» Articles » PMID: 31717365

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

Overview
Journal Cancers (Basel)
Publisher MDPI
Specialty Oncology
Date 2019 Nov 14
PMID 31717365
Citations 11
Authors
Affiliations
Soon will be listed here.
Abstract

Computer-aided diagnosis (CAD) techniques have emerged to complement qualitative assessment in the diagnosis of benign and malignant thyroid nodules. The aim of this review was to summarize the current evidence on the diagnostic performance of various ultrasound CAD in characterizing thyroid nodules. PUBMED, EMBASE and Cochrane databases were searched for studies published until August 2019. The Quality Assessment of Studies of Diagnostic Accuracy included in Systematic Review 2 (QUADAS-2) tool was used to assess the methodological quality of the studies. Reported diagnostic performance data were analyzed and discussed. Fourteen studies with 2232 patients and 2675 thyroid nodules met the inclusion criteria. The study quality based on QUADAS-2 assessment was moderate. At best performance, grey scale CAD had a sensitivity of 96.7% while Doppler CAD was 90%. Combined techniques of qualitative grey scale features and Doppler CAD assessment resulted in overall increased sensitivity (92%) and optimal specificity (85.1%). The experience of the CAD user, nodule size and the thyroid malignancy risk stratification system used for interpretation were the main potential factors affecting diagnostic performance outcomes. The diagnostic performance of CAD of thyroid ultrasound is comparable to that of qualitative visual assessment; however, combined techniques have the potential for better optimized diagnostic accuracy.

Citing Articles

An ultrasound image segmentation method for thyroid nodules based on dual-path attention mechanism-enhanced UNet+.

Dong P, Zhang R, Li J, Liu C, Liu W, Hu J BMC Med Imaging. 2024; 24(1):341.

PMID: 39695984 PMC: 11656873. DOI: 10.1186/s12880-024-01521-z.


Integration of Artificial Intelligence Decision Aids to Reduce Workload and Enhance Efficiency in Thyroid Nodule Management.

Tong W, Wu S, Cheng M, Huang H, Liang J, Li C JAMA Netw Open. 2023; 6(5):e2313674.

PMID: 37191957 PMC: 10189570. DOI: 10.1001/jamanetworkopen.2023.13674.


Study on diagnosis of thyroid nodules based on convolutional neural network.

Yin A, Lu Y, Xu F, Zhao Y, Sun Y, Huang M Radiologie (Heidelb). 2023; 63(Suppl 2):64-72.

PMID: 37074397 DOI: 10.1007/s00117-023-01137-4.


A Series-Based Deep Learning Approach to Lung Nodule Image Classification.

Balci M, Batrancea L, Akguller O, Nichita A Cancers (Basel). 2023; 15(3).

PMID: 36765801 PMC: 9913559. DOI: 10.3390/cancers15030843.


Endocrine Tumor Classification via Machine-Learning-Based Elastography: A Systematic Scoping Review.

Mao Y, Zha L, Tam A, Lim H, Cheung A, Zhang Y Cancers (Basel). 2023; 15(3).

PMID: 36765794 PMC: 9913672. DOI: 10.3390/cancers15030837.


References
1.
Gao L, Xi X, Jiang Y, Yang X, Wang Y, Zhu S . Comparison among TIRADS (ACR TI-RADS and KWAK- TI-RADS) and 2015 ATA Guidelines in the diagnostic efficiency of thyroid nodules. Endocrine. 2019; 64(1):90-96. DOI: 10.1007/s12020-019-01843-x. View

2.
Reverter J, Vazquez F, Puig-Domingo M . Diagnostic Performance Evaluation of a Computer-Assisted Imaging Analysis System for Ultrasound Risk Stratification of Thyroid Nodules. AJR Am J Roentgenol. 2019; 213(1):169-174. DOI: 10.2214/AJR.18.20740. View

3.
Iakovidis D, Keramidas E, Maroulis D . Fusion of fuzzy statistical distributions for classification of thyroid ultrasound patterns. Artif Intell Med. 2010; 50(1):33-41. DOI: 10.1016/j.artmed.2010.04.004. View

4.
Acharya U, Swapna G, Sree S, Molinari F, Gupta S, Bardales R . A review on ultrasound-based thyroid cancer tissue characterization and automated classification. Technol Cancer Res Treat. 2013; 13(4):289-301. DOI: 10.7785/tcrt.2012.500381. View

5.
Faust O, Rajendra Acharya U, Tamura T . Formal design methods for reliable computer-aided diagnosis: a review. IEEE Rev Biomed Eng. 2012; 5:15-28. DOI: 10.1109/RBME.2012.2184750. View