Diagnosis of lymph node metastasis in head and neck squamous cell carcinoma using deep learning
Haosheng Tang, Guo Li, Chao Liu, Donghai Huang, Xin Zhang, Yuanzheng Qiu, Yong Liu
Abstract
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.