Deep learning in cancer pathology: a new generation of clinical biomarkers.

British Journal of Cancer
Amelie EchleJakob Nikolas Kather

Abstract

Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making in routine daily oncology practice; furthermore, biomarkers often require tumour tissue on top of routine diagnostic material. Nevertheless, routinely available tumour tissue contains an abundance of clinically relevant information that is currently not fully exploited. Advances in deep learning (DL), an artificial intelligence (AI) technology, have enabled the extraction of previously hidden information directly from routine histology images of cancer, providing potentially clinically useful information. Here, we outline emerging concepts of how DL can extract biomarkers directly from histology images and summarise studies of basic and advanced image analysis for cancer histology. Basic image analysis tasks include detection, grading and subtyping of tumour tissue in histology images; they are aimed at automating pathology workflows and consequently do not immediately translate into clinical decisions. Exceeding such basic approaches, DL has also been used for advanced image analysis tasks, which have the potential of directly affe...Continue Reading

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Citations

Jul 22, 2021·Nature Communications·Frederick M HowardAlexander T Pearson
Jul 16, 2021·Expert Review of Hematology·Kennosuke KarubeKoichi Ohshima
Jul 15, 2021·Journal of the American Medical Informatics Association : JAMIA·Peter J SchüfflerThomas J Fuchs
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Aug 28, 2021·International Journal of Molecular Sciences·Nicole BussolaGiuseppe Jurman
Sep 26, 2021·The Journal of Pathology·Peter Leonard SchrammenJakob Nikolas Kather

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Methods Mentioned

BETA
biopsy
genotyping

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