Objective:

To establish a deep-learning model for automatically detecting metastatic lymph nodes (LN) of oralsquamous cell carcinoma (OSCC) patients from contrast-enhanced computed tomography (CT) images.

Methods:

Contrast-enhanced CT images of 114 oral cancer patien

ts were collected. The metastatic LNs of these patients

a total of 216

had been pathologically confirmed. All CT scans are with a slice thickness of 0.625 mm and resolution is 512512. It was randomly divided into a training set of 80 cases and a test set of 34 cases. The above results were trained and verified by a deep learning model. Performance in detecting metastasis were obtained.

Results:

Performance in detecting metastatic LNs showed FROC@1 of 0.391 5

FROC@2 of 0.518 3

FROC@3 of 0.647 8

FROC@4 of 0.740 8

FROC@5 of 0.816 9

FROC@6 of 0.853 5

mFROC of 0.661 5

maxF1-score of 0.438 5

the best performance of sensitivity is 87.32%.

Conclusion:

A deep-learning model can be used to automatically detect metastatic LNs in contrast-enhanced CT images of patients with OSCC

which provides a new idea for the rapid detection of metastatic LNs and realize the spread of knowledge of radiologists of head and neck imaging and improve the training efficiency of primary radiologists.