Diagnosis of lymph node metastasis in head and neck squamous cell carcinoma using deep learning

BACKGROUND: To build an automatic pathological diagnosis model to assess the lymph node metastasis status of head and neck squamous cell carcinoma (HNSCC) based on deep learning algorithms. STUDY DESIGN: A retrospective study. METHODS: A diagnostic model integrating two‐step deep learning networks was trained to analyze the metastasis status in 85 images of HNSCC lymph nodes. The diagnostic model was tested in a test set of 21 images with metastasis and 29 images without metastasis. All images were scanned from HNSCC lymph node sections stained with hematoxylin–eosin (HE). RESULTS: In the test set, the overall accuracy, sensitivity, and specificity of the diagnostic model reached 86%, 100%, and 75.9%, respectively. CONCLUSIONS: Our two‐step diagnostic model can be used to automatically assess the status of HNSCC lymph node metastasis with high sensitivity. LEVEL OF EVIDENCE: NA.