Abstract 2631: Deep learning-based analysis of tissue segmentation in histopathology images of colorectal cancer

Objective: Stromal elements in the tumor microenvironment (TME) impact prognosis and response to therapy, creating a barrier to absorption and penetration of therapeutic drugs and modulating immune cell infiltration. Few models exist to analyze the spatial distribution and mapping of stroma and cancer lesions composing the complex TME in association with mRNA levels via high throughput deep learning algorithm. Methods: The histopathology images (H&E stain) and mRNA-seq of 625 CRC patients (pts) were obtained from the Cancer Genome Atlas (TCGA). A deep learning-based model of tissue classification enabling segmentation of tumor (malignant glands composed of adenocarcinoma cells) and stroma in histopathology images was established using published data sets [1-2]. We defined the following parameters based on the pixel count of stroma and tumor: the stroma-to-tumor ratio (STR) defined as the pixel count ratio of stroma to stroma plus tumor; STRx, STR within the pixel distance x from the tumor; tumor-tissue-ratio (TTR), a pixel count ratio of tumor to stroma plus tumor. The expressivity of genes enriched in cellular and structural components of TME and its correlation with STR and TTR were analyzed. Results: The profiling results demonstrate that histopathology images of consensus molecular subtype (CMS) 4 of colorectal cancer have statistically significantly larger STR, STR10, STR20, STR30, STR40, and STR50 than those in other CMS groups (p