基于超声动态视频的深度学习模型在鉴别甲状腺结节中的价值。
Value of deep learning models based on ultrasonic dynamic videos for distinguishing thyroid nodules.
作者信息
Ni Chen, Feng Bojian, Yao Jincao, Zhou Xueqin, Shen Jiafei, Ou Di, Peng Chanjuan, Xu Dong
机构信息
The Second Clinical School of Zhejiang Chinese Medical University, Hangzhou, China.
Key Laboratory of Head and Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China.
出版信息
Front Oncol. 2023 Jan 17;12:1066508. doi: 10.3389/fonc.2022.1066508. eCollection 2022.
OBJECTIVE
This study was designed to distinguish benign and malignant thyroid nodules by using deep learning(DL) models based on ultrasound dynamic videos.
METHODS
Ultrasound dynamic videos of 1018 thyroid nodules were retrospectively collected from 657 patients in Zhejiang Cancer Hospital from January 2020 to December 2020 for the tests with 5 DL models.
RESULTS
In the internal test set, the area under the receiver operating characteristic curve (AUROC) was 0.929(95% CI: 0.888,0.970) for the best-performing model LSTM Two radiologists interpreted the dynamic video with AUROC values of 0.760 (95% CI: 0.653, 0.867) and 0.815 (95% CI: 0.778, 0.853). In the external test set, the best-performing DL model had AUROC values of 0.896(95% CI: 0.847,0.945), and two ultrasound radiologist had AUROC values of 0.754 (95% CI: 0.649,0.850) and 0.833 (95% CI: 0.797,0.869).
CONCLUSION
This study demonstrates that the DL model based on ultrasound dynamic videos performs better than the ultrasound radiologists in distinguishing thyroid nodules.
目的
本研究旨在通过基于超声动态视频的深度学习（DL）模型来区分甲状腺良恶性结节。
方法
回顾性收集了2020年1月至2020年12月期间浙江省肿瘤医院657例患者的1018个甲状腺结节的超声动态视频，用于5种DL模型的测试。
结果
在内部测试集中，表现最佳的模型LSTM的受试者操作特征曲线下面积（AUROC）为0.929（95%CI：0.888,0.970）。两位放射科医生对动态视频的解读AUROC值分别为0.760（95%CI：0.653,0.867）和0.815（95%CI：0.778,0.853）。在外部测试集中，表现最佳的DL模型的AUROC值为0.896（95%CI：0.847,0.945），两位超声放射科医生的AUROC值分别为0.754（95%CI：0.649,0.850）和0.833（95%CI：0.797,0.8).
结论
本研究表明，基于超声动态视频的DL模型在区分甲状腺结节方面比超声放射科医生表现更好。
Zotero 插件