LAUSR

Objective To clarify CT diagnostic performance in extranodal extension of cervical lymph node metastases using deep learning classification. Methods Seven-hundred and three CT images (178 with and 525 without extranodal extension) in 51 patients with cervical lymph node metastases from oral squamous cell carcinoma were enrolled in this study. CT images were cropped to an arbitrary size to include lymph nodes and surrounding tissues. All images were automatically divided into two datasets, assigning 80% as the training dataset and 20% as the testing dataset. The automated selection was repeated five times. Each training dataset was imported to a deep learning training system “DIGITS”. Five learning models were created after 300 epochs of the learning process using a neural network “AlexNet”. Each testing dataset was applied to each created learning model and resulting five performances were averaged as estimated diagnostic performances. A radiologist measured the minor axis and three radiologists evaluated central necrosis and irregular borders of each lymph node, and the diagnostic performances were obtained. Results The deep learning accuracy of extranodal extension was 84.0%. The radiologists’ accuracies based on minor axis ≥ 11 mm, central necrosis, and irregular borders were 55.7%, 51.1% and 62.6%, respectively. Conclusions The deep learning diagnostic performance in extranodal extension was significantly higher than that of radiologists. This method is expected to improve diagnostic accuracy by further study with increasing the number of patients.