{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T18:53:15Z","timestamp":1792176795576,"version":"4.4.0"},"reference-count":30,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"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":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Deep learning\u2010based virtual patient\u2010specific quality assurance (QA) is a novel technique that enables patient QA without measurement. However, this method could be improved by further evaluating the optimal data to be used as input. Therefore, a deep learning\u2010based model that uses multileaf collimator (MLC) information per control point and dose distribution in patient's CT as inputs was developed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Overall, 96 volumetric\u2010modulated arc therapy plans generated for prostate cancer treatment were used. We developed a model (Model 1) that can predict measurement\u2010based gamma passing rate (GPR) for a treatment plan using data stored as a map reflecting the MLC leaf position at each control point (MLPM) and data of the dose distribution in patient's CT as inputs. The evaluation of the model was based on the mean absolute error (MAE) and Pearson's correlation coefficient (<jats:italic>r<\/jats:italic>) between the measured and predicted GPR. For comparison, we also analyzed models trained with the dose distribution in patient's CT alone (Model 2) and with dose distributions recalculated on a virtual phantom CT (Model 3).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>At the 2%\/2\u00a0mm criterion, MAE[%] and <jats:italic>r<\/jats:italic> for Model 1, Model 2, and Model 3 were 2.32%\u00a0\u00b1\u00a00.43% and 0.54\u00a0\u00b1\u00a00.03, 2.70%\u00a0\u00b1\u00a00.26%, and 0.32\u00a0\u00b1\u00a00.08, and 2.96%\u00a0\u00b1\u00a00.23% and 0.24\u00a0\u00b1\u00a00.22, respectively; at the 3%\/3\u00a0mm criterion, these values were 1.25%\u00a0\u00b1\u00a00.05% and 0.36\u00a0\u00b1\u00a00.18, 1.57%\u00a0\u00b1\u00a00.35% and 0.19\u00a0\u00b1\u00a00.20, and 1.39%\u00a0\u00b1\u00a00.32% and 0.17\u00a0\u00b1\u00a00.22, respectively. This result showed that Model 1 exhibited the lowest MAE and highest r at both criteria of 2%\/2\u00a0mm and 3%3\u00a0mm.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>These findings showed that a model that combines the MLPM and dose distribution in patient's CT exhibited a better GPR prediction performance compared with the other two studied models.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.14055","type":"journal-article","created":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T15:13:00Z","timestamp":1685632380000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Improvement of deep learning prediction model in patient\u2010specific QA for VMAT with MLC leaf position map and patient's dose distribution"],"prefix":"10.1002","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5018-3800","authenticated-orcid":false,"given":"Ryota","family":"Tozuka","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Tohoku University Graduate School of Medicine  Sendai Miyagi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noriyuki","family":"Kadoya","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Tohoku University Graduate School of Medicine  Sendai Miyagi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seiji","family":"Tomori","sequence":"additional","affiliation":[{"name":"Department of Radiology National Hospital Organization Sendai Medical Center  Sendai Miyagi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuto","family":"Kimura","sequence":"additional","affiliation":[{"name":"Radiation Oncology Center Ofuna Chuo Hospital  Kamakura 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