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A Generalized Modeling Framework for Forecasting Radiation-Induced Lymphocyte Depletion: Application to Non-Small Cell Lung Cancer28 views
Author
Nguyen, Manh Cam, Physics - Graduate School of Arts and Sciences, University of Virginia0009-0000-7217-389X
Advisors
Wijesooriya, Krishni, MD-RONC Radiation Oncology, University of Virginia
Abstract
Radiation Therapy (RT) is a key treatment modality in treating many types of cancer, including lung cancer. One of the unwanted side effects of RT treatment is Radiation-Induced Lymphocyte Depletion (RILD), since the immune system, especially lymphocytes, are a radiosensitive organ. RILD varies substantially among patients and the extent to which different factors contribute to RILD remains incompletely understood. This dissertation presents a generalized modeling framework for forecasting RILD that includes two models and its application for Non-Small Cell Lung Cancer (NSCLC) patients treated with radiation therapy. Model I simulates the blood flow dynamic and calculates the dose distribution to the blood pool. Model II simulates the coupled blood flow with lymphatic flow dynamics using a multi-compartmental approach, and forecasts lymphocyte depletion due to radiation temporally. The models use real patients’ data to calculate dose distribution, estimate lymphocyte kill, and forecast lymphocyte depletion even before RT treatment initiation.
The models can forecast for both early-stage NSCLC treated with Stereotactic Body Radiation Therapy (SBRT) and locally advanced NSCLC treated with standard fractionation RT, two different RT regimens with substantial differences in fractionation scheme, overall treatment duration, tumor volumes, and radiation dose delivery, and can be adapted for other tumor sites. Beyond forecasting a continuous temporal lymphocyte depletion up to several months after RT treatment, Model II also accounts for contribution of radiation dose to primary and secondary lymphoid organs to RILD and indicates great benefits of sparing these organs during RT treatment planning for NSCLC.
This modeling framework has the potential to enable treatment plan selection to proactively reduce RILD and improve treatment outcomes while maintaining conventional RT treatment criteria. With the ability to provide a continuous lymphocyte count prediction, Model II also has the potential to help deciding whether immunotherapy is viable, and the optimal time for immunotherapy administration for a given patient.
Nguyen, Manh Cam. A Generalized Modeling Framework for Forecasting Radiation-Induced Lymphocyte Depletion: Application to Non-Small Cell Lung Cancer. University of Virginia, Physics - Graduate School of Arts and Sciences, PHD (Doctor of Philosophy), 2026-07-08, https://doi.org/10.18130/x7hg-8m69.