Improving Performance of Computer-aided Detection of Masses by Incorporating Bilateral Mammographic Density Asymmetry: An Assessment

Abstract

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 are positive cases with verified cancer associated with malignant masses and 300 are 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 (FROC) 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.

A clinical model for identifying the short-term risk of breast cancer

“…It has long been known that breast asymmetry is a risk factor for breast cancer [18]. In our model, the influence on risk from breast asymmetry was as strong as that from the total number of microcalcifications and masses (Table 3).…”

Left–right analysis of mammary gland development in retinoid X receptor-α +/− mice

“…Indeed, mammographic asymmetry is significantly associated with elevated breast cancer incidence even after adjusting for recognized risk factors such as parity, body mass index and age at menarche and menopause [1,6]. Furthermore, asymmetry outperforms the predictive power of mean parenchymal density, a well-established risk factor that is routinely assessed in standardized breast imaging-reporting and data system (BI-RADS) rating of mammographic images [6,8,9]. Yet, despite its emerging clinical utility, the origin of parenchymal asymmetry in breasts of healthy females is unknown.…”

A Modified Undecimated Discrete Wavelet Transform Based Approach to Mammographic Image Denoising

“…However, mammography is still far from being ideal, with its sensitivity only ranging from 70 % to 90 % [7]. The clinical significance of early breast cancer diagnosis and a clear need to reduce false-negative rate of screening mammography have motivated the development of computer-aided detection (CADe) systems for decision support [8][9][10][11][12][13][14][15][16][17]. These systems typically involve a series of steps; first applying a variety of image preprocessing to reduce the noise and/or to enhance suspicious structures in the image and then using morphological and textural analysis to better differentiate these structure between true positives and false positives.…”