{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T01:30:22Z","timestamp":1791250222579,"version":"4.1.0"},"reference-count":33,"publisher":"Elsevier BV","issue":"1","license":[{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003397","name":"Huazhong University of Science and Technology","doi-asserted-by":"publisher","award":["TJ-IRB20230326"],"award-info":[{"award-number":["TJ-IRB20230326"]}],"id":[{"id":"10.13039\/501100003397","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["oooojournal.net","clinicalkey.fr","clinicalkey.jp","clinicalkey.com.au","clinicalkey.es","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1016\/j.oooo.2024.01.016","type":"journal-article","created":{"date-parts":[[2024,2,3]],"date-time":"2024-02-03T16:43:18Z","timestamp":1706978598000},"page":"214-224","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":15,"title":["Magnetic resonance imaging-based radiomics and deep learning models for predicting lymph node metastasis of squamous cell carcinoma of the tongue"],"prefix":"10.1016","volume":"138","author":[{"given":"Dawei","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunming","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenqiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haosen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cicheng","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2427-2038","authenticated-orcid":false,"given":"Chuanyu","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"6","key":"10.1016\/j.oooo.2024.01.016_bib0001","doi-asserted-by":"crossref","first-page":"C1611","DOI":"10.1152\/ajpcell.00098.2022","article-title":"Squamous cell carcinoma of the tongue: subtypes and morphological features affecting prognosis","volume":"323","author":"Matsuo","year":"2022","journal-title":"Am J Physiol Cell Physiol"},{"issue":"3","key":"10.1016\/j.oooo.2024.01.016_bib0002","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1002\/cncr.11526","article-title":"Disparity in pathologic and clinical lymph node staging in oral tongue carcinoma. Implication for therapeutic decision making","volume":"98","author":"Greenberg","year":"2003","journal-title":"Cancer"},{"key":"10.1016\/j.oooo.2024.01.016_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.oraloncology.2020.104661","article-title":"Elective neck dissection versus observation for early-stage oral squamous cell carcinoma: systematic review and meta-analysis","volume":"105","author":"Oh","year":"2020","journal-title":"Oral Oncol"},{"issue":"8","key":"10.1016\/j.oooo.2024.01.016_bib0004","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1016\/j.oraloncology.2007.10.004","article-title":"Initial CT findings in early tongue and oral floor cancer as predictors of late neck metastasis","volume":"44","author":"Furusawa","year":"2008","journal-title":"Oral Oncol"},{"issue":"4","key":"10.1016\/j.oooo.2024.01.016_bib0005","doi-asserted-by":"crossref","first-page":"2739","DOI":"10.1007\/s00330-021-08310-0","article-title":"Histogram analysis of diffusion-weighted imaging and dynamic contrast-enhanced MRI for predicting occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma","volume":"32","author":"Ren","year":"2022","journal-title":"Eur Radiol"},{"issue":"2","key":"10.1016\/j.oooo.2024.01.016_bib0006","doi-asserted-by":"crossref","first-page":"e231","DOI":"10.1634\/theoncologist.2019-0427","article-title":"Lymph node imaging in patients with primary breast cancer: concurrent diagnostic tools","volume":"25","author":"Marino","year":"2020","journal-title":"Oncologist"},{"key":"10.1016\/j.oooo.2024.01.016_bib0007","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.ejrad.2017.02.039","article-title":"Lymph node metastasis in head and neck squamous carcinoma: efficacy of intravoxel incoherent motion magnetic resonance imaging for the differential diagnosis","volume":"90","author":"Liang","year":"2017","journal-title":"Eur J Radiol"},{"issue":"4","key":"10.1016\/j.oooo.2024.01.016_bib0008","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1002\/1097-0347(200007)22:4<380::AID-HED11>3.0.CO;2-E","article-title":"Cervical node metastases in laryngeal and hypopharyngeal cancer: a prospective analysis of prevalence and distribution","volume":"22","author":"Buckley","year":"2000","journal-title":"Head Neck"},{"issue":"4","key":"10.1016\/j.oooo.2024.01.016_bib0009","doi-asserted-by":"crossref","first-page":"488","DOI":"10.2967\/jnumed.118.222893","article-title":"Introduction to radiomics","volume":"61","author":"Mayerhoefer","year":"2020","journal-title":"J Nucl Med"},{"issue":"1","key":"10.1016\/j.oooo.2024.01.016_bib0010","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1186\/s12967-022-03688-x","article-title":"Radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer","volume":"20","author":"Su","year":"2022","journal-title":"J Transl Med"},{"key":"10.1016\/j.oooo.2024.01.016_bib0011","doi-asserted-by":"crossref","first-page":"937","DOI":"10.3389\/fonc.2020.00937","article-title":"Radiomics features predict CIC mutation status in lower grade glioma","volume":"10","author":"Zhang","year":"2020","journal-title":"Front Oncol"},{"issue":"27","key":"10.1016\/j.oooo.2024.01.016_bib0012","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/smll.202201300","article-title":"Tailored hydrogel delivering niobium carbide boosts ROS-scavenging and antimicrobial activities for diabetic wound healing","volume":"18","author":"Chen","year":"2022","journal-title":"Small"},{"issue":"9","key":"10.1016\/j.oooo.2024.01.016_bib0013","doi-asserted-by":"crossref","first-page":"6429","DOI":"10.1007\/s00330-021-07731-1","article-title":"Machine learning-based MRI texture analysis to predict occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma","volume":"31","author":"Yuan","year":"2021","journal-title":"Eur Radiol"},{"key":"10.1016\/j.oooo.2024.01.016_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102444","article-title":"Recent advances and clinical applications of deep learning in medical image analysis","volume":"79","author":"Chen","year":"2022","journal-title":"Med Image Anal"},{"issue":"1","key":"10.1016\/j.oooo.2024.01.016_bib0015","first-page":"1","article-title":"Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer","volume":"11","author":"Zheng","year":"2020","journal-title":"Nat Commun"},{"key":"10.1016\/j.oooo.2024.01.016_bib0016","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.ejca.2021.01.028","article-title":"Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer","volume":"147","author":"Jiang","year":"2021","journal-title":"Eur J Cancer"},{"issue":"1","key":"10.1016\/j.oooo.2024.01.016_bib0017","doi-asserted-by":"crossref","first-page":"4807","DOI":"10.1038\/s41467-020-18497-3","article-title":"Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics","volume":"11","author":"Yu","year":"2020","journal-title":"Nat Commun"},{"key":"10.1016\/j.oooo.2024.01.016_bib0018","series-title":"Applied Logistic Regression","first-page":"177","author":"Hosmer","year":"2013"},{"issue":"1","key":"10.1016\/j.oooo.2024.01.016_bib0019","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1136\/eb-2012-100645","article-title":"Principles for high-quality, high-value testing","volume":"18","author":"Power","year":"2013","journal-title":"Evid Based Med"},{"issue":"29","key":"10.1016\/j.oooo.2024.01.016_bib0020","doi-asserted-by":"crossref","first-page":"3960","DOI":"10.3748\/wjg.v28.i29.3960","article-title":"Radiomics for differentiating tumor deposits from lymph node metastasis in rectal cancer","volume":"28","author":"Zhang","year":"2022","journal-title":"World J Gastroenterol"},{"issue":"12","key":"10.1016\/j.oooo.2024.01.016_bib0021","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1016\/j.acra.2020.07.017","article-title":"Ultrasound-based radiomic nomogram for predicting lateral cervical lymph node metastasis in papillary thyroid carcinoma","volume":"28","author":"Tong","year":"2021","journal-title":"Acad Radiol"},{"issue":"6","key":"10.1016\/j.oooo.2024.01.016_bib0022","doi-asserted-by":"crossref","first-page":"e155","DOI":"10.1016\/j.acra.2020.03.045","article-title":"A CT-based radiomics model for prediction of lymph node metastasis in early stage gastric cancer","volume":"28","author":"Gao","year":"2021","journal-title":"Acad Radiol"},{"issue":"9","key":"10.1016\/j.oooo.2024.01.016_bib0023","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1007\/s11912-022-01249-5","article-title":"Predictors of nodal metastasis in cutaneous head and neck cancers","volume":"24","author":"Han","year":"2022","journal-title":"Curr Oncol Rep"},{"issue":"3","key":"10.1016\/j.oooo.2024.01.016_bib0024","doi-asserted-by":"crossref","first-page":"560","DOI":"10.3760\/cma.j.issn.0366-6999.20123235","article-title":"Lymph node metastasis in early gastric cancer","volume":"127","author":"Chen","year":"2014","journal-title":"Chin Med J (Engl)"},{"issue":"1","key":"10.1016\/j.oooo.2024.01.016_bib0025","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1002\/jmri.28019","article-title":"Magnetic resonance imaging-based radiomics features associated with depth of invasion predicted lymph node metastasis and prognosis in tongue cancer","volume":"56","author":"Wang","year":"2022","journal-title":"J Magn Reson Imaging"},{"issue":"2","key":"10.1016\/j.oooo.2024.01.016_bib0026","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.neurad.2021.07.006","article-title":"Tumor radiomics signature for artificial neural network-assisted detection of neck metastasis in patient with tongue cancer","volume":"49","author":"Zhong","year":"2022","journal-title":"J Neuroradiol"},{"key":"10.1016\/j.oooo.2024.01.016_bib0027","article-title":"Computed tomography-based deep-learning prediction of lymph node metastasis risk in locally advanced gastric cancer","volume":"12","author":"Zhang","year":"2022","journal-title":"Front Oncol"},{"key":"10.1016\/j.oooo.2024.01.016_bib0028","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.ejca.2021.08.039","article-title":"Deep learning can predict lymph node status directly from histology in colorectal cancer","volume":"157","author":"Kiehl","year":"2021","journal-title":"Eur J Cancer"},{"issue":"2","key":"10.1016\/j.oooo.2024.01.016_bib0029","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1002\/bit.27980","article-title":"A transfer learning approach for predictive modeling of bioprocesses using small data","volume":"119","author":"Rogers","year":"2022","journal-title":"Biotechnol Bioeng"},{"key":"10.1016\/j.oooo.2024.01.016_bib0030","article-title":"A nomogram based on radiomics signature and deep-learning signature for preoperative prediction of axillary lymph node metastasis in breast cancer","volume":"12","author":"Wang","year":"2022","journal-title":"Front Oncol"},{"key":"10.1016\/j.oooo.2024.01.016_bib0031","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2021.585942","article-title":"Development and validation of a nomogram for preoperative prediction of lymph node metastasis in lung adenocarcinoma based on radiomics signature and deep learning signature","volume":"11","author":"Ran","year":"2021","journal-title":"Front Oncol"},{"issue":"4","key":"10.1016\/j.oooo.2024.01.016_bib0032","doi-asserted-by":"crossref","first-page":"2324","DOI":"10.1007\/s00330-019-06621-x","article-title":"Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer","volume":"30","author":"Li","year":"2020","journal-title":"Eur Radiol"},{"issue":"7","key":"10.1016\/j.oooo.2024.01.016_bib0033","doi-asserted-by":"crossref","first-page":"912","DOI":"10.1016\/j.annonc.2020.04.003","article-title":"Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study","volume":"31","author":"Dong","year":"2020","journal-title":"Ann Oncol"}],"container-title":["Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2212440324000567?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2212440324000567?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T18:50:51Z","timestamp":1719514251000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2212440324000567"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7]]},"references-count":33,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["S2212440324000567"],"URL":"https:\/\/doi.org\/10.1016\/j.oooo.2024.01.016","relation":{},"ISSN":["2212-4403"],"issn-type":[{"value":"2212-4403","type":"print"}],"subject":[],"published":{"date-parts":[[2024,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Magnetic resonance imaging-based radiomics and deep learning models for predicting lymph node metastasis of squamous cell carcinoma of the tongue","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.2024.01.016","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}]}}