{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T21:40:35Z","timestamp":1789681235866,"version":"4.0.1"},"reference-count":36,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T00:00:00Z","timestamp":1684800000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007210","name":"Varian Medical Systems","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007210","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Radiotherapy (RT) is involved in about 50% of all cancer patients, making it a very important treatment modality. The most common type of RT is external beam RT, which consists of delivering the radiation to the tumor from outside the body. One novel treatment delivery method is volumetric modulated arc therapy (VMAT), where the gantry continuously rotates around the patient during the radiation delivery.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Accurate tumor position monitoring during stereotactic body radiotherapy (SBRT) for lung tumors can help to ensure that the tumor is only irradiated when it is inside the planning target volume. This can maximize tumor control and reduce uncertainty margins, lowering organ\u2010at\u2010risk dose. Conventional tracking methods are prone to errors, or have a low tracking rate, especially for small tumors that are in close vicinity to bony\u00a0structures.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We investigated patient\u2010specific deep Siamese networks for real\u2010time tumor tracking, during VMAT. Due to lack of ground truth tumor locations in the kilovoltage (kV) images, each patient\u2010specific model was trained on synthetic data (DRRs), generated from the 4D planning CT scans, and evaluated on clinical data (x\u2010rays). Since there are no annotated datasets with kV images, we evaluated the model on a 3D printed anthropomorphic phantom but also on six patients by computing the correlation coefficient with the breathing\u2010related vertical displacement of the surface\u2010mounted marker (RPM). For each patient\/phantom, we used 80% of DRRs for training and 20% for\u00a0validation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The proposed Siamese model outperformed the conventional benchmark template matching\u2010based method (RTR): (1) when evaluating both methods on the 3D phantom, the Siamese model obtained a 0.57\u20130.79\u2010mm mean absolute distance to the ground truth tumor locations, compared to 1.04\u20131.56 mm obtained by RTR; (2) on patient data, the Siamese\u2010determined longitudinal tumor position had a correlation coefficient of 0.71\u20130.98 with the RPM, compared to 0.07\u20130.85 for RTR; (3) the Siamese model had a 100% tracking rate, compared to 62%\u201382% for\u00a0RTR.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Based on these results, we argue that Siamese\u2010based real\u2010time 2D markerless tumor tracking during radiation delivery is possible. Further investigation and development of 3D tracking is\u00a0warranted.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16470","type":"journal-article","created":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T15:01:37Z","timestamp":1684854097000},"page":"6881-6893","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Deep learning\u2010based markerless lung tumor tracking in stereotactic radiotherapy using Siamese networks"],"prefix":"10.1002","volume":"50","author":[{"given":"Dragos","family":"Grama","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Amsterdam UMC Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Max","family":"Dahele","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Amsterdam UMC Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ward","family":"van Rooij","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Amsterdam UMC Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ben","family":"Slotman","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Amsterdam UMC Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deepak K.","family":"Gupta","sequence":"additional","affiliation":[{"name":"Transmute AI Research Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wilko F. 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