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

Abstract

The study aimed to evaluate the risk factors based on pathological findings comprehensively in oral squamous cell carcinoma (OSCC) using image analysis. 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. 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. We report our work on a possible descriptive model that can predict prognosis based on pathological and imaging findings regardless of the TNM stage.