Machine learning models reveal significant racial disparities and key predictors in breast cancer incidence across diverse groups.
Key Details
- 1ML and explainable AI models were applied to mammographic data from the Breast Cancer Surveillance Consortium.
- 2History of biopsy (50%) and age group (25.9%) were the strongest predictors identified.
- 3White women have the highest breast cancer incidence overall, especially in those aged 65 and older (18.1 per 100,000).
- 4Black women exhibit higher incidence rates among younger age groups (7.1 per 100,000 for ages 18–29).
- 5Triple-negative breast cancer occurs more often in Black women (15-30%), while HER2+ is more common in Asian/Pacific Islander women (30%).
- 6The study calls for refining ML models with more socioeconomic and lifestyle variables to reduce disparities.
Why It Matters
This research underscores the clinical relevance of AI in identifying risk factors and disparities using imaging data. Refining these models could facilitate earlier detection and improved outcomes, especially for underserved populations in radiological practice.
Source
AuntMinnie
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