Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Comment
. 2020 Aug;1(8):755-757.
doi: 10.1038/s43018-020-0099-2.

Deep learning links histology, molecular signatures and prognosis in cancer

Affiliations
Comment

Deep learning links histology, molecular signatures and prognosis in cancer

Nicolas Coudray et al. Nat Cancer. 2020 Aug.

Abstract

Deep learning can be used to predict genomic alterations based on morphological features learned from digital histopathology. Two independent pan-cancer studies now show that automated learning from digital pathology slides and genomics can potentially decipher broader classes of molecular signatures and prognostic associations across cancer types.

PubMed Disclaimer

Figures

Fig. 1:
Fig. 1:. Comparison of the two pan-cancer deep-learning-based approaches to unveil genotypes information from whole slide images.
(a) In Kather et al., each molecular feature in each cancer type is directly learned from pre-selected tumor regions using a modified version of shufflenet. (b) In Fu et al., a feature vector is first generated after classifying 42 different types of normal and cancer tissues using Google’s Inception v4, and then used to train generalized linear models to identify genotype properties. Both methods are based on tiles extracted at 20x magnification.

Comment on

  • Pan-cancer image-based detection of clinically actionable genetic alterations.
    Kather JN, Heij LR, Grabsch HI, Loeffler C, Echle A, Muti HS, Krause J, Niehues JM, Sommer KAJ, Bankhead P, Kooreman LFS, Schulte JJ, Cipriani NA, Buelow RD, Boor P, Ortiz-Brüchle NN, Hanby AM, Speirs V, Kochanny S, Patnaik A, Srisuwananukorn A, Brenner H, Hoffmeister M, van den Brandt PA, Jäger D, Trautwein C, Pearson AT, Luedde T. Kather JN, et al. Nat Cancer. 2020 Aug;1(8):789-799. doi: 10.1038/s43018-020-0087-6. Epub 2020 Jul 27. Nat Cancer. 2020. PMID: 33763651 Free PMC article.
  • Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis.
    Fu Y, Jung AW, Torne RV, Gonzalez S, Vöhringer H, Shmatko A, Yates LR, Jimenez-Linan M, Moore L, Gerstung M. Fu Y, et al. Nat Cancer. 2020 Aug;1(8):800-810. doi: 10.1038/s43018-020-0085-8. Epub 2020 Jul 27. Nat Cancer. 2020. PMID: 35122049

References

    1. El-Deiry WS et al. The current state of molecular testing in the treatment of patients with solid tumors, 2019. CA Cancer J. Clin 69, 305–343 (2019). - PMC - PubMed
    1. Mobadersany P et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc. Natl. Acad. Sci. U. S. A 115, E2970–E2979 (2018). - PMC - PubMed
    1. Coudray N et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med 24, 1559–1567 (2018). - PMC - PubMed
    1. Campanella G et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med 25, 1301–1309 (2019). - PMC - PubMed
    1. Ehteshami Bejnordi B et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA 318, 2199–2210 (2017). - PMC - PubMed

LinkOut - more resources