We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Deep learning to assist composition classification and thyroid solid nodule diagnosis: a multicenter diagnostic study.
- Authors
Chen, Chen; Jiang, Yitao; Yao, Jincao; Lai, Min; Liu, Yuanzhen; Jiang, Xianping; Ou, Di; Feng, Bojian; Zhou, Lingyan; Xu, Jinfeng; Wu, Linghu; Zhou, Yuli; Yue, Wenwen; Dong, Fajin; Xu, Dong
- 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.
- Subjects
THYROID cancer; THYROID nodules; DEEP learning; CONVOLUTIONAL neural networks; CLASSIFICATION of mental disorders; NEEDLE biopsy; ULTRASONIC imaging
- Publication
European Radiology, 2024, Vol 34, Issue 4, p2323
- ISSN
0938-7994
- Publication type
Article
- DOI
10.1007/s00330-023-10269-z