Mucoepidermoid Carcinoma: Enhancing Diagnostic Accuracy and Treatment Strategy through Machine Learning Models and Web-Based Prognostic Tool.

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).

Clinicopathological and prognostic significance of indoleamine 2,3-dioxygenase (IDO) expression in head and neck squamous cell carcinoma: A systematic review and meta-analysis.

Objective: Indoleamine 2,3-dioxygenase-1 (IDO1) is a promising antitumor target and predictive biomarker in a variety of cancers. Hence, we performed this meta-analysis to evaluate the clinicopathological and prognostic significance of IDO1 in head and neck squamous cell carcinoma (HNSCC).

Methods: We searched PubMed, Embase, Web of Science and Scopus databases from inception to May 2024, to identify studies measuring the clinicopathological and prognostic significance of IDO1 in HNSCC.