A computer-aided diagnosing system in the evaluation of thyroid nodules—experience in a specialized thyroid center

Abstract Background The evaluation of thyroid nodules with ultrasonography has created a large burden for radiologists. Artificial intelligence technology has been rapidly developed in recent years to reduce the cost of labor and improve the differentiation of thyroid malignancies. This study aimed...

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Main Authors: Shujun Xia, Jiejie Yao, Wei Zhou, Yijie Dong, Shangyan Xu, Jianqiao Zhou, Weiwei Zhan
Format: Article
Language:English
Published: BMC 2019-12-01
Series:World Journal of Surgical Oncology
Subjects:
Online Access:https://doi.org/10.1186/s12957-019-1752-z
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spelling doaj-287aad30d2164d22bef571cb59e78c642020-12-06T12:26:13ZengBMCWorld Journal of Surgical Oncology1477-78192019-12-011711810.1186/s12957-019-1752-zA computer-aided diagnosing system in the evaluation of thyroid nodules—experience in a specialized thyroid centerShujun Xia0Jiejie Yao1Wei Zhou2Yijie Dong3Shangyan Xu4Jianqiao Zhou5Weiwei Zhan6Department of Ultrasound, Rui Jin Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Ultrasound, Rui Jin Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Ultrasound, Rui Jin Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Ultrasound, Rui Jin Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Ultrasound, Rui Jin Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Ultrasound, Rui Jin Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Ultrasound, Rui Jin Hospital, Shanghai Jiao Tong University School of MedicineAbstract Background The evaluation of thyroid nodules with ultrasonography has created a large burden for radiologists. Artificial intelligence technology has been rapidly developed in recent years to reduce the cost of labor and improve the differentiation of thyroid malignancies. This study aimed to investigate the diagnostic performance of a novel computer-aided diagnosing system (CADs: S-detect) for the ultrasound (US) interpretation of thyroid nodule subtypes in a specialized thyroid center. Methods Our study prospectively included 180 thyroid nodules that underwent ultrasound interpretation. The CADs and radiologist assessed all nodules. The ultrasonographic features of different subtypes were analyzed, and the diagnostic performances of the CADs and radiologist were compared. Results There were seven subtypes of thyroid nodules, among which papillary thyroid cancer (PTC) accounted for 50.6% and follicular thyroid carcinoma (FTC) accounted for 2.2%. Among all thyroid nodules, the CADs presented a higher sensitivity and lower specificity than the radiologist (90.5% vs 81.1%; 41.2% vs 83.5%); the radiologist had a higher accuracy than the CADs (82.2% vs 67.2%) for diagnosing malignant thyroid nodules. The accuracy of the CADs was not as good as that of the radiologist in diagnosing PTCs (70.9% vs 82.1%). The CADs and radiologist presented accuracies of 43.8% and 60.9% in identifying FTCs, respectively. Conclusions The ultrasound CADs presented a higher sensitivity for identifying malignant thyroid nodules than experienced radiologists. The CADs was not as good as experienced radiologists in a specialized thyroid center in identifying PTCs. Radiologists maintained a higher specificity than the CADs for FTC detection.https://doi.org/10.1186/s12957-019-1752-zThyroid noduleCADsExperienced radiologists
collection DOAJ
language English
format Article
sources DOAJ
author Shujun Xia
Jiejie Yao
Wei Zhou
Yijie Dong
Shangyan Xu
Jianqiao Zhou
Weiwei Zhan
spellingShingle Shujun Xia
Jiejie Yao
Wei Zhou
Yijie Dong
Shangyan Xu
Jianqiao Zhou
Weiwei Zhan
A computer-aided diagnosing system in the evaluation of thyroid nodules—experience in a specialized thyroid center
World Journal of Surgical Oncology
Thyroid nodule
CADs
Experienced radiologists
author_facet Shujun Xia
Jiejie Yao
Wei Zhou
Yijie Dong
Shangyan Xu
Jianqiao Zhou
Weiwei Zhan
author_sort Shujun Xia
title A computer-aided diagnosing system in the evaluation of thyroid nodules—experience in a specialized thyroid center
title_short A computer-aided diagnosing system in the evaluation of thyroid nodules—experience in a specialized thyroid center
title_full A computer-aided diagnosing system in the evaluation of thyroid nodules—experience in a specialized thyroid center
title_fullStr A computer-aided diagnosing system in the evaluation of thyroid nodules—experience in a specialized thyroid center
title_full_unstemmed A computer-aided diagnosing system in the evaluation of thyroid nodules—experience in a specialized thyroid center
title_sort computer-aided diagnosing system in the evaluation of thyroid nodules—experience in a specialized thyroid center
publisher BMC
series World Journal of Surgical Oncology
issn 1477-7819
publishDate 2019-12-01
description Abstract Background The evaluation of thyroid nodules with ultrasonography has created a large burden for radiologists. Artificial intelligence technology has been rapidly developed in recent years to reduce the cost of labor and improve the differentiation of thyroid malignancies. This study aimed to investigate the diagnostic performance of a novel computer-aided diagnosing system (CADs: S-detect) for the ultrasound (US) interpretation of thyroid nodule subtypes in a specialized thyroid center. Methods Our study prospectively included 180 thyroid nodules that underwent ultrasound interpretation. The CADs and radiologist assessed all nodules. The ultrasonographic features of different subtypes were analyzed, and the diagnostic performances of the CADs and radiologist were compared. Results There were seven subtypes of thyroid nodules, among which papillary thyroid cancer (PTC) accounted for 50.6% and follicular thyroid carcinoma (FTC) accounted for 2.2%. Among all thyroid nodules, the CADs presented a higher sensitivity and lower specificity than the radiologist (90.5% vs 81.1%; 41.2% vs 83.5%); the radiologist had a higher accuracy than the CADs (82.2% vs 67.2%) for diagnosing malignant thyroid nodules. The accuracy of the CADs was not as good as that of the radiologist in diagnosing PTCs (70.9% vs 82.1%). The CADs and radiologist presented accuracies of 43.8% and 60.9% in identifying FTCs, respectively. Conclusions The ultrasound CADs presented a higher sensitivity for identifying malignant thyroid nodules than experienced radiologists. The CADs was not as good as experienced radiologists in a specialized thyroid center in identifying PTCs. Radiologists maintained a higher specificity than the CADs for FTC detection.
topic Thyroid nodule
CADs
Experienced radiologists
url https://doi.org/10.1186/s12957-019-1752-z
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