Background parenchymal enhancement at breast MR imaging and breast cancer risk.
ABSTRACT To examine the relationships between breast cancer and both amount of fibroglandular tissue (FGT) and level of background parenchymal enhancement (BPE) at magnetic resonance (MR) imaging.
A waiver of authorization was granted by the institutional review board for this retrospective HIPAA-compliant study. Among 1275 women who underwent breast MR imaging screening between December 2002 and February 2008, 39 breast carcinoma cases were identified. Two comparisons were performed: In one comparison, two normal controls--those of the women with negative (benign) findings at breast MR imaging--were matched to each breast cancer case on the basis of age and date of MR imaging. In the second comparison, one false-positive control--that of a woman with suspicious but nonmalignant findings at MR imaging--was similarly matched to each breast cancer case. Two readers independently rated the level of MR imaging-depicted BPE and the amount of MR imaging-depicted FGT by using a categorical scale: BPE was categorized as minimal, mild, moderate, or marked, and FGT was categorized as fatty, scattered, heterogeneously dense, or dense.
Compared with the odds ratio (OR) for a normal control, the OR for breast cancer increased significantly with increasing BPE: The ORs for moderate or marked BPE versus minimal or mild BPE were 10.1 (95% confidence interval [CI]: 2.9, 35.3; P < .001) and 3.3 (95% CI: 1.3, 8.3; P = .006) for readers 1 and 2, respectively. Similar odds were seen when the false-positive controls were compared with the breast cancer cases: The ORs for moderate or marked BPE versus minimal or mild BPE were 5.1 (95% CI: 1.4, 19.1; P = .005) and 3.7 (95% CI: 1.2, 11.2; P = .013) for readers 1 and 2, respectively. The breast cancer odds also increased with increasing FGT, but the BPE findings remained significant after adjustment for FGT.
Increased BPE is strongly predictive of breast cancer odds.
[Show abstract] [Hide abstract]
ABSTRACT: To investigate the feasibility of applying a new quantitative image analysis method to improve breast cancer diagnosis performance using dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) by integrating background parenchymal enhancement (BPE) features into the decision making process. A dataset involving 115 DCE-MRI examinations was used in this study. Each examination depicts one identified suspicious breast tumor. Among them, 75 cases were verified as malignant and 40 were benign by the biopsy results. A computer-aided detection scheme was applied to segment breast regions and the suspicious tumor depicted on the sequentially scanned MR images of each case. We then computed 18 kinetic features in which 6 were computed from the segmented breast tumor and 12 were BPE features from the background parenchymal regions (excluding the tumor). Support vector machine (SVM) based statistical learning classifiers were trained and optimized using different combinations of features that were computed either from tumor only or from both tumor and BPE. Each SVM was tested using a leave-one-case-out validation method and assessed using an area under the receiver operating characteristic curve (AUC). When using kinetic features computed from tumors only, the maximum AUC is 0.865 ± 0.035. After fusing with the BPE features, AUC increased to 0.919 ± 0.029. At 90% specificity, the tumor classification sensitivity increased by 13.2%. The proposed quantitative BPE features provide valuable supplementary information to the kinetic features of breast tumors in DCE-MRI. Their addition to computer-aided diagnosis methodologies could improve breast cancer diagnosis based on DCE-MRI examinations.Medical Physics 01/2015; 42(1):103. DOI:10.1118/1.4903280 · 3.01 Impact Factor
[Show abstract] [Hide abstract]
ABSTRACT: OBJECTIVE. The purpose of this article is to review the use of MRI in breast density measurement and breast cancer risk estimation and to discuss the role of MRI as an alternative screening to mammography for screening women with dense breasts. CONCLUSION. The potential of MRI for screening women with dense breasts remains controversial because of the paucity of clinical evidence, the possibility of overdiagnosis, and the cost-effectiveness of the technique in this population. Although methods of MRI measurement require standardization and automation, future addition of MRI density to risk models may positively impact their value.American Journal of Roentgenology 02/2015; 204(2):W141-W149. DOI:10.2214/AJR.14.13636 · 2.74 Impact Factor
[Show abstract] [Hide abstract]
ABSTRACT: Computerized algorithms are increasingly being developed for quantifying breast MRI features for facilitating lesion detection and breast tissue segmentation in various clinical applications. One of the current impediments is the intensity non-standardness of the breast tissue in the acquired MR images across different cases, scanners, and/or patients. This degrades the performance of quantitative image processing. In this work, we investigate the usefulness of post-hoc intensity standardization of breast MR images by using a landmark-based nonlinear intensity mapping algorithm. The standardization algorithm is applied after correction of the images for background bias field non-uniformity. We then quantitatively compare the percentage coefficient of variation (%CV) of image intensity in the fibroglandular (e.g., dense) tissue region before and after standardization to evaluate the standardization procedure. In our experiments, we use 9 representative 3D bilateral breast MRI scans/cases constituting 18 breasts (a total of 504 tomographic breast MRI slices), in which we observe a significant decrease of the %CV in the standardized images, indicating that standardization significantly reduces the intensity variation for the fibroglandular tissue across these cases. Furthermore, we demonstrate for two segmentation methods that the standardization process leads to improved segmentation of the fibroglandular tissue. Our work suggests that intensity standardization following bias field correction may serve as an effective preprocessing step to support improved quantitative breast MR image processing and analysis, particularly for breast density quantification.SPIE Medical Imaging; 03/2013