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.