{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T22:21:50Z","timestamp":1788387710915,"version":"build-2803163510"},"reference-count":20,"publisher":"Elsevier BV","issue":"6","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T00:00:00Z","timestamp":1738540800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003478","name":"Ministry of Health, Labour and Welfare","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003478","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","oooojournal.net","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1016\/j.oooo.2025.01.715","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T19:17:50Z","timestamp":1737746270000},"page":"760-769","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":8,"title":["A radiomics model combining machine learning and neural networks for high-accuracy prediction of cervical lymph node metastasis on ultrasound of head and neck squamous cell carcinoma"],"prefix":"10.1016","volume":"139","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0285-5008","authenticated-orcid":false,"given":"Motoki","family":"Fukuda","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sato","family":"Eida","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ikuo","family":"Katayama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yukinori","family":"Takagi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miho","family":"Sasaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Misa","family":"Sumi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshiko","family":"Ariji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.oooo.2025.01.715_bib0001","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1038\/s41572-020-00224-3","article-title":"Head and neck squamous cell carcinoma","volume":"6","author":"Johnson","year":"2020","journal-title":"Nat Rev Dis Primers"},{"key":"10.1016\/j.oooo.2025.01.715_bib0002","doi-asserted-by":"crossref","first-page":"417","DOI":"10.4103\/0019-509X.176750","article-title":"Lymph node metastasis: a bearing on prognosis in squamous cell carcinoma","volume":"52","author":"Kapoor","year":"2015","journal-title":"Indian J Cancer"},{"key":"10.1016\/j.oooo.2025.01.715_bib0003","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1007\/s00784-013-1050-z","article-title":"Diagnostic accuracy of ultrasound, \u00b9\u2078F-FDG-PET\/CT, and fused \u00b9\u2078F-FDG-PET-MR images with DWI for the detection of cervical lymph node metastases of HNSCC","volume":"18","author":"Heusch","year":"2014","journal-title":"Clin Oral Investig"},{"key":"10.1016\/j.oooo.2025.01.715_bib0004","first-page":"303","article-title":"Power Doppler sonography of cervical lymph nodes in patients with head and neck cancer","volume":"19","author":"Ariji","year":"1998","journal-title":"AJNR Am J Neuroradiol"},{"key":"10.1016\/j.oooo.2025.01.715_bib0005","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1102\/1470-7330.2007.0024","article-title":"The role of ultrasound in the detection of cervical lymph node metastases in clinically N0 squamous cell carcinoma of the head and neck","volume":"7","author":"Richards","year":"2007","journal-title":"Cancer Imaging"},{"key":"10.1016\/j.oooo.2025.01.715_bib0006","doi-asserted-by":"crossref","first-page":"274","DOI":"10.3390\/cancers16020274","article-title":"Metastatic lymph node detection on ultrasound images using YOLOv7 in patients with head and neck squamous cell carcinoma","volume":"16","author":"Eida","year":"2024","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.oooo.2025.01.715_bib0007","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1002\/med.21846","article-title":"A review in radiomics: making personalized medicine a reality via routine imaging","volume":"42","author":"Guiot","year":"2022","journal-title":"Med Res Rev"},{"key":"10.1016\/j.oooo.2025.01.715_bib0008","doi-asserted-by":"crossref","first-page":"374","DOI":"10.25259\/SNI_636_2020","article-title":"Easily created prediction model using deep learning software (Prediction One, Sony Network Communications Inc.) for subarachnoid hemorrhage outcomes from small dataset at admission","volume":"11","author":"Katsuki","year":"2020","journal-title":"Surg Neurol Int"},{"key":"10.1016\/j.oooo.2025.01.715_bib0009","doi-asserted-by":"crossref","first-page":"203","DOI":"10.25259\/SNI_222_2021","article-title":"Postsurgical functional outcome prediction model using deep learning framework (Prediction One, Sony Network Communications Inc.) for hypertensive intracerebral hemorrhage","volume":"12","author":"Katsuki","year":"2021","journal-title":"Surg Neurol Int"},{"key":"10.1016\/j.oooo.2025.01.715_bib0010","first-page":"e15695","article-title":"Easily created prediction model using automated artificial intelligence framework (Prediction One, Sony Network Communications Inc., Tokyo, Japan) for subarachnoid hemorrhage outcomes treated by coiling and delayed cerebral ischemia","volume":"13","author":"Katsuki","year":"2021","journal-title":"Cureus"},{"key":"10.1016\/j.oooo.2025.01.715_bib0011","doi-asserted-by":"crossref","first-page":"628","DOI":"10.21037\/qims-20-241","article-title":"CT radiomics features to predict lymph node metastasis in advanced esophageal squamous cell carcinoma and to discriminate between regional and non-regional lymph node metastasis: a case control study","volume":"11","author":"Ou","year":"2021","journal-title":"Quant Imaging Med Surg"},{"key":"10.1016\/j.oooo.2025.01.715_bib0012","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/8534262","article-title":"Radiomics analysis of lymph nodes with esophageal squamous cell carcinoma based on deep learning","volume":"2022","author":"Chen","year":"2022","journal-title":"J Oncol"},{"key":"10.1016\/j.oooo.2025.01.715_bib0013","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2022.988859","article-title":"Radiomics models based on CT at different phases predicting lymph node metastasis of esophageal squamous cell carcinoma (GASTO-1089)","volume":"12","author":"Peng","year":"2022","journal-title":"Front Oncol"},{"key":"10.1016\/j.oooo.2025.01.715_bib0014","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/s00784-023-05423-2","article-title":"A deep learning and radiomics fusion model based on contrast-enhanced computer tomography improves preoperative identification of cervical lymph node metastasis of oral squamous cell carcinoma","volume":"28","author":"Chen","year":"2023","journal-title":"Clin Oral Investig"},{"key":"10.1016\/j.oooo.2025.01.715_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.eclinm.2023.102385","article-title":"Comparing three-dimensional and two-dimensional deep-learning, radiomics, and fusion models for predicting occult lymph node metastasis in laryngeal squamous cell carcinoma based on CT imaging: a multicentre, retrospective, diagnostic study","volume":"67","author":"Wang","year":"2024","journal-title":"EClinicalMedicine"},{"key":"10.1016\/j.oooo.2025.01.715_bib0016","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2022.936040","article-title":"MRI-based radiomics analysis for preoperative evaluation of lymph node metastasis in hypopharyngeal squamous cell carcinoma","volume":"12","author":"Lu","year":"2022","journal-title":"Front Oncol"},{"key":"10.1016\/j.oooo.2025.01.715_bib0017","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20200358","article-title":"A preoperative radiomics model for the identification of lymph node metastasis in patients with early-stage cervical squamous cell carcinoma","volume":"93","author":"Yan","year":"2020","journal-title":"Br J Radiol"},{"key":"10.1016\/j.oooo.2025.01.715_bib0018","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s11282-022-00600-7","article-title":"Radiomics analysis of [18F]-fluoro-2-deoxyglucose positron emission tomography for the prediction of cervical lymph node metastasis in tongue squamous cell carcinoma","volume":"39","author":"Kudoh","year":"2023","journal-title":"Oral Radiol"},{"key":"10.1016\/j.oooo.2025.01.715_bib0019","doi-asserted-by":"crossref","first-page":"2579","DOI":"10.1002\/cam4.3634","article-title":"Quantitative ultrasound radiomics in predicting recurrence for patients with node-positive head-neck squamous cell carcinoma treated with radical radiotherapy","volume":"10","author":"Dasgupta","year":"2021","journal-title":"Cancer Med"},{"key":"10.1016\/j.oooo.2025.01.715_bib0020","doi-asserted-by":"crossref","first-page":"2619","DOI":"10.1002\/hed.27487","article-title":"Radiomics analysis of intraoral ultrasound images for prediction of late cervical lymph node metastasis in patients with tongue cancer","volume":"45","author":"Konishi","year":"2023","journal-title":"Head Neck"}],"container-title":["Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2212440325007187?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2212440325007187?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T12:47:48Z","timestamp":1762606068000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2212440325007187"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":20,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["S2212440325007187"],"URL":"https:\/\/doi.org\/10.1016\/j.oooo.2025.01.715","relation":{},"ISSN":["2212-4403"],"issn-type":[{"value":"2212-4403","type":"print"}],"subject":[],"published":{"date-parts":[[2025,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A radiomics model combining machine learning and neural networks for high-accuracy prediction of cervical lymph node metastasis on ultrasound of head and neck squamous cell carcinoma","name":"articletitle","label":"Article Title"},{"value":"Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.oooo.2025.01.715","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Author(s). Published by Elsevier Inc.","name":"copyright","label":"Copyright"}]}}