{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T17:39:29Z","timestamp":1784050769635,"version":"3.55.0"},"reference-count":39,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangdong Province Key Research and Development Program","award":["2021B0101420006"],"award-info":[{"award-number":["2021B0101420006"]}]},{"name":"Guangdong Province Key Research and Development Program","award":["2020B1515120046"],"award-info":[{"award-number":["2020B1515120046"]}]},{"name":"Guangdong Province Key Research and Development Program","award":["GDMUB2023009"],"award-info":[{"award-number":["GDMUB2023009"]}]},{"name":"Guangdong Province Key Research and Development Program","award":["2023YFC2411502"],"award-info":[{"award-number":["2023YFC2411502"]}]},{"name":"Guangdong Province Key Research and Development Program","award":["2024A1515010695"],"award-info":[{"award-number":["2024A1515010695"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2021B0101420006"],"award-info":[{"award-number":["2021B0101420006"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2020B1515120046"],"award-info":[{"award-number":["2020B1515120046"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["GDMUB2023009"],"award-info":[{"award-number":["GDMUB2023009"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2023YFC2411502"],"award-info":[{"award-number":["2023YFC2411502"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2024A1515010695"],"award-info":[{"award-number":["2024A1515010695"]}]},{"name":"Funds for PhD Researchers of Guangdong Medical University","award":["2021B0101420006"],"award-info":[{"award-number":["2021B0101420006"]}]},{"name":"Funds for PhD Researchers of Guangdong Medical University","award":["2020B1515120046"],"award-info":[{"award-number":["2020B1515120046"]}]},{"name":"Funds for PhD Researchers of Guangdong Medical University","award":["GDMUB2023009"],"award-info":[{"award-number":["GDMUB2023009"]}]},{"name":"Funds for PhD Researchers of Guangdong Medical University","award":["2023YFC2411502"],"award-info":[{"award-number":["2023YFC2411502"]}]},{"name":"Funds for PhD Researchers of Guangdong Medical University","award":["2024A1515010695"],"award-info":[{"award-number":["2024A1515010695"]}]},{"name":"National Key Research and Develop Program of China","award":["2021B0101420006"],"award-info":[{"award-number":["2021B0101420006"]}]},{"name":"National Key Research and Develop Program of China","award":["2020B1515120046"],"award-info":[{"award-number":["2020B1515120046"]}]},{"name":"National Key Research and Develop Program of China","award":["GDMUB2023009"],"award-info":[{"award-number":["GDMUB2023009"]}]},{"name":"National Key Research and Develop Program of China","award":["2023YFC2411502"],"award-info":[{"award-number":["2023YFC2411502"]}]},{"name":"National Key Research and Develop Program of China","award":["2024A1515010695"],"award-info":[{"award-number":["2024A1515010695"]}]},{"name":"Basic and Applied Basic Research Foundation of Guangdong","award":["2021B0101420006"],"award-info":[{"award-number":["2021B0101420006"]}]},{"name":"Basic and Applied Basic Research Foundation of Guangdong","award":["2020B1515120046"],"award-info":[{"award-number":["2020B1515120046"]}]},{"name":"Basic and Applied Basic Research Foundation of Guangdong","award":["GDMUB2023009"],"award-info":[{"award-number":["GDMUB2023009"]}]},{"name":"Basic and Applied Basic Research Foundation of Guangdong","award":["2023YFC2411502"],"award-info":[{"award-number":["2023YFC2411502"]}]},{"name":"Basic and Applied Basic Research Foundation of Guangdong","award":["2024A1515010695"],"award-info":[{"award-number":["2024A1515010695"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Respiratory-induced tumor motion presents a critical challenge in lung cancer radiotherapy, potentially impacting treatment precision and efficacy. This study introduces an innovative, deep learning-based approach for real-time, markerless lung tumor tracking utilizing orthogonal X-ray projection images. It incorporates three key components: (1) a sophisticated data augmentation technique combining a hybrid deformable model with 3D thin-plate spline transformation, (2) a state-of-the-art Transformer-based segmentation network for precise tumor boundary delineation, and (3) a CNN regression network for accurate 3D tumor position estimation. We rigorously evaluated this approach using both patient data from The Cancer Imaging Archive and dynamic thorax phantom data, assessing performance across various noise levels and comparing it with current leading algorithms. For TCIA patient data, the average DSC and HD95 values were 0.9789 and 1.8423 mm, respectively, with an average centroid localization deviation of 0.5441 mm. On CIRS phantoms, DSCs were 0.9671 (large tumor) and 0.9438 (small tumor) with corresponding HD95 values of 1.8178 mm and 1.9679 mm. The 3D centroid localization accuracy was consistently below 0.33 mm. The processing time averaged 90 ms\/frame. Even under high noise conditions (S2 = 25), errors for all data remained within 1 mm with tracking success rates mostly at 100%. In conclusion, the proposed markerless tracking method demonstrates superior accuracy, noise robustness, and real-time performance for lung tumor localization during radiotherapy. Its potential to enhance treatment precision, especially for small tumors, represents a significant step toward improving radiotherapy efficacy and personalizing cancer treatment.<\/jats:p>","DOI":"10.3390\/bioengineering11111051","type":"journal-article","created":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T04:10:14Z","timestamp":1729570214000},"page":"1051","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Robust Real-Time Cancer Tracking via Dual-Panel X-Ray Images for Precision Radiotherapy"],"prefix":"10.3390","volume":"11","author":[{"given":"Jing","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Medical Technology, Guangdong Medical University, Dongguan 523808, China"},{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Xueyuan, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Dai","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Xueyuan, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Guangdong Medical University, Xincheng, Dongguan 523808, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1149-1860","authenticated-orcid":false,"given":"Chulong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Xueyuan, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiankai","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Xueyuan, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zuledesi","family":"Silayi","sequence":"additional","affiliation":[{"name":"Friendship Hospital of Ili Kazakh Autonomous Prefecture, Yining 835000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haodi","family":"Wu","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Xueyuan, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1412-2354","authenticated-orcid":false,"given":"Yaoqing","family":"Xie","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Xueyuan, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1207-5726","authenticated-orcid":false,"given":"Xiaokun","family":"Liang","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Xueyuan, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huailing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Guangdong Medical University, Xincheng, Dongguan 523808, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21708","article-title":"Cancer statistics, 2022","volume":"72","author":"Siegel","year":"2022","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1038\/nrclinonc.2012.194","article-title":"Image-guided radiotherapy: From current concept to future perspectives","volume":"9","author":"Jaffray","year":"2012","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"102998","DOI":"10.1016\/j.media.2023.102998","article-title":"Volumetric tumor tracking from a single cone-beam X-ray projection image enabled by deep learning","volume":"91","author":"Dai","year":"2024","journal-title":"Med. Image Anal."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1093\/jrr\/rrs122","article-title":"Guidelines for respiratory motion management in radiation therapy","volume":"54","author":"Matsuo","year":"2013","journal-title":"J. Radiat. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.radonc.2007.07.021","article-title":"Defining internal target volume (ITV) for hepatocellular carcinoma using four-dimensional CT","volume":"84","author":"Xi","year":"2007","journal-title":"Radiother. Oncol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Vedam, S., Keall, P., Kini, V., Mostafavi, H., Shukla, H., and Mohan, R. (2002). Acquiring a four-dimensional computed tomography dataset using an external respiratory signal. Phys. Med. Biol., 48.","DOI":"10.1088\/0031-9155\/48\/1\/304"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1016\/S0360-3016(00)00681-7","article-title":"Active breathing control (ABC) for Hodgkin\u2019s disease: Reduction in normal tissue irradiation with deep inspiration and implications for treatment","volume":"48","author":"Stromberg","year":"2000","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1732","DOI":"10.1118\/1.1287110","article-title":"Compatibility of Varian 2100C gated operations with enhanced dynamic wedge and IMRT dose delivery","volume":"27","author":"Kubo","year":"2000","journal-title":"Med. Phys."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1016\/0360-3016(89)90078-3","article-title":"Irradiation synchronized with respiration gate","volume":"17","author":"Ohara","year":"1989","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Keall, P., Kini, V., Vedam, S., and Mohan, R. (2001). Motion adaptive X-ray therapy: A feasibility study. Phys. Med. Biol., 46.","DOI":"10.1088\/0031-9155\/46\/1\/301"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Rottmann, J., Keall, P., and Berbeco, R. (2013). Markerless EPID image guided dynamic multi-leaf collimator tracking for lung tumors. Phys. Med. Biol., 58.","DOI":"10.1088\/0031-9155\/58\/12\/4195"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shieh, C.C., Caillet, V., Dunbar, M., Keall, P.J., Booth, J.T., Hardcastle, N., Haddad, C., Eade, T., and Feain, I. (2017). A Bayesian approach for three-dimensional markerless tumor tracking using kV imaging during lung radiotherapy. Phys. Med. Biol., 62.","DOI":"10.1088\/1361-6560\/aa6393"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1016\/j.gie.2012.07.006","article-title":"Comparative analysis of traditional and coiled fiducials implanted during EUS for pancreatic cancer patients receiving stereotactic body radiation therapy","volume":"76","author":"Khashab","year":"2012","journal-title":"Gastrointest. Endosc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.ijrobp.2013.05.001","article-title":"Interfractional position variation of pancreatic tumors quantified using intratumoral fiducial markers and daily cone beam computed tomography","volume":"87","author":"Wognum","year":"2013","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13014-019-1373-2","article-title":"Safety and efficacy of fiducial marker implantation for robotic stereotactic body radiation therapy with fiducial tracking","volume":"14","author":"Scher","year":"2019","journal-title":"Radiat. Oncol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1007\/s00270-010-9949-0","article-title":"Complications associated with the percutaneous insertion of fiducial markers in the thorax","volume":"33","author":"Bhagat","year":"2010","journal-title":"Cardiovasc. Interv. Radiol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cui, Y., Dy, J.G., Sharp, G.C., Alexander, B., and Jiang, S.B. (2007). Multiple template-based fluoroscopic tracking of lung tumor mass without implanted fiducial markers. Phys. Med. Biol., 52.","DOI":"10.1088\/0031-9155\/52\/20\/010"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4738","DOI":"10.1118\/1.2799886","article-title":"Concurrent multimodality image segmentation by active contours for radiotherapy treatment planning a","volume":"34","author":"Yang","year":"2007","journal-title":"Med Phys."},{"key":"ref_19","unstructured":"Jain, M., Narayan, S., Balaji, P., Bharath, K.P., Bhowmick, A., Karthik, R., and Muthu, R.K. (2020). Speech emotion recognition using support vector machine. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1561\/0600000035","article-title":"Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning","volume":"7","author":"Criminisi","year":"2012","journal-title":"Found. Trends Comput. Graph. Vis."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"20190420","DOI":"10.1259\/bjr.20190420","article-title":"Real-time markerless tumour tracking with patient-specific deep learning using a personalised data generation strategy: Proof of concept by phantom study","volume":"93","author":"Takahashi","year":"2020","journal-title":"Br. J. Radiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.ijrobp.2019.05.071","article-title":"Markerless pancreatic tumor target localization enabled by deep learning","volume":"105","author":"Zhao","year":"2019","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.radonc.2019.06.027","article-title":"Incorporating imaging information from deep neural network layers into image guided radiation therapy (IGRT)","volume":"140","author":"Zhao","year":"2019","journal-title":"Radiother. Oncol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.ejmp.2020.04.029","article-title":"Deep Learning model for markerless tracking in spinal SBRT","volume":"74","author":"Roggen","year":"2020","journal-title":"Phys. Medica"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.1002\/mp.15456","article-title":"Development of a deep learning-based patient-specific target contour prediction model for markerless tumor positioning","volume":"49","author":"Zhou","year":"2022","journal-title":"Med. Phys."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"133111","DOI":"10.1109\/ACCESS.2019.2941154","article-title":"An augmentation strategy for medical image processing based on statistical shape model and 3D thin plate spline for deep learning","volume":"7","author":"Tang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"102156","DOI":"10.1016\/j.media.2021.102156","article-title":"Incorporating the hybrid deformable model for improving the performance of abdominal CT segmentation via multi-scale feature fusion network","volume":"73","author":"Liang","year":"2021","journal-title":"Med Image Anal."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"102984","DOI":"10.1016\/j.media.2023.102984","article-title":"A statistical deformation model-based data augmentation method for volumetric medical image segmentation","volume":"91","author":"He","year":"2024","journal-title":"Med. Image Anal."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Dong, G., Dai, J., Li, N., Zhang, C., He, W., Liu, L., Chan, Y., Li, Y., Xie, Y., and Liang, X. (2023). 2D\/3D non-rigid image registration via two orthogonal X-ray projection images for lung tumor tracking. Bioengineering, 10.","DOI":"10.3390\/bioengineering10020144"},{"key":"ref_31","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., and Zhou, Y. (2021). Transunet: Transformers make strong encoders for medical image segmentation. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Koonce, B. (2021). ResNet 50. Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization, Springer.","DOI":"10.1007\/978-1-4842-6168-2"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1002\/mp.12059","article-title":"Data from 4D lung imaging of nsclc patients","volume":"44","author":"Hugo","year":"2017","journal-title":"Med. Phys."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.ijrobp.2012.12.023","article-title":"Evaluation of 4-dimensional computed tomography to 4-dimensional cone-beam computed tomography deformable image registration for lung cancer adaptive radiation therapy","volume":"86","author":"Balik","year":"2013","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","article-title":"The Cancer Imaging Archive (TCIA): Maintaining and operating a public information repository","volume":"26","author":"Clark","year":"2013","journal-title":"J. Digit. Imaging"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1566","DOI":"10.1016\/j.ijrobp.2011.10.051","article-title":"Interfractional positional variability of fiducial markers and primary tumors in locally advanced non-small-cell lung cancer during audiovisual biofeedback radiotherapy","volume":"83","author":"Roman","year":"2012","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xue, Y., Tang, H., Qiao, Z., Gong, G., Yin, Y., Qian, Z., Huang, C., Fan, W., and Huang, X. (2020, January 7\u201312). Shape-aware organ segmentation by predicting signed distance maps. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6946"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3874","DOI":"10.1118\/1.2349696","article-title":"The management of respiratory motion in radiation oncology report of AAPM Task Group 76 a","volume":"33","author":"Keall","year":"2006","journal-title":"Med. Phys."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"650463","DOI":"10.1155\/2013\/650463","article-title":"Tracking Lung Tumors in Orthogonal X-rays","volume":"2013","author":"Li","year":"2013","journal-title":"Comput. Math. Methods Med."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/11\/1051\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:17:48Z","timestamp":1760113068000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/11\/1051"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,22]]},"references-count":39,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["bioengineering11111051"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering11111051","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,22]]}}}