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).

Does Adjuvant Radiotherapy Enhance Survival in Intracranial Solitary Fibrous Tumor Patients?

Purpose: Intracranial solitary fibrous tumor (SFT) is a rare central nervous system (CNS) tumor that lacks a reliable prognostic clinical model. Uncertainty persists regarding the treatment outcomes of surgery and adjuvant radiotherapy (ART). To address this, we investigated the efficacy of ART and applied machine learning (ML) to develop accurate prognostic models.

Integrating machine learning with web-based tools for personalized prognosis in oral adenoid cystic carcinoma.

Background: Adenoid cystic carcinoma (ACC) of the oral cavity is a rare head and neck cancer. This rarity contributes to the paucity of comprehensive research on this cancer thereby complicating the development of evidence-based treatment strategies. This study aims to use machine learning (ML) techniques to analyze survival outcomes and optimize treatment approaches of ACC.