A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer
Open accessPublished version of a preprint
Abstract
Breast cancer is a heterogeneous disease with variable survival outcomes. Pathologists grade the microscopic appearance of breast tissue using the Nottingham criteria, which are qualitative and do not account for noncancerous elements within the tumor microenvironment. Here we present the Histomic Prognostic Signature (HiPS), a comprehensive, interpretable scoring of the survival risk incurred by breast tumor microenvironment morphology. HiPS uses deep learning to accurately map cellular and tissue structures to measure epithelial, stromal, immune, and spatial interaction features. It was developed using a population-level cohort from the Cancer Prevention Study-II and validated using data from three independent cohorts, including the Prostate, Lung, Colorectal, and Ovarian Cancer trial, Cancer Prevention Study-3, and The Cancer Genome Atlas. HiPS consistently outperformed pathologists in predicting survival outcomes, independent of tumor-node-metastasis stage and pertinent variables. This was largely driven by stromal and immune features. In conclusion, HiPS is a robustly validated biomarker to support pathologists and improve patient prognosis.
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Amgad and colleagues used deep learning to extract human-interpretable features and then generated a risk score for breast cancer prognosis.
“Amgad and colleagues (84) explicitly enforce interpretability by using deep learning to extract human-interpretable features, and subsequently use these features to generate a risk score for breast cancer prognosis.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [84]
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References 130 · Cited by 137
- Authors
- Amgad, M., Hodge, J. M., Elsebaie, M. A. T., Bodelon, C., Puvanesarajah, S., Gutman, D. A., Siziopikou, K. P., Goldstein, J. A., Gaudet, M. M., Teras, L. R., & Cooper, L. A. D.
- Affiliations
- Northwestern University (US)
- American Cancer Society (US)
- John H. Stroger, Jr. Hospital of Cook County (US)
- Emory University (US)
- National Cancer Institute (US)
- Corresponding author
- Cooper, Lee A. D.
- ORCID
- Venue
- Nature Medicine
- Published
- 2023-11-27
- Volume
- 30
- Issue
- 1
- Pages
- 85-97
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC10246230
- OpenAlex
- W4389058230
- Semantic Scholar
- d5d7dfd5ed000c85c934fad085fb11862e5c02c9
- Cited by
- 137
- References
- 130
- Citations by year
- 2026: 38 · 2025: 67 · 2024: 32
- FWCI
- 13.61
- Type
- journal article
- Language
- en
- Open access
- green
- dx.doi.org
- Licence
- Locations
- Nature Medicine (publishedVersion)
- Research Square (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- Funders
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (U01CA220401, U24CA19436201)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- invasive breast cancer, digital histology, tumor microenvironment, prognostic factors, survival prediction, stromal-epithelial interactions, deep learning, Cancer Prevention Study II, prognostic scoring
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