作者
Jakob Nikolas Kather, Lara R Heij, Heike I Grabsch, Chiara Loeffler, Amelie Echle, Hannah Sophie Muti, Jeremias Krause, Jan M Niehues, Kai AJ Sommer, Peter Bankhead, Loes FS Kooreman, Jefree J Schulte, Nicole A Cipriani, Roman D Buelow, Peter Boor, Nadina Ortiz-Brüchle, Andrew M Hanby, Valerie Speirs, Sara Kochanny, Akash Patnaik, Andrew Srisuwananukorn, Hermann Brenner, Michael Hoffmeister, Piet A van den Brandt, Dirk Jäger, Christian Trautwein, Alexander T Pearson, Tom Luedde
发表日期
2020/8
期刊
Nature cancer
卷号
1
期号
8
页码范围
789-799
出版商
Nature Publishing Group US
简介
Molecular alterations in cancer can cause phenotypic changes in tumor cells and their microenvironment. 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 5,000 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 …
学术搜索中的文章
JN Kather, LR Heij, HI Grabsch, C Loeffler, A Echle… - Nature cancer, 2020