{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T18:06:59Z","timestamp":1790791619781,"version":"4.1.0"},"reference-count":25,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2023,8,26]],"date-time":"2023-08-26T00:00:00Z","timestamp":1693008000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>The purpose of this study was to create and evaluate deep learning\u2010based models to detect and classify errors of multi\u2010leaf collimator (MLC) modeling parameters in volumetric modulated radiation therapy (VMAT), namely the transmission factor (TF) and the dosimetric leaf gap (DLG).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A total of 33 clinical VMAT plans for prostate and head\u2010and\u2010neck cancer were used, assuming a cylindrical and homogeneous phantom, and error plans were created by altering the original value of the TF and the DLG by\u00a0\u00b1\u00a010, 20, and 30% in the treatment planning system (TPS). The Gaussian filters of and 1.0 were applied to the planar dose maps of the error\u2010free plan to mimic the measurement dose map, and thus dose difference maps between the error\u2010free and error plans were obtained. We evaluated 3 deep learning\u2010based models, created to perform the following detections\/classifications: (1) error\u2010free versus TF error, (2) error\u2010free versus DLG error, and (3) TF versus DLG error. Models to classify the sign of the errors were also created and evaluated. A gamma analysis was performed for comparison.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The detection and classification of TF and DLG error were feasible for ; however, a considerable reduction of accuracy was observed for depending on the magnitude of error and treatment site. The sign of errors was detectable by the specifically trained models for and 1.0. The gamma analysis could not detect errors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We demonstrated that the deep learning\u2010based models could feasibly detect and classify TF and DLG errors in VMAT dose distributions, depending on the magnitude of the error, treatment site, and the degree of mimicked measurement doses.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.14136","type":"journal-article","created":{"date-parts":[[2023,8,27]],"date-time":"2023-08-27T01:54:50Z","timestamp":1693101290000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Deep learning\u2010based detection and classification of multi\u2010leaf collimator modeling errors in volumetric modulated radiation therapy"],"prefix":"10.1002","volume":"24","author":[{"given":"Sae","family":"Nakamura","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Niigata Neurosurgical Hospital, Nishi\u2010ku Niigata City Niigata Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Madoka","family":"Sakai","sequence":"additional","affiliation":[{"name":"Department of Radiology Nagaoka Chuo General Hospital, Nagaoka Nagaoka Niigata Japan"},{"name":"Department of Radiology and Radiation Oncology Niigata University Graduate School of Medical and Dental Sciences, Chuo\u2010ku Niigata City Niigata Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natsuki","family":"Ishizaka","sequence":"additional","affiliation":[{"name":"Department of Radiology Niigata Prefectural Shibata Hospital Shibata City Niigata Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazuki","family":"Mayumi","sequence":"additional","affiliation":[{"name":"Department of Radiological Technology Niigata University Graduate School of Health Sciences, Chuo\u2010ku Niigata City Niigata 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