Rationale and Objectives
Bilateral mammographic density asymmetry is a promising indicator in assessing risk of having or developing breast cancer. This study aims to assess the performance improvement of a computer-aided detection (CAD) scheme in detecting masses by incorporating bilateral mammographic density asymmetrical information.
Materials and Methods
A testing dataset containing 2400 full-field digital mammograms (FFDM) acquired from 600 examination cases was established. Among them, 300 were positive cases with verified cancer associated with malignant masses and 300 were negative cases. Two computerized schemes were applied to process images of each case. The first single-image based CAD scheme detected suspicious mass regions and the second scheme computed average and difference of mammographic tissue density depicted between the left and right breast. A fusion method based on rotation of the CAD scoring projection reference axis was then applied to combine CAD-generated mass detection scores and either the computed average or difference (asymmetry) of bilateral mammographic density scores. The CAD performance levels with and without incorporating mammographic density information were evaluated and compared using a free-response receiver operating characteristic type data analysis method.
Results
CAD achieved a case-based mass detection sensitivity of 0.74 and a region-based sensitivity of 0.56 at a false-positive rate of 0.25 per image. By fusing the CAD and bilateral mammographic density asymmetry scores, the case-based and region-based sensitivity levels of the CAD scheme were increased to 0.84 and 0.69, respectively, at the same false-positive rate. Fusion with average mammographic density only slightly increased CAD sensitivity to 0.75 (case-based) and 0.57 (region-based).
Conclusions
This study indicated that 1) bilateral mammographic density asymmetry was a stronger indicator of the case depicting suspicious masses than the average density computed from two breasts and 2) fusion between the conventional CAD scores and bilateral mammographic density asymmetry information could substantially increase CAD performance in mass detection.
Breast cancer is one of the leading cancers in women older than age 40 worldwide and scientific evidence has shown the breast cancer screening resulted in the early cancer detection and reduced patients’ mortality and morbidity rates . Although mammography is a most cost-effective and widely used imaging modality for breast cancer screening for the last several decades, interpreting mammograms to achieve high accuracy in terms of cancer detection and recall rates is a difficult and time-consuming task because of the large variability of breast abnormalities, overlapping dense fibroglandular tissue, and low cancer prevalence in the screening environment . As a result, a large number of computer-aided detection (CAD) schemes of mammograms have been developed to detect microcalcification clusters and masses in the last two decades and the commercialized CAD systems have also be routinely used to assist radiologists in interpreting mammograms in the clinical practice to date . Although there is no universal agreement of whether using current CAD systems as “a second reader” actually helped improve radiologists’ performance in the screening environment , improving CAD performance alone is important and has scientific merit . Current CAD schemes are able to detect microcalcification clusters with high sensitivity and low false-positive detection rates, which helps improve radiologists’ efficiency in interpreting mammograms and detect more cancers that are associated with microcalcifications . However, current CAD schemes have relatively lower performance in mass detection , which may reduce the overall performance level of radiologists as measured by the areas under receiver operating characteristic (ROC) curves . Previous studies have also found high correlation of mass detection results between radiologists and CAD schemes , which could substantially reduce the clinical utility of using CAD as “the second reader.” Hence, improving CAD sensitivity while maintaining or reducing the false-positive rates remains a challenge in developing CAD schemes of mammographic masses.
Because mammographic tissue density is one of the strongest known breast cancer risk indicator to date , several research groups have attempted to improve CAD performance in mass detection by taking mammographic density information into account in CAD decision-making . Because of considerable inter- and intraobserver variability, subjectively rating mammographic density using Breast Imaging Reporting and Data System (BIRADS) is often unreliable . Hence, a number of computerized schemes have been developed and tested to segment fibroglandular tissue and compute mammographic density. The results were proved to highly correlate to radiologists’ subjective assessment . As a result, one recent study reported that incorporating automatically computed mammographic density information into CAD schemes enabled CAD to achieve higher performance than incorporating CAD with manually (subjectively) assessed mammographic density . In our previous studies, we have pursued a new approach to extract and use mammographic density information. Based on the scientific evidence of that bilateral breast tissue pattern asymmetry is an important phenotype related to the abnormal biological processes that may increase the risk of developing breast cancer and our observation that radiologists routinely examined regional breast tissue density asymmetry depicted on bilateral mammograms of the left and right breast when identifying and/or classifying suspicious lesions, we demonstrated in our previous studies that computerized bilateral mammographic tissue density asymmetry was also an useful indicator in assessing the risk of developing breast cancer . Hence, in this study, we investigated the feasibility of improving CAD performance in mass detection by incorporating bilateral mammographic density asymmetry information, in particular enabling CAD scheme detecting more subtle masses without increasing false-positive rates. The details of our experimental procedures and results are presented in the following sections.
Materials and methods
Testing Image Dataset
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Table 1
Distribution of 314 Verified Malignant Masses in the Testing Dataset
Mass Type Not Specified Smoothed Irregular Spiculated Focal Asymmetry Mass Number 26 7 179 80 22 Percentage 8.3 2.2 57.0 25.5 7.0
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CAD Scheme of Mass Detection
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Computing Mammographic Tissue Density and its Bilateral Asymmetry
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Fusion of CAD Scores and Mammographic Density Information
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Results
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Discussion
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