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Ultrasound Computer-Aided Diagnosis (CAD) Based on the Thyroid Imaging Reporting and Data System (TI-RADS) to Distinguish Benign from Malignant Thyroid Nodules and the Diagnostic Performance of Radiologists with Different Diagnostic Experience

BACKGROUND: The diagnosis of thyroid cancer and distinguishing benign from malignant thyroid nodules by junior radiologists can be challenging. This study aimed to develop a computer-aided diagnosis (CAD) system based on the Thyroid Imaging Reporting and Data System (TI-RADS) to distinguish benign f...

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Autores principales: Jin, Zhuang, Zhu, Yaqiong, Zhang, Shijie, Xie, Fang, Zhang, Mingbo, Zhang, Ying, Tian, Xiaoqi, Zhang, Jue, Luo, Yukun, Cao, Junying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Scientific Literature, Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6977643/
https://www.ncbi.nlm.nih.gov/pubmed/31929498
http://dx.doi.org/10.12659/MSM.918452
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author Jin, Zhuang
Zhu, Yaqiong
Zhang, Shijie
Xie, Fang
Zhang, Mingbo
Zhang, Ying
Tian, Xiaoqi
Zhang, Jue
Luo, Yukun
Cao, Junying
author_facet Jin, Zhuang
Zhu, Yaqiong
Zhang, Shijie
Xie, Fang
Zhang, Mingbo
Zhang, Ying
Tian, Xiaoqi
Zhang, Jue
Luo, Yukun
Cao, Junying
author_sort Jin, Zhuang
collection PubMed
description BACKGROUND: The diagnosis of thyroid cancer and distinguishing benign from malignant thyroid nodules by junior radiologists can be challenging. This study aimed to develop a computer-aided diagnosis (CAD) system based on the Thyroid Imaging Reporting and Data System (TI-RADS) to distinguish benign from malignant thyroid nodules by analyzing ultrasound images to improve the diagnostic performance of junior radiologists. MATERIAL/METHODS: A modified TI-RADS based on a convolutional neural network (CNN) was used to develop the CAD system. This retrospective study reviewed 789 thyroid nodules from 695 patients and included radiologists with different diagnostic experience. Five study groups included the CAD group, the junior radiologist group, the intermediate-level radiologist group, the senior radiologist group, and the group in which the junior radiologist used the CAD system. The ultrasound findings were reviewed and compared with the histopathology diagnosis. RESULTS: The CAD system for the diagnosis of thyroid cancer showed an accuracy of 80.35%, a sensitivity of 80.64%, a specificity of 80.13%, a positive predictive value (PPV) of 76.02%, a negative predictive value (NPV) of 84.12%, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.87. The accuracy of the junior radiologists in diagnosing thyroid cancer using CAD was similar to that of intermediate-level radiologists (79.21% vs. 77.57%; P=0.427). CONCLUSIONS: The use of ultrasound CAD based on the TI-RADS showed potential for distinguishing between benign and malignant thyroid nodules and improved the diagnostic performance of junior radiologists.
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spelling pubmed-69776432020-02-03 Ultrasound Computer-Aided Diagnosis (CAD) Based on the Thyroid Imaging Reporting and Data System (TI-RADS) to Distinguish Benign from Malignant Thyroid Nodules and the Diagnostic Performance of Radiologists with Different Diagnostic Experience Jin, Zhuang Zhu, Yaqiong Zhang, Shijie Xie, Fang Zhang, Mingbo Zhang, Ying Tian, Xiaoqi Zhang, Jue Luo, Yukun Cao, Junying Med Sci Monit Clinical Research BACKGROUND: The diagnosis of thyroid cancer and distinguishing benign from malignant thyroid nodules by junior radiologists can be challenging. This study aimed to develop a computer-aided diagnosis (CAD) system based on the Thyroid Imaging Reporting and Data System (TI-RADS) to distinguish benign from malignant thyroid nodules by analyzing ultrasound images to improve the diagnostic performance of junior radiologists. MATERIAL/METHODS: A modified TI-RADS based on a convolutional neural network (CNN) was used to develop the CAD system. This retrospective study reviewed 789 thyroid nodules from 695 patients and included radiologists with different diagnostic experience. Five study groups included the CAD group, the junior radiologist group, the intermediate-level radiologist group, the senior radiologist group, and the group in which the junior radiologist used the CAD system. The ultrasound findings were reviewed and compared with the histopathology diagnosis. RESULTS: The CAD system for the diagnosis of thyroid cancer showed an accuracy of 80.35%, a sensitivity of 80.64%, a specificity of 80.13%, a positive predictive value (PPV) of 76.02%, a negative predictive value (NPV) of 84.12%, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.87. The accuracy of the junior radiologists in diagnosing thyroid cancer using CAD was similar to that of intermediate-level radiologists (79.21% vs. 77.57%; P=0.427). CONCLUSIONS: The use of ultrasound CAD based on the TI-RADS showed potential for distinguishing between benign and malignant thyroid nodules and improved the diagnostic performance of junior radiologists. International Scientific Literature, Inc. 2020-01-02 /pmc/articles/PMC6977643/ /pubmed/31929498 http://dx.doi.org/10.12659/MSM.918452 Text en © Med Sci Monit, 2020 This work is licensed under Creative Common Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) )
spellingShingle Clinical Research
Jin, Zhuang
Zhu, Yaqiong
Zhang, Shijie
Xie, Fang
Zhang, Mingbo
Zhang, Ying
Tian, Xiaoqi
Zhang, Jue
Luo, Yukun
Cao, Junying
Ultrasound Computer-Aided Diagnosis (CAD) Based on the Thyroid Imaging Reporting and Data System (TI-RADS) to Distinguish Benign from Malignant Thyroid Nodules and the Diagnostic Performance of Radiologists with Different Diagnostic Experience
title Ultrasound Computer-Aided Diagnosis (CAD) Based on the Thyroid Imaging Reporting and Data System (TI-RADS) to Distinguish Benign from Malignant Thyroid Nodules and the Diagnostic Performance of Radiologists with Different Diagnostic Experience
title_full Ultrasound Computer-Aided Diagnosis (CAD) Based on the Thyroid Imaging Reporting and Data System (TI-RADS) to Distinguish Benign from Malignant Thyroid Nodules and the Diagnostic Performance of Radiologists with Different Diagnostic Experience
title_fullStr Ultrasound Computer-Aided Diagnosis (CAD) Based on the Thyroid Imaging Reporting and Data System (TI-RADS) to Distinguish Benign from Malignant Thyroid Nodules and the Diagnostic Performance of Radiologists with Different Diagnostic Experience
title_full_unstemmed Ultrasound Computer-Aided Diagnosis (CAD) Based on the Thyroid Imaging Reporting and Data System (TI-RADS) to Distinguish Benign from Malignant Thyroid Nodules and the Diagnostic Performance of Radiologists with Different Diagnostic Experience
title_short Ultrasound Computer-Aided Diagnosis (CAD) Based on the Thyroid Imaging Reporting and Data System (TI-RADS) to Distinguish Benign from Malignant Thyroid Nodules and the Diagnostic Performance of Radiologists with Different Diagnostic Experience
title_sort ultrasound computer-aided diagnosis (cad) based on the thyroid imaging reporting and data system (ti-rads) to distinguish benign from malignant thyroid nodules and the diagnostic performance of radiologists with different diagnostic experience
topic Clinical Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6977643/
https://www.ncbi.nlm.nih.gov/pubmed/31929498
http://dx.doi.org/10.12659/MSM.918452
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