Artificial intelligence for the study of colorectal cancer tissue slides

doi:10.35712/aig.v1.i3.51

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Artificial Intelligence in Gastroenterology

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2644-3236

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Artif Intell Gastroenterol. Sep 28, 2020; 1(3): 51-59
Published online Sep 28, 2020. doi: 10.35712/aig.v1.i3.51

Artificial intelligence for the study of colorectal cancer tissue slides

Vincenzo Formica, Cristina Morelli, Silvia Riondino, Nicola Renzi, Daniele Nitti, Mario Roselli

Vincenzo Formica, Cristina Morelli, Silvia Riondino, Nicola Renzi, Daniele Nitti, Mario Roselli, Department of Systems Medicine, Medical Oncology Unit, Tor Vergata University Hospital, Rome 00133, Italy

Author contributions: Formica V, Morelli C, Riondino S and Roselli M designed the research study; Nitti D and Renzi N performed the research for retrieval of relevant articles; Formica V, Morelli C and Riondino S analyzed the articles and wrote the manuscript; All authors have read and approve the final manuscript.

Conflict-of-interest statement: None to declare.

Corresponding author: Vincenzo Formica, MD, PhD, Chief Doctor, Department of Systems Medicine, Medical Oncology Unit, Tor Vergata University Hospital, Viale Oxford 81, Rome 00133, Italy. vincenzo.formica@uniroma2.it

Received: June 29, 2020
Peer-review started: June 29, 2020
First decision: July 28, 2020
Revised: September 25, 2020
Accepted: September 27, 2020
Article in press: September 27, 2020
Published online: September 28, 2020
Processing time: 91 Days and 9.4 Hours

Abstract

Artificial intelligence (AI) is gaining incredible momentum as a companion diagnostic in a number of fields in oncology. In the present mini-review, we summarize the main uses and findings of AI applied to the analysis of digital histopathological images of slides from colorectal cancer (CRC) patients. Machine learning tools have been developed to automatically and objectively recognize specific CRC subtypes, such as those with microsatellite instability and high lymphocyte infiltration that would optimally respond to specific therapies. Also, AI-based classification in distinct prognostic groups with no studies of the basic biological features of the tumor have been attempted in a methodological approach that we called “biology-agnostic”.

Keywords: Artificial intelligence; Colorectal cancer; Digital pathology; Deep learning; Machine learning; Tumor-infiltrating lymphocytes

Core Tip: Artificial intelligence (AI) is gaining incredible momentum as a companion diagnostic in a number of fields in oncology. In the present mini-review, we summarize the main uses and findings of AI applied to the analysis of digital histopathological images of slides from colorectal cancer patients.