Abstract
Computer-aided diagnosis (CAD) systems hold potential to improve the diagnostic accuracy of thyroid ultrasound (US). We aimed to develop a deep learning-based US CAD system (dCAD) for the diagnosis of thyroid nodules and compare its performance with those of a support vector machine (SVM)-based US CAD system (sCAD) and radiologists. dCAD was developed by using US images of 4919 thyroid nodules from three institutions. Its diagnostic performance was prospectively evaluated between June 2016 and February 2017 in 286 nodules, and was compared with those of sCAD and radiologists, using logistic regression with the generalized estimating equation. Subgroup analyses were performed according to experience level and separately for small thyroid nodules 1–2 cm. There was no difference in overall sensitivity, specificity, positive predictive value (PPV), negative predictive value and accuracy (all p > 0.05) between radiologists and dCAD. Radiologists and dCAD showed higher specificity, PPV, and accuracy than sCAD (all p < 0.001). In small nodules, experienced radiologists showed higher specificity, PPV and accuracy than sCAD (all p < 0.05). In conclusion, dCAD showed overall comparable diagnostic performance with radiologists and assessed thyroid nodules more effectively than sCAD, without loss of sensitivity.
Highlights
Computer-aided diagnosis (CAD) systems hold potential to improve the diagnostic accuracy of thyroid ultrasound (US)
Our study results demonstrated that deep learning-based US CAD system (dCAD) had performance comparable to radiologists for diagnosing thyroid malignancy, regardless of the experience level of the radiologists
Gao et al assessed the diagnostic performance of an US CAD system based on a convolutional neural networks (CNNs) framework, and reported similar sensitivity (96.7%) but lower specificity (48.5%) than an experienced radiologist[17]
Summary
Computer-aided diagnosis (CAD) systems hold potential to improve the diagnostic accuracy of thyroid ultrasound (US). DCAD showed overall comparable diagnostic performance with radiologists and assessed thyroid nodules more effectively than sCAD, without loss of sensitivity. Assessments based on individual US features have shown lower sensitivity and accuracy than assessments based on combined features, and many professional societies and investigators have proposed US-based risk stratification systems that incorporate multiple US features for thyroid nodules[1,2,6,7,8,9] Such systems are based on subjective assessments, and reported values for interobserver agreement are somewhat higher, observer variation still exists for reporting US classifications and recommending biopsy[4,10,11,12]. Characteristic Age (years)a Sexb No of men No of women Nodule size (mm)c Benign/Malignantc No of benign nodules No of malignant nodules
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