Optimization of Oncogenic Risk Assessment in Patients with Oral Leukoplakia Associated with Type 2 Diabetes Mellitus: A Precision Diagnostic Approach
oral leukoplakia; Type 2 diabetes mellitus; oral potentially malignant disorders; epithelial dysplasia; precision diagnostics; digital pathology; immunohistochemistry; oral squamous cell carcinoma.
AuthorsAbstractOral leukoplakia is the most common oral potentially malignant disorder (OPMD) and remains a significant precursor of oral squamous cell carcinoma (OSCC). Although histopathological grading is the current standard for evaluating malignant transformation, its predictive accuracy is limited, particularly in patients with Type 2 diabetes mellitus (T2DM), where chronic hyperglycemia, oxidative stress, persistent inflammation, and metabolic dysregulation may accelerate epithelial carcinogenesis. The present study aimed to optimize oncogenic risk assessment in patients with oral leukoplakia associated with T2DM through the development of an integrated precision diagnostic approach combining clinicopathological, metabolic, immunohistochemical, and digital pathological parameters. A prospective comparative observational study was conducted involving patients with oral leukoplakia with and without T2DM and healthy controls. Comprehensive clinical examination, histopathological grading, immunohistochemical evaluation of Ki-67, p53, p16, VEGF, and E-cadherin expression, glycemic assessment using fasting plasma glucose and glycated hemoglobin (HbA1c), inflammatory and oxidative stress biomarker analysis, digital pathology, morphometric image analysis, and artificial intelligenceassisted risk prediction were performed. Multivariate logistic regression, Cox proportional hazards modeling, receiver operating characteristic (ROC) analysis, and machine-learning algorithms were used to identify independent predictors of malignant transformation and to construct a personalized oncogenic risk model. The integrated diagnostic framework demonstrated superior predictive performance compared with conventional histopathological evaluation by more accurately stratifying patients according to their individualized risk profiles. Elevated HbA1c, severe epithelial dysplasia, increased Ki-67 and p53 expression, enhanced VEGFmediated angiogenesis, reduced E-cadherin expression, and digital morphometric abnormalities collectively contributed to a significantly improved prediction of malignant progression. The proposed Integrated Precision Oral Oncogenic Risk Assessment Framework (IPORAF) represents an original multidisciplinary model that integrates metabolic status, molecular biomarkers, digital pathology, and artificial intelligence into precision oral oncology. This approach has the potential to facilitate earlier diagnosis, improve individualized surveillance strategies, optimize therapeutic decision-making, and reduce the incidence of malignant transformation among high-risk patients with oral leukoplakia and Type 2 diabetes mellitus.
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