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Comment
. 2022 Dec 20;3(12):100873.
doi: 10.1016/j.xcrm.2022.100873.

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

Yoni Schirris  1 , Hugo Mark Horlings  2
Affiliations
Comment

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

Yoni Schirris et al. Cell Rep Med. .

Abstract

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.

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Figures

Figure 1
Figure 1
Interplay of PARPi response, genotype and genomic phenotype, confounding factors, and resulting morphology The confounders may be correlated with the genotype and used by the AI model. Lazard et al. partly block the technical confounders by focusing on a single-center dataset, controlling for technical artifacts. Additionally, they partly block the possibility of using biological confounders by modeling only basal BC samples. The resulting model is forced to focus on HRD-related morphologies. By analyzing the model predictions, they conclude that the morphologies described in the “Possible HRD-related morphologies” box are indicative of HRD. The figure draws heavily on Figure 2 from Stewart et al. The spurious correlation flow is inspired by Figure S2 in Ilse et al.
See this image and copyright information in PMC

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References

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