Predicting Mutational Status of Driver and Suppressor Genes Directly from Histopathology With Deep Learning: A Systematic Study Across 23 Solid Tumor Types

In the last four years, advances in Deep Learning technology have enabled the inference of selected mutational alterations directly from routine histopathology slides. In particular, recent studies have shown that genetic changes in clinically relevant driver genes are reflected in the histological...

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Detalles Bibliográficos
Autores principales: Loeffler, Chiara Maria Lavinia, Gaisa, Nadine T., Muti, Hannah Sophie, van Treeck, Marko, Echle, Amelie, Ghaffari Laleh, Narmin, Trautwein, Christian, Heij, Lara R., Grabsch, Heike I., Ortiz Bruechle, Nadina, Kather, Jakob Nikolas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Genetics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889144/
https://www.ncbi.nlm.nih.gov/pubmed/35251119
http://dx.doi.org/10.3389/fgene.2021.806386

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889144/
https://www.ncbi.nlm.nih.gov/pubmed/35251119
http://dx.doi.org/10.3389/fgene.2021.806386

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