Machine Learning-Driven Insights into ctDNA for Oral Cancer: Applications, Models, and Future Prospects

Abstract

Circulating tumor DNA (ctDNA) offers a promising non-invasive approach for early cancer detection, treatment monitoring, and personalized medicine, particularly in oral cancer. This review explores the clinical applications, challenges, and future prospects of ctDNA analysis. We highlight the integration of advanced machine learning (ML) models—Support Vector Machines (SVM), Random Forests (RF), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN)—in ctDNA detection and analysis. These models significantly enhance the accuracy and reliability of ctDNA analysis, with accuracies reaching up to 93%. SVM and RF models excel in classification and feature selection, while ANN and CNN models capture complex and spatial patterns, respectively. Despite challenges such as low ctDNA abundance and the need for standardized protocols, ML-driven ctDNA analysis holds immense potential for revolutionizing cancer diagnostics and treatment.