{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T06:31:48Z","timestamp":1777185108098,"version":"3.51.4"},"reference-count":44,"publisher":"IOP Publishing","issue":"23","license":[{"start":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T00:00:00Z","timestamp":1606176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T00:00:00Z","timestamp":1606176000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100007210","name":"Varian Medical Systems","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100007210","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100007052","name":"Memorial Sloan-Kettering Cancer Center","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100007052","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":[[2020,12,7]]},"abstract":"Abstract<\/jats:title>\n \n During radiation therapy (RT) of head and neck (HN) cancer, the shape and volume of the parotid glands (PG) may change significantly, resulting in clinically relevant deviations of delivered dose from the planning dose. Early and accurate longitudinal prediction of PG anatomical changes during the RT can be valuable to inform decisions on plan adaptation. We developed a deep neural network for longitudinal predictions using the displacement fields (DFs) between the planning computed tomography (pCT) and weekly cone beam computed tomography (CBCT). Sixty-three HN patients treated with volumetric modulated arc were retrospectively studied. We calculated DFs between pCT and week 1\u20133 CBCT by B-spline and Demon deformable image registration (DIR). The resultant DFs were subsequently used as input to our novel network to predict the week 4 to 6 DFs for generating predicted weekly PG contours and weekly dose distributions. For evaluation, we measured dice similarity (DICE), and the uncertainty of accumulated dose. Moreover, we compared the detection accuracies of candidates for adaptive radiotherapy (ART) when the trigger criteria were mean dose difference more than 10%, 7.5%, and 5%, respectively. The DICE of ipsilateral\/contralateral PG at week 4 to 6 using the prediction model trained with B-spline were 0.81\n \n \n \n <\/jats:tex-math>\n \n \u00b1<\/mml:mo>\n <\/mml:math>\n <\/jats:inline-formula>\n 0.07\/0.81\n \n \n \n <\/jats:tex-math>\n \n \u00b1<\/mml:mo>\n <\/mml:math>\n <\/jats:inline-formula>\n 0.04 (week 4), 0.79\n \n \n \n <\/jats:tex-math>\n \n \u00b1<\/mml:mo>\n <\/mml:math>\n <\/jats:inline-formula>\n 0.06\/0.81\n \n \n \n <\/jats:tex-math>\n \n \u00b1<\/mml:mo>\n <\/mml:math>\n <\/jats:inline-formula>\n 0.05 (week 5) and 0.78\n \n \n \n <\/jats:tex-math>\n \n \u00b1<\/mml:mo>\n <\/mml:math>\n <\/jats:inline-formula>\n 0.06\/0.82\n \n \n \n <\/jats:tex-math>\n \n \u00b1<\/mml:mo>\n \u2009<\/mml:mtext>\n 0.02<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n (week 6). The DICE with the Demons model were 0.78\n \n \n \n <\/jats:tex-math>\n \n \u00b1<\/mml:mo>\n <\/mml:math>\n <\/jats:inline-formula>\n 0.08\/0.82\n \n \n \n <\/jats:tex-math>\n \n \u00b1<\/mml:mo>\n <\/mml:math>\n <\/jats:inline-formula>\n 0.03 (week 4), 0.77\n \n \n \n <\/jats:tex-math>\n \n \u00b1<\/mml:mo>\n <\/mml:math>\n <\/jats:inline-formula>\n 0.07\/0.82\n \n \n \n <\/jats:tex-math>\n \n \u00b1<\/mml:mo>\n <\/mml:math>\n <\/jats:inline-formula>\n 0.04 (week 5) and 0.75\n \n \n \n <\/jats:tex-math>\n \n \u00b1<\/mml:mo>\n <\/mml:math>\n <\/jats:inline-formula>\n 0.07\/0.82\n \n \n \n <\/jats:tex-math>\n \n \u00b1<\/mml:mo>\n <\/mml:math>\n <\/jats:inline-formula>\n 0.02 (week 6). The dose volume histogram (DVH) analysis with the predicted accumulated dose showed the feasibility of predicting dose uncertainty due to the PG anatomical changes. The AUC of ART candidate detection with our predictive model was over 0.90. In conclusion, the proposed network was able to predict future anatomical changes and dose uncertainty of PGs with clinically acceptable accuracy, and hence can be readily integrated into the ART workflow.\n <\/jats:p>","DOI":"10.1088\/1361-6560\/abbdb8","type":"journal-article","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T18:15:28Z","timestamp":1601662528000},"page":"235011","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Predictive dose accumulation for HN adaptive radiotherapy"],"prefix":"10.1088","volume":"65","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6786-1118","authenticated-orcid":false,"given":"Donghoon","family":"Lee","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2622-9367","authenticated-orcid":false,"given":"Pengpeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5030-9792","authenticated-orcid":false,"given":"Saad","family":"Nadeem","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7845-4959","authenticated-orcid":false,"given":"Sadegh","family":"Alam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9642-3515","authenticated-orcid":false,"given":"Jue","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amanda","family":"Caringi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Natasha","family":"Allgood","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michalis","family":"Aristophanous","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9373-836X","authenticated-orcid":false,"given":"James","family":"Mechalakos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7742-2971","authenticated-orcid":false,"given":"Yu-Chi","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2020,11,24]]},"reference":[{"key":"pmbabbdb8bib1","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1109\/TITS.2019.2900426","type":"journal-article","article-title":"A 3D CNN-LSTM based image-to-image foreground segmentation","volume":"21","author":"Akilan","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"pmbabbdb8bib2","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.phro.2020.03.002","type":"journal-article","article-title":"Quantification of accumulated dose and associated anatomical changes of esophagus using weekly magnetic resonance imaging acquired during radiotherapy of locally advanced lung cancer 2020","volume":"13","author":"Alam","year":"2020","journal-title":"J. Phys. Imaging Radiat. Oncol."},{"key":"pmbabbdb8bib3","doi-asserted-by":"publisher","first-page":"2366","DOI":"10.1109\/ICPR.2010.579","type":"conference-proceedings","article-title":"Image quality metric: PSNR vs SSIM","author":"Alian","year":"2010"},{"key":"pmbabbdb8bib4","article-title":"Dosimetric changes of the parotid as a result of head and neck adaptive radiotherapy","author":"Aristophanous","year":"2020","type":"other"},{"key":"pmbabbdb8bib5","doi-asserted-by":"publisher","first-page":"193","DOI":"10.7150\/ijms.3635","type":"journal-article","article-title":"Cancer and radiation therapy: current advances and future directions","volume":"9","author":"Baskar","year":"2012","journal-title":"Int. J. Med. Sci."},{"key":"pmbabbdb8bib6","doi-asserted-by":"publisher","first-page":"2054","DOI":"10.1002\/mp.12226","type":"journal-article","article-title":"Toward robust adaptive radiation therapy strategies","volume":"44","author":"B\u00f6ck","year":"2017","journal-title":"Med. Phys."},{"key":"pmbabbdb8bib7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.radonc.2008.04.010","type":"journal-article","article-title":"Comparison of 12 deformable registration strategies in adaptive radiation therapy for the treatment of head and neck tumors","volume":"89","author":"Castadot","year":"2008","journal-title":"Radiother. Oncol."},{"key":"pmbabbdb8bib8","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.semradonc.2019.02.002","type":"journal-article","article-title":"Deformable registration for dose accumulation","volume":"29","author":"Chetty","year":"2019","journal-title":"Seminal Radiat. Oncol."},{"key":"pmbabbdb8bib9","first-page":"103","type":"preprint","article-title":"On the properties of neural machine translation: encoder-decoder approaches","author":"Cho","year":"2014"},{"key":"pmbabbdb8bib10","first-page":"628","type":"preprint","article-title":"3D-R2N2: a unified approach for single and multi-view 3D object reconstruction","volume":"vol 9912","author":"Choy","year":"2016"},{"key":"pmbabbdb8bib11","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.1016\/0360-3016(94)00421-G","type":"journal-article","article-title":"Late effects of radiation therapy in the head and neck region","volume":"31","author":"Cooper","year":"1995","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbabbdb8bib12","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.1016\/j.ijrobp.2007.07.2345","type":"journal-article","article-title":"Parotid gland dose in intensity-modulated radiotherapy for head and neck cancer: is what you plan what you get?","volume":"69","author":"Daniel","year":"2007","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbabbdb8bib13","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.ijrobp.2014.05.027","type":"journal-article","article-title":"Feasibility of multimodal deformable registration for head and neck tumor treatment planning","volume":"90","author":"Fortunati","year":"2014","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbabbdb8bib14","article-title":"Robust learning with jacobian regularization","author":"Hoffman","year":"2019","type":"preprint"},{"key":"pmbabbdb8bib15","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1016\/j.ijrobp.2004.07.696","type":"journal-article","article-title":"The impact of daily setup variations on head-and-neck intensity-modulated radiation therapy","volume":"61","author":"Hong","year":"2005","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbabbdb8bib16","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1016\/j.ijrobp.2013.07.040","type":"journal-article","article-title":"Parotid glands dose-effect relationships based on their actually delivered dose: implications for adaptive replanning in radiation therapy of head-and-neck cancer","volume":"87","author":"Hunter","year":"2013","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbabbdb8bib17","doi-asserted-by":"publisher","DOI":"10.1186\/s41241-017-0037-5","type":"journal-article","article-title":"Clinical features and preventive therapies of radiation-induced xerostomia in head and neck cancer patient: a literature review","volume":"37, 31","author":"Jaguar","year":"2017","journal-title":"Appl. Cancer Res."},{"key":"pmbabbdb8bib18","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1016\/j.clon.2011.02.002","type":"journal-article","article-title":"Volumetric-modulated arc therapy in head and neck radiotherapy: a planning comparison using simultaneous integrated boost for nasopharynx and oropharynx carcinoma","volume":"23","author":"Johnston","year":"2011","journal-title":"Clin. Oncol."},{"key":"pmbabbdb8bib19","doi-asserted-by":"publisher","first-page":"570","DOI":"10.3348\/kjr.2017.18.4.570","type":"journal-article","article-title":"Deep learning in medical imaging: general overview","volume":"18","author":"Lee","year":"2017","journal-title":"Korean J. Radiol."},{"key":"pmbabbdb8bib20","doi-asserted-by":"publisher","first-page":"2221","DOI":"10.1088\/0031-9155\/55\/8\/007","type":"journal-article","article-title":"A hybrid strategy of offline adaptive planning and online image guidance for prostate cancer radiotherapy","volume":"55","author":"Lei","year":"2010","journal-title":"Phys. Med. Biol."},{"key":"pmbabbdb8bib21","first-page":"1744","type":"preprint","article-title":"Dual motion GAN for future flow embedded video prediction","author":"Liang","year":"2017"},{"key":"pmbabbdb8bib22","doi-asserted-by":"publisher","first-page":"1400","DOI":"10.1016\/S0360-3016(01)01740-0","type":"journal-article","article-title":"The effect of setup uncertainty on normal tissue sparing with IMRT for head-and-neck cancer","volume":"51","author":"Manning","year":"2001","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbabbdb8bib23","doi-asserted-by":"publisher","first-page":"e422","DOI":"10.1016\/j.prro.2019.02.014","type":"journal-article","article-title":"Predictive models to determine clinically relevant deviations in delivered dose for head and neck cancer","volume":"9","author":"McCFulloch","year":"2019","journal-title":"Pract. Radiat. Oncol."},{"key":"pmbabbdb8bib24","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR42600.2020.00470","type":"preprint","article-title":"Fast symmetric diffeomorphic image registration with convolutional neural network","author":"Mok","year":"2020"},{"key":"pmbabbdb8bib25","article-title":"Towards a better understanding and regularization of GAN training dynamics","author":"Nie","year":"2019","type":"preprint"},{"key":"pmbabbdb8bib26","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1093\/jjco\/hyi108","type":"journal-article","article-title":"Importance of the initial volume of parotid glands in xerostomia for patients with head and neck cancer treated with IMRT","volume":"35","author":"Nishimaura","year":"2005","journal-title":"Jpn. J. Clin. Oncol."},{"key":"pmbabbdb8bib27","type":"web-resource","article-title":"Plastimatch\u2013an open source software suite for radiotherapy image processing","volume":"vol 7","author":"Sharp","year":"2010"},{"key":"pmbabbdb8bib28","doi-asserted-by":"publisher","first-page":"3699","DOI":"10.1118\/1.4735041","type":"conference-proceedings","article-title":"What is the importance of dose recalculation for adaptive radiotherapy dose assessment?","volume":"vol","author":"Pukala","year":"2012"},{"key":"pmbabbdb8bib29","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.ijrobp.2010.01.071","type":"journal-article","article-title":"Volumetric change of selected organs at risk during IMRT for oropharyngeal cancer","volume":"80","author":"Ricchetti","year":"2011","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbabbdb8bib30","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aacd22","type":"journal-article","article-title":"Quantifying local tumor morphological changes with Jacobian map for prediction of pathologic tumor response to chemo-radiotherapy in locally advanced esophageal cancer","volume":"63","author":"Riyahi-Alam","year":"2018","journal-title":"Phys. Med. Biol."},{"key":"pmbabbdb8bib31","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.radonc.2012.10.010","type":"journal-article","article-title":"Adaptive radiotherapy for head and neck cancer-dosimetric results from a prospective clinical trial","volume":"106","author":"Schwartz","year":"2013","journal-title":"Radiother. Oncol."},{"key":"pmbabbdb8bib32","doi-asserted-by":"publisher","first-page":"986","DOI":"10.1016\/j.ijrobp.2011.08.017","type":"journal-article","article-title":"Adaptive radiotherapy for head-and-neck cancer: initial clinical outcomes from a prospective trial","volume":"83","author":"Schwartz","year":"2012","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbabbdb8bib33","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.radonc.2012.10.011","type":"journal-article","article-title":"Dose deformation-invariance in adaptive prostate radiation therapy: implication for treatment simulations","volume":"105","author":"Sharma","year":"2012","journal-title":"Radiother. Oncol."},{"key":"pmbabbdb8bib34","first-page":"843","type":"preprint","article-title":"Unsupervised learning of video representation using LSTMs","volume":"37","author":"Srivastava","year":"2015"},{"key":"pmbabbdb8bib35","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1093\/jjco\/hyp113","type":"journal-article","article-title":"Radiation-induced parotid gland changes in oral cancer patients: correlation between parotid volume and saliva production","volume":"40","author":"Teshima","year":"2009","journal-title":"Jpn. J. Clin. Oncol."},{"key":"pmbabbdb8bib36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0360-3016(99)00558-1","type":"journal-article","article-title":"Toxicity in head and neck cancer: a review of trends and issues","volume":"47","author":"Trotti","year":"2000","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbabbdb8bib37","doi-asserted-by":"publisher","first-page":"760","DOI":"10.1118\/1.4905050","type":"journal-article","article-title":"Toward adaptive radiotherapy for head and neck patients: uncertainties in dose warping due to the choice of deformable registration algorithm","volume":"42","author":"Veiga","year":"2015","journal-title":"Med. Phys."},{"key":"pmbabbdb8bib38","doi-asserted-by":"publisher","DOI":"10.1118\/1.4864240","type":"journal-article","article-title":"Toward adaptive radiotherapy for head and neck patients: feasibility study on using CT-to-CBCT deformable registration for \u2018dose of the day\u2019 calulations","volume":"41","author":"Veiga","year":"2014","journal-title":"Med. Phys."},{"key":"pmbabbdb8bib39","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/abb1d9","type":"journal-article","article-title":"Predicting spatial esophageal changes in a multimodal longitudinal imaging study via a convolutional recurrent neural network","author":"Wang","year":"2020","journal-title":"Phys. Med. Biol."},{"key":"pmbabbdb8bib40","doi-asserted-by":"publisher","first-page":"4699","DOI":"10.1002\/mp.13765","type":"journal-article","article-title":"Toward predicting the evolution of lung tumors during radiotherapy observed on a longitudinal MR imaging study via a deep learning algorithm","volume":"46","author":"Wang","year":"2019","journal-title":"Med. Phys."},{"key":"pmbabbdb8bib41","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1016\/j.ijrobp.2009.04.047","type":"journal-article","article-title":"Adaptive replanning strategies accounting for shrinkage in head and neck IMRT","volume":"75","author":"Wu","year":"2009","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbabbdb8bib42","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/S0360-3016(99)00304-1","type":"journal-article","article-title":"The potential for sparing of parotids and escalation of biologically effective dose with intensity-modulated radiation treatments of head and neck cancers: a treatment design study","volume":"46","author":"Wu","year":"2000","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbabbdb8bib43","first-page":"208","type":"conference-proceedings","article-title":"Action recognition with 3D ConvNet-GRU architecture","author":"Yao","year":"2018"},{"key":"pmbabbdb8bib44","doi-asserted-by":"publisher","first-page":"1114","DOI":"10.1109\/TMI.2019.2943841","type":"journal-article","article-title":"Spatio-temporal convolutional LSTMs for tumor growth prediction by learning 4D longitudinal patient data","volume":"39","author":"Zang","year":"2020","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Physics in Medicine & Biology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abbdb8","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abbdb8\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abbdb8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abbdb8\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abbdb8\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abbdb8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abbdb8\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abbdb8\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abbdb8\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abbdb8\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abbdb8\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abbdb8","content-type":"text\/html","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T14:38:28Z","timestamp":1765636708000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abbdb8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,24]]},"references-count":44,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2020,11,24]]},"published-print":{"date-parts":[[2020,12,7]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6560\/abbdb8","relation":{},"ISSN":["0031-9155","1361-6560"],"issn-type":[{"value":"0031-9155","type":"print"},{"value":"1361-6560","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,24]]},"assertion":[{"value":"Predictive dose accumulation for HN adaptive radiotherapy","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 2020 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":"2020-06-25","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2020-10-02","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2020-11-24","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}