{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T15:25:58Z","timestamp":1777994758166,"version":"3.51.4"},"reference-count":23,"publisher":"IOP Publishing","issue":"23","license":[{"start":{"date-parts":[[2019,12,5]],"date-time":"2019-12-05T00:00:00Z","timestamp":1575504000000},"content-version":"vor","delay-in-days":4,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2019,12,5]],"date-time":"2019-12-05T00:00:00Z","timestamp":1575504000000},"content-version":"tdm","delay-in-days":4,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["11375278"],"award-info":[{"award-number":["11375278"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2019,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In this study, we developed a gated recurrent unit (GRU)-based recurrent neural network (RNN) for dose-volume histogram (DVH) prediction in volumetric modulated arc therapy (VMAT) planning for nasopharyngeal carcinomas (NPCs) based on uniform-intensity radiation with equal angle intervals and investigated the feasibility and usefulness of this method for treatment optimization.<\/jats:p>\n                  <jats:p>\n                    One hundred twenty-four NPC patients were selected from a database containing clinical VMAT plans from 2015 to 2018; of these, the data from 100 patients were used to train the GRU-RNN, and the data of the other 24 patients were used for testing. For the prescribed doses to D\n                    <jats:sub>95<\/jats:sub>\n                    (the absorbed dose for 95% of the planning target volume) of all the plans in 30 or 31 fractions, 70 Gy were delivered to PTV70 (the gross tumour volume with circumferential margin), 60 Gy were delivered to PTV60, 54 Gy were delivered to PTV54 and 66 Gy were delivered to PTV66 (lymph node gross tumour volume with circumferential margin). For each NPC patient, an equal-field-weight conformal radiotherapy plan was generated by a treatment planning system (TPS) to offer uniform-intensity radiation. By adjusting the field weights, the dose distribution induced by individual conformal beams was acquired, and the corresponding DVH was calculated. Direction-dependent DVHs were employed to predict the DVH for VMAT with the GRU-RNN, and the regenerated VMAT experimental plans (EPs), guided by the predicted DVHs, were evaluated by comparing them with the clinical plans (CPs).\n                  <\/jats:p>\n                  <jats:p>\n                    For the 24 test patients, the regenerated EPs guided by the GRU-RNN predictive model achieved good consistency relative to the CPs. The EPs resulted in better dose sparing for many organs at risk (OARs) while still meeting the acceptable criteria for the PTVs. Significant differences were found in the maximum\/mean doses to the optic nerves, temporal lobes, lenses, mandibles, temporomandibular joints (TMJs), larynx and inner ears, with\n                    <jats:italic>P<\/jats:italic>\n                    -values of 0.03, 0.01, 0.01, &lt;0.01, 0.02, 0.02 and\u2009\u2009&lt;0.01, respectively. On average, compared to the CPs, the maximum\/mean doses to these OARs were altered by\u2009\u2009\u22121.38 Gy, \u22120.92 Gy, 0.53 Gy, \u22121.19 Gy, \u22121.16 Gy, 2.39 Gy and\u2009\u2009\u22121.71 Gy, respectively.\n                  <\/jats:p>\n                  <jats:p>The results showed the accuracy and effectiveness of the proposed uniform-intensity radiation approach. The regenerated plans guided by the predictive method were not inferior to the manual plans, indicating their great potential for improved planning and quality control in clinical applications.<\/jats:p>","DOI":"10.1088\/1361-6560\/ab5433","type":"journal-article","created":{"date-parts":[[2019,11,4]],"date-time":"2019-11-04T20:04:56Z","timestamp":1572897896000},"page":"23NT03","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Dose-volume histogram prediction in volumetric modulated arc therapy for nasopharyngeal carcinomas based on uniform-intensity radiation with equal angle intervals"],"prefix":"10.1088","volume":"64","author":[{"given":"Yongdong","family":"Zhuang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lixin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7276-0787","authenticated-orcid":false,"given":"Xiaowei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2019,12,5]]},"reference":[{"key":"pmbab5433bib001","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1016\/j.ijrobp.2016.02.017","type":"journal-article","article-title":"Comparison of planning quality and efficiency between conventional and knowledge-based algorithms in nasopharyngeal cancer patients using intensity modulated radiation therapy","volume":"95","author":"Chang","year":"2016","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbab5433bib002","doi-asserted-by":"publisher","first-page":"2515","DOI":"10.1118\/1.3574874","type":"journal-article","article-title":"Knowledge-based IMRT treatment planning for prostate cancer","volume":"38","author":"Chanyavanich","year":"2011","journal-title":"Med. Phys."},{"key":"pmbab5433bib003","doi-asserted-by":"crossref","DOI":"10.3115\/v1\/D14-1179","type":"preprint","article-title":"Learning phrase representations using RNN encoder-decoder for statistical machine translation","author":"Cho","year":"2014"},{"key":"pmbab5433bib004","doi-asserted-by":"publisher","first-page":"3191","DOI":"10.1088\/0031-9155\/48\/19\/007","type":"journal-article","article-title":"Algorithm and performance of a clinical IMRT beam-angle optimization system","volume":"48","author":"Djajaputra","year":"2003","journal-title":"Phys. Med. Biol."},{"key":"pmbab5433bib005","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1186\/s13014-017-0808-x","type":"journal-article","article-title":"RapidPlan head and neck model: the objectives and possible clinical benefit","volume":"12","author":"Fogliata","year":"2017","journal-title":"Radiat. Oncol."},{"key":"pmbab5433bib006","article-title":"Adam: a method for stochastic optimization","author":"Kingma","year":"2014","type":"preprint"},{"key":"pmbab5433bib007","doi-asserted-by":"publisher","first-page":"3724","DOI":"10.1118\/1.4957369","type":"journal-article","article-title":"Deep-learning based prediction of achievable dose for personalizing inverse treatment planning","volume":"43","author":"Korani","year":"2016","journal-title":"Med. Phys."},{"key":"pmbab5433bib008","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1002\/mp.13334","type":"journal-article","article-title":"Dosimetric features-driven machine learning model for DVH prediction in VMAT treatment planning","volume":"46","author":"Ma","year":"2019","journal-title":"Med. Phys."},{"key":"pmbab5433bib009","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1016\/j.ijrobp.2010.11.030","type":"journal-article","article-title":"Experience-based quality control of clinical intensity-modulated radiotherapy planning","volume":"81","author":"Moore","year":"2011","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbab5433bib010","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.radonc.2011.05.025","type":"journal-article","article-title":"Increased organ sparing using shape-based treatment plan optimization for intensity modulated radiation therapy of pancreatic adenocarcinoma","volume":"102","author":"Petit","year":"2012","journal-title":"Radiother. Oncol. J. Eur. Soc. Ther. Radiol. Oncol."},{"key":"pmbab5433bib011","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/S0360-3016(01)02552-4","type":"journal-article","article-title":"Optimal dose\/volume constraints of sensitive structures in inverse planning for nasopharyngeal carcinoma","volume":"51","author":"Poon","year":"2001","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbab5433bib012","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1016\/S0360-3016(01)01502-4","type":"journal-article","article-title":"Role of beam orientation optimization in intensity-modulated radiation therapy","volume":"50","author":"Pugachev","year":"2001","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbab5433bib013","first-page":"1929","type":"journal-article","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"pmbab5433bib014","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.1016\/j.ijrobp.2009.01.018","type":"journal-article","article-title":"Treatment of nasopharyngeal carcinoma using intensity-modulated radiotherapy\u2014the National Cancer Centre Singapore experience","volume":"75","author":"Tham","year":"2009","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbab5433bib015","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1016\/j.ijrobp.2014.11.014","type":"journal-article","article-title":"Evaluation of a knowledge-based planning solution for head and neck cancer","volume":"91","author":"Tol","year":"2015a","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbab5433bib016","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1186\/s13014-015-0542-1","type":"journal-article","article-title":"Can knowledge-based DVH predictions be used for automated, individualized quality assurance of radiotherapy treatment plans?","volume":"10","author":"Tol","year":"2015b","journal-title":"Radiat. Oncol."},{"key":"pmbab5433bib017","doi-asserted-by":"publisher","first-page":"5497","DOI":"10.1118\/1.3253464","type":"journal-article","article-title":"Patient geometry-driven information retrieval for IMRT treatment plan quality control","volume":"36","author":"Wu","year":"2009","journal-title":"Med. Phys."},{"key":"pmbab5433bib018","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1016\/j.ijrobp.2010.05.026","type":"journal-article","article-title":"Data-driven approach to generating achievable dose\u2013volume histogram objectives in intensity-modulated radiotherapy planning","volume":"79","author":"Wu","year":"2011","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbab5433bib019","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13014-017-0947-0","type":"journal-article","article-title":"Knowledge-based IMRT planning for individual liver cancer patients using a novel specific model","volume":"13","author":"Yu","year":"2018","journal-title":"Radiat. Oncol."},{"key":"pmbab5433bib020","doi-asserted-by":"publisher","first-page":"6868","DOI":"10.1118\/1.4757927","type":"journal-article","article-title":"Quantitative analysis of the factors which affect the interpatient organ\u2010at\u2010risk dose sparing variation in IMRT plans","volume":"39","author":"Yuan","year":"2012","journal-title":"Med. Phys."},{"key":"pmbab5433bib021","doi-asserted-by":"publisher","first-page":"1869","DOI":"10.1088\/0031-9155\/58\/6\/1869","type":"journal-article","article-title":"A moment-based approach for DVH-guided radiotherapy treatment plan optimization","volume":"58","author":"Zarepisheh","year":"2013","journal-title":"Phys. Med. Biol."},{"key":"pmbab5433bib022","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1118\/1.3539749","type":"journal-article","article-title":"A planning quality evaluation tool for prostate adaptive IMRT based on machine learning","volume":"38","author":"Zhu","year":"2011a","journal-title":"Med. Phys."},{"key":"pmbab5433bib023","doi-asserted-by":"publisher","first-page":"2821","DOI":"10.1118\/1.3578613","type":"journal-article","article-title":"Response to Comment on \u2018A planning quality evaluation tool for prostate adaptive IMRT based on machine learning (2011 Med. Phys. 38 719)\u2019","volume":"38","author":"Zhu","year":"2011b","journal-title":"Med. Phys."}],"container-title":["Physics in Medicine &amp; Biology"],"original-title":[],"link":[{"URL":"http:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab5433","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"http:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab5433\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab5433\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab5433","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab5433","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab5433\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab5433\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab5433\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab5433\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab5433\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab5433","content-type":"text\/html","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab5433\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T12:36:39Z","timestamp":1770467799000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab5433"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,1]]},"references-count":23,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2019,12,5]]},"published-print":{"date-parts":[[2019,12,1]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6560\/ab5433","relation":{},"ISSN":["0031-9155","1361-6560"],"issn-type":[{"value":"0031-9155","type":"print"},{"value":"1361-6560","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,1]]},"assertion":[{"value":"Dose-volume histogram prediction in volumetric modulated arc therapy for nasopharyngeal carcinomas based on uniform-intensity radiation with equal angle intervals","name":"article_title","label":"Article Title"},{"value":"Physics in Medicine & Biology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2019 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2019-07-19","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2019-11-04","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2019-12-05","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}