Radiation-Induced Lymphopenia: From Mathematical Modeling Toward Mechanistic Learning.

Abstract

Radiation-induced lymphopenia (RIL) is a critical adverse factor that worsens outcomes in patients undergoing radiation therapy. Numerous mechanistic models have been proposed to better understand RIL mechanisms or screen patients at risk. This scoping review presents a comprehensive overview of these models, ranging from basic dosimetry approaches to more advanced models that incorporate dose-response relationships, lymphocyte repopulation and homeostasis, and interactions with tumors. We then critically analyzed the key components, assumptions, and available data underlying these models. In particular, we highlighted some important but overlooked indirect effects of irradiation that could influence the incidence of RIL, such as the role of cytokines, radiation-induced myeloid-derived suppressor cells, or radiation-impaired lymphocyte recirculation. Finally, we proposed leveraging mechanistic learning as a novel approach to develop both clinically translatable and mechanistically insightful models.