Formica V, Morelli C, Riondino S, Renzi N, Nitti D, Roselli M. Artificial intelligence for the study of colorectal cancer tissue slides. Artif Intell Gastroenterol 2020; 1(3): 51-59 [DOI: 10.35712/aig.v1.i3.51]
Corresponding Author of This Article
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
Research Domain of This Article
Oncology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
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.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
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”.
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.
