Context: Radiation-induced lymphopenia (RIL) is characterized by a decrease in the absolute lymphocyte count (ALC) following radiotherapy and is associated with a significant decline in patient outcomes [1]. In this context, several mathematical models have been developed to study RIL, many of which focus on the radiation dose received by circulating blood. In 2024, our team introduced LymphoDose [2], a novel framework that goes beyond blood dose by incorporating both lymphocyte-specific recirculation mechanisms and the dose to lymphoid organs. When applied to brain tumor irradiations, we highlighted that the dose received by circulating lymphocytes appears to be too low to fully explain the high prevalence of RIL, suggesting that other mechanisms likely play an important role. This observation has been recently supported by another independent modeling study [3]. Methods and Results: To explore this hypothesis further, 184 patients with high-grade gliomas treated with chemoradiotherapy have been retrospectively identified. Patients had an average of 7.2 ± 2.2 lymphocyte blood samples collected between baseline and one-year post-treatment. The LymphoDose framework was applied to each patient. The computed lymphocyte dose metrics, along with several relevant clinical and treatment characteristics, were used for statistical analysis. In univariate analysis, each variable was assessed for its association with ALC nadir. To address multicollinearity among the selected variables, a stepwise variable selection process using Variance Inflation Factors have been performed. Finally, a multivariate analysis revealed a moderate correlation between median lymphocyte dose and ALC nadir (p=0.06). Perspectives: These results support the idea that the direct cytotoxic effect of irradiation does not fully explain the occurrence of RIL. In our last paper [4], we critically analyze the key components, assumptions, and available data underlying RIL mathematical models published to date. We propose leveraging mechanistic learning as a promising approach integrating mechanistic modeling with data-driven techniques. In particular, by providing several concrete examples, we illustrate how mechanistic learning can be utilized to develop models that are both clinically translatable and mechanistically insightful for studying RIL. [1] de Kermenguy, F. et al. Radio-induced lymphopenia in the era of anti-cancer immunotherapy. in International Review of Cell and Molecular Biology. (2023). [2 de Kermenguy, F. et al. LymphoDose: a lymphocyte dose estimation framework - application to brain radiotherapy. Phys. Med. Biol. (2024). [3] Beekman, C. et al. Radiation-Induced Lymphopenia: In Silico Replications of Preclinical Studies Suggest Importance of Dose to Lymphoid Organs. Int. J. Radiat. Oncol. Biol. Phys. 0, (2025). [4] de Kermenguy, F. et al. Radiation-Induced Lymphopenia: From Mathematical Modeling Towards Mechanistic Learning. Int. J. Radiat. Oncol. Biol. Phys. 0, (2025).