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Cited by 662

The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.

Pan-cancer image-based detection of clinically actionable genetic alterations

Open access
Kather, Jakob Nikolas, Heij, Lara R., Grabsch, Heike I., Loeffler, Chiara, Echle, Amelie, Muti, Hannah Sophie, Krause, Jeremias, Niehues, Jan M., Sommer, Kai A. J., Bankhead, Peter, Kooreman, Loes F. S., Schulte, Jefree J., Cipriani, Nicole A., Buelow, Roman D., Boor, Peter, Ortiz-Brüchle, Nadina, Hanby, Andrew M., Speirs, Valerie, Kochanny, Sara, Patnaik, Akash, Srisuwananukorn, Andrew, Brenner, Hermann, Hoffmeister, Michael, van den Brandt, Piet A., Jäger, Dirk, Trautwein, Christian, Pearson, Alexander T., Luedde, Tom·2020·Nature Cancer
DOI 10.1038/s43018-020-0087-6

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Abstract

Molecular alterations in cancer can cause phenotypic changes in tumor cells and their micro-environment. Routine histopathology tissue slides - which are ubiquitously available - can reflect such morphological changes. Here, we show that deep learning can consistently infer a wide range of genetic mutations, molecular tumor subtypes, gene expression signatures and standard pathology biomarkers directly from routine histology. We developed, optimized, validated and publicly released a one-stop-shop workflow and applied it to tissue slides of more than 5000 patients across multiple solid tumors. Our findings show that a single deep learning algorithm can be trained to predict a wide range of molecular alterations from routine, paraffin-embedded histology slides stained with hematoxylin and eosin. These predictions generalize to other populations and are spatially resolved. Our method can be implemented on mobile hardware, potentially enabling point-of-care diagnostics for personalized cancer treatment. More generally, this approach could elucidate and quantify genotype-phenotype links in cancer.

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Show all 24 references

Research works printed but not linked: 27

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Metadata

Authors
Kather, J. N., Heij, L. R., Grabsch, H. I., Loeffler, C., Echle, A., Muti, H. S., Krause, J., Niehues, J. M., Sommer, K. A. J., Bankhead, P., Kooreman, L. F. S., Schulte, J. J., Cipriani, N. A., Buelow, R. D., Boor, P., Ortiz-Brüchle, N., Hanby, A. M., Speirs, V., Kochanny, S., Patnaik, A., Srisuwananukorn, A., Brenner, H., Hoffmeister, M., van den Brandt, P. A., Jäger, D., Trautwein, C., Pearson, A. T., & Luedde, T.
Venue
Nature Cancer
Publisher
Springer Science and Business Media LLC
Published
2020-07-27
Volume
1
Issue
8
Pages
789-799
ISSN
2662-1347 (ISSN-L)
DOI
10.1038/s43018-020-0087-6
OpenAlex
W3044996171
Cited by
662
References
24
FWCI
32.30
Type
journal article
Language
en
Update notices
correction (2020-11-03)
Open access
hybrid
Licence

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CoChat. (2026). Citation check: Pan-cancer image-based detection of clinically actionable genetic alterations. https://app.cochat.ai/papers/10.1038/s43018-020-0087-6
The paper
Kather, J. N., Heij, L. R., Grabsch, H. I., Loeffler, C., Echle, A., Muti, H. S., Krause, J., Niehues, J. M., Sommer, K. A. J., Bankhead, P., Kooreman, L. F. S., Schulte, J. J., Cipriani, N. A., Buelow, R. D., Boor, P., Ortiz-Brüchle, N., Hanby, A. M., Speirs, V., Kochanny, S., . . . Luedde, T. (2020). Pan-cancer image-based detection of clinically actionable genetic alterations. Nature Cancer. https://doi.org/10.1038/s43018-020-0087-6

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.