Abstract PO-001: Deep learning-based image analysis of the histological Glasgow Microenvironment Score in patients with colorectal cancer

Background: Efforts to prognostically stratify colorectal cancer (CRC) patients using histological samples has led to the development of the Glasgow Microenvironment Score (GMS). However, the subjective nature of the GMS assessment criteria makes reproducing the score challenging. Therefore, the aim of the current study was to assess the clinical utility of an image analysis-based approach. Methods: In two cohorts of patients (a test cohort of 297 Stage II/III CRC patients from Norway and a validation cohort of 233 Stage II/III CRC patients from Scotland), one HE GMS 1 – manual = 136 months vs digital = 139 months ; GMS2 – manual 101 months vs 97 months). To negate the need for any annotation prior to analysis, the lymphocyte detection algorithm was applied to the stroma areas determined by the UNET algorithm. This fully automated digital version of the GMS was able to stratify validation cohort patients into the 3 prognostic groups based on CSS (P