{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T01:52:38Z","timestamp":1781920358823,"version":"3.54.5"},"reference-count":43,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education (MOE), Taiwan","award":["NSTC 114-2321-B-A49-015"],"award-info":[{"award-number":["NSTC 114-2321-B-A49-015"]}]},{"name":"National Science and Technology Council (NSTC), Taiwan","award":["NSTC 114-2221-E-A49-104-MY3"],"award-info":[{"award-number":["NSTC 114-2221-E-A49-104-MY3"]}]},{"name":"Department of Health, Taipei City Government, Taiwan","award":["11401-62-026"],"award-info":[{"award-number":["11401-62-026"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Occult lymph node metastasis (OLNM) and depth of invasion (DOI) are key determinants of elective neck dissection in clinically node-negative oral tongue squamous cell carcinoma (OTSCC), yet accurate preoperative risk stratification remains challenging. This study evaluated the diagnostic performance of artificial intelligence (AI)-based predictive models for OLNM and DOI in OTSCC. Methods: A systematic review and meta-analysis were conducted in accordance with PRISMA 2020 guidelines. A structured search of PubMed identified twelve eligible studies, nine of which provided extractable 2 \u00d7 2 contingency data for inclusion in the primary bivariate meta-analysis. One additional study modeling DOI-derived pT stage was synthesized narratively. Pooled sensitivity and specificity were estimated using a bivariate random-effects model. Heterogeneity, threshold effects, and publication bias (Deeks\u2019 test) were assessed. Methodological quality was evaluated using QUADAS-2 supplemented by an AI-specific methodological appraisal. Results: Across nine studies included in the primary meta-analysis, pooled sensitivity was 0.679 (95% CI: 0.604\u20130.745) and pooled specificity was 0.762 (95% CI: 0.705\u20130.811), with a summary AUC of 0.786. Heterogeneity was moderate for sensitivity (I2 = 41.8%) and low for specificity (I2 = 23.4%), with no significant threshold effect (\u03c1 = \u22120.117, p = 0.776). No significant publication bias was detected (p = 0.596). Subgroup analyses showed comparable performance between OLNM-specific and general LNM models, whereas deep learning or hybrid approaches demonstrated higher accuracy than traditional machine learning methods. Notably, only one out of nine primary studies incorporated true external validation. Conclusions: AI-based models demonstrate moderate discriminative performance for predicting LNM and DOI in OTSCC and may serve as adjunctive tools in preoperative risk stratification rather than standalone decision-makers. However, the near absence of external validation, limited calibration reporting, and lack of clinician-comparator analyses substantially constrain current clinical translation. Future research should prioritize multi-center prospective validation, systematic calibration and decision-curve analyses, and adherence to TRIPOD-AI and CLAIM reporting standards.<\/jats:p>","DOI":"10.3390\/diagnostics16050774","type":"journal-article","created":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T16:17:31Z","timestamp":1772641051000},"page":"774","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial Intelligence for Preoperative Prediction of Lymph Node Metastasis and Depth of Invasion in Oral Tongue Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0471-265X","authenticated-orcid":false,"given":"Yi-Yun","family":"Ho","sequence":"first","affiliation":[{"name":"Oral & Maxillofacial Surgery, Department of Stomatology, Taipei Veterans General Hospital, Taipei 11217, Taiwan"},{"name":"Faculty of Dentistry, Institute of Oral Biology, School of Dentistry, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun-Wei","family":"Hsu","sequence":"additional","affiliation":[{"name":"Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ta-Yi","family":"Chu","sequence":"additional","affiliation":[{"name":"Oral & Maxillofacial Surgery, Department of Stomatology, Chang Gung Memorial Hospital, Linkou 33305, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun-Ju","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Dentistry, School of Dentistry, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Hsin","family":"Ho","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Taipei Veterans General Hospital, Taipei 11217, Taiwan"},{"name":"School of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0613-7214","authenticated-orcid":false,"given":"Cheng-Hsien","family":"Wu","sequence":"additional","affiliation":[{"name":"Oral & Maxillofacial Surgery, Department of Stomatology, Taipei Veterans General Hospital, Taipei 11217, Taiwan"},{"name":"Faculty of Dentistry, Institute of Oral Biology, School of Dentistry, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ching-Po","family":"Lin","sequence":"additional","affiliation":[{"name":"Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan"},{"name":"Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan"},{"name":"Department of Education and Research, Taipei City Hospital, Taipei 10341, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100055","DOI":"10.1016\/j.soi.2024.100055","article-title":"Trends in incidence of oral cavity squamous cell carcinoma in the United States, 2001\u20132019","volume":"1","author":"Ramadan","year":"2024","journal-title":"Surg. Oncol. Insights"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1177\/01945998221098011","article-title":"Changing epidemiology of oral cavity cancer in the United States","volume":"168","author":"Stepan","year":"2023","journal-title":"Otolaryngol. Head Neck Surg."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sakr, Y., Hamdy, O., Eldeghedi, M., Abdelaziz, R., El Moctar, E.M.S., Alharazin, M., and Awny, S. (2023). Shifting epidemiology trends in tongue cancer: A retrospective cohort study. Cancers, 15.","DOI":"10.3390\/cancers15235680"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"75","DOI":"10.4103\/jhnps.jhnps_17_17","article-title":"Incidence of occult metastasis in clinically N0 oral tongue squamous cell carcinoma and its association with tumour staging, thickness, and differentiation","volume":"5","author":"Chaudhary","year":"2017","journal-title":"J. Head Neck Physicians Surg."},{"key":"ref_5","unstructured":"(2024, September 15). Merck Manual Professional Version. Oral Squamous Cell Carcinoma. Available online: https:\/\/www.merckmanuals.com\/professional\/ear-nose-and-throat-disorders\/tumors-of-the-head-and-neck\/oral-squamous-cell-carcinoma."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ghantous, Y., Nashef, A., Sidransky, D., Abdelraziq, M., Alkeesh, K., Araidy, S., Koch, W., Brait, M., and Abu El-Naaj, I. (2022). Clinical and prognostic significance of the eighth edition oral cancer staging system. Cancers, 14.","DOI":"10.3390\/cancers14194632"},{"key":"ref_7","first-page":"e225","article-title":"Artificial intelligence and radiomics in head and neck cancer care: Opportunities, mechanics, and challenges","volume":"41","author":"Fuller","year":"2021","journal-title":"ASCO Educ. Book"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Broggi, G., Maniaci, A., Lentini, M., Palicelli, A., Zanelli, M., Zizzo, M., Koufopoulos, N., Salzano, S., Mazzucchelli, M., and Caltabiano, R. (2024). Artificial intelligence in head and neck cancer diagnosis: A comprehensive review with emphasis on radiomics, histopathological, and molecular applications. Cancers, 16.","DOI":"10.3390\/cancers16213623"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"108683","DOI":"10.1016\/j.cmpb.2025.108683","article-title":"Prognostic power of radiomics in head and neck cancers: Insights from a meta-analysis","volume":"262","author":"Wang","year":"2025","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105464","DOI":"10.1016\/j.ijmedinf.2024.105464","article-title":"Artificial intelligence-driven radiomics in head and neck cancer: Current status and future prospects","volume":"188","author":"Alabi","year":"2024","journal-title":"Int. J. Med. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s00234-024-03485-x","article-title":"Diagnostic accuracy of radiomics and artificial intelligence models in diagnosing lymph node metastasis in head and neck cancers: A systematic review and meta-analysis","volume":"67","author":"Valizadeh","year":"2025","journal-title":"Neuroradiology"},{"key":"ref_12","first-page":"e472464","article-title":"Artificial intelligence for head and neck squamous cell carcinoma: From diagnosis to treatment","volume":"45","author":"Song","year":"2025","journal-title":"ASCO Educ. Book"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tortora, M., Gemini, L., Scaravilli, A., Ugga, L., Ponsiglione, A., Stanzione, A., D\u2019arco, F., D\u2019anna, G., and Cuocolo, R. (2023). Radiomics applications in head and neck tumor imaging: A narrative review. Cancers, 15.","DOI":"10.3390\/cancers15041174"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Huynh, B.N., Groendahl, A.R., Tomic, O., Liland, K.H., Knudtsen, I.S., Hoebers, F., van Elmpt, W., Malinen, E., Dale, E., and Futsaether, C.M. (2023). Head and neck cancer treatment outcome prediction: A comparison between machine learning with conventional radiomics features and deep learning radiomics. Front. Med., 10.","DOI":"10.3389\/fmed.2023.1217037"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1038\/s41746-022-00592-y","article-title":"Machine learning for medical imaging: Methodological failures and recommendations for the future","volume":"5","author":"Varoquaux","year":"2022","journal-title":"npj Digit. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"ref_17","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":"ref_18","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1016\/j.jclinepi.2005.02.022","article-title":"Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews","volume":"58","author":"Reitsma","year":"2005","journal-title":"J. Clin. Epidemiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1093\/biostatistics\/kxl004","article-title":"A unification of models for meta-analysis of diagnostic accuracy studies","volume":"8","author":"Harbord","year":"2007","journal-title":"Biostatistics"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1016\/j.jclinepi.2005.01.016","article-title":"The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed","volume":"58","author":"Deeks","year":"2005","journal-title":"J. Clin. Epidemiol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Committeri, U., Fusco, R., Di Bernardo, E., Abbate, V., Salzano, G., Maglitto, F., Dell\u2019Aversana Orabona, G., Piombino, P., Bonavolont\u00e0, P., and Arena, A. (2022). Radiomics metrics combined with clinical data in the surgical management of early-stage (cT1\u2013T2 N0) tongue squamous cell carcinomas: A preliminary study. Biology, 11.","DOI":"10.3390\/biology11030468"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e6824","DOI":"10.1002\/cam4.6824","article-title":"The modified Polsby\u2013Popper score, a novel quantitative histomorphological biomarker and its potential to predict lymph node positivity and cancer-specific survival in oral tongue squamous cell carcinoma","volume":"13","author":"Balk","year":"2024","journal-title":"Cancer Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e17254","DOI":"10.7717\/peerj.17254","article-title":"A CT-based integrated model for preoperative prediction of occult lymph node metastasis in early tongue cancer","volume":"12","author":"Han","year":"2024","journal-title":"PeerJ"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.oooo.2021.12.122","article-title":"Development of a radiomics and machine learning model for predicting occult cervical lymph node metastasis in patients with tongue cancer","volume":"134","author":"Kubo","year":"2022","journal-title":"Oral Surg. Oral Med. Oral Pathol. Oral Radiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4648","DOI":"10.1097\/JS9.0000000000001578","article-title":"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","volume":"110","author":"Lan","year":"2024","journal-title":"Int. J. Surg."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"15330338231207006","DOI":"10.1177\/15330338231207006","article-title":"Prediction of prognosis of tongue squamous cell carcinoma based on clinical MR imaging data modeling","volume":"22","author":"Liu","year":"2023","journal-title":"Technol. Cancer Res. Treat."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4277","DOI":"10.1016\/j.csbj.2023.08.020","article-title":"Magnetic resonance imaging-based prediction models for tumor stage and cervical lymph node metastasis of tongue squamous cell carcinoma","volume":"21","author":"Vidiri","year":"2023","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.oooo.2024.01.016","article-title":"Magnetic resonance imaging-based radiomics and deep learning models for predicting lymph node metastasis of squamous cell carcinoma of the tongue","volume":"138","author":"Wang","year":"2024","journal-title":"Oral Surg. Oral Med. Oral Pathol. Oral Radiol."},{"key":"ref_29","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"},{"key":"ref_30","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":"ref_31","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 patients with tongue cancer","volume":"49","author":"Zhong","year":"2022","journal-title":"J. Neuroradiol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1038\/s41591-021-01517-0","article-title":"A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI","volume":"27","author":"Sounderajah","year":"2021","journal-title":"Nat. Med."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Deng, C., Hu, J., Tang, P., Xu, T., He, L., Zeng, Z., and Sheng, J. (2024). Application of CT and MRI images based on artificial intelligence to predict lymph node metastases in patients with oral squamous cell carcinoma: A subgroup meta-analysis. Front. Oncol., 14.","DOI":"10.3389\/fonc.2024.1395159"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1038\/s41591-022-01981-2","article-title":"Multimodal biomedical AI","volume":"28","author":"Acosta","year":"2022","journal-title":"Nat. Med."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"485","DOI":"10.5152\/dir.2019.19321","article-title":"Radiomics with artificial intelligence: A practical guide for beginners","volume":"25","author":"Durmaz","year":"2019","journal-title":"Diagn. Interv. Radiol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1186\/s40644-016-0075-3","article-title":"Cross-sectional imaging in cancers of the head and neck: How we review and report","volume":"16","author":"Vogel","year":"2016","journal-title":"Cancer Imaging"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Van Calster, B., McLernon, D.J., van Smeden, M., Wynants, L., and Steyerberg, E.W. (2019). Calibration: The Achilles heel of predictive analytics. BMC Med., 17.","DOI":"10.1186\/s12916-019-1466-7"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G., and King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Med., 17.","DOI":"10.1186\/s12916-019-1426-2"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"e210064","DOI":"10.1148\/ryai.210064","article-title":"External validation of deep learning algorithms for radiologic diagnosis: A systematic review","volume":"4","author":"Yu","year":"2022","journal-title":"Radiol. Artif. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","article-title":"Radiomics: The bridge between medical imaging and personalized medicine","volume":"14","author":"Lambin","year":"2017","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s00330-019-06360-z","article-title":"Quality of science and reporting of radiomics in oncologic studies: Room for improvement according to radiomics quality score and TRIPOD statement","volume":"30","author":"Park","year":"2020","journal-title":"Eur. Radiol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1148\/radiol.2020191145","article-title":"The Image Biomarker Standardization Initiative: Standardized quantitative radiomics for high-throughput image-based phenotyping","volume":"295","author":"Zwanenburg","year":"2020","journal-title":"Radiology"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e200029","DOI":"10.1148\/ryai.2020200029","article-title":"Checklist for artificial intelligence in medical imaging (CLAIM): A guide for authors and reviewers","volume":"2","author":"Mongan","year":"2020","journal-title":"Radiol. Artif. Intell."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/5\/774\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T05:13:07Z","timestamp":1772860387000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/5\/774"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,4]]},"references-count":43,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["diagnostics16050774"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16050774","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,4]]}}}