A prognostic model for invasive breast cancer that is based on interpretable measurements of epithelial, stromal, and immune components outperforms histologic grading by expert pathologists. This model could improve clinical management of patients diagnosed with invasive breast cancer and address the concerns of pathologists about artificial intelligence (AI) trustworthiness by providing transparent and explainable predictions.
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This is a summary of: Amgad, M. et al. A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer. Nat. Med. https://doi.org/10.1038/s41591-023-02643-7 (2023).
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Machine learning improves prediction of clinical outcomes for invasive breast cancers. Nat Med 30, 41–42 (2024). https://doi.org/10.1038/s41591-023-02667-z
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DOI: https://doi.org/10.1038/s41591-023-02667-z
