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Deep learning modeling using normal mammograms for predicting breast cancer risk.

Dooman Arefan1, Aly A Mohamed1, Wendie A Berg1,2

  • 1Department of Radiology, University of Pittsburgh, School of Medicine, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA.

Medical Physics
|November 1, 2019
PubMed
Summary

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Deep learning models can predict short-term breast cancer risk from normal mammograms. The GoogLeNet-LDA model showed superior performance, outperforming traditional breast density measures.

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Early breast cancer detection is crucial for effective treatment.
  • Risk prediction models can aid in personalized screening strategies.
  • Digital mammography is a standard screening tool.

Purpose of the Study:

  • To evaluate two deep learning models for predicting short-term breast cancer risk.
  • To utilize prior normal screening digital mammograms for risk assessment.
  • To compare deep learning performance against traditional methods.

Main Methods:

  • A case-control study involving 113 breast cancer patients and 113 controls.
  • Analysis of 452 prior normal digital mammograms (MLO and CC views).
  • Implementation and comparison of an end-to-end deep learning model and a GoogLeNet-LDA model.
Keywords:
breast cancerbreast densitydeep learningdigital mammographyrisk biomarkers

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Main Results:

  • The GoogLeNet-LDA model achieved the highest Area Under the Curve (AUC) of 0.73 on CC view images.
  • GoogLeNet-LDA significantly outperformed the end-to-end model and logistic regression of breast density (AUC=0.54).
  • Craniocaudal (CC) view mammograms were more predictive than mediolateral oblique (MLO) views.

Conclusions:

  • Deep learning models can predict short-term breast cancer risk using normal screening mammograms.
  • Further research with larger cohorts is necessary to validate and enhance deep learning for breast cancer risk assessment.