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
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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From Large language models and multimodal foundation models for precision oncology (2024) · cites this paper as [1]
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“Between 2012 and 2022, neural networks were applied in numerous studies that primarily focused on the analysis of oncological imaging or text1.”
From Large language models and multimodal foundation models for precision oncology (2024) · cites this paper as [1]
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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
- Venue
- Nature Cancer
- Published
- 2022-09-22
- Volume
- 3
- Issue
- 9
- Pages
- 1026-1038
- ISSN
- 2662-1347 (ISSN-L)
- 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
- 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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