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Updated: Jan 15, 2026
Author Spotlight: Unlocking the Mysteries of Oral Potential Malignancies
Published on: August 11, 2023
Molecular Biomarkers and Machine Learning in Oral Cancer: A Systematic Review and Meta-Analysis
Carlos M Ardila1,2, Anny Marcela Vivares-Builes2,3, Eliana Pineda-Vélez2,3
1Department of Periodontics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India.
Objective:
This systematic review and meta-analysis aimed to synthesize diagnostic and prognostic performance metrics of machine learning (ML)-based biomarker models in oral squamous cell carcinoma (OSCC) and to integrate biological insights through a functional metasynthesis.
Methods:
Following PRISMA 2020 guidelines, a comprehensive search was conducted up to July 2025. Eligible studies applied ML algorithms to molecular or imaging biomarkers from OSCC patients. Data synthesis incorporated meta-analysis when endpoints and designs were sufficiently comparable; otherwise, study-level results were summarized narratively.
Results:
Twenty-five studies encompassing 4408 patients were included. Diagnostic performance was strongest for salivary DNA methylation (AUC up to 1.00), metabolomics (AUC ≈0.92), and FTIR imaging (AUC ≈0.91), while autoantibody and microbiome models showed more variable accuracy. Prognostic models based on immune-feature signatures outperformed conventional scores, while multimodal approaches integrating imaging and metabolomics retained strong performance under external validation. Models based on pathomics and MRI radiomics also achieved clinically meaningful accuracy across independent cohorts. Functional metasynthesis revealed convergent biological processes-metabolic reprogramming, immune-inflammatory remodeling, microbiome dysbiosis, and epithelial/extracellular matrix disruption-that underpin predictive accuracy.
Conclusion:
ML models leveraging molecular and imaging biomarkers show strong potential to improve OSCC diagnosis, risk stratification, and prognosis, particularly through multimodal integration.
