Multi-institutional validation of a mammography-based breast cancer risk model
Open accessAbstract
PURPOSE: Accurate risk assessment is essential for the success of population screening programs in breast cancer. Models with high sensitivity and specificity would enable programs to target more elaborate screening efforts to high-risk populations, while minimizing overtreatment for the rest. Artificial intelligence (AI)-based risk models have demonstrated a significant advance over risk models used today in clinical practice. However, the responsible deployment of novel AI requires careful validation across diverse populations. To this end, we validate our AI-based model, Mirai, across globally diverse screening populations. METHODS: We collected screening mammograms and pathology-confirmed breast cancer outcomes from Massachusetts General Hospital, USA; Novant, USA; Emory, USA; Maccabi-Assuta, Israel; Karolinska, Sweden; Chang Gung Memorial Hospital, Taiwan; and Barretos, Brazil. We evaluated Uno's concordance index for Mirai in predicting risk of breast cancer at one to five years from the mammogram. RESULTS: A total of 128,793 mammograms from 62,185 patients were collected across the seven sites, of which 3,815 were followed by a cancer diagnosis within 5 years. Mirai obtained concordance indices of 0.75 (95% CI, 0.72 to 0.78), 0.75 (95% CI, 0.70 to 0.80), 0.77 (95% CI, 0.75 to 0.79), 0.77 (95% CI, 0.73 to 0.81), 0.81 (95% CI, 0.79 to 0.82), 0.79 (95% CI, 0.76 to 0.83), and 0.84 (95% CI, 0.81 to 0.88) at Massachusetts General Hospital, Novant, Emory, Maccabi-Assuta, Karolinska, Chang Gung Memorial Hospital, and Barretos, respectively. CONCLUSION: Mirai, a mammography-based risk model, maintained its accuracy across globally diverse test sets from seven hospitals across five countries. This is the broadest validation to date of an AI-based breast cancer model and suggests that the technology can offer broad and equitable improvements in care.
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The Mirai system predicts 5-year breast cancer risk directly from mammograms and has been retrospectively evaluated across multiple hospitals.
“the Mirai system developed by Yala and colleagues ( 12) is designed to predict 5-year cancer risk directly from mammograms and has been retrospectively evaluated across multiple hospitals.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [12]
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- Authors
- Yala, A., Mikhael, P. G., Strand, F., Lin, G., Satuluru, S., Kim, T., Banerjee, I., Gichoya, J., Trivedi, H., Lehman, C. D., Hughes, K., Sheedy, D. J., Matthis, L. M., Karunakaran, B., Hegarty, K. E., Sabino, S., Silva, T. B., Evangelista, M. C., Caron, R. F., Souza, B., Mauad, E. C., Patalon, T., Handelman-Gotlib, S., Guindy, M., & Barzilay, R.
- Affiliations
- Massachusetts Institute of Technology (US)
- Karolinska University Hospital (SE)
- Chang Gung Memorial Hospital (TW)
- University of California, Los Angeles (US)
- Georgia Institute of Technology (US)
- Emory University (US)
- Harvard University (US)
- Novant Health (US)
- Hospital de Câncer de Barretos (BR)
- Maccabi Healthcare Services (IL)
- Assuta Medical Center (IL)
- ORCID
- Venue
- Journal of Clinical Oncology
- Published
- 2022-06-01
- Volume
- 40
- Issue
- 16
- Pages
- 1732-1740
- ISSN
- 0732-183X (ISSN-L)
- 1527-7755
- PMC
- PMC9148689
- OpenAlex
- W3213232584
- Cited by
- 204
- References
- 15
- Citations by year
- 2026: 43 · 2025: 43 · 2024: 51 · 2023: 51 · 2022: 16
- FWCI
- 10.37
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Clinical Oncology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Digital Radiography and Breast Imaging
- Health Sciences › Medicine › Oncology › Global Cancer Incidence and Screening
- Keywords
- breast cancer risk prediction, artificial intelligence, mammography, multi-institutional validation, cross-population generalizability, breast cancer screening, concordance index
- MeSH terms
- Artificial Intelligence, Breast Neoplasms, Female, Humans, Mammography, Mass Screening, Early Detection of Cancer
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