No abstract

Colorectal cancer (CRC) is the most common malignancy of the gastrointestinal (GI) tract and accounts for 9% of all cancers. The stroma and the tumoral microenvironment represent brave new frontiers for patients with colorectal cancer. Here we demonstrate novel superpixel image segmentation (SIS) techniques for whole slide images (WSI) to unravel this biology. Findings of significance include the association of low proportionated stromal area (PSA), high immature stromal percentage (ISP) and high myxoid stroma ratio (MSR) with worse prognostic outcomes in our CRC patients. Overall, stromal markers outperformed all others at predicting clinical outcomes. In particular, MSR may be able to prognosticate patients independent of tumor stage and may be the most optimal way to effectively prognosticate CRC patients which circumvents the need for more extensive deep learning (DL) based computational profiling. Approaches demonstrated here can be performed by a trained pathologist and very easily recorded during synoptic cancer reporting with appropriate quality assurance. Future well-designed, robust clinical trials will have the ultimate say in determining whether digital image analysis and superpixel image segmentation can better tailor the need for adjuvant therapy in patients with colorectal cancer.