{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T14:08:59Z","timestamp":1775657339539,"version":"3.50.1"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"vor","delay-in-days":9,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Oral Health"],"DOI":"10.1186\/s12903-026-08165-1","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T09:48:56Z","timestamp":1774864136000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning radiomics based on multimodal MRI for preoperative prediction of N stage in tongue squamous cell carcinoma: a multicenter study"],"prefix":"10.1186","volume":"26","author":[{"given":"Xin","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sen","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pingqing","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yikang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Ling","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,30]]},"reference":[{"key":"8165_CR1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21708","volume":"72","author":"RL Siegel","year":"2022","unstructured":"Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. Cancer J Clin. 2022;72:7\u201333. https:\/\/doi.org\/10.3322\/caac.21708.","journal-title":"Cancer J Clin"},{"key":"8165_CR2","doi-asserted-by":"publisher","unstructured":"Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209\u201349. https:\/\/doi.org\/10.3322\/caac.21660.","DOI":"10.3322\/caac.21660"},{"key":"8165_CR3","doi-asserted-by":"publisher","first-page":"104799","DOI":"10.1016\/j.oraloncology.2020.104799","volume":"107","author":"A Almangush","year":"2020","unstructured":"Almangush A, M\u00e4kitie AA, Triantafyllou A, de Bree R, Strojan P, Rinaldo A, et al. Staging and grading of oral squamous cell carcinoma: An update. Oral Oncol. 2020;107:104799. https:\/\/doi.org\/10.1016\/j.oraloncology.2020.104799.","journal-title":"Oral Oncol"},{"key":"8165_CR4","doi-asserted-by":"publisher","first-page":"C1611","DOI":"10.1152\/ajpcell.00098.2022","volume":"323","author":"K Matsuo","year":"2022","unstructured":"Matsuo K, Akiba J, Kusukawa J, Yano H. Squamous cell carcinoma of the tongue: Subtypes and morphological features affecting prognosis. Am J Physiol Cell Physiol. 2022;323:C1611\u201323. https:\/\/doi.org\/10.1152\/ajpcell.00098.2022.","journal-title":"Am J Physiol Cell Physiol"},{"key":"8165_CR5","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1002\/hed.24589","volume":"39","author":"JH Ng","year":"2017","unstructured":"Ng JH, Iyer NG, Tan M-H, Edgren G. Changing epidemiology of oral squamous cell carcinoma of the tongue: A global study. Head Neck. 2017;39:297\u2013304. https:\/\/doi.org\/10.1002\/hed.24589.","journal-title":"Head Neck"},{"key":"8165_CR6","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1186\/s13000-023-01371-3","volume":"18","author":"K Kato","year":"2023","unstructured":"Kato K, Miyazawa H, Kobayashi H, Kishikawa Y, Funaki H, Noguchi N, et al. The pattern and spread of invasion can predict late cervical lymph node metastasis in early tongue squamous cell carcinoma. Diagn Pathol. 2023;18:87. https:\/\/doi.org\/10.1186\/s13000-023-01371-3.","journal-title":"Diagn Pathol"},{"key":"8165_CR7","doi-asserted-by":"publisher","first-page":"105586","DOI":"10.1016\/j.oraloncology.2021.105586","volume":"123","author":"P Arun","year":"2021","unstructured":"Arun P, Arun I, Jain P, Manikantan K, Sharan R. Determinants of prognosis in patients with oral squamous cell carcinoma metastasizing to a single cervical lymph node. Oral Oncol. 2021;123:105586. https:\/\/doi.org\/10.1016\/j.oraloncology.2021.105586.","journal-title":"Oral Oncol"},{"key":"8165_CR8","doi-asserted-by":"publisher","first-page":"2739","DOI":"10.1007\/s00330-021-08310-0","volume":"32","author":"J Ren","year":"2022","unstructured":"Ren J, Yuan Y, Tao X. 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. Eur Radiol. 2022;32:2739\u201347. https:\/\/doi.org\/10.1007\/s00330-021-08310-0.","journal-title":"Eur Radiol"},{"key":"8165_CR9","doi-asserted-by":"publisher","first-page":"1313","DOI":"10.1177\/00220345211014795","volume":"100","author":"S Warnakulasuriya","year":"2021","unstructured":"Warnakulasuriya S, Kerr AR. Oral cancer screening: Past, present, and future. J Dent Res. 2021;100:1313\u201320. https:\/\/doi.org\/10.1177\/00220345211014795.","journal-title":"J Dent Res"},{"key":"8165_CR10","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1007\/s00784-024-05897-8","volume":"28","author":"X Yang","year":"2024","unstructured":"Yang X, Xiang W, Sun Y, Li W, Ji X, Gao S, et al. Risk factors and impact of occult and skip metastasis in early-stage oral tongue squamous cell carcinoma. Clin Oral Investig. 2024;28:510. https:\/\/doi.org\/10.1007\/s00784-024-05897-8.","journal-title":"Clin Oral Investig"},{"key":"8165_CR11","doi-asserted-by":"publisher","first-page":"6429","DOI":"10.1007\/s00330-021-07731-1","volume":"31","author":"Y Yuan","year":"2021","unstructured":"Yuan Y, Ren J, Tao X. Machine learning-based MRI texture analysis to predict occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma. Eur Radiol. 2021;31:6429\u201337. https:\/\/doi.org\/10.1007\/s00330-021-07731-1.","journal-title":"Eur Radiol"},{"key":"8165_CR12","doi-asserted-by":"publisher","first-page":"106201","DOI":"10.1016\/j.oraloncology.2022.106201","volume":"135","author":"A Gurmeet Singh","year":"2022","unstructured":"Gurmeet Singh A, Sathe P, Roy S, Thiagrajan S, Chaukar D, Chaturvedi P. Incidence and impact of skip metastasis in the neck in early oral cancer: Reality or a myth? Oral Oncol. 2022;135:106201. https:\/\/doi.org\/10.1016\/j.oraloncology.2022.106201.","journal-title":"Oral Oncol"},{"key":"8165_CR13","doi-asserted-by":"publisher","first-page":"105510","DOI":"10.1016\/j.oraloncology.2021.105510","volume":"122","author":"G van Hinte","year":"2021","unstructured":"van Hinte G, Sancak T, Weijs WLJ, Merkx MAW, Leijendekkers RA, Nijhuis-van der Sanden MWG, et al. Effect of elective neck dissection versus sentinel lymph node biopsy on shoulder morbidity and health-related quality of life in patients with oral cavity cancer: A longitudinal comparative cohort study. Oral Oncol. 2021;122:105510. https:\/\/doi.org\/10.1016\/j.oraloncology.2021.105510.","journal-title":"Oral Oncol"},{"key":"8165_CR14","doi-asserted-by":"publisher","first-page":"3886","DOI":"10.1200\/JCO.2016.68.4563","volume":"34","author":"JR Acevedo","year":"2016","unstructured":"Acevedo JR, Fero KE, Wilson B, Sacco AG, Mell LK, Coffey CS, et al. Cost-effectiveness analysis of elective neck dissection in patients with clinically node-negative oral cavity cancer. J Clin Oncol. 2016;34:3886\u201391. https:\/\/doi.org\/10.1200\/JCO.2016.68.4563.","journal-title":"J Clin Oncol"},{"key":"8165_CR15","doi-asserted-by":"publisher","first-page":"224","DOI":"10.6004\/jnccn.2022.0016","volume":"20","author":"JJ Caudell","year":"2022","unstructured":"Caudell JJ, Gillison ML, Maghami E, Spencer S, Pfister DG, Adkins D, et al. NCCN guidelines\u00ae insights: Head and neck cancers, version 1.2022. J Natl Compr Canc Netw. 2022;20:224\u201334. https:\/\/doi.org\/10.6004\/jnccn.2022.0016.","journal-title":"J Natl Compr Canc Netw"},{"key":"8165_CR16","doi-asserted-by":"publisher","unstructured":"Hou S, Sun Y, Yang Z, Tang M, Yin T, Shao C et al. The diagnostic value of GICA used for intraoperative lymph node FNA-tg measurement to evaluate thyroid cancer metastases. 2024. https:\/\/doi.org\/10.1530\/ETJ-23-0182.","DOI":"10.1530\/ETJ-23-0182"},{"key":"8165_CR17","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1148\/radiol.2015150959","volume":"279","author":"JT Park","year":"2016","unstructured":"Park JT, Roh J-L, Kim JS, Lee JH, Cho K-J, Choi S-H, et al. (18)F FDG PET\/CT versus CT\/MR imaging and the prognostic value of contralateral neck metastases in patients with head and neck squamous cell carcinoma. Radiology. 2016;279:481\u201391. https:\/\/doi.org\/10.1148\/radiol.2015150959.","journal-title":"Radiology"},{"key":"8165_CR18","doi-asserted-by":"publisher","first-page":"994","DOI":"10.1245\/s10434-014-4062-2","volume":"22","author":"N-Y Shin","year":"2015","unstructured":"Shin N-Y, Lee J-H, Kang WJ, Koh YW, Sohn B, Kim J. Clinical usefulness of [18F]FDG PET-CT and CT\/MRI for detecting nodal metastasis in patients with hypopharyngeal squamous cell carcinoma. Ann Surg Oncol. 2015;22:994\u20139. https:\/\/doi.org\/10.1245\/s10434-014-4062-2.","journal-title":"Ann Surg Oncol"},{"key":"8165_CR19","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.2967\/jnumed.124.268049","volume":"65","author":"A Al-Ibraheem","year":"2024","unstructured":"Al-Ibraheem A, Abdlkadir A, Herrmann K, Bomanji J, Jadvar H, Shi H, et al. Diagnostic accuracy of [18F]FDG PET\/MRI in head and neck squamous cell carcinoma: A systematic review and metaanalysis. J Nucl Med. 2024;65:1533\u20139. https:\/\/doi.org\/10.2967\/jnumed.124.268049.","journal-title":"J Nucl Med"},{"key":"8165_CR20","doi-asserted-by":"publisher","first-page":"4648","DOI":"10.1097\/JS9.0000000000001578","volume":"110","author":"T Lan","year":"2024","unstructured":"Lan T, Kuang S, Liang P, Ning C, Li Q, Wang L, et al. MRI-based deep learning and radiomics for prediction of occult cervical lymph node metastasis and prognosis in early-stage oral and oropharyngeal squamous cell carcinoma: A diagnostic study. Int J Surg. 2024;110:4648\u201359. https:\/\/doi.org\/10.1097\/JS9.0000000000001578.","journal-title":"Int J Surg"},{"key":"8165_CR21","doi-asserted-by":"publisher","first-page":"3022","DOI":"10.1016\/j.acra.2023.06.029","volume":"30","author":"X Chen","year":"2023","unstructured":"Chen X, Yu Q, Peng J, He Z, Li Q, Ning Y, et al. A combined model integrating radiomics and deep learning based on contrast-enhanced CT for preoperative staging of laryngeal carcinoma. Acad Radiol. 2023;30:3022\u201331. https:\/\/doi.org\/10.1016\/j.acra.2023.06.029.","journal-title":"Acad Radiol"},{"key":"8165_CR22","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s00784-023-05423-2","volume":"28","author":"Z Chen","year":"2023","unstructured":"Chen Z, Yu Y, Liu S, Du W, Hu L, Wang C, et al. 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. Clin Oral Investig. 2023;28:39. https:\/\/doi.org\/10.1007\/s00784-023-05423-2.","journal-title":"Clin Oral Investig"},{"key":"8165_CR23","doi-asserted-by":"publisher","first-page":"6054","DOI":"10.1007\/s00330-023-09568-2","volume":"33","author":"Q Yu","year":"2023","unstructured":"Yu Q, Ning Y, Wang A, Li S, Gu J, Li Q, et al. Deep learning-assisted diagnosis of benign and malignant parotid tumors based on contrast-enhanced CT: A multicenter study. Eur Radiol. 2023;33:6054\u201365. https:\/\/doi.org\/10.1007\/s00330-023-09568-2.","journal-title":"Eur Radiol"},{"key":"8165_CR24","doi-asserted-by":"publisher","first-page":"e17254","DOI":"10.7717\/peerj.17254","volume":"12","author":"W Han","year":"2024","unstructured":"Han W, Wang Y, Li T, Dong Y, Dang Y, He L, et al. A CT-based integrated model for preoperative prediction of occult lymph node metastasis in early tongue cancer. PeerJ. 2024;12:e17254. https:\/\/doi.org\/10.7717\/peerj.17254.","journal-title":"PeerJ"},{"key":"8165_CR25","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1038\/s41568-020-00327-9","volume":"21","author":"A Kleppe","year":"2021","unstructured":"Kleppe A, Skrede O-J, De Raedt S, Liest\u00f8l K, Kerr DJ, Danielsen HE. Designing deep learning studies in cancer diagnostics. Nat Rev Cancer. 2021;21:199\u2013211. https:\/\/doi.org\/10.1038\/s41568-020-00327-9.","journal-title":"Nat Rev Cancer"},{"key":"8165_CR26","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1186\/s13244-023-01550-2","volume":"14","author":"L-Q Zhou","year":"2023","unstructured":"Zhou L-Q, Zeng S-E, Xu J-W, Lv W-Z, Mei D, Tu J-J, et al. Deep learning predicts cervical lymph node metastasis in clinically node-negative papillary thyroid carcinoma. Insights Imaging. 2023;14:222. https:\/\/doi.org\/10.1186\/s13244-023-01550-2.","journal-title":"Insights Imaging"},{"key":"8165_CR27","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1002\/hed.27605","volume":"46","author":"S Liu","year":"2024","unstructured":"Liu S, Zhang A, Xiong J, Su X, Zhou Y, Li Y, et al. The application of radiomics machine learning models based on multimodal MRI with different sequence combinations in predicting cervical lymph node metastasis in oral tongue squamous cell carcinoma patients. Head Neck. 2024;46:513\u201327. https:\/\/doi.org\/10.1002\/hed.27605.","journal-title":"Head Neck"},{"key":"8165_CR28","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","volume":"29","author":"NJ Tustison","year":"2010","unstructured":"Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, et al. N4ITK: Improved N3 bias correction. IEEE Trans Med Imaging. 2010;29:1310\u201320. https:\/\/doi.org\/10.1109\/TMI.2010.2046908.","journal-title":"IEEE Trans Med Imaging"},{"key":"8165_CR29","doi-asserted-by":"publisher","DOI":"10.1097\/JS9.0000000000004012","author":"H Yan","year":"2025","unstructured":"Yan H, Yao J, Hou J, Liu L, Li Y, Wang G, et al. A transformer-based deep learning model for preoperative prediction of lympho-vascular invasion in laryngeal squamous cell carcinoma: A multicenter study. Int J Surg. 2025. https:\/\/doi.org\/10.1097\/JS9.0000000000004012.","journal-title":"Int J Surg"},{"key":"8165_CR30","doi-asserted-by":"publisher","first-page":"2275","DOI":"10.1002\/lary.32043","volume":"135","author":"H Yan","year":"2025","unstructured":"Yan H, Liu L, Xie M, Sun M, Yao J, Guo J, et al. Deep learning radiomics based on MRI for differentiating benign and malignant parapharyngeal space tumors. Laryngoscope. 2025;135:2275\u201382. https:\/\/doi.org\/10.1002\/lary.32043.","journal-title":"Laryngoscope"},{"key":"8165_CR31","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/BF00178467","volume":"249","author":"GB Snow","year":"1992","unstructured":"Snow GB, Patel P, Leemans CR, Tiwari R. Management of cervical lymph nodes in patients with head and neck cancer. Eur Arch Otorhinolaryngol. 1992;249:187\u201394. https:\/\/doi.org\/10.1007\/BF00178467.","journal-title":"Eur Arch Otorhinolaryngol"},{"key":"8165_CR32","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1016\/j.acra.2012.02.009","volume":"19","author":"R Matsubara","year":"2012","unstructured":"Matsubara R, Kawano S, Chikui T, Kiyosue T, Goto Y, Hirano M, et al. Clinical significance of combined assessment of the maximum standardized uptake value of F-18 FDG PET with nodal size in the diagnosis of cervical lymph node metastasis of oral squamous cell carcinoma. Acad Radiol. 2012;19:708\u201317. https:\/\/doi.org\/10.1016\/j.acra.2012.02.009.","journal-title":"Acad Radiol"},{"key":"8165_CR33","doi-asserted-by":"publisher","first-page":"180","DOI":"10.4248\/IJOS11068","volume":"3","author":"S Fan","year":"2011","unstructured":"Fan S, Tang Q-L, Lin Y-J, Chen W-L, Li J-S, Huang Z-Q, et al. A review of clinical and histological parameters associated with contralateral neck metastases in oral squamous cell carcinoma. Int J Oral Sci. 2011;3:180\u201391. https:\/\/doi.org\/10.4248\/IJOS11068.","journal-title":"Int J Oral Sci"},{"key":"8165_CR34","doi-asserted-by":"publisher","first-page":"4958","DOI":"10.3390\/jcm12154958","volume":"12","author":"S Jiang","year":"2023","unstructured":"Jiang S, Locatello LG, Maggiore G, Gallo O. Radiomics-based analysis in the prediction of occult lymph node metastases in patients with oral cancer: A systematic review. J Clin Med. 2023;12:4958. https:\/\/doi.org\/10.3390\/jcm12154958.","journal-title":"J Clin Med"},{"key":"8165_CR35","first-page":"1136","volume":"46","author":"S-H Ng","year":"2005","unstructured":"Ng S-H, Yen T-C, Liao C-T, Chang JT-C, Chan S-C, Ko S-F, et al. 18F-FDG PET and CT\/MRI in oral cavity squamous cell carcinoma: A prospective study of 124 patients with histologic correlation. J Nucl Med. 2005;46:1136\u201343.","journal-title":"J Nucl Med"},{"key":"8165_CR36","doi-asserted-by":"publisher","first-page":"1489","DOI":"10.1002\/cncr.24161","volume":"115","author":"SH Huang","year":"2009","unstructured":"Huang SH, Hwang D, Lockwood G, Goldstein DP, O\u2019Sullivan B. Predictive value of tumor thickness for cervical lymph-node involvement in squamous cell carcinoma of the oral cavity: A meta-analysis of reported studies. Cancer. 2009;115:1489\u201397. https:\/\/doi.org\/10.1002\/cncr.24161.","journal-title":"Cancer"},{"key":"8165_CR37","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1002\/hed.27207","volume":"45","author":"SS Jang","year":"2023","unstructured":"Jang SS, Davis ME, Vera DR, Lai SY, Guo TW. Role of sentinel lymph node biopsy for oral squamous cell carcinoma: Current evidence and future challenges. Head Neck. 2023;45:251\u201365. https:\/\/doi.org\/10.1002\/hed.27207.","journal-title":"Head Neck"},{"key":"8165_CR38","doi-asserted-by":"publisher","first-page":"102385","DOI":"10.1016\/j.eclinm.2023.102385","volume":"67","author":"W Wang","year":"2024","unstructured":"Wang W, Liang H, Zhang Z, Xu C, Wei D, Li W, et al. 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. EClinicalMedicine. 2024;67:102385. https:\/\/doi.org\/10.1016\/j.eclinm.2023.102385.","journal-title":"EClinicalMedicine"},{"key":"8165_CR39","doi-asserted-by":"publisher","first-page":"5228","DOI":"10.1007\/s00330-025-11473-9","volume":"35","author":"W Li","year":"2025","unstructured":"Li W, Li Y, Wang L, Yang M, Iikubo M, Huang N, et al. Evaluating fusion models for predicting occult lymph node metastasis in tongue squamous cell carcinoma. Eur Radiol. 2025;35:5228\u201338. https:\/\/doi.org\/10.1007\/s00330-025-11473-9.","journal-title":"Eur Radiol"},{"key":"8165_CR40","doi-asserted-by":"publisher","first-page":"107165","DOI":"10.1016\/j.oraloncology.2024.107165","volume":"161","author":"L Yang","year":"2025","unstructured":"Yang L, Zhang S, Li J, Feng C, Zhu L, Li J, et al. Diagnosis of lymph node metastasis in oral squamous cell carcinoma by an MRI-based deep learning model. Oral Oncol. 2025;161:107165. https:\/\/doi.org\/10.1016\/j.oraloncology.2024.107165.","journal-title":"Oral Oncol"},{"key":"8165_CR41","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies RJ, Kinahan PE, Hricak H, Radiomics. Images are more than pictures, they are data. Radiology. 2016;278:563\u201377. https:\/\/doi.org\/10.1148\/radiol.2015151169.","journal-title":"Radiology"},{"key":"8165_CR42","doi-asserted-by":"publisher","first-page":"5213","DOI":"10.1073\/pnas.0801279105","volume":"105","author":"M Diehn","year":"2008","unstructured":"Diehn M, Nardini C, Wang DS, McGovern S, Jayaraman M, Liang Y, et al. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci USA. 2008;105:5213\u20138. https:\/\/doi.org\/10.1073\/pnas.0801279105.","journal-title":"Proc Natl Acad Sci USA"},{"key":"8165_CR43","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1007\/s13244-012-0196-6","volume":"3","author":"F Davnall","year":"2012","unstructured":"Davnall F, Yip CSP, Ljungqvist G, Selmi M, Ng F, Sanghera B, et al. Assessment of tumor heterogeneity: An emerging imaging tool for clinical practice? Insights Into Imaging. 2012;3:573\u201389. https:\/\/doi.org\/10.1007\/s13244-012-0196-6.","journal-title":"Insights Into Imaging"},{"key":"8165_CR44","doi-asserted-by":"publisher","first-page":"4006","DOI":"10.1038\/ncomms5006","volume":"5","author":"HJWL Aerts","year":"2014","unstructured":"Aerts HJWL, Velazquez ER, Leijenaar RTH, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006. https:\/\/doi.org\/10.1038\/ncomms5006.","journal-title":"Nat Commun"},{"key":"8165_CR45","doi-asserted-by":"publisher","first-page":"2786","DOI":"10.1002\/hed.27189","volume":"44","author":"Y Wang","year":"2022","unstructured":"Wang Y, Yu T, Yang Z, Zhou Y, Kang Z, Wang Y, et al. Radiomics based on magnetic resonance imaging for preoperative prediction of lymph node metastasis in head and neck cancer: Machine learning study. Head Neck. 2022;44:2786\u201395. https:\/\/doi.org\/10.1002\/hed.27189.","journal-title":"Head Neck"},{"key":"8165_CR46","doi-asserted-by":"publisher","first-page":"7440","DOI":"10.1007\/s00330-021-07758-4","volume":"31","author":"H Tomita","year":"2021","unstructured":"Tomita H, Yamashiro T, Heianna J, Nakasone T, Kimura Y, Mimura H, et al. Nodal-based radiomics analysis for identifying cervical lymph node metastasis at levels I and II in patients with oral squamous cell carcinoma using contrast-enhanced computed tomography. Eur Radiol. 2021;31:7440\u20139. https:\/\/doi.org\/10.1007\/s00330-021-07758-4.","journal-title":"Eur Radiol"},{"key":"8165_CR47","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1002\/jmri.28019","volume":"56","author":"F Wang","year":"2022","unstructured":"Wang F, Tan R, Feng K, Hu J, Zhuang Z, Wang C, et al. Magnetic resonance imaging-based radiomics features associated with depth of invasion predicted lymph node metastasis and prognosis in tongue cancer. J Magn Reson Imaging. 2022;56:196\u2013209. https:\/\/doi.org\/10.1002\/jmri.28019.","journal-title":"J Magn Reson Imaging"},{"key":"8165_CR48","doi-asserted-by":"publisher","first-page":"2669","DOI":"10.1002\/jmri.29390","volume":"60","author":"Y-J Pan","year":"2024","unstructured":"Pan Y-J, Wu S-J, Zeng Y, Cao Z-R, Shan Y, Lin J, et al. Intra- and peri-tumoral radiomics based on dynamic contrast enhanced-MRI to identify lymph node metastasis and prognosis in intrahepatic cholangiocarcinoma. J Magn Reson Imaging. 2024;60:2669\u201380. https:\/\/doi.org\/10.1002\/jmri.29390.","journal-title":"J Magn Reson Imaging"},{"key":"8165_CR49","doi-asserted-by":"publisher","first-page":"e2028086","DOI":"10.1001\/jamanetworkopen.2020.28086","volume":"3","author":"Y Yu","year":"2020","unstructured":"Yu Y, Tan Y, Xie C, Hu Q, Ouyang J, Chen Y, et al. Development and validation of a preoperative magnetic resonance imaging radiomics-based signature to predict axillary lymph node metastasis and disease-free survival in patients with early-stage breast cancer. JAMA Netw Open. 2020;3:e2028086. https:\/\/doi.org\/10.1001\/jamanetworkopen.2020.28086.","journal-title":"JAMA Netw Open"},{"key":"8165_CR50","doi-asserted-by":"publisher","first-page":"3795","DOI":"10.1097\/JS9.0000000000001239","volume":"110","author":"E Abbaspour","year":"2024","unstructured":"Abbaspour E, Karimzadhagh S, Monsef A, Joukar F, Mansour-Ghanaei F, Hassanipour S. Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: A systematic review and meta-analysis. Int J Surg. 2024;110:3795\u2013813. https:\/\/doi.org\/10.1097\/JS9.0000000000001239.","journal-title":"Int J Surg"},{"key":"8165_CR51","doi-asserted-by":"publisher","first-page":"4576","DOI":"10.1097\/JS9.0000000000002488","volume":"111","author":"J Liu","year":"2025","unstructured":"Liu J, Jiang S, Wu Y, Zou R, Bao Y, Wang N, et al. Deep learning-based radiomics and machine learning for prognostic assessment in IDH-wildtype glioblastoma after maximal safe surgical resection: A multicenter study. Int J Surg. 2025;111:4576\u201385. https:\/\/doi.org\/10.1097\/JS9.0000000000002488.","journal-title":"Int J Surg"},{"key":"8165_CR52","doi-asserted-by":"publisher","first-page":"111136","DOI":"10.1016\/j.ejrad.2023.111136","volume":"168","author":"H Zhou","year":"2023","unstructured":"Zhou H, Bai HX, Jiao Z, Cui B, Wu J, Zheng H, et al. Deep learning-based radiomic nomogram to predict risk categorization of thymic epithelial tumors: A multicenter study. Eur J Radiol. 2023;168:111136. https:\/\/doi.org\/10.1016\/j.ejrad.2023.111136.","journal-title":"Eur J Radiol"},{"key":"8165_CR53","doi-asserted-by":"publisher","first-page":"1217037","DOI":"10.3389\/fmed.2023.1217037","volume":"10","author":"BN Huynh","year":"2023","unstructured":"Huynh BN, Groendahl AR, Tomic O, Liland KH, Knudtsen IS, Hoebers F, et al. Head and neck cancer treatment outcome prediction: A comparison between machine learning with conventional radiomics features and deep learning radiomics. Front Med. 2023;10:1217037. https:\/\/doi.org\/10.3389\/fmed.2023.1217037.","journal-title":"Front Med"},{"key":"8165_CR54","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1186\/s40644-023-00609-z","volume":"23","author":"H Song","year":"2023","unstructured":"Song H, Yang S, Yu B, Li N, Huang Y, Sun R, et al. CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: A multicenter study. Cancer Imaging. 2023;23:89. https:\/\/doi.org\/10.1186\/s40644-023-00609-z.","journal-title":"Cancer Imaging"},{"key":"8165_CR55","doi-asserted-by":"publisher","first-page":"5312","DOI":"10.1002\/cam4.5343","volume":"12","author":"K Kawamura","year":"2023","unstructured":"Kawamura K, Lee C, Yoshikawa T, Hani A-S, Usami Y, Toyosawa S, et al. Prediction of cervical lymph node metastasis from immunostained specimens of tongue cancer using a multilayer perceptron neural network. Cancer Med. 2023;12:5312\u201322. https:\/\/doi.org\/10.1002\/cam4.5343.","journal-title":"Cancer Med"},{"key":"8165_CR56","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.neurad.2021.07.006","volume":"49","author":"Y-W Zhong","year":"2022","unstructured":"Zhong Y-W, Jiang Y, Dong S, Wu W-J, Wang L-X, Zhang J, et al. Tumor radiomics signature for artificial neural network-assisted detection of neck metastasis in patient with tongue cancer. J Neuroradiol. 2022;49:213\u20138. https:\/\/doi.org\/10.1016\/j.neurad.2021.07.006.","journal-title":"J Neuroradiol"},{"key":"8165_CR57","doi-asserted-by":"publisher","first-page":"106948","DOI":"10.1016\/j.cmpb.2022.106948","volume":"222","author":"Y Wang","year":"2022","unstructured":"Wang Y, Lombardo E, Avanzo M, Zschaek S, Weing\u00e4rtner J, Holzgreve A, et al. Deep learning based time-to-event analysis with PET, CT and joint PET\/CT for head and neck cancer prognosis. Comput Methods Programs Biomed. 2022;222:106948. https:\/\/doi.org\/10.1016\/j.cmpb.2022.106948.","journal-title":"Comput Methods Programs Biomed"},{"key":"8165_CR58","doi-asserted-by":"publisher","first-page":"3948","DOI":"10.1158\/1078-0432.CCR-20-4935","volume":"27","author":"N-M Cheng","year":"2021","unstructured":"Cheng N-M, Yao J, Cai J, Ye X, Zhao S, Zhao K, et al. Deep learning for fully automated prediction of overall survival in patients with oropharyngeal cancer using FDG-PET imaging. Clin Cancer Res. 2021;27:3948\u201359. https:\/\/doi.org\/10.1158\/1078-0432.CCR-20-4935.","journal-title":"Clin Cancer Res"},{"key":"8165_CR59","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1186\/s13073-021-00968-x","volume":"13","author":"KA Tran","year":"2021","unstructured":"Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med. 2021;13:152. https:\/\/doi.org\/10.1186\/s13073-021-00968-x.","journal-title":"Genome Med"},{"key":"8165_CR60","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.oooo.2024.01.016","volume":"138","author":"D Wang","year":"2024","unstructured":"Wang D, He X, Huang C, Li W, Li H, Huang C, et al. Magnetic resonance imaging-based radiomics and deep learning models for predicting lymph node metastasis of squamous cell carcinoma of the tongue. Oral Surg Oral Med Oral Pathol Oral Radiol. 2024;138:214\u201324. https:\/\/doi.org\/10.1016\/j.oooo.2024.01.016.","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radiol"},{"key":"8165_CR61","doi-asserted-by":"publisher","first-page":"3638","DOI":"10.1007\/s00330-023-09450-1","volume":"33","author":"L Wan","year":"2023","unstructured":"Wan L, Hu J, Chen S, Zhao R, Peng W, Liu Y, et al. Prediction of lymph node metastasis in stage T1-2 rectal cancers with MRI-based deep learning. Eur Radiol. 2023;33:3638\u201346. https:\/\/doi.org\/10.1007\/s00330-023-09450-1.","journal-title":"Eur Radiol"},{"key":"8165_CR62","doi-asserted-by":"publisher","first-page":"105010","DOI":"10.1016\/j.compbiomed.2021.105010","volume":"139","author":"A Aghabiglou","year":"2021","unstructured":"Aghabiglou A, Eksioglu EM. MR image reconstruction using densely connected residual convolutional networks. Comput Biol Med. 2021;139:105010. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.105010.","journal-title":"Comput Biol Med"},{"key":"8165_CR63","doi-asserted-by":"publisher","first-page":"e1002683","DOI":"10.1371\/journal.pmed.1002683","volume":"15","author":"JR Zech","year":"2018","unstructured":"Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLoS Med. 2018;15:e1002683. https:\/\/doi.org\/10.1371\/journal.pmed.1002683.","journal-title":"PLoS Med"},{"key":"8165_CR64","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1007\/s00330-020-07184-y","volume":"31","author":"X Bai","year":"2021","unstructured":"Bai X, Huang Q, Zuo P, Zhang X, Yuan J, Zhang X, et al. MRI radiomics-based nomogram for individualised prediction of synchronous distant metastasis in patients with clear cell renal cell carcinoma. Eur Radiol. 2021;31:1029\u201342. https:\/\/doi.org\/10.1007\/s00330-020-07184-y.","journal-title":"Eur Radiol"},{"key":"8165_CR65","doi-asserted-by":"publisher","first-page":"106366","DOI":"10.1016\/j.compbiomed.2022.106366","volume":"152","author":"N Ye","year":"2023","unstructured":"Ye N, Yang Q, Liu P, Chen Z, Li X. A comprehensive machine-learning model applied to MRI to classify germinomas of the pineal region. Comput Biol Med. 2023;152:106366. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106366.","journal-title":"Comput Biol Med"},{"key":"8165_CR66","doi-asserted-by":"publisher","first-page":"638185","DOI":"10.3389\/fonc.2021.638185","volume":"11","author":"L Lu","year":"2021","unstructured":"Lu L, Ahmed FS, Akin O, Luk L, Guo X, Yang H, et al. Uncontrolled confounders may lead to false or overvalued radiomics signature: A proof of concept using survival analysis in a multicenter cohort of kidney cancer. Front Oncol. 2021;11:638185. https:\/\/doi.org\/10.3389\/fonc.2021.638185.","journal-title":"Front Oncol"}],"container-title":["BMC Oral Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12903-026-08165-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12903-026-08165-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12903-026-08165-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T13:31:08Z","timestamp":1775655068000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12903-026-08165-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,30]]},"references-count":66,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["8165"],"URL":"https:\/\/doi.org\/10.1186\/s12903-026-08165-1","relation":{},"ISSN":["1472-6831"],"issn-type":[{"value":"1472-6831","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,30]]},"assertion":[{"value":"6 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved by the Medical Ethics Committees of the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University (approval No. SBQLL-2024-064) and the First Affiliated Hospital of Guangxi Medical University (approval No. 2025-E0785). Due to the retrospective nature of the study, the requirement for written informed consent was waived by both committees.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"606"}}