Diagnosis of lymph node metastasis in head and neck squamous cell carcinoma using deep learning
Haosheng Tang1,2,3, Guo Li1,2,3,4, Chao Liu1,2,3
1Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital Central South University Changsha Hunan China.
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.
