A Digital Twin Framework for Per-Fraction Prediction of Normal Tissue Toxicity in Radiotherapy of Non-Small Cell Lung Cancer

Background/Objectives: Stereotactic body radiation therapy (SBRT), often delivered in 3–5 fractions, is a highly effective treatment strategy for early-stage non-small cell lung cancer (NSCLC) in medically inoperable patients. Biology-guided radiotherapy (BGRT), a PET-guided SBRT, enables acquisition of patient-specific metabolic, anatomical, and dose information at each fraction. While prior models have focused on static, population-level prediction of radiation-induced toxicity, there remains a lack of approaches to characterize how normal tissue response evolves longitudinally within individual patients over the course of radiation treatment. We therefore hypothesize that AI-based models can quantify temporal toxicity patterns in such longitudinal data, allowing clinicians to balance tumor control against normal tissue complications to guide individualized treatment planning.