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Yuan Y, Pan B, Mo H, Wu X, Long Z, Yang Z, Zhu J, Ming J, Qiu L, Sun Y, Yin S, Zhang F. Deep learning-based computer-aided diagnosis system for the automatic detection and classification of lateral cervical lymph nodes on original ultrasound images of papillary thyroid carcinoma: a prospective diagnostic study. Endocrine 2024:10.1007/s12020-024-03808-1. [PMID: 38570388 DOI: 10.1007/s12020-024-03808-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 03/26/2024] [Indexed: 04/05/2024]
Abstract
PURPOSE This study aims to develop a deep learning-based computer-aided diagnosis (CAD) system for the automatic detection and classification of lateral cervical lymph nodes (LNs) on original ultrasound images of papillary thyroid carcinoma (PTC) patients. METHODS A retrospective data set of 1801 cervical LN ultrasound images from 1675 patients with PTC and a prospective test set including 185 images from 160 patients were collected. Four different deep leaning models were trained and validated in the retrospective data set. The best model was selected for CAD system development and compared with three sonographers in the retrospective and prospective test sets. RESULTS The Deformable Detection Transformer (DETR) model showed the highest diagnostic efficacy, with a mean average precision score of 86.3% in the retrospective test set, and was therefore used in constructing the CAD system. The detection performance of the CAD system was superior to the junior sonographer and intermediate sonographer with accuracies of 86.3% and 92.4% in the retrospective and prospective test sets, respectively. The classification performance of the CAD system was better than all sonographers with the areas under the curve (AUCs) of 94.4% and 95.2% in the retrospective and prospective test sets, respectively. CONCLUSIONS This study developed a Deformable DETR model-based CAD system for automatically detecting and classifying lateral cervical LNs on original ultrasound images, which showed excellent diagnostic efficacy and clinical utility. It can be an important tool for assisting sonographers in the diagnosis process.
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Affiliation(s)
- Yuquan Yuan
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
- Graduate School of Medicine, Chongqing Medical University, Chongqing, China
| | - Bin Pan
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
- Graduate School of Medicine, Chongqing Medical University, Chongqing, China
| | - Hongbiao Mo
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Xing Wu
- College of Computer Science, Chongqing University, Chongqing, China
| | - Zhaoxin Long
- College of Computer Science, Chongqing University, Chongqing, China
| | - Zeyu Yang
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
- Graduate School of Medicine, Chongqing Medical University, Chongqing, China
| | - Junping Zhu
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Jing Ming
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Lin Qiu
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Yiceng Sun
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Supeng Yin
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China.
- Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China.
| | - Fan Zhang
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China.
- Graduate School of Medicine, Chongqing Medical University, Chongqing, China.
- Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China.
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