Lei Xu1,2, Junling Gao1, Quan Wang3, Jichao Yin2, Pengfei Yu4, Bin Bai4, Ruixia Pei2, Dingzhang Chen4, Guochun Yang2, Shiqi Wang4, Mingxi Wan1. 1. Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China. 2. Xi'an Hospital of Traditional Chinese Medicine, Xi'an, China. 3. Laboratory of Surgical Oncology, Peking University People's Hospital, Peking University, Beijing, China. 4. Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Abstract
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.
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.
Authors: Mary C Frates; Carol B Benson; J William Charboneau; Edmund S Cibas; Orlo H Clark; Beverly G Coleman; John J Cronan; Peter M Doubilet; Douglas B Evans; John R Goellner; Ian D Hay; Barbara S Hertzberg; Charles M Intenzo; R Brooke Jeffrey; Jill E Langer; P Reed Larsen; Susan J Mandel; William D Middleton; Carl C Reading; Steven I Sherman; Franklin N Tessler Journal: Radiology Date: 2005-12 Impact factor: 11.105
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Authors: J L Reverter; L Ferrer-Estopiñan; F Vázquez; S Ballesta; S Batule; A Perez-Montes de Oca; C Puig-Jové; M Puig-Domingo Journal: Eur Thyroid J Date: 2022-01-01