Cited by 448AI in cancer detectionRadiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and Education

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Artificial intelligence in histopathology: enhancing cancer research and clinical oncology

Shmatko, Artem, Ghaffari Laleh, Narmin, Gerstung, Moritz, Kather, Jakob Nikolas·2022·Nature Cancer

DOI 10.1038/s43018-022-00436-4

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Abstract

Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative information from digital histopathology images. AI is expected to reduce workload for human experts, improve the objectivity and consistency of pathology reports, and have a clinical impact by extracting hidden information from routinely available data. Here, we describe how AI can be used to predict cancer outcome, treatment response, genetic alterations and gene expression from digitized histopathology slides. We summarize the underlying technologies and emerging approaches, noting limitations, including the need for data sharing and standards. Finally, we discuss the broader implications of AI in cancer research and oncology.

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Authors: Shmatko, A., Ghaffari Laleh, N., Gerstung, M., & Kather, J. N.
Affiliations: European Bioinformatics Institute (GB)
University of Leeds (GB)
Corresponding author: Gerstung, Moritz
Kather, Jakob Nikolas
ORCID: Shmatko, Artem
Gerstung, Moritz
Kather, Jakob Nikolas
Venue: Nature Cancer
Publisher: Springer Science and Business Media LLC
Published: 2022-09-22
Volume: 3
Issue: 9
Pages: 1026-1038
ISSN: 2662-1347 (ISSN-L)
DOI: 10.1038/s43018-022-00436-4
OpenAlex: W4296693015
Semantic Scholar: d7524f2ec8ad9866190c1b2d030fcac95cc3f565
Cited by: 446
References: 147
Citations by year: 2026: 98 · 2025: 121 · 2024: 121 · 2023: 95 · 2022: 10
FWCI: 6.07
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature Cancer (publishedVersion)
PubMed (publishedVersion)
RWTH Publications (RWTH Aachen) (submittedVersion)
Funders: Deutsche Krebshilfe (Kather)
Bundesministerium für Gesundheit (DEEP LIVER)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords: artificial intelligence, digital histopathology, cancer diagnosis, cancer prognosis, cancer classification, treatment response prediction, genetic alterations, gene expression, data sharing, data standards
MeSH terms: Artificial Intelligence, Humans, Medical Oncology, Neoplasms, Research

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Artificial intelligence in histopathology: enhancing cancer research and clinical oncology

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