{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T01:07:06Z","timestamp":1780103226985,"version":"3.54.0"},"reference-count":32,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100010265","name":"Guizhou Medical University","doi-asserted-by":"publisher","award":["BSH-Q-2020-09"],"award-info":[{"award-number":["BSH-Q-2020-09"]}],"id":[{"id":"10.13039\/501100010265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82260160"],"award-info":[{"award-number":["82260160"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017957","name":"Health Commission of Guizhou Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100017957","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018555","name":"Science and Technology Program of Guizhou Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100018555","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","ejradiology.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Radiology"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.ejrad.2026.112843","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T06:13:59Z","timestamp":1776233639000},"page":"112843","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Region-guided decoupled fusion network for ultrasound-based classification of thyroid nodules with and without Hashimoto\u2019s thyroiditis"],"prefix":"10.1016","volume":"200","author":[{"given":"Jing","family":"Wen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qijian","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijuan","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongqing","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caihong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyu","family":"Hua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinge","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Limei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juang","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lihui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shi","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhu","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ejrad.2026.112843_b1","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.1016\/S0140-6736(16)30172-6","article-title":"Thyroid cancer","volume":"388","author":"Cabanillas","year":"2016","journal-title":"Lancet"},{"key":"10.1016\/j.ejrad.2026.112843_b2","article-title":"Epidemiology of thyroid cancer: A review of the national cancer database, 2000\u20132013","volume":"11","author":"Olson","year":"2019","journal-title":"Cureus"},{"key":"10.1016\/j.ejrad.2026.112843_b3","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1111\/j.1365-2362.2009.02162.x","article-title":"Very high prevalence of thyroid nodules detected by high frequency (13 mhz) ultrasound examination","volume":"39","author":"Guth","year":"2009","journal-title":"Eur. J. Clin. Invest."},{"key":"10.1016\/j.ejrad.2026.112843_b4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1089\/thy.2015.0020","article-title":"2015 American thyroid association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: The American thyroid association guidelines task force on thyroid nodules and differentiated thyroid cancer","volume":"26","author":"Haugen","year":"2016","journal-title":"Thyroid"},{"key":"10.1016\/j.ejrad.2026.112843_b5","doi-asserted-by":"crossref","first-page":"e250","DOI":"10.1016\/S2589-7500(21)00041-8","article-title":"Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicentre diagnostic study","volume":"3","author":"Peng","year":"2021","journal-title":"Lancet Digit. Health"},{"key":"10.1016\/j.ejrad.2026.112843_b6","doi-asserted-by":"crossref","DOI":"10.1117\/1.JMI.9.3.034501","article-title":"A machine-learning algorithm for distinguishing malignant from benign indeterminate thyroid nodules using ultrasound radiomic features","volume":"9","author":"Keutgen","year":"2022","journal-title":"J. Med. Imaging"},{"key":"10.1016\/j.ejrad.2026.112843_b7","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1007\/s12020-023-03407-6","article-title":"A machine learning-based sonomics for prediction of thyroid nodule malignancies","volume":"82","author":"Arabi","year":"2023","journal-title":"Endocrine"},{"key":"10.1016\/j.ejrad.2026.112843_b8","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1007\/s12020-023-03461-0","article-title":"Comparative analysis of machine learning-based ultrasound radiomics in predicting malignancy of partially cystic thyroid nodules","volume":"83","author":"Zhou","year":"2024","journal-title":"Endocrine"},{"key":"10.1016\/j.ejrad.2026.112843_b9","article-title":"Ultrasound image analysis using deep learning algorithm for the diagnosis of thyroid nodules","volume":"98","author":"Song","year":"2019","journal-title":"Medicine"},{"key":"10.1016\/j.ejrad.2026.112843_b10","doi-asserted-by":"crossref","first-page":"e250","DOI":"10.1016\/S2589-7500(21)00041-8","article-title":"Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicentre diagnostic study","volume":"3","author":"Peng","year":"2021","journal-title":"Lancet Digit. Health"},{"key":"10.1016\/j.ejrad.2026.112843_b11","doi-asserted-by":"crossref","first-page":"2538","DOI":"10.1177\/15353702231220664","article-title":"Deep learning diagnostic performance and visual insights in differentiating benign and malignant thyroid nodules on ultrasound images","volume":"248","author":"Liu","year":"2023","journal-title":"Exp. Biol. Med."},{"key":"10.1016\/j.ejrad.2026.112843_b12","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1007\/s00330-023-10269-z","article-title":"Deep learning to assist composition classification and thyroid solid nodule diagnosis: a multicenter diagnostic study","volume":"34","author":"Chen","year":"2024","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.ejrad.2026.112843_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101665","article-title":"Automatic diagnosis for thyroid nodules in ultrasound images by deep neural networks","volume":"61","author":"Wang","year":"2020","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.ejrad.2026.112843_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106468","article-title":"Automatic detection and diagnosis of thyroid ultrasound images based on attention mechanism","volume":"155","author":"Yu","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.ejrad.2026.112843_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.medengphy.2024.104101","article-title":"Srt: Swin-residual transformer for benign and malignant nodules classification in thyroid ultrasound images","volume":"124","author":"Huang","year":"2024","journal-title":"Med. Eng. Phys."},{"key":"10.1016\/j.ejrad.2026.112843_b16","article-title":"Advancing thyroid diagnosis: integrating ai-driven cad framework with numerical data and ultrasound images","volume":"11","author":"Almutairi","year":"2025","journal-title":"PeerJ Comput. Sci."},{"key":"10.1016\/j.ejrad.2026.112843_b17","article-title":"Checklist for artificial intelligence in medical imaging (claim): 2024 update","volume":"6","author":"Tejani","year":"2024","journal-title":"Radiol.: Artif. Intell."},{"key":"10.1016\/j.ejrad.2026.112843_b18","doi-asserted-by":"crossref","unstructured":"H. Touvron, M. Cord, A. Sablayrolles, G. Synnaeve, H. J\u00e9gou, Going deeper with image transformers, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 32\u201342.","DOI":"10.1109\/ICCV48922.2021.00010"},{"key":"10.1016\/j.ejrad.2026.112843_b19","doi-asserted-by":"crossref","unstructured":"W. Xu, Y. Xu, T. Chang, Z. Tu, Co-scale conv-attentional image transformers, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 9981\u20139990.","DOI":"10.1109\/ICCV48922.2021.00983"},{"key":"10.1016\/j.ejrad.2026.112843_b20","series-title":"International Conference on Machine Learning","first-page":"2286","article-title":"Convit: Improving vision transformers with soft convolutional inductive biases","author":"d\u2019Ascoli","year":"2021"},{"key":"10.1016\/j.ejrad.2026.112843_b21","doi-asserted-by":"crossref","unstructured":"Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, S. Xie, A convnet for the 2020s, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 11976\u201311986.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"10.1016\/j.ejrad.2026.112843_b22","series-title":"International Conference on Machine Learning","first-page":"10347","article-title":"Training data-efficient image transformers & distillation through attention","author":"Touvron","year":"2021"},{"key":"10.1016\/j.ejrad.2026.112843_b23","doi-asserted-by":"crossref","first-page":"5314","DOI":"10.1109\/TPAMI.2022.3206148","article-title":"Resmlp: Feedforward networks for image classification with data-efficient training","volume":"45","author":"Touvron","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.ejrad.2026.112843_b24","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s13534-023-00325-4","article-title":"A multi-task model for reliable classification of thyroid nodules in ultrasound images","volume":"14","author":"Xing","year":"2024","journal-title":"Biomed. Eng. Lett."},{"key":"10.1016\/j.ejrad.2026.112843_b25","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.1109\/TMI.2023.3236011","article-title":"Hover-trans: Anatomy-aware hover-transformer for roi-free breast cancer diagnosis in ultrasound images","volume":"42","author":"Mo","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.ejrad.2026.112843_b26","doi-asserted-by":"crossref","first-page":"7959","DOI":"10.1002\/mp.15319","article-title":"A deep learning-based method for detecting and classifying the ultrasound images of suspicious thyroid nodules","volume":"48","author":"Zhao","year":"2021","journal-title":"Med. Phys."},{"key":"10.1016\/j.ejrad.2026.112843_b27","first-page":"1","article-title":"A segmentation-based algorithm for classification of benign and malignancy thyroid nodules with multi-feature information","author":"Zheng","year":"2024","journal-title":"Biomed. Eng. Lett."},{"key":"10.1016\/j.ejrad.2026.112843_b28","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1007\/s12020-020-02441-y","article-title":"2020 Chinese guidelines for ultrasound malignancy risk stratification of thyroid nodules: the c-tirads","volume":"70","author":"Zhou","year":"2020","journal-title":"Endocrine"},{"key":"10.1016\/j.ejrad.2026.112843_b29","series-title":"Statistical Power Analysis for the Behavioral Sciences","author":"Cohen","year":"2013"},{"key":"10.1016\/j.ejrad.2026.112843_b30","doi-asserted-by":"crossref","first-page":"837","DOI":"10.2307\/2531595","article-title":"Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach","author":"DeLong","year":"1988","journal-title":"Biometrics"},{"key":"10.1016\/j.ejrad.2026.112843_b31","series-title":"Bonferroni correction","author":"Weisstein","year":"2004"},{"key":"10.1016\/j.ejrad.2026.112843_b32","series-title":"Hashimoto\u2019s Thyroiditis","author":"Akamizu","year":"2000"}],"container-title":["European Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X26001919?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X26001919?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:41:25Z","timestamp":1780101685000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0720048X26001919"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":32,"alternative-id":["S0720048X26001919"],"URL":"https:\/\/doi.org\/10.1016\/j.ejrad.2026.112843","relation":{},"ISSN":["0720-048X"],"issn-type":[{"value":"0720-048X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Region-guided decoupled fusion network for ultrasound-based classification of thyroid nodules with and without Hashimoto\u2019s thyroiditis","name":"articletitle","label":"Article Title"},{"value":"European Journal of Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejrad.2026.112843","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"112843"}}