Differential Diagnosis of Benign and Malignant Thyroid Nodules Using Deep Learning Radiomics of Thyroid Ultrasound Images
Zhou, Hui1; Jin, Yinhua2; Dai, Lei2; Zhang, Meiwu2; Qiu, Yuqin2; Wang, Kun1; Tian, Jie1; Zheng, Jianjun2
Source PublicationEuropean Journal of Radiology
2020
Issue127Pages:0
Abstract

Purpose: We aimed to propose a highly automatic and objective model named deep learning Radiomics of
thyroid (DLRT) for the differential diagnosis of benign and malignant thyroid nodules from ultrasound (US)
images.
Methods: We retrospectively enrolled and finally include US images and fine-needle aspiration biopsies from
1734 patients with 1750 thyroid nodules. A basic convolutional neural network (CNN) model, a transfer learning
(TL) model, and a newly designed model named deep learning Radiomics of thyroid (DLRT) were used for the
investigation. Their diagnostic accuracy was further compared with human observers (one senior and one junior
US radiologist). Moreover, the robustness of DLRT over different US instruments was also validated. Analysis of
receiver operating characteristic (ROC) curves were performed to calculate optimal area under it (AUC) for
benign and malignant nodules. One observer helped to delineate the nodules.
Results: AUCs of DLRT were 0.96 (95% confidence interval [CI]: 0.94-0.98), 0.95 (95% confidence interval [CI]:
0.93-0.97) and 0.97 (95% confidence interval [CI]: 0.95-0.99) in the training, internal and external validation
cohort, respectively, which were significantly better than other deep learning models (P < 0.01) and human
observers (P < 0.001). No significant difference was found when applying DLRT on thyroid US images acquired
from different US instruments.
Conclusions: DLRT shows the best overall performance comparing with other deep learning models and human
observers. It holds great promise for improving the differential diagnosis of benign and malignant thyroid nodules.
 

KeywordThyroid Nodules Thyroid Ultrasound Deep Learning Ultrasound Radiomics Diagnosis
Indexed BySCI
Language英语
WOS IDWOS:000533552200008
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/38566
Collection复杂系统管理与控制国家重点实验室_影像分析与机器视觉
Corresponding AuthorWang, Kun; Tian, Jie; Zheng, Jianjun
Affiliation1.CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing, 100190, China
2.HwaMei Hospital, University of Chinese Academy of Sciences, 41 Xibei Street, Ningbo, 315010, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhou, Hui,Jin, Yinhua,Dai, Lei,et al. Differential Diagnosis of Benign and Malignant Thyroid Nodules Using Deep Learning Radiomics of Thyroid Ultrasound Images[J]. European Journal of Radiology,2020(127):0.
APA Zhou, Hui.,Jin, Yinhua.,Dai, Lei.,Zhang, Meiwu.,Qiu, Yuqin.,...&Zheng, Jianjun.(2020).Differential Diagnosis of Benign and Malignant Thyroid Nodules Using Deep Learning Radiomics of Thyroid Ultrasound Images.European Journal of Radiology(127),0.
MLA Zhou, Hui,et al."Differential Diagnosis of Benign and Malignant Thyroid Nodules Using Deep Learning Radiomics of Thyroid Ultrasound Images".European Journal of Radiology .127(2020):0.
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