Reframing Oral Epithelial Dysplasia as a Public Health Priority: Diagnostic Advances, Molecular Insights, and Preventive Strategies

Abstract

Oral potentially malignant disorders (OPMDs) represent a significant public health concern due to their measurable risk of malignant transformation into oral squamous cell carcinoma (OSCC), one of the most prevalent cancers in low- and middle-income countries. The global burden of oral cancer, largely driven by tobacco, alcohol, and areca nut consumption, underscores the necessity for early detection, effective screening, and evidence-based management of precursor lesions. Histopathological assessment for the presence and grading of oral epithelial dysplasia (OED) remains the current gold standard for predicting malignant transformation; however, this approach faces challenges of subjectivity, variability, and limited reproducibility across observers and regions. Several grading systems such as those proposed by Smith and Pindborg (1969), the Ljubljana classification, and successive World Health Organization (WHO) iterations (1978, 2005, 2017, 2022) have attempted to standardize diagnosis, yet inconsistencies persist. These limitations can directly affect early detection programs, delay intervention, and increase the disease burden at the community level. Hence, improving diagnostic objectivity is not merely a laboratory refinement but a critical public health goal.

Recent advances in molecular markers including p53, p63, Ki-67, β-catenin, and E-cadherin, along with genomic profiling through next-generation sequencing (NGS), have enhanced understanding of dysplasia progression and its molecular drivers. Incorporating such biomarkers into community screening models could significantly strengthen early cancer risk stratification and surveillance programs. Furthermore, artificial intelligence (AI) and deep learning (DL)-based systems show promise for large-scale, low-cost, and automated detection of dysplastic changes, particularly in resource-limited settings.

This review examines the evolution of grading systems for OED, emphasizing their implications for public health policy, screening efficiency, and preventive oncology. By integrating histopathological evaluation with molecular diagnostics and AI-driven technologies, healthcare systems can move toward a more standardized, objective, and scalable diagnostic framework. Strengthening these integrative diagnostic pathways is essential not only for clinical precision but also for reducing the incidence, mortality, and socioeconomic impact of oral cancer at the population level.