Share Email Print
cover

Proceedings Paper • new

Use of convolutional neural networks to predict risk of masking by mammographic density
Author(s): Theo Cleland; James G. Mainprize; Olivier Alonzo-Proulx; Jennifer A. Harvey; Roberta A Jong; Anne L. Martel; Martin J. Yaffe
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 13 March 2019
PDF: 8 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109501X (13 March 2019); doi: 10.1117/12.2513063
Show Author Affiliations
Theo Cleland, Sunnybrook Research Institute (Canada)
James G. Mainprize, Sunnybrook Research Institute (Canada)
Olivier Alonzo-Proulx, Sunnybrook Research Institute (Canada)
Jennifer A. Harvey, Univ. of Virginia Health System (United States)
Roberta A Jong, Sunnybrook Health Sciences Ctr. (Canada)
Anne L. Martel, Sunnybrook Research Institute (Canada)
Martin J. Yaffe, Sunnybrook Research Institute (Canada)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

© SPIE. Terms of Use
Back to Top