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Deep learning links histology, molecular signatures and prognosis in cancer

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

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Fig. 1: Comparison of the two pan-cancer deep-learning-based approaches to unveil genotypes information from whole slide images.


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Correspondence to Nicolas Coudray or Aristotelis Tsirigos.

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Coudray, N., Tsirigos, A. Deep learning links histology, molecular signatures and prognosis in cancer. Nat Cancer 1, 755–757 (2020).

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