Computer-Aided Diagnosis of Thyroid Nodules via Ultrasonography: Initial Clinical Experience

Yoo, Young Jin; Ha, Eun Ju; Cho, Yoon Joo; Kim, Hye Lin; Han, Miran; Kang, So Young

doi:10.3348/kjr.2018.19.4.665

Cited by 74 publications

(86 citation statements)

References 21 publications

(46 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 risk of bias from the reference standard was considered to be unclear in 2 of the included studies [14,23]. The risk of bias from flow and timing was considered to be high or unclear in 7 of the included studies, and these studies adopted pathological examination, fine needle aspiration, and ultrasound as reference standards for diagnosing benign nodules [19,22,25,27,28,30,31].…”

Section: Methodological Quality Of the Included Studiesmentioning

confidence: 99%

“…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%

“…Five studies with 237 malignant and 362 benign thyroid nodules were included in the analysis [25][26][27][28]31]. All 5 studies compared the diagnostic performances of CAD systems and radiologists.…”

Section: Diagnostic Performance Of the Cad System And Real-time Diagnmentioning

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: Methodological Quality Of the Included Studiesmentioning

confidence: 99%

Section: Literature Searches and Description Of Studiesmentioning

confidence: 99%

Section: Diagnostic Performance Of Deep Learning-based Cad Systemsmentioning

confidence: 99%

Section: Diagnostic Performance Of the Cad System And Real-time Diagnmentioning

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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The Value of Thyroid Ultrasound Computer‐Aided Diagnosis System in the Evaluation of Thyroid Nodules With Concurrent Hashimoto's Thyroiditis

Zong

et al. 2021

J of Ultrasound Medicine

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Objective To investigate the value of computer‐aided diagnosis (CAD) system in assessing thyroid nodules concurrent with Hashimoto's thyroiditis (HT). Methods Totally 148 patients with 193 thyroid nodules were enrolled. A radiologist assessed the nodules using a thyroid ultrasound CAD system. Additionally, the nodules were evaluated by one experienced radiologist alone, and one training radiologist without and with CAD assistance. The diagnostic performance was compared between the CAD system and the experienced radiologist, and the training radiologist without and with CAD assistance. Results The CAD system demonstrated a similar sensitivity to that of the experienced radiologist in diagnosing thyroid cancers (89.8% versus 92.4%, P > .05). The specificity and accuracy of the CAD system were lower than that of the experienced radiologist in assessing the nodules with diffusedly altered glands (specificity, 60.0% versus 81.7%, P = .007; accuracy, 77.5% versus 88.1%, P = .011). With CAD assistance, the training radiologist had improved sensitivity and accuracy that increased to 87.9% and 86.8% in classifying nodules with sonographically evident HT (both P = .012). Conclusion The CAD system has comparable sensitivity, but lower specificity compared with the experienced radiologist in diagnosing thyroid malignancies concurrent with HT. For a radiologist with less experience, the CAD system can help improve the diagnostic performance by increasing sensitivity and accuracy in assessing thyroid nodules with diffusely altered parenchyma.

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Machine Learning Assisted Doppler Features for Enhancing Thyroid Cancer Diagnosis

Zhu

Jiang

et al. 2021

J of Ultrasound Medicine

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Background This pilot study aims at exploiting machine learning techniques to extract color Doppler ultrasound (CDUS) features and to build an artificial neural network (ANN) model based on these CDUS features for improving the diagnostic performance of thyroid cancer classification. Methods A total of 674 patients with 712 thyroid nodules (TNs) (512 from internal dataset and 200 from external dataset) were randomly selected in this retrospective study. We used ANN to build a model (TDUS‐Net) for classifying malignant and benign TNs using both the automatically extracted quantitative CDUS features (whole ratio, intranodular ratio, peripheral ratio, and number of vessels) and gray‐scale ultrasound (US) features defined by the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI‐RADS). Then, we compared the diagnostic performance of the model, the performance of another ANN model based on the gray‐scale US features alone (TUS‐Net), and that of radiologists. Results The TDUS‐Net (0.898, 95% CI: 0.868–0.922) achieved a higher area under the curve (AUC) than that of TUS‐Net (0.881, 95% CI: 0.850–0.908) in the internal tests. Compared with radiologists, TDUS‐Net (AUC: 0.925, 95% CI: 0.880–0.958) performed better than radiologists (AUC: 0.810, 95% CI: 0.749–0.862) in the external tests. Conclusions Applying a machine learning model by combining both gray‐scale US features and CDUS features can achieve comparable or even higher performance than radiologists in classifying TNs.

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Computer-Aided Diagnosis of Thyroid Nodules via Ultrasonography: Initial Clinical Experience

Cited by 74 publications

References 21 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

The Value of Thyroid Ultrasound Computer‐Aided Diagnosis System in the Evaluation of Thyroid Nodules With Concurrent Hashimoto's Thyroiditis

Machine Learning Assisted Doppler Features for Enhancing Thyroid Cancer Diagnosis

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