Capability and reliability of deep learning models to make density predictions on low-dose mammograms

Steven Squires; Alistair Mackenzie; Dafydd Gareth Evans; Sacha J. Howell; Susan M. Astley

doi:10.1117/1.JMI.11.4.044506

6 August 2024 Capability and reliability of deep learning models to make density predictions on low-dose mammograms

Steven Squires, Alistair Mackenzie, Dafydd Gareth Evans, Sacha J. Howell, Susan M. Astley

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue 4, 044506 (August 2024). https://doi.org/10.1117/1.JMI.11.4.044506

Abstract

Purpose

Breast density is associated with the risk of developing cancer and can be automatically estimated using deep learning models from digital mammograms. Our aim is to evaluate the capacity and reliability of such models to predict density from low-dose mammograms taken to enable risk estimates for younger women.

Approach

We trained deep learning models on standard-dose and simulated low-dose mammograms. The models were then tested on a mammography dataset with paired standard- and low-dose images. The effect of different factors (including age, density, and dose ratio) on the differences between predictions on standard and low doses is analyzed. Methods to improve performance are assessed, and factors that reduce the model quality are demonstrated.

Results

We showed that, although many factors have no significant effect on the quality of low-dose density prediction, both density and breast area have an impact. The correlation between density predictions on low- and standard-dose images of breasts with the largest breast area is 0.985 (0.949 to 0.995), whereas that with the smallest is 0.882 (0.697 to 0.961). We also demonstrated that averaging across craniocaudal-mediolateral oblique (CC-MLO) images and across repeatedly trained models can improve predictive performance.

Conclusions

Low-dose mammography can be used to produce density and risk estimates that are comparable to standard-dose images. Averaging across CC-MLO and model predictions should improve this performance. The model quality is reduced when making predictions on denser and smaller breasts.

Citation Download Citation

Steven Squires, Alistair Mackenzie, Dafydd Gareth Evans, Sacha J. Howell, and Susan M. Astley "Capability and reliability of deep learning models to make density predictions on low-dose mammograms," Journal of Medical Imaging 11(4), 044506 (6 August 2024). https://doi.org/10.1117/1.JMI.11.4.044506

Received: 5 March 2024; Accepted: 19 July 2024; Published: 6 August 2024

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KEYWORDS

Breast

Breast density

Mammography

Data modeling

Education and training

Performance modeling

Deep learning

Purpose

Approach

Results

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