Artificial intelligence to identify genetic alterations in conventional histopathology

The Journal of Pathology, vol. 257, pp. 430–444

Abstract

Precision oncology relies on the identification of targetable molecular alterations in tumor tissues. In many tumor types, a limited set of molecular tests is currently part of standard diagnostic workflows. However, universal testing for all targetable alterations, especially rare ones, is limited by the cost and availability of molecular assays. From 2017 to 2021, multiple studies have shown that artificial intelligence (AI) methods can predict the probability of specific genetic alterations directly from conventional hematoxylin and eosin (H&E) tissue slides. Although these methods are currently less accurate than gold standard testing (e.g. immunohistochemistry, polymerase chain reaction or next-generation sequencing), they could be used as pre-screening tools to reduce the workload of genetic analyses. In this systematic literature review, we summarize the state of the art in predicting molecular alterations from H&E using AI. We found that AI methods perform reasonably well across multiple tumor types, although few algorithms have been broadly validated. In addition, we found that genetic alterations in FGFR, IDH, PIK3CA, BRAF, TP53, and DNA repair pathways are predictable from H&E in multiple tumor types, while many other genetic alterations have rarely been investigated or were only poorly predictable. Finally, we discuss the next steps for the implementation of AI-based surrogate tests in diagnostic workflows. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Authors 3

1. Didem Çifçi Aachen Department of Medicine III

   RWTH Aachen University · Universitätsklinikum Aachen

   Affiliation as printed

   Department of Medicine III University Hospital RWTH Aachen Aachen Germany

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

2. Sebastian Foersch

   Johannes Gutenberg University Mainz · University Medical Center of the Johannes Gutenberg University Mainz

   Affiliation as printed

   Institute of Pathology University Medical Center of the Johannes Gutenberg‐University Mainz Germany

   Institute of Pathology, University Medical Center of the Johannes Gutenberg-University, Mainz, Germany

3. Jakob Nikolas Kather corresponding Aachen Department of Medicine III

   RWTH Aachen University · Universitätsklinikum Aachen · Heidelberg University · University Hospital Heidelberg · National Center for Tumor Diseases · University of Leeds

   Affiliation as printed

   Department of Medicine III University Hospital RWTH Aachen Aachen Germany

   Medical Oncology National Center for Tumor Diseases, University Hospital Heidelberg Heidelberg Germany

   Pathology and Data Analytics Leeds Institute of Medical Research at St James's, University of Leeds Leeds UK

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

   Medical Oncology, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany

   Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK