphocyte homeostasis, yet prognostic assessment in clinical practice largely relies on simple summaries such as absolute lymphocyte count (ALC) nadirs, which overlook rich temporal information encoded in the full ALC trajectory during and after chemoradiation. We propose a stochastic modeling framework to quantitatively characterize patient-specific radiation-induced lymphocyte kinetics. Built on state-dependent multi-type branching processes and diffusion approximations, this two-phase mechanistic model captures treatment-driven cytotoxicity and post-treatment regenerative dynamics through distinct self-renewal, death, and egress rates. Application to longitudinal ALC data from esophageal cancer patients receiving chemoradiation shows the estimated individual-level kinetic parameters from ALC trajectories provide prognostic information beyond nadir-based metrics. These results highlight the proposed framework as a mechanistically interpretable modeling of radiation-induced lymphocyte kinetics with potential to inform risk stratification and treatment planning in radiation oncology.

Stochastic modeling


Longitudinal data

Oncology

Radiation therapy

Survival analysis

Immune kinetics 

Biometrics Section