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Deep-learning model for background parenchymal enhancement classification in contrast-enhanced mammography.

E Ripaud¹, C Jailin¹, G I Quintana¹

¹GE HealthCare, Buc, France.

Physics in Medicine and Biology

|April 24, 2024

Summary

A new deep learning tool automates breast background parenchymal enhancement (BPE) classification on contrast-enhanced mammography (CEM). This method offers accurate and repeatable BPE assessment, addressing limitations of human interpretation in breast cancer risk evaluation.

Keywords:

background parenchymal enhancement breast imaging contrast-enhanced mammography deep learning

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Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Healthcare
Breast Cancer Diagnostics

Background:

Breast background parenchymal enhancement (BPE) is a known risk factor for breast cancer.
Current BPE assessment in contrast-enhanced mammography (CEM) relies on subjective radiologist interpretation (BI-RADS categories), leading to variability.
Automated BPE classification methods exist for MRI but are lacking for CEM.

Purpose of the Study:

To develop and evaluate a deep learning-based tool for automated BPE level classification in CEM.
To assess the tool's performance, robustness to lesions, and compare it with existing methods.

Main Methods:

A deep learning model was trained on 7012 CEM image pairs (low-energy and recombined).
Model optimization involved analyzing image resolution, backbone architecture, and loss functions.
Performance was evaluated using 4-class balanced accuracy and mean absolute error on a 1013 image pair dataset, including analysis of lesion presence.

Main Results:

The optimized model achieved 71.5% 4-class balanced accuracy, with 98.8% of errors between adjacent classes.
Binary classification (minimal/mild vs. moderate/marked) reached 93.0% accuracy.
Model performance showed robustness to the presence of lesions, with only a slight, non-significant decrease in accuracy.

Conclusions:

The developed deep learning tool provides accurate and repeatable BPE classification for CEM.
This automated approach has the potential to overcome the limitations of subjective human assessment.
The tool's performance is comparable to methods reported for CE-MRI, marking a significant advancement for CEM.