Literature DB >> 30973776

Diagnostic Performance Evaluation of a Computer-Assisted Imaging Analysis System for Ultrasound Risk Stratification of Thyroid Nodules.

Jordi L Reverter1,2, Federico Vázquez1, Manuel Puig-Domingo1,2.   

Abstract

OBJECTIVE. Ultrasound-based stratification of the malignancy risk of thyroid nodules has potential variability. The purpose of this study is to evaluate the diagnostic effectiveness of the first commercially available system for computer-aided diagnosis (CADx) imaging analysis. MATERIALS AND METHODS. Ultrasound images of 300 thyroid nodules (135 of which were malignant) acquired before surgical treatment were retrospectively reviewed by a thyroid expert, and his classification of each image was then compared with the classification rendered by an image analysis program (AmCAD-UT, AmCAD Biomed). The American Thyroid Association (ATA) classification system, the European Thyroid Imaging Reporting and Data System (EU-TIRADS), and the classification system jointly proposed by American and Italian associations of clinical endocrinologists (the American Association of Clinical Endocrinologists [AACE], the American College of Endocrinology [ACE], and Associazione Medici Endocrinologi [AME]) were used for risk stratification. RESULTS. The diagnostic performance of the thyroid expert when the ATA system was used was as follows: sensitivity, 87.0%; specificity, 91.2%; positive predictive value, 90.5%; and negative predictive value, 90.9%. Compared with the expert, the CADx program, when used with the three classification systems, had a similar sensitivity but a lower specificity and positive predictive value. Regarding the negative predictive value, the results of the expert did not differ from those of the CADx program when it applied the ATA classification system (90.9% vs 86.3%; p = 0.07). The ROC AUC value was 0.88 for the expert clinician and 0.72 for the CADx program when the ATA classification system was used. CONCLUSION. The CADx ultrasound image analysis program described in the present study is useful for risk stratification of thyroid nodules, but it does not perform better than a sonography expert.

Entities:  

Keywords:  computer-aided diagnosis; imaging analysis; malignancy; thyroid nodules; ultrasound

Year:  2019        PMID: 30973776     DOI: 10.2214/AJR.18.20740

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  14 in total

1.  Applications of machine learning and deep learning to thyroid imaging: where do we stand?

Authors:  Eun Ju Ha; Jung Hwan Baek
Journal:  Ultrasonography       Date:  2020-07-03

Review 2.  Artificial Intelligence for Precision Oncology.

Authors:  Sherry Bhalla; Alessandro Laganà
Journal:  Adv Exp Med Biol       Date:  2022       Impact factor: 2.622

3.  A comparison of artificial intelligence versus radiologists in the diagnosis of thyroid nodules using ultrasonography: a systematic review and meta-analysis.

Authors:  Pimrada Potipimpanon; Natamon Charakorn; Prakobkiat Hirunwiwatkul
Journal:  Eur Arch Otorhinolaryngol       Date:  2022-06-29       Impact factor: 3.236

4.  A Clinical Assessment of an Ultrasound Computer-Aided Diagnosis System in Differentiating Thyroid Nodules With Radiologists of Different Diagnostic Experience.

Authors:  Yichun Zhang; Qiong Wu; Yutong Chen; Yan Wang
Journal:  Front Oncol       Date:  2020-09-11       Impact factor: 6.244

5.  Evaluation of the Diagnostic Performance of EU-TIRADS in Discriminating Benign from Malignant Thyroid Nodules: A Prospective Study in One Referral Center.

Authors:  Roussanka D Kovatcheva; Alexander D Shinkov; Inna D Dimitrova; Ralitsa B Ivanova; Kalin N Vidinov; Radina S Ivanova
Journal:  Eur Thyroid J       Date:  2020-05-18

6.  An efficient deep convolutional neural network model for visual localization and automatic diagnosis of thyroid nodules on ultrasound images.

Authors:  Jialin Zhu; Sheng Zhang; Ruiguo Yu; Zhiqiang Liu; Hongyan Gao; Bing Yue; Xun Liu; Xiangqian Zheng; Ming Gao; Xi Wei
Journal:  Quant Imaging Med Surg       Date:  2021-04

7.  Reliability of a computer-aided system in the evaluation of indeterminate ultrasound images of thyroid nodules.

Authors:  J L Reverter; L Ferrer-Estopiñan; F Vázquez; S Ballesta; S Batule; A Perez-Montes de Oca; C Puig-Jové; M Puig-Domingo
Journal:  Eur Thyroid J       Date:  2022-01-01

8.  Diagnostic performance evaluation of different TI-RADS using ultrasound computer-aided diagnosis of thyroid nodules: An experience with adjusted settings.

Authors:  Nonhlanhla Chambara; Shirley Y W Liu; Xina Lo; Michael Ying
Journal:  PLoS One       Date:  2021-01-15       Impact factor: 3.240

9.  Visual Interpretability in Computer-Assisted Diagnosis of Thyroid Nodules Using Ultrasound Images.

Authors:  Xi Wei; Jialin Zhu; Haozhi Zhang; Hongyan Gao; Ruiguo Yu; Zhiqiang Liu; Xiangqian Zheng; Ming Gao; Sheng Zhang
Journal:  Med Sci Monit       Date:  2020-08-15

10.  The Diagnostic Efficiency of Ultrasound Computer-Aided Diagnosis in Differentiating Thyroid Nodules: A Systematic Review and Narrative Synthesis.

Authors:  Nonhlanhla Chambara; Michael Ying
Journal:  Cancers (Basel)       Date:  2019-11-08       Impact factor: 6.639

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.