{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T12:21:23Z","timestamp":1778674883919,"version":"3.51.4"},"reference-count":26,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MMH-114-22"],"award-info":[{"award-number":["MMH-114-22"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["NSTC 113-2314-B-195-011-MY3"],"award-info":[{"award-number":["NSTC 113-2314-B-195-011-MY3"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["NSTC 113-2221-E-A49-167-MY3"],"award-info":[{"award-number":["NSTC 113-2221-E-A49-167-MY3"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["110-2314-B-195-033"],"award-info":[{"award-number":["110-2314-B-195-033"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004861","name":"Mackay Memorial Hospital","doi-asserted-by":"publisher","award":["180503"],"award-info":[{"award-number":["180503"]}],"id":[{"id":"10.13039\/501100004861","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","thegreenjournal.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Radiotherapy and Oncology"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1016\/j.radonc.2025.111066","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:51:15Z","timestamp":1753890675000},"page":"111066","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":5,"special_numbering":"C","title":["Explainable machine learning for predicting aspiration pneumonia after radiotherapy in oral cavity cancer"],"prefix":"10.1016","volume":"211","author":[{"given":"Yu-Fan","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jhen-Bin","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi-Shing","family":"Leu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang-Ju","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Jen","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1445-2393","authenticated-orcid":false,"given":"Jie","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.radonc.2025.111066_b0005","first-page":"229","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J Clin"},{"key":"10.1016\/j.radonc.2025.111066_b0010","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1016\/j.ijrobp.2019.08.058","article-title":"Precision adjuvant therapy based on detailed pathologic risk factors for resected oral cavity squamous cell carcinoma: long-term outcome comparison of CGMH and NCCN guidelines","volume":"106","author":"Lin","year":"2020","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.radonc.2025.111066_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.oraloncology.2024.107102","article-title":"Margin-to-depth ratio as an independent prognostic factor in resected oral cavity squamous cell carcinoma: a nationwide cohort study","volume":"159","author":"Klibngern","year":"2024","journal-title":"Oral Oncol"},{"key":"10.1016\/j.radonc.2025.111066_b0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.oraloncology.2024.106745","article-title":"Should sub-millimeter margins be deemed positive in oral cavity squamous cell carcinoma?","volume":"151","author":"Kang","year":"2024","journal-title":"Oral Oncol"},{"key":"10.1016\/j.radonc.2025.111066_b0025","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1002\/cncr.29207","article-title":"Aspiration pneumonia after concurrent chemoradiotherapy for head and neck cancer","volume":"121","author":"Xu","year":"2015","journal-title":"Cancer"},{"key":"10.1016\/j.radonc.2025.111066_b0030","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.oraloncology.2016.11.010","article-title":"Laryngeal tumours and radiotherapy dose to the cricopharyngeus are predictive of death from aspiration pneumonia","volume":"64","author":"O'Hare","year":"2017","journal-title":"Oral Oncol"},{"key":"10.1016\/j.radonc.2025.111066_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.oraloncology.2025.107291","article-title":"Impact of radiation dose to the swallowing organs on death from aspiration pneumonia in oral cavity cancer","volume":"164","author":"Lin","year":"2025","journal-title":"Oral Oncol"},{"key":"10.1016\/j.radonc.2025.111066_b0040","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.radonc.2020.08.015","article-title":"Death unrelated to cancer and death from aspiration pneumonia after definitive radiotherapy for head and neck cancer","volume":"151","author":"Kanayama","year":"2020","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.radonc.2025.111066_b0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.radonc.2025.110871","article-title":"Improved NTCP model for late radiation-induced aspiration based on dose delivered to specific aspiration-related OARs","volume":"207","author":"Gawryszuk","year":"2025","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.radonc.2025.111066_b0050","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.radonc.2021.09.017","article-title":"Relationship between videofluoroscopic and subjective (physician- and patient- rated) assessment of late swallowing dysfunction after (chemo) radiation: results of a prospective observational study","volume":"164","author":"Gawryszuk","year":"2021","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.radonc.2025.111066_b0055","doi-asserted-by":"crossref","first-page":"e93","DOI":"10.1016\/j.ijrobp.2010.12.067","article-title":"Chemo-IMRT of oropharyngeal cancer aiming to reduce dysphagia: swallowing organs late complication probabilities and dosimetric correlates","volume":"81","author":"Eisbruch","year":"2011","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.radonc.2025.111066_b0060","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1002\/hed.23275","article-title":"Aspiration pneumonia after chemo-intensity-modulated radiation therapy of oropharyngeal carcinoma and its clinical and dysphagia-related predictors","volume":"36","author":"Hunter","year":"2014","journal-title":"Head Neck"},{"key":"10.1016\/j.radonc.2025.111066_b0065","article-title":"Artificial intelligence to predict outcomes of head and neck radiotherapy","volume":"39","author":"Bang","year":"2023","journal-title":"Clin Transl Radiat Oncol"},{"key":"10.1016\/j.radonc.2025.111066_b0070","series-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","first-page":"4768","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017"},{"key":"10.1016\/j.radonc.2025.111066_b0075","doi-asserted-by":"crossref","first-page":"2044","DOI":"10.1002\/jcsm.13282","article-title":"Explainable machine learning model for predicting skeletal muscle loss during surgery and adjuvant chemotherapy in ovarian cancer","volume":"14","author":"Hsu","year":"2023","journal-title":"J Cachexia Sarcopenia Muscle"},{"key":"10.1016\/j.radonc.2025.111066_b0080","first-page":"544","article-title":"Interpretable machine learning model based on clinical factors for predicting muscle radiodensity loss after treatment in ovarian cancer","volume":"32","author":"Lin","year":"2024","journal-title":"Support Care Cancer"},{"key":"10.1016\/j.radonc.2025.111066_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.radonc.2024.110693","article-title":"Prediction of the need of enteral nutrition during radiation therapy for head and neck cancers","volume":"203","author":"Giraud","year":"2025","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.radonc.2025.111066_b0090","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1016\/j.ijrobp.2023.06.019","article-title":"Explainable artificial intelligence to identify dosimetric predictors of toxicity in patients with locally advanced non-small cell lung cancer: a secondary analysis of RTOG 0617","volume":"117","author":"Ladbury","year":"2023","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.radonc.2025.111066_b0095","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1038\/bjc.2014.639","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement","volume":"112","author":"Collins","year":"2015","journal-title":"Br J Cancer"},{"key":"10.1016\/j.radonc.2025.111066_b0100","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1002\/hed.23692","article-title":"Guideline and preliminary clinical practice results for dose specification and target delineation for postoperative radiotherapy for oral cavity cancer","volume":"37","author":"Liu","year":"2015","journal-title":"Head Neck"},{"key":"10.1016\/j.radonc.2025.111066_b0105","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.radonc.2021.02.014","article-title":"Progressive muscle loss is an independent predictor for survival in locally advanced oral cavity cancer: a longitudinal study","volume":"158","author":"Lee","year":"2021","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.radonc.2025.111066_b0110","doi-asserted-by":"crossref","first-page":"4289","DOI":"10.1007\/s00330-024-11303-4","article-title":"Identifying threshold of CT-defined muscle loss after radiotherapy for survival in oral cavity cancer using machine learning","volume":"35","author":"Lee","year":"2025","journal-title":"Eur Radiol"},{"key":"10.1016\/j.radonc.2025.111066_b0115","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.radonc.2025.111066_b0120","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"},{"key":"10.1016\/j.radonc.2025.111066_b0125","first-page":"2825","article-title":"Scikit-learn: machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.radonc.2025.111066_b0130","series-title":"Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning","first-page":"878","author":"Han","year":"2005"}],"container-title":["Radiotherapy and Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167814025045700?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167814025045700?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T11:53:44Z","timestamp":1778673224000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167814025045700"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":26,"alternative-id":["S0167814025045700"],"URL":"https:\/\/doi.org\/10.1016\/j.radonc.2025.111066","relation":{},"ISSN":["0167-8140"],"issn-type":[{"value":"0167-8140","type":"print"}],"subject":[],"published":{"date-parts":[[2025,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Explainable machine learning for predicting aspiration pneumonia after radiotherapy in oral cavity cancer","name":"articletitle","label":"Article Title"},{"value":"Radiotherapy and Oncology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.radonc.2025.111066","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111066"}}