A transformer‐based patient‐agnostic tumor motion tracking model (TransTracking): A feasibility study in radiotherapy for lung cancer

This study proposed a novel Transformer-based deep learning method aimed at training a patient-agnostic tumor motion tracking model in radiotherapy. The model enables real-time, high-precision tracking of surface markers using vision cameras, offering a cost-effective and compact solution. Additionally, we demonstrated that our method can accurately locate tumor target areas in DRR images with high precision, without the need for individualized training or the implantation of fiducial markers. This feasibility study demonstrates the strong potential of our strategy as a clinically viable solution for moving tumor IGRT.