Collaborative machine learning-guided overall survival prediction of oral squamous cell carcinoma

Abstract

Background There is a lack of prognosticators of overall survival (OS) for Oral Squamous Cell Carcinoma (OSCC).

Objectives We examined collaborative machine learning (cML) in estimating the OS of OSCC patients. The prognostic significance of the clinicopathological parameters was examined.

Methodology Altogether, 9439 OSCC patients were extracted from the Surveillance, Epidemiology, and End Results database (US). Five ML models – voting ensemble, stacked ensemble, extreme gradient boosting, light boosting, and logistic regression were used to predict OS. Three of these ML algorithms were combined to form a cluster of cML models. The performance of the cML was compared with the best performing individual ML algorithm following model training.

Results The performance accuracy of the voting ensemble, stacked ensemble, extreme gradient boosting, light boosting, and logistic regression models was 70.2%, 69.9%, 69.1%, 69.4%, and 69.5% respectively, following model training. When the voting ensemble model was compared with cML using temporal validation, the cML showed a comparable performance accuracy. The most significant prognostic factors were age of the patient at diagnosis, T stage, tumor grade, marital status, gender, primary site, surgery, N stage, radiotherapy, ethnicity, chemotherapy, and M stage.

Conclusions cML appears to give reliability to the final prediction and thereby may mark a paradigm shift from model individualism to a more cooperative paradigm. This approach may aid in determining an enhanced individualized treatment for OSCC patients.