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000168179 1001_ $$0P:(DE-He78)761f5d0f73e0d8f170394b29448a9e8d$$aKather, Jakob Nikolas$$b0$$udkfz
000168179 245__ $$aPan-cancer image-based detection of clinically actionable genetic alterations.
000168179 260__ $$aLondon$$bNature Research$$c2020
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000168179 520__ $$aMolecular alterations in cancer can cause phenotypic changes in tumor cells and their micro-environment. Routine histopathology tissue slides - which are ubiquitously available - can reflect such morphological changes. Here, we show that deep learning can consistently infer a wide range of genetic mutations, molecular tumor subtypes, gene expression signatures and standard pathology biomarkers directly from routine histology. We developed, optimized, validated and publicly released a one-stop-shop workflow and applied it to tissue slides of more than 5000 patients across multiple solid tumors. Our findings show that a single deep learning algorithm can be trained to predict a wide range of molecular alterations from routine, paraffin-embedded histology slides stained with hematoxylin and eosin. These predictions generalize to other populations and are spatially resolved. Our method can be implemented on mobile hardware, potentially enabling point-of-care diagnostics for personalized cancer treatment. More generally, this approach could elucidate and quantify genotype-phenotype links in cancer.
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000168179 7001_ $$aHeij, Lara R$$b1
000168179 7001_ $$aGrabsch, Heike I$$b2
000168179 7001_ $$aLoeffler, Chiara$$b3
000168179 7001_ $$aEchle, Amelie$$b4
000168179 7001_ $$aMuti, Hannah Sophie$$b5
000168179 7001_ $$aKrause, Jeremias$$b6
000168179 7001_ $$aNiehues, Jan M$$b7
000168179 7001_ $$aSommer, Kai A J$$b8
000168179 7001_ $$aBankhead, Peter$$b9
000168179 7001_ $$aKooreman, Loes F S$$b10
000168179 7001_ $$aSchulte, Jefree J$$b11
000168179 7001_ $$aCipriani, Nicole A$$b12
000168179 7001_ $$aBuelow, Roman D$$b13
000168179 7001_ $$aBoor, Peter$$b14
000168179 7001_ $$aOrtiz-Brüchle, Nadi-Na$$b15
000168179 7001_ $$aHanby, Andrew M$$b16
000168179 7001_ $$aSpeirs, Valerie$$b17
000168179 7001_ $$aKochanny, Sara$$b18
000168179 7001_ $$aPatnaik, Akash$$b19
000168179 7001_ $$aSrisuwananukorn, Andrew$$b20
000168179 7001_ $$0P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2$$aBrenner, Hermann$$b21$$udkfz
000168179 7001_ $$0P:(DE-He78)6c5d058b7552d071a7fa4c5e943fff0f$$aHoffmeister, Michael$$b22$$udkfz
000168179 7001_ $$avan den Brandt, Piet A$$b23
000168179 7001_ $$0P:(DE-He78)ed0321409c9cde20b380ae663dbcefd1$$aJäger, Dirk$$b24$$udkfz
000168179 7001_ $$aTrautwein, Christian$$b25
000168179 7001_ $$aPearson, Alexander T$$b26
000168179 7001_ $$aLuedde, Tom$$b27
000168179 773__ $$0PERI:(DE-600)3005299-3$$a10.1038/s43018-020-0087-6$$gVol. 1, no. 8, p. 789 - 799$$n8$$p789 - 799$$tNature cancer$$v1$$x2662-1347$$y2020
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