{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T02:28:40Z","timestamp":1793068120129,"version":"4.5.0"},"reference-count":36,"publisher":"Elsevier BV","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T00:00:00Z","timestamp":1723075200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008897","name":"Nihon Superior","doi-asserted-by":"publisher","award":["R01CA225190"],"award-info":[{"award-number":["R01CA225190"]}],"id":[{"id":"10.13039\/501100008897","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008897","name":"Nihon Superior","doi-asserted-by":"publisher","award":["NSF-1933369"],"award-info":[{"award-number":["NSF-1933369"]}],"id":[{"id":"10.13039\/501100008897","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001826","name":"ZonMw","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004622","name":"KWF Kankerbestrijding","doi-asserted-by":"publisher","award":["KWF-13529"],"award-info":[{"award-number":["KWF-13529"]}],"id":[{"id":"10.13039\/501100004622","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018286","name":"Dutch Arthritis Society","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100018286","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100015388","name":"Kansas NSF EPSCoR","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100015388","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017751","name":"NIHR BioResource","doi-asserted-by":"publisher","award":["R01CA258827"],"award-info":[{"award-number":["R01CA258827"]}],"id":[{"id":"10.13039\/100017751","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017751","name":"NIHR BioResource","doi-asserted-by":"publisher","award":["R01CA214825"],"award-info":[{"award-number":["R01CA214825"]}],"id":[{"id":"10.13039\/100017751","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017751","name":"NIHR BioResource","doi-asserted-by":"publisher","award":["R01CA218148"],"award-info":[{"award-number":["R01CA218148"]}],"id":[{"id":"10.13039\/100017751","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017751","name":"NIHR BioResource","doi-asserted-by":"publisher","award":["R01DE028290"],"award-info":[{"award-number":["R01DE028290"]}],"id":[{"id":"10.13039\/100017751","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017751","name":"NIHR BioResource","doi-asserted-by":"publisher","award":["R25EB025787"],"award-info":[{"award-number":["R25EB025787"]}],"id":[{"id":"10.13039\/100017751","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017751","name":"NIHR BioResource","doi-asserted-by":"publisher","award":["1R21DE031082-01"],"award-info":[{"award-number":["1R21DE031082-01"]}],"id":[{"id":"10.13039\/100017751","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017751","name":"NIHR BioResource","doi-asserted-by":"publisher","award":["1K01DE030524-01A1"],"award-info":[{"award-number":["1K01DE030524-01A1"]}],"id":[{"id":"10.13039\/100017751","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017751","name":"NIHR BioResource","doi-asserted-by":"publisher","award":["R21DE031082"],"award-info":[{"award-number":["R21DE031082"]}],"id":[{"id":"10.13039\/100017751","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","redjournal.org","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Radiation Oncology*Biology*Physics"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1016\/j.ijrobp.2024.07.2334","type":"journal-article","created":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T17:10:15Z","timestamp":1723569015000},"page":"269-280","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":18,"title":["Three-Dimensional Deep Learning Normal Tissue Complication Probability Model to Predict Late Xerostomia in Patients With Head and Neck Cancer"],"prefix":"10.1016","volume":"121","author":[{"given":"Hung","family":"Chu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suzanne P.M.","family":"de Vette","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hendrike","family":"Neh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nanna M.","family":"Sijtsema","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roel J.H.M.","family":"Steenbakkers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amy","family":"Moreno","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes A.","family":"Langendijk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter M.A.","family":"van Ooijen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clifton D.","family":"Fuller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9515-5616","authenticated-orcid":false,"given":"Lisanne V.","family":"van Dijk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0001","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.radonc.2013.05.007","article-title":"Selection of patients for radiotherapy with protons aiming at reduction of side effects: the model-based approach","volume":"107","author":"Langendijk","year":"2013","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0002","doi-asserted-by":"crossref","first-page":"3770","DOI":"10.1200\/JCO.2007.14.6647","article-title":"Impact of late treatment-related toxicity on quality of life among patients with head and neck cancer treated with radiotherapy","volume":"26","author":"Langendijk","year":"2008","journal-title":"J Clin Oncol"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0003","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1111\/odi.12222","article-title":"Current ideas to reduce or salvage radiation damage to salivary glands","volume":"21","author":"Vissink","year":"2015","journal-title":"Oral Dis"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0004","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1016\/j.ijrobp.2009.07.1685","article-title":"A comparison of dose-response models for the parotid gland in a large group of head-and-neck cancer patients","volume":"76","author":"Houweling","year":"2010","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0005","doi-asserted-by":"crossref","first-page":"938","DOI":"10.1016\/S0360-3016(01)01717-5","article-title":"Quantitative dose-volume response analysis of changes in parotid gland function after radiotherapy in the head-and-neck region","volume":"51","author":"Roesink","year":"2001","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0006","doi-asserted-by":"crossref","first-page":"S58","DOI":"10.1016\/j.ijrobp.2009.06.090","article-title":"Radiotherapy dose-volume effects on salivary gland function","volume":"76","author":"Deasy","year":"2010","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0007","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.radonc.2017.06.020","article-title":"Dose-volume toxicity modeling for de-intensified chemo-radiation therapy for HPV-positive oropharynx cancer","volume":"124","author":"Mavroidis","year":"2017","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0008","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.radonc.2021.01.024","article-title":"Comprehensive toxicity risk profiling in radiation therapy for head and neck cancer: a new concept for individually optimised treatment","volume":"157","author":"Van den Bosch","year":"2021","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0009","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.radonc.2011.05.010","article-title":"Development of NTCP models for head and neck cancer patients treated with three-dimensional conformal radiotherapy for xerostomia and sticky saliva the role of dosimetric and clinical factors","volume":"105","author":"Beetz","year":"2012","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0010","article-title":"Machine learning for normal tissue complication probability prediction: predictive power with versatility and easy implementation","volume":"39","author":"Samant","year":"2023","journal-title":"Clin Transl Radiat Oncol"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0011","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.adro.2019.08.009","article-title":"Spatial radiation dose influence on xerostomia recovery and its comparison to acute incidence in patients with head and neck cancer","volume":"5","author":"Guo","year":"2020","journal-title":"Adv Radiat Oncol"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0012","doi-asserted-by":"crossref","first-page":"3616","DOI":"10.1038\/s41598-019-40228-y","article-title":"Dose\/volume histogram patterns in Salivary Gland subvolumes influence xerostomia injury and recovery","volume":"9","author":"Han","year":"2019","journal-title":"Sci Rep"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0013","doi-asserted-by":"crossref","first-page":"7220","DOI":"10.1038\/s41598-017-07586-x","article-title":"Voxel-based analysis unveils regional dose differences associated with radiation-induced morbidity in head and neck cancer patients","volume":"7","author":"Monti","year":"2017","journal-title":"Sci Rep"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0014","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.radonc.2012.03.004","article-title":"NTCP models for patient-rated xerostomia and sticky saliva after treatment with intensity modulated radiotherapy for head and neck cancer: the role of dosimetric and clinical factors","volume":"105","author":"Beetz","year":"2012","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0015","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/S0167-8140(17)30514-5","article-title":"CT image biomarkers to improve patient-specific prediction of radiation-induced xerostomia and sticky saliva","volume":"122","author":"van Dijk","year":"2017","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0016","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fonc.2018.00035","article-title":"Design and selection of machine learning methods using radiomics and dosiomics for normal tissue complication probability modeling of xerostomia","volume":"8","author":"Gabry\u015b","year":"2018","journal-title":"Front Oncol"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0017","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/j.ijrobp.2019.06.009","article-title":"A deep learning model for predicting xerostomia due to radiation therapy for head and neck squamous cell carcinoma in the RTOG 0522 clinical trial","volume":"105","author":"Men","year":"2019","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0018","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.radonc.2011.05.015","article-title":"Delineation of organs at risk involved in swallowing for radiotherapy treatment planning","volume":"101","author":"Christianen","year":"2011","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0019","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.radonc.2013.05.004","article-title":"Swallowing-sparing intensity-modulated radiotherapy for head and neck cancer patients: treatment planning optimization and clinical introduction","volume":"107","author":"van der Laan","year":"2013","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0020","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ijrobp.2008.07.059","article-title":"Intensity-modulated radiotherapy reduces radiation-induced morbidity and improves health-related quality of life: results of a nonrandomized prospective study using a standardized follow-up program","volume":"74","author":"Vergeer","year":"2009","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0021","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1002\/hed.20602","article-title":"Measuring head and neck cancer symptom burden: the development and validation of the M. D. Anderson symptom inventory, head and neck module","volume":"29","author":"Rosenthal","year":"2007","journal-title":"Head Neck"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0022","first-page":"25","article-title":"ImageNet classification with deep convolutional neural networks","author":"Krizhevsky","year":"2012","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0023","unstructured":"Tan M, Le Qv. EfficientNetV2: Smaller Models and Faster Training; 2021."},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0024","series-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2016","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0025","series-title":"MONAI: an open-source framework for deep learning in healthcare","author":"Cardoso","year":"2022"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0026","series-title":"PyTorch: an imperative style, high-performance deep learning library","author":"Paszke","year":"2019"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0027","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1093\/biomet\/78.3.691","article-title":"A note on a general definition of the coefficient of determination","volume":"78","author":"Nagelkerke","year":"1991","journal-title":"Biometrika"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0028","series-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2921","article-title":"Learning deep features for discriminative localization","author":"Zhou","year":"2015"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0029","series-title":"Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018","first-page":"839","article-title":"Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks","author":"Chattopadhay","year":"2018"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0030","doi-asserted-by":"crossref","unstructured":"Gotkowski K, Gonzalez C, Bucher A, Mukhopadhyay A. M3d-CAM: a PyTorch library to generate 3D data attention maps for medical deep learning. DeepAI; 2020. Accessed January 21, 2022.https:\/\/deepai.org\/publication\/m3d-cam-a-pytorch-library-to-generate-3d-data-attention-maps-for-medical-deep-learning","DOI":"10.1007\/978-3-658-33198-6_52"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0031","series-title":"7th International Conference on Learning Representations","first-page":"1","article-title":"Adaptive gradient methods with dynamic bound of learning rate","volume":"2019","author":"Luo","year":"2019"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0032","series-title":"SGDR: stochastic gradient descent with warm restarts","author":"Loshchilov","year":"2016"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0033","doi-asserted-by":"crossref","first-page":"305ra147","DOI":"10.1126\/scitranslmed.aac4441","article-title":"Sparing the region of the salivary gland containing stem cells preserves saliva production after radiotherapy for head and neck cancer","volume":"7","author":"van Luijk","year":"2015","journal-title":"Sci Transl Med"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0034","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J Big Data"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0035","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.radonc.2022.05.001","article-title":"Prospective assessment of sparing the parotid ducts via MRI sialography for reducing patient reported xerostomia","volume":"172","author":"Fried","year":"2022","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.ijrobp.2024.07.2334_bib0036","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.radonc.2020.04.012","article-title":"Key challenges in normal tissue complication probability model development and validation: towards a comprehensive strategy","volume":"148","author":"Van den Bosch","year":"2020","journal-title":"Radiother Oncol"}],"container-title":["International Journal of Radiation Oncology*Biology*Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0360301624031729?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0360301624031729?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T23:34:23Z","timestamp":1741649663000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0360301624031729"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":36,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["S0360301624031729"],"URL":"https:\/\/doi.org\/10.1016\/j.ijrobp.2024.07.2334","relation":{},"ISSN":["0360-3016"],"issn-type":[{"value":"0360-3016","type":"print"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Three-Dimensional Deep Learning Normal Tissue Complication Probability Model to Predict Late Xerostomia in Patients With Head and Neck Cancer","name":"articletitle","label":"Article Title"},{"value":"International Journal of Radiation Oncology*Biology*Physics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ijrobp.2024.07.2334","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Author(s). Published by Elsevier Inc.","name":"copyright","label":"Copyright"}]}}