Development of a machine learning model for the prediction of nodal metastasis in early T classification oral squamous cell carcinoma: SEER-based population study.
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.
['Kwak|Min Seob|MS|', 'Eun|Young-Gyu|YG|0000-0003-4081-5207', 'Lee|Jung-Woo|JW|', 'Lee|Young Chan|YC|0000-0001-5577-7288']
D002294:Carcinoma, Squamous Cell D006258:Head and Neck Neoplasms D006801:Humans D000069550:Machine Learning D009062:Mouth Neoplasms D000077195:Squamous Cell Carcinoma of Head and Neck
machine learning prediction metastasis squamous cell carcinoma oral cancer