HRD-related morphology discovery in breast cancer by controlling for confounding factors

Lazard et al.(1) predict homologous recombination deficiency from hematoxylin and eosin-stained slides of breast cancer tissue using deep learning. By controlling for technical artifacts on a curated dataset, the model puts forward novel HRD-related morphologies in luminal breast cancers.

Detalles Bibliográficos
Autores principales: Schirris, Yoni, Horlings, Hugo Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
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Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9798077/
https://www.ncbi.nlm.nih.gov/pubmed/36543118
http://dx.doi.org/10.1016/j.xcrm.2022.100873

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9798077/
https://www.ncbi.nlm.nih.gov/pubmed/36543118
http://dx.doi.org/10.1016/j.xcrm.2022.100873

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