Enhancing lymph node diagnosis: integrating deep learning with endoscopic ultrasonography: a retrospective study in China.
Authors
Affiliations (3)
Affiliations (3)
- Department of Gastroenterology, Second Affiliated Hospital of Soochow University, Suzhou, China.
- Department of Gastroenterology, Affiliated Hospital of Jiangnan University, Wuxi, China.
- Suzhou Institute of Biomedical Engineering and Technology, Suzhou, China.
Abstract
Lymphadenopathy presents diagnostic challenges, particularly for the mediastinal and intra-abdominal lymph nodes (LNs). Endoscopic ultrasonography (EUS) has emerged as a tool for LN detection; however, its accuracy varies. To enhance the diagnostic performance and minimize medical costs, assisting LN assessment using EUS is necessary. Machine learning (ML) offers potential for medical image analysis. This study aimed to develop an ML model for classifying mediastinal and intra-abdominal LNs using gastrointestinal EUS. EUS images of mediastinal and intra-abdominal LNs were randomly split into training and validation datasets. U-Net was selected for LN segmentation, and six deep-learning architectures were combined with the k-nearest-neighbor algorithm for LN classification. Physicians, comprising one expert group and one trainee group, reviewed the validation dataset and made individual diagnoses. A logistic regression model was generated based on LN features. We compared the diagnostic yields of ML, expert and trainee groups, logistic regression analysis, and a combination of the various methods mentioned above for diagnosing LNs. In total, 93 patients were enrolled, providing 630 images. The ResNet-50+logistic regression analysis+expert group achieved the best F1 score and sensitivity of 0.89 and 100.0%, respectively. Paired comparisons revealed that the combination outperformed both experts and trainees in terms of the area under the curve (p<0.01). ML assists in predicting the mediastinal and intra-abdominal LNs based on gastrointestinal EUS images, particularly when combined with expert expertise and logistic regression models.