The diagnostic efficiency of ultrasound computer–aided diagnosis in differentiating thyroid nodules: a systematic review and narrative synthesis

Chambara, Nonhlanhla ORCID: https://orcid.org/0000-0002-3183-883X and Ying, Michael 2019. The diagnostic efficiency of ultrasound computer–aided diagnosis in differentiating thyroid nodules: a systematic review and narrative synthesis. Cancers 11 (11) , 1759. 10.3390/cancers11111759

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Healthcare Sciences
Publisher:	MDPI
ISSN:	2072-6694
Date of Acceptance:	6 November 2019
Last Modified:	21 Mar 2024 17:15
URI:	https://orca.cardiff.ac.uk/id/eprint/167153

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