Intelligent Diagnosis of Cervical Lymph Node Metastasis Using a CNN Model.

Abstract

Lymph node (LN) metastasis is a prevalent cause of recurrence in oral squamous cell carcinoma (OSCC). However, accurately identifying metastatic LNs (LNs+) remains challenging. This prospective clinical study aims to test the effectiveness of our convolutional neural network (CNN) model for identifying OSCC cervical LN+ in contrast-enhanced computed tomography (CECT) in clinical practice. A CNN model was developed and trained using a dataset of 8,380 CECT images from previous OSCC patients. It was then prospectively validated on 17,777 preoperative CECT images from 354 OSCC patients between October 17, 2023, and August 31, 2024. The model's predicted LN results were provided to the surgical team without influencing surgical or treatment plans. During surgery, the predicted LN+ were identified and sent for separate pathological examination. The accuracy of the model's predictions was compared with those of human experts and verified against pathology reports. The capacity of the model to assist radiologists in LN+ diagnosis was also assessed. The CNN model was trained over 40 epochs and successfully validated after each. Compared with human experts (2 radiologists, 2 surgeons, and 2 students), the CNN model achieved higher sensitivity (81.89% vs. 81.48%, 46.91%, 50.62%), specificity (99.31% vs. 99.15%, 98.36%, 96.27%), LN+ accuracy (76.19% vs. 75.43%, P = 0.854; 40.64%, P < 0.001; 37.44%, P < 0.001), and clinical accuracy (86.16% vs. 83%, 61%, 56%). With the model's assistance, the radiologists surpassed both the previous predictive results without the model's support and the model's performance alone. The CNN model demonstrated an accuracy comparable to that of radiologists in identifying, locating, and predicting cervical LN+ in OSCC patients. Furthermore, the model has the potential to assist radiologists in making more accurate diagnoses.