Digital pattern recognition-based image analysis quantifies immune infiltrates in distinct tissue regions of colorectal cancer and identifies a metastatic phenotype

Background: Several studies in colorectal cancer (CRC) indicate a relationship between tumour immune infiltrates and clinical outcome. We tested the utility of a digital pattern recognition-based image analysis (DPRIA) system to segregate tissue regions and facilitate automated quantification of immune infiltrates in CRC. Methods: Primary CRC with matched hepatic metastatic (n = 7), primary CRC alone (n = 18) and primary CRC with matched normal (n = 40) tissue were analysed immunohistochemically. Genie pattern recognition software was used to segregate distinct tissue regions in combination with image analysis algorithms to quantify immune cells. Results: Immune infiltrates were observed predominately at the invasive margin. Quantitative image analysis revealed a significant increase in the prevalence of Foxp3 (P<0.0001), CD8 (P<0.0001), CD68 <0.0001) and CD31 (<0.0001) positive cells in the stroma of primary and metastatic CRC, compared with tumour cell mass. A direct comparison between non-metastatic primary CRC (MET-) and primary CRC that resulted in metastasis (MET+) showed an immunosuppressive phenotype, with elevated Foxp3 (P<0.05) and reduced numbers of CD8 (P<0.05) cells in the stroma of MET+ compared with MET- samples. Conclusion: By combining immunohistochemistry with DPRIA, we demonstrate a potential metastatic phenotype in CRC. Our study accelerates wider acceptance and use of automated systems as an adjunct to traditional histopathological techniques.