Modeling of Respiratory-Induced Lung Tumor Motion

This dataset supports a patient-specific computational study of lung tumor motion induced by respiration. Using CT-based anatomical reconstructions and finite element modeling, tumor trajectories were simulated under varying lung tissue stiffness conditions. The dataset includes segmentation outputs, mesh models, material property parameters, and tumor displacement results under elastic moduli derived from multiple biomechanical characterization techniques. These data provide insight into how localized tissue stiffness, especially values obtained from cavitation rheology, affects tumor mobility during breathing cycles. This work aims to improve the biomechanical fidelity of tumor motion prediction in support of personalized radiotherapy planning. Research was conducted under the Marie Skłodowska-Curie Postdoctoral Fellowship (Grant No. 101067240).