Computer‐aided system for diagnosing thyroid nodules on ultrasound: A comparison with radiologist‐based clinical assessments

Gao, Luying; Liu, Ruyu; Jiang, Yuxin; Song, Wenfeng; Wang, Ying; Liu, Jia; Wang, Juanjuan; Wu, Dongqian; Li, Shuai; Hao, Aimin; Zhang, Bo

doi:10.1002/hed.25049

Cited by 57 publications

(70 citation statements)

References 13 publications

(30 reference statements)

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“…The pooled sensitivity, specificity, AUC, and DOR are demonstrated in Figure 2b. Eleven of the 13 studies compared the diagnostic performances of CAD systems and radiologists [7,20,21,[23][24][25][26][27][28]. The pooled sensitivity, specificity, AUC, and DOR were comparable between the CAD systems and the radiologists (Fig.…”

Section: Diagnostic Performance Of Deep Learning-based Cad Systemsmentioning

confidence: 94%

“…The flow diagram of the literature search is shown in Figure 1. Nineteen studies with 4,781 nodules used in external validation sets were included in the study, including 6 studies on classic machine learning-based CAD sys-DOI: 10.1159/000504390 tems [14][15][16][17][18][19] and 13 studies on deep learning-based CAD systems [7,[20][21][22][23][24][25][26][27][28][29][30][31]. The general characteristics of the included studies are shown in Table 1, and the detailed characteristics are demonstrated in online supplementary Table 1 (see www.karger.com/doi/10.1159/000504390 for all online suppl.…”

Section: Literature Searches and Description Of Studiesmentioning

confidence: 99%

“…Thirteen studies with 1,667 malignant and 1,415 benign nodules were included in the analysis [7,[20][21][22][23][24][25][26][27][28][29][30][31]. The pooled sensitivity, specificity, AUC, and DOR are demonstrated in Figure 2b.…”

Section: Diagnostic Performance Of Deep Learning-based Cad Systemsmentioning

confidence: 99%

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Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis

Xu¹,

Gao²,

Wang³

et al. 2019

Eur Thyroid J

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Background: Computer-aided diagnosis (CAD) systems are being applied to the ultrasonographic diagnosis of malignant thyroid nodules, but it remains controversial whether the systems add any accuracy for radiologists. Objective: To determine the accuracy of CAD systems in diagnosing malignant thyroid nodules. Methods: PubMed, EMBASE, and the Cochrane Library were searched for studies on the diagnostic performance of CAD systems. The diagnostic performance was assessed by pooled sensitivity and specificity, and their accuracy was compared with that of radiologists. The present systematic review was registered in PROSPERO (CRD42019134460). Results: Nineteen studies with 4,781 thyroid nodules were included. Both the classic machine learning-and the deep learning-based CAD system had good performance in diagnosing malignant thyroid nodules (classic machine learning: sensitivity 0.86 [95% CI 0.79-0.92], specificity 0.85 [95% CI 0.77-0.91], diagnostic odds ratio (DOR) 37.41 [95% CI 24.91-56.20]; deep learning: sensitivity 0.89 [95% CI 0.81-0.93], specificity 0.84 [95% CI 0.75-0.90], DOR 40.87 [95% CI 18.13-92.13]). The diagnostic performance of the deep learning-based CAD system was comparable to that of the radiologists (sensitivity 0.87 [95% CI 0.78-0.93] vs. 0.87 [95% CI 0.85-0.89], specificity 0.85 [95% CI 0.76-0.91] vs. 0.87 [95% CI 0.81-0.91], DOR 40.12 [95% CI 15.58-103.33] vs. DOR 44.88 [95% CI 30.71-65.57]). Conclusions: The CAD systems demonstrated good performance in diagnosing malignant thyroid nodules. However, experienced radiologists may still have an advantage over CAD systems during real-time diagnosis.

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Section: Diagnostic Performance Of Deep Learning-based Cad Systemsmentioning

confidence: 94%

Section: Literature Searches and Description Of Studiesmentioning

confidence: 99%

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Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis

Xu¹,

Gao²,

Wang³

et al. 2019

Eur Thyroid J

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“…Prior studies of CAD systems based on deep learning algorithms have consistently observed high sensitivity and low specificity, especially for studies with small sample sizes. For instance, in a retrospective study that developed a CNN model using 342 cases of thyroid nodules, the sensitivity, specificity, PPV, NPV, accuracy, and AUC of the CNN model were 96.7%, 48.5%, 87.3%, 86.2%, 82.2%, and 0.73, respectively, compared with values of 96.2%, 75.7%, 90.2%, 89.7%, 90.1%, and 0.87, respectively, for 2017 ACR TI-RADS [19]. Another study compared the deep learning system with the Automated Breast Ultrasound (ABUS) based on BI-RADS [20].…”

Section: Discussionmentioning

confidence: 96%

Convolutional Neural Network for Breast and Thyroid Nodules Diagnosis in Ultrasound Imaging

Liang

Liao

et al. 2020

BioMed Research International

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Objective. The incidence of superficial organ diseases has increased rapidly in recent years. New methods such as computer-aided diagnosis (CAD) are widely used to improve diagnostic efficiency. Convolutional neural networks (CNNs) are one of the most popular methods, and further improvements of CNNs should be considered. This paper aims to develop a multiorgan CAD system based on CNNs for classifying both thyroid and breast nodules and investigate the impact of this system on the diagnostic efficiency of different preprocessing approaches. Methods. The training and validation sets comprised randomly selected thyroid and breast nodule images. The data were subgrouped into 4 models according to the different preprocessing methods (depending on segmentation and the classification method). A prospective data set was selected to verify the clinical value of the CNN model by comparison with ultrasound guidelines. Diagnostic efficiency was assessed based on receiver operating characteristic (ROC) curves. Results. Among the 4 models, the CNN model using segmented images for classification achieved the best result. For the validation set, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the curve (AUC) of our CNN model were 84.9%, 69.0%, 62.5%, 88.2%, 75.0%, and 0.769, respectively. There was no statistically significant difference between the CNN model and the ultrasound guidelines. The combination of the two methods achieved superior diagnostic efficiency compared with their use individually. Conclusions. The study demonstrates the probability, feasibility, and clinical value of CAD in the ultrasound diagnosis of multiple organs. The use of segmented images and classification by the nature of the disease are the main factors responsible for the improvement of the CNN model. Moreover, the combination of the CNN model and ultrasound guidelines results in better diagnostic performance, which will contribute to the improved diagnostic efficiency of CAD systems.

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“…Most image datasets used for training only included fewer than 200 thyroid nodules. Gao 25 used a computer-aided system that was based on multiple-scale CNN model and trained by a large ultrasound image dataset with 1700 benign nodules and 2000 malignant nodules. The sensitivity of their CAD system was similar to that of an experienced radiologist.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of a deep learning‐based computer‐aided diagnosis system for distinguishing benign from malignant thyroid nodules in ultrasound images

et al. 2020

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Purpose: Computer-aided diagnosis (CAD) systems assist in solving subjective diagnosis problems that typically rely on personal experience. A CAD system has been developed to differentiate malignant thyroid nodules from benign thyroid nodules in ultrasound images based on deep learning methods. The diagnostic performance was compared between the CAD system and the experienced attending radiologists. Methods: The ultrasound image dataset for training the CAD system included 651 malignant nodules and 386 benign nodules while the database for testing included 422 malignant nodules and 128 benign nodules. All the nodules were confirmed by pathology results. In the proposed CAD system, a support vector machine (SVM) is used for classification and fused features which combined the deep features extracted by a convolutional neural network (CNN) with the hand-crafted features such as the histogram of oriented gradient (HOG), local binary patterns (LBP), and scale invariant feature transform (SIFT) were obtained. The optimal feature subset was formed by selecting these fused features based on the maximum class separation distance and used as the training sample for the SVM. Results: The accuracy, sensitivity, and specificity of the CAD system were 92.5%, 96.4%, and 83.1%, respectively, which were higher than those of the experienced attending radiologists. The areas under the ROC curves of the CAD system and the attending radiologists were 0.881 and 0.819, respectively. Conclusions: The CAD system for thyroid nodules exhibited a better diagnostic performance than experienced attending radiologists. The CAD system could be a reliable supplementary tool to diagnose thyroid nodules using ultrasonography. Macroscopic features in ultrasound images, such as the margins and shape of thyroid nodules, could influence the diagnostic efficiency of the CAD system.

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Computer‐aided system for diagnosing thyroid nodules on ultrasound: A comparison with radiologist‐based clinical assessments

Abstract: The sensitivity of a thyroid ultrasound CAD system in differentiating nodules was similar to that of an experienced radiologist. However, the CAD system had lower specificity.

Cited by 57 publications

References 13 publications

Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis

Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis

Convolutional Neural Network for Breast and Thyroid Nodules Diagnosis in Ultrasound Imaging

Evaluation of a deep learning‐based computer‐aided diagnosis system for distinguishing benign from malignant thyroid nodules in ultrasound images

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