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.

Keywords: image analysis; oral squamous cell carcinoma; tumor budding; tumor infiltrating lymphocytes; whole slide image.

FIGURE 1

Schematic image of the components of oral squamous cell carcinoma, with the microenvironment and measurements of each variable. TIL, tumor infiltrating lymphocyte [Color figure can be viewed at wileyonlinelibrary.com]

FIGURE 2

Image analysis process of oral squamous cell carcinoma from a representative case. Based on the whole slide image of the pan‐cytokeratin‐stained slide, the tumor bed was manually designated, followed by automated tumor and tumor budding detection; based on the whole slide image of the CD3‐stained slide, the TIL bed was manually designated, followed by automated processing of CD3+ T cells and CD8+ T cells. TIL, tumor infiltrating lymphocyte [Color figure can be viewed at wileyonlinelibrary.com]

FIGURE 3

Image and graphs showing tumor budding and measurement of the area. (A) Representative image of tumor budding with variable tumor cell numbers and areas. (B) Graph showing the area range of each group of tumor cell nests consisting of one to five tumor cells. (C) A receiver operating characteristic (ROC) curve was generated to determine the optimal cutoff value to detect tumor budding [Color figure can be viewed at wileyonlinelibrary.com]