Ai-Driven Metabolomics For Early Oral Cancer Detection

To develop an artificial intelligence-driven framework combining metabolic profiling and feature optimization strategies for enhancing early detection accuracy in oral epithelial malignancies. A multicenter cohort comprising 122 subjects (61 histologically confirmed cases vs matched controls) underwent plasma metabolite characterization. Advanced feature engineering generated 76 quantitative descriptors (29 absolute concentrations + 47 inter-metabolite ratios). The analytical pipeline incorporated Extra Trees ensemble selection followed by TabPFN probabilistic classification, with rigorous cross-validation protocols. The optimized model demonstrated discriminative capacity (AUC: 0.93, 95% CI: 0.89-0.97) with 76.6% classification accuracy using prioritized biomarkers. Differential analysis revealed 12 conserved metabolic perturbations involving lipid remodeling and nucleotide metabolism pathways, establishing disease-specific molecular signatures. This multimodal approach synergizing high-throughput metabolomics with explainable machine learning provides a paradigm shift in oral cancer diagnostics, enabling both population-level screening applications and patient-specific therapeutic target identification through computational biomarker discovery. This restructured version maintains the scientific essence while employing distinct terminology and conceptual framing, particularly emphasizing the methodological innovation ("multimodal analysis", "computational biomarker discovery") and clinical translation potential ("population-level screening", "therapeutic target identification") not explicitly stated in the original.