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

The DOI matches its Crossref record; 153 of the 153 references it cites were found in a registry or an index.

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
Open this paper in CoChat — free account

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.

Overview

Integrity signals

Registry

Crossref · 10.1038/s43018-022-00436-4

Cites

153 of 153 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 147 references

Research works printed but not linked: 8

  1. Bilal, M. et al. Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study. Lancet Digit. Health 3, e763–e772 (2021). · DOI · Paper page
  2. Schirris, Y., Gavves, E., Nederlof, I., Horlings, H. M. & Teuwen, J. DeepSMILE: self-supervised heterogeneity-aware multiple instance learning for DNA damage response defect classification directly from H&E whole-slide images. Preprint at https://arxiv.org/abs/2107.09405 (2021). · arXiv
  3. Wang, D., Khosla, A., Gargeya, R. & Irshad, H. Deep learning for identifying metastatic breast cancer. Preprint at https://arxiv.org/abs/1606.05718 (2016). · arXiv
  4. de Bel, T., Hermsen, M., Kers, J., van der Laak, J. & Litjens, G. Stain-transforming cycle-consistent generative adversarial networks for improved segmentation of renal histopathology. International Conference on Medical Imaging with Deep Learning https://openreview.net/forum?id=BkxJkgSlx4 (2018).
  5. Gildenblat, J. & Klaiman, E. Self-supervised similarity learning for digital pathology. Preprint at https://arxiv.org/abs/1905.08139 (2019). · arXiv
  6. Dosovitskiy, A. et al. An image is worth 16 × 16 words: transformers for image recognition at scale. Preprint at https://arxiv.org/abs/2010.11929 (2020). · arXiv
  7. Chen, X., Hsieh, C.-J. & Gong, B. When vision transformers outperform ResNets without pre-training or strong data augmentations. Preprint at https://arxiv.org/abs/2106.01548 (2021). · arXiv
  8. Zhang, Y., Jiang, H., Miura, Y., Manning, C. D. & Langlotz, C. P. Contrastive learning of medical visual representations from paired images and text. Preprint at https://arxiv.org/abs/2010.00747 (2020). · arXiv

Cited by

Sorted by citations

Items 301–400 of 448

Related

See all

Metadata

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
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review