医学
淋巴结转移
深度学习
转移
甲状腺乳突癌
淋巴结
放射科
癌症
人工智能
超声波
颈淋巴结
节点(物理)
甲状腺癌
计算机科学
肿瘤科
内科学
结构工程
工程类
作者
Jincao Yao,Zhikai Lei,Wenwen Yue,Bojian Feng,Wei Li,Dawei Ou,Na Feng,Yidan Lu,Jing Xu,Wen-Cong Chen,Chen Yang,Lijing Wang,Liping Wang,Junping Liu,Peiying Wei,Hui‐Xiong Xu,Dong Xu
标识
DOI:10.1002/aisy.202200100
摘要
Papillary thyroid cancer (PTC) accounts for more than 80% of thyroid cancers, and ultrasound (US) imaging is the preferred method for the diagnosis of PTC. 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 years are collected, and a multimodal deep learning model called DeepThy‐Net 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.
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