Computationally-Guided Development of a Stromal Inflammation Histologic Biomarker in Lung Squamous Cell Carcinoma.

ABSTRACT: The goal of this study is to use computational pathology to help guide the development of human-based prognostic H&E biomarker(s) suitable for research and potential clinical use in lung squamous cell carcinoma (SCC). We started with high-throughput computational image analysis with tissue microarrays (TMAs) to screen for histologic features associated with patient overall survival, and found that features related to stromal inflammation were the most strongly prognostic. Based on this, we developed an H&E stromal inflammation (SI) score. The prognostic value of the SI score was validated by two blinded human observers on two large cohorts from a single institution. The SI score was found to be reproducible on TMAs (Spearman rho = 0.88 between the two observers), and highly prognostic (e.g

SUBMITTER: Xia D 

PROVIDER: S-EPMC5834457 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature