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However, accurate prediction of different patterns of cervical lymph node metastasis (CLNM) in PTC continues to be a challenge. Herein, US images and clinical factors of PTC patients from three hospitals for more than 11\u2009years are collected, and a multimodal deep learning model called DeepThy\u2010Net is then developed to predict different CLNM patterns. The proposed model not only uses the convolutional features extracted by deep learning but also integrates traditional clinical factors that are highly related to lymph node metastasis. Finally, the model is tested in two independent test sets, and the experimental results show that the area under curve (AUC) is between 0.870 and 0.905, indicating clinical applicability. The proposed method provides an important reference for the treatment and management of PTC. Moreover, for PTC cases involving an active surveillance strategy, the proposed method can serve as an important CLNM early warning tool.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.202200100","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T22:05:06Z","timestamp":1660169106000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["DeepThy\u2010Net: A Multimodal Deep Learning Method for Predicting Cervical Lymph Node Metastasis in Papillary Thyroid Cancer"],"prefix":"10.1002","volume":"4","author":[{"given":"Jincao","family":"Yao","sequence":"first","affiliation":[{"name":"Medical Ultrasonics Department Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)  No.1 East Banshan Road Hangzhou 310022 P. R. China"},{"name":"Institute of Basic Medicine and Cancer (IBMC) Chinese Academy of Sciences  No.150 Dongfangjie Hangzhou 310000 P. R. 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