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Related Experiment Video

Updated: May 5, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

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A Deep Learning Breast Cancer Risk Model for Precise Supplemental Screening.

Leslie R Lamb1,2, Sarah F Mercaldo1,2, Andrew Carney1

  • 1Department of Radiology, Massachusetts General Hospital, Boston.

JAMA Network Open
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

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A deep learning (DL) breast cancer risk model significantly outperformed radiologist-assessed breast density in predicting future cancer and false-negative screening results. This suggests a shift towards DL models for personalized breast cancer screening.

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Current federal legislation requires informing patients of breast density, a factor associated with increased breast cancer risk and potential masking of tumors.
  • Breast density assessment is subjective and varies between readers, limiting its effectiveness in guiding supplemental imaging decisions.
  • A binary dense/nondense classification applies to 40-50% of women, highlighting the need for more precise risk stratification methods.

Purpose of the Study:

  • To compare the performance of a deep learning (DL) breast cancer risk model against radiologist-assessed breast density.
  • To evaluate the models' efficacy in estimating future breast cancer risk and identifying false-negative (FN) screening results.

Main Methods:

  • A retrospective cohort study analyzed screening mammograms from 67,019 women aged 30+ over a 10-year period (2009-2018).

Related Experiment Videos

Last Updated: May 5, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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Published on: August 16, 2020

5.8K
  • A DL risk model was applied to mammograms, and breast density was categorized using BI-RADS criteria.
  • Primary outcomes included 5-year breast cancer diagnoses and FN screening results; performance was assessed using AUROC.

    • Main Results:

    • The DL model showed significantly higher accuracy in predicting future cancer (AUROC 0.71) compared to breast density (AUROC 0.53).
    • False-negative rates increased across DL risk groups, while women with dense breasts had higher FN rates than those with nondense breasts.
    • Adding breast density information to the DL model did not enhance its predictive performance.

    Conclusions:

    • A DL risk model demonstrates superior performance over subjective breast density assessment for predicting breast cancer risk and FN screening results.
    • These findings advocate for transitioning from density-based policies to more accurate, image-derived risk models for guiding supplemental screening.
    • Implementing DL models can lead to more precise and personalized breast cancer screening strategies.