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Five-Year Breast Cancer Risk Prediction From Screening Breast Ultrasound Using Deep Learning.

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    A new deep learning model, BUS-Risk-Net, accurately predicts 5-year breast cancer risk from screening ultrasounds. This AI tool outperforms existing models and stratifies risk beyond breast density alone.

    Area of Science:

    • Radiology and Medical Imaging
    • Artificial Intelligence in Healthcare
    • Oncology

    Background:

    • Accurate breast cancer risk prediction is crucial for personalized screening and prevention strategies.
    • Current risk assessment models have limitations in predicting long-term risk from screening mammography alone.

    Purpose of the Study:

    • To develop and evaluate a deep learning model, BUS-Risk-Net, for predicting 5-year breast cancer risk using screening breast ultrasound (BUS) examinations.
    • To assess the performance of BUS-Risk-Net compared to established risk prediction models.

    Main Methods:

    • A retrospective study of 295,298 BUS examinations from 122,072 women (2012-2020).
    • BUS-Risk-Net aggregated image features using attention-based multiple instance learning, combined with age and breast density.

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  • Performance was compared against the full and reduced Tyrer-Cuzick models in matched case-control and held-out test sets.

    • Main Results:

    • BUS-Risk-Net achieved a 5-year AUC of 0.679 in the test set, outperforming the reduced Tyrer-Cuzick model (AUC 0.594).
    • In a case-control cohort, BUS-Risk-Net (AUC 0.632) significantly outperformed the full Tyrer-Cuzick model (AUC 0.514).
    • AI-driven risk stratification identified distinct 5-year cancer incidence groups within BI-RADS density categories.

    Conclusions:

    • Deep learning models applied to screening BUS can enable long-term breast cancer risk prediction.
    • BUS-Risk-Net demonstrates potential for risk stratification beyond breast density alone.
    • Further external and prospective validation is necessary before widespread clinical implementation.