Use of convolutional neural networks to predict risk of masking by mammographic density

Theo Cleland; James G. Mainprize; Olivier Alonzo-Proulx; Jennifer A. Harvey; Roberta A Jong; Anne L. Martel; Martin J. Yaffe

doi:10.1117/12.2513063

13 March 2019 Use of convolutional neural networks to predict risk of masking by mammographic density

Theo Cleland, James G. Mainprize, Olivier Alonzo-Proulx, Jennifer A. Harvey, Roberta A Jong, Anne L. Martel, Martin J. Yaffe

Author Affiliations +

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109501X (2019) https://doi.org/10.1117/12.2513063
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Sensitivity of screening mammography is reduced by increased mammographic density (MD). MD can obscure or “mask” developing lesions making them harder to detect. Predicting masking risk may be an effective tool for a stratified screening program where selected women can receive alternative screening modalities that are less susceptible to masking. Here, we investigate whether the use of artificial intelligence can accurately predict the masking risk and compare its performance to that of conventional BI-RADS density classification. The analysis was based on mammograms of 214 subjects comprised of 147 women with a screen-detected (SD) or “non-masked” cancer and 67 that developed a non-screen detected (NSD) or presumably masked cancer within 2 years following a negative screen. Prior to analysis, mammograms were pre-processed into quantitative MD maps using an in-house algorithm. A transfer learning approach was used to train a convolutional neural network (CNN) based on VGG-16 in a seven cross-fold approach to classify masking status. A two-step transfer learning method was also used where the pre-trained CNN was initially trained on 5,865 mammograms to classify by BI-RADS density category and then trained for masking status. Using BI-RADS density as a masking risk predictor has an AUC of 0.64 [0.57 - 0.71 95CI]. The CNN-mask yielded an AUC of 0.76 [0.68 - 0.81]. Combining the CNN-mask with our previous hand-crafted masking risk predictor, the AUC improved to 0.78 [0.70 - 0.83]. The combined AUC improved to 0.81 [0.72-0.90] when analysis was restricted to NSD cancers surfacing clinically within one year after a negative screen. The two-step transfer learning yielded similar performance. This work suggests that a CNN masking risk predictor can be used to guide a stratified screening program to overcome the limitations of screening mammography in dense breasts.

Conference Presentation

Citation Download Citation

Theo Cleland, James G. Mainprize, Olivier Alonzo-Proulx, Jennifer A. Harvey, Roberta A Jong, Anne L. Martel, and Martin J. Yaffe "Use of convolutional neural networks to predict risk of masking by mammographic density", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109501X (13 March 2019); https://doi.org/10.1117/12.2513063

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available