{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T18:44:28Z","timestamp":1792003468889,"version":"4.4.0"},"reference-count":58,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T00:00:00Z","timestamp":1749513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>This meta-analysis aims to evaluate the diagnostic performance of ultrasound (US)-based artificial intelligence (AI) in assessing cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A comprehensive literature search was conducted in PubMed, Embase, Web of Science, and the Cochrane Library to identify relevant studies published up to November 19, 2024. Studies focused on the diagnostic performance of AI in the detection of CLNM of PTC were included. A bivariate random-effects model was used to calculate the pooled sensitivity and specificity, both with 95% confidence intervals (CI). The I<jats:sup>2<\/jats:sup> statistic was used to assess heterogeneity among studies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Among the 593 studies identified, 27 studies were included (involving over 23,170 patients or images). For the internal validation set, the pooled sensitivity, specificity, and AUC for detecting CLNM of PTC were 0.80 (95% CI: 0.75\u20130.84), 0.83 (95% CI: 0.80\u20130.87), and 0.89 (95% CI: 0.86\u20130.91), respectively. For the external validation set, the pooled sensitivity, specificity, and AUC were 0.77 (95% CI: 0.49\u20130.92), 0.82 (95% CI: 0.75\u20130.88), and 0.86 (95% CI: 0.83\u20130.89), respectively. For US physicians, the overall sensitivity, specificity, and AUC for detecting CLNM were 0.51 (95% CI: 0.38\u20130.64), 0.84 (95% CI: 0.76\u20130.89), and 0.77 (95% CI: 0.73\u20130.81), respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>US-based AI demonstrates higher diagnostic performance than US physicians. However, the high heterogeneity among studies and the limited number of externally validated studies constrain the generalizability of these findings, and further research on external validation datasets is needed to confirm the results and assess their practical clinical value.<\/jats:p><\/jats:sec><jats:sec><jats:title>Systematic review registration<\/jats:title><jats:p><jats:uri>https:\/\/www.crd.york.ac.uk\/PROSPERO\/view\/CRD42024625725<\/jats:uri>, identifier CRD42024625725.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fendo.2025.1570811","type":"journal-article","created":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T04:10:35Z","timestamp":1749528635000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Ultrasound-based artificial intelligence for predicting cervical lymph node metastasis in papillary thyroid cancer: a systematic review and meta-analysis"],"prefix":"10.3389","volume":"16","author":[{"given":"Xi","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiting","family":"Qi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,6,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1038\/s41420-024-02157-2","article-title":"High aggressiveness of papillary thyroid cancer: from clinical evidence to regulatory cellular networks","volume":"10","author":"Zhang","year":"2024","journal-title":"Cell Death Discov"},{"key":"B2","doi-asserted-by":"publisher","first-page":"5266","DOI":"10.3390\/cancers14215266","article-title":"Evaluation of cervical lymph node metastasis in papillary thyroid carcinoma using Clinical-Ultrasound Radiomic Machine Learning-Based model","volume":"14","author":"Agyekum","year":"2022","journal-title":"Cancers"},{"key":"B3","doi-asserted-by":"publisher","first-page":"3641","DOI":"10.3390\/jcm12113641","article-title":"Prediction of cervical lymph node metastasis in clinically node-negative T1 and T2 papillary thyroid carcinoma using supervised machine learning approach","volume":"12","author":"Popovi\u0107 Krneta","year":"2023","journal-title":"J Clin Med"},{"key":"B4","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1038\/s41598-019-55991-1","article-title":"Predictive risk-scoring model for central lymph node metastasis and predictors of recurrence in papillary thyroid carcinoma","volume":"10","author":"Jiang","year":"2020","journal-title":"Sci Rep"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.2174\/1566524023666230915103723","article-title":"Nuclear imaging modalities in the diagnosis and management of thyroid cancer","volume":"24","author":"Singh","year":"2024","journal-title":"Curr Mol Med"},{"key":"B6","article-title":"Chapter 53 - molecular and functional imaging and theranostics of the tumor microenvironment","volume-title":"Molecular Imaging (Second Edition)","author":"Penet","year":"2021"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2023.12.008","article-title":"Development and validation of clinical-radiomics nomogram for preoperative prediction of central lymph node metastasis in papillary thyroid carcinoma","volume":"31","author":"Feng","year":"2024","journal-title":"Acad Radiol"},{"key":"B8","doi-asserted-by":"publisher","first-page":"562.e1","DOI":"10.1016\/j.crad.2020.03.025","article-title":"Diagnostic performance of MRI to detect metastatic cervical lymph nodes in patients with thyroid cancer: a systematic review and meta-analysis","volume":"75","author":"Cho","year":"2020","journal-title":"Clin Radiol"},{"key":"B9","doi-asserted-by":"publisher","first-page":"e051568","DOI":"10.1136\/bmjopen-2021-051568","article-title":"Diagnostic accuracy of ultrasound, CT and their combination in detecting cervical lymph node metastasis in patients with papillary thyroid cancer: a systematic review and meta-analysis","volume":"12","author":"Yang","year":"2022","journal-title":"BMJ Open"},{"key":"B10","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1007\/s12020-023-03644-9","article-title":"Integration of ultrasound-based radiomics with clinical features for predicting cervical lymph node metastasis in postoperative patients with differentiated thyroid carcinoma","volume":"84","author":"Fan","year":"2024","journal-title":"Endocrine"},{"key":"B11","doi-asserted-by":"publisher","first-page":"e40238","DOI":"10.2196\/40238","article-title":"Artificial intelligence applications in health care practice: scoping review","volume":"24","author":"Sharma","year":"2022","journal-title":"J Med Internet Res"},{"key":"B12","article-title":"The use of AI in advanced medical imaging","volume":"6","author":"Tadiboina","year":"2022","journal-title":"J Positive School Psychol"},{"key":"B13","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1186\/s12885-024-11838-1","article-title":"An integrated model incorporating deep learning, hand-crafted radiomics and clinical and US features to diagnose central lymph node metastasis in patients with papillary thyroid cancer","volume":"24","author":"Gao","year":"2024","journal-title":"BMC Cancer"},{"key":"B14","doi-asserted-by":"publisher","first-page":"3676","DOI":"10.21037\/qims-23-1650","article-title":"Diagnostic performance of artificial intelligence in interpreting thyroid nodules on ultrasound images: a multicenter retrospective study","volume":"14","author":"Namsena","year":"2024","journal-title":"Quantitative Imaging Med Surg"},{"key":"B15","doi-asserted-by":"publisher","first-page":"e10010","DOI":"10.2196\/10010","article-title":"Artificial intelligence versus clinicians in disease diagnosis: systematic review","volume":"7","author":"Shen","year":"2019","journal-title":"JMIR Med Inf"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.19163","article-title":"Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA statement","volume":"319","author":"McInnes","year":"2018","journal-title":"Jama"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.3760\/cma.j.issn.0254-6450.2018.04.028","article-title":"Risk on bias assessment:(6) a revised tool for the quality assessment on diagnostic accuracy studies (QUADAS-2)","volume":"39","author":"Qu","year":"2018","journal-title":"Zhonghua Liuxingbingxue Zazhi"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1177\/0272989X08319957","article-title":"Bivariate random effects meta-analysis of ROC curves","volume":"28","author":"Arends","year":"2008","journal-title":"Med Decision Making"},{"key":"B19","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1037\/1082-989X.11.2.193","article-title":"Assessing heterogeneity in meta-analysis: Q statistic or I\u00b2 index","volume":"11","author":"Huedo-Medina","year":"2006","journal-title":"psychol Methods"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-011-2221-4","article-title":"Diagnosis of bone metastases: a meta-analysis comparing 18 FDG PET, CT, MRI and bone scintigraphy","volume":"21","author":"Yang","year":"2011","journal-title":"Eur Radiol"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2023.964074","article-title":"An integrated nomogram combining deep learning, clinical characteristics and ultrasound features for predicting central lymph node metastasis in papillary thyroid cancer: A multicenter study","volume":"14","author":"Chang","year":"2023","journal-title":"Front Endocrinol"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.18280\/ts.380310","article-title":"Predictions for central lymph node metastasis of papillary thyroid carcinoma via CNN-based fusion modeling of ultrasound images","volume":"38","author":"Chen","year":"2021","journal-title":"Traitement Du Signal"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1261080","article-title":"Ultrasound radiomics models based on multimodal imaging feature fusion of papillary thyroid carcinoma for predicting central lymph node metastasis","volume":"13","author":"Dai","year":"2023","journal-title":"Front Oncol"},{"key":"B24","doi-asserted-by":"publisher","first-page":"8370","DOI":"10.21037\/qims-23-318","article-title":"A model for predicting lymph node metastasis of thyroid carcinoma: a multimodality convolutional neural network study","volume":"13","author":"Guang","year":"2023","journal-title":"Quantitative Imaging Med Surg"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2021.734900","article-title":"Web-based ultrasonic nomogram predicts preoperative central lymph node metastasis of cN0 papillary thyroid microcarcinoma","volume":"12","author":"Huang","year":"2021","journal-title":"Front Endocrinol"},{"key":"B26","doi-asserted-by":"publisher","first-page":"1701","DOI":"10.7150\/ijms.95022","article-title":"Predictive value of an ultrasound-based radiomics model for central lymph node metastasis of papillary thyroid carcinoma","volume":"21","author":"Jia","year":"2024","journal-title":"Int J Med Sci"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1089\/thy.2019.0780","article-title":"Nomogram based on shear-wave elastography radiomics can improve preoperative cervical lymph node staging for papillary thyroid carcinoma","volume":"30","author":"Jiang","year":"2020","journal-title":"Thyroid"},{"key":"B28","doi-asserted-by":"publisher","first-page":"1613","DOI":"10.3390\/cancers15051613","article-title":"Clinical-radiomics nomogram based on contrast-enhanced ultrasound for preoperative prediction of cervical lymph node metastasis in papillary thyroid carcinoma","volume":"15","author":"Jiang","year":"2023","journal-title":"Cancers"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1007\/s12020-024-04091-w","article-title":"Deep learning based analysis of dynamic video ultrasonography for predicting cervical lymph node metastasis in papillary thyroid carcinoma","volume":"87","author":"Qian","year":"2024","journal-title":"Endocrine"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.897596","article-title":"Ultrasound-based radiomics XGBoost model to assess the risk of central cervical lymph node metastasis in patients with papillary thyroid carcinoma: Individual application of SHAP","volume":"12","author":"Shi","year":"2022","journal-title":"Front Oncol"},{"key":"B31","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1186\/s12880-022-00809-2","article-title":"Ultrasound-based radiomics analysis for preoperative prediction of central and lateral cervical lymph node metastasis in papillary thyroid carcinoma: a multi-institutional study","volume":"22","author":"Tong","year":"2022","journal-title":"BMC Med Imaging"},{"key":"B32","doi-asserted-by":"publisher","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"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.3904\/kjim.2024.122","article-title":"Fisher discriminant analysis of multimodal ultrasound in diagnosis of cervical metastatic lymph nodes in papillary thyroid cancer","volume":"40","author":"Wang","year":"2025","journal-title":"Korean J Internal Med"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.2147\/IJGM.S424880","article-title":"Development of a clinical-radiomics nomogram that used contrast-enhanced ultrasound images to anticipate the occurrence of preoperative cervical lymph node metastasis in papillary thyroid carcinoma patients","volume":"16","author":"Wei","year":"2023","journal-title":"Int J Gen Med"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2022.1064434","article-title":"A radiomics nomogram for the ultrasound-based evaluation of central cervical lymph node metastasis in papillary thyroid carcinoma","volume":"13","author":"Wen","year":"2022","journal-title":"Front Endocrinol"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2024.1354288","article-title":"Application of ultrasound elastography and radiomic for predicting central cervical lymph node metastasis in papillary thyroid microcarcinoma","author":"Wu","year":"2024","journal-title":"Front Oncol"},{"key":"B37","doi-asserted-by":"publisher","first-page":"e0227315","DOI":"10.1371\/journal.pone.0227315","article-title":"Radiomics signature for prediction of lateral lymph node metastasis in conventional papillary thyroid carcinoma","volume":"15","author":"Park","year":"2020","journal-title":"PloS One"},{"key":"B38","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1186\/s12880-023-01085-4","article-title":"Predicting central lymph node metastasis in patients with papillary thyroid carcinoma based on ultrasound radiomic and morphological features analysis","volume":"23","author":"Yan","year":"2023","journal-title":"BMC Med Imaging"},{"key":"B39","doi-asserted-by":"publisher","first-page":"2200100","DOI":"10.1002\/aisy.202200100","article-title":"DeepThy-Net: a multimodal deep learning method for predicting cervical lymph node metastasis in papillary thyroid cancer","volume":"4","author":"Yao","year":"2022","journal-title":"Adv Intelligent Syst"},{"key":"B40","doi-asserted-by":"publisher","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":"B41","doi-asserted-by":"publisher","DOI":"10.1016\/j.asjsur.2024.02.140","article-title":"Application of deep-learning to the automatic segmentation and classification of lateral lymph nodes on ultrasound images of papillary thyroid carcinoma","volume":"47","author":"Yuan","year":"2024","journal-title":"Asian J Surg"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.1002\/mp.17498","article-title":"Automatic tumor segmentation and lymph node metastasis prediction in papillary thyroid carcinoma using ultrasound keyframes","volume":"52","author":"Zhang","year":"2025","journal-title":"Med Phys"},{"key":"B43","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1159114","article-title":"Ultrasound radiomics nomogram for predicting large-number cervical lymph node metastasis in papillary thyroid carcinoma","volume":"13","author":"Zhang","year":"2023","journal-title":"Front Oncol"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.01591","article-title":"An ultrasound radiomics nomogram for preoperative prediction of central neck lymph node metastasis in papillary thyroid carcinoma","volume":"10","author":"Zhou","year":"2020","journal-title":"Front Oncol"},{"key":"B45","doi-asserted-by":"publisher","first-page":"e14546","DOI":"10.7717\/peerj.14546","article-title":"Models of ultrasonic radiomics and clinical characters for lymph node metastasis assessment in thyroid cancer: a retrospective study","volume":"11","author":"Zhu","year":"2023","journal-title":"PeerJ"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2788044","article-title":"Deep learning applications in medical image analysis","volume":"6","author":"Ker","year":"2017","journal-title":"IEEE Access"},{"key":"B47","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3086530","article-title":"Deep neural architectures for medical image semantic segmentation","volume":"9","author":"Khan","year":"2021","journal-title":"IEEE Access"},{"key":"B48","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2305.03219","article-title":"All models are local: time to replace external validation with recurrent local validation","author":"Youssef","year":"2023","journal-title":"arXiv preprint, arXiv:2305.03219"},{"key":"B49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s42492-019-0026-5","article-title":"Developing global image feature analysis models to predict cancer risk and prognosis","volume":"2","author":"Zheng","year":"2019","journal-title":"Visual Computing Industry Biomed Art"},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3198996","article-title":"Mediastinal lymph node detection and segmentation using deep learning","volume":"10","author":"Nayan","year":"2022","journal-title":"IEEE Access"},{"key":"B51","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1148\/radiol.2019190372","article-title":"Lymph node metastasis prediction from primary breast cancer US images using deep learning","volume":"294","author":"Zhou","year":"2020","journal-title":"Radiology"},{"key":"B52","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1186\/s12885-024-12277-8","article-title":"Deep learning-assisted diagnosis of benign and Malignant parotid tumors based on ultrasound: a retrospective study","volume":"24","author":"Jiang","year":"2024","journal-title":"BMC Cancer"},{"key":"B53","doi-asserted-by":"publisher","first-page":"55","DOI":"10.4236\/jct.2018.91007","article-title":"Clinical examination and ultrasonography as predictors of lateral neck lymph nodes metastasis in primary well differentiated thyroid cancer","volume":"9","author":"Amin","year":"2018","journal-title":"J Cancer Ther"},{"key":"B54","doi-asserted-by":"publisher","first-page":"111129","DOI":"10.1016\/j.ejrad.2023.111129","article-title":"Radiomics diagnostic performance in predicting lymph node metastasis of papillary thyroid carcinoma: a systematic review and meta-analysis","volume":"168","author":"HajiEsmailPoor","year":"2023","journal-title":"Eur J Radiol"},{"key":"B55","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/978-3-031-21506-3_2","article-title":"Application of AI in novel biomarkers detection that induces drug resistance, enhance treatment regimens, and advancing precision oncology","volume-title":"Artificial intelligence and precision oncology: bridging cancer research and clinical decision support","author":"Marima","year":"2023"},{"key":"B56","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2024.03.012","article-title":"Ultrasound-base radiomics for discerning lymph node metastasis in thyroid cancer: A systematic review and meta-analysis","volume":"31","author":"Zhang","year":"2024","journal-title":"Acad Radiol"},{"key":"B57","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1186\/s43055-024-01356-2","article-title":"Explainability, transparency and black box challenges of AI in radiology: Impact on patient care in cardiovascular radiology","volume":"55","author":"Marey","year":"2024","journal-title":"Egyptian J Radiol Nucl Med"},{"key":"B58","doi-asserted-by":"publisher","DOI":"10.60087\/jaigs.v6i1.289","article-title":"The role of explainable AI in bias mitigation for hyper-personalization","volume":"6","author":"Para","year":"2024","journal-title":"J Artif Intell Gen Sci (JAIGS)"}],"container-title":["Frontiers in Endocrinology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2025.1570811\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T04:10:37Z","timestamp":1749528637000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2025.1570811\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,10]]},"references-count":58,"alternative-id":["10.3389\/fendo.2025.1570811"],"URL":"https:\/\/doi.org\/10.3389\/fendo.2025.1570811","relation":{},"ISSN":["1664-2392"],"issn-type":[{"value":"1664-2392","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,10]]},"article-number":"1570811"}}