TY  - JOUR
AU  - Kather, Jakob Nikolas
AU  - Heij, Lara R
AU  - Grabsch, Heike I
AU  - Loeffler, Chiara
AU  - Echle, Amelie
AU  - Muti, Hannah Sophie
AU  - Krause, Jeremias
AU  - Niehues, Jan M
AU  - Sommer, Kai A J
AU  - Bankhead, Peter
AU  - Kooreman, Loes F S
AU  - Schulte, Jefree J
AU  - Cipriani, Nicole A
AU  - Buelow, Roman D
AU  - Boor, Peter
AU  - Ortiz-Brüchle, Nadi-Na
AU  - Hanby, Andrew M
AU  - Speirs, Valerie
AU  - Kochanny, Sara
AU  - Patnaik, Akash
AU  - Srisuwananukorn, Andrew
AU  - Brenner, Hermann
AU  - Hoffmeister, Michael
AU  - van den Brandt, Piet A
AU  - Jäger, Dirk
AU  - Trautwein, Christian
AU  - Pearson, Alexander T
AU  - Luedde, Tom
TI  - Pan-cancer image-based detection of clinically actionable genetic alterations.
JO  - Nature cancer
VL  - 1
IS  - 8
SN  - 2662-1347
CY  - London
PB  - Nature Research
M1  - DKFZ-2021-00719
SP  - 789 - 799
PY  - 2020
AB  - Molecular alterations in cancer can cause phenotypic changes in tumor cells and their micro-environment. 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 5000 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 mobile hardware, potentially enabling point-of-care diagnostics for personalized cancer treatment. More generally, this approach could elucidate and quantify genotype-phenotype links in cancer.
LB  - PUB:(DE-HGF)16
C6  - pmid:33763651
C2  - pmc:PMC7610412
DO  - DOI:10.1038/s43018-020-0087-6
UR  - https://inrepo02.dkfz.de/record/168179
ER  -