# Evaluation of Commercial Mammography-Based Artificial Intelligence Algorithms for Breast Cancer Risk Prediction in U.S. Screening Populations

> **NIH NIH R37** · UNIVERSITY OF WASHINGTON · 2024 · $989,122

## Abstract

PROJECT SUMMARY
Women known to be at high risk for breast cancer have opportunities to reduce their risk
through primary and secondary breast cancer prevention, including risk-reducing medications
and supplemental screening beyond mammography. However, breast cancer risk models used
to identify women eligible for risk reduction have only modest accuracy for predicting individual-
level breast cancer risk and perform even less well in Black and Hispanic women compared to
White women. Mammography-based AI algorithms have the potential to improve breast cancer
risk prediction, with early studies suggesting image-based AI technologies outperform traditional
clinical risk factor-based models commonly used in current practice. Multiple commercial
mammography-based AI breast cancer risk algorithms will soon obtain U.S. Food and & Drug
Administration approval for clinical use. Although promising, these models have limited
performance data in real-world screening settings and there is a critical need for rigorous,
independent evaluation prior to their adoption in clinical practice. The goal of this proposal is to
use a large, diverse screening population to examine whether mammography-based AI breast
cancer risk models can improve clinical risk prediction and reduce the inequities associated with
currently used models. The accuracy and performance of four commercial mammography-
based AI breast cancer risk algorithms will be evaluated using mammograms and cancer
outcomes for women undergoing routine screening mammography at seven facilities across the
Breast Cancer Surveillance Consortium. Model performance will be evaluated across race and
ethnicity groups and compared to currently used clinical risk-factor based models. Finally, an
established and externally validated breast cancer simulation model will be used to estimate the
population-level health impact of adoption of AI-based breast cancer risk models for targeted
risk reduction approaches. Overall, this work will provide robust performance and patient
outcomes data that will guide physicians and policymakers for more precise applications of AI to
identify women most likely to benefit from risk reduction measures beyond mammography and
ultimately improve population-level breast cancer outcomes.

## Key facts

- **NIH application ID:** 10941606
- **Project number:** 1R37CA292399-01
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Kathryn Paige Lowry
- **Activity code:** R37 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $989,122
- **Award type:** 1
- **Project period:** 2024-09-09 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10941606

## Citation

> US National Institutes of Health, RePORTER application 10941606, Evaluation of Commercial Mammography-Based Artificial Intelligence Algorithms for Breast Cancer Risk Prediction in U.S. Screening Populations (1R37CA292399-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10941606. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*