Current Trends in Diagnosis and Early Monitoring of Oral Cavity Cancer: Techniques and Biomarkers

Background/Objectives: Oral cavity squamous cell carcinoma (OSCC) remains a major global health burden, with outcomes strongly dependent on stage at diagnosis. Although the oral cavity is directly accessible to clinical examination, many cases are still detected at advanced stages. This narrative review aimed to summarize current trends in OSCC diagnosis and early monitoring, with emphasis on non-invasive and minimally invasive techniques and biomarkers. Methods: A semi-systematic narrative literature search was conducted in PubMed/MEDLINE, Scopus, and Web of Science using predefined combinations of OSCC-, early-detection-, imaging-, cytology-, liquid-biopsy-, salivaomics-, artificial-intelligence-, and biosensor-related terms. English-language systematic reviews, meta-analyses, reviews of reviews, translational studies, and clinically relevant original articles were prioritized, with explicit attention to oral cavity-specific evidence and clearly identified extrapolation from broader head-and-neck or oropharyngeal cancer settings. Results: Conventional oral examination and histopathological assessment of biopsy specimens remain the diagnostic foundation. Adjunctive methods may support lesion triage, biopsy-site selection, risk stratification, and early monitoring, but cannot replace tissue diagnosis. Narrow-band imaging, optical coherence tomography, and molecular brush cytology appear particularly promising for specialist assessment and surveillance. Liquid biopsy and saliva-based biomarker platforms offer translational potential, particularly for repeatable monitoring, but clinical implementation is limited by methodological heterogeneity, pre-analytical variability, inconsistent thresholds, and insufficient external validation. Artificial intelligence and biosensor platforms remain promising but largely developmental. Conclusions: Progress in early OSCC diagnosis and monitoring will most likely depend on integrated diagnostic models combining clinical examination, adjunctive imaging, minimally invasive sampling, molecular biomarkers, and computational decision-support tools, validated in prospective multicenter studies.