{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T15:02:14Z","timestamp":1789570934686,"version":"build-2803163510"},"reference-count":39,"publisher":"Elsevier BV","issue":"3","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,3,14]],"date-time":"2022-03-14T00:00:00Z","timestamp":1647216000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004377","name":"The Hong Kong Polytechnic University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004377","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010877","name":"Shenzhen Science and Technology Innovation Committee","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010877","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":[[2022,7]]},"DOI":"10.1016\/j.ijrobp.2022.03.011","type":"journal-article","created":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T16:52:08Z","timestamp":1647363128000},"page":"685-694","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":21,"title":["Deep Learning-Based Automatic Assessment of Radiation Dermatitis in Patients With Nasopharyngeal Carcinoma"],"prefix":"10.1016","volume":"113","author":[{"given":"Ruiyan","family":"Ni","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ta","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ge","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanpeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongrong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victor C.W.","family":"Tam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wan Shun","family":"Leung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Ge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shara W.Y.","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ijrobp.2022.03.011_bib0001","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s40257-016-0186-4","article-title":"Radiodermatitis: A review of our current understanding","volume":"17","author":"Singh","year":"2016","journal-title":"Am J Clin Dermatol"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0002","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1016\/j.jaad.2019.02.047","article-title":"Management of acute radiation dermatitis: A review of the literature and proposal for treatment algorithm","volume":"81","author":"Rosenthal","year":"2019","journal-title":"J Am Acad Dermatol"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0003","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.breast.2015.01.008","article-title":"RTOG, CTCAE and WHO criteria for acute radiation dermatitis correlate with cutaneous blood flow measurements","volume":"24","author":"Huang","year":"2015","journal-title":"Breast"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0004","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1093\/jrr\/rrv092","article-title":"A prospective picture collection study for a grading atlas of radiation dermatitis for clinical trials in head-and-neck cancer patients","volume":"57","author":"Zenda","year":"2016","journal-title":"J Radiat Res"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0005","doi-asserted-by":"crossref","first-page":"S28","DOI":"10.12968\/bjon.2014.23.Sup2.S28","article-title":"Radiotherapy-induced skin reactions: assessment and management","volume":"23","author":"Glover","year":"2014","journal-title":"Br J Nurs"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0006","doi-asserted-by":"crossref","DOI":"10.1186\/s13014-019-1215-2","article-title":"A scoring system predicting acute radiation dermatitis in patients with head and neck cancer treated with intensity-modulated radiotherapy","volume":"14","author":"Kawamura","year":"2019","journal-title":"Radiat Oncol"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0007","first-page":"885","article-title":"Radiation dermatitis: Recognition, prevention, and management","volume":"31","author":"Leventhal","year":"2017","journal-title":"Oncology-Ny"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0008","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1016\/0360-3016(95)00060-C","article-title":"Toxicity criteria of the Radiation Therapy Oncology Group (RTOG) and the European Organization for Research and Treatment of Cancer (EORTC)","volume":"31","author":"Cox","year":"1995","journal-title":"Int J Radiat Oncol"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0009","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/S1053-4296(03)00031-6","article-title":"CTCAE v3.0: Development of a comprehensive grading system for the adverse effects of cancer treatment","volume":"13","author":"Trotti","year":"2003","journal-title":"Semin Radiat Oncol"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0010","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1002\/1097-0142(19810101)47:1<207::AID-CNCR2820470134>3.0.CO;2-6","article-title":"Reporting results of cancer treatment","volume":"47","author":"Miller","year":"1981","journal-title":"Cancer"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0011","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s00520-011-1110-3","article-title":"Trolamine emulsion for the prevention of radiation dermatitis in patients with squamous cell carcinoma of the head and neck","volume":"20","author":"Abbas","year":"2012","journal-title":"Support Care Cancer"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0012","doi-asserted-by":"crossref","first-page":"E345","DOI":"10.3747\/co.20.1356","article-title":"Aloe vera for prevention of radiation-induced dermatitis: A self-controlled clinical trial","volume":"20","author":"Haddad","year":"2013","journal-title":"Curr Oncol"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0013","doi-asserted-by":"crossref","first-page":"3245","DOI":"10.1007\/s12652-020-02537-3","article-title":"Deep learning based an automated skin lesion segmentation and intelligent classification model","volume":"12","author":"Sikkandar","year":"2021","journal-title":"J Amb Intel Hum Comp"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0014","doi-asserted-by":"crossref","first-page":"208264","DOI":"10.1109\/ACCESS.2020.3037258","article-title":"Deep learning in skin disease image recognition: A review","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0015","doi-asserted-by":"crossref","DOI":"10.1007\/s10916-019-1414-2","article-title":"The application of deep learning in the risk grading of skin tumors for patients using clinical images","volume":"43","author":"Zhao","year":"2019","journal-title":"J Med Syst"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0016","doi-asserted-by":"crossref","first-page":"513","DOI":"10.2174\/1573405615666190129120449","article-title":"Deep learning approaches toward skin lesion segmentation and classification from dermoscopic images - a review","volume":"16","author":"Baig","year":"2020","journal-title":"Curr Med Imaging"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2020.105351","article-title":"Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification","volume":"190","author":"Al-Masni","year":"2020","journal-title":"Comput Meth Prog Bio"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0018","series-title":"Automatic Skin Lesion Segmentation and Melanoma Detection: Transfer Learning Approach With U-Net and DCNN-SVM","first-page":"371","author":"Al Nazi","year":"2020"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0019","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","article-title":"U-Net: Convolutional networks for biomedical image segmentation","volume":"9351","author":"Ronneberger","year":"2015","journal-title":"Lect Notes Comput Sc"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0020","doi-asserted-by":"crossref","first-page":"S59","DOI":"10.1016\/j.ijrobp.2018.06.167","article-title":"Applying a machine learning approach to predict acute toxicities during radiation for breast cancer patients","volume":"102","author":"Reddy","year":"2018","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0021","doi-asserted-by":"crossref","first-page":"E130","DOI":"10.1016\/j.ijrobp.2019.06.2259","article-title":"Predicting radiation-adverse effects using three-dimensional dose and fractionation data: Radiation dermatitis","volume":"105","author":"Lim","year":"2019","journal-title":"Int J Radiat Oncol"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0022","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1016\/j.ijrobp.2019.12.032","article-title":"Quantitative thermal imaging biomarkers to detect acute skin toxicity from breast radiation therapy using supervised machine learning","volume":"106","author":"Saednia","year":"2020","journal-title":"Int J Radiat Oncol"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0023","first-page":"2261","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017","journal-title":"Proc Cvpr Ieee"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0024","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.radonc.2021.03.002","article-title":"Photobiomodulation therapy for the prevention of acute radiation dermatitis in head and neck cancer patients (DERMISHEAD trial)","volume":"158","author":"Robijns","year":"2021","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0025","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.neucom.2016.02.060","article-title":"A deep feature based framework for breast masses classification","volume":"197","author":"Jiao","year":"2016","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0026","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TIP.2007.901808","article-title":"Edge-based color constancy","volume":"16","author":"Van de Weijer","year":"2007","journal-title":"Ieee T Image Process"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0027","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016","journal-title":"Proc Cvpr Ieee"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0028","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/mp.13891","article-title":"Clinical implementation of AI technologies will require interpretable AI models","volume":"47","author":"Jia","year":"2020","journal-title":"Med Phys"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0029","doi-asserted-by":"crossref","first-page":"1791","DOI":"10.1002\/mp.12831","article-title":"Artificial intelligence will soon change the landscape of medical physics research and practice","volume":"45","author":"Xing","year":"2018","journal-title":"Med Phys"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0030","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0031","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J Big Data-Ger"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0032","doi-asserted-by":"crossref","first-page":"e357","DOI":"10.1016\/j.ijrobp.2012.05.009","article-title":"Re-examining the evidence in radiation dermatitis management literature: An overview and a critical appraisal of systematic reviews","volume":"84","author":"Chan","year":"2012","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0033","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1007\/s00520-016-3521-7","article-title":"Topical interventions to prevent acute radiation dermatitis in head and neck cancer patients: A systematic review","volume":"25","author":"Ferreira","year":"2017","journal-title":"Support Care Cancer"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0034","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1097\/00129334-200209000-00007","article-title":"Wound care after radiation therapy","volume":"15","author":"Mendelsohn","year":"2002","journal-title":"Adv Skin Wound Care"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0035","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.jaad.2005.08.054","article-title":"Radiation dermatitis: Clinical presentation, pathophysiology, and treatment 2006","volume":"54","author":"Hymes","year":"2006","journal-title":"J Am Acad Dermatol"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0036","first-page":"337","article-title":"Skin lesion segmentation in clinical images using deep learning","author":"Jafari","year":"2016","journal-title":"Int C Patt Recog"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0037","first-page":"1","article-title":"SkinNet: A deep learning framework for skin lesion segmentation","author":"Vesal","year":"2018","journal-title":"2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS\/MIC)"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0038","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.ebiom.2019.04.055","article-title":"Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope","volume":"43","author":"Dascalu","year":"2019","journal-title":"Ebiomedicine"},{"key":"10.1016\/j.ijrobp.2022.03.011_bib0039","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/CIBEC.2018.8641762","article-title":"Skin cancer classification using deep learning and transfer learning","author":"Hosny","year":"2018","journal-title":"2018 9th Cairo International Biomedical Engineering Conference (CIBEC)"}],"container-title":["International Journal of Radiation Oncology*Biology*Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0360301622002449?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0360301622002449?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T08:10:29Z","timestamp":1724659829000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0360301622002449"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":39,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["S0360301622002449"],"URL":"https:\/\/doi.org\/10.1016\/j.ijrobp.2022.03.011","relation":{},"ISSN":["0360-3016"],"issn-type":[{"value":"0360-3016","type":"print"}],"subject":[],"published":{"date-parts":[[2022,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deep Learning-Based Automatic Assessment of Radiation Dermatitis in Patients With Nasopharyngeal Carcinoma","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.2022.03.011","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 The Author(s). Published by Elsevier Inc.","name":"copyright","label":"Copyright"}]}}