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Breast glandularity and mean glandular dose assessment using a deep learning framework: Virtual patients study.

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This study introduces a deep learning framework to estimate breast glandularity from mammograms, enabling accurate Mean Glandular Dose (MGD) calculations for improved breast cancer screening radiation protection.

Keywords:
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Area of Science:

  • Medical Imaging
  • Radiological Physics
  • Artificial Intelligence in Healthcare

Background:

  • Breast dosimetry is crucial for radioprotection in mammography screening.
  • Accurate Mean Glandular Dose (MGD) calculation requires reliable breast glandularity estimation.
  • Current methods for glandularity assessment can be complex and time-consuming.

Purpose of the Study:

  • To develop and validate a deep learning framework for estimating Volume Glandular Fraction (VGF) from mammography images.
  • To utilize predicted VGF for calculating MGD, enhancing radioprotection in breast cancer screening.
  • To provide a more efficient and accurate method for breast dosimetry.

Main Methods:

  • Generation of 208 virtual breast phantoms with Monte Carlo simulations.
  • Utilizing XNet and multilayer perceptron architectures for VGF prediction.
  • Comparison of predicted VGF and MGD with ground truth values using coefficient of determination (r²).

Main Results:

  • High accuracy achieved in inner breast segmentation (r²=0.999), breast volume prediction (r²=0.982), and VGF prediction (r²=0.935).
  • DgN coefficients derived from predicted VGF showed minimal deviation (1.3%) from ground truth.
  • Estimated MGD values for a clinical cohort were within clinically relevant ranges.

Conclusions:

  • A deep learning framework for VGF and MGD calculation was successfully implemented.
  • The proposed method offers a promising approach for accurate breast dosimetry in mammography.
  • This framework can contribute to enhanced radiation protection strategies in breast cancer screening programs.