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Fully Automated Quantitative Estimation of Volumetric Breast Density from Digital Breast Tomosynthesis Images: Preliminary Results and Comparison with Digital Mammography and MR Imaging. - Radiology
Purpose To assess a fully automated method for volumetric breast density (VBD) estimation in digital breast tomosynthesis (DBT) and to compare the findings with those of full-field digital mammography (FFDM) and magnetic resonance (MR) imaging. Materials and Methods Bilateral DBT images, FFDM images, and sagittal breast MR images were retrospectively collected from 68 women who underwent breast cancer screening from October 2011 to September 2012 with institutional review board-approved, HIPAA-compliant protocols. A fully automated computer algorithm was developed for quantitative estimation of VBD from DBT images. FFDM images were processed with U.S. Food and Drug Administration-cleared software, and the MR images were processed with a previously validated automated algorithm to obtain corresponding VBD estimates. Pearson correlation and analysis of variance with Tukey-Kramer post hoc correction were used to compare the multimodality VBD estimates. Results Estimates of VBD from DBT were significantly correlated with FFDM-based and MR imaging-based estimates with r = 0.83 (95% confidence interval [CI]: 0.74, 0.90) and r = 0.88 (95% CI: 0.82, 0.93), respectively (P < .001). The corresponding correlation between FFDM and MR imaging was r = 0.84 (95% CI: 0.76, 0.90). However, statistically significant differences after post hoc correction (Î± = 0.05) were found among VBD estimates from FFDM (mean Â± standard deviation, 11.1% Â± 7.0) relative to MR imaging (16.6% Â± 11.2) and DBT (19.8% Â± 16.2). Differences between VDB estimates from DBT and MR imaging were not significant (P = .26). Conclusion Fully automated VBD estimates from DBT, FFDM, and MR imaging are strongly correlated but show statistically significant differences. Therefore, absolute differences in VBD between FFDM, DBT, and MR imaging should be considered in breast cancer risk assessment. (Â©) RSNA, 2015 Online supplemental material is available for this article.
Quantitative assessment of background parenchymal enhancement in breast MRI predicts response to risk-reducing salpingo-oophorectomy: preliminary evaluation in a cohort of BRCA1/2 mutation carriers. - Breast cancer research : BCR
We present a fully automated method for deriving quantitative measures of background parenchymal enhancement (BPE) from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and perform a preliminary evaluation of these measures to assess the effect of risk-reducing salpingo-oophorectomy (RRSO) in a cohort of breast cancer susceptibility gene 1/2 (BRCA1/2) mutation carriers.Breast DCE-MRI data from 50 BRCA1/2 carriers were retrospectively analyzed in compliance with the Health Insurance Portability and Accountability Act and with institutional review board approval. Both the absolute (| |) and relative (%) measures of BPE and fibroglandular tissue (FGT) were computed from the MRI scans acquired before and after RRSO. These pre-RRSO and post-RRSO measures were compared using paired Student's t test. The area under the curve (AUC) of the receiver operating characteristic (ROC) was used to evaluate the performance of relative changes in the BPE and FGT measures in predicting breast cancer that developed in these women after the RRSO surgery.For the 44 women who did not develop breast cancer after RRSO, the absolute volume of BPE and FGT had a significant decrease (P < 0.05) post-RRSO, whereas for the 6 women who developed breast cancer, there were no significant changes in these measures. Higher values in all BPE and FGT measures were also observed post-RRSO for the women who developed breast cancer, compared with women who did not. Relative changes in BPE percentage were most predictive of women who developed breast cancer after RRSO (P < 0.05), whereas combining BPE percentage and |FGT| yielded an AUC of 0.80, higher than BPE percentage (AUC = 0.78) or |FGT| (AUC = 0.66) alone (both P > 0.02).Quantitative measures of BPE and FGT are different before and after RRSO, and their relative changes are associated with prediction of developing breast cancer, potentially indicative of women who are more susceptible to develop breast cancer after RRSO in BRCA1/2 mutation carriers.
Breast MRI fibroglandular volume and parenchymal enhancement in BRCA1 and BRCA2 mutation carriers before and immediately after risk-reducing salpingo-oophorectomy. - AJR. American journal of roentgenology
OBJECTIVE. The purpose of this article is to assess the difference in fibroglandular volume and background parenchymal enhancement in BRCA1 and BRCA2 mutation carriers on contrast-enhanced breast MRI (CE-MRI) performed before and immediately after risk-reducing salpingo-oophorectomy (RRSO). MATERIALS AND METHODS. We retrospectively compared fibroglandular volume and background parenchymal enhancement in 55 female BRCA1 and BRCA2 mutation carriers before and after RRSO using standard BI-RADS categories and a paired Wilcoxon and Mann-Whitney U test. A two-sample Wilcoxon test was performed to compare fibroglandular volume and background parenchymal enhancement in women with and without subsequent breast cancer diagnosis on follow-up. RESULTS. The median time to post-RRSO CE-MRI was 8 months (range, 1-40 months). There was no difference in fibroglandular volume before and after RRSO (p = 0.65). The mean background parenchymal enhancement was 2.5 (range, 1-4) before and 1.5 (range, 1-4) after RRSO (overall range, -2.5 to 1.5; p = 0.0001). Breast cancer was detected in nine women at a median time of 4.8 years (range, 1.8-13.3 years) after RRSO. For women who received a diagnosis of breast cancer after RRSO compared with those who did not, mean background parenchymal enhancement before RRSO was 3 (range, 2-4) versus 2.5 (range, 1-4; p = 0.001), and mean background parenchymal enhancement after RRSO was 2.5 (range, 1.5-4) versus 1.5 (range 2-4; p = 0.0018). There was no difference in fibroglandular volume before and after RRSO. CONCLUSION. In BRCA1 and BRCA2 mutation carriers, we observed a significant reduction in background parenchymal enhancement on the first CE-MRI after RRSO and no significant change in fibroglandular volume. Higher background parenchymal enhancement before and after RRSO was observed in women who subsequently received a diagnosis of breast cancer. This suggests that background parenchymal enhancement, rather than fibro-glandular volume, may be a more sensitive imaging biomarker of breast cancer risk.
Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy. - Magnetic resonance in medicine
To evaluate DRAMMS, an attribute-based deformable registration algorithm, compared to other intensity-based algorithms, for longitudinal breast MRI registration, and to show its applicability in quantifying tumor changes over the course of neoadjuvant chemotherapy.Breast magnetic resonance images from 14 women undergoing neoadjuvant chemotherapy were analyzed. The accuracy of DRAMMS versus five intensity-based deformable registration methods was evaluated based on 2,380 landmarks independently annotated by two experts, for the entire image volume, different image subregions, and patient subgroups. The registration method with the smallest landmark error was used to quantify tumor changes, by calculating the Jacobian determinant maps of the registration deformation.DRAMMS had the smallest landmark errors (6.05 Â± 4.86 mm), followed by the intensity-based methods CC-FFD (8.07 Â± 3.86 mm), NMI-FFD (8.21 Â± 3.81 mm), SSD-FFD (9.46 Â± 4.55 mm), Demons (10.76 Â± 6.01 mm), and Diffeomorphic Demons (10.82 Â± 6.11 mm). Results show that registration accuracy also depends on tumor versus normal tissue regions and different patient subgroups.The DRAMMS deformable registration method, driven by attribute-matching and mutual-saliency, can register longitudinal breast magnetic resonance images with a higher accuracy than several intensity-matching methods included in this article. As such, it could be valuable for more accurately quantifying heterogeneous tumor changes as a marker of response to treatment.Â© 2014 Wiley Periodicals, Inc.
Digital breast tomosynthesis: lessons learned from early clinical implementation. - Radiographics : a review publication of the Radiological Society of North America, Inc
The limitations of mammography are well known and are partly related to the fact that with conventional imaging, the three-dimensional volume of the breast is imaged and presented in a two-dimensional format. Because normal breast tissue is similar in x-ray attenuation to some breast cancers, clinically relevant malignancies may be obscured by normal overlapping tissue. In addition, complex areas of normal tissue may be perceived as suspicious. The limitations of two-dimensional breast imaging lead to low sensitivity in detecting some cancers and high false-positive recall rates. Although mammographic screening has been shown to reduce breast cancer deaths by approximately 30%, controversy exists over when and how often screening mammography should occur. Digital breast tomosynthesis (DBT) is rapidly being implemented in breast imaging clinics around the world as early clinical data demonstrate that it may address some of the limitations of conventional mammography. With DBT, multiple low-dose x-ray images are acquired in an arc and reconstructed to create a three-dimensional image, thus minimizing the impact of overlapping breast tissue and improving lesion conspicuity. Early studies of screening DBT have shown decreased false-positive callback rates and increased rates of cancer detection (particularly for invasive cancers), resulting in increased sensitivity and specificity. In our clinical practice, we have completed more than 2 years of using two-view digital mammography combined with two-view DBT for all screening and select diagnostic imaging examinations (over 25,000 patients). Our experience, combined with previously published data, demonstrates that the combined use of DBT and digital mammography is associated with improved outcomes for screening and diagnostic imaging. Online supplemental material is available for this article.Â©RSNA, 2014.
Interspecific aggression by a rabid eastern red bat (Lasiurus borealis). - Journal of wildlife diseases
On 16 March 2012 a rabid eastern red bat (Lasiurus borealis) was found attached to an evening bat (Nycticeius humeralis) in Randolph County, Arkansas, USA. This appears to be the first confirmed case of a rabid bat attacking a bat of another species.
Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method. - Medical physics
Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Studies suggest that the relative amount of fibroglandular (i.e., dense) tissue in the breast as quantified in MR images can be predictive of the risk for developing breast cancer, especially for high-risk women. Automated segmentation of the fibroglandular tissue and volumetric density estimation in breast MRI could therefore be useful for breast cancer risk assessment.In this work the authors develop and validate a fully automated segmentation algorithm, namely, an atlas-aided fuzzy C-means (FCM-Atlas) method, to estimate the volumetric amount of fibroglandular tissue in breast MRI. The FCM-Atlas is a 2D segmentation method working on a slice-by-slice basis. FCM clustering is first applied to the intensity space of each 2D MR slice to produce an initial voxelwise likelihood map of fibroglandular tissue. Then a prior learned fibroglandular tissue likelihood atlas is incorporated to refine the initial FCM likelihood map to achieve enhanced segmentation, from which the absolute volume of the fibroglandular tissue (|FGT|) and the relative amount (i.e., percentage) of the |FGT| relative to the whole breast volume (FGT%) are computed. The authors' method is evaluated by a representative dataset of 60 3D bilateral breast MRI scans (120 breasts) that span the full breast density range of the American College of Radiology Breast Imaging Reporting and Data System. The automated segmentation is compared to manual segmentation obtained by two experienced breast imaging radiologists. Segmentation performance is assessed by linear regression, Pearson's correlation coefficients, Student's paired t-test, and Dice's similarity coefficients (DSC).The inter-reader correlation is 0.97 for FGT% and 0.95 for |FGT|. When compared to the average of the two readers' manual segmentation, the proposed FCM-Atlas method achieves a correlation of r = 0.92 for FGT% and r = 0.93 for |FGT|, and the automated segmentation is not statistically significantly different (p = 0.46 for FGT% and p = 0.55 for |FGT|). The bilateral correlation between left breasts and right breasts for the FGT% is 0.94, 0.92, and 0.95 for reader 1, reader 2, and the FCM-Atlas, respectively; likewise, for the |FGT|, it is 0.92, 0.92, and 0.93, respectively. For the spatial segmentation agreement, the automated algorithm achieves a DSC of 0.69 Â± 0.1 when compared to reader 1 and 0.61 Â± 0.1 for reader 2, respectively, while the DSC between the two readers' manual segmentation is 0.67 Â± 0.15. Additional robustness analysis shows that the segmentation performance of the authors' method is stable both with respect to selecting different cases and to varying the number of cases needed to construct the prior probability atlas. The authors' results also show that the proposed FCM-Atlas method outperforms the commonly used two-cluster FCM-alone method. The authors' method runs at âˆ¼5 min for each 3D bilateral MR scan (56 slices) for computing the FGT% and |FGT|, compared to âˆ¼55 min needed for manual segmentation for the same purpose.The authors' method achieves robust segmentation and can serve as an efficient tool for processing large clinical datasets for quantifying the fibroglandular tissue content in breast MRI. It holds a great potential to support clinical applications in the future including breast cancer risk assessment.
Automated chest wall line detection for whole-breast segmentation in sagittal breast MR images. - Medical physics
Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Computerized analysis is increasingly used to quantify breast MRI features in applications such as computer-aided lesion detection and fibroglandular tissue estimation for breast cancer risk assessment. Automated segmentation of the whole-breast as an organ from the other parts imaged is an important step in aiding lesion localization and fibroglandular tissue quantification. For this task, identifying the chest wall line (CWL) is most challenging due to image contrast variations, intensity discontinuity, and bias field.In this work, the authors develop and validate a fully automated image processing algorithm for accurate delineation of the CWL in sagittal breast MRI. The CWL detection is based on an integrated scheme of edge extraction and CWL candidate evaluation. The edge extraction consists of applying edge-enhancing filters and an edge linking algorithm. Increased accuracy is achieved by the synergistic use of multiple image inputs for edge extraction, where multiple CWL candidates are evaluated by the dynamic time warping algorithm coupled with the construction of a CWL reference. Their method is quantitatively validated by a dataset of 60 3D bilateral sagittal breast MRI scans (in total 3360 2D MR slices) that span the full American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) breast density range. Agreement with manual segmentation obtained by an experienced breast imaging radiologist is assessed by both volumetric and boundary-based metrics, including four quantitative measures.In terms of breast volume agreement with manual segmentation, the overlay percentage expressed by the Dice's similarity coefficient is 95.0% and the difference percentage is 10.1%. More specifically, for the segmentation accuracy of the CWL boundary, the CWL overlay percentage is 92.7% and averaged deviation distance is 2.3 mm. Their method requires â‰ˆ 4.5 min for segmenting each 3D breast MRI scan (56 slices) in comparison to â‰ˆ 35 min required for manual segmentation. Further analysis indicates that the segmentation performance of their method is relatively stable across the different BI-RADS density categories and breast volume, and also robust with respect to a varying range of the major parameters of the algorithm.Their fully automated method achieves high segmentation accuracy in a time-efficient manner. It could support large scale quantitative breast MRI analysis and holds the potential to become integrated into the clinical workflow for breast cancer clinical applications in the future.
Atlas-based probabilistic fibroglandular tissue segmentation in breast MRI. - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
In this paper we propose an atlas-aided probabilistic model-based segmentation method for estimating the fibroglandular tissue in breast MRI, where a novel fibroglandular tissue atlas is learned to aid the segmentation. The atlas represents a pixel-wise likelihood of being fibroglandular tissue in the breast, which is derived by combining deformable image warping, using aligned breast contour points as landmarks, with a kernel density estimation technique. A mixture multivariate model is learned to characterize the breast tissue using MR image features, and the segmentation is subsequently based on examining the posterior probability where the learned atlas is incorporated as the prior probability. In our experiments, the algorithm-generated segmentation results of 10 cases are compared to the manual segmentations, verified by an experienced breast imaging radiologist, to assess the accuracy of the algorithm, where the Dice's Similarity Coefficient (DSC) shows a 0.85 agreement. The proposed automated segmentation method could be used to estimate the volumetric amount of fibroglandular tissue in the breast for breast cancer risk estimation.
HER-2 pulsed dendritic cell vaccine can eliminate HER-2 expression and impact ductal carcinoma in situ. - Cancer
HER-2/neu overexpression plays a critical role in breast cancer development, and its expression in ductal carcinoma in situ (DCIS) is associated with development of invasive breast cancer. A vaccine targeting HER-2/neu expression in DCIS may initiate immunity against invasive cancer.A HER-2/neu dendritic cell vaccine was administered to 27 patients with HER-2/neu-overexpressing DCIS. The HER-2/neu vaccine was administered before surgical resection, and pre- and postvaccination analysis was conducted to assess clinical results.At surgery, 5 of 27 (18.5%) vaccinated subjects had no evidence of remaining disease, whereas among 22 subjects with residual DCIS, HER-2/neu expression was eradicated in 11 (50%). When comparing estrogen receptor (ER)(neg) with ER(pos) DCIS lesions, vaccination was more effective in hormone-independent DCIS. After vaccination, no residual DCIS was found in 40% of ER(neg) subjects compared with 5.9% in ER(pos) subjects. Sustained HER-2/neu expression was found in 10% of ER(neg) subjects compared with 47.1% in ER(pos) subjects (P = .04). Postvaccination phenotypes were significantly different between ER(pos) and ER(neg) subjects (P = .01), with 7 of 16 (43.8%) initially presenting with ER(pos) HER-2/neu(pos) luminal B phenotype finishing with the ER(pos) HER-2/neu(neg) luminal A phenotype, and 3 of 6 (50%) with the ER(neg) HER-2/neu(pos) phenotype changing to the ER(neg) HER-2/neu(neg) phenotype.Results suggest that vaccination against HER-2/neu is safe and well tolerated and induces decline and/or eradication of HER-2/neu expression. These findings warrant further exploration of HER-2/neu vaccination in estrogen-independent breast cancer and highlight the need to target additional tumor-associated antigens and pathways.Copyright Â© 2012 American Cancer Society.
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