Mucoepidermoid carcinoma: Enhancing diagnostic accuracy and treatment strategy through machine learning models and web-based prognostic tool.

Abstract

Background: Oral cancer, particularly mucoepidermoid carcinoma (MEC), presents diagnostic challenges due to its histological diversity and rarity. This study aimed to develop machine learning (ML) models to predict survival outcomes for MEC patients and pioneer a clinically accessible prognostic tool.

Methods: Using the SEER database (2000-2020), we constructed predictive models with five ML algorithms: Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Multilayer Perceptron (MLP). Predictive variables were identified via Cox regression, and Kaplan-Meier analysis assessed survival trends. Model performance was validated through the area under the curve (AUC) of receiver operating characteristic (ROC) curves.

Results: This study included 1314 patients diagnosed with MEC of the oral cavity. The RFC demonstrated the highest predictive accuracy (AUC = 0.55), followed by the GBC and RFC (AUC = 0.53). The most affected primary site was the hard palate, followed by the retromolar and cheek mucosa. Survival rates varied with the treatment modality, with the highest rates observed in patients undergoing surgery alone. ML models have identified age, sex, and metastasis as significant prognostic factors influencing survival outcomes, underscoring the complexity and heterogeneity of MEC.

Conclusions: This study highlights ML's potential to enhance survival predictions and personalize treatment for MEC patients. We developed the first web-based prognostic tool, providing a novel, accessible solution for improving clinical decision-making in MEC.