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.
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ABSTRACT: OBJECTIVE. The purposes of this study were to describe the prevalence of background parenchymal uptake categories observed at screening molecular breast imaging (MBI) and to examine the association of background parenchymal uptake with mammographic density and other clinical factors. MATERIALS AND METHODS. Adjunct MBI screening was performed for women with dense breasts on previous mammograms. Two radiologists reviewed images from the MBI examinations and subjectively categorized background parenchymal uptake into four groups: photopenic, minimal-mild, moderate, or marked. Women with breast implants or a personal history of breast cancer were excluded. The association between background parenchymal uptake categories and patient characteristics was examined with Kruskal-Wallis and chi-square tests as appropriate. RESULTS. In 1149 eligible participants, background parenchymal uptake was photopenic in 252 (22%), minimal-mild in 728 (63%), and moderate or marked in 169 (15%). The distribution of categories differed across BI-RADS density categories (p < 0.0001). In 164 participants with extremely dense breasts, background parenchymal uptake was photopenic in 72 (44%), minimal-mild in 55 (34%), and moderate or marked in 37 (22%). The moderate-marked group was younger on average, more likely to be premenopausal or perimenopausal, and more likely to be using postmenopausal hormone therapy than the photopenic or minimal-mild groups (p < 0.0001). CONCLUSION. Among women with similar-appearing mammographic density, background parenchymal uptake ranged from photopenic to marked. Background parenchymal uptake was associated with menopausal status and postmenopausal hormone therapy but not with premenopausal hormonal contraceptives, phase of menstrual cycle, or Gail model 5-year risk of breast cancer. Additional work is necessary to fully characterize the underlying cause of background parenchymal uptake and determine its utility in predicting subsequent risk of breast cancer.American Journal of Roentgenology 03/2015; 204(3):W363-70. DOI:10.2214/AJR.14.12979 · 2.74 Impact Factor
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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
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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