1. Deep learning to assist composition classification and thyroid solid nodule diagnosis: a multicenter diagnostic study.
- Author
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Chen C, Jiang Y, Yao J, Lai M, Liu Y, Jiang X, Ou D, Feng B, Zhou L, Xu J, Wu L, Zhou Y, Yue W, Dong F, and Xu D
- Subjects
- Humans, Diagnosis, Differential, Sensitivity and Specificity, Ultrasonography methods, Retrospective Studies, Thyroid Nodule diagnostic imaging, Thyroid Nodule pathology, Deep Learning, Thyroid Neoplasms diagnostic imaging, Thyroid Neoplasms pathology
- Abstract
Objectives: This study aimed to propose a deep learning (DL)-based framework for identifying the composition of thyroid nodules and assessing their malignancy risk., Methods: We conducted a retrospective multicenter study using ultrasound images from four hospitals. Convolutional neural network (CNN) models were constructed to classify ultrasound images of thyroid nodules into solid and non-solid, as well as benign and malignant. A total of 11,201 images of 6784 nodules were used for training, validation, and testing. The area under the receiver-operating characteristic curve (AUC) was employed as the primary evaluation index., Results: The models had AUCs higher than 0.91 in the benign and malignant grading of solid thyroid nodules, with the Inception-ResNet AUC being the highest at 0.94. In the test set, the best algorithm for identifying benign and malignant thyroid nodules had a sensitivity of 0.88, and a specificity of 0.86. In the human vs. DL test set, the best algorithm had a sensitivity of 0.93, and a specificity of 0.86. The Inception-ResNet model performed better than the senior physicians (p < 0.001). The sensitivity and specificity of the optimal model based on the external test set were 0.90 and 0.75, respectively., Conclusions: This research demonstrates that CNNs can assist thyroid nodule diagnosis and reduce the rate of unnecessary fine-needle aspiration (FNA)., Clinical Relevance Statement: High-resolution ultrasound has led to increased detection of thyroid nodules. This results in unnecessary fine-needle aspiration and anxiety for patients whose nodules are benign. Deep learning can solve these problems to some extent., Key Points: • Thyroid solid nodules have a high probability of malignancy. • Our models can improve the differentiation between benign and malignant solid thyroid nodules. • The differential performance of one model was superior to that of senior radiologists. Applying this could reduce the rate of unnecessary fine-needle aspiration of solid thyroid nodules., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)
- Published
- 2024
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