Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10549-018-4690-5
Title: A comprehensive tool for measuring mammographic density changes over time
Authors: Eriksson, M
Li, J 
Leifland, K
Czene, K
Hall, P
Keywords: adult
algorithm
Article
breast cancer
breast density
cancer risk
cancer screening
controlled study
digital imaging
female
human
image processing
machine learning
major clinical study
mammography
priority journal
aged
breast
breast tumor
diagnostic imaging
mammography
middle aged
pathology
procedures
risk factor
software
Aged
Breast
Breast Density
Breast Neoplasms
Female
Humans
Image Processing, Computer-Assisted
Mammography
Middle Aged
Risk Factors
Software
Issue Date: 2018
Publisher: Springer New York LLC
Citation: Eriksson, M, Li, J, Leifland, K, Czene, K, Hall, P (2018). A comprehensive tool for measuring mammographic density changes over time. Breast Cancer Research and Treatment 169 (2) : 371-379. ScholarBank@NUS Repository. https://doi.org/10.1007/s10549-018-4690-5
Rights: Attribution 4.0 International
Abstract: Background: Mammographic density is a marker of breast cancer risk and diagnostics accuracy. Density change over time is a strong proxy for response to endocrine treatment and potentially a stronger predictor of breast cancer incidence. We developed STRATUS to analyse digital and analogue images and enable automated measurements of density changes over time. Method: Raw and processed images from the same mammogram were randomly sampled from 41,353 healthy women. Measurements from raw images (using FDA approved software iCAD) were used as templates for STRATUS to measure density on processed images through machine learning. A similar two-step design was used to train density measures in analogue images. Relative risks of breast cancer were estimated in three unique datasets. An alignment protocol was developed using images from 11,409 women to reduce non-biological variability in density change. The protocol was evaluated in 55,073 women having two regular mammography screens. Differences and variances in densities were compared before and after image alignment. Results: The average relative risk of breast cancer in the three datasets was 1.6 [95% confidence interval (CI) 1.3–1.8] per standard deviation of percent mammographic density. The discrimination was AUC 0.62 (CI 0.60–0.64). The type of image did not significantly influence the risk associations. Alignment decreased the non-biological variability in density change and re-estimated the yearly overall percent density decrease from 1.5 to 0.9%, p < 0.001. Conclusions: The quality of STRATUS density measures was not influenced by mammogram type. The alignment protocol reduced the non-biological variability between images over time. STRATUS has the potential to become a useful tool for epidemiological studies and clinical follow-up. © 2018, The Author(s).
Source Title: Breast Cancer Research and Treatment
URI: https://scholarbank.nus.edu.sg/handle/10635/179036
ISSN: 01676806
DOI: 10.1007/s10549-018-4690-5
Rights: Attribution 4.0 International
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