AUTHOR=Ni Chen , Feng Bojian , Yao Jincao , Zhou Xueqin , Shen Jiafei , Ou Di , Peng Chanjuan , Xu Dong 

TITLE=Value of deep learning models based on ultrasonic dynamic videos for distinguishing thyroid nodules

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.1066508

DOI=10.3389/fonc.2022.1066508

ISSN=2234-943X

ABSTRACT=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.