Artificial intelligence for the study of colorectal cancer tissue slides

Table 1 Summary of main applications and findings of artificial intelligence in colorectal cancer digital pathology

Target	Description	Ref.
Cancer cell shape and organization to predict N+	Digital assessment of cancer cells using Feret’s diameters allows to predict lymph node metastasis in pT1 colon cancer	[11]
Assessment of anti-cancer immune response	Simultaneous assessment of all immune cell subpopulations and demonstration that eosinophils, other than T cells, may play a role in CRC immune response	[12,14,15,17]
Identification of Microsatellite instable tumors	Rapid and large size screening of histopathologic features in conventional HE-stained slides increasing the probability of being a MSI-H tumor, without the need of specific immunohistochemical or molecular testing	[18]
Quantification of stroma within the tumor	Algorithms for tissue –specific recognition, even when sparse within the tumor mass have been development. These algorithms have allowed the validation of “deep stroma score” which is significantly associated with survival in CRC	[20]
Biology-agnostic prediction of survival	Development of tissue “digital” profiles without specific underlying biologic background or significance that are predictive of distinct survivals, bad vs good outcome	[21]

CRC: Colorectal cancer; MSI-H: Microsatellite instability high.