Diagnosis of lymph node metastasis in oral squamous cell carcinoma by an MRI-based deep learning model.

BACKGROUND

Cervical lymph node metastasis (LNM) is a well-established poor prognosticator of oral squamous cell carcinoma (OSCC), in which occult metastasis is a subtype that makes prediction challenging. Here, we developed and validated a deep learning (DL) model using magnetic resonance imaging (MRI) for the identification of LNM in OSCC patients.

METHODS

This retrospective diagnostic study developed a three-stage DL model by 45,664 preoperative MRI images from 723 patients in 10 Chinese hospitals between January 2015 and October 2020. It was comprehensively processed from training (8:2), multicenter external validation to reader study. The performance of the DL model was accessed and compared with general and specialized radiologists.

RESULTS

LNM was found in 36.51% of all patients, and the occult metastasis rate was 16.45%. The three-stage DL model together with a random forest classifier achieved the performance in identification of LNM with areas under curve (AUC) of 0.97 (0.93-0.99) in training cohort and AUC of 0.81 (0.74-0.86) in external validation cohorts. The models can reduce the occult metastasis rate up to 89.50% and add more benefit in guiding neck dissection in cN0 patients. DL models tied or exceeded average performance relative to both general and specialized radiologists.

CONCLUSION

Our three-stage DL model based on MRI with three-dimensional sequences was beneficial in detecting LNM and reducing the occult metastasis rate of OSCC patients.