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.