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

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 c

SUBMITTER: Tang H 

PROVIDER: S-EPMC8823170 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature