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Recently, Transformer architectures have demonstrated strong global reasoning capabilities across multiple frames by leveraging both self\u2010attention and cross\u2010attention mechanisms. Transformers have therefore been applied to object tracking with great success. By combining Image Guided Radiation Therapy (IGRT) technologies and deep learning\u2010based object tracking architecture, it is possible to deliver radiation doses to the target area with high accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study develops a transformer\u2010based patient\u2010agnostic tracking model (TransTracking) for surface and markerless internal target tracking in thoracic tumor radiotherapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We trained the TransTracking model using the training splits of publicly available object tracking datasets. Subsequently, for internal target tracking, the model is fine\u2010tuned using 10,000 digitally reconstructed radiograph (DRR) images generated from the actual 4DCT datasets of 25 patients. The DRR images are annotated with bounding boxes of the moving tumor. Our method learns to directly predict the target classification and bounding\u2010box regression weights through end\u2010to\u2010end training, enabling accurate target localization in each frame for both surface and internal target tracking sequences. The tracking performance of the trained model was evaluated in 20 volunteers for surface tracking and using DRR images generated from 20 4DCT datasets for internal tumor tracking. To address the limited availability of medical images for training, we conducted the data augmentation procedure to 4DCT datasets and expanded the data scale 40\u2010fold in total.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>For the surface marker tracking, the mean absolute deviation (MAD)\u00a0\u00b1\u00a0standard deviation (SD) between the model\u2010predicted and the actual positions for 20 volunteers was 0.07\u00a0\u00b1\u00a00.06\u00a0mm, 0.12\u00a0\u00b1\u00a00.13\u00a0mm, and 0.29\u00a0\u00b1\u00a00.20\u00a0mm in left\u2010right, superior\u2010inferior, and anterior\u2010posterior directions, respectively. In each directional axis, over 85% of frames exhibited a model\u2010predicted target position within 0.5\u00a0mm of the corresponding ground\u2010truth position. For the internal tumor tracking, the MAD\u00a0\u00b1\u00a0SD between the predicted and annotated center positions of the tumor bounding boxes is 1.49\u00a0\u00b1\u00a01.39\u00a0mm, with a mean Intersection over Union (IoU) value of 0.83 and an area under curve (AUC) score of 82% for 20 patients. Additionally, our transformer\u2010based model can extract the target position in 81\u00a0ms after an image is acquired.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study proposed a novel Transformer\u2010based deep learning method aimed at training a patient\u2010agnostic tumor motion tracking model in radiotherapy. The model enables real\u2010time, high\u2010precision tracking of surface markers using vision cameras, offering a cost\u2010effective 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70449","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T08:11:46Z","timestamp":1777018306000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A transformer\u2010based patient\u2010agnostic tumor motion tracking model (TransTracking): A feasibility study in radiotherapy for lung cancer"],"prefix":"10.1002","volume":"53","author":[{"given":"Maidina","family":"Abuduxiku","sequence":"first","affiliation":[{"name":"Institute of Modern Physics, Chinese Academy of Sciences  Lanzhou China"},{"name":"Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences  Lanzhou China"},{"name":"Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu 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