Bilateral Mammographic Density Research Articles

Risk Analysis of Breast Cancer by Using Bilateral Mammographic Density Differences: A Case-Control Study.

The identification of risk factors helps radiologists assess the risk of breast cancer. Quantitative factors such as age and mammographic density are established risk factors for breast cancer. Asymmetric breast findings are frequently encountered during diagnostic mammography. The asymmetric area may indicate a developing mass in the early stage, causing a difference in mammographic density between the left and right sides. Therefore, this paper aims to propose a quantitative parameter named bilateral mammographic density difference (BMDD) for the quantification of breast asymmetry and to verify BMDD as a risk factor for breast cancer. To quantitatively evaluate breast asymmetry, we developed a semi-automatic method to estimate mammographic densities and calculate BMDD as the absolute difference between the left and right mammographic densities. And then, a retrospective case-control study, covering the period from July 2006 to October 2014, was conducted to analyse breast cancer risk in association with BMDD. The study included 364 women diagnosed with breast cancer and 364 matched control patients. As a result, a significant difference in BMDD was found between cases and controls (P < 0.001) and the case-control study demonstrated that women with BMDD > 10% had a 2.4-fold higher risk of breast cancer (odds ratio, 2.4; 95% confidence interval, 1.3-4.5) than women with BMDD ≤ 10%. In addition, we also demonstrated the positive association between BMDD and breast cancer risk among the subgroups with different ages and the Breast Imaging Reporting and Data System (BI-RADS) mammographic density categories. This study demonstrated that BMDD could be a potential risk factor for breast cancer.

Abstract NG15: Investigation of relationships between breast cancer risk factors and bilateral mammographic breast density asymmetry among women undergoing diagnostic image-guided breast biopsies

Introduction: The etiology of breast cancer remains an area of ongoing investigation. Improving our understanding of factors associated with breast cancer development will strengthen the utility of risk prediction strategies and improve risk stratification. Mammographic breast density remains one of the most influential breast cancer risk factors, with 4 to 6 fold elevated risk observed among women with the highest compared to the lowest levels. These associations have consistently been observed irrespective of the method (visual or automated), used to quantify breast density, showing the robustness of the associations. An understudied breast cancer risk factor is the bilateral asymmetry of mammographic features, with prior studies suggesting that women with higher levels of breast asymmetry may be at elevated breast cancer risk. Increasingly, studies are recognizing the potential of bilateral breast asymmetry defined by mammographic breast density in determining breast cancer risk. Recently, Eriksson and colleagues highlighted a strong influence of breast asymmetry within a model they developed to predict short-term breast cancer risk among women attending breast screening. They observed that the contribution to the model of asymmetry in mammographic breast density, microcalcifications, and masses between the breasts was as substantial as the total number of microcalcifications and masses found within a mammogram, indicating that differences between the breasts may be an important risk marker. To help further understand associations between breast cancer risk factors and asymmetry of breast density, particularly among women at elevated risk for breast cancer, we evaluated risk factor relationships with the bilateral asymmetry of volumetric measures of global and local breast density across the spectrum of premalignant and invasive breast cancer diagnoses. Methods: This study included 882 women enrolled as part of the National Cancer Institute9s Breast Radiology Evaluation and Study of Tissues (BREAST)-Stamp Project (2007-2010). The BREAST-Stamp Project is a cross-sectional molecular epidemiologic study that aims to understand how novel breast density measures are related to breast cancer etiology. Women were enrolled if they were referred for diagnostic image-guided breast biopsy following an abnormal mammogram at the University of Vermont Medical Center, and had not previously been diagnosed or treated for cancer, undergone breast surgery within one year or received chemoprevention. Risk factor data were collected at study enrolment via interview and self-administered questionnaires. Breast density measures were estimated using Single X-ray Absorptiometry (SXA), a technique in which an SXA breast density phantom was affixed to the compression paddle of the mammography machine during routine mammography so that it was included in the X-ray field. It served as a reference standard to estimate volumetric breast density. Breast density was assessed using pre-biopsy craniocaudal full-field digital mammograms of both the ipsilateral (affected) and contralateral (unaffected) breast. Firstly, global density from each laterality was determined as percent fibroglandular volume (%FGV). Secondly, localized volumetric density measures were estimated following identification of the biopsy site on the ipsilateral pre-biopsy mammogram by the study radiologist and identifying the corresponding site on the contralateral mammogram. The SXA estimated %FGV in a perilesional volume twice the size of, but excluding, the biopsy target, centered at the biopsy site. Previous estimates of reproducibility for the SXA test phantoms demonstrated a repeatability SD of 2%, with a ±2% accuracy for the entire thickness and density ranges. Breast density asymmetry was defined as an absolute bilateral difference in measures when subtracting the breast density measures of the contralateral breast from the ipsilateral measures. Spearman9s correlations (rho) examined associations between breast density measures of the left and right breasts. To determine relationships between breast cancer risk factors (defined as categorical variables) and measures of bilateral breast density asymmetry, analysis of covariance (ANCOVA) models (PROC GLM) were used. Analyses were conducted at the per woman level using SAS. Probability values of Citation Format: Maeve Mullooly, Shaoqi Fan, Ruth M. Pfeiffer, Brian Sprague, Pamela M. Vacek, Donald L. Weaver, John A. Shepherd, Amir Pasha Mahmoudzadeh, Jeff Wang, Serghei Malkov, Jason M. Johnson, Sally D. Herschorn, Gretchen L. Gierach. Investigation of relationships between breast cancer risk factors and bilateral mammographic breast density asymmetry among women undergoing diagnostic image-guided breast biopsies [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr NG15.

Applying a new bilateral mammographic density segmentation method to improve accuracy of breast cancer risk prediction.

How to optimally detect bilateral mammographic asymmetry and improve risk prediction accuracy remains a difficult and unsolved issue. Our aim was to find an effective mammographic density segmentation method to improve accuracy of breast cancer risk prediction. A dataset including 168 negative mammography screening cases was used. We applied a mutual threshold to bilateral mammograms of left and right breasts to segment the dense breast regions. The mutual threshold was determined by the median grayscale value of all pixels in both left and right breast regions. For each case, we then computed three types of image features representing asymmetry, mean and the maximum of the image features, respectively. A two-stage classification scheme was developed to fuse the three types of features. The risk prediction performance was tested using a leave-one-case-out cross-validation method. By using the new density segmentation method, the computed area under the receiver operating characteristic curve was 0.830±0.033 and overall prediction accuracy was 81.0%, significantly higher than those of 0.633±0.043 and 57.1% achieved by using the previous density segmentation method ([Formula: see text], t-test). A new mammographic density segmentation method based on a bilateral mutual threshold can be used to more effectively detect bilateral mammographic density asymmetry and help significantly improve accuracy of near-term breast cancer risk prediction.

Association between Computed Tissue Density Asymmetry in Bilateral Mammograms and Near-term Breast Cancer Risk

This study investigated association between bilateral mammographic density asymmetry and near-term breast cancer risk. A data base of digital mammograms acquired from 690 women was retrospectively collected. All images were originally interpreted as negative by radiologists. During the next subsequent screening examinations (between 12 and 36months later), 230 women were diagnosed positive for cancer, 230 were recalled for additional diagnostic workups and proved to be benign, and 230 remained negative (not recalled). We applied a computerized scheme to compute the differences of five image features between the left and right mammograms, and trained an artificial neural network (ANN) to compute a bilateral mammographic density asymmetry score. Odds ratios (ORs) were used to assess associations between the ANN-generated scores and risk of women having detectable cancers during the next screening examinations. A logistic regression method was applied to test for trend as a function of the increase in ANN-generated scores. The results were also compared with ORs computed using other existing cancer risk factors. The ORs showed an increasing risk trend with the increase in ANN-generated scores (from 1.00 to 9.07 between positive and negative case groups). The regression analysis also showed a significant increase trend in slope (p<0.05). No significant increase trends of the ORs were found when using woman's age, subjectively rated breast density, or family history of breast cancer. This study demonstrated that the computed bilateral mammographic density asymmetry had potential to be used as a new risk factor to improve discriminatory power in predicting near-term risk of women developing breast cancer.

Prediction of near-term risk of developing breast cancer using computerized features from bilateral mammograms

Asymmetry of bilateral mammographic tissue density and patterns is a potentially strong indicator of having or developing breast abnormalities or early cancers. The purpose of this study is to design and test the global asymmetry features from bilateral mammograms to predict the near-term risk of women developing detectable high risk breast lesions or cancer in the next sequential screening mammography examination. The image dataset includes mammograms acquired from 90 women who underwent routine screening examinations, all interpreted as negative and not recalled by the radiologists during the original screening procedures. A computerized breast cancer risk analysis scheme using four image processing modules, including image preprocessing, suspicious region segmentation, image feature extraction, and classification was designed to detect and compute image feature asymmetry between the left and right breasts imaged on the mammograms. The highest computed area under curve (AUC) is 0.754±0.024 when applying the new computerized aided diagnosis (CAD) scheme to our testing dataset. The positive predictive value and the negative predictive value were 0.58 and 0.80, respectively.

Bilateral mammographic density asymmetry and breast cancer risk: A preliminary assessment

To improve efficacy of breast cancer screening and prevention programs, it requires a risk assessment model with high discriminatory power. This study aimed to assess classification performance of using computed bilateral mammographic density asymmetry to predict risk of individual women developing breast cancer in near-term. The database includes 451 cases with multiple screening mammography examinations. The first (baseline) examinations of all case were interpreted negative. In the next sequential examinations, 187 cases developed cancer or surgically excised high-risk lesions, 155 remained negative (not-recalled), and 109 were recalled benign cases. From each of two bilateral cranio-caudal view images acquired from the baseline examination, we computed two features of average pixel value and local pixel value fluctuation. We then computed mean and difference of each feature computed from two images. When applying the computed features and other two risk factors (woman's age and subjectively rated mammographic density) to predict risk of cancer development, areas under receiver operating characteristic curves (AUC) were computed to evaluate the discriminatory/classification performance. The AUCs are 0.633±0.030, 0.535±0.036, 0.567±0.031, and 0.719±0.027 when using woman's age, subjectively rated, computed mean and asymmetry of mammographic density, to classify between two groups of cancer-verified and negative cases, respectively. When using an equal-weighted fusion method to combine woman's age and computed density asymmetry, AUC increased to 0.761±0.025 (p<0.05). The study demonstrated that bilateral mammographic density asymmetry could be a significantly stronger risk factor associated to the risk of women developing breast cancer in near-term than woman's age and assessed mean mammographic density.

Improving the performance of computer-aided detection of subtle breast masses using an adaptive cueing method

Current computer-aided detection (CAD) schemes for detecting mammographic masses have several limitations including high correlation with radiologists’ detection and cueing most subtle masses only on one view. To increase CAD sensitivity in cueing more subtle masses that are likely missed and/or overlooked by radiologists without increasing false-positive rates, we investigated a new case-dependent cueing method by combining the original CAD-generated detection scores with a computed bilateral mammographic density asymmetry index. Using the new method, we adaptively raise the CAD-generated scores of the regions detected on ‘high-risk’ cases to cue more subtle mass regions and reduce the CAD scores of the regions detected on ‘low-risk’ cases to discard more false-positive regions. A testing dataset involving 78 positive and 338 negative cases was used to test this adaptive cueing method. Each positive case involves two sequential examinations in which the mass was detected in ‘current’ examination and missed in ‘prior’ examination but detected in a retrospective review by radiologists. Applying to this dataset, a pre-optimized CAD scheme yielded 75% case-based and 55% region-based sensitivity on ‘current’ examinations at a false-positive rate of 0.25 per image. CAD sensitivity was reduced to 42% (case based) and 27% (region based) on ‘prior’ examinations. Using the new cueing method, case-based and region-based sensitivity could maximally increase 9% and 33% on the ‘prior’ examinations, respectively. The percentages of the masses cued on two views also increased from 27% to 65%. The study demonstrated that using this adaptive cueing method enabled us to help CAD cue more subtle cancers without increasing the false-positive cueing rate.

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

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. 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. 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). 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.