Proposal of a scoring system for predicting pathological risk based on a semiautomated analysis of whole slide images in oral squamous cell carcinoma

Abstract

Background: The study aimed to evaluate the risk factors based on pathological findings comprehensively in oral squamous cell carcinoma (OSCC) using image analysis. Methods: Scanned images of hematoxylin and eosin-, pan-cytokeratin-, CD3-, and CD8-stained slides of OSCC cases from 256 patients were analyzed, and six variables were obtained including the tumor–stroma ratio, tumor budding per tumor bed area, and tumor infiltrating lymphocytes-associated variables. We determined the “score” of all cases based on the variables, and all cases were classified into low-, intermediate-, and high-risk groups. Results: A significant difference in prognosis was confirmed between the risk groups (p < 0.001), and even when evaluated within different tumor-node-metastasis (TNM) stages, the high-risk groups were associated with poor survival. Conclusions: We report our work on a possible descriptive model that can predict prognosis based on pathological and imaging findings regardless of the TNM stage.