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

Nonhlanhla Chambara,Shirley Y W Liu,Xina Lo,Michael Ying,Chung-Ming Lo

doi:10.1371/journal.pone.0245617

Nonhlanhla Chambara, Shirley Y W Liu + Show 3 more

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https://doi.org/10.1371/journal.pone.0245617

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Abstract

BackgroundThyroid cancer diagnosis has evolved to include computer-aided diagnosis (CAD) approaches to overcome the limitations of human ultrasound feature assessment. This study aimed to evaluate the diagnostic performance of a CAD system in thyroid nodule differentiation using varied settings.MethodsUltrasound images of 205 thyroid nodules from 198 patients were analysed in this retrospective study. AmCAD-UT software was used at default settings and 3 adjusted settings to diagnose the nodules. Six risk-stratification systems in the software were used to classify the thyroid nodules: The American Thyroid Association (ATA), American College of Radiology Thyroid Imaging, Reporting, and Data System (ACR-TIRADS), British Thyroid Association (BTA), European Union (EU-TIRADS), Kwak (2011) and the Korean Society of Thyroid Radiology (KSThR). The diagnostic performance of CAD was determined relative to the histopathology and/or cytology diagnosis of each nodule.ResultsAt the default setting, EU-TIRADS yielded the highest sensitivity, 82.6% and lowest specificity, 42.1% while the ATA-TIRADS yielded the highest specificity, 66.4%. Kwak had the highest AUROC (0.74) which was comparable to that of ACR, ATA, and KSThR TIRADS (0.72, 0.73, and 0.70 respectively). At a hyperechoic foci setting of 3.5 with other settings at median values; ATA had the best-balanced sensitivity, specificity and good AUROC (70.4%; 67.3% and 0.71 respectively).ConclusionThe default setting achieved the best diagnostic performance with all TIRADS and was best for maximizing the sensitivity of EU-TIRADS. Adjusting the settings by only reducing the sensitivity to echogenic foci may be most helpful for improving specificity with minimal change in sensitivity.

Highlights

Thyroid cancer is the most common endocrine malignancy which constitutes about 5% of all cancers [1, 2]
This study aimed to evaluate the diagnostic performance of a computer-aided diagnosis (CAD) system in thyroid nodule differentiation using varied settings
Adjusting the settings by only reducing the sensitivity to echogenic foci may be most helpful for improving specificity with minimal change in sensitivity

Summary

Introduction

Thyroid cancer is the most common endocrine malignancy which constitutes about 5% of all cancers [1, 2]. With the advancement and increased sensitivity of diagnostic imaging tools such as ultrasound, the incidence of thyroid cancers is rising for subclinical cases [3]. Ultrasound is the recommended primary imaging modality for thyroid nodule assessment, it has drawbacks of being operator-dependent and the subjective interpretation of results. Various thyroid malignancy risk classification guidelines have been designed to assist with categorizing risk of malignancy based on several predictive sonographic features. The diversity of sonographic features highly predictive of malignancy in the different guidelines augments the dependence on the experience and clinical approach of the clinician [11]. Thyroid cancer diagnosis has evolved to include computer-aided diagnosis (CAD) approaches to overcome the limitations of human ultrasound feature assessment. This study aimed to evaluate the diagnostic performance of a CAD system in thyroid nodule differentiation using varied settings

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