scholarly journals Convolutional Neural Network to Stratify the Malignancy Risk of Thyroid Nodules: Diagnostic Performance Compared with the American College of Radiology Thyroid Imaging Reporting and Data System Implemented by Experienced Radiologists

2021 ◽  
Author(s):  
G.R. Kim ◽  
E. Lee ◽  
H.R. Kim ◽  
J.H. Yoon ◽  
V.Y. Park ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Data System ◽  
Malignancy Risk ◽  
Thyroid Imaging

Malignancy risk of initially benign thyroid nodules: validation with various Thyroid Imaging Reporting and Data System guidelines

2018 ◽  
Vol 29 (1) ◽  
pp. 133-140 ◽  
Author(s):  
Su Min Ha ◽  
Jung Hwan Baek ◽  
Young Jun Choi ◽  
Sae Rom Chung ◽  
Tae Yon Sung ◽  
...  
Keyword(s):  
Thyroid Nodules ◽  
Data System ◽  
Malignancy Risk ◽  
Thyroid Imaging ◽  
Benign Thyroid Nodules ◽  
Benign Thyroid

scholarly journals Using the Deep Convolutional Neural Network to Evaluate Thyroid Nodules With Atypia of Undetermined Significance/follicular Lesion of Undetermined Significance Cytology: Multicenter Study

2021 ◽  
Author(s):  
Inyoung Youn ◽  
Eunjung Lee ◽  
Jung Hyun Yoon ◽  
Hye Sun Lee ◽  
Mi-Ri Kwon ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Interobserver Variability ◽  
Needle Aspiration ◽  
Deep Convolutional Neural Network ◽  
Fine Needle ◽  
Experience Levels ◽  
Undetermined Significance

Abstract To compare the diagnostic performances of physicians and a deep convolutional neural network (CNN) predicting malignancy with ultrasonography images of thyroid nodules with atypia of undetermined significance (AUS)/follicular lesion of undetermined significance (FLUS) results on fine-needle aspiration (FNA). This study included 202 patients with 202 nodules ≥ 1cm AUS/FLUS on FNA, and underwent surgery in one of 3 different institutions. Diagnostic performances were compared between 8 physicians (4 radiologists, 4 endocrinologists) with varying experience levels and CNN, and AUS/FLUS subgroups were analyzed. Interobserver variability was assessed among the 8 physicians. Of the 202 nodules, 158 were AUS, and 44 were FLUS; 86 were benign, and 116 were malignant. The area under the curves (AUCs) of the 8 physicians and CNN were 0.680-0.722 and 0.666, without significant differences (P > 0.05). In the subgroup analysis, the AUCs for the 8 physicians and CNN were 0.657–0.768 and 0.652 for AUS, 0.469-0.674 and 0.622 for FLUS. Interobserver agreements were moderate (k=0.543), substantial (k=0.652), and moderate (k=0.455) among the 8 physicians, 4 radiologists, and 4 endocrinologists. For thyroid nodules with AUS/FLUS cytology, the diagnostic performance of CNN to differentiate malignancy with US images was comparable to that of physicians with variable experience levels.

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