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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.
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier
2022
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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 |