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The technique's heterogeneous dose distribution triggers complex biological effects\u2014including bystander signaling and immune activation\u2014that are not captured by conventional dose\u2010response models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aims to develop a proof\u2010of\u2010concept computational framework to simulate tumor and immune responses during combined Lattice radiotherapy and SBRT) as an initial step toward patient\u2010specific modeling.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A four\u2010compartment ordinary differential equation (ODE) model was established to simulate tumor and lymphocyte dynamics, integrating Gompertz tumor growth kinetics, direct radiation\u2010induced cell killing, and indirect biological effects mediated by intercellular signaling and immune activation. Bystander signaling was described by reaction\u2013diffusion equations modeling spatial propagation from high\u2010 to low\u2010dose regions. Immune responses were modeled with coupled lymphocyte\u2010tumor 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\u2010small cell lung cancer (NSCLC) patients treated with SFRT and SBRT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model demonstrated feasibility in reproducing tumor volume (normalized root\u2010mean\u2010square error [NRMSE]: 0.071\u20130.197) and ALC dynamics (NRMSE: 0.017\u20130.385). Simulations revealed substantial inter\u2010patient heterogeneity in the estimated contributions of direct radiation and immune\u2010mediated effects, with immune\u2010mediated killing exceeding direct radiation in 2 patients. Combined SFRT\u2010SBRT achieved superior tumor control over either modality alone in our simulations. Notably, the optimal SFRT\u2010SBRT interval appeared patient\u2010specific: extending the interval to 2 months improved outcomes in some patients, while others showed limited benefit, depending on the balance between treatment\u2010induced cell kill and tumor regrowth.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We developed a radiobiological model that simulates tumor and lymphocyte dynamics under combined SFRT\u2013SBRT regimens, providing a preliminary framework for exploring personalized SFRT\u2010SBRT scheduling. These findings warrant further prospective validation in larger patient cohorts before clinical translation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70643","type":"journal-article","created":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:08:19Z","timestamp":1787029699000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploratory personalized radiobiological modeling of bystander and immune effects to inform SFRT\u2010SBRT scheduling"],"prefix":"10.1002","volume":"53","author":[{"given":"Jiaxin","family":"Li","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology University of Texas Southwestern Medical Center  Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Kansas Medical Center  Kansas City Kansas 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