Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers

Gut, vol. 70, pp. 1183–1193

Abstract

Artificial intelligence (AI) can extract complex information from visual data. Histopathology images of gastrointestinal (GI) and liver cancer contain a very high amount of information which human observers can only partially make sense of. Complementing human observers, AI allows an in-depth analysis of digitised histological slides of GI and liver cancer and offers a wide range of clinically relevant applications. First, AI can automatically detect tumour tissue, easing the exponentially increasing workload on pathologists. In addition, and possibly exceeding pathologist's capacities, AI can capture prognostically relevant tissue features and thus predict clinical outcome across GI and liver cancer types. Finally, AI has demonstrated its capacity to infer molecular and genetic alterations of cancer tissues from histological digital slides. These are likely only the first of many AI applications that will have important clinical implications. Thus, pathologists and clinicians alike should be aware of the principles of AI-based pathology and its ability to solve clinically relevant problems, along with its limitations and biases.

Authors 2

1. Julien Caldéraro corresponding

   Inserm · Hôpitaux Universitaires Henri-Mondor · Centre Hospitalier Universitaire Henri-Mondor

   Affiliation as printed

   Pathology, Hopital Henri Mondor, Creteil, Île-de-France, France

   U955, INSERM, Créteil, France

   U955, INSERM, Créteil 75010, France

2. Jakob Nikolas Kather Aachen Department of Medicine III

   RWTH Aachen University · Universitätsklinikum Aachen · German Cancer Research Center

   Affiliation as printed

   Applied Tumor Immunity, Deutsches Krebsforschungszentrum, Heidelberg, BW, Germany

   Department of Medicine III, University Hospital RWTH, Aachen, Germany