Development of a machine learning model for the prediction of nodal metastasis in early T classification oral
Min Seob Kwak1, Young-Gyu Eun2, Jung-Woo Lee3
1Department of Internal Medicine, School of Medicine, Kyung Hee University, Seoul, South Korea.
Background:
This study aimed to develop and compare machine learning (ML) based predictive models for lymph node metastasis (LNM) in early T classification oral squamous cell carcinoma (OSCC).
Methods:
We used data from the Surveillance Epidemiology and End Results Database to develop and validate the predictive models for LNM in patients with T1, T2 OSCC. Using simple clinical and histopathological data, we developed six ML algorithms to predict LNM. The predictive performance of models was compared.
Results:
The areas under the receiver operating characteristic curves (AUCs) of the six models ranged from 0.768 to 0.956. The best prediction performance was achieved with a XGBoost (AUC = 0.956). Permutation importance analysis showed that tumor size is the most important feature in predicting metastasis.
Conclusions:
We developed a simplified and reproducible ML-based predictive model for metastasis in early T classification OSCC that could be helpful for the decision of a treatment strategy.
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