RESUMEN
Recent advancements in oral cancer detection prioritize non-invasive and minimally invasive techniques for efficient and accurate screening. This review outlines progress in methods such as narrow band imaging, fluorescence imaging, and optical coherence tomography, showing promise in early lesion detection. Biomarker detection in saliva and targeted nanoparticles enhance early diagnosis, while machine learning improves diagnostic accuracy. However, clinical validation and large-scale studies are needed for widespread adoption.
RESUMEN
Background: One of the main risk factors for the occurrence of oral cancer is oral precancerous lesions (OPLs). Early management and preventive efforts depend on knowing the transformation rate and detecting predictive signs of malignancy. Methods: For 6 months, a group of 200 individuals with clinically diagnosed OPLs was followed up on in this longitudinal research. To examine biomarker expression levels and describe the lesions, examinations using immunohistochemistry, histopathology, and clinical methods were carried out. Findings: Over the course of 2 years, 200 patients with OPLs were monitored in this study. Most lesions had mild dysplasia, according to histopathological examination. The expression of many biomarkers that were correlated with the dysplasia grade were p53 (60.0%), Ki-67 (40.0%), CDKN2A (30.0%), and epidermal growth factor receptor (EGFR) (25.0%). Conclusion: In summary, this study emphasizes how crucial it is to provide patients with OPLs with individualized care plans and routine surveillance. Certain biomarkers, such EGFR, Ki-67, and p53, can be useful prognostic markers for identifying malignant transformation. To confirm these results and create tailored therapies for high-risk patients, more study is necessary.