Background: Oral squamous cell carcinoma (OSCC) accounts for nearly 90% of all oral malignancies and remains a major cause of cancer-related morbidity and mortality worldwide. Conventional histopathological grading is considered the gold standard for diagnosis; however, subjective interpretation often results in interobserver variability. Digital morphometric analysis provides objective quantitative assessment of nuclear and cellular alterations associated with malignant transformation and tumor progression.

Objective: To evaluate digital morphometric parameters in oral squamous cell carcinoma and determine their correlation with histological grade and prognostic indicators.

Materials and Methods: A retrospective observational study was conducted on 180 histopathologically confirmed OSCC surgical specimens over five years. Digital image analysis was performed using ImageJ software. Nuclear area, nuclear perimeter, nuclear diameter, nuclear circularity, nuclear optical density, cellular area, nuclear-cytoplasmic ratio, and nuclear pleomorphism were measured from representative microscopic fields. Morphometric parameters were correlated with histological grade, lymphovascular invasion, perineural invasion, lymph node metastasis, and pathological stage.

Results: Well-differentiated OSCC constituted 44.4% of cases, moderately differentiated tumors 36.7%, and poorly differentiated tumors 18.9%. Progressive increases in nuclear area, nuclear perimeter, nuclear diameter, nuclear optical density, and nuclear-cytoplasmic ratio were observed with increasing histological grade (p < 0.001). Poorly differentiated tumors demonstrated significantly greater nuclear pleomorphism, higher nuclear density, lymphovascular invasion, and lymph node metastasis. Digital morphometric assessment achieved an overall diagnostic accuracy of 95.1% in differentiating tumor grades.

Conclusion: Digital morphometry provides an objective, reproducible, and highly accurate method for evaluating oral squamous cell carcinoma. Quantitative nuclear measurements significantly correlate with tumor aggressiveness and may serve as valuable prognostic biomarkers.