Abstract
BackgroundMammographic 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.MethodRaw 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.ResultsThe 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.ConclusionsThe 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.
Highlights
High mammographic density is a strong risk factor for breast cancer [1]
The first risk estimation was done on a nested case–control study sample with a two-year follow-up using the available 433 incident breast cancer cases age-matched in one-year bands with 1732 controls in KARMA (Table 1)
A longitudinal study showed that individual differences in mammographic density changes over time were not associated with breast cancer risk [23]
Summary
High mammographic density is a strong risk factor for breast cancer [1]. The density consists of epithelium and stroma and is radiographically dense. Several commercial tools measure density automated on digital raw mammograms [7]. There is currently no automated tool that measures density of raw and processed images regardless of vendor and accounts for technical difference between images from the same women. This is unfortunate since most digital images are stored as processed images and precise measures are needed to monitor treatment response and density change over time. We present a new algorithm which measures density on all type of images, regardless of vendor, and controls for non-biological differences seen in time series of mammograms from the same women
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