Development of clinico-histopathological predictive model for the assessment of metastatic risk of oral squamous cell carcinoma

CONTEXT: Oral cancer metastasis is the leading cause of death globally. The decision-making on the mode of surgical treatment in clinically negative lymph nodes is challenging. AIM: The aim of this study was to develop a predictive model using clinical and histopathologic parameters that may help in the assessment of the metastatic risk of oral squamous cell carcinoma (OSCC). SETTINGS AND DESIGN: Clinical data of histopathologically confirmed primary OSCC from 2014 to 2017 were retrieved from the archives. Histopathological parameters for metastasis that were considered for evaluation in the study were tumor buds, cytoplasmic pseudofragments, tumor grade, depth of invasion, invasive tumor front (ITF) pattern, and lymphovascular invasion (LVI). METHODS: Hematoxylin and eosin and pan-cytokeratin immunostained sections of metastatic and nonmetastatic OSCC were assessed for histopathological features and correlated with clinical parameters. STATISTICAL ANALYSIS USED: SPSS software (Statistical Package for Social Sciences for Windows, Version 22.0 (2013) (IBM Corp., Armonk, NY, USA)) was used for the statistical analysis. Pearson's Chi-square test was done to assess the grades of histopathological and clinical parameters between the study groups. Univariate analysis was performed to develop a clinicopathologic predictive model. RESULTS: The clinicopathologic model signifies that OSCC with clinical Stage IV, high grades of tumor buds and cytoplasmic pseudofragments, Type V ITF pattern, positive LVI, deeply invasive tumors, and poorly differentiated grades of OSCC have a high risk of developing nodal metastasis. These parameters may be used as early predictors for metastasis of OSCC both in incisional and excisional biopsy specimens. CONCLUSIONS: The proposed predictive model is simple, cost-effective, and user-friendly for the early assessment of nodal metastatic risk in clinically negative lymph nodes.