Deep Learning-Based Computer-Aided Diagnosis System for Localization and Diagnosis of Metastatic Lymph Nodes on Ultrasound: A Pilot Study

Background: The presence of metastatic lymph nodes is a prognostic indicator for patients with thyroid carcinomas and is an important determinant of clinical decision making. However, evaluating neck lymph nodes requires experience and is labor- and time-intensive. Therefore, the development of a computer-aided diagnosis (CAD) system to identify and differentiate metastatic lymph nodes may be useful. Methods: From January 2008 to December 2016, we retrieved clinical records for 804 consecutive patients with 812 lymph nodes. The status of all lymph nodes was confirmed by fine-needle aspiration. The datasets were split into training (263 benign and 286 metastatic lymph nodes), validation (30 benign and 33 metastatic lymph nodes), and test (100 benign and 100 metastatic lymph nodes). Using the VGG-Class Activation Map model, we developed a CAD system to localize and differentiate the metastatic lymph nodes. We then evaluated the diagnostic performance of this CAD system in our test set. Results: In the test set, the accuracy, sensitivity, and specificity of our model for predicting lymph node malignancy were 83.0%, 79.5%, and 87.5%, respectively. The CAD system clearly detected the locations of the lymph nodes, which not only provided identifying data, but also demonstrated the basis of decisions. Conclusion: We developed a deep learning-based CAD system for the localization and differentiation of metastatic lymph nodes from thyroid cancer on ultrasound. This CAD system is highly sensitive and may be used as a screening tool; however, as it is relatively less specific, the screening results should be validated by experienced physicians.