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Article

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis.

Nature cancer - 1 Aug 2020

Fu Yu, Jung Alexander W, Torne Ramon Viñas, Gonzalez Santiago, Vöhringer Harald, Shmatko Artem, Yates Lucy R, Jimenez-Linan Mercedes, Moore Luiza, Gerstung Moritz

Abstract excerpt

We use deep transfer learning to quantify histopathological patterns across 17,355 hematoxylin and eosin-stained histopathology slide images from 28 cancer types and correlate these with matched genomic, transcriptomic and survival data. This approach accurately classifies cancer types and provides spatially resolved tumor and normal tissue distinction. Automatically learned computational histopathological...

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PMID: 35122049
DOI: 10.1038/s43018-020-0085-8

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