Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation.

PubMed

Keller, Brad M; Nathan, Diane L; Wang, Yan; Zheng, Yuanjie; Gee, James C; Conant, Emily F; Kontos, Despina

2012-08-01

The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screen-film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., "FOR PROCESSING") and vendor postprocessed (i.e., "FOR PRESENTATION"), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis. This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely fibroglandular, which are then aggregated into a final dense tissue segmentation that is used to compute breast PD%. Our method is validated on a group of 81 women for whom bilateral, mediolateral oblique, raw and processed screening digital mammograms were available, and agreement is assessed with both continuous and categorical density estimates made by a trained breast-imaging radiologist. Strong association between algorithm-estimated and radiologist-provided breast PD% was detected for both raw (r = 0.82, p < 0.001) and processed (r = 0.85, p < 0.001) digital mammograms on a per-breast basis. Stronger agreement was found when overall breast density was assessed on a per-woman basis for both raw (r = 0.85, p < 0.001) and processed (0.89, p < 0.001) mammograms. Strong agreement between categorical density estimates was also seen (weighted Cohen's κ ≥ 0.79). Repeated measures analysis of variance demonstrated no statistically significant differences between the PD% estimates (p > 0.1) due to either presentation of the image (raw vs processed) or method of PD% assessment (radiologist vs algorithm). The proposed fully automated algorithm was successful in estimating breast percent density from both raw and processed digital mammographic images. Accurate assessment of a woman's breast density is critical in order for the estimate to be incorporated into risk assessment models. These results show promise for the clinical application of the algorithm in quantifying breast density in a repeatable manner, both at time of imaging as well as in retrospective studies.

Time domain diffuse optical spectroscopy: In vivo quantification of collagen in breast tissue

NASA Astrophysics Data System (ADS)

Taroni, Paola; Pifferi, Antonio; Quarto, Giovanna; Farina, Andrea; Ieva, Francesca; Paganoni, Anna Maria; Abbate, Francesca; Cassano, Enrico; Cubeddu, Rinaldo

2015-05-01

Time-resolved diffuse optical spectroscopy provides non-invasively the optical characterization of highly diffusive media, such as biological tissues. Light pulses are injected into the tissue and the effects of light propagation on re-emitted pulses are interpreted with the diffusion theory to assess simultaneously tissue absorption and reduced scattering coefficients. Performing spectral measurements, information on tissue composition and structure is derived applying the Beer law to the measured absorption and an empiric approximation to Mie theory to the reduced scattering. The absorption properties of collagen powder were preliminarily measured in the range of 600-1100 nm using a laboratory set-up for broadband time-resolved diffuse optical spectroscopy. Optical projection images were subsequently acquired in compressed breast geometry on 218 subjects, either healthy or bearing breast lesions, using a portable instrument for optical mammography that operates at 7 wavelengths selected in the range 635-1060 nm. For all subjects, tissue composition was estimated in terms of oxy- and deoxy-hemoglobin, water, lipids, and collagen. Information on tissue microscopic structure was also derived. Good correlation was obtained between mammographic breast density (a strong risk factor for breast cancer) and an optical index based on collagen content and scattering power (that accounts mostly for tissue collagen). Logistic regression applied to all optically derived parameters showed that subjects at high risk for developing breast cancer for their high breast density can effectively be identified based on collagen content and scattering parameters. Tissue composition assessed in breast lesions with a perturbative approach indicated that collagen and hemoglobin content are significantly higher in malignant lesions than in benign ones.

Development and Optimization of a Dedicated, Hybrid Dual-Modality SPECT-CmT System for Improved Breast Lesion Diagnosis

DTIC Science & Technology

2010-01-01

throughout the entire 3D volume which made quantification of the different tissues in the breast possible. The p eaks representing glandular and fat in...coefficients. Keywords: tissue quantification , absolute attenuation coefficient, scatter correction, computed tomography, tomography... tissue types. 1-4 Accurate measurements of t he quantification and di fferentiation of numerous t issues can be useful to identify di sease from

Quantification of breast lesion compositions using low-dose spectral mammography: A feasibility study

PubMed Central

Ding, Huanjun; Sennung, David; Cho, Hyo-Min; Molloi, Sabee

2016-01-01

Purpose: The positive predictive power for malignancy can potentially be improved, if the chemical compositions of suspicious breast lesions can be reliably measured in screening mammography. The purpose of this study is to investigate the feasibility of quantifying breast lesion composition, in terms of water and lipid contents, with spectral mammography. Methods: Phantom and tissue samples were imaged with a spectral mammography system based on silicon-strip photon-counting detectors. Dual-energy calibration was performed for material decomposition, using plastic water and adipose-equivalent phantoms as the basis materials. The step wedge calibration phantom consisted of 20 calibration configurations, which ranged from 2 to 8 cm in thickness and from 0% to 100% in plastic water density. A nonlinear rational fitting function was used in dual-energy calibration of the imaging system. Breast lesion phantoms, made from various combinations of plastic water and adipose-equivalent disks, were embedded in a breast mammography phantom with a heterogeneous background pattern. Lesion phantoms with water densities ranging from 0% to 100% were placed at different locations of the heterogeneous background phantom. The water density in the lesion phantoms was measured using dual-energy material decomposition. The thickness and density of the background phantom were varied to test the accuracy of the decomposition technique in different configurations. In addition, an in vitro study was also performed using mixtures of lean and fat bovine tissue of 25%, 50%, and 80% lean weight percentages as the background. Lesions were simulated by using breast lesion phantoms, as well as small bovine tissue samples, composed of carefully weighed lean and fat bovine tissues. The water densities in tissue samples were measured using spectral mammography and compared to measurement using chemical decomposition of the tissue. Results: The thickness of measured and known water contents was compared for various lesion configurations. There was a good linear correlation between the measured and the known values. The root-mean-square errors in water thickness measurements were 0.3 and 0.2 mm for the plastic phantom and bovine tissue backgrounds, respectively. Conclusions: The results indicate that spectral mammography can be used to accurately characterize breast lesion composition in terms of their equivalent water and lipid contents. PMID:27782705

Analyzing the management and disturbance in European forest based on self-thinning theory

NASA Astrophysics Data System (ADS)

Yan, Y.; Gielen, B.; Schelhaas, M.; Mohren, F.; Luyssaert, S.; Janssens, I. A.

2012-04-01

There is increasing awareness that natural and anthropogenic disturbance in forests affects exchange of CO2, H2O and energy between the ecosystem and the atmosphere. Consequently quantification of land use and disturbance intensity is one of the next steps needed to improve our understanding of the carbon cycle, its interactions with the atmosphere and its main drivers at local as well as at global level. The conventional NPP-based approaches to quantify the intensity of land management are limited because they lack a sound ecological basis. Here we apply a new way of characterising the degree of management and disturbance in forests using the self- thinning theory and observations of diameter at breast height and stand density. We used plot level information on dominant tree species, diameter at breast height, stand density and soil type from the French national forest inventory from 2005 to 2010. Stand density and diameter at breast height were used to parameterize the intercept of the self-thinning relationship and combined with theoretical slope to obtain an upper boundary for stand productivity given its density. Subsequently, we tested the sensitivity of the self-thinning relationship for tree species, soil type, climate and other environmental characteristics. We could find statistical differences in the self-thinning relationship between species and soil types, mainly due to the large uncertainty of the parameter estimates. Deviation from the theoretical self-thinning line defined as DBH=αN-3/4, was used as a proxy for disturbances, allowing to make spatially explicit maps of forest disturbance over France. The same framework was used to quantify the density-DBH trajectory of even-aged stand management of beech and oak over France. These trajectories will be used as a driver of forest management in the land surface model ORCHIDEE.

A liquid chromatography-tandem mass spectrometry-based targeted proteomics assay for monitoring P-glycoprotein levels in human breast tissue.

PubMed

Yang, Ting; Chen, Fei; Xu, Feifei; Wang, Fengliang; Xu, Qingqing; Chen, Yun

2014-09-25

P-glycoprotein (P-gp) can efflux drugs from cancer cells, and its overexpression is commonly associated with multi-drug resistance (MDR). Thus, the accurate quantification of P-gp would help predict the response to chemotherapy and for prognosis of breast cancer patients. An advanced liquid chromatography-tandem mass spectrometry (LC/MS/MS)-based targeted proteomics assay was developed and validated for monitoring P-gp levels in breast tissue. Tryptic peptide 368IIDNKPSIDSYSK380 was selected as a surrogate analyte for quantification, and immuno-depleted tissue extract was used as a surrogate matrix. Matched pairs of breast tissue samples from 60 patients who were suspected to have drug resistance were subject to analysis. The levels of P-gp were quantified. Using data from normal tissue, we suggested a P-gp reference interval. The experimental values of tumor tissue samples were compared with those obtained from Western blotting and immunohistochemistry (IHC). The result indicated that the targeted proteomics approach was comparable to IHC but provided a lower limit of quantification (LOQ) and could afford more reliable results at low concentrations than the other two methods. LC/MS/MS-based targeted proteomics may allow the quantification of P-gp in breast tissue in a more accurate manner. Copyright © 2014 Elsevier B.V. All rights reserved.

Apparent exchange rate for breast cancer characterization.

PubMed

Lasič, Samo; Oredsson, Stina; Partridge, Savannah C; Saal, Lao H; Topgaard, Daniel; Nilsson, Markus; Bryskhe, Karin

2016-05-01

Although diffusion MRI has shown promise for the characterization of breast cancer, it has low specificity to malignant subtypes. Higher specificity might be achieved if the effects of cell morphology and molecular exchange across cell membranes could be disentangled. The quantification of exchange might thus allow the differentiation of different types of breast cancer cells. Based on differences in diffusion rates between the intra- and extracellular compartments, filter exchange spectroscopy/imaging (FEXSY/FEXI) provides non-invasive quantification of the apparent exchange rate (AXR) of water between the two compartments. To test the feasibility of FEXSY for the differentiation of different breast cancer cells, we performed experiments on several breast epithelial cell lines in vitro. Furthermore, we performed the first in vivo FEXI measurement of water exchange in human breast. In cell suspensions, pulsed gradient spin-echo experiments with large b values and variable pulse duration allow the characterization of the intracellular compartment, whereas FEXSY provides a quantification of AXR. These experiments are very sensitive to the physiological state of cells and can be used to establish reliable protocols for the culture and harvesting of cells. Our results suggest that different breast cancer subtypes can be distinguished on the basis of their AXR values in cell suspensions. Time-resolved measurements allow the monitoring of the physiological state of cells in suspensions over the time-scale of hours, and reveal an abrupt disintegration of the intracellular compartment. In vivo, exchange can be detected in a tumor, whereas, in normal tissue, the exchange rate is outside the range experimentally accessible for FEXI. At present, low signal-to-noise ratio and limited scan time allows the quantification of AXR only in a region of interest of relatively large tumors. © 2016 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.

Combination of elastography and tissue quantification using the acoustic radiation force impulse (ARFI) technology for differential diagnosis of breast masses.

PubMed

Tozaki, Mitsuhiro; Isobe, Sachiko; Sakamoto, Masaaki

2012-10-01

We evaluated the diagnostic performance of elastography and tissue quantification using acoustic radiation force impulse (ARFI) technology for differential diagnosis of breast masses. There were 161 mass lesions. First, lesion correspondence on ARFI elastographic images to those on the B-mode images was evaluated: no findings on ARFI images (pattern 1), lesions that were bright inside (pattern 2), lesions that were dark inside (pattern 4), lesions that contained both bright and dark areas (pattern 3). In addition, pattern 4 was subdivided into 4a (dark area same as B-mode lesion) and 4b (dark area larger than lesion). Next, shear wave velocity (SWV) was measured using virtual touch tissue quantification. There were 13 pattern 1 lesions and five pattern 2 lesions; all of these lesions were benign, whereas all pattern 4b lesions (n = 43) were malignant. When the value of 3.59 m/s was chosen as the cutoff value, the combination of elastography and tissue quantification showed 91 % (83-91) sensitivity, 93 % (65-70) specificity, and 92 % (148-161) accuracy. The combination of elastography and tissue quantification is thought to be a promising ultrasound technique for differential diagnosis of breast-mass lesions.

Are breast density and bone mineral density independent risk factors for breast cancer?

PubMed

Kerlikowske, Karla; Shepherd, John; Creasman, Jennifer; Tice, Jeffrey A; Ziv, Elad; Cummings, Steve R

2005-03-02

Mammographic breast density and bone mineral density (BMD) are markers of cumulative exposure to estrogen. Previous studies have suggested that women with high mammographic breast density or high BMD are at increased risk of breast cancer. We determined whether mammographic breast density and BMD of the hip and spine are correlated and independently associated with breast cancer risk. We conducted a cross-sectional study (N = 15,254) and a nested case-control study (of 208 women with breast cancer and 436 control subjects) among women aged 28 years or older who had a screening mammography examination and hip BMD measurement within 2 years. Breast density for 3105 of the women was classified using the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) categories, and percentage mammographic breast density among the case patients and control subjects was quantified with a computer-based threshold method. Spearman rank partial correlation coefficient and Pearson's correlation coefficient were used to examine correlations between BI-RADS breast density and BMD and between percentage mammographic breast density and BMD, respectively, in women without breast cancer. Logistic regression was used to examine the association of breast cancer with percentage mammographic breast density and BMD. All statistical tests were two-sided. Neither BI-RADS breast density nor percentage breast density was correlated with hip or spine BMD (correlation coefficient = -.02 and -.01 for BI-RADS, respectively, and -.06 and .01 for percentage breast density, respectively). Neither hip BMD nor spine BMD had a statistically significant relationship with breast cancer risk. Women with breast density in the highest sextile had an approximately threefold increased risk of breast cancer compared with women in the lowest sextile (odds ratio = 2.7, 95% confidence interval = 1.4 to 5.4); adjusting for hip or spine BMD did not change the association between breast density and breast cancer risk. Breast density is strongly associated with increased risk of breast cancer, even after taking into account reproductive and hormonal risk factors, whereas BMD, although a possible marker of lifetime exposure to estrogen, is not. Thus, a component of breast density that is independent of estrogen-mediated effects may contribute to breast cancer risk.

Current and Future Methods for Measuring Breast Density: A Brief Comparative Review

PubMed Central

Sak, Mark A.; Littrup, Peter J.; Duric, Neb; Mullooly, Maeve; Sherman, Mark E.; Gierach, Gretchen L.

2017-01-01

Breast density is one of the strongest predictors of breast cancer risk. Women with the densest breasts are 4 to 6 times more likely to develop cancer compared with those with the lowest densities. Breast density is generally assessed using mammographic imaging; however, this approach has limitations. Magnetic resonance imaging and ultrasound tomography are some alternative imaging modalities that can aid mammography in patient screening and the measurement of breast density. As breast density becomes more commonly discussed, knowledge of the advantages and limitations of breast density as a marker of risk will become more critical. This review article discusses the relationship between breast density and breast cancer risk, lists the benefits and drawbacks of using multiple different imaging modalities to measure density and briefly discusses how breast density will be applied to aid in breast cancer prevention and treatment. PMID:28943893

Template-based automatic breast segmentation on MRI by excluding the chest region

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lin, Muqing; Chen, Jeon-Hor; Wang, Xiaoyong

2013-12-15

Purpose: Methods for quantification of breast density on MRI using semiautomatic approaches are commonly used. In this study, the authors report on a fully automatic chest template-based method. Methods: Nonfat-suppressed breast MR images from 31 healthy women were analyzed. Among them, one case was randomly selected and used as the template, and the remaining 30 cases were used for testing. Unlike most model-based breast segmentation methods that use the breast region as the template, the chest body region on a middle slice was used as the template. Within the chest template, three body landmarks (thoracic spine and bilateral boundary ofmore » the pectoral muscle) were identified for performing the initial V-shape cut to determine the posterior lateral boundary of the breast. The chest template was mapped to each subject's image space to obtain a subject-specific chest model for exclusion. On the remaining image, the chest wall muscle was identified and excluded to obtain clean breast segmentation. The chest and muscle boundaries determined on the middle slice were used as the reference for the segmentation of adjacent slices, and the process continued superiorly and inferiorly until all 3D slices were segmented. The segmentation results were evaluated by an experienced radiologist to mark voxels that were wrongly included or excluded for error analysis. Results: The breast volumes measured by the proposed algorithm were very close to the radiologist's corrected volumes, showing a % difference ranging from 0.01% to 3.04% in 30 tested subjects with a mean of 0.86% ± 0.72%. The total error was calculated by adding the inclusion and the exclusion errors (so they did not cancel each other out), which ranged from 0.05% to 6.75% with a mean of 3.05% ± 1.93%. The fibroglandular tissue segmented within the breast region determined by the algorithm and the radiologist were also very close, showing a % difference ranging from 0.02% to 2.52% with a mean of 1.03% ± 1.03%. The total error by adding the inclusion and exclusion errors ranged from 0.16% to 11.8%, with a mean of 2.89% ± 2.55%. Conclusions: The automatic chest template-based breast MRI segmentation method worked well for cases with different body and breast shapes and different density patterns. Compared to the radiologist-established truth, the mean difference in segmented breast volume was approximately 1%, and the total error by considering the additive inclusion and exclusion errors was approximately 3%. This method may provide a reliable tool for MRI-based segmentation of breast density.« less

Breast Density Legislation in New England: A Survey Study of Practicing Radiologists.

PubMed

Lourenco, Ana P; DiFlorio-Alexander, Roberta M; Slanetz, Priscilla J

2017-10-01

This study aimed to assess radiologists' knowledge about breast density legislation as well as perceived practice changes resulting from the enactment of breast density legislation. This is an institutional review board-exempt anonymous email survey of 523 members of the New England Roentgen Ray Society. In addition to radiologist demographics, survey questions addressed radiologist knowledge of breast density legislation, knowledge of breast density as a risk factor for breast cancer, recommendations for supplemental screening, and perceived practice changes resulting from density notification legislation. Of the 523 members, 96 responded, yielding an 18% response rate. Seventy-three percent of respondents practiced in a state with breast density legislation. Sixty-nine percent felt that breast density notification increased patient anxiety about breast cancer, but also increased patient (74%) and provider (66%) understanding of the effect of breast density on mammographic sensitivity. Radiologist knowledge of the relative risk of breast cancer when comparing breasts of different density was variable. Considerable confusion and controversy regarding breast density persists, even among practicing radiologists. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Knowledge of Breast Density and Awareness of Related Breast Cancer Risk

PubMed Central

Duric, Neb; Littrup, Peter; Bey-Knight, Lisa; Penner, Louis; Albrecht, Terrance L.

2013-01-01

Little is known about women’s knowledge of breast density or between-race differences in this knowledge. In the current study, we examined knowledge of breast density and awareness of its role as a breast cancer risk factor among women who had previously taken part in a breast imaging study. Seventy-seven women (54.5 % Black) returned a survey assessing perceptions and accuracy of breast density knowledge, knowledge of one’s own breast density, and breast cancer risk awareness. White women had greater perceived knowledge of breast density compared to Black women; however, differences in the accuracy of definitions of breast density were due to education. Black women were less likely to know how dense their own breasts were. Black and White women both lacked awareness that having dense breast increased breast cancer risk. The results highlight the need to disseminate information regarding breast density to women, while ensuring that the information is equally accessible to both Black and White women. PMID:23467999

Knowledge of breast density and awareness of related breast cancer risk.

PubMed

Manning, Mark A; Duric, Neb; Littrup, Peter; Bey-Knight, Lisa; Penner, Louis; Albrecht, Terrance L

2013-06-01

Little is known about women's knowledge of breast density or between-race differences in this knowledge. In the current study, we examined knowledge of breast density and awareness of its role as a breast cancer risk factor among women who had previously taken part in a breast imaging study. Seventy-seven women (54.5 % Black) returned a survey assessing perceptions and accuracy of breast density knowledge, knowledge of one's own breast density, and breast cancer risk awareness. White women had greater perceived knowledge of breast density compared to Black women; however, differences in the accuracy of definitions of breast density were due to education. Black women were less likely to know how dense their own breasts were. Black and White women both lacked awareness that having dense breast increased breast cancer risk. The results highlight the need to disseminate information regarding breast density to women, while ensuring that the information is equally accessible to both Black and White women.

The variation in breast density and its relationship to delayed wound healing: a prospective study of 40 reduction mammoplasties.

PubMed

Baldwin, C J; Kelly, E J; Batchelor, A G

2010-04-01

The proportions of glandular and adipose tissue within the breast vary. This study records the variation in density of breast tissue excised at 40 consecutive bilateral breast reductions. Age, body mass index (BMI), breast size and wound healing problems were related to breast density. The removed breast tissue was weighed and volume determined by water displacement. Delayed wound healing was defined as any breast unhealed after 2 weeks. The density of excised tissue varied between 0.8 and 1.2g/cm(3). There was no correlation between age or BMI and breast density. Delayed wound healing occurred in 32% of patients. There was no correlation between delayed wound healing and breast density. However, there was a direct relationship between increasing BMI and delayed wound healing. In this study, breast density varied by up to 50%. The density of breast tissue cannot be predicted by age, BMI or breast size. There is no relationship between delayed wound healing and breast density. Copyright 2009. Published by Elsevier Ltd.

Imaging Management of Breast Density, a Controversial Risk Factor for Breast Cancer.

PubMed

Falcon, Shannon; Williams, Angela; Weinfurtner, Jared; Drukteinis, Jennifer S

2017-04-01

Breast density is well recognized as an independent risk factor for the development of breast cancer. However, the magnitude of risk is controversial. As the public becomes increasingly aware of breast density as a risk factor, legislation and notification laws in relation to breast density have become common throughout the United States. Awareness of breast density as a risk factor for breast cancer presents new challenges for the clinician in the approach to the management and screening of women with dense breasts. The evidence and controversy surrounding breast density as a risk factor for the development of breast cancer are discussed. Common supplemental screening modalities for breast cancer are also discussed, including tomosynthesis, ultrasonography, and magnetic resonance imaging. A management strategy for screening women with dense breasts is also presented. The American College of Radiology recognizes breast density as a controversial risk factor for breast cancer, whereas the American Congress of Obstetricians and Gynecologists recognizes breast density as a modest risk factor. Neither organization recommends the routine use of supplemental screening in women with dense breasts without considering additional patient-related risk factors. Breast density is a poorly understood and controversial risk factor for the development of breast cancer. Mammography is a screening modality proven to reduce breast cancer-related mortality rates and is the single most appropriate tool for population-based screening. Use of supplemental screening modalities should be tailored to individual risk assessment.

Breast Density Awareness and Knowledge, and Intentions for Breast Cancer Screening in a Diverse Sample of Women Age Eligible for Mammography.

PubMed

Santiago-Rivas, Marimer; Benjamin, Shayna; Andrews, Janna Z; Jandorf, Lina

2017-08-14

The objectives of this study were to assess breast density knowledge and breast density awareness, and to identify information associated with intention to complete routine and supplemental screening for breast cancer in a diverse sample of women age eligible for mammography. We quantitatively (self-report) assessed breast density awareness and knowledge (N = 264) in black (47.7%), Latina (35.2%), and white (17%) women recruited online and in the community. Most participants reported having heard about breast density (69.2%); less than one third knew their own breast density status (30.4%). Knowing their own breast density, believing that women should be notified of their breast density in their mammogram report, and feeling informed if being provided this information are associated with likelihood of completing mammogram. Intending mammogram completion and knowledge regarding the impact of breast density on mammogram accuracy are associated with likelihood of completing supplemental ultrasound tests of the breast. These findings help inform practitioners and policy makers about information and communication factors that influence breast cancer screening concerns and decisions. Knowing this information should prepare practitioners to better identify women who may have not been exposed to breast density messages.

MO-F-CAMPUS-I-01: Accuracy of Radiologists Interpretation of Mammographic Breast Density

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vedantham, S; Shi, L; Karellas, A

2015-06-15

Purpose: Several commercial and non-commercial software and techniques are available for determining breast density from mammograms. However, where mandated by law the breast density information communicated to the subject/patient is based on radiologist’s interpretation of breast density from mammograms. Several studies have reported on the concordance among radiologists in interpreting mammographic breast density. In this work, we investigated the accuracy of radiologist’s interpretation of breast density. Methods: Volumetric breast density (VBD) determined from 134 unilateral dedicated breast CT scans from 134 subjects was considered the truth. An MQSA-qualified study radiologist with more than 20 years of breast imaging experience reviewedmore » the DICOM “for presentation” standard 2-view mammograms of the corresponding breasts and assigned BIRADS breast density categories. For statistical analysis, the breast density categories were dichotomized in two ways; fatty vs. dense breasts where “fatty” corresponds to BIRADS breast density categories A/B, and “dense” corresponds to BIRADS breast density categories C/D, and extremely dense vs. fatty to heterogeneously dense breasts, where extremely dense corresponds to BIRADS breast density category D and BIRADS breast density categories A through C were grouped as fatty to heterogeneously dense breasts. Logistic regression models (SAS 9.3) were used to determine the association between radiologist’s interpretation of breast density and VBD from breast CT, from which the area under the ROC (AUC) was determined. Results: Both logistic regression models were statistically significant (Likelihood Ratio test, p<0.0001). The accuracy (AUC) of the study radiologist for classification of fatty vs. dense breasts was 88.4% (95% CI: 83–94%) and for classification of extremely dense breast was 94.3% (95% CI: 90–98%). Conclusion: The accuracy of the radiologist in classifying dense and extremely dense breasts is high. Considering the variability in VBD estimates from commercial software, the breast density information communicated to the patient should be based on radiologist’s interpretation. This work was supported in part by NIH R21 CA176470 and R21 CA134128. The contents are solely the responsibility of the authors and do not reflect the official views of the NIH or NCI.« less

Initial In Vivo Quantification of Tc-99m Sestamibi Uptake as a Function of Tissue Type in Healthy Breasts Using Dedicated Breast SPECT-CT

PubMed Central

Mann, Steve D.; Perez, Kristy L.; McCracken, Emily K. E.; Shah, Jainil P.; Wong, Terence Z.; Tornai, Martin P.

2012-01-01

A pilot study is underway to quantify in vivo the uptake and distribution of Tc-99m Sestamibi in subjects without previous history of breast cancer using a dedicated SPECT-CT breast imaging system. Subjects undergoing diagnostic parathyroid imaging studies were consented and imaged as part of this IRB-approved breast imaging study. For each of the seven subjects, one randomly selected breast was imaged prone-pendant using the dedicated, compact breast SPECT-CT system underneath the shielded patient support. Iteratively reconstructed and attenuation and/or scatter corrected images were coregistered; CT images were segmented into glandular and fatty tissue by three different methods; the average concentration of Sestamibi was determined from the SPECT data using the CT-based segmentation and previously established quantification techniques. Very minor differences between the segmentation methods were observed, and the results indicate an average image-based in vivo Sestamibi concentration of 0.10 ± 0.16 μCi/mL with no preferential uptake by glandular or fatty tissues. PMID:22956950

Fully automated segmentation of the pectoralis muscle boundary in breast MR images

NASA Astrophysics Data System (ADS)

Wang, Lei; Filippatos, Konstantinos; Friman, Ola; Hahn, Horst K.

2011-03-01

Dynamic Contrast Enhanced MRI (DCE-MRI) of the breast is emerging as a novel tool for early tumor detection and diagnosis. The segmentation of the structures in breast DCE-MR images, such as the nipple, the breast-air boundary and the pectoralis muscle, serves as a fundamental step for further computer assisted diagnosis (CAD) applications, e.g. breast density analysis. Moreover, the previous clinical studies show that the distance between the posterior breast lesions and the pectoralis muscle can be used to assess the extent of the disease. To enable automatic quantification of the distance from a breast tumor to the pectoralis muscle, a precise delineation of the pectoralis muscle boundary is required. We present a fully automatic segmentation method based on the second derivative information represented by the Hessian matrix. The voxels proximal to the pectoralis muscle boundary exhibit roughly the same Eigen value patterns as a sheet-like object in 3D, which can be enhanced and segmented by a Hessian-based sheetness filter. A vector-based connected component filter is then utilized such that only the pectoralis muscle is preserved by extracting the largest connected component. The proposed method was evaluated quantitatively with a test data set which includes 30 breast MR images by measuring the average distances between the segmented boundary and the annotated surfaces in two ground truth sets, and the statistics showed that the mean distance was 1.434 mm with the standard deviation of 0.4661 mm, which shows great potential for integration of the approach in the clinical routine.

Evaluation of Quantra Hologic Volumetric Computerized Breast Density Software in Comparison With Manual Interpretation in a Diverse Population.

PubMed

Richard-Davis, Gloria; Whittemore, Brianna; Disher, Anthony; Rice, Valerie Montgomery; Lenin, Rathinasamy B; Dollins, Camille; Siegel, Eric R; Eswaran, Hari

2018-01-01

Increased mammographic breast density is a well-established risk factor for breast cancer development, regardless of age or ethnic background. The current gold standard for categorizing breast density consists of a radiologist estimation of percent density according to the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) criteria. This study compares paired qualitative interpretations of breast density on digital mammograms with quantitative measurement of density using Hologic's Food and Drug Administration-approved R2 Quantra volumetric breast density assessment tool. Our goal was to find the best cutoff value of Quantra-calculated breast density for stratifying patients accurately into high-risk and low-risk breast density categories. Screening digital mammograms from 385 subjects, aged 18 to 64 years, were evaluated. These mammograms were interpreted by a radiologist using the ACR's BI-RADS density method, and had quantitative density measured using the R2 Quantra breast density assessment tool. The appropriate cutoff for breast density-based risk stratification using Quantra software was calculated using manually determined BI-RADS scores as a gold standard, in which scores of D3/D4 denoted high-risk densities and D1/D2 denoted low-risk densities. The best cutoff value for risk stratification using Quantra-calculated breast density was found to be 14.0%, yielding a sensitivity of 65%, specificity of 77%, and positive and negative predictive values of 75% and 69%, respectively. Under bootstrap analysis, the best cutoff value had a mean ± SD of 13.70% ± 0.89%. Our study is the first to publish on a North American population that assesses the accuracy of the R2 Quantra system at breast density stratification. Quantitative breast density measures will improve accuracy and reliability of density determination, assisting future researchers to accurately calculate breast cancer risks associated with density increase.

Associations of Breast Density With Demographic, Reproductive, and Lifestyle Factors in a Developing Southeast Asian Population.

PubMed

Dung Yun Trieu, Phuong; Mello-Thoms, Claudia; Peat, Jennifer K; Doan Do, Thuan; Brennan, Patrick C

2017-07-01

The aim of this study was to investigate how breast density interacted with demographic, reproductive, and lifestyle features among Vietnamese women. Mammographic density and established risk factors for breast cancer were collected from 1651 women (345 cancer cases and 1306 normal cases) in Vietnam. The association of breast density categories with potential risk factors was investigated using Spearman's test for continuous variables and χ 2 tests for categorical variables. Independent factors associated with high breast density and breast cancer in specific density groupings were assessed using logistic regression. Results showed that high breast density was significantly associated with young age, low body mass index, low number of children, early age at having the last child, premenopausal status, and increased vegetable consumption. Reproductive factors were key agents associated with breast cancer for women with high breast density, which was not so evident for women with low breast density.

Evaluation of Quantra Hologic Volumetric Computerized Breast Density Software in Comparison With Manual Interpretation in a Diverse Population

PubMed Central

Richard-Davis, Gloria; Whittemore, Brianna; Disher, Anthony; Rice, Valerie Montgomery; Lenin, Rathinasamy B; Dollins, Camille; Siegel, Eric R; Eswaran, Hari

2018-01-01

Objective: Increased mammographic breast density is a well-established risk factor for breast cancer development, regardless of age or ethnic background. The current gold standard for categorizing breast density consists of a radiologist estimation of percent density according to the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) criteria. This study compares paired qualitative interpretations of breast density on digital mammograms with quantitative measurement of density using Hologic’s Food and Drug Administration–approved R2 Quantra volumetric breast density assessment tool. Our goal was to find the best cutoff value of Quantra-calculated breast density for stratifying patients accurately into high-risk and low-risk breast density categories. Methods: Screening digital mammograms from 385 subjects, aged 18 to 64 years, were evaluated. These mammograms were interpreted by a radiologist using the ACR’s BI-RADS density method, and had quantitative density measured using the R2 Quantra breast density assessment tool. The appropriate cutoff for breast density–based risk stratification using Quantra software was calculated using manually determined BI-RADS scores as a gold standard, in which scores of D3/D4 denoted high-risk densities and D1/D2 denoted low-risk densities. Results: The best cutoff value for risk stratification using Quantra-calculated breast density was found to be 14.0%, yielding a sensitivity of 65%, specificity of 77%, and positive and negative predictive values of 75% and 69%, respectively. Under bootstrap analysis, the best cutoff value had a mean ± SD of 13.70% ± 0.89%. Conclusions: Our study is the first to publish on a North American population that assesses the accuracy of the R2 Quantra system at breast density stratification. Quantitative breast density measures will improve accuracy and reliability of density determination, assisting future researchers to accurately calculate breast cancer risks associated with density increase. PMID:29511356

Lesion Quantification in Dual-Modality Mammotomography

NASA Astrophysics Data System (ADS)

Li, Heng; Zheng, Yibin; More, Mitali J.; Goodale, Patricia J.; Williams, Mark B.

2007-02-01

This paper describes a novel x-ray/SPECT dual modality breast imaging system that provides 3D structural and functional information. While only a limited number of views on one side of the breast can be acquired due to mechanical and time constraints, we developed a technique to compensate for the limited angle artifact in reconstruction images and accurately estimate both the lesion size and radioactivity concentration. Various angular sampling strategies were evaluated using both simulated and experimental data. It was demonstrated that quantification of lesion size to an accuracy of 10% and quantification of radioactivity to an accuracy of 20% are feasible from limited-angle data acquired with clinically practical dosage and acquisition time

Quantitative breast density analysis using tomosynthesis and comparison with MRI and digital mammography.

PubMed

Moon, Woo Kyung; Chang, Jie-Fan; Lo, Chung-Ming; Chang, Jung Min; Lee, Su Hyun; Shin, Sung Ui; Huang, Chiun-Sheng; Chang, Ruey-Feng

2018-02-01

Breast density at mammography has been used as markers of breast cancer risk. However, newly introduced tomosynthesis and computer-aided quantitative method could provide more reliable breast density evaluation. In the experiment, 98 tomosynthesis image volumes were obtained from 98 women. For each case, an automatic skin removal was used and followed by a fuzzy c-mean (FCM) classifier which separated the fibroglandular tissues from other tissues in breast area. Finally, percent of breast density and breast volume were calculated and the results were compared with MRI. In addition, the percent of breast density and breast area of digital mammography calculated using the software Cumulus (University of Toronto, Toronto, ON, Canada.) were also compared with 3-D modalities. Percent of breast density and breast volume, which were computed from tomosynthesis, MRI and digital mammography were 17.37% ± 4.39% and 607.12 cm 3  ± 323.01 cm 3 , 20.3% ± 8.6% and 537.59 cm 3  ± 287.74 cm 3 , and 12.03% ± 4.08%, respectively. There were significant correlations on breast density as well as volume between tomosynthesis and MRI (R = 0.482 and R = 0.805), tomosynthesis and breast density with breast area of digital mammography (R = 0.789 and R = 0.877), and MRI and breast density with breast area of digital mammography (R = 0.482 and R = 0.857) (all P values < .001). Breast density and breast volume evaluated from tomosynthesis, MRI and breast density and breast area of digital mammographic images have significant correlations and indicate that tomosynthesis could provide useful 3-D information on breast density through proposed method. Copyright © 2017 Elsevier B.V. All rights reserved.

A fully automated system for quantification of background parenchymal enhancement in breast DCE-MRI

NASA Astrophysics Data System (ADS)

Ufuk Dalmiş, Mehmet; Gubern-Mérida, Albert; Borelli, Cristina; Vreemann, Suzan; Mann, Ritse M.; Karssemeijer, Nico

2016-03-01

Background parenchymal enhancement (BPE) observed in breast dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has been identified as an important biomarker associated with risk for developing breast cancer. In this study, we present a fully automated framework for quantification of BPE. We initially segmented fibroglandular tissue (FGT) of the breasts using an improved version of an existing method. Subsequently, we computed BPEabs (volume of the enhancing tissue), BPErf (BPEabs divided by FGT volume) and BPErb (BPEabs divided by breast volume), using different relative enhancement threshold values between 1% and 100%. To evaluate and compare the previous and improved FGT segmentation methods, we used 20 breast DCE-MRI scans and we computed Dice similarity coefficient (DSC) values with respect to manual segmentations. For evaluation of the BPE quantification, we used a dataset of 95 breast DCE-MRI scans. Two radiologists, in individual reading sessions, visually analyzed the dataset and categorized each breast into minimal, mild, moderate and marked BPE. To measure the correlation between automated BPE values to the radiologists' assessments, we converted these values into ordinal categories and we used Spearman's rho as a measure of correlation. According to our results, the new segmentation method obtained an average DSC of 0.81 0.09, which was significantly higher (p<0.001) compared to the previous method (0.76 0.10). The highest correlation values between automated BPE categories and radiologists' assessments were obtained with the BPErf measurement (r=0.55, r=0.49, p<0.001 for both), while the correlation between the scores given by the two radiologists was 0.82 (p<0.001). The presented framework can be used to systematically investigate the correlation between BPE and risk in large screening cohorts.

Weight versus volume in breast surgery: an observational study

PubMed Central

Parmar, Chetan; West, Malcolm; Pathak, Samir; Nelson, J; Martin, Lee

2011-01-01

Objectives The study hypothesis is to assess correlation of breast specimen weight versus volume. Design Consecutive patients undergoing breast surgery at a single tertiary referral centre during a 6-month period were included. Specimen weight was measured in grams. Direct volume measurements were performed using water displacement. Data including side of the breast, age and menstrual status of the patient were noted. Setting Knowledge of breast volume provides an objective guide in facilitating the achievements of balance in reconstructive operations. Surgeons use intraoperative weight measurements from individual breasts to calculate the breast volume assuming that weight is equal to the volume of the specimen. However, it is unclear whether weight accurately reveals the true volume of resection. Participants Forty-one patients were included in the study with 28 having bilateral surgeries, 13 having unilateral procedures giving a total of 69 breast specimens. Main outcome measures Breast specimen weight correlation to breast specimen volume. Results The mean age of the group was 42.4 years. Fifty-two specimens were from premenopausal patients and 17 were of postmenopausal. Thirty-five were left-sided. Twenty-six patients had bilateral breast reduction, two had bilateral mastectomy, nine had a unilateral mastectomy and four patients had a unilateral breast reduction. The difference between weight and volume of these breasts was 36.4 units (6.6% difference). The difference in measurement of weight and volume in premenopausal was 37.6 units compared to 32.6 units in postmenopausal women. The density was 1.07 and 1.06, respectively. This was statistically not significant. Conclusions No significant difference between volume and weight was seen in this series. Furthermore, we are unable to support the notion that premenopausal patients have a significant difference in the proportion of fatty and glandular tissue as there was little difference between the weight and the volume. An easy, clinically proper formula for the quantification of actual breast volume has yet to be derived. PMID:22140613

Weight versus volume in breast surgery: an observational study.

PubMed

Parmar, Chetan; West, Malcolm; Pathak, Samir; Nelson, J; Martin, Lee

2011-11-01

The study hypothesis is to assess correlation of breast specimen weight versus volume. Consecutive patients undergoing breast surgery at a single tertiary referral centre during a 6-month period were included. Specimen weight was measured in grams. Direct volume measurements were performed using water displacement. Data including side of the breast, age and menstrual status of the patient were noted. Knowledge of breast volume provides an objective guide in facilitating the achievements of balance in reconstructive operations. Surgeons use intraoperative weight measurements from individual breasts to calculate the breast volume assuming that weight is equal to the volume of the specimen. However, it is unclear whether weight accurately reveals the true volume of resection. Forty-one patients were included in the study with 28 having bilateral surgeries, 13 having unilateral procedures giving a total of 69 breast specimens. Breast specimen weight correlation to breast specimen volume. The mean age of the group was 42.4 years. Fifty-two specimens were from premenopausal patients and 17 were of postmenopausal. Thirty-five were left-sided. Twenty-six patients had bilateral breast reduction, two had bilateral mastectomy, nine had a unilateral mastectomy and four patients had a unilateral breast reduction. The difference between weight and volume of these breasts was 36.4 units (6.6% difference). The difference in measurement of weight and volume in premenopausal was 37.6 units compared to 32.6 units in postmenopausal women. The density was 1.07 and 1.06, respectively. This was statistically not significant. No significant difference between volume and weight was seen in this series. Furthermore, we are unable to support the notion that premenopausal patients have a significant difference in the proportion of fatty and glandular tissue as there was little difference between the weight and the volume. An easy, clinically proper formula for the quantification of actual breast volume has yet to be derived.

Breast density estimation from high spectral and spatial resolution MRI

PubMed Central

Li, Hui; Weiss, William A.; Medved, Milica; Abe, Hiroyuki; Newstead, Gillian M.; Karczmar, Gregory S.; Giger, Maryellen L.

2016-01-01

Abstract. A three-dimensional breast density estimation method is presented for high spectral and spatial resolution (HiSS) MR imaging. Twenty-two patients were recruited (under an Institutional Review Board--approved Health Insurance Portability and Accountability Act-compliant protocol) for high-risk breast cancer screening. Each patient received standard-of-care clinical digital x-ray mammograms and MR scans, as well as HiSS scans. The algorithm for breast density estimation includes breast mask generating, breast skin removal, and breast percentage density calculation. The inter- and intra-user variabilities of the HiSS-based density estimation were determined using correlation analysis and limits of agreement. Correlation analysis was also performed between the HiSS-based density estimation and radiologists’ breast imaging-reporting and data system (BI-RADS) density ratings. A correlation coefficient of 0.91 (p<0.0001) was obtained between left and right breast density estimations. An interclass correlation coefficient of 0.99 (p<0.0001) indicated high reliability for the inter-user variability of the HiSS-based breast density estimations. A moderate correlation coefficient of 0.55 (p=0.0076) was observed between HiSS-based breast density estimations and radiologists’ BI-RADS. In summary, an objective density estimation method using HiSS spectral data from breast MRI was developed. The high reproducibility with low inter- and low intra-user variabilities shown in this preliminary study suggest that such a HiSS-based density metric may be potentially beneficial in programs requiring breast density such as in breast cancer risk assessment and monitoring effects of therapy. PMID:28042590

Dense Breasts: Answers to Commonly Asked Questions

Cancer.gov

Mammograms detect breast density, breast cancer, and breast changes that are not cancer (benign breast changes). Breast density describes the amount of glandular and fibrous tissue, as compared to fatty tissue. Learn what factors are associated with breast density and about other risk factors for breast cancer.

Automatic quantification of mammary glands on non-contrast x-ray CT by using a novel segmentation approach

NASA Astrophysics Data System (ADS)

Zhou, Xiangrong; Kano, Takuya; Cai, Yunliang; Li, Shuo; Zhou, Xinxin; Hara, Takeshi; Yokoyama, Ryujiro; Fujita, Hiroshi

2016-03-01

This paper describes a brand new automatic segmentation method for quantifying volume and density of mammary gland regions on non-contrast CT images. The proposed method uses two processing steps: (1) breast region localization, and (2) breast region decomposition to accomplish a robust mammary gland segmentation task on CT images. The first step detects two minimum bounding boxes of left and right breast regions, respectively, based on a machine-learning approach that adapts to a large variance of the breast appearances on different age levels. The second step divides the whole breast region in each side into mammary gland, fat tissue, and other regions by using spectral clustering technique that focuses on intra-region similarities of each patient and aims to overcome the image variance caused by different scan-parameters. The whole approach is designed as a simple structure with very minimum number of parameters to gain a superior robustness and computational efficiency for real clinical setting. We applied this approach to a dataset of 300 CT scans, which are sampled with the equal number from 30 to 50 years-old-women. Comparing to human annotations, the proposed approach can measure volume and quantify distributions of the CT numbers of mammary gland regions successfully. The experimental results demonstrated that the proposed approach achieves results consistent with manual annotations. Through our proposed framework, an efficient and effective low cost clinical screening scheme may be easily implemented to predict breast cancer risk, especially on those already acquired scans.

Volumetric breast density evaluation by ultrasound tomography and magnetic resonance imaging: a preliminary comparative study

NASA Astrophysics Data System (ADS)

Myc, Lukasz; Duric, Neb; Littrup, Peter; Li, Cuiping; Ranger, Bryan; Lupinacci, Jessica; Schmidt, Steven; Rama, Olsi; Bey-Knight, Lisa

2010-03-01

Since a 1976 study by Wolfe, high breast density has gained recognition as a factor strongly correlating with an increased incidence of breast cancer. These observations have led to mammographic density being designated a "risk factor" for breast cancer. Clinically, the exclusive reliance on mammography for breast density measurement has forestalled the inclusion of breast density into statistical risk models. This exclusion has in large part been due to the ionizing radiation associated with the method. Additionally, the use of mammography as valid tool for measuring a three dimensional characteristic (breast density) has been criticized for its prima facie incongruity. These shortfalls have prompted MRI studies of breast density as an alternative three-dimensional method of assessing breast density. Although, MRI is safe and can be used to measure volumetric density, its cost has prohibited its use in screening. Here, we report that sound speed measurements using a prototype ultrasound tomography device have potential for use as surrogates for breast density measurement. Accordingly, we report a strong positive linear correlation between volume-averaged sound speed of the breast and percent glandular tissue volume as assessed by MR.

Mammographic density estimation with automated volumetric breast density measurement.

PubMed

Ko, Su Yeon; Kim, Eun-Kyung; Kim, Min Jung; Moon, Hee Jung

2014-01-01

To compare automated volumetric breast density measurement (VBDM) with radiologists' evaluations based on the Breast Imaging Reporting and Data System (BI-RADS), and to identify the factors associated with technical failure of VBDM. In this study, 1129 women aged 19-82 years who underwent mammography from December 2011 to January 2012 were included. Breast density evaluations by radiologists based on BI-RADS and by VBDM (Volpara Version 1.5.1) were compared. The agreement in interpreting breast density between radiologists and VBDM was determined based on four density grades (D1, D2, D3, and D4) and a binary classification of fatty (D1-2) vs. dense (D3-4) breast using kappa statistics. The association between technical failure of VBDM and patient age, total breast volume, fibroglandular tissue volume, history of partial mastectomy, the frequency of mass > 3 cm, and breast density was analyzed. The agreement between breast density evaluations by radiologists and VBDM was fair (k value = 0.26) when the four density grades (D1/D2/D3/D4) were used and moderate (k value = 0.47) for the binary classification (D1-2/D3-4). Twenty-seven women (2.4%) showed failure of VBDM. Small total breast volume, history of partial mastectomy, and high breast density were significantly associated with technical failure of VBDM (p = 0.001 to 0.015). There is fair or moderate agreement in breast density evaluation between radiologists and VBDM. Technical failure of VBDM may be related to small total breast volume, a history of partial mastectomy, and high breast density.

Breast density in multiethnic women presenting for screening mammography.

PubMed

Oppong, Bridget A; Dash, Chiranjeev; O'Neill, Suzanne; Li, Yinan; Makambi, Kepher; Pien, Edward; Makariou, Erini; Coleman, Tesha; Adams-Campbell, Lucile L

2018-05-01

Data on ethnic variations in breast density are limited and often not inclusive of underrepresented minorities. As breast density is associated with elevated breast cancer risk, investigating racial and ethnic difference may elucidate the observed differences in breast cancer risk among different populations. We reviewed breast density from initial screening of women from the Capital Breast Care Center and Georgetown University Hospital from 2010 to 2014. Patient demographics including race, age at screening, education, menopausal status, and body mass index were abstracted. We recorded the BI-RADS density categories: (1) "fatty," (2) "scattered fibroglandular densities," (3) "heterogeneously dense," and (4) "extremely dense." Multivariable unconditional logistic regression was used to identify predictors of breast density. Density categorization was recorded for 2146 women over the 5-year period, comprising Blacks (n = 940), Hispanics (n = 893), and Whites (n = 314). Analysis of subject characteristics by breast density showed that high category is observed in younger, Hispanic, nulliparous, premenopausal, and nonobese women (t-test or chi-square test, P-values <.0001). Obese women are 70% less likely to have high density. Being Hispanic, premenopausal, and nonobese were predictive of high density on logistic regression. In this analysis of density distribution in a diverse sample, Hispanic women have the highest breast density, followed by Blacks and Whites. Unique in our findings is women who identify as Hispanic have the highest breast density and lower rates of obesity. Further investigation of the impact of obesity on breast density, especially in the understudied Hispanic group is needed. © 2017 Wiley Periodicals, Inc.

Breast Density Estimation with Fully Automated Volumetric Method: Comparison to Radiologists' Assessment by BI-RADS Categories.

PubMed

Singh, Tulika; Sharma, Madhurima; Singla, Veenu; Khandelwal, Niranjan

2016-01-01

The objective of our study was to calculate mammographic breast density with a fully automated volumetric breast density measurement method and to compare it to breast imaging reporting and data system (BI-RADS) breast density categories assigned by two radiologists. A total of 476 full-field digital mammography examinations with standard mediolateral oblique and craniocaudal views were evaluated by two blinded radiologists and BI-RADS density categories were assigned. Using a fully automated software, mean fibroglandular tissue volume, mean breast volume, and mean volumetric breast density were calculated. Based on percentage volumetric breast density, a volumetric density grade was assigned from 1 to 4. The weighted overall kappa was 0.895 (almost perfect agreement) for the two radiologists' BI-RADS density estimates. A statistically significant difference was seen in mean volumetric breast density among the BI-RADS density categories. With increased BI-RADS density category, increase in mean volumetric breast density was also seen (P < 0.001). A significant positive correlation was found between BI-RADS categories and volumetric density grading by fully automated software (ρ = 0.728, P < 0.001 for first radiologist and ρ = 0.725, P < 0.001 for second radiologist). Pairwise estimates of the weighted kappa between Volpara density grade and BI-RADS density category by two observers showed fair agreement (κ = 0.398 and 0.388, respectively). In our study, a good correlation was seen between density grading using fully automated volumetric method and density grading using BI-RADS density categories assigned by the two radiologists. Thus, the fully automated volumetric method may be used to quantify breast density on routine mammography. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

New method for predicting estrogen receptor status utilizing breast MRI texture kinetic analysis

NASA Astrophysics Data System (ADS)

Chaudhury, Baishali; Hall, Lawrence O.; Goldgof, Dmitry B.; Gatenby, Robert A.; Gillies, Robert; Drukteinis, Jennifer S.

2014-03-01

Magnetic Resonance Imaging (MRI) of breast cancer typically shows that tumors are heterogeneous with spatial variations in blood flow and cell density. Here, we examine the potential link between clinical tumor imaging and the underlying evolutionary dynamics behind heterogeneity in the cellular expression of estrogen receptors (ER) in breast cancer. We assume, in an evolutionary environment, that ER expression will only occur in the presence of significant concentrations of estrogen, which is delivered via the blood stream. Thus, we hypothesize, the expression of ER in breast cancer cells will correlate with blood flow on gadolinium enhanced breast MRI. To test this hypothesis, we performed quantitative analysis of blood flow on dynamic contrast enhanced MRI (DCE-MRI) and correlated it with the ER status of the tumor. Here we present our analytic methods, which utilize a novel algorithm to analyze 20 volumetric DCE-MRI breast cancer tumors. The algorithm generates post initial enhancement (PIE) maps from DCE-MRI and then performs texture features extraction from the PIE map, feature selection, and finally classification of tumors into ER positive and ER negative status. The combined gray level co-occurrence matrices, gray level run length matrices and local binary pattern histogram features allow quantification of breast tumor heterogeneity. The algorithm predicted ER expression with an accuracy of 85% using a Naive Bayes classifier in leave-one-out cross-validation. Hence, we conclude that our data supports the hypothesis that imaging characteristics can, through application of evolutionary principles, provide insights into the cellular and molecular properties of cancer cells.

Minimal impact of adjuvant exemestane or tamoxifen treatment on mammographic breast density in postmenopausal breast cancer patients: a Dutch TEAM trial analysis.

PubMed

van Nes, Johanna G H; Beex, Louk V A M; Seynaeve, Caroline; Putter, Hein; Sramek, Alexandr; Lardenoije, Susanne; Duijm-de Carpentier, Marjolijn; Van Rongen, Inge; Nortier, Johan W R; Zonderland, Harmien M; van de Velde, Cornelis J H

2015-03-01

Mammographic breast density is one of the strongest independent risk factors for developing breast cancer. We examined the effect of exemestane and tamoxifen on breast density in Dutch postmenopausal early breast cancer patients participating in the Tamoxifen Exemestane Adjuvant Multinational (TEAM) trial. Analogue mammograms of selected TEAM participants before start, and after one and two (and if available after three) years of adjuvant endocrine therapy were collected centrally and reviewed. Study endpoints were change in breast density over time, and correlations between breast density and locoregional recurrence (LRR), distance recurrence (DR), and contralateral breast cancer (CBC). Mammograms of 378 patients (181 tamoxifen, 197 exemestane) were included in the current per protocol analyses. Baseline breast density was low (breast density score<50% in 75% of patients) and not different between patients randomised to exemestane or tamoxifen (coefficient 0.16, standard error 0.17). Breast density did not change during treatment in exemestane (p=0.25) or tamoxifen users (p=0.59). No relation was observed between breast density and the occurrence of a LRR [hazards ratio (HR) 0.87, 95% CI 0.45-1.68, p=0.67], a DR (HR 1.02, 95% CI 0.77-1.35, p=0.90), or CBC (HR 1.31, 95% CI 0.63-2.72, p=0.48). The in general low breast density score in early postmenopausal breast cancer patients did not substantially change over time, and this pattern was not different between tamoxifen and exemestane users. Breast density was not a predictive marker for efficacy of adjuvant endocrine therapy.

Impact of the California breast density law on primary care physicians.

PubMed

Khong, Kathleen A; Hargreaves, Jonathan; Aminololama-Shakeri, Shadi; Lindfors, Karen K

2015-03-01

To investigate primary physician awareness of the California Breast Density Notification Law and its impact on primary care practice. An online survey was distributed to 174 physicians within a single primary care network system 10 months after California's breast density notification law took effect. The survey assessed physicians' awareness of the law, perceived changes in patient levels of concern about breast density, and physician comfort levels in handling breast density management issues. The survey was completed by 77 physicians (45%). Roughly half of those surveyed (49%) reported no knowledge of the breast density notification legislation. Only 32% of respondents noted an increase in patient levels of concern about breast density compared to prior years. The majority were only "somewhat comfortable" (55%) or "not comfortable" (12%) with breast density questions, and almost one-third (32%) had referred patients to a breast health clinic for these discussions. A total of 75% of those surveyed would be interested in more specific education on the subject. Awareness among primary care clinicians of the California Breast Density Notification Law is low, and many do not feel comfortable answering breast density-related patient questions. Breast imagers and institutions may need to devote additional time and resources to primary physician education in order for density notification laws to have significant impact on patient care. Copyright © 2015 American College of Radiology. Published by Elsevier Inc. All rights reserved.

One vs. Two Breast Density Measures to Predict 5- and 10- Year Breast Cancer Risk

PubMed Central

Kerlikowske, Karla; Gard, Charlotte C.; Sprague, Brian L.; Tice, Jeffrey A.; Miglioretti, Diana L.

2015-01-01

Background One measure of Breast Imaging Reporting and Data System (BI-RADS) breast density improves 5-year breast cancer risk prediction, but the value of sequential measures is unknown. We determined if two BI-RADS density measures improves the predictive accuracy of the Breast Cancer Surveillance Consortium 5-year risk model compared to one measure. Methods We included 722,654 women aged 35–74 years with two mammograms with BI-RADS density measures on average 1.8 years apart; 13,715 developed invasive breast cancer. We used Cox regression to estimate the relative hazards of breast cancer for age, race/ethnicity, family history of breast cancer, history of breast biopsy, and one or two density measures. We developed a risk prediction model by combining these estimates with 2000–2010 Surveillance, Epidemiology, and End Results incidence and 2010 vital statistics for competing risk of death. Results The two-measure density model had marginally greater discriminatory accuracy than the one-measure model (AUC=0.640 vs. 0.635). Of 18.6% of women (134,404/722,654) who decreased density categories, 15.4% (20,741/134,404) of women whose density decreased from heterogeneously or extremely dense to a lower density category with one other risk factor had a clinically meaningful increase in 5-year risk from <1.67% with the one-density model to ≥1.67% with the two-density model. Conclusion The two-density model has similar overall discrimination to the one-density model for predicting 5-year breast cancer risk and improves risk classification for women with risk factors and a decrease in density. Impact A two-density model should be considered for women whose density decreases when calculating breast cancer risk. PMID:25824444

One versus Two Breast Density Measures to Predict 5- and 10-Year Breast Cancer Risk.

PubMed

Kerlikowske, Karla; Gard, Charlotte C; Sprague, Brian L; Tice, Jeffrey A; Miglioretti, Diana L

2015-06-01

One measure of Breast Imaging Reporting and Data System (BI-RADS) breast density improves 5-year breast cancer risk prediction, but the value of sequential measures is unknown. We determined whether two BI-RADS density measures improve the predictive accuracy of the Breast Cancer Surveillance Consortium 5-year risk model compared with one measure. We included 722,654 women of ages 35 to 74 years with two mammograms with BI-RADS density measures on average 1.8 years apart; 13,715 developed invasive breast cancer. We used Cox regression to estimate the relative hazards of breast cancer for age, race/ethnicity, family history of breast cancer, history of breast biopsy, and one or two density measures. We developed a risk prediction model by combining these estimates with 2000-2010 Surveillance, Epidemiology, and End Results incidence and 2010 vital statistics for competing risk of death. The two-measure density model had marginally greater discriminatory accuracy than the one-measure model (AUC, 0.640 vs. 0.635). Of 18.6% of women (134,404 of 722,654) who decreased density categories, 15.4% (20,741 of 134,404) of women whose density decreased from heterogeneously or extremely dense to a lower density category with one other risk factor had a clinically meaningful increase in 5-year risk from <1.67% with the one-density model to ≥1.67% with the two-density model. The two-density model has similar overall discrimination to the one-density model for predicting 5-year breast cancer risk and improves risk classification for women with risk factors and a decrease in density. A two-density model should be considered for women whose density decreases when calculating breast cancer risk. ©2015 American Association for Cancer Research.

Estimation of effective x-ray tissue attenuation differences for volumetric breast density measurement

NASA Astrophysics Data System (ADS)

Chen, Biao; Ruth, Chris; Jing, Zhenxue; Ren, Baorui; Smith, Andrew; Kshirsagar, Ashwini

2014-03-01

Breast density has been identified to be a risk factor of developing breast cancer and an indicator of lesion diagnostic obstruction due to masking effect. Volumetric density measurement evaluates fibro-glandular volume, breast volume, and breast volume density measures that have potential advantages over area density measurement in risk assessment. One class of volume density computing methods is based on the finding of the relative fibro-glandular tissue attenuation with regards to the reference fat tissue, and the estimation of the effective x-ray tissue attenuation differences between the fibro-glandular and fat tissue is key to volumetric breast density computing. We have modeled the effective attenuation difference as a function of actual x-ray skin entrance spectrum, breast thickness, fibro-glandular tissue thickness distribution, and detector efficiency. Compared to other approaches, our method has threefold advantages: (1) avoids the system calibration-based creation of effective attenuation differences which may introduce tedious calibrations for each imaging system and may not reflect the spectrum change and scatter induced overestimation or underestimation of breast density; (2) obtains the system specific separate and differential attenuation values of fibroglandular and fat for each mammographic image; and (3) further reduces the impact of breast thickness accuracy to volumetric breast density. A quantitative breast volume phantom with a set of equivalent fibro-glandular thicknesses has been used to evaluate the volume breast density measurement with the proposed method. The experimental results have shown that the method has significantly improved the accuracy of estimating breast density.

Dose-dependent effect of mammographic breast density on the risk of contralateral breast cancer.

PubMed

Chowdhury, Marzana; Euhus, David; O'Donnell, Maureen; Onega, Tracy; Choudhary, Pankaj K; Biswas, Swati

2018-07-01

Increased mammographic breast density is a significant risk factor for breast cancer. It is not clear if it is also a risk factor for the development of contralateral breast cancer. The data were obtained from Breast Cancer Surveillance Consortium and included women diagnosed with invasive breast cancer or ductal carcinoma in situ between ages 18 and 88 and years 1995 and 2009. Each case of contralateral breast cancer was matched with three controls based on year of first breast cancer diagnosis, race, and length of follow-up. A total of 847 cases and 2541 controls were included. The risk factors included in the study were mammographic breast density, age of first breast cancer diagnosis, family history of breast cancer, anti-estrogen treatment, hormone replacement therapy, menopausal status, and estrogen receptor status, all from the time of first breast cancer diagnosis. Both univariate analysis and multivariate conditional logistic regression analysis were performed. In the final multivariate model, breast density, family history of breast cancer, and anti-estrogen treatment remained significant with p values less than 0.01. Increasing breast density had a dose-dependent effect on the risk of contralateral breast cancer. Relative to 'almost entirely fat' category of breast density, the adjusted odds ratios (and p values) in the multivariate analysis for 'scattered density,' 'heterogeneously dense,' and 'extremely dense' categories were 1.65 (0.036), 2.10 (0.002), and 2.32 (0.001), respectively. Breast density is an independent and significant risk factor for development of contralateral breast cancer. This risk factor should contribute to clinical decision making.

Benign Breast Disease, Mammographic Breast Density, and the Risk of Breast Cancer

PubMed Central

2013-01-01

Background Benign breast disease and high breast density are prevalent, strong risk factors for breast cancer. Women with both risk factors may be at very high risk. Methods We included 42818 women participating in the Breast Cancer Surveillance Consortium who had no prior diagnosis of breast cancer and had undergone at least one benign breast biopsy and mammogram; 1359 women developed incident breast cancer in 6.1 years of follow-up (78.1% invasive, 21.9% ductal carcinoma in situ). We calculated hazard ratios (HRs) using Cox regression analysis. The referent group was women with nonproliferative changes and average density. All P values are two-sided. Results Benign breast disease and breast density were independently associated with breast cancer. The combination of atypical hyperplasia and very high density was uncommon (0.6% of biopsies) but was associated with the highest risk for breast cancer (HR = 5.34; 95% confidence interval [CI] = 3.52 to 8.09, P < .001). Proliferative disease without atypia (25.6% of biopsies) was associated with elevated risk that varied little across levels of density: average (HR = 1.37; 95% CI = 1.11 to 1.69, P = .003), high (HR = 2.02; 95% CI = 1.68 to 2.44, P < .001), or very high (HR = 2.05; 95% CI = 1.54 to 2.72, P < .001). Low breast density (4.5% of biopsies) was associated with low risk (HRs <1) for all benign pathology diagnoses. Conclusions Women with high breast density and proliferative benign breast disease are at very high risk for future breast cancer. Women with low breast density are at low risk, regardless of their benign pathologic diagnosis. PMID:23744877

Benign breast disease, mammographic breast density, and the risk of breast cancer.

PubMed

Tice, Jeffrey A; O'Meara, Ellen S; Weaver, Donald L; Vachon, Celine; Ballard-Barbash, Rachel; Kerlikowske, Karla

2013-07-17

Benign breast disease and high breast density are prevalent, strong risk factors for breast cancer. Women with both risk factors may be at very high risk. We included 42818 women participating in the Breast Cancer Surveillance Consortium who had no prior diagnosis of breast cancer and had undergone at least one benign breast biopsy and mammogram; 1359 women developed incident breast cancer in 6.1 years of follow-up (78.1% invasive, 21.9% ductal carcinoma in situ). We calculated hazard ratios (HRs) using Cox regression analysis. The referent group was women with nonproliferative changes and average density. All P values are two-sided. Benign breast disease and breast density were independently associated with breast cancer. The combination of atypical hyperplasia and very high density was uncommon (0.6% of biopsies) but was associated with the highest risk for breast cancer (HR = 5.34; 95% confidence interval [CI] = 3.52 to 8.09, P < .001). Proliferative disease without atypia (25.6% of biopsies) was associated with elevated risk that varied little across levels of density: average (HR = 1.37; 95% CI = 1.11 to 1.69, P = .003), high (HR = 2.02; 95% CI = 1.68 to 2.44, P < .001), or very high (HR = 2.05; 95% CI = 1.54 to 2.72, P < .001). Low breast density (4.5% of biopsies) was associated with low risk (HRs <1) for all benign pathology diagnoses. Women with high breast density and proliferative benign breast disease are at very high risk for future breast cancer. Women with low breast density are at low risk, regardless of their benign pathologic diagnosis.

Mammographic Density Estimation with Automated Volumetric Breast Density Measurement

PubMed Central

Ko, Su Yeon; Kim, Eun-Kyung; Kim, Min Jung

2014-01-01

Objective To compare automated volumetric breast density measurement (VBDM) with radiologists' evaluations based on the Breast Imaging Reporting and Data System (BI-RADS), and to identify the factors associated with technical failure of VBDM. Materials and Methods In this study, 1129 women aged 19-82 years who underwent mammography from December 2011 to January 2012 were included. Breast density evaluations by radiologists based on BI-RADS and by VBDM (Volpara Version 1.5.1) were compared. The agreement in interpreting breast density between radiologists and VBDM was determined based on four density grades (D1, D2, D3, and D4) and a binary classification of fatty (D1-2) vs. dense (D3-4) breast using kappa statistics. The association between technical failure of VBDM and patient age, total breast volume, fibroglandular tissue volume, history of partial mastectomy, the frequency of mass > 3 cm, and breast density was analyzed. Results The agreement between breast density evaluations by radiologists and VBDM was fair (k value = 0.26) when the four density grades (D1/D2/D3/D4) were used and moderate (k value = 0.47) for the binary classification (D1-2/D3-4). Twenty-seven women (2.4%) showed failure of VBDM. Small total breast volume, history of partial mastectomy, and high breast density were significantly associated with technical failure of VBDM (p = 0.001 to 0.015). Conclusion There is fair or moderate agreement in breast density evaluation between radiologists and VBDM. Technical failure of VBDM may be related to small total breast volume, a history of partial mastectomy, and high breast density. PMID:24843235

Racial Differences in Quantitative Measures of Area and Volumetric Breast Density

PubMed Central

McCarthy, Anne Marie; Keller, Brad M.; Pantalone, Lauren M.; Hsieh, Meng-Kang; Synnestvedt, Marie; Conant, Emily F.; Armstrong, Katrina; Kontos, Despina

2016-01-01

Abstract Background: Increased breast density is a strong risk factor for breast cancer and also decreases the sensitivity of mammographic screening. The purpose of our study was to compare breast density for black and white women using quantitative measures. Methods: Breast density was assessed among 5282 black and 4216 white women screened using digital mammography. Breast Imaging-Reporting and Data System (BI-RADS) density was obtained from radiologists’ reports. Quantitative measures for dense area, area percent density (PD), dense volume, and volume percent density were estimated using validated, automated software. Breast density was categorized as dense or nondense based on BI-RADS categories or based on values above and below the median for quantitative measures. Logistic regression was used to estimate the odds of having dense breasts by race, adjusted for age, body mass index (BMI), age at menarche, menopause status, family history of breast or ovarian cancer, parity and age at first birth, and current hormone replacement therapy (HRT) use. All statistical tests were two-sided. Results: There was a statistically significant interaction of race and BMI on breast density. After accounting for age, BMI, and breast cancer risk factors, black women had statistically significantly greater odds of high breast density across all quantitative measures (eg, PD nonobese odds ratio [OR] = 1.18, 95% confidence interval [CI] = 1.02 to 1.37, P = .03, PD obese OR = 1.26, 95% CI = 1.04 to 1.53, P = .02). There was no statistically significant difference in BI-RADS density by race. Conclusions: After accounting for age, BMI, and other risk factors, black women had higher breast density than white women across all quantitative measures previously associated with breast cancer risk. These results may have implications for risk assessment and screening. PMID:27130893

Correlation between quantified breast densities from digital mammography and 18F-FDG PET uptake.

PubMed

Lakhani, Paras; Maidment, Andrew D A; Weinstein, Susan P; Kung, Justin W; Alavi, Abass

2009-01-01

To correlate breast density quantified from digital mammograms with mean and maximum standardized uptake values (SUVs) from positron emission tomography (PET). This was a prospective study that included 56 women with a history of suspicion of breast cancer (mean age 49.2 +/- 9.3 years), who underwent 18F-fluoro-2-deoxyglucose (FDG)-PET imaging of their breasts as well as digital mammography. A computer thresholding algorithm was applied to the contralateral nonmalignant breasts to quantitatively estimate the breast density on digital mammograms. The breasts were also classified into one of four Breast Imaging Reporting and Data System categories for density. Comparisons between SUV and breast density were made using linear regression and the Student's t-test. Linear regression of mean SUV versus average breast density showed a positive relationship with a Pearson's correlation coefficient of R(2) = 0.83. The quantified breast densities and mean SUVs were significantly greater for mammographically dense than nondense breasts (p < 0.0001 for both). The average quantified densities and mean SUVs of the breasts were significantly greater for premenopausal than postmenopausal patients (p < 0.05). 8/51 (16%) of the patients had maximum SUVs that equaled 1.6 or greater. There is a positive linear correlation between quantified breast density on digital mammography and FDG uptake on PET. Menopausal status affects the metabolic activity of normal breast tissue, resulting in higher SUVs in pre- versus postmenopausal patients.

Mammographic density and breast cancer risk in breast screening assessment cases and women with a family history of breast cancer.

PubMed

Duffy, Stephen W; Morrish, Oliver W E; Allgood, Prue C; Black, Richard; Gillan, Maureen G C; Willsher, Paula; Cooke, Julie; Duncan, Karen A; Michell, Michael J; Dobson, Hilary M; Maroni, Roberta; Lim, Yit Y; Purushothaman, Hema N; Suaris, Tamara; Astley, Susan M; Young, Kenneth C; Tucker, Lorraine; Gilbert, Fiona J

2018-01-01

Mammographic density has been shown to be a strong independent predictor of breast cancer and a causative factor in reducing the sensitivity of mammography. There remain questions as to the use of mammographic density information in the context of screening and risk management, and of the association with cancer in populations known to be at increased risk of breast cancer. To assess the association of breast density with presence of cancer by measuring mammographic density visually as a percentage, and with two automated volumetric methods, Quantra™ and VolparaDensity™. The TOMosynthesis with digital MammographY (TOMMY) study of digital breast tomosynthesis in the Breast Screening Programme of the National Health Service (NHS) of the United Kingdom (UK) included 6020 breast screening assessment cases (of whom 1158 had breast cancer) and 1040 screened women with a family history of breast cancer (of whom two had breast cancer). We assessed the association of each measure with breast cancer risk in these populations at enhanced risk, using logistic regression adjusted for age and total breast volume as a surrogate for body mass index (BMI). All density measures showed a positive association with presence of cancer and all declined with age. The strongest effect was seen with Volpara absolute density, with a significant 3% (95% CI 1-5%) increase in risk per 10 cm 3 of dense tissue. The effect of Volpara volumetric density on risk was stronger for large and grade 3 tumours. Automated absolute breast density is a predictor of breast cancer risk in populations at enhanced risk due to either positive mammographic findings or family history. In the screening context, density could be a trigger for more intensive imaging. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Multi-modal diffuse optical techniques for breast cancer neoadjuvant chemotherapy monitoring (Conference Presentation)

NASA Astrophysics Data System (ADS)

Cochran, Jeffrey M.; Busch, David R.; Ban, Han Y.; Kavuri, Venkaiah C.; Schweiger, Martin J.; Arridge, Simon R.; Yodh, Arjun G.

2017-02-01

We present high spatial density, multi-modal, parallel-plate Diffuse Optical Tomography (DOT) imaging systems for the purpose of breast tumor detection. One hybrid instrument provides time domain (TD) and continuous wave (CW) DOT at 64 source fiber positions. The TD diffuse optical spectroscopy with PMT- detection produces low-resolution images of absolute tissue scattering and absorption while the spatially dense array of CCD-coupled detector fibers (108 detectors) provides higher-resolution CW images of relative tissue optical properties. Reconstruction of the tissue optical properties, along with total hemoglobin concentration and tissue oxygen saturation, is performed using the TOAST software suite. Comparison of the spatially-dense DOT images and MR images allows for a robust validation of DOT against an accepted clinical modality. Additionally, the structural information from co-registered MR images is used as a spatial prior to improve the quality of the functional optical images and provide more accurate quantification of the optical and hemodynamic properties of tumors. We also present an optical-only imaging system that provides frequency domain (FD) DOT at 209 source positions with full CCD detection and incorporates optical fringe projection profilometry to determine the breast boundary. This profilometry serves as a spatial constraint, improving the quality of the DOT reconstructions while retaining the benefits of an optical-only device. We present initial images from both human subjects and phantoms to display the utility of high spatial density data and multi-modal information in DOT reconstruction with the two systems.

TU-CD-207-02: Quantification of Breast Lesion Compositions Using Low-Dose Spectral Mammography: A Feasibility Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cho, H; Ding, H; Sennung, D

2015-06-15

Purpose: To investigate the feasibility of measuring breast lesion composition with spectral mammography using physical phantoms and bovine tissue. Methods: Phantom images were acquired with a spectral mammography system with a silicon-strip based photon-counting detector. Plastic water and adipose-equivalent phantoms were used to calibrate the system for dual-energy material decomposition. The calibration phantom was constructed in range of 2–8 cm thickness and water densities in the range of 0% to 100%. A non-linear rational fitting function was used to calibrate the imaging system. The phantom studies were performed with uniform background phantom and non-uniform background phantom. The breast lesion phantomsmore » (2 cm in diameter and 0.5 cm in thickness) were made with water densities ranging from 0 to 100%. The lesion phantoms were placed in different positions and depths on the phantoms to investigate the accuracy of the measurement under various conditions. The plastic water content of the lesion was measured by subtracting the total decomposed plastic water signal from a surrounding 2.5 mm thick border outside the lesion. In addition, bovine tissue samples composed of 80 % lean were imaged as background for the simulated lesion phantoms. Results: The thickness of measured and known water contents was compared. The rootmean-square (RMS) errors in water thickness measurements were 0.01 cm for the uniform background phantom, 0.04 cm for non-uniform background phantom, and 0.03 cm for 80% lean bovine tissue background. Conclusion: The results indicate that the proposed technique using spectral mammography can be used to accurately characterize breast lesion compositions.« less

Concentration of Trichloroethylene in Breast Milk and Household Water from Nogales, Arizona

PubMed Central

Beamer, Paloma I.; Luik, Catherine E.; Abrell, Leif; Campos, Swilma; Martínez, María Elena; Sáez, A. Eduardo

2013-01-01

The United States Environmental Protection Agency has identified quantification of trichloroethylene (TCE), an industrial solvent, in breast milk as a high priority need for risk assessment. Water and milk samples were collected from 20 households by a lactation consultant in Nogales, Arizona. Separate water samples (including tap, bottled and vending machine) were collected for all household uses: drinking, bathing, cooking, and laundry. A risk factor questionnaire was administered. Liquid-liquid extraction with diethyl ether was followed by GC-MS for TCE quantification in water. Breast milk underwent homogenization, lipid hydrolysis and centrifugation prior to extraction. The limit of detection was 1.5 ng/mL. TCE was detected in 7 of 20 mothers’ breast milk samples. The maximum concentration was 6 ng/mL. TCE concentration in breast milk was significantly correlated with the concentration in water used for bathing (ρ=0.59, p=0.008). Detection of TCE in breast milk was more likely if the infant had a body mass index <14 (RR=5.2, p=0.02). Based on average breast milk consumption, TCE intake for 5% of the infants may exceed the proposed US EPA Reference Dose. Results of this exploratory study warrant more in depth studies to understand risk of TCE exposures from breast milk intake. PMID:22827160

Screen-detected versus interval cancers: Effect of imaging modality and breast density in the Flemish Breast Cancer Screening Programme.

PubMed

Timmermans, Lore; Bleyen, Luc; Bacher, Klaus; Van Herck, Koen; Lemmens, Kim; Van Ongeval, Chantal; Van Steen, Andre; Martens, Patrick; De Brabander, Isabel; Goossens, Mathieu; Thierens, Hubert

2017-09-01

To investigate if direct radiography (DR) performs better than screen-film mammography (SF) and computed radiography (CR) in dense breasts in a decentralized organised Breast Cancer Screening Programme. To this end, screen-detected versus interval cancers were studied in different BI-RADS density classes for these imaging modalities. The study cohort consisted of 351,532 women who participated in the Flemish Breast Cancer Screening Programme in 2009 and 2010. Information on screen-detected and interval cancers, breast density scores of radiologist second readers, and imaging modality was obtained by linkage of the databases of the Centre of Cancer Detection and the Belgian Cancer Registry. Overall, 67% of occurring breast cancers are screen detected and 33% are interval cancers, with DR performing better than SF and CR. The interval cancer rate increases gradually with breast density, regardless of modality. In the high-density class, the interval cancer rate exceeds the cancer detection rate for SF and CR, but not for DR. DR is superior to SF and CR with respect to cancer detection rates for high-density breasts. To reduce the high interval cancer rate in dense breasts, use of an additional imaging technique in screening can be taken into consideration. • Interval cancer rate increases gradually with breast density, regardless of modality. • Cancer detection rate in high-density breasts is superior in DR. • IC rate exceeds CDR for SF and CR in high-density breasts. • DR performs better in high-density breasts for third readings and false-positives.

Comparison of subjective and fully automated methods for measuring mammographic density.

PubMed

Moshina, Nataliia; Roman, Marta; Sebuødegård, Sofie; Waade, Gunvor G; Ursin, Giske; Hofvind, Solveig

2018-02-01

Background Breast radiologists of the Norwegian Breast Cancer Screening Program subjectively classified mammographic density using a three-point scale between 1996 and 2012 and changed into the fourth edition of the BI-RADS classification since 2013. In 2015, an automated volumetric breast density assessment software was installed at two screening units. Purpose To compare volumetric breast density measurements from the automated method with two subjective methods: the three-point scale and the BI-RADS density classification. Material and Methods Information on subjective and automated density assessment was obtained from screening examinations of 3635 women recalled for further assessment due to positive screening mammography between 2007 and 2015. The score of the three-point scale (I = fatty; II = medium dense; III = dense) was available for 2310 women. The BI-RADS density score was provided for 1325 women. Mean volumetric breast density was estimated for each category of the subjective classifications. The automated software assigned volumetric breast density to four categories. The agreement between BI-RADS and volumetric breast density categories was assessed using weighted kappa (k w ). Results Mean volumetric breast density was 4.5%, 7.5%, and 13.4% for categories I, II, and III of the three-point scale, respectively, and 4.4%, 7.5%, 9.9%, and 13.9% for the BI-RADS density categories, respectively ( P for trend < 0.001 for both subjective classifications). The agreement between BI-RADS and volumetric breast density categories was k w  = 0.5 (95% CI = 0.47-0.53; P < 0.001). Conclusion Mean values of volumetric breast density increased with increasing density category of the subjective classifications. The agreement between BI-RADS and volumetric breast density categories was moderate.

Comparison of breast density measurements made using ultrasound tomography and mammography

NASA Astrophysics Data System (ADS)

Sak, Mark; Duric, Neb; Littrup, Peter; Bey-Knight, Lisa; Krycia, Mark; Sherman, Mark E.; Boyd, Norman; Gierach, Gretchen L.

2015-03-01

Women with elevated mammographic percent density, defined as the ratio of fibroglandular tissue area to total breast area on a mammogram are at an increased risk of developing breast cancer. Ultrasound tomography (UST) is an imaging modality that can create tomographic sound speed images of a patient's breast, which can then be used to measure breast density. These sound speed images are useful because physical tissue density is directly proportional to sound speed. The work presented here updates previous results that compared mammographic breast density measurements with UST breast density measurements within an ongoing study. The current analysis has been expanded to include 158 women with negative digital mammographic screens who then underwent a breast UST scan. Breast density was measured for both imaging modalities and preliminary analysis demonstrated strong and positive correlations (Spearman correlation coefficient rs = 0.703). Additional mammographic and UST related imaging characteristics were also analyzed and used to compare the behavior of both imaging modalities. Results suggest that UST can be used among women with negative mammographic screens as a quantitative marker of breast density that may avert shortcomings of mammography.

Association between mammogram density and background parenchymal enhancement of breast MRI

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Danala, Gopichandh; Wang, Yunzhi; Zarafshani, Ali; Qian, Wei; Liu, Hong; Zheng, Bin

2018-02-01

Breast density has been widely considered as an important risk factor for breast cancer. The purpose of this study is to examine the association between mammogram density results and background parenchymal enhancement (BPE) of breast MRI. A dataset involving breast MR images was acquired from 65 high-risk women. Based on mammography density (BIRADS) results, the dataset was divided into two groups of low and high breast density cases. The Low-Density group has 15 cases with mammographic density (BIRADS 1 and 2), while the High-density group includes 50 cases, which were rated by radiologists as mammographic density BIRADS 3 and 4. A computer-aided detection (CAD) scheme was applied to segment and register breast regions depicted on sequential images of breast MRI scans. CAD scheme computed 20 global BPE features from the entire two breast regions, separately from the left and right breast region, as well as from the bilateral difference between left and right breast regions. An image feature selection method namely, CFS method, was applied to remove the most redundant features and select optimal features from the initial feature pool. Then, a logistic regression classifier was built using the optimal features to predict the mammogram density from the BPE features. Using a leave-one-case-out validation method, the classifier yields the accuracy of 82% and area under ROC curve, AUC=0.81+/-0.09. Also, the box-plot based analysis shows a negative association between mammogram density results and BPE features in the MRI images. This study demonstrated a negative association between mammogram density and BPE of breast MRI images.

Comparison of Visual Assessment of Breast Density in BI-RADS 4th and 5th Editions With Automated Volumetric Measurement.

PubMed

Youk, Ji Hyun; Kim, So Jung; Son, Eun Ju; Gweon, Hye Mi; Kim, Jeong-Ah

2017-09-01

The purpose of this study was to compare visual assessments of mammographic breast density by radiologists using BI-RADS 4th and 5th editions in correlation with automated volumetric breast density measurements. A total of 337 consecutive full-field digital mammographic examinations with standard views were retrospectively assessed by two radiologists for mammographic breast density according to BI-RADS 4th and 5th editions. Fully automated measurement of the volume of fibroglandular tissue and total breast and percentage breast density was performed with a commercially available software program. Interobserver and intraobserver agreement was assessed with kappa statistics. The distributions of breast density categories for both editions of BI-RADS were compared and correlated with volumetric data. Interobserver agreement on breast density category was moderate to substantial (κ = 0.58-0.63) with use of BI-RADS 4th edition and substantial (κ = 0.63-0.66) with use of the 5th edition but without significant difference between the two editions. For intraobserver agreement between the two editions, the distributions of density category were significantly different (p < 0.0001), the proportions of dense breast increased, and the proportion of fatty breast decreased with use of the 5th edition compared with the 4th edition (p < 0.0001). All volumetric breast density data, including percentage breast density, were significantly different among density categories (p < 0.0001) and had significant correlation with visual assessment for both editions of BI-RADS (p < 0.01). Assessment using BI-RADS 5th edition revealed a higher proportion of dense breast than assessment using BI-RADS 4th edition. Nevertheless, automated volumetric density assessment had good correlation with visual assessment for both editions of BI-RADS.

Sentinel Lymph Node Biopsy: Quantification of Lymphedema Risk Reduction

DTIC Science & Technology

2006-10-01

dimensional internal mammary lymphoscintigraphy: implications for radiation therapy treatment planning for breast carcinoma. Int J Radiat Oncol Biol Phys...techniques based on conventional photon beams, intensity modulated photon beams and proton beams for therapy of intact breast. Radiother Oncol. Feb...Harris JR. Three-dimensional internal mammary lymphoscintigraphy: implications for radiation therapy treatment planning for breast carcinoma. Int J

Imaging Breast Density: Established and Emerging Modalities1

PubMed Central

Chen, Jeon-Hor; Gulsen, Gultekin; Su, Min-Ying

2015-01-01

Mammographic density has been proven as an independent risk factor for breast cancer. Women with dense breast tissue visible on a mammogram have a much higher cancer risk than women with little density. A great research effort has been devoted to incorporate breast density into risk prediction models to better estimate each individual’s cancer risk. In recent years, the passage of breast density notification legislation in many states in USA requires that every mammography report should provide information regarding the patient’s breast density. Accurate definition and measurement of breast density are thus important, which may allow all the potential clinical applications of breast density to be implemented. Because the two-dimensional mammography-based measurement is subject to tissue overlapping and thus not able to provide volumetric information, there is an urgent need to develop reliable quantitative measurements of breast density. Various new imaging technologies are being developed. Among these new modalities, volumetric mammographic density methods and three-dimensional magnetic resonance imaging are the most well studied. Besides, emerging modalities, including different x-ray–based, optical imaging, and ultrasound-based methods, have also been investigated. All these modalities may either overcome some fundamental problems related to mammographic density or provide additional density and/or compositional information. The present review article aimed to summarize the current established and emerging imaging techniques for the measurement of breast density and the evidence of the clinical use of these density methods from the literature. PMID:26692524

Personalizing mammography by breast density and other risk factors for breast cancer: analysis of health benefits and cost-effectiveness.

PubMed

Schousboe, John T; Kerlikowske, Karla; Loh, Andrew; Cummings, Steven R

2011-07-05

Current guidelines recommend mammography every 1 or 2 years starting at age 40 or 50 years, regardless of individual risk for breast cancer. To estimate the cost-effectiveness of mammography by age, breast density, history of breast biopsy, family history of breast cancer, and screening interval. Markov microsimulation model. Surveillance, Epidemiology, and End Results program, Breast Cancer Surveillance Consortium, and the medical literature. U.S. women aged 40 to 49, 50 to 59, 60 to 69, and 70 to 79 years with initial mammography at age 40 years and breast density of Breast Imaging Reporting and Data System (BI-RADS) categories 1 to 4. Lifetime. National health payer. Mammography annually, biennially, or every 3 to 4 years or no mammography. Costs per quality-adjusted life-year (QALY) gained and number of women screened over 10 years to prevent 1 death from breast cancer. Biennial mammography cost less than $100,000 per QALY gained for women aged 40 to 79 years with BI-RADS category 3 or 4 breast density or aged 50 to 69 years with category 2 density; women aged 60 to 79 years with category 1 density and either a family history of breast cancer or a previous breast biopsy; and all women aged 40 to 79 years with both a family history of breast cancer and a previous breast biopsy, regardless of breast density. Biennial mammography cost less than $50,000 per QALY gained for women aged 40 to 49 years with category 3 or 4 breast density and either a previous breast biopsy or a family history of breast cancer. Annual mammography was not cost-effective for any group, regardless of age or breast density. Mammography is expensive if the disutility of false-positive mammography results and the costs of detecting nonprogressive and nonlethal invasive cancer are considered. Results are not applicable to carriers of BRCA1 or BRCA2 mutations. Mammography screening should be personalized on the basis of a woman's age, breast density, history of breast biopsy, family history of breast cancer, and beliefs about the potential benefit and harms of screening. Eli Lilly, Da Costa Family Foundation for Research in Breast Cancer Prevention of the California Pacific Medical Center, and Breast Cancer Surveillance Consortium.

Measurement of breast density with digital breast tomosynthesis—a systematic review

PubMed Central

McEntee, M F

2014-01-01

Digital breast tomosynthesis (DBT) has gained acceptance as an adjunct to digital mammography in screening. Now that breast density reporting is mandated in several states in the USA, it is increasingly important that the methods of breast density measurement be robust, reliable and consistent. Breast density assessment with DBT needs some consideration since quantitative methods are modelled for two-dimensional (2D) mammography. A review of methods used for breast density assessment with DBT was performed. Existing evidence shows Cumulus has better reproducibility than that of the breast imaging reporting and data system (BI-RADS®) but still suffers from subjective variability; MedDensity is limited by image noise, whilst Volpara and Quantra are robust and consistent. The reported BI-RADs inter-reader breast density agreement (k) ranged from 0.65 to 0.91, with inter-reader correlation (r) ranging from 0.70 to 0.93. The correlation (r) between BI-RADS and Cumulus ranged from 0.54–0.94, whilst that of BI-RADs and MedDensity ranged from 0.48–0.78. The reported agreement (k) between BI-RADs and Volpara is 0.953. Breast density correlation between DBT and 2D mammography ranged from 0.73 to 0.97, with agreement (k) ranging from 0.56 to 0.96. To avoid variability and provide more reliable breast density information for clinicians, automated volumetric methods are preferred. PMID:25146640

Using ultrasound tomography to identify the distributions of density throughout the breast

NASA Astrophysics Data System (ADS)

Sak, Mark; Duric, Neb; Littrup, Peter; Sherman, Mark E.; Gierach, Gretchen L.

2016-04-01

Women with high breast density are at increased risk of developing breast cancer. Breast density has usually been defined using mammography as the ratio of fibroglandular tissue to total breast area. Ultrasound tomography (UST) is an emerging modality that can also be used to measure breast density. UST creates tomographic sound speed images of the patient's breast which is useful as sound speed is directly proportional to tissue density. Furthermore, the volumetric and quantitative information contained in the sound speed images can be used to describe the distribution of breast density. The work presented here measures the UST sound speed density distributions of 165 women with negative screening mammography. Frequency distributions of the sound speed voxel information were examined for each patient. In a preliminary analysis, the UST sound speed distributions were averaged across patients and grouped by various patient and density-related factors (e.g., age, body mass index, menopausal status, average mammographic breast density). It was found that differences in the distribution of density could be easily visualized for different patient groupings. Furthermore, findings suggest that the shape of the distributions may be used to identify participants with varying amounts of dense and non-dense tissue.

A novel ultrasonic method for measuring breast density and breast cancer risk

NASA Astrophysics Data System (ADS)

Glide-Hurst, Carri K.; Duric, Neb; Littrup, Peter J.

2008-03-01

Women with high mammographic breast density are at 4- to 6-fold increased risk of developing breast cancer compared to women with fatty breasts. However, current breast density estimations rely on mammography, which cannot provide accurate volumetric breast representation. Therefore, we explored two techniques of breast density evaluation via ultrasound tomography. A sample of 93 patients was imaged with our clinical prototype; each dataset contained 45-75 tomograms ranging from near the chest wall through the nipple. Whole breast acoustic velocity was determined by creating image stacks and evaluating the sound speed frequency distribution. Ultrasound percent density (USPD) was determined by segmenting high sound speed areas from each tomogram using k-means clustering, integrating over the entire breast, and dividing by total breast area. Both techniques were independently evaluated using two mammographic density measures: (1) qualitative, determined by a radiologist's visual assessment using BI-RADS Categories, and (2) quantitative, via semi-automatic segmentation to calculate mammographic percent density (MPD) for craniocaudal and medio-lateral oblique mammograms. ~140 m/s difference in acoustic velocity was observed between fatty and dense BI-RADS Categories. Increased sound speed was found with increased BI-RADS Category and quantitative MPD. Furthermore, strong positive associations between USPD, BI-RADS Category, and calculated MPD were observed. These results confirm that utilizing sound speed, both for whole-breast evaluation and segmenting locally, can be implemented to evaluate breast density.

Computerized image analysis: estimation of breast density on mammograms

NASA Astrophysics Data System (ADS)

Zhou, Chuan; Chan, Heang-Ping; Petrick, Nicholas; Sahiner, Berkman; Helvie, Mark A.; Roubidoux, Marilyn A.; Hadjiiski, Lubomir M.; Goodsitt, Mitchell M.

2000-06-01

An automated image analysis tool is being developed for estimation of mammographic breast density, which may be useful for risk estimation or for monitoring breast density change in a prevention or intervention program. A mammogram is digitized using a laser scanner and the resolution is reduced to a pixel size of 0.8 mm X 0.8 mm. Breast density analysis is performed in three stages. First, the breast region is segmented from the surrounding background by an automated breast boundary-tracking algorithm. Second, an adaptive dynamic range compression technique is applied to the breast image to reduce the range of the gray level distribution in the low frequency background and to enhance the differences in the characteristic features of the gray level histogram for breasts of different densities. Third, rule-based classification is used to classify the breast images into several classes according to the characteristic features of their gray level histogram. For each image, a gray level threshold is automatically determined to segment the dense tissue from the breast region. The area of segmented dense tissue as a percentage of the breast area is then estimated. In this preliminary study, we analyzed the interobserver variation of breast density estimation by two experienced radiologists using BI-RADS lexicon. The radiologists' visually estimated percent breast densities were compared with the computer's calculation. The results demonstrate the feasibility of estimating mammographic breast density using computer vision techniques and its potential to improve the accuracy and reproducibility in comparison with the subjective visual assessment by radiologists.

Using Clinical Factors and Mammographic Breast Density to Estimate Breast Cancer Risk: Development and Validation of a New Predictive Model

PubMed Central

Tice, Jeffrey A.; Cummings, Steven R.; Smith-Bindman, Rebecca; Ichikawa, Laura; Barlow, William E.; Kerlikowske, Karla

2009-01-01

Background Current models for assessing breast cancer risk are complex and do not include breast density, a strong risk factor for breast cancer that is routinely reported with mammography. Objective To develop and validate an easy-to-use breast cancer risk prediction model that includes breast density. Design Empirical model based on Surveillance, Epidemiology, and End Results incidence, and relative hazards from a prospective cohort. Setting Screening mammography sites participating in the Breast Cancer Surveillance Consortium. Patients 1 095 484 women undergoing mammography who had no previous diagnosis of breast cancer. Measurements Self-reported age, race or ethnicity, family history of breast cancer, and history of breast biopsy. Community radiologists rated breast density by using 4 Breast Imaging Reporting and Data System categories. Results During 5.3 years of follow-up, invasive breast cancer was diagnosed in 14 766 women. The breast density model was well calibrated overall (expected–observed ratio, 1.03 [95% CI, 0.99 to 1.06]) and in racial and ethnic subgroups. It had modest discriminatory accuracy (concordance index, 0.66 [CI, 0.65 to 0.67]). Women with low-density mammograms had 5-year risks less than 1.67% unless they had a family history of breast cancer and were older than age 65 years. Limitation The model has only modest ability to discriminate between women who will develop breast cancer and those who will not. Conclusion A breast cancer prediction model that incorporates routinely reported measures of breast density can estimate 5-year risk for invasive breast cancer. Its accuracy needs to be further evaluated in independent populations before it can be recommended for clinical use. PMID:18316752

Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: comparison of fully automated area and volumetric density measures in a case-control study with digital mammography.

PubMed

Keller, Brad M; Chen, Jinbo; Daye, Dania; Conant, Emily F; Kontos, Despina

2015-08-25

Breast density, commonly quantified as the percentage of mammographically dense tissue area, is a strong breast cancer risk factor. We investigated associations between breast cancer and fully automated measures of breast density made by a new publicly available software tool, the Laboratory for Individualized Breast Radiodensity Assessment (LIBRA). Digital mammograms from 106 invasive breast cancer cases and 318 age-matched controls were retrospectively analyzed. Density estimates acquired by LIBRA were compared with commercially available software and standard Breast Imaging-Reporting and Data System (BI-RADS) density estimates. Associations between the different density measures and breast cancer were evaluated by using logistic regression after adjustment for Gail risk factors and body mass index (BMI). Area under the curve (AUC) of the receiver operating characteristic (ROC) was used to assess discriminatory capacity, and odds ratios (ORs) for each density measure are provided. All automated density measures had a significant association with breast cancer (OR = 1.47-2.23, AUC = 0.59-0.71, P < 0.01) which was strengthened after adjustment for Gail risk factors and BMI (OR = 1.96-2.64, AUC = 0.82-0.85, P < 0.001). In multivariable analysis, absolute dense area (OR = 1.84, P < 0.001) and absolute dense volume (OR = 1.67, P = 0.003) were jointly associated with breast cancer (AUC = 0.77, P < 0.01), having a larger discriminatory capacity than models considering the Gail risk factors alone (AUC = 0.64, P < 0.001) or the Gail risk factors plus standard area percent density (AUC = 0.68, P = 0.01). After BMI was further adjusted for, absolute dense area retained significance (OR = 2.18, P < 0.001) and volume percent density approached significance (OR = 1.47, P = 0.06). This combined area-volume density model also had a significantly (P < 0.001) improved discriminatory capacity (AUC = 0.86) relative to a model considering the Gail risk factors plus BMI (AUC = 0.80). Our study suggests that new automated density measures may ultimately augment the current standard breast cancer risk factors. In addition, the ability to fully automate density estimation with digital mammography, particularly through the use of publically available breast density estimation software, could accelerate the translation of density reporting in routine breast cancer screening and surveillance protocols and facilitate broader research into the use of breast density as a risk factor for breast cancer.

Equol-producing status, isoflavone intake, and breast density in a sample of U.S. Chinese women.

PubMed

Tseng, Marilyn; Byrne, Celia; Kurzer, Mindy S; Fang, Carolyn Y

2013-11-01

Differences in ability to metabolize daidzein to equol might help explain inconsistent findings about isoflavones and breast cancer. We examined equol-producing status in relation to breast density, a marker of breast cancer risk, and evaluated whether an association of isoflavone intake with breast density differs by equol-producing status in a sample of Chinese immigrant women. Participants were 224 women, ages 36 to 58 years, enrolled in a study on diet and breast density. All women completed dietary recall interviews, underwent a soy challenge to assess equol-producing status, and received a mammogram assessed for breast density using a computer-assisted method. In our sample, 30% were classified as equol producers. In adjusted linear regression models, equol producers had significantly lower mean dense tissue area (32.8 vs. 37.7 cm(2), P = 0.03) and lower mean percent breast density (32% vs. 35%, P = 0.03) than nonproducers. Significant inverse associations of isoflavone intake with dense area and percent density were apparent, but only in equol producers (interaction P = 0.05 for both). These results support the possibility that equol-producing status affects breast density and that effects of isoflavones on breast density depend on ability to metabolize daidzein to equol. Although these findings warrant confirmation in a larger sample, they offer a possible explanation for the inconsistent findings about soy intake and breast density and possibly breast cancer risk as well. The findings further suggest the importance of identifying factors that influence equol-producing status and exploring appropriate targeting of interventions. ©2013 AACR.

Mammographic breast density as a risk factor for breast cancer: awareness in a recently screened clinical sample.

PubMed

O'Neill, Suzanne C; Leventhal, Kara Grace; Scarles, Marie; Evans, Chalanda N; Makariou, Erini; Pien, Edward; Willey, Shawna

2014-01-01

Breast density is an established, independent risk factor for breast cancer. Despite this, density has not been included in standard risk models or routinely disclosed to patients. However, this is changing in the face of legal mandates and advocacy efforts. Little information exists regarding women's awareness of density as a risk factor, their personal risk, and risk management options. We assessed awareness of density as a risk factor and whether sociodemographic variables, breast cancer risk factors. and perceived breast cancer risk were associated with awareness in 344 women with a recent screening mammogram at a tertiary care center. Overall, 62% of women had heard about density as a risk factor and 33% had spoken to a provider about breast density. Of the sample, 18% reported that their provider indicated that they had high breast density. Awareness of density as a risk factor was greater among White women and those with other breast cancer risk factors. Our results suggest that although a growing number of women are aware of breast density as a risk factor, this awareness varies. Growing mandates for disclosure suggest the need for patient education interventions for women at increased risk for the disease and to ensure all women are equally aware of their risks. Copyright © 2014 Jacobs Institute of Women's Health. Published by Elsevier Inc. All rights reserved.

Breast density characterization using texton distributions.

PubMed

Petroudi, Styliani; Brady, Michael

2011-01-01

Breast density has been shown to be one of the most significant risks for developing breast cancer, with women with dense breasts at four to six times higher risk. The Breast Imaging Reporting and Data System (BI-RADS) has a four class classification scheme that describes the different breast densities. However, there is great inter and intra observer variability among clinicians in reporting a mammogram's density class. This work presents a novel texture classification method and its application for the development of a completely automated breast density classification system. The new method represents the mammogram using textons, which can be thought of as the building blocks of texture under the operational definition of Leung and Malik as clustered filter responses. The new proposed method characterizes the mammographic appearance of the different density patterns by evaluating the texton spatial dependence matrix (TDSM) in the breast region's corresponding texton map. The TSDM is a texture model that captures both statistical and structural texture characteristics. The normalized TSDM matrices are evaluated for mammograms from the different density classes and corresponding texture models are established. Classification is achieved using a chi-square distance measure. The fully automated TSDM breast density classification method is quantitatively evaluated on mammograms from all density classes from the Oxford Mammogram Database. The incorporation of texton spatial dependencies allows for classification accuracy reaching over 82%. The breast density classification accuracy is better using texton TSDM compared to simple texton histograms.

Breast density measurements using ultrasound tomography for patients undergoing tamoxifen treatment

NASA Astrophysics Data System (ADS)

Sak, Mark; Duric, Neb; Littrup, Peter; Li, Cuiping; Bey-Knight, Lisa; Sherman, Mark; Boyd, Norman; Gierach, Gretchen

2013-03-01

Women with high breast density have an increased risk of developing breast cancer. Women treated with the selective estrogen receptor modulator tamoxifen for estrogen receptor positive breast cancer experience a 50% reduction in risk of contralateral breast cancer and overall reduction of similar magnitude has been identified among high-risk women receiving the drug for prevention. Tamoxifen has been shown to reduce mammographic density, and in the IBIS-1 chemoprevention trial, risk reduction and decline in density were significantly associated. Ultrasound tomography (UST) is an imaging modality that can create tomographic sound speed images of the breast. These sound speed images are useful because breast density is proportional to sound speed. The aim of this work is to examine the relationship between USTmeasured breast density and the use of tamoxifen. So far, preliminary results for a small number of patients have been observed and are promising. Correlations between the UST-measured density and mammographic density are strong and positive, while relationships between UST density with some patient specific risk factors behave as expected. Initial results of UST examinations of tamoxifen treated patients show that approximately 45% of the patients have a decrease in density in the contralateral breast after only several months of treatment. The true effect of tamoxifen on UST-measured density cannot yet be fully determined until more data are collected. However, these promising results suggest that UST can be used to reliably assess quantitative changes in breast density over short intervals and therefore suggest that UST may enable rapid assessment of density changes associated with therapeutic and preventative interventions.

Development of a phantom to test fully automated breast density software - A work in progress.

PubMed

Waade, G G; Hofvind, S; Thompson, J D; Highnam, R; Hogg, P

2017-02-01

Mammographic density (MD) is an independent risk factor for breast cancer and may have a future role for stratified screening. Automated software can estimate MD but the relationship between breast thickness reduction and MD is not fully understood. Our aim is to develop a deformable breast phantom to assess automated density software and the impact of breast thickness reduction on MD. Several different configurations of poly vinyl alcohol (PVAL) phantoms were created. Three methods were used to estimate their density. Raw image data of mammographic images were processed using Volpara to estimate volumetric breast density (VBD%); Hounsfield units (HU) were measured on CT images; and physical density (g/cm 3 ) was calculated using a formula involving mass and volume. Phantom volume versus contact area and phantom volume versus phantom thickness was compared to values of real breasts. Volpara recognized all deformable phantoms as female breasts. However, reducing the phantom thickness caused a change in phantom density and the phantoms were not able to tolerate same level of compression and thickness reduction experienced by female breasts during mammography. Our results are promising as all phantoms resulted in valid data for automated breast density measurement. Further work should be conducted on PVAL and other materials to produce deformable phantoms that mimic female breast structure and density with the ability of being compressed to the same level as female breasts. We are the first group to have produced deformable phantoms that are recognized as breasts by Volpara software. Copyright © 2016 The College of Radiographers. All rights reserved.

Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective.

PubMed

Mohamed, Aly A; Luo, Yahong; Peng, Hong; Jankowitz, Rachel C; Wu, Shandong

2017-09-20

Mammographic breast density has been established as an independent risk marker for developing breast cancer. Breast density assessment is a routine clinical need in breast cancer screening and current standard is using the Breast Imaging and Reporting Data System (BI-RADS) criteria including four qualitative categories (i.e., fatty, scattered density, heterogeneously dense, or extremely dense). In each mammogram examination, a breast is typically imaged with two different views, i.e., the mediolateral oblique (MLO) view and cranial caudal (CC) view. The BI-RADS-based breast density assessment is a qualitative process made by visual observation of both the MLO and CC views by radiologists, where there is a notable inter- and intra-reader variability. In order to maintain consistency and accuracy in BI-RADS-based breast density assessment, gaining understanding on radiologists' reading behaviors will be educational. In this study, we proposed to leverage the newly emerged deep learning approach to investigate how the MLO and CC view images of a mammogram examination may have been clinically used by radiologists in coming up with a BI-RADS density category. We implemented a convolutional neural network (CNN)-based deep learning model, aimed at distinguishing the breast density categories using a large (15,415 images) set of real-world clinical mammogram images. Our results showed that the classification of density categories (in terms of area under the receiver operating characteristic curve) using MLO view images is significantly higher than that using the CC view. This indicates that most likely it is the MLO view that the radiologists have predominately used to determine the breast density BI-RADS categories. Our study holds a potential to further interpret radiologists' reading characteristics, enhance personalized clinical training to radiologists, and ultimately reduce reader variations in breast density assessment.

Soy isoflavone supplementation and breast density in postmenopausal women

USDA-ARS?s Scientific Manuscript database

Soy isoflavones may protect against breast cancer. Breast density, a marker for breast cancer risk, increases as a result of hormone replacement therapy. We examined the relation between isoflavone supplementation and breast density using the mammograms from 358 women who participated in the multi-s...

Digital volumetric measurement of mammographic density and the risk of overlooking cancer in Japanese women.

PubMed

Sawada, Terumasa; Akashi, Sadako; Nakamura, Seigo; Kuwayama, Takashi; Enokido, Katsutoshi; Yoshida, Miwa; Hashimoto, Rikako; Ide, Toshimi; Masuda, Hiroko; Taruno, Kanae; Oyama, Hiroto; Takamaru, Tomoko; Kanada, Yoko; Ikeda, Murasaki; Kosugi, Natsuko; Sato, Hiroki; Nakayama, Sayuka; Ata, Arisa; Tonouchi, Yumi; Sakai, Haruna; Matsunaga, Yuki; Matsutani, Akiko

2017-09-01

Breast density often affects cancer detection via mammography (MMG). Because of this, additional tests are recommended for women with dense breasts. This study aimed to reveal trends in breast density among Japanese women and determine whether differences in breast density differentially affected the detection of abnormalities via MMG. We retrospectively analyzed 397 control women who underwent MMG screening as well as 269 patients who underwent surgery for breast cancer for whom preoperative MMG data were available. VolparaDensity™ (Volpara), a three-dimensional image analysis software with high reproducibility, was used to calculate breast density. Breasts were categorized according to the volumetric density grade (VDG), a measure of the percentage of dense tissue. The associations between age, VDG, and MMG density categories were analyzed. In the control group, 78% of women had dense breasts, while in the breast cancer group, 87% of patients had dense breasts. One of 36 patients with non-dense breasts (2.7%) was classified as category 1 or 2 (C-1 or C-2), indicating that abnormal findings could not be detected by MMG. The proportion of patients with breast cancer who had dense breasts and were classified as C-1 or C-2 was as high as 22.3%. The proportions of Japanese women with dense breasts were high. In addition, the false-negative rate for women with dense breasts was also high. Owing to this, Japanese women with dense breasts may need to commonly undergo additional tests to ensure detection of breast cancer in the screening MMG.

Automated Breast Density Computation in Digital Mammography and Digital Breast Tomosynthesis: Influence on Mean Glandular Dose and BIRADS Density Categorization.

PubMed

Castillo-García, Maria; Chevalier, Margarita; Garayoa, Julia; Rodriguez-Ruiz, Alejandro; García-Pinto, Diego; Valverde, Julio

2017-07-01

The study aimed to compare the breast density estimates from two algorithms on full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) and to analyze the clinical implications. We selected 561 FFDM and DBT examinations from patients without breast pathologies. Two versions of a commercial software (Quantra 2D and Quantra 3D) calculated the volumetric breast density automatically in FFDM and DBT, respectively. Other parameters such as area breast density and total breast volume were evaluated. We compared the results from both algorithms using the Mann-Whitney U non-parametric test and the Spearman's rank coefficient for data correlation analysis. Mean glandular dose (MGD) was calculated following the methodology proposed by Dance et al. Measurements with both algorithms are well correlated (r ≥ 0.77). However, there are statistically significant differences between the medians (P < 0.05) of most parameters. The volumetric and area breast density median values from FFDM are, respectively, 8% and 77% higher than DBT estimations. Both algorithms classify 35% and 55% of breasts into BIRADS (Breast Imaging-Reporting and Data System) b and c categories, respectively. There are no significant differences between the MGD calculated using the breast density from each algorithm. DBT delivers higher MGD than FFDM, with a lower difference (5%) for breasts in the BIRADS d category. MGD is, on average, 6% higher than values obtained with the breast glandularity proposed by Dance et al. Breast density measurements from both algorithms lead to equivalent BIRADS classification and MGD values, hence showing no difference in clinical outcomes. The median MGD values of FFDM and DBT examinations are similar for dense breasts (BIRADS d category). Published by Elsevier Inc.

Relationship Between Mammographic Density and Breast Cancer Death in the Breast Cancer Surveillance Consortium

PubMed Central

2012-01-01

Background Women with elevated mammographic density have an increased risk of developing breast cancer. However, among women diagnosed with breast cancer, it is unclear whether higher density portends reduced survival, independent of other factors. Methods We evaluated relationships between mammographic density and risk of death from breast cancer and all causes within the US Breast Cancer Surveillance Consortium. We studied 9232 women diagnosed with primary invasive breast carcinoma during 1996–2005, with a mean follow-up of 6.6 years. Mammographic density was assessed using the Breast Imaging Reporting and Data System (BI-RADS) density classification. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated by Cox proportional hazards regression; women with scattered fibroglandular densities (BI-RADS 2) were the referent group. All statistical tests were two-sided. Results A total of 1795 women died, of whom 889 died of breast cancer. In multivariable analyses (adjusted for site, age at and year of diagnosis, American Joint Committee on Cancer stage, body mass index, mode of detection, treatment, and income), high density (BI-RADS 4) was not related to risk of death from breast cancer (HR = 0.92, 95% CI = 0.71 to 1.19) or death from all causes (HR = 0.83, 95% CI = 0.68 to 1.02). Analyses stratified by stage and other prognostic factors yielded similar results, except for an increased risk of breast cancer death among women with low density (BI-RADS 1) who were either obese (HR = 2.02, 95% CI = 1.37 to 2.97) or had tumors of at least 2.0cm (HR = 1.55, 95% CI = 1.14 to 2.09). Conclusions High mammographic breast density was not associated with risk of death from breast cancer or death from any cause after accounting for other patient and tumor characteristics. Thus, risk factors for the development of breast cancer may not necessarily be the same as factors influencing the risk of death after breast cancer has developed. PMID:22911616

Breast density in screening mammography in Indian population - Is it different from western population?

PubMed

Singh, Tulika; Khandelwal, Niranjan; Singla, Veenu; Kumar, Dileep; Gupta, Madhu; Singh, Gurpreet; Bal, Amanjit

2018-05-01

Mammography is the only method presently considered appropriate for mass screening of breast cancer. However, higher breast density was strongly associated with lower mammographic sensitivity. Breast density is also identified as independent and strongest risk factors for breast cancer. Studies have shown women with high breast density have four to six times increased risk of breast cancer as compare to women with fatty breast. It varies between different age group it generally decreases with increasing age in postmenopausal women and it can be different in different ethnic groups and people from different geographical areas. This study evaluates the breast density in Indian population and its relationship with the age. We reviewed of all screening mammography examinations performed from May 2012 to January 2015 at our institute PGIMER, Chandigarh, INDIA. Descriptive analyses were used to examine the association between age and breast density. A total of 6132 screening mammograms were performed. Each subgroup categorized by decade of age. There was a significant inverse relationship between age and breast density (P < .001). Twenty-two percent of patients between 40 and 49 years old had dense breasts. This percentage decreased to 9% of women in their 50s. Only 7% of women in their 60s and 8% of women in their 70s had dense breasts. This data has been compared with the Western study done in New York University (NYU) shows there is significant difference (P value <.05) in the breast density in Indian and Western population with more Indians having ACR Grade 1 and 2 and Western population having 2 and 3. We found an inverse relationship between patient age and mammographic breast density. However, there were a large proportion of young women who had lower grades of mammographic density which could potentially benefit from the use of routine screening mammography in this subgroup of patients. Moreover, the breast density of Indian population is less when compared to the Western population. This might suggest that mammography is a good modality of choice for screening Indian population. © 2017 Wiley Periodicals, Inc.

Breast cancer screening effect across breast density strata: A case-control study.

PubMed

van der Waal, Daniëlle; Ripping, Theodora M; Verbeek, André L M; Broeders, Mireille J M

2017-01-01

Breast cancer screening is known to reduce breast cancer mortality. A high breast density may affect this reduction. We assessed the effect of screening on breast cancer mortality in women with dense and fatty breasts separately. Analyses were performed within the Nijmegen (Dutch) screening programme (1975-2008), which invites women (aged 50-74 years) biennially. Performance measures were determined. Furthermore, a case-control study was performed for women having dense and women having fatty breasts. Breast density was assessed visually with a dichotomized Wolfe scale. Breast density data were available for cases. The prevalence of dense breasts among controls was estimated with age-specific rates from the general population. Sensitivity analyses were performed on these estimates. Screening performance was better in the fatty than in the dense group (sensitivity 75.7% vs 57.8%). The mortality reduction appeared to be smaller for women with dense breasts, with an odds ratio (OR) of 0.87 (95% CI 0.52-1.45) in the dense and 0.59 (95% CI 0.44-0.79) in the fatty group. We can conclude that high density results in lower screening performance and appears to be associated with a smaller mortality reduction. Breast density is thus a likely candidate for risk-stratified screening. More research is needed on the association between density and screening harms. © 2016 UICC.

Mammographic breast density in recent and longer-standing ethiopian immigrants to israel.

PubMed

Sklair-Levy, Miri; Segev, Anat; Sella, Tamar; Calderon-Margalit, Ronit; Zippel, Douglas

2018-04-23

High breast density is associated with an increased risk of breast cancer development. Little is known concerning ethnic variations in breast density and its relevant contributing factors. We aimed to study breast density among Ethiopian immigrants to Israel in comparison with Israeli-born women and to determine any effect on breast density of the length of residency in the immigrant population. Mammographic breast density using the BI-RADS system was estimated and compared between 77 women of Ethiopian origin who live in Israel and 177 Israeli-born controls. Logistic regression analysis was performed to estimate the odds ratios (OR) for high density (BI-RADS score ≥ 3) vs low density (BI-RADS score < 3) cases, comparing the 2 origin groups. Ethiopian-born women had a crude OR of 0.15 (95% CI: 0.08-0.26) for high breast density compared with Israeli-born women. Adjustments for various cofounders did not affect the results. Time since immigration to Israel seemed to modify the relationship, with a stronger association for women who immigrated within 2 years prior to mammography (OR:0.07, 95% CI: 0.03-0.17) as opposed to women with a longer residency stay in Israel (OR:0.23, 95% CI:0.10-0.50). Adjustments of various confounders did not alter these findings. Breast density in Ethiopian immigrants to Israel is significantly lower than that of Israeli-born controls. Our study suggests a positive association between time since immigration and breast density. Future studies are required to define the possible effects of dietary change on mammographic density following immigration. © 2018 Wiley Periodicals, Inc.

Relationship between breast sound speed and mammographic percent density

NASA Astrophysics Data System (ADS)

Sak, Mark; Duric, Nebojsa; Boyd, Norman; Littrup, Peter; Myc, Lukasz; Faiz, Muhammad; Li, Cuiping; Bey-Knight, Lisa

2011-03-01

Despite some shortcomings, mammography is currently the standard of care for breast cancer screening and diagnosis. However, breast ultrasound tomography is a rapidly developing imaging modality that has the potential to overcome the drawbacks of mammography. It is known that women with high breast densities have a greater risk of developing breast cancer. Measuring breast density is accomplished through the use of mammographic percent density, defined as the ratio of fibroglandular to total breast area. Using an ultrasound tomography (UST) prototype, we created sound speed images of the patient's breast, motivated by the fact that sound speed in a tissue is proportional to the density of the tissue. The purpose of this work is to compare the acoustic performance of the UST system with the measurement of mammographic percent density. A cohort of 251 patients was studied using both imaging modalities and the results suggest that the volume averaged breast sound speed is significantly related to mammographic percent density. The Spearman correlation coefficient was found to be 0.73 for the 175 film mammograms and 0.69 for the 76 digital mammograms obtained. Since sound speed measurements do not require ionizing radiation or physical compression, they have the potential to form the basis of a safe, more accurate surrogate marker of breast density.

Vision 20/20: Mammographic breast density and its clinical applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ng, Kwan-Hoong, E-mail: ngkh@ummc.edu.my; Lau, Susie

2015-12-15

Breast density is a strong predictor of the failure of mammography screening to detect breast cancer and is a strong predictor of the risk of developing breast cancer. The many imaging options that are now available for imaging dense breasts show great promise, but there is still the question of determining which women are “dense” and what imaging modality is suitable for individual women. To date, mammographic breast density has been classified according to the Breast Imaging-Reporting and Data System (BI-RADS) categories from visual assessment, but this is known to be very subjective. Despite many research reports, the authors believemore » there has been a lack of physics-led and evidence-based arguments about what breast density actually is, how it should be measured, and how it should be used. In this paper, the authors attempt to start correcting this situation by reviewing the history of breast density research and the debates generated by the advocacy movement. The authors review the development of breast density estimation from pattern analysis to area-based analysis, and the current automated volumetric breast density (VBD) analysis. This is followed by a discussion on seeking the ground truth of VBD and mapping volumetric methods to BI-RADS density categories. The authors expect great improvement in VBD measurements that will satisfy the needs of radiologists, epidemiologists, surgeons, and physicists. The authors believe that they are now witnessing a paradigm shift toward personalized breast screening, which is going to see many more cancers being detected early, with the use of automated density measurement tools as an important component.« less

Common Breast Cancer Susceptibility Variants in LSP1 and RAD51L1 Are Associated with Mammographic Density Measures that Predict Breast Cancer Risk

PubMed Central

Vachon, Celine M.; Scott, Christopher G.; Fasching, Peter A.; Hall, Per; Tamimi, Rulla M.; Li, Jingmei; Stone, Jennifer; Apicella, Carmel; Odefrey, Fabrice; Gierach, Gretchen L.; Jud, Sebastian M.; Heusinger, Katharina; Beckmann, Matthias W.; Pollan, Marina; Fernández-Navarro, Pablo; González-Neira, Anna; Benítez, Javier; van Gils, Carla H.; Lokate, Mariëtte; Onland-Moret, N. Charlotte; Peeters, Petra H.M.; Brown, Judith; Leyland, Jean; Varghese, Jajini S.; Easton, Douglas F.; Thompson, Deborah J.; Luben, Robert N.; Warren, Ruth ML; Wareham, Nicholas J.; Loos, Ruth JF; Khaw, Kay-Tee; Ursin, Giske; Lee, Eunjung; Gayther, Simon A.; Ramus, Susan J.; Eeles, Rosalind A.; Leach, Martin O.; Kwan-Lim, Gek; Couch, Fergus J.; Giles, Graham G.; Baglietto, Laura; Krishnan, Kavitha; Southey, Melissa C.; Le Marchand, Loic; Kolonel, Laurence N.; Woolcott, Christy; Maskarinec, Gertraud; Haiman, Christopher A; Walker, Kate; Johnson, Nichola; McCormack, Valerie A.; Biong, Margarethe; Alnæs, Grethe I.G.; Gram, Inger Torhild; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lindström, Sara; Hankinson, Susan E.; Hunter, David J.; Andrulis, Irene L.; Knight, Julia A.; Boyd, Norman F.; Figueroa, Jonine D.; Lissowska, Jolanta; Wesolowska, Ewa; Peplonska, Beata; Bukowska, Agnieszka; Reszka, Edyta; Liu, JianJun; Eriksson, Louise; Czene, Kamila; Audley, Tina; Wu, Anna H.; Pankratz, V. Shane; Hopper, John L.; dos-Santos-Silva, Isabel

2013-01-01

Background Mammographic density adjusted for age and body mass index (BMI) is a heritable marker of breast cancer susceptibility. Little is known about the biological mechanisms underlying the association between mammographic density and breast cancer risk. We examined whether common low-penetrance breast cancer susceptibility variants contribute to inter-individual differences in mammographic density measures. Methods We established an international consortium (DENSNP) of 19 studies from 10 countries, comprising 16,895 Caucasian women, to conduct a pooled cross-sectional analysis of common breast cancer susceptibility variants in 14 independent loci and mammographic density measures. Dense and non-dense areas, and percent density, were measured using interactive-thresholding techniques. Mixed linear models were used to assess the association between genetic variants and the square roots of mammographic density measures adjusted for study, age, case status, body mass index (BMI) and menopausal status. Results Consistent with their breast cancer associations, the C-allele of rs3817198 in LSP1 was positively associated with both adjusted dense area (p=0.00005) and adjusted percent density (p=0.001) whereas the A-allele of rs10483813 in RAD51L1 was inversely associated with adjusted percent density (p=0.003), but not with adjusted dense area (p=0.07). Conclusion We identified two common breast cancer susceptibility variants associated with mammographic measures of radio-dense tissue in the breast gland. Impact We examined the association of 14 established breast cancer susceptibility loci with mammographic density phenotypes within a large genetic consortium and identified two breast cancer susceptibility variants, LSP1-rs3817198 and RAD51L1-rs10483813, associated with mammographic measures and in the same direction as the breast cancer association. PMID:22454379

Decreased contralateral breast volume after mastectomy, adjuvant chemotherapy, and anti-estrogen therapy, in particular in breasts with high density.

PubMed

Ishii, Naohiro; Ando, Jiro; Harao, Michiko; Takemae, Masaru; Kishi, Kazuo

2017-10-01

Adjuvant chemotherapy and anti-estrogenic therapy can result in decreased volume of the contralateral breast, following mastectomy for the treatment of breast cancer. However, no data on the effect of adjuvant therapy on contralateral breast volume have previously been reported. We aimed to evaluate the extent to which adjuvant therapy and differences in breast density contribute to decreased breast volume. We conducted a prospective cohort study, selecting 40 nonconsecutive patients who underwent immediate breast reconstruction with mastectomy and expander insertion followed by expander replacement. We measured the contralateral breast volume before each procedure. The extent of the change was analyzed with respect to adjuvant therapy and breast density measured by preoperative mammography. The greatest decrease in breast volume was 135.1 cm 3 . The decrease in breast volume was significantly larger in the adjuvant therapy (+) group, particularly in patients with high breast density, than in the adjuvant therapy (-) group. Significant differences between the chemotherapy (+), tamoxifen (+) group and the chemotherapy (-), tamoxifen (+) group were not found. Breast density scores had a range of 2.0-3.3 (mean: 2.8). In breast reconstruction, particularly when performed in one stage, preoperative mammography findings are valuable to plastic surgeons, and possible decreases in the contralateral breast volume due to adjuvant therapy, particularly in patients with high breast density, should be considered carefully. Copyright © 2017 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

Fully automated breast density assessment from low-dose chest CT

NASA Astrophysics Data System (ADS)

Liu, Shuang; Margolies, Laurie R.; Xie, Yiting; Yankelevitz, David F.; Henschke, Claudia I.; Reeves, Anthony P.

2017-03-01

Breast cancer is the most common cancer diagnosed among US women and the second leading cause of cancer death 1 . Breast density is an independent risk factor for breast cancer and more than 25 states mandate its reporting to patients as part of the lay mammogram report 2 . Recent publications have demonstrated that breast density measured from low-dose chest CT (LDCT) correlates well with that measured from mammograms and MRIs 3-4 , thereby providing valuable information for many women who have undergone LDCT but not recent mammograms. A fully automated framework for breast density assessment from LDCT is presented in this paper. The whole breast region is first segmented using an anatomy-orientated novel approach based on the propagation of muscle fronts for separating the fibroglandular tissue from the underlying muscles. The fibroglandular tissue regions are then identified from the segmented whole breast and the percentage density is calculated based on the volume ratio of the fibroglandular tissue to the local whole breast region. The breast region segmentation framework was validated with 1270 LDCT scans, with 96.1% satisfactory outcomes based on visual inspection. The density assessment was evaluated by comparing with BI-RADS density grades established by an experienced radiologist in 100 randomly selected LDCT scans of female subjects. The continuous breast density measurement was shown to be consistent with the reference subjective grading, with the Spearman's rank correlation 0.91 (p-value < 0.001). After converting the continuous density to categorical grades, the automated density assessment was congruous with the radiologist's reading in 91% cases.

Breast density evaluation using spectral mammography, radiologist reader assessment and segmentation techniques: a retrospective study based on left and right breast comparison

PubMed Central

Molloi, Sabee; Ding, Huanjun; Feig, Stephen

2015-01-01

Purpose The purpose of this study was to compare the precision of mammographic breast density measurement using radiologist reader assessment, histogram threshold segmentation, fuzzy C-mean segmentation and spectral material decomposition. Materials and Methods Spectral mammography images from a total of 92 consecutive asymptomatic women (50–69 years old) who presented for annual screening mammography were retrospectively analyzed for this study. Breast density was estimated using 10 radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm and spectral material decomposition. The breast density correlation between left and right breasts was used to assess the precision of these techniques to measure breast composition relative to dual-energy material decomposition. Results In comparison to the other techniques, the results of breast density measurements using dual-energy material decomposition showed the highest correlation. The relative standard error of estimate for breast density measurements from left and right breasts using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm and dual-energy material decomposition was calculated to be 1.95, 2.87, 2.07 and 1.00, respectively. Conclusion The results indicate that the precision of dual-energy material decomposition was approximately factor of two higher than the other techniques with regard to better correlation of breast density measurements from right and left breasts. PMID:26031229

No relationship between circulating levels of sex steroids and mammographic breast density: the Prospect-EPIC cohort

PubMed Central

Verheus, Martijn; Peeters, Petra HM; van Noord, Paulus AH; van der Schouw, Yvonne T; Grobbee, Diederick E; van Gils, Carla H

2007-01-01

Background High breast density is associated with increased breast cancer risk. Epidemiologic studies have shown an increase in breast cancer risk in postmenopausal women with high levels of sex steroids. Hence, sex steroids may increase postmenopausal breast cancer risk via an increase of breast density. The objective of the present study was to study the relation between circulating oestrogens and androgens as well as sex hormone binding globulin (SHBG) in relation to breast density. Methods We conducted a cross-sectional study among 775 postmenopausal women, using baseline data of a random sample of the Prospect-EPIC study. Prospect-EPIC is one of two Dutch cohorts participating in the European Prospective Investigation into Cancer and Nutrition, and women were recruited via a breast cancer screening programme. At enrolment a nonfasting blood sample was taken and a mammogram was made. Oestrone, oestradiol, dehydroepiandrosterone sulfate, androstenedione, testosterone and SHBG levels were measured, using double-antibody radioimmunoassays. Concentrations of free oestradiol and free testosterone were calculated from the measured oestradiol, testosterone and SHBG levels Mammographic dense and nondense areas were measured using a semiquantitative computerized method and the percentage breast density was calculated. Mean breast measures for quintiles of hormone or SHBG levels were estimated using linear regression analyses. Results Both oestrogens and testosterone were inversely related with percent breast density, but these relationships disappeared after adjustment for BMI. None of the sex steroids or SHBG was associated with the absolute measure of breast density, the dense area. Conclusion The results of our study do not support the hypothesis that sex steroids increase postmenopausal breast cancer risk via an increase in breast density. PMID:17692133

Breast-density assessment with hand-held ultrasound: A novel biomarker to assess breast cancer risk and to tailor screening?

PubMed

Sanabria, Sergio J; Goksel, Orcun; Martini, Katharina; Forte, Serafino; Frauenfelder, Thomas; Kubik-Huch, Rahel A; Rominger, Marga B

2018-03-19

To assess feasibility and diagnostic accuracy of a novel hand-held ultrasound (US) method for breast density assessment that measures the speed of sound (SoS), in comparison to the ACR mammographic (MG) categories. ACR-MG density (a=fatty to d=extremely dense) and SoS-US were assessed in the retromamillary, inner and outer segments of 106 women by two radiographers. A conventional US system was used for SoS-US. A reflector served as timing reference for US signals transmitted through the breasts. Four blinded readers assessed average SoS (m/s), ΔSoS (segment-variation SoS; m/s) and the ACR-MG density. The highest SoS and ΔSoS values of the three segments were used for MG-ACR whole breast comparison. SoS-US breasts were examined in <2 min. Mean SoS values of densities a-d were 1,421 m/s (SD 14), 1,432 m/s (SD 17), 1,448 m/s (SD 20) and 1,500 m/s (SD 31), with significant differences between all groups (p<0.001). The SoS-US comfort scores and inter-reader agreement were significantly better than those for MG (1.05 vs. 2.05 and 0.982 vs. 0.774; respectively). A strong segment correlation between SoS and ACR-MG breast density was evident (r s =0.622, p=<0.001) and increased for full breast classification (r s =0.746, p=<0.001). SoS-US allowed diagnosis of dense breasts (ACR c and d) with sensitivity 86.2 %, specificity 85.2 % and AUC 0.887. Using hand-held SoS-US, radiographers measured breast density without discomfort, readers evaluated measurements with high inter-reader agreement, and SoS-US correlated significantly with ACR-MG breast-density categories. • The novel speed-of-sound ultrasound correlated significantly with mammographic ACR breast density categories. • Radiographers measured breast density without women discomfort or radiation. • SoS-US can be implemented on a standard US machine. • SoS-US shows potential for a quantifiable, cost-effective assessment of breast density.

Simultaneous quantification of four antiretroviral drugs in breast milk samples from HIV-positive women by an ultra-high performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method

PubMed Central

Rodríguez-Delgado, Rosa Georgina; Figueroa-Damián, Ricardo; Domínguez-Castro, Mauricio; López-Martínez, Margarita; Flores-García, Zayra

2018-01-01

The primary strategy to avoid mother-to-child transmission of human immunodeficiency virus (HIV) through breastfeeding is administration of highly active antiretroviral therapy (HAART) to HIV-positive pregnant women. Because significant changes in the pharmacokinetics of antiretroviral (ARV) drugs occur during pregnancy, quantifying HAART and the viral load in breast milk in this population is essential. Here, we developed an analytical assay for the simultaneous quantification of four ARV drugs in breast milk using ultra-performance liquid chromatography coupled to tandem mass spectrometry. We validated this method following Mexican and international guidelines. ARV drugs. We extracted the ARV drugs from 200 μL samples of breast milk and detected these drugs in a triple quadrupole mass spectrometer with positive electrospray ionization. The validated concentration ranges (ng/mL) for zidovudine, lamivudine, lopinavir, and ritonavir were 12.5–750, 50–2500, 100–5000 and 5 to 250, respectively. Additionally, the absolute recovery percentages (and matrix effects) were 91.4 (8.39), 88.78 (28.75), 91.38 (11.77) and 89.78 (12.37), respectively. We determined that ARV drugs are stable for 24 h at 8°C and 24°C for 15 days at –80°C. This methodology had the capacity for simultaneous detection; separation; and accurate, precise quantification of ARV drugs in human breast milk samples according to Mexican standard laws and United States Food and Drug Administration guidelines. PMID:29351333

Preoperative Breast Magnetic Resonance Imaging Use by Breast Density and Family History of Breast Cancer.

PubMed

Henderson, Louise M; Hubbard, Rebecca A; Zhu, Weiwei; Weiss, Julie; Wernli, Karen J; Goodrich, Martha E; Kerlikowske, Karla; DeMartini, Wendy; Ozanne, Elissa M; Onega, Tracy

2018-01-15

Use of preoperative breast magnetic resonance imaging (MRI) among women with a new breast cancer has increased over the past decade. MRI use is more frequent in younger women and those with lobular carcinoma, but associations with breast density and family history of breast cancer are unknown. Data for 3075 women ages >65 years with stage 0-III breast cancer who underwent breast conserving surgery or mastectomy from 2005 to 2010 in the Breast Cancer Surveillance Consortium were linked to administrative claims data to assess associations of preoperative MRI use with mammographic breast density and first-degree family history of breast cancer. Multivariable logistic regression estimated adjusted odds ratios (OR) and 95% confidence intervals (95% CI) for the association of MRI use with breast density and family history, adjusting for woman and tumor characteristics. Overall, preoperative MRI use was 16.4%. The proportion of women receiving breast MRI was similar by breast density (17.6% dense, 16.9% nondense) and family history (17.1% with family history, 16.5% without family history). After adjusting for potential confounders, we found no difference in preoperative MRI use by breast density (OR = 0.95 for dense vs. nondense, 95% CI: 0.73-1.22) or family history (OR = 0.99 for family history vs. none, 95% CI: 0.73-1.32). Among women aged >65 years with breast cancer, having dense breasts or a first-degree relative with breast cancer was not associated with greater preoperative MRI use. This utilization is in keeping with lack of evidence that MRI has higher yield of malignancy in these subgroups.

Development of a sampling strategy and sample size calculation to estimate the distribution of mammographic breast density in Korean women.

PubMed

Jun, Jae Kwan; Kim, Mi Jin; Choi, Kui Son; Suh, Mina; Jung, Kyu-Won

2012-01-01

Mammographic breast density is a known risk factor for breast cancer. To conduct a survey to estimate the distribution of mammographic breast density in Korean women, appropriate sampling strategies for representative and efficient sampling design were evaluated through simulation. Using the target population from the National Cancer Screening Programme (NCSP) for breast cancer in 2009, we verified the distribution estimate by repeating the simulation 1,000 times using stratified random sampling to investigate the distribution of breast density of 1,340,362 women. According to the simulation results, using a sampling design stratifying the nation into three groups (metropolitan, urban, and rural), with a total sample size of 4,000, we estimated the distribution of breast density in Korean women at a level of 0.01% tolerance. Based on the results of our study, a nationwide survey for estimating the distribution of mammographic breast density among Korean women can be conducted efficiently.

Affinity proteomic profiling of plasma for proteins associated to area-based mammographic breast density.

PubMed

Byström, Sanna; Eklund, Martin; Hong, Mun-Gwan; Fredolini, Claudia; Eriksson, Mikael; Czene, Kamila; Hall, Per; Schwenk, Jochen M; Gabrielson, Marike

2018-02-14

Mammographic breast density is one of the strongest risk factors for breast cancer, but molecular understanding of how breast density relates to cancer risk is less complete. Studies of proteins in blood plasma, possibly associated with mammographic density, are well-suited as these allow large-scale analyses and might shed light on the association between breast cancer and breast density. Plasma samples from 1329 women in the Swedish KARMA project, without prior history of breast cancer, were profiled with antibody suspension bead array (SBA) assays. Two sample sets comprising 729 and 600 women were screened by two different SBAs targeting a total number of 357 proteins. Protein targets were selected through searching the literature, for either being related to breast cancer or for being linked to the extracellular matrix. Association between proteins and absolute area-based breast density (AD) was assessed by quantile regression, adjusting for age and body mass index (BMI). Plasma profiling revealed linear association between 20 proteins and AD, concordant in the two sets of samples (p < 0.05). Plasma levels of seven proteins were positively associated and 13 proteins negatively associated with AD. For eleven of these proteins evidence for gene expression in breast tissue existed. Among these, ABCC11, TNFRSF10D, F11R and ERRF were positively associated with AD, and SHC1, CFLAR, ACOX2, ITGB6, RASSF1, FANCD2 and IRX5 were negatively associated with AD. Screening proteins in plasma indicates associations between breast density and processes of tissue homeostasis, DNA repair, cancer development and/or progression in breast cancer. Further validation and follow-up studies of the shortlisted protein candidates in independent cohorts will be needed to infer their role in breast density and its progression in premenopausal and postmenopausal women.

Longitudinal Association of Anthropometry with Mammographic Breast Density in the Study of Women's Health Across the Nation (Swan)

PubMed Central

Reeves, Katherine W.; Stone, Roslyn A.; Modugno, Francesmary; Ness, Roberta B.; Vogel, Victor G.; Weissfeld, Joel L.; Habel, Laurel A.; Sternfeld, Barbara; Cauley, Jane A.

2009-01-01

High percent mammographic breast density is strongly associated with increased breast cancer risk. Though body mass index (BMI) is positively associated with risk of postmenopausal breast cancer, BMI is negatively associated with percent breast density in cross-sectional studies. Few longitudinal studies have evaluated associations between BMI and weight and mammographic breast density. We studied the longitudinal relationships between anthropometry and breast density in a prospective cohort of 834 pre- and perimenopausal women enrolled in an ancillary study to the Study of Women's Health Across the Nation (SWAN). Routine screening mammograms were collected and read for breast density. Random intercept regression models were used to evaluate whether annual BMI change was associated with changes over time in dense breast area and percent density. The study population was 7.4% African American, 48.8% Caucasian, 21.8% Chinese, and 21.9% Japanese. Mean follow-up was 4.8 years. Mean annual weight change was +0.32 kg/year, mean change in dense area was -0.77 cm2/year, and mean change in percent density was -1.14%/year. In fully adjusted models, annual change in BMI was not significantly associated with changes in dense breast area (-0.17 cm2, 95% CI -0.64, 0.29). Borderline significant negative associations were observed between annual BMI change and annual percent density change, with percent density decreasing 0.36% (95% CI -0.74, 0.02) for a one unit increase in BMI over a year. This longitudinal study provides modest evidence that changes in BMI are not associated with changes in dense area, yet may be negatively associated with percent density. PMID:19065651

Natural History of Breast Density and Breast Cancer Risk

DTIC Science & Technology

2001-07-01

mammography database. We have estimated breast density on the oldest mammogram from both cases and controls, using our semi-automated software and...using the oldest mammogram) with breast cancer risk. Next winter, we will continue these analyses investigating the change in density over time and

Family History of Breast Cancer, Breast Density, and Breast Cancer Risk in a U.S. Breast Cancer Screening Population.

PubMed

Ahern, Thomas P; Sprague, Brian L; Bissell, Michael C S; Miglioretti, Diana L; Buist, Diana S M; Braithwaite, Dejana; Kerlikowske, Karla

2017-06-01

Background: The utility of incorporating detailed family history into breast cancer risk prediction hinges on its independent contribution to breast cancer risk. We evaluated associations between detailed family history and breast cancer risk while accounting for breast density. Methods: We followed 222,019 participants ages 35 to 74 in the Breast Cancer Surveillance Consortium, of whom 2,456 developed invasive breast cancer. We calculated standardized breast cancer risks within joint strata of breast density and simple (1 st -degree female relative) or detailed (first-degree, second-degree, or first- and second-degree female relative) breast cancer family history. We fit log-binomial models to estimate age-specific breast cancer associations for simple and detailed family history, accounting for breast density. Results: Simple first-degree family history was associated with increased breast cancer risk compared with no first-degree history [Risk ratio (RR), 1.5; 95% confidence interval (CI), 1.0-2.1 at age 40; RR, 1.5; 95% CI, 1.3-1.7 at age 50; RR, 1.4; 95% CI, 1.2-1.6 at age 60; RR, 1.3; 95% CI, 1.1-1.5 at age 70). Breast cancer associations with detailed family history were strongest for women with first- and second-degree family history compared with no history (RR, 1.9; 95% CI, 1.1-3.2 at age 40); this association weakened in higher age groups (RR, 1.2; 95% CI, 0.88-1.5 at age 70). Associations did not change substantially when adjusted for breast density. Conclusions: Even with adjustment for breast density, a history of breast cancer in both first- and second-degree relatives is more strongly associated with breast cancer than simple first-degree family history. Impact: Future efforts to improve breast cancer risk prediction models should evaluate detailed family history as a risk factor. Cancer Epidemiol Biomarkers Prev; 26(6); 938-44. ©2017 AACR . ©2017 American Association for Cancer Research.

InforMD: a new initiative to raise public awareness about breast density

PubMed Central

Hugo, Honor J; Zysk, Aneta; Dasari, Pallave; Britt, Kara; Hopper, John L; Stone, Jennifer; Thompson, Erik W; Ingman, Wendy V

2018-01-01

On a mammogram, breast density (also known as mammographic density) is shown as white and bright regions and is associated with reduced sensitivity in cancer detection and increased breast cancer risk. However, many Australian women are unaware of the significance of breast density as it is not routinely reported or discussed. In order to address this lack of knowledge, Australian breast cancer researchers with expertise in mammographic density formed the InforMD alliance (INformation FORum on Mammographic Density) in 2016. The alliance is working to raise awareness of breast density with the goal of improving breast cancer diagnosis and health outcomes for women. The InforMD website (www.InforMD.org.au) was launched in October 2016, coinciding with a major nationwide public awareness campaign by the alliance during breast cancer awareness month. The website contains unbiased, accurate, updated information on breast density. The website also provides summaries of major research articles in layperson language, recent news items related to breast density, links to relevant information for health professionals, events, and feature articles. Members of the public and health professionals can also subscribe for news updates. The interactive online Forum section facilitates discussion between health professionals, scientists and members of the public. To increase online traffic to the website, Facebook (www.facebook.com/BeInforMD) and Twitter (https://twitter.com/BeInforMD_) pages were launched in December 2016. Since its launch, InforMD has generated considerable interest. The public awareness campaign reached over 7 million Australians through a combination of newspaper, TV, radio, and online news. The website has attracted 13,058 unique visitors and 30,353 page views (data as of 19/12/2017). Breast cancer researchers have a significant role to play in disseminating information to the public on breast density. A combination of mainstream and social media, together with a well-informed and updated website, has laid the groundwork for the InforMD alliance to reach a wide audience. PMID:29492101

InforMD: a new initiative to raise public awareness about breast density.

PubMed

Hugo, Honor J; Zysk, Aneta; Dasari, Pallave; Britt, Kara; Hopper, John L; Stone, Jennifer; Thompson, Erik W; Ingman, Wendy V

2018-01-01

On a mammogram, breast density (also known as mammographic density) is shown as white and bright regions and is associated with reduced sensitivity in cancer detection and increased breast cancer risk. However, many Australian women are unaware of the significance of breast density as it is not routinely reported or discussed. In order to address this lack of knowledge, Australian breast cancer researchers with expertise in mammographic density formed the InforMD alliance (INformation FORum on Mammographic Density) in 2016. The alliance is working to raise awareness of breast density with the goal of improving breast cancer diagnosis and health outcomes for women. The InforMD website (www.InforMD.org.au) was launched in October 2016, coinciding with a major nationwide public awareness campaign by the alliance during breast cancer awareness month. The website contains unbiased, accurate, updated information on breast density. The website also provides summaries of major research articles in layperson language, recent news items related to breast density, links to relevant information for health professionals, events, and feature articles. Members of the public and health professionals can also subscribe for news updates. The interactive online Forum section facilitates discussion between health professionals, scientists and members of the public. To increase online traffic to the website, Facebook (www.facebook.com/BeInforMD) and Twitter (https://twitter.com/BeInforMD_) pages were launched in December 2016. Since its launch, InforMD has generated considerable interest. The public awareness campaign reached over 7 million Australians through a combination of newspaper, TV, radio, and online news. The website has attracted 13,058 unique visitors and 30,353 page views (data as of 19/12/2017). Breast cancer researchers have a significant role to play in disseminating information to the public on breast density. A combination of mainstream and social media, together with a well-informed and updated website, has laid the groundwork for the InforMD alliance to reach a wide audience.

Technical evaluation of Virtual Touch™ tissue quantification and elastography in benign and malignant breast tumors

PubMed Central

JIANG, QUAN; ZHANG, YUAN; CHEN, JIAN; ZHANG, YUN-XIAO; HE, ZHU

2014-01-01

The aim of this study was to investigate the diagnostic value of the Virtual Touch™ tissue quantification (VTQ) and elastosonography technologies in benign and malignant breast tumors. Routine preoperative ultrasound, elastosonography and VTQ examinations were performed on 86 patients with breast lesions. The elastosonography score and VTQ speed grouping of each lesion were measured and compared with the pathological findings. The difference in the elastosonography score between the benign and malignant breast tumors was statistically significant (P<0.05). The detection rate for an elastosonography score of 1–3 points in benign tumors was 68.09% and that for an elastosonography score of 4–5 points in malignant tumors was 82.05%. The difference in VTQ speed values between the benign and malignant tumors was also statistically significant (P<0.05). In addition, the diagnostic accuracy of conventional ultrasound, elastosonography, VTQ technology and the combined methods showed statistically significant differences (P<0.05). The use of the three technologies in combination significantly improved the diagnostic accuracy to 91.86%. In conclusion, the combination of conventional ultrasound, elastosonography and VTQ technology can significantly improve accuracy in the diagnosis of breast cancer. PMID:25187797

Breast density and parenchymal texture measures as potential risk factors for estrogen-receptor positive breast cancer

NASA Astrophysics Data System (ADS)

Keller, Brad M.; Chen, Jinbo; Conant, Emily F.; Kontos, Despina

2014-03-01

Accurate assessment of a woman's risk to develop specific subtypes of breast cancer is critical for appropriate utilization of chemopreventative measures, such as with tamoxifen in preventing estrogen-receptor positive breast cancer. In this context, we investigate quantitative measures of breast density and parenchymal texture, measures of glandular tissue content and tissue structure, as risk factors for estrogen-receptor positive (ER+) breast cancer. Mediolateral oblique (MLO) view digital mammograms of the contralateral breast from 106 women with unilateral invasive breast cancer were retrospectively analyzed. Breast density and parenchymal texture were analyzed via fully-automated software. Logistic regression with feature selection and was performed to predict ER+ versus ER- cancer status. A combined model considering all imaging measures extracted was compared to baseline models consisting of density-alone and texture-alone features. Area under the curve (AUC) of the receiver operating characteristic (ROC) and Delong's test were used to compare the models' discriminatory capacity for receptor status. The density-alone model had a discriminatory capacity of 0.62 AUC (p=0.05). The texture-alone model had a higher discriminatory capacity of 0.70 AUC (p=0.001), which was not significantly different compared to the density-alone model (p=0.37). In contrast the combined density-texture logistic regression model had a discriminatory capacity of 0.82 AUC (p<0.001), which was statistically significantly higher than both the density-alone (p<0.001) and texture-alone regression models (p=0.04). The combination of breast density and texture measures may have the potential to identify women specifically at risk for estrogen-receptor positive breast cancer and could be useful in triaging women into appropriate risk-reduction strategies.

Breast density and impacts on health

PubMed Central

Cruwys, Cheryl; Pushkin, JoAnn

2017-01-01

The World Health Organization states ‘Early detection in order to improve breast cancer outcome and survival remains the cornerstone of breast cancer control’ [WHO (World Health Organization) (2017) Breast cancer: prevention and control Available from: http://www.who.int/cancer/detection/breastcancer/en/]. Breast Density Matters UK is a non-profit breast cancer organization. The organization’s mission is to educate about breast density and its screening and risk implications with the goal of achieving the earliest stage diagnosis possible for women with dense breasts. This educational mission is endorsed by breast imaging experts worldwide [Berg WA (2015–2017) Dense Breast-info Inc Available from: http://densebreast-info.org/about.aspx]. PMID:28900477

Breast percent density estimation from 3D reconstructed digital breast tomosynthesis images

NASA Astrophysics Data System (ADS)

Bakic, Predrag R.; Kontos, Despina; Carton, Ann-Katherine; Maidment, Andrew D. A.

2008-03-01

Breast density is an independent factor of breast cancer risk. In mammograms breast density is quantitatively measured as percent density (PD), the percentage of dense (non-fatty) tissue. To date, clinical estimates of PD have varied significantly, in part due to the projective nature of mammography. Digital breast tomosynthesis (DBT) is a 3D imaging modality in which cross-sectional images are reconstructed from a small number of projections acquired at different x-ray tube angles. Preliminary studies suggest that DBT is superior to mammography in tissue visualization, since superimposed anatomical structures present in mammograms are filtered out. We hypothesize that DBT could also provide a more accurate breast density estimation. In this paper, we propose to estimate PD from reconstructed DBT images using a semi-automated thresholding technique. Preprocessing is performed to exclude the image background and the area of the pectoral muscle. Threshold values are selected manually from a small number of reconstructed slices; a combination of these thresholds is applied to each slice throughout the entire reconstructed DBT volume. The proposed method was validated using images of women with recently detected abnormalities or with biopsy-proven cancers; only contralateral breasts were analyzed. The Pearson correlation and kappa coefficients between the breast density estimates from DBT and the corresponding digital mammogram indicate moderate agreement between the two modalities, comparable with our previous results from 2D DBT projections. Percent density appears to be a robust measure for breast density assessment in both 2D and 3D x-ray breast imaging modalities using thresholding.

A case-control study to assess the impact of mammographic density on breast cancer risk in women aged 40-49 at intermediate familial risk.

PubMed

Assi, Valentina; Massat, Nathalie J; Thomas, Susan; MacKay, James; Warwick, Jane; Kataoka, Masako; Warsi, Iqbal; Brentnall, Adam; Warren, Ruth; Duffy, Stephen W

2015-05-15

Mammographic density is a strong risk factor for breast cancer, but its potential application in risk management is not clear, partly due to uncertainties about its interaction with other breast cancer risk factors. We aimed to quantify the impact of mammographic density on breast cancer risk in women aged 40-49 at intermediate familial risk of breast cancer (average lifetime risk of 23%), in particular in premenopausal women, and to investigate its relationship with other breast cancer risk factors in this population. We present the results from a case-control study nested with the FH01 cohort study of 6,710 women mostly aged 40-49 at intermediate familial risk of breast cancer. One hundred and three cases of breast cancer were age-matched to one or two controls. Density was measured by semiautomated interactive thresholding. Absolute density, but not percent density, was a significant risk factor for breast cancer in this population after adjusting for area of nondense tissue (OR per 10 cm(2) = 1.07, 95% CI 1.00-1.15, p = 0.04). The effect was stronger in premenopausal women, who made up the majority of the study population. Absolute density remained a significant predictor of breast cancer risk after adjusting for age at menarche, age at first live birth, parity, past or present hormone replacement therapy, and the Tyrer-Cuzick 10-year relative risk estimate of breast cancer. Absolute density can improve breast cancer risk stratification and delineation of high-risk groups alongside the Tyrer-Cuzick 10-year relative risk estimate. © 2014 UICC.

Increased vitamin D and calcium intake associated with reduced mammographic breast density among premenopausal women

PubMed Central

Fair, Alecia Malin; Lewis, Toni J.; Sanderson, Maureen; Dupont, William D.; Fletcher, Sarah; Egan, Kathleen M.; Disher, Anthony C.

2015-01-01

Vitamin D has been identified as a weak protective factor for postmenopausal breast cancer (relative risk [RR]~0.9), while high breast density has been identified as a strong risk factor (RR~4–6). To test the hypothesis that there is an association between vitamin D intake, but not circulating vitamin D levels, and mammographic breast density among women in our study we conducted a cross-sectional study of 165 screening mammography patients at Nashville General Hospital’s Breast Health Center (NGH-BHC), a public facility serving medically indigent and underserved women. Dietary and total (dietary plus supplements) vitamin D, calcium intakes were estimated by the AAFQ and blood samples were analyzed for 25-Hydroxyvitamin D [25(OH)D3]. Average percent breast density for the left and right breasts combined was estimated from digitized films using an interactive-thresholding method available through Cumulus software. After statistical adjustment for age, race and body mass index, the results revealed there were significant trends of decreasing breast density with increasing vitamin D and calcium intake among premenopausal, but not among postmenopausal women. There was no association between serum vitamin D and breast density in pre- or postmenopausal women. Confirmation of our findings in larger studies may assist in clarifying the role of vitamin D in breast density. PMID:26321093

Detection of lobular structures in normal breast tissue.

PubMed

Apou, Grégory; Schaadt, Nadine S; Naegel, Benoît; Forestier, Germain; Schönmeyer, Ralf; Feuerhake, Friedrich; Wemmert, Cédric; Grote, Anne

2016-07-01

Ongoing research into inflammatory conditions raises an increasing need to evaluate immune cells in histological sections in biologically relevant regions of interest (ROIs). Herein, we compare different approaches to automatically detect lobular structures in human normal breast tissue in digitized whole slide images (WSIs). This automation is required to perform objective and consistent quantitative studies on large data sets. In normal breast tissue from nine healthy patients immunohistochemically stained for different markers, we evaluated and compared three different image analysis methods to automatically detect lobular structures in WSIs: (1) a bottom-up approach using the cell-based data for subsequent tissue level classification, (2) a top-down method starting with texture classification at tissue level analysis of cell densities in specific ROIs, and (3) a direct texture classification using deep learning technology. All three methods result in comparable overall quality allowing automated detection of lobular structures with minor advantage in sensitivity (approach 3), specificity (approach 2), or processing time (approach 1). Combining the outputs of the approaches further improved the precision. Different approaches of automated ROI detection are feasible and should be selected according to the individual needs of biomarker research. Additionally, detected ROIs could be used as a basis for quantification of immune infiltration in lobular structures. Copyright © 2016 Elsevier Ltd. All rights reserved.

Computing mammographic density from a multiple regression model constructed with image-acquisition parameters from a full-field digital mammographic unit

PubMed Central

Lu, Lee-Jane W.; Nishino, Thomas K.; Khamapirad, Tuenchit; Grady, James J; Leonard, Morton H.; Brunder, Donald G.

2009-01-01

Breast density (the percentage of fibroglandular tissue in the breast) has been suggested to be a useful surrogate marker for breast cancer risk. It is conventionally measured using screen-film mammographic images by a labor intensive histogram segmentation method (HSM). We have adapted and modified the HSM for measuring breast density from raw digital mammograms acquired by full-field digital mammography. Multiple regression model analyses showed that many of the instrument parameters for acquiring the screening mammograms (e.g. breast compression thickness, radiological thickness, radiation dose, compression force, etc) and image pixel intensity statistics of the imaged breasts were strong predictors of the observed threshold values (model R2=0.93) and %density (R2=0.84). The intra-class correlation coefficient of the %-density for duplicate images was estimated to be 0.80, using the regression model-derived threshold values, and 0.94 if estimated directly from the parameter estimates of the %-density prediction regression model. Therefore, with additional research, these mathematical models could be used to compute breast density objectively, automatically bypassing the HSM step, and could greatly facilitate breast cancer research studies. PMID:17671343

Radiological assessment of breast density by visual classification (BI-RADS) compared to automated volumetric digital software (Quantra): implications for clinical practice.

PubMed

Regini, Elisa; Mariscotti, Giovanna; Durando, Manuela; Ghione, Gianluca; Luparia, Andrea; Campanino, Pier Paolo; Bianchi, Caterina Chiara; Bergamasco, Laura; Fonio, Paolo; Gandini, Giovanni

2014-10-01

This study was done to assess breast density on digital mammography and digital breast tomosynthesis according to the visual Breast Imaging Reporting and Data System (BI-RADS) classification, to compare visual assessment with Quantra software for automated density measurement, and to establish the role of the software in clinical practice. We analysed 200 digital mammograms performed in 2D and 3D modality, 100 of which positive for breast cancer and 100 negative. Radiological density was assessed with the BI-RADS classification; a Quantra density cut-off value was sought on the 2D images only to discriminate between BI-RADS categories 1-2 and BI-RADS 3-4. Breast density was correlated with age, use of hormone therapy, and increased risk of disease. The agreement between the 2D and 3D assessments of BI-RADS density was high (K 0.96). A cut-off value of 21% is that which allows us to best discriminate between BI-RADS categories 1-2 and 3-4. Breast density was negatively correlated to age (r = -0.44) and positively to use of hormone therapy (p = 0.0004). Quantra density was higher in breasts with cancer than in healthy breasts. There is no clear difference between the visual assessments of density on 2D and 3D images. Use of the automated system requires the adoption of a cut-off value (set at 21%) to effectively discriminate BI-RADS 1-2 and 3-4, and could be useful in clinical practice.

Associations between breast density and a panel of single nucleotide polymorphisms linked to breast cancer risk: a cohort study with digital mammography.

PubMed

Keller, Brad M; McCarthy, Anne Marie; Chen, Jinbo; Armstrong, Katrina; Conant, Emily F; Domchek, Susan M; Kontos, Despina

2015-03-18

Breast density and single-nucleotide polymorphisms (SNPs) have both been associated with breast cancer risk. To determine the extent to which these two breast cancer risk factors are associated, we investigate the association between a panel of validated SNPs related to breast cancer and quantitative measures of mammographic density in a cohort of Caucasian and African-American women. In this IRB-approved, HIPAA-compliant study, we analyzed a screening population of 639 women (250 African American and 389 Caucasian) who were tested with a validated panel assay of 12 SNPs previously associated to breast cancer risk. Each woman underwent digital mammography as part of routine screening and all were interpreted as negative. Both absolute and percent estimates of area and volumetric density were quantified on a per-woman basis using validated software. Associations between the number of risk alleles in each SNP and the density measures were assessed through a race-stratified linear regression analysis, adjusted for age, BMI, and Gail lifetime risk. The majority of SNPs were not found to be associated with any measure of breast density. SNP rs3817198 (in LSP1) was significantly associated with both absolute area (p = 0.004) and volumetric (p = 0.019) breast density in Caucasian women. In African-American women, SNPs rs3803662 (in TNRC9/TOX3) and rs4973768 (in NEK10) were significantly associated with absolute (p = 0.042) and percent (p = 0.028) volume density respectively. The majority of SNPs investigated in our study were not found to be significantly associated with breast density, even when accounting for age, BMI, and Gail risk, suggesting that these two different risk factors contain potentially independent information regarding a woman's risk to develop breast cancer. Additionally, the few statistically significant associations between breast density and SNPs were different for Caucasian versus African American women. Larger prospective studies are warranted to validate our findings and determine potential implications for breast cancer risk assessment.

Correlations between female breast density and biochemical markers.

PubMed

Kim, Ji-Hye; Lee, Hae-Kag; Cho, Jae-Hwan; Park, Hyong-Keun; Yang, Han-Jun

2015-07-01

[Purpose] The aim of this study was to identify biochemical markers related to breast density. The study was performed with 200 patients who received mammography and biochemical marker testing between March 1, 2014 to October 1, 2014. [Subjects and Methods] Following the American College of Radiology, Breast Imaging Reporting and Data System (ACR BI-RADS), breast parenchymal pattern density from mammography was categorized into four grades: grade 1, almost entirely fat; grade 2, fibroglandular densities; grade 3, heterogeneously dense; and grade 4, extremely dense. Regarding biochemical markers, subjects underwent blood and urine tests after a 12-h fast. We analyzed correlations among breast density, general characteristics, and biochemical markers. [Results] Breast density-related factors were age, height, weight, body mass index (BMI), hematocrit, MCH, RDW, AST, ALT, ALP, uric acid, γGT, triglycerides, total cholesterol, HDL-cholesterol, and LDL-cholesterol. [Conclusion] The results can be used as basic and comparative data for the prevention and early control of breast cancer.

The Problem of Mammographic Breast Density - The Position of the DEGUM Working Group on Breast Ultrasound.

PubMed

Mueller-Schimpfle, M P; Brandenbusch, V C; Degenhardt, F; Duda, V; Madjar, H; Mundinger, A; Rathmann, R; Hahn, M

2016-04-01

Mammographic breast density correlates with breast cancer risk and also with the number of false-negative calls. In the USA these facts lead to the "Breast Density and Mammography Reporting Act" of 2011. In the case of mammographically dense breasts, the Working Group on Breast Ultrasound in Germany recommends explaining the advantages of adjunct imaging to women, depending on the individual breast cancer risk. Due to the particular structure of German healthcare, quality-assured breast ultrasound would be the first choice. Possible overdiagnosis, costs, potentially increased emotional stress should be addressed. In high familial breast cancer risk, genetic counselling and an intensified early detection program should be performed. © Georg Thieme Verlag KG Stuttgart · New York.

Automatic breast tissue density estimation scheme in digital mammography images

NASA Astrophysics Data System (ADS)

Menechelli, Renan C.; Pacheco, Ana Luisa V.; Schiabel, Homero

2017-03-01

Cases of breast cancer have increased substantially each year. However, radiologists are subject to subjectivity and failures of interpretation which may affect the final diagnosis in this examination. The high density features in breast tissue are important factors related to these failures. Thus, among many functions some CADx (Computer-Aided Diagnosis) schemes are classifying breasts according to the predominant density. In order to aid in such a procedure, this work attempts to describe automated software for classification and statistical information on the percentage change in breast tissue density, through analysis of sub regions (ROIs) from the whole mammography image. Once the breast is segmented, the image is divided into regions from which texture features are extracted. Then an artificial neural network MLP was used to categorize ROIs. Experienced radiologists have previously determined the ROIs density classification, which was the reference to the software evaluation. From tests results its average accuracy was 88.7% in ROIs classification, and 83.25% in the classification of the whole breast density in the 4 BI-RADS density classes - taking into account a set of 400 images. Furthermore, when considering only a simplified two classes division (high and low densities) the classifier accuracy reached 93.5%, with AUC = 0.95.

Tailoring Breast Cancer Screening Intervals by Breast Density and Risk for Women Aged 50 Years or Older: Collaborative Modeling of Screening Outcomes.

PubMed

Trentham-Dietz, Amy; Kerlikowske, Karla; Stout, Natasha K; Miglioretti, Diana L; Schechter, Clyde B; Ergun, Mehmet Ali; van den Broek, Jeroen J; Alagoz, Oguzhan; Sprague, Brian L; van Ravesteyn, Nicolien T; Near, Aimee M; Gangnon, Ronald E; Hampton, John M; Chandler, Young; de Koning, Harry J; Mandelblatt, Jeanne S; Tosteson, Anna N A

2016-11-15

Biennial screening is generally recommended for average-risk women aged 50 to 74 years, but tailored screening may provide greater benefits. To estimate outcomes for various screening intervals after age 50 years based on breast density and risk for breast cancer. Collaborative simulation modeling using national incidence, breast density, and screening performance data. United States. Women aged 50 years or older with various combinations of breast density and relative risk (RR) of 1.0, 1.3, 2.0, or 4.0. Annual, biennial, or triennial digital mammography screening from ages 50 to 74 years (vs. no screening) and ages 65 to 74 years (vs. biennial digital mammography from ages 50 to 64 years). Lifetime breast cancer deaths, life expectancy and quality-adjusted life-years (QALYs), false-positive mammograms, benign biopsy results, overdiagnosis, cost-effectiveness, and ratio of false-positive results to breast cancer deaths averted. Screening benefits and overdiagnosis increase with breast density and RR. False-positive mammograms and benign results on biopsy decrease with increasing risk. Among women with fatty breasts or scattered fibroglandular density and an RR of 1.0 or 1.3, breast cancer deaths averted were similar for triennial versus biennial screening for both age groups (50 to 74 years, median of 3.4 to 5.1 vs. 4.1 to 6.5 deaths averted; 65 to 74 years, median of 1.5 to 2.1 vs. 1.8 to 2.6 deaths averted). Breast cancer deaths averted increased with annual versus biennial screening for women aged 50 to 74 years at all levels of breast density and an RR of 4.0, and those aged 65 to 74 years with heterogeneously or extremely dense breasts and an RR of 4.0. However, harms were almost 2-fold higher. Triennial screening for the average-risk subgroup and annual screening for the highest-risk subgroup cost less than $100 000 per QALY gained. Models did not consider women younger than 50 years, those with an RR less than 1, or other imaging methods. Average-risk women with low breast density undergoing triennial screening and higher-risk women with high breast density receiving annual screening will maintain a similar or better balance of benefits and harms than average-risk women receiving biennial screening. National Cancer Institute.

Clinical application of a microfluidic chip for immunocapture and quantification of circulating exosomes to assist breast cancer diagnosis and molecular classification.

PubMed

Fang, Shimeng; Tian, Hongzhu; Li, Xiancheng; Jin, Dong; Li, Xiaojie; Kong, Jing; Yang, Chun; Yang, Xuesong; Lu, Yao; Luo, Yong; Lin, Bingcheng; Niu, Weidong; Liu, Tingjiao

2017-01-01

Increasing attention has been attracted by exosomes in blood-based diagnosis because cancer cells release more exosomes in serum than normal cells and these exosomes overexpress a certain number of cancer-related biomarkers. However, capture and biomarker analysis of exosomes for clinical application are technically challenging. In this study, we developed a microfluidic chip for immunocapture and quantification of circulating exosomes from small sample volume and applied this device in clinical study. Circulating EpCAM-positive exosomes were measured in 6 cases breast cancer patients and 3 healthy controls to assist diagnosis. A significant increase in the EpCAM-positive exosome level in these patients was detected, compared to healthy controls. Furthermore, we quantified circulating HER2-positive exosomes in 19 cases of breast cancer patients for molecular classification. We demonstrated that the exosomal HER2 expression levels were almost consistent with that in tumor tissues assessed by immunohistochemical staining. The microfluidic chip might provide a new platform to assist breast cancer diagnosis and molecular classification.

Fully automated gynecomastia quantification from low-dose chest CT

NASA Astrophysics Data System (ADS)

Liu, Shuang; Sonnenblick, Emily B.; Azour, Lea; Yankelevitz, David F.; Henschke, Claudia I.; Reeves, Anthony P.

2018-02-01

Gynecomastia is characterized by the enlargement of male breasts, which is a common and sometimes distressing condition found in over half of adult men over the age of 44. Although the majority of gynecomastia is physiologic or idiopathic, its occurrence may also associate with an extensive variety of underlying systemic disease or drug toxicity. With the recent large-scale implementation of annual lung cancer screening using low-dose chest CT (LDCT), gynecomastia is believed to be a frequent incidental finding on LDCT. A fully automated system for gynecomastia quantification from LDCT is presented in this paper. The whole breast region is first segmented using an anatomyorientated approach based on the propagation of pectoral muscle fronts in the vertical direction. The subareolar region is then localized, and the fibroglandular tissue within it is measured for the assessment of gynecomastia. The presented system was validated using 454 breast regions from non-contrast LDCT scans of 227 adult men. The ground truth was established by an experienced radiologist by classifying each breast into one of the five categorical scores. The automated measurements have been demonstrated to achieve promising performance for the gynecomastia diagnosis with the AUC of 0.86 for the ROC curve and have statistically significant Spearman correlation r=0.70 (p < 0.001) with the reference categorical grades. The encouraging results demonstrate the feasibility of fully automated gynecomastia quantification from LDCT, which may aid the early detection as well as the treatment of both gynecomastia and the underlying medical problems, if any, that cause gynecomastia.

Breast Density Assessment by Dual Energy X-ray Absorptiometry in Women and Girls

DTIC Science & Technology

2009-07-01

of this project among adult women and adolescent girls, who will be recruited as mothers and daughters, will be to 1. Correlate breast density...scans to their association with mammographic density; 3. Assess DXA breast density by Tanner stage of breast maturation among adolescent girls; 4...cancer risk, adolescents 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF RESPONSIBLE PERSON USAMRMC

Combining quantitative and qualitative breast density measures to assess breast cancer risk.

PubMed

Kerlikowske, Karla; Ma, Lin; Scott, Christopher G; Mahmoudzadeh, Amir P; Jensen, Matthew R; Sprague, Brian L; Henderson, Louise M; Pankratz, V Shane; Cummings, Steven R; Miglioretti, Diana L; Vachon, Celine M; Shepherd, John A

2017-08-22

Accurately identifying women with dense breasts (Breast Imaging Reporting and Data System [BI-RADS] heterogeneously or extremely dense) who are at high breast cancer risk will facilitate discussions of supplemental imaging and primary prevention. We examined the independent contribution of dense breast volume and BI-RADS breast density to predict invasive breast cancer and whether dense breast volume combined with Breast Cancer Surveillance Consortium (BCSC) risk model factors (age, race/ethnicity, family history of breast cancer, history of breast biopsy, and BI-RADS breast density) improves identifying women with dense breasts at high breast cancer risk. We conducted a case-control study of 1720 women with invasive cancer and 3686 control subjects. We calculated ORs and 95% CIs for the effect of BI-RADS breast density and Volpara™ automated dense breast volume on invasive cancer risk, adjusting for other BCSC risk model factors plus body mass index (BMI), and we compared C-statistics between models. We calculated BCSC 5-year breast cancer risk, incorporating the adjusted ORs associated with dense breast volume. Compared with women with BI-RADS scattered fibroglandular densities and second-quartile dense breast volume, women with BI-RADS extremely dense breasts and third- or fourth-quartile dense breast volume (75% of women with extremely dense breasts) had high breast cancer risk (OR 2.87, 95% CI 1.84-4.47, and OR 2.56, 95% CI 1.87-3.52, respectively), whereas women with extremely dense breasts and first- or second-quartile dense breast volume were not at significantly increased breast cancer risk (OR 1.53, 95% CI 0.75-3.09, and OR 1.50, 95% CI 0.82-2.73, respectively). Adding continuous dense breast volume to a model with BCSC risk model factors and BMI increased discriminatory accuracy compared with a model with only BCSC risk model factors (C-statistic 0.639, 95% CI 0.623-0.654, vs. C-statistic 0.614, 95% CI 0.598-0.630, respectively; P < 0.001). Women with dense breasts and fourth-quartile dense breast volume had a BCSC 5-year risk of 2.5%, whereas women with dense breasts and first-quartile dense breast volume had a 5-year risk ≤ 1.8%. Risk models with automated dense breast volume combined with BI-RADS breast density may better identify women with dense breasts at high breast cancer risk than risk models with either measure alone.

Impact of Breast Density Legislation on Breast Cancer Risk Assessment and Supplemental Screening: A Survey of 110 Radiology Facilities.

PubMed

Nayak, Lina; Miyake, Kanae K; Leung, Jessica W T; Price, Elissa R; Liu, Yueyi I; Joe, Bonnie N; Sickles, Edward A; Thomas, William R; Lipson, Jafi A; Daniel, Bruce L; Hargreaves, Jonathan; Brenner, R James; Bassett, Lawrence W; Ojeda-Fournier, Haydee; Lindfors, Karen K; Feig, Stephen A; Ikeda, Debra M

2016-09-01

Breast density notification laws, passed in 19 states as of October 2014, mandate that patients be informed of their breast density. The purpose of this study is to assess the impact of this legislation on radiology practices, including performance of breast cancer risk assessment and supplemental screening studies. A 20-question anonymous web-based survey was emailed to radiologists in the Society of Breast Imaging between August 2013 and March 2014. Statistical analysis was performed using Fisher's exact test. Around 121 radiologists from 110 facilities in 34 USA states and 1 Canadian site responded. About 50% (55/110) of facilities had breast density legislation, 36% of facilities (39/109) performed breast cancer risk assessment (one facility did not respond). Risk assessment was performed as a new task in response to density legislation in 40% (6/15) of facilities in states with notification laws. However, there was no significant difference in performing risk assessment between facilities in states with a law and those without (p < 0.831). In anticipation of breast density legislation, 33% (16/48), 6% (3/48), and 6% (3/48) of facilities in states with laws implemented handheld whole breast ultrasound (WBUS), automated WBUS, and tomosynthesis, respectively. The ratio of facilities offering handheld WBUS was significantly higher in states with a law than in states without (p < 0.001). In response to breast density legislation, more than 33% of facilities are offering supplemental screening with WBUS and tomosynthesis, and many are performing formal risk assessment for determining patient management. © 2016 Wiley Periodicals, Inc.

The role of cone-beam breast-CT for breast cancer detection relative to breast density.

PubMed

Wienbeck, Susanne; Uhlig, Johannes; Luftner-Nagel, Susanne; Zapf, Antonia; Surov, Alexey; von Fintel, Eva; Stahnke, Vera; Lotz, Joachim; Fischer, Uwe

2017-12-01

To evaluate the impact of breast density on the diagnostic accuracy of non-contrast cone-beam breast computed tomography (CBBCT) in comparison to mammography for the detection of breast masses. A retrospective study was conducted from August 2015 to July 2016. Fifty-nine patients (65 breasts, 112 lesions) with BI-RADS, 5th edition 4 or 5 assessment in mammography and/or ultrasound of the breast received an additional non-contrast CBBCT. Independent double blind reading by two radiologists was performed for mammography and CBBCT imaging. Sensitivity, specificity and AUC were compared between the modalities. Breast lesions were histologically examined in 85 of 112 lesions (76%). The overall sensitivity for CBBCT (reader 1: 91%, reader 2: 88%) was higher than in mammography (both: 68%, p<0.001), and also for the high-density group (p<0.05). The specificity and AUC was higher for mammography in comparison to CBBCT (p<0.05 and p<0.001). The interobserver agreement (ICC) between the readers was 90% (95% CI: 86-93%) for mammography and 87% (95% CI: 82-91%) for CBBCT. Compared with two-view mammography, non-contrast CBBCT has higher sensitivity, lower specificity, and lower AUC for breast mass detection in both high and low density breasts. • Overall sensitivity for non-contrast CBBCT ranged between 88%-91%. • Sensitivity was higher for CBBCT than mammography in both density types (p<0.001). • Specificity was higher for mammography than CBBCT in both density types (p<0.05). • AUC was larger for mammography than CBBCT in both density types (p<0.001).

CFS-SMO based classification of breast density using multiple texture models.

PubMed

Sharma, Vipul; Singh, Sukhwinder

2014-06-01

It is highly acknowledged in the medical profession that density of breast tissue is a major cause for the growth of breast cancer. Increased breast density was found to be linked with an increased risk of breast cancer growth, as high density makes it difficult for radiologists to see an abnormality which leads to false negative results. Therefore, there is need for the development of highly efficient techniques for breast tissue classification based on density. This paper presents a hybrid scheme for classification of fatty and dense mammograms using correlation-based feature selection (CFS) and sequential minimal optimization (SMO). In this work, texture analysis is done on a region of interest selected from the mammogram. Various texture models have been used to quantify the texture of parenchymal patterns of breast. To reduce the dimensionality and to identify the features which differentiate between breast tissue densities, CFS is used. Finally, classification is performed using SMO. The performance is evaluated using 322 images of mini-MIAS database. Highest accuracy of 96.46% is obtained for two-class problem (fatty and dense) using proposed approach. Performance of selected features by CFS is also evaluated by Naïve Bayes, Multilayer Perceptron, RBF Network, J48 and kNN classifier. The proposed CFS-SMO method outperforms all other classifiers giving a sensitivity of 100%. This makes it suitable to be taken as a second opinion in classifying breast tissue density.

Estimation of percentage breast tissue density: comparison between digital mammography (2D full field digital mammography) and digital breast tomosynthesis according to different BI-RADS categories.

PubMed

Tagliafico, A S; Tagliafico, G; Cavagnetto, F; Calabrese, M; Houssami, N

2013-11-01

To compare breast density estimated from two-dimensional full-field digital mammography (2D FFDM) and from digital breast tomosynthesis (DBT) according to different Breast Imaging-Reporting and Data System (BI-RADS) categories, using automated software. Institutional review board approval and written informed patient consent were obtained. DBT and 2D FFDM were performed in the same patients to allow within-patient comparison. A total of 160 consecutive patients (mean age: 50±14 years; mean body mass index: 22±3) were included to create paired data sets of 40 patients for each BI-RADS category. Automatic software (MedDensity(©), developed by Giulio Tagliafico) was used to compare the percentage breast density between DBT and 2D FFDM. The estimated breast percentage density obtained using DBT and 2D FFDM was examined for correlation with the radiologists' visual BI-RADS density classification. The 2D FFDM differed from DBT by 16.0% in BI-RADS Category 1, by 11.9% in Category 2, by 3.5% in Category 3 and by 18.1% in Category 4. These differences were highly significant (p<0.0001). There was a good correlation between the BI-RADS categories and the density evaluated using 2D FFDM and DBT (r=0.56, p<0.01 and r=0.48, p<0.01, respectively). Using DBT, breast density values were lower than those obtained using 2D FFDM, with a non-linear relationship across the BI-RADS categories. These data are relevant for clinical practice and research studies using density in determining the risk. On DBT, breast density values were lower than with 2D FFDM, with a non-linear relationship across the classical BI-RADS categories.

Reproducible automated breast density measure with no ionizing radiation using fat-water decomposition MRI.

PubMed

Ding, Jie; Stopeck, Alison T; Gao, Yi; Marron, Marilyn T; Wertheim, Betsy C; Altbach, Maria I; Galons, Jean-Philippe; Roe, Denise J; Wang, Fang; Maskarinec, Gertraud; Thomson, Cynthia A; Thompson, Patricia A; Huang, Chuan

2018-04-06

Increased breast density is a significant independent risk factor for breast cancer, and recent studies show that this risk is modifiable. Hence, breast density measures sensitive to small changes are desired. Utilizing fat-water decomposition MRI, we propose an automated, reproducible breast density measurement, which is nonionizing and directly comparable to mammographic density (MD). Retrospective study. The study included two sample sets of breast cancer patients enrolled in a clinical trial, for concordance analysis with MD (40 patients) and reproducibility analysis (10 patients). The majority of MRI scans (59 scans) were performed with a 1.5T GE Signa scanner using radial IDEAL-GRASE sequence, while the remaining (seven scans) were performed with a 3T Siemens Skyra using 3D Cartesian 6-echo GRE sequence with a similar fat-water separation technique. After automated breast segmentation, breast density was calculated using FraGW, a new measure developed to reliably reflect the amount of fibroglandular tissue and total water content in the entire breast. Based on its concordance with MD, FraGW was calibrated to MR-based breast density (MRD) to be comparable to MD. A previous breast density measurement, Fra80-the ratio of breast voxels with <80% fat fraction-was also calculated for comparison with FraGW. Pearson correlation was performed between MD (reference standard) and FraGW (and Fra80). Test-retest reproducibility of MRD was evaluated using the difference between test-retest measures (Δ 1-2 ) and intraclass correlation coefficient (ICC). Both FraGW and Fra80 were strongly correlated with MD (Pearson ρ: 0.96 vs. 0.90, both P < 0.0001). MRD converted from FraGW showed higher test-retest reproducibility (Δ 1-2 variation: 1.1% ± 1.2%; ICC: 0.99) compared to MD itself (literature intrareader ICC ≤0.96) and Fra80. The proposed MRD is directly comparable with MD and highly reproducible, which enables the early detection of small breast density changes and treatment response. 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018. © 2018 International Society for Magnetic Resonance in Medicine.

Breast Density and Risk of Breast Cancer in Asian Women: A Meta-analysis of Observational Studies.

PubMed

Bae, Jong-Myon; Kim, Eun Hee

2016-11-01

The established theory that breast density is an independent predictor of breast cancer risk is based on studies targeting white women in the West. More Asian women than Western women have dense breasts, but the incidence of breast cancer is lower among Asian women. This meta-analysis investigated the association between breast density in mammography and breast cancer risk in Asian women. PubMed and Scopus were searched, and the final date of publication was set as December 31, 2015. The effect size in each article was calculated using the interval-collapse method. Summary effect sizes (sESs) and 95% confidence intervals (CIs) were calculated by conducting a meta-analysis applying a random effect model. To investigate the dose-response relationship, random effect dose-response meta-regression (RE-DRMR) was conducted. Six analytical epidemiology studies in total were selected, including one cohort study and five case-control studies. A total of 17 datasets were constructed by type of breast density index and menopausal status. In analyzing the subgroups of premenopausal vs. postmenopausal women, the percent density (PD) index was confirmed to be associated with a significantly elevated risk for breast cancer (sES, 2.21; 95% CI, 1.52 to 3.21; I 2 =50.0%). The RE-DRMR results showed that the risk of breast cancer increased 1.73 times for each 25% increase in PD in postmenopausal women (95% CI, 1.20 to 2.47). In Asian women, breast cancer risk increased with breast density measured using the PD index, regardless of menopausal status. We propose the further development of a breast cancer risk prediction model based on the application of PD in Asian women.

Diffuse optical tomography with structured-light patterns to quantify breast density

NASA Astrophysics Data System (ADS)

Kwong, Jessica; Nouizi, Farouk; Cho, Jaedu; Zheng, Jie; Li, Yifan; Chen, Jeon-hor; Su, Min-Ying; Gulsen, Gultekin

2016-02-01

Breast density is an independent risk factor for breast cancer, where women with denser breasts are more likely to develop cancer. By identifying women at higher risk, healthcare providers can suggest screening at a younger age to effectively diagnose and treat breast cancer in its earlier stages. Clinical risk assessment models currently do not incorporate breast density, despite its strong correlation with breast cancer. Current methods to measure breast density rely on mammography and MRI, both of which may be difficult to use as a routine risk assessment tool. We propose to use diffuse optical tomography with structured-light to measure the dense, fibroglandular (FGT) tissue volume, which has a different chromophore signature than the surrounding adipose tissue. To test the ability of this technique, we performed simulations by creating numerical breast phantoms from segmented breast MR images. We looked at two different cases, one with a centralized FGT distribution and one with a dispersed distribution. As expected, the water and lipid volumes segmented at half-maximum were overestimated for the dispersed case. However, it was noticed that the recovered water and lipid concentrations were lower and higher, respectively, than the centralized case. This information may provide insight into the morphological distribution of the FGT and can be a correction in estimating the breast density.

Positive association between mammographic breast density and bone mineral density in the Postmenopausal Estrogen/Progestin Interventions Study

PubMed Central

Crandall, Carolyn; Palla, Shana; Reboussin, Beth A; Ursin, Giske; Greendale, Gail A

2005-01-01

Introduction Mammographic breast density is a strong independent risk factor for breast cancer. We hypothesized that demonstration of an association between mammographic breast density and bone mineral density (BMD) would suggest a unifying underlying mechanism influencing both breast density and BMD. Methods In a cross-sectional analysis of baseline data from the Postmenopausal Estrogen/Progestin Interventions Study (PEPI), participants were aged 45 to 64 years and were at least 1 year postmenopausal. Mammographic breast density (percentage of the breast composed of dense tissue), the outcome, was assessed with a computer-assisted percentage-density method. BMD, the primary predictor, was measured with dual-energy X-ray absorptiometry. Women quitting menopausal hormone therapy to join PEPI were designated recent hormone users. Results The mean age of the 594 women was 56 years. The average time since menopause was 5.6 years. After adjustment for age, body mass index, and cigarette smoking, in women who were not recent hormone users before trial enrollment (n = 415), mammographic density was positively associated with total hip (P = 0.04) and lumbar (P = 0.08) BMD. Mammographic density of recent hormone users (n = 171) was not significantly related to either total hip (P = 0.51) or lumbar (P = 0.44) BMD. In participants who were not recent hormone users, mammographic density was 4% greater in the highest quartile of total hip BMD than in the lowest. In participants who were not recent hormone users, mammographic density was 5% greater in the highest quartile of lumbar spine BMD than in the lowest. Conclusion Mammographic density and BMD are positively associated in women who have not recently used postmenopausal hormones. A unifying biological mechanism may link mammographic density and BMD. Recent exogenous postmenopausal hormone use may obscure the association between mammographic density and BMD by having a persistent effect on breast tissue. PMID:16280044

The relationship between breast density and bone mineral density in postmenopausal women.

PubMed

Buist, Diana S M; Anderson, Melissa L; Taplin, Stephen H; LaCroix, Andrea Z

2004-11-01

It is not well understood whether breast density is a marker of cumulative exposure to estrogen or a marker of recent exposure to estrogen. The authors examined the relationship between bone mineral density (BMD; a marker of lifetime estrogen exposure) and breast density. The authors conducted a cross-sectional analysis among 1800 postmenopausal women > or = 54 years. BMD data were taken from two population-based studies conducted in 1992-1993 (n = 1055) and in 1998-1999 (n = 753). The authors linked BMD data with breast density information collected as part of a mammography screening program. They used linear regression to evaluate the density relationship, adjusted for age, hormone therapy use, body mass index (BMI), and reproductive covariates. There was a small but significant negative association between BMD and breast density. The negative correlation between density measures was not explained by hormone therapy or age, and BMI was the only covariate that notably influenced the relationship. Stratification by BMI only revealed the negative correlation between bone and breast densities in women with normal BMI. There was no relationship in overweight or obese women. The same relationship was seen for all women who had never used hormone therapy, but it was not significant once stratified by BMI. BMD and breast density were not positively associated although both are independently associated with estrogen exposure. It is likely that unique organ responses obscure the relationship between the two as indicators of cumulative estrogen exposure.

Misclassification of Breast Imaging Reporting and Data System (BI-RADS) mammographic density and implications for breast density reporting legislation

PubMed Central

Gard, Charlotte C.; Aiello Bowles, Erin J.; Miglioretti, Diana L.; Taplin, Stephen H.; Rutter, Carolyn M.

2015-01-01

U.S. states have begun legislating mammographic breast density reporting to women, requiring that women undergoing screening mammography who have dense breast tissue (BI-RADS density c or d) receive written notification of their breast density; however, the impact that misclassification of breast density will have on this reporting remains unclear. The aim of this study was to assess reproducibility of the four-category Breast Imaging Reporting and Data System (BI-RADS) density measure and examine its relationship with a continuous measure of percent density. We enrolled 19 radiologists, experienced in breast imaging, from a single integrated healthcare system. Radiologists interpreted 341 screening mammograms at two points in time six months apart. We assessed intra- and inter-observer agreement in radiologists’ interpretations of BI-RADS density and explored whether agreement depended upon radiologist characteristics. We examined the relationship between BI-RADS density and percent density in a subset of 282 examinations. Intra-radiologist agreement was moderate to substantial, with kappa varying across radiologists from 0.50–0.81 (mean=0.69, 95% CI (0.63, 0.73)). Intra-radiologist agreement was higher for radiologists with ≥10 years experience interpreting mammograms (difference in mean kappa=0.10, 95% CI (0.01, 0.24)). Inter-radiologist agreement varied widely across radiologist pairs from slight to substantial, with kappa ranging from 0.02–0.72 (mean=0.46, 95% CI (0.36, 0.55)). Of 145 examinations interpreted as “non-dense” (BI-RADS density a or b) by the majority of radiologists, 82.8% were interpreted as “dense” (BI-RADS density c or d) by at least one radiologist. Of 187 examinations interpreted as “dense” by the majority of radiologists, 47.1% were interpreted as “non-dense” by at least one radiologist. While the examinations of almost half of the women in our study were interpreted clinically as having BI-RADS density c or d, only about 10% of examinations had percent density >50%. Our results suggest that breast density reporting based on a single BI-RADS density interpretation may be misleading due to high inter-radiologist variability and a lack of correspondence between BI-RADS density and percent density. PMID:26133090

Analysis of parenchymal patterns using conspicuous spatial frequency features in mammograms applied to the BI-RADS density rating scheme

NASA Astrophysics Data System (ADS)

Perconti, Philip; Loew, Murray

2006-03-01

Automatic classification of the density of breast parenchyma is shown using a measure that is correlated to the human observer performance, and compared against the BI-RADS density rating. Increasingly popular in the United States, the Breast Imaging Reporting and Data System (BI-RADS) is used to draw attention to the increased screening difficulty associated with greater breast density; however, the BI-RADS rating scheme is subjective and is not intended as an objective measure of breast density. So, while popular, BI-RADS does not define density classes using a standardized measure, which leads to increased variability among observers. The adaptive thresholding technique is a more quantitative approach for assessing the percentage breast density, but considerable reader interaction is required. We calculate an objective density rating that is derived using a measure of local feature salience. Previously, this measure was shown to correlate well with radiologists' localization and discrimination of true positive and true negative regions-of-interest. Using conspicuous spatial frequency features, an objective density rating is obtained and correlated with adaptive thresholding, and the subjectively ascertained BI-RADS density ratings. Using 100 cases, obtained from the University of South Florida's DDSM database, we show that an automated breast density measure can be derived that is correlated with the interactive thresholding method for continuous percentage breast density, but not with the BI-RADS density rating categories for the selected cases. Comparison between interactive thresholding and the new salience percentage density resulted in a Pearson correlation of 76.7%. Using a four-category scale equivalent to the BI-RADS density categories, a Spearman correlation coefficient of 79.8% was found.

Does breast density measured through population-based screening independently increase breast cancer risk in Asian females?

PubMed Central

Park, Boyoung; Cho, Hye Mi; Lee, Eun Hye; Song, Seunghoon; Suh, Mina; Choi, Kui Son; Kang, Bong Joo; Ko, Kyungran; Yi, Ann; Jung, Hae Kyoung; Cha, Joo Hee; Jun, Jae Kwan

2018-01-01

Purpose The purpose of this study was to investigate the effects of breast density on breast cancer risk among women screened via a nationwide mammographic screening program. Patients and methods We conducted a nested case–control study for a randomly selected population of 1,561 breast cancer patients and 6,002 matched controls from the National Cancer Screening Program. Breast density was measured and recorded by two independent radiologists using the Breast Imaging Reporting and Data System (BI-RADS). Associations between BI-RADS density and breast cancer risk were evaluated according to screening results, time elapsed since receiving non-recall results, age, and menopausal status after adjusting for possible covariates. Results Breast cancer risk for women with extremely dense breasts was five times higher (adjusted odds ratio [aOR] =5.0; 95% confidence interval [CI]) =3.7–6.7) than that for women with an almost entirely fatty breast, although the risk differed between recalled women (aOR =3.3, 95% CI =2.3–3.6) and women with non-recalled results (aOR =12.1, 95% CI =6.3–23.3, P-heterogeneity =0.001). aORs for BI-RADS categories of breast density were similar when subjects who developed cancer after showing non-recall findings during initial screening were grouped according to time until cancer diagnosis thereafter (<1 and ≥1 year). The prevalence of dense breasts was higher in younger women, and the association between a denser breast and breast cancer was stronger in younger women (heterogeneously dense breast: aOR =7.0, 95% CI =2.4–20.3, women in their 40s) than older women (aOR =2.5, 95% CI =1.1–6.0, women in their 70s or more). In addition, while the positive association remained, irrespective of menopausal status, the effect of a dense breast on breast cancer risk was stronger in premenopausal women. Conclusion This study confirmed an increased risk of breast cancer with greater breast density in Korean women which was consistent regardless of BI-RADS assessment category, time interval after initially non-recall results, and menopausal status. PMID:29343988

Investigation of mammographic breast density as a risk factor for ovarian cancer.

PubMed

Wernli, Karen J; O'Meara, Ellen S; Kerlikowske, Karla; Miglioretti, Diana L; Muller, Carolyn Y; Onega, Tracy; Sprague, Brian L; Henderson, Louise M; Buist, Diana S M

2014-01-01

Endogenous hormones and growth factors that increase mammographic breast density could increase ovarian cancer risk. We examined whether high breast density is associated with ovarian cancer risk. We conducted a cohort study of 724,603 women aged 40 to 79 years with 2,506,732 mammograms participating in the Breast Cancer Surveillance Consortium from 1995 to 2009. Incident epithelial ovarian cancer was diagnosed in 1373 women. We used partly conditional Cox regression to estimate the association between breast density and 5-year risk of incident epithelial ovarian cancer overall and stratified by 10-year age group. All statistical tests were two-sided. Compared with women with scattered fibroglandular densities, women with heterogeneously dense and extremely dense breast tissue had 20% and 18% increased 5-year risk of incident epithelial ovarian cancer (hazard ratio [HR] = 1.20, 95% confidence interval [CI] = 1.06 to 1.36; HR = 1.18, 95% CI = 0.93 to 1.50, respectively; P(trend) = .01). Among women aged 50 to 59 years, we observed a trend in elevated risk associated with increased breast density (P(trend) = .02); women with heterogeneously and extremely dense breast tissue had 30% (HR = 1.30; 95% CI = 1.03 to 1.64) and 65% (HR = 1.65; 95% CI = 1.12 to 2.44) increased risk, respectively, compared with women with scattered fibroglandular densities. The pattern was similar but not statistically significant at age 40 to 49 years. There were no consistent patterns of breast density and ovarian cancer risk at age 60 to 79 years. Dense breast tissue was associated with a modest increase in 5-year ovarian cancer risk in women aged 50 to 59 years but was not associated with ovarian cancer at ages 40 to 49 or 60 to 79 years.

A comparative study of volumetric breast density estimation in digital mammography and magnetic resonance imaging: results from a high-risk population

NASA Astrophysics Data System (ADS)

Kontos, Despina; Xing, Ye; Bakic, Predrag R.; Conant, Emily F.; Maidment, Andrew D. A.

2010-03-01

We performed a study to compare methods for volumetric breast density estimation in digital mammography (DM) and magnetic resonance imaging (MRI) for a high-risk population of women. DM and MRI images of the unaffected breast from 32 women with recently detected abnormalities and/or previously diagnosed breast cancer (age range 31-78 yrs, mean 50.3 yrs) were retrospectively analyzed. DM images were analyzed using QuantraTM (Hologic Inc). The MRI images were analyzed using a fuzzy-C-means segmentation algorithm on the T1 map. Both methods were compared to Cumulus (Univ. Toronto). Volumetric breast density estimates from DM and MRI are highly correlated (r=0.90, p<=0.001). The correlation between the volumetric and the area-based density measures is lower and depends on the training background of the Cumulus software user (r=0.73-84, p<=0.001). In terms of absolute values, MRI provides the lowest volumetric estimates (mean=14.63%), followed by the DM volumetric (mean=22.72%) and area-based measures (mean=29.35%). The MRI estimates of the fibroglandular volume are statistically significantly lower than the DM estimates for women with very low-density breasts (p<=0.001). We attribute these differences to potential partial volume effects in MRI and differences in the computational aspects of the image analysis methods in MRI and DM. The good correlation between the volumetric and the area-based measures, shown to correlate with breast cancer risk, suggests that both DM and MRI volumetric breast density measures can aid in breast cancer risk assessment. Further work is underway to fully-investigate the association between volumetric breast density measures and breast cancer risk.

Comparing Visually Assessed BI-RADS Breast Density and Automated Volumetric Breast Density Software: A Cross-Sectional Study in a Breast Cancer Screening Setting.

PubMed

van der Waal, Daniëlle; den Heeten, Gerard J; Pijnappel, Ruud M; Schuur, Klaas H; Timmers, Johanna M H; Verbeek, André L M; Broeders, Mireille J M

2015-01-01

The objective of this study is to compare different methods for measuring breast density, both visual assessments and automated volumetric density, in a breast cancer screening setting. These measures could potentially be implemented in future screening programmes, in the context of personalised screening or screening evaluation. Digital mammographic exams (N = 992) of women participating in the Dutch breast cancer screening programme (age 50-75y) in 2013 were included. Breast density was measured in three different ways: BI-RADS density (5th edition) and with two commercially available automated software programs (Quantra and Volpara volumetric density). BI-RADS density (ordinal scale) was assessed by three radiologists. Quantra (v1.3) and Volpara (v1.5.0) provide continuous estimates. Different comparison methods were used, including Bland-Altman plots and correlation coefficients (e.g., intraclass correlation coefficient [ICC]). Based on the BI-RADS classification, 40.8% of the women had 'heterogeneously or extremely dense' breasts. The median volumetric percent density was 12.1% (IQR: 9.6-16.5) for Quantra, which was higher than the Volpara estimate (median 6.6%, IQR: 4.4-10.9). The mean difference between Quantra and Volpara was 5.19% (95% CI: 5.04-5.34) (ICC: 0.64). There was a clear increase in volumetric percent dense volume as BI-RADS density increased. The highest accuracy for predicting the presence of BI-RADS c+d (heterogeneously or extremely dense) was observed with a cut-off value of 8.0% for Volpara and 13.8% for Quantra. Although there was no perfect agreement, there appeared to be a strong association between all three measures. Both volumetric density measures seem to be usable in breast cancer screening programmes, provided that the required data flow can be realized.

Comparing Visually Assessed BI-RADS Breast Density and Automated Volumetric Breast Density Software: A Cross-Sectional Study in a Breast Cancer Screening Setting

PubMed Central

van der Waal, Daniëlle; den Heeten, Gerard J.; Pijnappel, Ruud M.; Schuur, Klaas H.; Timmers, Johanna M. H.; Verbeek, André L. M.; Broeders, Mireille J. M.

2015-01-01

Introduction The objective of this study is to compare different methods for measuring breast density, both visual assessments and automated volumetric density, in a breast cancer screening setting. These measures could potentially be implemented in future screening programmes, in the context of personalised screening or screening evaluation. Materials and Methods Digital mammographic exams (N = 992) of women participating in the Dutch breast cancer screening programme (age 50–75y) in 2013 were included. Breast density was measured in three different ways: BI-RADS density (5th edition) and with two commercially available automated software programs (Quantra and Volpara volumetric density). BI-RADS density (ordinal scale) was assessed by three radiologists. Quantra (v1.3) and Volpara (v1.5.0) provide continuous estimates. Different comparison methods were used, including Bland-Altman plots and correlation coefficients (e.g., intraclass correlation coefficient [ICC]). Results Based on the BI-RADS classification, 40.8% of the women had ‘heterogeneously or extremely dense’ breasts. The median volumetric percent density was 12.1% (IQR: 9.6–16.5) for Quantra, which was higher than the Volpara estimate (median 6.6%, IQR: 4.4–10.9). The mean difference between Quantra and Volpara was 5.19% (95% CI: 5.04–5.34) (ICC: 0.64). There was a clear increase in volumetric percent dense volume as BI-RADS density increased. The highest accuracy for predicting the presence of BI-RADS c+d (heterogeneously or extremely dense) was observed with a cut-off value of 8.0% for Volpara and 13.8% for Quantra. Conclusion Although there was no perfect agreement, there appeared to be a strong association between all three measures. Both volumetric density measures seem to be usable in breast cancer screening programmes, provided that the required data flow can be realized. PMID:26335569

Feature extraction using convolutional neural network for classifying breast density in mammographic images

NASA Astrophysics Data System (ADS)

Thomaz, Ricardo L.; Carneiro, Pedro C.; Patrocinio, Ana C.

2017-03-01

Breast cancer is the leading cause of death for women in most countries. The high levels of mortality relate mostly to late diagnosis and to the direct proportionally relationship between breast density and breast cancer development. Therefore, the correct assessment of breast density is important to provide better screening for higher risk patients. However, in modern digital mammography the discrimination among breast densities is highly complex due to increased contrast and visual information for all densities. Thus, a computational system for classifying breast density might be a useful tool for aiding medical staff. Several machine-learning algorithms are already capable of classifying small number of classes with good accuracy. However, machinelearning algorithms main constraint relates to the set of features extracted and used for classification. Although well-known feature extraction techniques might provide a good set of features, it is a complex task to select an initial set during design of a classifier. Thus, we propose feature extraction using a Convolutional Neural Network (CNN) for classifying breast density by a usual machine-learning classifier. We used 307 mammographic images downsampled to 260x200 pixels to train a CNN and extract features from a deep layer. After training, the activation of 8 neurons from a deep fully connected layer are extracted and used as features. Then, these features are feedforward to a single hidden layer neural network that is cross-validated using 10-folds to classify among four classes of breast density. The global accuracy of this method is 98.4%, presenting only 1.6% of misclassification. However, the small set of samples and memory constraints required the reuse of data in both CNN and MLP-NN, therefore overfitting might have influenced the results even though we cross-validated the network. Thus, although we presented a promising method for extracting features and classifying breast density, a greater database is still required for evaluating the results.

Characterization of breast density in Vietnam and its association with demographic, reproductive and lifestyle factors

NASA Astrophysics Data System (ADS)

Trieu, Phuong Dung (Yun); Mello-Thoms, Claudia; Peat, Jenny; Do, Thuan Doan; Brennan, Patrick C.

2017-03-01

This study aims to investigate patterns of breast density among women in Vietnam and their association with demographic, reproductive and lifestyle features. Mammographic densities of 1,651 women were collected from the two largest breast cancer screening and treatment centers in Ha Noi and Ho Chi Minh city. Putative factors associated with breast density were obtained from self-administered questionnaires which considered demographic, reproductive and lifestyle elements and were provided by women who attended mammography examinations. Results show that a large proportion of Vietnamese women (78.4%) had a high breast density. With multivariable logistic regression, significant associations of high breast density were evident with women with less than 55 years old (OR=3.0), having BMI less than 23 (OR=2.2), experiencing pre-menopausal status (OR=2.9), having less than three children (OR=1.7), and being less than 32 years old when having their last child (OR=1.8). Participants who consumed more than two vegetable servings per day also had an increased risk of higher density (OR=2.6). The findings suggest some unique features regarding mammographic density amongst Vietnamese compared with westernized women.

Epidemiologic Studies of Isoflavones & Mammographic Density

PubMed Central

Maskarinec, Gertraud; Verheus, Martijn; Tice, Jeffrey A.

2010-01-01

Isoflavones, phytoestrogens in soy beans with estrogen-like properties, have been examined for their cancer protective effects. Mammographic density is a strong predictor of breast cancer. This review summarizes studies that have examined the association between isoflavones and breast density. Observational investigations in Hawaii and Singapore suggest slightly lower breast density among women of Asian descent with regular soy intake, but two larger studies from Japan and Singapore did not observe a protective effect. The findings from seven randomized trials with primarily Caucasian women indicate that soy or isoflavones do not modify mammographic density. Soy foods and isoflavone supplements within a nutritional range do not appear to modify breast cancer risk as assessed by mammographic density. PMID:22253990

The relationships between breast volume, breast dense volume and volumetric breast density with body mass index, body fat mass and ethnicity

NASA Astrophysics Data System (ADS)

Zakariyah, N.; Pathy, N. B.; Taib, N. A. M.; Rahmat, K.; Judy, C. W.; Fadzil, F.; Lau, S.; Ng, K. H.

2016-03-01

It has been shown that breast density and obesity are related to breast cancer risk. The aim of this study is to investigate the relationships of breast volume, breast dense volume and volumetric breast density (VBD) with body mass index (BMI) and body fat mass (BFM) for the three ethnic groups (Chinese, Malay and Indian) in Malaysia. We collected raw digital mammograms from 2450 women acquired on three digital mammography systems. The mammograms were analysed using Volpara software to obtain breast volume, breast dense volume and VBD. Body weight, BMI and BFM of the women were measured using a body composition analyser. Multivariable logistic regression was used to determine the independent predictors of increased overall breast volume, breast dense volume and VBD. Indians have highest breast volume and breast dense volume followed by Malays and Chinese. While Chinese are highest in VBD, followed by Malay and Indian. Multivariable analysis showed that increasing BMI and BFM were independent predictors of increased overall breast volume and dense volume. Moreover, BMI and BFM were independently and inversely related to VBD.

Automated mammographic breast density estimation using a fully convolutional network.

PubMed

Lee, Juhun; Nishikawa, Robert M

2018-03-01

The purpose of this study was to develop a fully automated algorithm for mammographic breast density estimation using deep learning. Our algorithm used a fully convolutional network, which is a deep learning framework for image segmentation, to segment both the breast and the dense fibroglandular areas on mammographic images. Using the segmented breast and dense areas, our algorithm computed the breast percent density (PD), which is the faction of dense area in a breast. Our dataset included full-field digital screening mammograms of 604 women, which included 1208 mediolateral oblique (MLO) and 1208 craniocaudal (CC) views. We allocated 455, 58, and 91 of 604 women and their exams into training, testing, and validation datasets, respectively. We established ground truth for the breast and the dense fibroglandular areas via manual segmentation and segmentation using a simple thresholding based on BI-RADS density assessments by radiologists, respectively. Using the mammograms and ground truth, we fine-tuned a pretrained deep learning network to train the network to segment both the breast and the fibroglandular areas. Using the validation dataset, we evaluated the performance of the proposed algorithm against radiologists' BI-RADS density assessments. Specifically, we conducted a correlation analysis between a BI-RADS density assessment of a given breast and its corresponding PD estimate by the proposed algorithm. In addition, we evaluated our algorithm in terms of its ability to classify the BI-RADS density using PD estimates, and its ability to provide consistent PD estimates for the left and the right breast and the MLO and CC views of the same women. To show the effectiveness of our algorithm, we compared the performance of our algorithm against a state of the art algorithm, laboratory for individualized breast radiodensity assessment (LIBRA). The PD estimated by our algorithm correlated well with BI-RADS density ratings by radiologists. Pearson's rho values of our algorithm for CC view, MLO view, and CC-MLO-averaged were 0.81, 0.79, and 0.85, respectively, while those of LIBRA were 0.58, 0.71, and 0.69, respectively. For CC view and CC-MLO averaged cases, the difference in rho values between the proposed algorithm and LIBRA showed statistical significance (P < 0.006). In addition, our algorithm provided reliable PD estimates for the left and the right breast (Pearson's ρ > 0.87) and for the MLO and CC views (Pearson's ρ = 0.76). However, LIBRA showed a lower Pearson's rho value (0.66) for both the left and right breasts for the CC view. In addition, our algorithm showed an excellent ability to separate each sub BI-RADS breast density class (statistically significant, p-values = 0.0001 or less); only one comparison pair, density 1 and density 2 in the CC view, was not statistically significant (P = 0.54). However, LIBRA failed to separate breasts in density 1 and 2 for both the CC and MLO views (P > 0.64). We have developed a new deep learning based algorithm for breast density segmentation and estimation. We showed that the proposed algorithm correlated well with BI-RADS density assessments by radiologists and outperformed an existing state of the art algorithm. © 2018 American Association of Physicists in Medicine.

Automated and Clinical Breast Imaging Reporting and Data System Density Measures Predict Risk for Screen-Detected and Interval Cancers: A Case-Control Study.

PubMed

Kerlikowske, Karla; Scott, Christopher G; Mahmoudzadeh, Amir P; Ma, Lin; Winham, Stacey; Jensen, Matthew R; Wu, Fang Fang; Malkov, Serghei; Pankratz, V Shane; Cummings, Steven R; Shepherd, John A; Brandt, Kathleen R; Miglioretti, Diana L; Vachon, Celine M

2018-06-05

In 30 states, women who have had screening mammography are informed of their breast density on the basis of Breast Imaging Reporting and Data System (BI-RADS) density categories estimated subjectively by radiologists. Variation in these clinical categories across and within radiologists has led to discussion about whether automated BI-RADS density should be reported instead. To determine whether breast cancer risk and detection are similar for automated and clinical BI-RADS density measures. Case-control. San Francisco Mammography Registry and Mayo Clinic. 1609 women with screen-detected cancer, 351 women with interval invasive cancer, and 4409 matched control participants. Automated and clinical BI-RADS density assessed on digital mammography at 2 time points from September 2006 to October 2014, interval and screen-detected breast cancer risk, and mammography sensitivity. Of women whose breast density was categorized by automated BI-RADS more than 6 months to 5 years before diagnosis, those with extremely dense breasts had a 5.65-fold higher interval cancer risk (95% CI, 3.33 to 9.60) and a 1.43-fold higher screen-detected risk (CI, 1.14 to 1.79) than those with scattered fibroglandular densities. Associations of interval and screen-detected cancer with clinical BI-RADS density were similar to those with automated BI-RADS density, regardless of whether density was measured more than 6 months to less than 2 years or 2 to 5 years before diagnosis. Automated and clinical BI-RADS density measures had similar discriminatory accuracy, which was higher for interval than screen-detected cancer (c-statistics: 0.70 vs. 0.62 [P < 0.001] and 0.72 vs. 0.62 [P < 0.001], respectively). Mammography sensitivity was similar for automated and clinical BI-RADS categories: fatty, 93% versus 92%; scattered fibroglandular densities, 90% versus 90%; heterogeneously dense, 82% versus 78%; and extremely dense, 63% versus 64%, respectively. Neither automated nor clinical BI-RADS density was assessed on tomosynthesis, an emerging breast screening method. Automated and clinical BI-RADS density similarly predict interval and screen-detected cancer risk, suggesting that either measure may be used to inform women of their breast density. National Cancer Institute.

Physical Activity and Change in Mammographic Density

PubMed Central

Conroy, Shannon M.; Butler, Lesley M.; Harvey, Danielle; Gold, Ellen B.; Sternfeld, Barbara; Oestreicher, Nina; Greendale, Gail A.; Habel, Laurel A.

2010-01-01

One potential mechanism by which physical activity may protect against breast cancer is by decreasing mammographic density. Percent mammographic density, the proportion of dense breast tissue area to total breast area, declines with age and is a strong risk factor for breast cancer. The authors hypothesized that women who were more physically active would have a greater decline in percent mammographic density with age, compared with less physically active women. The authors tested this hypothesis using longitudinal data (1996–2004) from 722 participants in the Study of Women's Health Across the Nation (SWAN), a multiethnic cohort of women who were pre- and early perimenopausal at baseline, with multivariable, repeated-measures linear regression analyses. During an average of 5.6 years, the mean annual decline in percent mammographic density was 1.1% (standard deviation = 0.1). A 1-unit increase in total physical activity score was associated with a weaker annual decline in percent mammographic density by 0.09% (standard error = 0.03; P = 0.01). Physical activity was inversely associated with the change in nondense breast area (P < 0.01) and not associated with the change in dense breast area (P = 0.17). Study results do not support the hypothesis that physical activity reduces breast cancer through a mechanism that includes reduced mammographic density. PMID:20354074

CPTAC Evaluates Long-Term Reproducibility of Quantitative Proteomics Using Breast Cancer Xenografts | Office of Cancer Clinical Proteomics Research

Cancer.gov

Liquid chromatography tandem-mass spectrometry (LC-MS/MS)- based methods such as isobaric tags for relative and absolute quantification (iTRAQ) and tandem mass tags (TMT) have been shown to provide overall better quantification accuracy and reproducibility over other LC-MS/MS techniques. However, large scale projects like the Clinical Proteomic Tumor Analysis Consortium (CPTAC) require comparisons across many genomically characterized clinical specimens in a single study and often exceed the capability of traditional iTRAQ-based quantification.

Reader variability in breast density estimation from full-field digital mammograms: the effect of image postprocessing on relative and absolute measures.

PubMed

Keller, Brad M; Nathan, Diane L; Gavenonis, Sara C; Chen, Jinbo; Conant, Emily F; Kontos, Despina

2013-05-01

Mammographic breast density, a strong risk factor for breast cancer, may be measured as either a relative percentage of dense (ie, radiopaque) breast tissue or as an absolute area from either raw (ie, "for processing") or vendor postprocessed (ie, "for presentation") digital mammograms. Given the increasing interest in the incorporation of mammographic density in breast cancer risk assessment, the purpose of this study is to determine the inherent reader variability in breast density assessment from raw and vendor-processed digital mammograms, because inconsistent estimates could to lead to misclassification of an individual woman's risk for breast cancer. Bilateral, mediolateral-oblique view, raw, and processed digital mammograms of 81 women were retrospectively collected for this study (N = 324 images). Mammographic percent density and absolute dense tissue area estimates for each image were obtained from two radiologists using a validated, interactive software tool. The variability of interreader agreement was not found to be affected by the image presentation style (ie, raw or processed, F-test: P > .5). Interreader estimates of relative and absolute breast density are strongly correlated (Pearson r > 0.84, P < .001) but systematically different (t-test, P < .001) between the two readers. Our results show that mammographic density may be assessed with equal reliability from either raw or vendor postprocessed images. Furthermore, our results suggest that the primary source of density variability comes from the subjectivity of the individual reader in assessing the absolute amount of dense tissue present in the breast, indicating the need to use standardized tools to mitigate this effect. Copyright © 2013 AUR. Published by Elsevier Inc. All rights reserved.

Mammographic density and breast cancer risk by family history in women of white and Asian ancestry.

PubMed

Maskarinec, Gertraud; Nakamura, Kaylae L; Woolcott, Christy G; Conroy, Shannon M; Byrne, Celia; Nagata, Chisato; Ursin, Giske; Vachon, Celine M

2015-04-01

Mammographic density, i.e., the radiographic appearance of the breast, is a strong predictor of breast cancer risk. To determine whether the association of breast density with breast cancer is modified by a first-degree family history of breast cancer (FHBC) in women of white and Asian ancestry, we analyzed data from four case-control studies conducted in the USA and Japan. The study population included 1,699 breast cancer cases and 2,422 controls, of whom 45% reported white (N = 1,849) and 40% Asian (N = 1,633) ancestry. To standardize mammographic density assessment, a single observer re-read all mammograms using one type of interactive thresholding software. Logistic regression was applied to estimate odds ratios (OR) while adjusting for confounders. Overall, 496 (12%) of participants reported a FHBC, which was significantly associated with breast cancer risk in the adjusted model (OR 1.51; 95% CI 1.23-1.84). There was a statistically significant interaction on a multiplicative scale between FHBC and continuous percent density (per 10 % density: p = 0.03). The OR per 10% increase in percent density was higher among women with a FHBC (OR 1.30; 95% CI 1.13-1.49) than among those without a FHBC (OR 1.14; 1.09-1.20). This pattern was apparent in whites and Asians. The respective ORs were 1.45 (95% CI 1.17-1.80) versus 1.22 (95% CI 1.14-1.32) in whites, whereas the values in Asians were only 1.24 (95% CI 0.97-1.58) versus 1.09 (95% CI 1.00-1.19). These findings support the hypothesis that women with a FHBC appear to have a higher risk of breast cancer associated with percent mammographic density than women without a FHBC.

A web-based personalized risk communication and decision-making tool for women with dense breasts: Design and methods of a randomized controlled trial within an integrated health care system.

PubMed

Knerr, Sarah; Wernli, Karen J; Leppig, Kathleen; Ehrlich, Kelly; Graham, Amanda L; Farrell, David; Evans, Chalanda; Luta, George; Schwartz, Marc D; O'Neill, Suzanne C

2017-05-01

Mammographic breast density is one of the strongest risk factors for breast cancer after age and family history. Mandatory breast density disclosure policies are increasing nationally without clear guidance on how to communicate density status to women. Coupling density disclosure with personalized risk counseling and decision support through a web-based tool may be an effective way to allow women to make informed, values-consistent risk management decisions without increasing distress. This paper describes the design and methods of Engaged, a prospective, randomized controlled trial examining the effect of online personalized risk counseling and decision support on risk management decisions in women with dense breasts and increased breast cancer risk. The trial is embedded in a large integrated health care system in the Pacific Northwest. A total of 1250 female health plan members aged 40-69 with a recent negative screening mammogram who are at increased risk for interval cancer based on their 5-year breast cancer risk and BI-RADS® breast density will be randomly assigned to access either a personalized web-based counseling and decision support tool or standard educational content. Primary outcomes will be assessed using electronic health record data (i.e., chemoprevention and breast MRI utilization) and telephone surveys (i.e., distress) at baseline, six weeks, and twelve months. Engaged will provide evidence about whether a web-based personalized risk counseling and decision support tool is an effective method for communicating with women about breast density and risk management. An effective intervention could be disseminated with minimal clinical burden to align with density disclosure mandates. Clinical Trials Registration Number:NCT03029286. Copyright © 2017 Elsevier Inc. All rights reserved.

The Impact of Acquisition Dose on Quantitative Breast Density Estimation with Digital Mammography: Results from ACRIN PA 4006.

PubMed

Chen, Lin; Ray, Shonket; Keller, Brad M; Pertuz, Said; McDonald, Elizabeth S; Conant, Emily F; Kontos, Despina

2016-09-01

Purpose To investigate the impact of radiation dose on breast density estimation in digital mammography. Materials and Methods With institutional review board approval and Health Insurance Portability and Accountability Act compliance under waiver of consent, a cohort of women from the American College of Radiology Imaging Network Pennsylvania 4006 trial was retrospectively analyzed. All patients underwent breast screening with a combination of dose protocols, including standard full-field digital mammography, low-dose digital mammography, and digital breast tomosynthesis. A total of 5832 images from 486 women were analyzed with previously validated, fully automated software for quantitative estimation of density. Clinical Breast Imaging Reporting and Data System (BI-RADS) density assessment results were also available from the trial reports. The influence of image acquisition radiation dose on quantitative breast density estimation was investigated with analysis of variance and linear regression. Pairwise comparisons of density estimations at different dose levels were performed with Student t test. Agreement of estimation was evaluated with quartile-weighted Cohen kappa values and Bland-Altman limits of agreement. Results Radiation dose of image acquisition did not significantly affect quantitative density measurements (analysis of variance, P = .37 to P = .75), with percent density demonstrating a high overall correlation between protocols (r = 0.88-0.95; weighted κ = 0.83-0.90). However, differences in breast percent density (1.04% and 3.84%, P < .05) were observed within high BI-RADS density categories, although they were significantly correlated across the different acquisition dose levels (r = 0.76-0.92, P < .05). Conclusion Precision and reproducibility of automated breast density measurements with digital mammography are not substantially affected by variations in radiation dose; thus, the use of low-dose techniques for the purpose of density estimation may be feasible. (©) RSNA, 2016 Online supplemental material is available for this article.

The Impact of Acquisition Dose on Quantitative Breast Density Estimation with Digital Mammography: Results from ACRIN PA 4006

PubMed Central

Chen, Lin; Ray, Shonket; Keller, Brad M.; Pertuz, Said; McDonald, Elizabeth S.; Conant, Emily F.

2016-01-01

Purpose To investigate the impact of radiation dose on breast density estimation in digital mammography. Materials and Methods With institutional review board approval and Health Insurance Portability and Accountability Act compliance under waiver of consent, a cohort of women from the American College of Radiology Imaging Network Pennsylvania 4006 trial was retrospectively analyzed. All patients underwent breast screening with a combination of dose protocols, including standard full-field digital mammography, low-dose digital mammography, and digital breast tomosynthesis. A total of 5832 images from 486 women were analyzed with previously validated, fully automated software for quantitative estimation of density. Clinical Breast Imaging Reporting and Data System (BI-RADS) density assessment results were also available from the trial reports. The influence of image acquisition radiation dose on quantitative breast density estimation was investigated with analysis of variance and linear regression. Pairwise comparisons of density estimations at different dose levels were performed with Student t test. Agreement of estimation was evaluated with quartile-weighted Cohen kappa values and Bland-Altman limits of agreement. Results Radiation dose of image acquisition did not significantly affect quantitative density measurements (analysis of variance, P = .37 to P = .75), with percent density demonstrating a high overall correlation between protocols (r = 0.88–0.95; weighted κ = 0.83–0.90). However, differences in breast percent density (1.04% and 3.84%, P < .05) were observed within high BI-RADS density categories, although they were significantly correlated across the different acquisition dose levels (r = 0.76–0.92, P < .05). Conclusion Precision and reproducibility of automated breast density measurements with digital mammography are not substantially affected by variations in radiation dose; thus, the use of low-dose techniques for the purpose of density estimation may be feasible. © RSNA, 2016 Online supplemental material is available for this article. PMID:27002418

Qualitative Versus Quantitative Mammographic Breast Density Assessment: Applications for the US and Abroad

PubMed Central

Destounis, Stamatia; Arieno, Andrea; Morgan, Renee; Roberts, Christina; Chan, Ariane

2017-01-01

Mammographic breast density (MBD) has been proven to be an important risk factor for breast cancer and an important determinant of mammographic screening performance. The measurement of density has changed dramatically since its inception. Initial qualitative measurement methods have been found to have limited consistency between readers, and in regards to breast cancer risk. Following the introduction of full-field digital mammography, more sophisticated measurement methodology is now possible. Automated computer-based density measurements can provide consistent, reproducible, and objective results. In this review paper, we describe various methods currently available to assess MBD, and provide a discussion on the clinical utility of such methods for breast cancer screening. PMID:28561776

A stepwedge-based method for measuring breast density: observer variability and comparison with human reading

NASA Astrophysics Data System (ADS)

Diffey, Jenny; Berks, Michael; Hufton, Alan; Chung, Camilla; Verow, Rosanne; Morrison, Joanna; Wilson, Mary; Boggis, Caroline; Morris, Julie; Maxwell, Anthony; Astley, Susan

2010-04-01

Breast density is positively linked to the risk of developing breast cancer. We have developed a semi-automated, stepwedge-based method that has been applied to the mammograms of 1,289 women in the UK breast screening programme to measure breast density by volume and area. 116 images were analysed by three independent operators to assess inter-observer variability; 24 of these were analysed on 10 separate occasions by the same operator to determine intra-observer variability. 168 separate images were analysed using the stepwedge method and by two radiologists who independently estimated percentage breast density by area. There was little intra-observer variability in the stepwedge method (average coefficients of variation 3.49% - 5.73%). There were significant differences in the volumes of glandular tissue obtained by the three operators. This was attributed to variations in the operators' definition of the breast edge. For fatty and dense breasts, there was good correlation between breast density assessed by the stepwedge method and the radiologists. This was also observed between radiologists, despite significant inter-observer variation. Based on analysis of thresholds used in the stepwedge method, radiologists' definition of a dense pixel is one in which the percentage of glandular tissue is between 10 and 20% of the total thickness of tissue.

Biologic and Computational Modeling of Mammographic Density and Stromal Patterning

DTIC Science & Technology

2009-07-01

research effort. INTRODUCTION: Mammographic density serves as independent marker of short term breast cancer risk and a surrogate marker of...response to a variety of prevention agents1-3. Although a majority of breast cancers are epithelial in origin, there is evidence that stromal content of...the breast is an important predictor or mammographic density. There is increasing evidence that the stroma plays a role in breast cancer initiation4

A deep learning method for classifying mammographic breast density categories.

PubMed

Mohamed, Aly A; Berg, Wendie A; Peng, Hong; Luo, Yahong; Jankowitz, Rachel C; Wu, Shandong

2018-01-01

Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS categories, i.e., "scattered density" and "heterogeneously dense". The aim of this work was to investigate a deep learning-based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI-RADS category in current clinical workflow. In this study, we constructed a convolutional neural network (CNN)-based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography screening from 2005 to 2016 at our institution. The truths of the density categories were based on standard clinical assessment made by board-certified breast imaging radiologists. Effects of direct training from scratch solely using digital mammogram images and transfer learning of a pretrained model on a large nonmedical imaging dataset were evaluated for the specific task of breast density classification. In order to measure the classification performance, the CNN classifier was also tested on a refined version of the mammogram image dataset by removing some potentially inaccurately labeled images. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to measure the accuracy of the classifier. The AUC was 0.9421 when the CNN-model was trained from scratch on our own mammogram images, and the accuracy increased gradually along with an increased size of training samples. Using the pretrained model followed by a fine-tuning process with as few as 500 mammogram images led to an AUC of 0.9265. After removing the potentially inaccurately labeled images, AUC was increased to 0.9882 and 0.9857 for without and with the pretrained model, respectively, both significantly higher (P < 0.001) than when using the full imaging dataset. Our study demonstrated high classification accuracies between two difficult to distinguish breast density categories that are routinely assessed by radiologists. We anticipate that our approach will help enhance current clinical assessment of breast density and better support consistent density notification to patients in breast cancer screening. © 2017 American Association of Physicists in Medicine.

Does the prediction of breast cancer improve using a combination of mammographic density measures compared to individual measures alone?

NASA Astrophysics Data System (ADS)

Wong Sik Hee, Joseph Ryan; Harkness, Elaine F.; Gadde, Soujanya; Lim, Yit Y.; Maxwell, Anthony J.; Evans, D. Gareth; Howell, Anthony; Astley, Susan M.

2017-03-01

High mammographic density is associated with an increased risk of breast cancer, however whether the association is stronger when there is agreement across measures is unclear. This study investigates whether a combination of density measures is a better predictor of breast cancer risk than individual methods alone. Women recruited to the Predicting Risk of Cancer At Screening (PROCAS) study and with mammographic density assessed using three different methods were included (n=33,304). Density was assessed visually using Visual Analogue Scales (VAS) and by two fully automated methods, Quantra and Volpara. Percentage breast density was divided into (high, medium and low) and combinations of measures were used to further categorise individuals (e.g. `all high'). A total of 667 breast cancers were identified and logistic regression was used to determine the relationship between breast density and breast cancer risk. In total, 44% of individuals were in the same tertile for all three measures, 8.6% were in non-adjacent (high and low) or mixed categories (high, medium and low). For individual methods the strongest association with breast cancer risk was for medium and high tertiles of VAS with odds ratios (OR) adjusted for age and BMI of 1.63 (95% CI 1.31-2.03) and 2.33 (1.87-2.90) respectively. For the combination of density methods the strongest association was for `all high' (OR 2.42, 1.77-3.31) followed by "two high" (OR 1.90, 1.35-3.31) and "two medium" (OR 1.88, 1.40-2.52). Combining density measures did not affect the magnitude of risk compared to using individual methods.

Correlation between Na/K ratio and electron densities in blood samples of breast cancer patients.

PubMed

Topdağı, Ömer; Toker, Ozan; Bakırdere, Sezgin; Bursalıoğlu, Ertuğrul Osman; Öz, Ersoy; Eyecioğlu, Önder; Demir, Mustafa; İçelli, Orhan

2018-05-31

The main purpose of this study was to investigate the relationship between the electron densities and Na/K ratio which has important role in breast cancer disease. Determinations of sodium and potassium concentrations in blood samples performed with inductive coupled plasma-atomic emission spectrometry. Electron density values of blood samples were determined via ZXCOM. Statistical analyses were performed for electron densities and Na/K ratio including Kolmogorov-Smirnov normality tests, Spearman's rank correlation test and Mann-Whitney U test. It was found that the electron densities significantly differ between control and breast cancer groups. In addition, statistically significant positive correlation was found between the electron density and Na/K ratios in breast cancer group.

Nearest Neighbor Classification Using a Density Sensitive Distance Measurement

DTIC Science & Technology

2009-09-01

both the proposed density sensitive distance measurement and Euclidean distance are compared on the Wisconsin Diagnostic Breast Cancer dataset and...proposed density sensitive distance measurement and Euclidean distance are compared on the Wisconsin Diagnostic Breast Cancer dataset and the MNIST...35 1. The Wisconsin Diagnostic Breast Cancer (WDBC) Dataset..........35 2. The

Do pathological parameters differ with regard to breast density and mode of detection in breast cancer? The Malmö Diet and Cancer Study.

PubMed

Sartor, Hanna; Borgquist, Signe; Hartman, Linda; Zackrisson, Sophia

2015-02-01

Our aim was to study how breast density relates to tumor characteristics in breast cancer with emphasis on mode of detection. Among 17,035 women in the Malmö Diet and Cancer Study 826 incident cases have been diagnosed (1991-2007). Data on tumor characteristics, mode of detection, and density at diagnosis were collected. Associations between density and tumor characteristics were analyzed using logistic and ordinal logistic regression models yielding OR and 95% CI. Adjustments for age at diagnosis, BMI at baseline, and the mode of detection, were performed. In denser breasts, large tumor size was more frequent (ORadj 1.59 (1.26-2.01)) as was lymph node involvement (ORadj 1.32 (1.00-1.74)). Further, the higher the density, the lower the grade (ORadj 0.73 (0.53-1.02) for having higher grade), in screening-detected invasive breast cancer. Our findings stress the importance of considering the impact of density in mammography image interpretation and the possible associations with tumor aggressiveness. Copyright © 2014 Elsevier Ltd. All rights reserved.

Assessment of two mammographic density related features in predicting near-term breast cancer risk

NASA Astrophysics Data System (ADS)

Zheng, Bin; Sumkin, Jules H.; Zuley, Margarita L.; Wang, Xingwei; Klym, Amy H.; Gur, David

2012-02-01

In order to establish a personalized breast cancer screening program, it is important to develop risk models that have high discriminatory power in predicting the likelihood of a woman developing an imaging detectable breast cancer in near-term (e.g., <3 years after a negative examination in question). In epidemiology-based breast cancer risk models, mammographic density is considered the second highest breast cancer risk factor (second to woman's age). In this study we explored a new feature, namely bilateral mammographic density asymmetry, and investigated the feasibility of predicting near-term screening outcome. The database consisted of 343 negative examinations, of which 187 depicted cancers that were detected during the subsequent screening examination and 155 that remained negative. We computed the average pixel value of the segmented breast areas depicted on each cranio-caudal view of the initial negative examinations. We then computed the mean and difference mammographic density for paired bilateral images. Using woman's age, subjectively rated density (BIRADS), and computed mammographic density related features we compared classification performance in estimating the likelihood of detecting cancer during the subsequent examination using areas under the ROC curves (AUC). The AUCs were 0.63+/-0.03, 0.54+/-0.04, 0.57+/-0.03, 0.68+/-0.03 when using woman's age, BIRADS rating, computed mean density and difference in computed bilateral mammographic density, respectively. Performance increased to 0.62+/-0.03 and 0.72+/-0.03 when we fused mean and difference in density with woman's age. The results suggest that, in this study, bilateral mammographic tissue density is a significantly stronger (p<0.01) risk indicator than both woman's age and mean breast density.

The association between mammographic breast density and bone mineral density in the study of women's health across the nation.

PubMed

Crandall, Carolyn J; Zheng, Yan; Karlamangla, Arun; Sternfeld, Barbara; Habel, Laurel A; Oestreicher, Nina; Johnston, Janet; Cauley, Jane A; Greendale, Gail A

2007-08-01

Bone mineral density and mammographic breast density are each associated with markers of lifetime estrogen exposure. The association between mammographic breast density and bone mineral density in early perimenopausal women is unknown. We analyzed data from a cohort (n = 501) of premenopausal (no change in menstrual regularity) and early perimenopausal (decreased menstrual regularity in past 3 months) participants of African-American, Caucasian, Chinese, and Japanese ethnicity in the Study of Women's Health Across the Nation. Using multivariable linear regression, we examined the cross-sectional association between percent mammographic density and bone mineral density (BMD). Percent mammographic density was statistically significantly inversely associated with hip BMD and lumbar spine BMD after adjustment (body mass index, ethnicity, age, study site, parity, alcohol intake, cigarette smoking, physical activity, age at first childbirth) in early perimenopausal, but not premenopausal, women. In early perimenopausal women, every 0.1g/cm(2) greater hip BMD predicted a 2% lower percent mammographic density (95% confidence interval -37.0 to -0.6%, p = 0.04). Mammographic breast density is inversely associated with BMD in the perimenopausal participants of this community-based cohort. The biological underpinnings of these findings may reflect differential responsiveness of breast and bone mineral density to the steroid milieu.

Breast Density Notification Legislation and Breast Cancer Stage at Diagnosis: Early Evidence from the SEER Registry.

PubMed

Richman, Ilana; Asch, Steven M; Bendavid, Eran; Bhattacharya, Jay; Owens, Douglas K

2017-06-01

Twenty-eight states have passed breast density notification laws, which require physicians to inform women of a finding of dense breasts on mammography. To evaluate changes in breast cancer stage at diagnosis after enactment of breast density notification legislation. Using a difference-in-differences analysis, we examined changes in stage at diagnosis among women with breast cancer in Connecticut, the first state to enact legislation, compared to changes among women in control states. We used data from the Surveillance, Epidemiology, and End Results Program (SEER) registry, 2005-2013. Women ages 40-74 with breast cancer. Breast density notification legislation, enacted in Connecticut in October of 2009. Breast cancer stage at diagnosis. Our study included 466,930 women, 25,592 of whom lived in Connecticut. Legislation was associated with a 1.38-percentage-point (95 % CI 0.12 to 2.63) increase in the proportion of women in Connecticut versus control states who had localized invasive cancer at the time of diagnosis, and a 1.12-percentage-point (95 % CI -2.21 to -0.08) decline in the proportion of women with ductal carcinoma in situ at diagnosis. Breast density notification legislation was not associated with a change in the proportion of women in Connecticut versus control states with regional-stage (-0.09 percentage points, 95 % CI -1.01 to 1.02) or metastatic disease (-0.24, 95 % CI -0.75 to 0.28). County-level analyses and analyses limited to women younger than 50 found no statistically significant associations. Single intervention state, limited follow-up, potential confounding from unobserved trends. Breast density notification legislation in Connecticut was associated with a small increase in the proportion of women diagnosed with localized invasive breast cancer in individual-level but not county-level analyses. Whether this finding reflects potentially beneficial early detection or potentially harmful overdiagnosis is not known. Legislation was not associated with changes in regional or metastatic disease.

Insulin-like growth factor-I (IGF-1), IGF-binding protein-3 (IGFBP-3) and mammographic features.

PubMed

Izzo, L; Meggiorini, M L; Nofroni, I; Pala, A; De Felice, C; Meloni, P; Simari, T; Izzo, S; Pugliese, F; Impara, L; Merlini, G; Di Cello, P; Cipolla, V; Forcione, A R; Paliotta, A; Domenici, L; Bolognese, A

2012-05-01

The IGF system has recently been shown to play an important role in the regulation of breast tumor cell proliferation. However, also breast density is currently considered as the strongest breast cancer risk factor. It is not yet clear whether these factors are interrelated and if and how they are influenced by menopausal status. The purpose of this study was to examine the possible effects of IGF-1 and IGFBP-3 and IGF-1/IGFBP-3 molar ratio on mammographic density stratified by menopausal status. A group of 341 Italian women were interviewed to collect the following data: family history of breast cancer, reproductive and menstrual factors, breast biopsies, previous administration of hormonal contraceptive therapy, hormone replacement therapy (HRT) in menopause and lifestyle information. A blood sample was drawn for determination of IGF-1, IGFBP-3 levels. IGF-1/ IGFBP-3 molar ratio was then calculated. On the basis of recent mammograms the women were divided into two groups: dense breast (DB) and non-dense breast (NDB). Student's t-test was employed to assess the association between breast density and plasma level of IGF-1, IGFBP-3 and molar ratio. To assess if this relationship was similar in subgroups of pre- and postmenopausal women, the study population was stratified by menopausal status and Student's t-test was performed. Finally, multivariate analysis was employed to evaluate if there were confounding factors that might influence the relationship between growth factors and breast density. The analysis of the relationship between mammographic density and plasma level of IGF-1, IGFBP-3 and IGF-1/ IGFBP-3 molar ratio showed that IGF-1 levels and molar ratio varied in the two groups resulting in higher mean values in the DB group (IGF-1: 109.6 versus 96.6 ng/ml; p= 0.001 and molar ratio 29.4 versus 25.5 ng/ml; p= 0.001) whereas IGFBP-3 showed similar values in both groups (DB and NDB). Analysis of plasma level of IGF-1, IGFBP-3 and IGF-1/IGFBP-3 molar ratio compared to breast density after stratification of the study population by menopausal status (premenopausal and postmenopausal) showed that there was no association between the plasma of growth factors and breast density, neither in premenopausal nor in postmenopausal patients. Multivariate analysis showed that only nulliparity, premenopausal status and body mass index (BMI) are determinants of breast density. Our study provides a strong evidence of a crude association between breast density and plasma levels of IGF-1 and molar ratio. On the basis of our results, it is reasonable to assume that the role of IGF-1 and molar ratio in the pathogenesis of breast cancer might be mediated through mammographic density. IGF-1 and molar ratio might thus increase the risk of cancer by increasing mammographic density.

Breast Density and Benign Breast Disease: Risk Assessment to Identify Women at High Risk of Breast Cancer.

PubMed

Tice, Jeffrey A; Miglioretti, Diana L; Li, Chin-Shang; Vachon, Celine M; Gard, Charlotte C; Kerlikowske, Karla

2015-10-01

Women with proliferative breast lesions are candidates for primary prevention, but few risk models incorporate benign findings to assess breast cancer risk. We incorporated benign breast disease (BBD) diagnoses into the Breast Cancer Surveillance Consortium (BCSC) risk model, the only breast cancer risk assessment tool that uses breast density. We developed and validated a competing-risk model using 2000 to 2010 SEER data for breast cancer incidence and 2010 vital statistics to adjust for the competing risk of death. We used Cox proportional hazards regression to estimate the relative hazards for age, race/ethnicity, family history of breast cancer, history of breast biopsy, BBD diagnoses, and breast density in the BCSC. We included 1,135,977 women age 35 to 74 years undergoing mammography with no history of breast cancer; 17% of the women had a prior breast biopsy. During a mean follow-up of 6.9 years, 17,908 women were diagnosed with invasive breast cancer. The BCSC BBD model slightly overpredicted risk (expected-to-observed ratio, 1.04; 95% CI, 1.03 to 1.06) and had modest discriminatory accuracy (area under the receiver operator characteristic curve, 0.665). Among women with proliferative findings, adding BBD to the model increased the proportion of women with an estimated 5-year risk of 3% or higher from 9.3% to 27.8% (P<.001). The BCSC BBD model accurately estimates women's risk for breast cancer using breast density and BBD diagnoses. Greater numbers of high-risk women eligible for primary prevention after BBD diagnosis are identified using the BCSC BBD model. © 2015 by American Society of Clinical Oncology.

Breast Density and Benign Breast Disease: Risk Assessment to Identify Women at High Risk of Breast Cancer

PubMed Central

Tice, Jeffrey A.; Miglioretti, Diana L.; Li, Chin-Shang; Vachon, Celine M.; Gard, Charlotte C.; Kerlikowske, Karla

2015-01-01

Purpose Women with proliferative breast lesions are candidates for primary prevention, but few risk models incorporate benign findings to assess breast cancer risk. We incorporated benign breast disease (BBD) diagnoses into the Breast Cancer Surveillance Consortium (BCSC) risk model, the only breast cancer risk assessment tool that uses breast density. Methods We developed and validated a competing-risk model using 2000 to 2010 SEER data for breast cancer incidence and 2010 vital statistics to adjust for the competing risk of death. We used Cox proportional hazards regression to estimate the relative hazards for age, race/ethnicity, family history of breast cancer, history of breast biopsy, BBD diagnoses, and breast density in the BCSC. Results We included 1,135,977 women age 35 to 74 years undergoing mammography with no history of breast cancer; 17% of the women had a prior breast biopsy. During a mean follow-up of 6.9 years, 17,908 women were diagnosed with invasive breast cancer. The BCSC BBD model slightly overpredicted risk (expected-to-observed ratio, 1.04; 95% CI, 1.03 to 1.06) and had modest discriminatory accuracy (area under the receiver operator characteristic curve, 0.665). Among women with proliferative findings, adding BBD to the model increased the proportion of women with an estimated 5-year risk of 3% or higher from 9.3% to 27.8% (P < .001). Conclusion The BCSC BBD model accurately estimates women's risk for breast cancer using breast density and BBD diagnoses. Greater numbers of high-risk women eligible for primary prevention after BBD diagnosis are identified using the BCSC BBD model. PMID:26282663

Breast Tenderness after Initiation of Conjugated Equine Estrogens and Mammographic Density Change

PubMed Central

Crandall, Carolyn J.; Aragaki, Aaron K.; Cauley, Jane A.; McTiernan, Anne; Manson, JoAnn E.; Anderson, Garnet L.; Wactawski-Wende, Jean; Chlebowski, Rowan T.

2013-01-01

Background We examined the association between new-onset breast tenderness and change in mammographic density after initiation of conjugated equine estrogens (CEE). Methods We analyzed baseline, year 1, and year 2 data from 695 participants of the Women's Health Initiative Estrogen + Progestin (daily CEE 0.625 mg + medroxyprogesterone acetate 2.5 mg [MPA] or placebo) and Estrogen-Alone (CEE 0.625 mg or placebo) trials who participated in the Mammogram Density Ancillary Study. Using multivariable repeated measures models, we analyzed the association between new-onset breast tenderness (i.e. absence of baseline tenderness and presence of tenderness at year 1 follow-up) and change from baseline in percent mammographic density. Results Active therapy increased the odds of new-onset breast tenderness (CEE + MPA vs. placebo risk ratio [RR] 3.01, 95% confidence interval [95% CI] 1.96-4.62; CEE vs. placebo RR 1.70, 95% CI 1.14-2.53). Among women assigned to CEE + MPA, mean increase in mammographic density was greater among participants reporting new-onset of breast tenderness than among participants without new-onset breast tenderness (11.3% vs. 3.9% at year 1, 9.4% vs. 3.2% at year 2, P < 0.001). Among women assigned to CEE alone, increase in mammographic density at year 1 follow-up was not significantly different in women with new-onset breast tenderness compared to women without new-onset breast tenderness (2.4% vs. 0.6% at year 1, 2.2% vs. 1.0% at year 2, P = 0.30). Conclusions The new-onset of breast tenderness after initiation of CEE + MPA, but not CEE alone, is associated with greater increases in mammographic density. PMID:21979747

Automated assessment of breast tissue density in non-contrast 3D CT images without image segmentation based on a deep CNN

NASA Astrophysics Data System (ADS)

Zhou, Xiangrong; Kano, Takuya; Koyasu, Hiromi; Li, Shuo; Zhou, Xinxin; Hara, Takeshi; Matsuo, Masayuki; Fujita, Hiroshi

2017-03-01

This paper describes a novel approach for the automatic assessment of breast density in non-contrast three-dimensional computed tomography (3D CT) images. The proposed approach trains and uses a deep convolutional neural network (CNN) from scratch to classify breast tissue density directly from CT images without segmenting the anatomical structures, which creates a bottleneck in conventional approaches. Our scheme determines breast density in a 3D breast region by decomposing the 3D region into several radial 2D-sections from the nipple, and measuring the distribution of breast tissue densities on each 2D section from different orientations. The whole scheme is designed as a compact network without the need for post-processing and provides high robustness and computational efficiency in clinical settings. We applied this scheme to a dataset of 463 non-contrast CT scans obtained from 30- to 45-year-old-women in Japan. The density of breast tissue in each CT scan was assigned to one of four categories (glandular tissue within the breast <25%, 25%-50%, 50%-75%, and >75%) by a radiologist as ground truth. We used 405 CT scans for training a deep CNN and the remaining 58 CT scans for testing the performance. The experimental results demonstrated that the findings of the proposed approach and those of the radiologist were the same in 72% of the CT scans among the training samples and 76% among the testing samples. These results demonstrate the potential use of deep CNN for assessing breast tissue density in non-contrast 3D CT images.

Double-blind randomized 12-month soy intervention had no effects on breast MRI fibroglandular tissue density or mammographic density

PubMed Central

Wu, Anna H.; Spicer, Darcy; Garcia, Agustin; Tseng, Chiu-Chen; Hovanessian-Larsen, Linda; Sheth, Pulin; Martin, Sue Ellen; Hawes, Debra; Russell, Christy; McDonald, Heather; Tripathy, Debu; Su, Min-Ying; Ursin, Giske; Pike, Malcolm C.

2015-01-01

Soy supplementation by breast cancer patients remains controversial. No controlled intervention studies have investigated the effects of soy supplementation on mammographic density in breast cancer patients. We conducted a double-blind, randomized, placebo-controlled intervention study in previously treated breast cancer patients (n=66) and high-risk women (n=29). We obtained digital mammograms and breast magnetic resonance imaging (MRI) scans at baseline and after 12 months of daily soy (50 mg isoflavones per day) (n=46) or placebo (n=49) tablet supplementation. The total breast area (MA) and the area of mammographic density (MD) on the mammogram was measured using a validated computer-assisted method, and mammographic density percent (MD% = 100 × MD/MA) was determined. A well-tested computer algorithm was used to quantitatively measure the total breast volume (TBV) and fibroglandular tissue volume (FGV) on the breast MRI, and the FGV percent (FGV% = 100 × FGV/TBV) was calculated. On the basis of plasma soy isoflavone levels, compliance was excellent. Small decreases in MD% measured by the ratios of month 12 to baseline levels, were seen in the soy (0.95) and the placebo (0.87) groups; these changes did not differ between the treatments (P=0.38). Small decreases in FGV% were also found in both the soy (0.90) and the placebo (0.92) groups; these changes also did not differ between the treatments (P=0.48). Results were comparable in breast cancer patients and high-risk women. We found no evidence that soy supplementation would decrease mammographic density and that MRI might be more sensitive to changes in density than mammography. PMID:26276750

Enhancement of breast periphery region in digital mammography

NASA Astrophysics Data System (ADS)

Menegatti Pavan, Ana Luiza; Vacavant, Antoine; Petean Trindade, Andre; Quini, Caio Cesar; Rodrigues de Pina, Diana

2018-03-01

Volumetric breast density has been shown to be one of the strongest risk factor for breast cancer diagnosis. This metric can be estimated using digital mammograms. During mammography acquisition, breast is compressed and part of it loses contact with the paddle, resulting in an uncompressed region in periphery with thickness variation. Therefore, reliable density estimation in the breast periphery region is a problem, which affects the accuracy of volumetric breast density measurement. The aim of this study was to enhance breast periphery to solve the problem of thickness variation. Herein, we present an automatic algorithm to correct breast periphery thickness without changing pixel value from internal breast region. The correction pixel values from periphery was based on mean values over iso-distance lines from the breast skin-line using only adipose tissue information. The algorithm detects automatically the periphery region where thickness should be corrected. A correction factor was applied in breast periphery image to enhance the region. We also compare our contribution with two other algorithms from state-of-the-art, and we show its accuracy by means of different quality measures. Experienced radiologists subjectively evaluated resulting images from the tree methods in relation to original mammogram. The mean pixel value, skewness and kurtosis from histogram of the three methods were used as comparison metric. As a result, the methodology presented herein showed to be a good approach to be performed before calculating volumetric breast density.

Amount of stroma is associated with mammographic density and stromal expression of oestrogen receptor in normal breast tissues.

PubMed

Gabrielson, Marike; Chiesa, Flaminia; Paulsson, Janna; Strell, Carina; Behmer, Catharina; Rönnow, Katarina; Czene, Kamila; Östman, Arne; Hall, Per

2016-07-01

Following female sex and age, mammographic density is considered one of the strongest risk factors for breast cancer. Despite the association between mammographic density and breast cancer risk, little is known about the underlying histology and biological basis of breast density. To better understand the mechanisms behind mammographic density we assessed morphology, proliferation and hormone receptor status in relation to mammographic density in breast tissues from healthy women. Tissues were obtained from 2012-2013 by ultrasound-guided core needle biopsy from 160 women as part of the Karma (Karolinska mammography project for risk prediction for breast cancer) project. Mammograms were collected through routine mammography screening and mammographic density was calculated using STRATUS. The histological composition, epithelial and stromal proliferation status and hormone receptor status were assessed through immunohistochemical staining. Higher mammographic density was significantly associated with a greater proportion of stromal and epithelial tissue and a lower proportion of adipose tissue. Epithelial expression levels of Ki-67, oestrogen receptor (ER) and progesterone receptor (PR) were not associated with mammographic density. Epithelial Ki-67 was associated with a greater proportion of epithelial tissue, and epithelial PR was associated with a greater proportion of stromal and a lower proportion of adipose tissue. Epithelial ER was not associated with any tissues. In contrast, expression of ER in the stroma was significantly associated with a greater proportion of stroma, and negatively associated with the amount of adipose tissue. High mammographic density is associated with higher amount of stroma and epithelium and less amount of fat, but is not associated with a change in epithelial proliferation or receptor status. Increased expressions of both epithelial PR and stromal ER are associated with a greater proportion of stroma, suggesting hormonal involvement in regulating breast tissue composition.

Patient awareness of breast density and interest in supplemental screening tests: comparison of an academic facility and a county hospital.

PubMed

Trinh, Long; Ikeda, Debra M; Miyake, Kanae K; Trinh, Jennifer; Lee, Kevin K; Dave, Haatal; Hanafusa, Kei; Lipson, Jafi

2015-03-01

The aim of this study was to measure women's knowledge of breast density and their attitudes toward supplemental screening tests in the setting of the California Breast Density Notification Law at an academic facility and a county hospital, serving women with higher and lower socioeconomic status, respectively. Institutional review board exemptions were obtained. A survey was administered during screening mammography at two facilities, assessing women's awareness of and interest in knowing their breast density and interest in and willingness to pay for supplemental whole breast ultrasound and contrast-enhanced spectral mammography (CEMG). The results were compared by using Fisher exact tests between groups. A total of 105 of 130 and 132 of 153 women responded to the survey at the academic and county facilities, respectively. Among respondents at the academic and county facilities, 23% and 5% were aware of their breast density, and 94% and 79% wanted to know their density. A majority were interested in supplemental ultrasonography and CEMG at both sites; however, fewer women had a willingness to pay for the supplemental tests at the county hospital compared with those at the academic facility (22% and 70%, respectively, for ultrasound, P < .0001; 20% and 65%, respectively, for CEMG, P < .0001). Both groups of women were interested in knowing their breast density and in supplemental screening tests. However, women at the county hospital were less willing to incur out-of-pocket expenses, suggesting a potential for a disparity in health care access for women of lower socioeconomic status after the enactment of breast density notification legislation. Published by Elsevier Inc.

High-performance broad-band spectroscopy for breast cancer risk assessment

NASA Astrophysics Data System (ADS)

Pawluczyk, Olga; Blackmore, Kristina; Dick, Samantha; Lilge, Lothar

2005-09-01

Medical diagnostics and screening are becoming increasingly demanding applications for spectroscopy. Although for many years the demand was satisfied with traditional spectrometers, analysis of complex biological samples has created a need for instruments capable of detecting small differences between samples. One such application is the measurement of absorbance of broad spectrum illumination by breast tissue, in order to quantify the breast tissue density. Studies have shown that breast cancer risk is closely associated with the measurement of radiographic breast density measurement. Using signal attenuation in transillumination spectroscopy in the 550-1100nm spectral range to measure breast density, has the potential to reduce the frequency of ionizing radiation, or making the test accessible to younger women; lower the cost and make the procedure more comfortable for the patient. In order to determine breast density, small spectral variances over a total attenuation of up to 8 OD have to be detected with the spectrophotometer. For this, a high performance system has been developed. The system uses Volume Phase Holographic (VPH) transmission grating, a 2D detector array for simultaneous registration of the whole spectrum with high signal to noise ratio, dedicated optical system specifically optimized for spectroscopic applications and many other improvements. The signal to noise ratio exceeding 50,000 for a single data acquisition eliminates the need for nitrogen cooled detectors and provides sufficient information to predict breast tissue density. Current studies employing transillumination breast spectroscopy (TIBS) relating to breast cancer risk assessment and monitoring are described.

Aluminum concentrations in central and peripheral areas of malignant breast lesions do not differ from those in normal breast tissues

PubMed Central

2013-01-01

Background Aluminum is used in a wide range of applications and is a potential environmental hazard. The known genotoxic effects of aluminum might play a role in the development of breast cancer. However, the data currently available on the subject are not sufficient to establish a causal relationship between aluminum exposure and the augmented risk of developing breast cancer. To achieve maximum sensitivity and specificity in the determination of aluminum levels, we have developed a detection protocol using graphite furnace atomic absorption spectrometry (GFAAS). The objective of the present study was to compare the aluminum levels in the central and peripheral areas of breast carcinomas with those in the adjacent normal breast tissues, and to identify patient and/or tumor characteristics associated with these aluminum levels. Methods A total of 176 patients with breast cancer were included in the study. Samples from the central and peripheral areas of their tumors were obtained, as well as from the surrounding normal breast tissue. Aluminum quantification was performed using GFAAS. Results The average (mean ± SD) aluminum concentrations were as follows: central area, 1.88 ± 3.60 mg/kg; peripheral area, 2.10 ± 5.67 mg/kg; and normal area, 1.68 ± 11.1 mg/kg. Overall and two-by-two comparisons of the aluminum concentrations in these areas indicated no significant differences. We detected a positive relationship between aluminum levels in the peripheral areas of the tumors, age and menopausal status of the patients (P = .02). Conclusions Using a sensitive quantification technique we detected similar aluminum concentrations in the central and peripheral regions of breast tumors, and in normal tissues. In addition, we did not detect significant differences in aluminum concentrations as related to the location of the breast tumor within the breast, or to other relevant tumor features such as stage, size and steroid receptor status. The next logical step is the assessment of whether the aluminum concentration is related to the key genomic abnormalities associated with breast carcinogenesis. PMID:23496847

Aluminum concentrations in central and peripheral areas of malignant breast lesions do not differ from those in normal breast tissues.

PubMed

Rodrigues-Peres, Raquel Mary; Cadore, Solange; Febraio, Stefanny; Heinrich, Juliana Karina; Serra, Katia Piton; Derchain, Sophie F M; Vassallo, Jose; Sarian, Luis Otavio

2013-03-08

Aluminum is used in a wide range of applications and is a potential environmental hazard. The known genotoxic effects of aluminum might play a role in the development of breast cancer. However, the data currently available on the subject are not sufficient to establish a causal relationship between aluminum exposure and the augmented risk of developing breast cancer. To achieve maximum sensitivity and specificity in the determination of aluminum levels, we have developed a detection protocol using graphite furnace atomic absorption spectrometry (GFAAS). The objective of the present study was to compare the aluminum levels in the central and peripheral areas of breast carcinomas with those in the adjacent normal breast tissues, and to identify patient and/or tumor characteristics associated with these aluminum levels. A total of 176 patients with breast cancer were included in the study. Samples from the central and peripheral areas of their tumors were obtained, as well as from the surrounding normal breast tissue. Aluminum quantification was performed using GFAAS. The average (mean ± SD) aluminum concentrations were as follows: central area, 1.88 ± 3.60 mg/kg; peripheral area, 2.10 ± 5.67 mg/kg; and normal area, 1.68 ± 11.1 mg/kg. Overall and two-by-two comparisons of the aluminum concentrations in these areas indicated no significant differences. We detected a positive relationship between aluminum levels in the peripheral areas of the tumors, age and menopausal status of the patients (P = .02). Using a sensitive quantification technique we detected similar aluminum concentrations in the central and peripheral regions of breast tumors, and in normal tissues. In addition, we did not detect significant differences in aluminum concentrations as related to the location of the breast tumor within the breast, or to other relevant tumor features such as stage, size and steroid receptor status. The next logical step is the assessment of whether the aluminum concentration is related to the key genomic abnormalities associated with breast carcinogenesis.

Quantification of osteolytic bone lesions in a preclinical rat trial

NASA Astrophysics Data System (ADS)

Fränzle, Andrea; Bretschi, Maren; Bäuerle, Tobias; Giske, Kristina; Hillengass, Jens; Bendl, Rolf

2013-10-01

In breast cancer, most of the patients who died, have developed bone metastasis as disease progression. Bone metastases in case of breast cancer are mainly bone destructive (osteolytic). To understand pathogenesis and to analyse response to different treatments, animal models, in our case rats, are examined. For assessment of treatment response to bone remodelling therapies exact segmentations of osteolytic lesions are needed. Manual segmentations are not only time-consuming but lack in reproducibility. Computerized segmentation tools are essential. In this paper we present an approach for the computerized quantification of osteolytic lesion volumes using a comparison to a healthy reference model. The presented qualitative and quantitative evaluation of the reconstructed bone volumes show, that the automatically segmented lesion volumes complete missing bone in a reasonable way.

Virtual touch tissue quantification using acoustic radiation force impulse technology: initial clinical experience with solid breast masses.

PubMed

Bai, Min; Du, Lianfang; Gu, Jiying; Li, Fan; Jia, Xiao

2012-02-01

The purpose of this study was to investigate the clinical usage of Virtual Touch tissue quantification (VTQ; Siemens Medical Solutions, Mountain View, CA) implementing sonographic acoustic radiation force impulse technology for differentiation between benign and malignant solid breast masses. A total of 143 solid breast masses were examined with VTQ, and their shear wave velocities (SWVs) were measured. From all of the masses, 30 were examined by two independent operators to evaluate the reproducibility of the results of VTQ measurement. All masses were later surgically resected, and the histologic results were correlated with the SWV results. A receiver operating characteristic curve was calculated to assess the diagnostic performance of VTQ. A total of 102 benign lesions and 41 carcinomas were diagnosed on the basis of histologic examination. The VTQ measurements performed by the two independent operators yielded a correlation coefficient of 0.885. Applying a cutoff point of 3.065 m/s, a significant difference (P < .001) was found between the SWVs of the benign (mean ± SD, 2.25 ± 0.59 m/s) and malignant (5.96 ± 2.96 m/s) masses. The sensitivity, specificity, and area under the receiver operating characteristic curve for the differentiation were 75.6%, 95.1%, and 85.6%, respectively. When the repeated non-numeric result X.XX of the SWV measurements was designated as an indicator of malignancy, the sensitivity, specificity, and accuracy were 63.4%, 100%, and 89.5%. Virtual Touch tissue quantification can yield reproducible and quantitative diagnostic information on solid breast masses and serve as an effective diagnostic tool for differentiation between benign and malignant solid masses.

Volumetric breast density is essential for predicting cosmetic outcome at the late stage after breast-conserving surgery.

PubMed

Shiina, N; Sakakibara, M; Fujisaki, K; Iwase, T; Nagashima, T; Sangai, T; Kubota, Y; Akita, S; Takishima, H; Miyazaki, M

2016-04-01

The critical issue related to breast-conserving therapy (BCT) is that cosmetic outcomes deteriorate with long-term follow-up. There is little research for breast density as a predictor of cosmetic outcomes at the late stage after BCT. To improve the long-term quality of life after BCT of breast cancer patients, the correlation of volumetric breast density (VBD) and cosmetic outcome at the late stage after BCT was evaluated. Breast volume, fibroglandular tissue volume, adipose tissue volume, and VBD were calculated on mammography using image analysis software (Volpara(®)) in 151 patients with BCT. Furthermore, the correlation of breast density and the change of breast volume over time was analyzed on mammography in 99 patients who were followed-up long-term after BCT. On multivariate analysis, VBD was a predictor of cosmetic outcome after BCT with percent breast volume excised (PBVE). Decreased adipose tissue volume and increased fibrosis were more common in patients with VBD < 15%. Furthermore, remnant breast volume continued to decrease over time in low breast density patients during long-term follow-up. 93% of patients with VBD ≥ 15% and PBVE < 10% had a better cosmetic outcome, while 60% of patients with VBD < 15% and PBVE ≥ 10% had a worse cosmetic outcome after BCT. While PBVE was involved in cosmetic outcome at the early stage after BCT, VBD was associated with cosmetic outcome at the late stage after BCT. Thus, a combination of VBD and PBVE could predict cosmetic outcome after BCT and contribute to the selection for the appropriate BCT. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Short-Term Effect of Weight Loss Surgery on Volumetric Breast Density and Fibroglandular Volume.

PubMed

Vohra, Nasreen A; Kachare, Swapnil D; Vos, Paul; Schroeder, Bruce F; Schuth, Olga; Suttle, Dylan; Fitzgerald, Timothy L; Wong, Jan H; Verbanac, Kathryn M

2017-04-01

Obesity and breast density are both associated with an increased risk of breast cancer and are potentially modifiable. Weight loss surgery (WLS) causes a significant reduction in the amount of body fat and a decrease in breast cancer risk. The effect of WLS on breast density and its components has not been documented. Here, we analyze the impact of WLS on volumetric breast density (VBD) and on each of its components (fibroglandular volume and breast volume) by using three-dimensional methods. Fibroglandular volume, breast volume, and their ratio, the VBD, were calculated from mammograms before and after WLS by using Volpara™ automated software. For the 80 women included, average body mass index decreased from 46.0 ± 7.22 to 33.7 ± 7.06 kg/m 2 . Mammograms were performed on average 11.6 ± 9.4 months before and 10.1 ± 7 months after WLS. There was a significant reduction in average breast volume (39.4 % decrease) and average fibroglandular volume (15.5 % decrease), and thus, the average VBD increased from 5.15 to 7.87 % (p < 1 × 10 -9 ) after WLS. When stratified by menopausal status and diabetic status, VBD increased significantly in all groups but only perimenopausal and postmenopausal women and non-diabetics experienced a significant reduction in fibroglandular volume. Breast volume and fibroglandular volume decreased, and VBD increased following WLS, with the most significant change observed in postmenopausal women and non-diabetics. Further studies are warranted to determine how physical and biological alterations in breast density components after WLS may impact breast cancer risk.

Comparison of volumetric breast density estimations from mammography and thorax CT

NASA Astrophysics Data System (ADS)

Geeraert, N.; Klausz, R.; Cockmartin, L.; Muller, S.; Bosmans, H.; Bloch, I.

2014-08-01

Breast density has become an important issue in current breast cancer screening, both as a recognized risk factor for breast cancer and by decreasing screening efficiency by the masking effect. Different qualitative and quantitative methods have been proposed to evaluate area-based breast density and volumetric breast density (VBD). We propose a validation method comparing the computation of VBD obtained from digital mammographic images (VBDMX) with the computation of VBD from thorax CT images (VBDCT). We computed VBDMX by applying a conversion function to the pixel values in the mammographic images, based on models determined from images of breast equivalent material. VBDCT is computed from the average Hounsfield Unit (HU) over the manually delineated breast volume in the CT images. This average HU is then compared to the HU of adipose and fibroglandular tissues from patient images. The VBDMX method was applied to 663 mammographic patient images taken on two Siemens Inspiration (hospL) and one GE Senographe Essential (hospJ). For the comparison study, we collected images from patients who had a thorax CT and a mammography screening exam within the same year. In total, thorax CT images corresponding to 40 breasts (hospL) and 47 breasts (hospJ) were retrieved. Averaged over the 663 mammographic images the median VBDMX was 14.7% . The density distribution and the inverse correlation between VBDMX and breast thickness were found as expected. The average difference between VBDMX and VBDCT is smaller for hospJ (4%) than for hospL (10%). This study shows the possibility to compare VBDMX with the VBD from thorax CT exams, without additional examinations. In spite of the limitations caused by poorly defined breast limits, the calibration of mammographic images to local VBD provides opportunities for further quantitative evaluations.

Average glandular dose in paired digital mammography and digital breast tomosynthesis acquisitions in a population based screening program: effects of measuring breast density, air kerma and beam quality

NASA Astrophysics Data System (ADS)

Helge Østerås, Bjørn; Skaane, Per; Gullien, Randi; Catrine Trægde Martinsen, Anne

2018-02-01

The main purpose was to compare average glandular dose (AGD) for same-compression digital mammography (DM) and digital breast tomosynthesis (DBT) acquisitions in a population based screening program, with and without breast density stratification, as determined by automatically calculated breast density (Quantra™). Secondary, to compare AGD estimates based on measured breast density, air kerma and half value layer (HVL) to DICOM metadata based estimates. AGD was estimated for 3819 women participating in the screening trial. All received craniocaudal and mediolateral oblique views of each breasts with paired DM and DBT acquisitions. Exposure parameters were extracted from DICOM metadata. Air kerma and HVL were measured for all beam qualities used to acquire the mammograms. Volumetric breast density was estimated using Quantra™. AGD was estimated using the Dance model. AGD reported directly from the DICOM metadata was also assessed. Mean AGD was 1.74 and 2.10 mGy for DM and DBT, respectively. Mean DBT/DM AGD ratio was 1.24. For fatty breasts: mean AGD was 1.74 and 2.27 mGy for DM and DBT, respectively. For dense breasts: mean AGD was 1.73 and 1.79 mGy, for DM and DBT, respectively. For breasts of similar thickness, dense breasts had higher AGD for DM and similar AGD for DBT. The DBT/DM dose ratio was substantially lower for dense compared to fatty breasts (1.08 versus 1.33). The average c-factor was 1.16. Using previously published polynomials to estimate glandularity from thickness underestimated the c-factor by 5.9% on average. Mean AGD error between estimates based on measurements (air kerma and HVL) versus DICOM header data was 3.8%, but for one mammography unit as high as 7.9%. Mean error of using the AGD value reported in the DICOM header was 10.7 and 13.3%, respectively. Thus, measurement of breast density, radiation dose and beam quality can substantially affect AGD estimates.

Average glandular dose in paired digital mammography and digital breast tomosynthesis acquisitions in a population based screening program: effects of measuring breast density, air kerma and beam quality.

PubMed

Østerås, Bjørn Helge; Skaane, Per; Gullien, Randi; Martinsen, Anne Catrine Trægde

2018-01-25

The main purpose was to compare average glandular dose (AGD) for same-compression digital mammography (DM) and digital breast tomosynthesis (DBT) acquisitions in a population based screening program, with and without breast density stratification, as determined by automatically calculated breast density (Quantra ™ ). Secondary, to compare AGD estimates based on measured breast density, air kerma and half value layer (HVL) to DICOM metadata based estimates. AGD was estimated for 3819 women participating in the screening trial. All received craniocaudal and mediolateral oblique views of each breasts with paired DM and DBT acquisitions. Exposure parameters were extracted from DICOM metadata. Air kerma and HVL were measured for all beam qualities used to acquire the mammograms. Volumetric breast density was estimated using Quantra ™ . AGD was estimated using the Dance model. AGD reported directly from the DICOM metadata was also assessed. Mean AGD was 1.74 and 2.10 mGy for DM and DBT, respectively. Mean DBT/DM AGD ratio was 1.24. For fatty breasts: mean AGD was 1.74 and 2.27 mGy for DM and DBT, respectively. For dense breasts: mean AGD was 1.73 and 1.79 mGy, for DM and DBT, respectively. For breasts of similar thickness, dense breasts had higher AGD for DM and similar AGD for DBT. The DBT/DM dose ratio was substantially lower for dense compared to fatty breasts (1.08 versus 1.33). The average c-factor was 1.16. Using previously published polynomials to estimate glandularity from thickness underestimated the c-factor by 5.9% on average. Mean AGD error between estimates based on measurements (air kerma and HVL) versus DICOM header data was 3.8%, but for one mammography unit as high as 7.9%. Mean error of using the AGD value reported in the DICOM header was 10.7 and 13.3%, respectively. Thus, measurement of breast density, radiation dose and beam quality can substantially affect AGD estimates.

Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk

PubMed Central

Lindström, Sara; Thompson, Deborah J.; Paterson, Andrew D.; Li, Jingmei; Gierach, Gretchen L.; Scott, Christopher; Stone, Jennifer; Douglas, Julie A.; dos-Santos-Silva, Isabel; Fernandez-Navarro, Pablo; Verghase, Jajini; Smith, Paula; Brown, Judith; Luben, Robert; Wareham, Nicholas J.; Loos, Ruth J.F.; Heit, John A.; Pankratz, V. Shane; Norman, Aaron; Goode, Ellen L.; Cunningham, Julie M.; deAndrade, Mariza; Vierkant, Robert A.; Czene, Kamila; Fasching, Peter A.; Baglietto, Laura; Southey, Melissa C.; Giles, Graham G.; Shah, Kaanan P.; Chan, Heang-Ping; Helvie, Mark A.; Beck, Andrew H.; Knoblauch, Nicholas W.; Hazra, Aditi; Hunter, David J.; Kraft, Peter; Pollan, Marina; Figueroa, Jonine D.; Couch, Fergus J.; Hopper, John L.; Hall, Per; Easton, Douglas F.; Boyd, Norman F.; Vachon, Celine M.; Tamimi, Rulla M.

2015-01-01

Mammographic density reflects the amount of stromal and epithelial tissues in relation to adipose tissue in the breast and is a strong risk factor for breast cancer. Here we report the results from meta-analysis of genome-wide association studies (GWAS) of three mammographic density phenotypes: dense area, non-dense area and percent density in up to 7,916 women in stage 1 and an additional 10,379 women in stage 2. We identify genome-wide significant (P<5×10−8) loci for dense area (AREG, ESR1, ZNF365, LSP1/TNNT3, IGF1, TMEM184B, SGSM3/MKL1), non-dense area (8p11.23) and percent density (PRDM6, 8p11.23, TMEM184B). Four of these regions are known breast cancer susceptibility loci, and four additional regions were found to be associated with breast cancer (P<0.05) in a large meta-analysis. These results provide further evidence of a shared genetic basis between mammographic density and breast cancer and illustrate the power of studying intermediate quantitative phenotypes to identify putative disease susceptibility loci. PMID:25342443

Mammographic density, breast cancer risk and risk prediction

PubMed Central

Vachon, Celine M; van Gils, Carla H; Sellers, Thomas A; Ghosh, Karthik; Pruthi, Sandhya; Brandt, Kathleen R; Pankratz, V Shane

2007-01-01

In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models. PMID:18190724

Automatic breast density classification using a convolutional neural network architecture search procedure

NASA Astrophysics Data System (ADS)

Fonseca, Pablo; Mendoza, Julio; Wainer, Jacques; Ferrer, Jose; Pinto, Joseph; Guerrero, Jorge; Castaneda, Benjamin

2015-03-01

Breast parenchymal density is considered a strong indicator of breast cancer risk and therefore useful for preventive tasks. Measurement of breast density is often qualitative and requires the subjective judgment of radiologists. Here we explore an automatic breast composition classification workflow based on convolutional neural networks for feature extraction in combination with a support vector machines classifier. This is compared to the assessments of seven experienced radiologists. The experiments yielded an average kappa value of 0.58 when using the mode of the radiologists' classifications as ground truth. Individual radiologist performance against this ground truth yielded kappa values between 0.56 and 0.79.

Parenchymal Texture Analysis in Digital Breast Tomosynthesis for Breast Cancer Risk Estimation: A Preliminary Study

PubMed Central

Kontos, Despina; Bakic, Predrag R.; Carton, Ann-Katherine; Troxel, Andrea B.; Conant, Emily F.; Maidment, Andrew D.A.

2009-01-01

Rationale and Objectives Studies have demonstrated a relationship between mammographic parenchymal texture and breast cancer risk. Although promising, texture analysis in mammograms is limited by tissue superimposition. Digital breast tomosynthesis (DBT) is a novel tomographic x-ray breast imaging modality that alleviates the effect of tissue superimposition, offering superior parenchymal texture visualization compared to mammography. Our study investigates the potential advantages of DBT parenchymal texture analysis for breast cancer risk estimation. Materials and Methods DBT and digital mammography (DM) images of 39 women were analyzed. Texture features, shown in studies with mammograms to correlate with cancer risk, were computed from the retroareolar breast region. We compared the relative performance of DBT and DM texture features in correlating with two measures of breast cancer risk: (i) the Gail and Claus risk estimates, and (ii) mammographic breast density. Linear regression was performed to model the association between texture features and increasing levels of risk. Results No significant correlation was detected between parenchymal texture and the Gail and Claus risk estimates. Significant correlations were observed between texture features and breast density. Overall, the DBT texture features demonstrated stronger correlations with breast percent density (PD) than DM (p ≤0.05). When dividing our study population in groups of increasing breast PD, the DBT texture features appeared to be more discriminative, having regression lines with overall lower p-values, steeper slopes, and higher R2 estimates. Conclusion Although preliminary, our results suggest that DBT parenchymal texture analysis could provide more accurate characterization of breast density patterns, which could ultimately improve breast cancer risk estimation. PMID:19201357

Computerized breast parenchymal analysis on DCE-MRI

NASA Astrophysics Data System (ADS)

Li, Hui; Giger, Maryellen L.; Yuan, Yading; Jansen, Sanaz A.; Lan, Li; Bhooshan, Neha; Newstead, Gillian M.

2009-02-01

Breast density has been shown to be associated with the risk of developing breast cancer, and MRI has been recommended for high-risk women screening, however, it is still unknown how the breast parenchymal enhancement on DCE-MRI is associated with breast density and breast cancer risk. Ninety-two DCE-MRI exams of asymptomatic women with normal MR findings were included in this study. The 3D breast volume was automatically segmented using a volume-growing based algorithm. The extracted breast volume was classified into fibroglandular and fatty regions based on the discriminant analysis method. The parenchymal kinetic curves within the breast fibroglandular region were extracted and categorized by use of fuzzy c-means clustering, and various parenchymal kinetic characteristics were extracted from the most enhancing voxels. Correlation analysis between the computer-extracted percent dense measures and radiologist-noted BIRADS density ratings yielded a correlation coefficient of 0.76 (p<0.0001). From kinetic analyses, 70% (64/92) of most enhancing curves showed persistent curve type and reached peak parenchymal intensity at the last postcontrast time point; with 89% (82/92) of most enhancing curves reaching peak intensity at either 4th or 5th post-contrast time points. Women with dense breast (BIRADS 3 and 4) were found to have more parenchymal enhancement at their peak time point (Ep) with an average Ep of 116.5% while those women with fatty breasts (BIRADS 1 and 2) demonstrated an average Ep of 62.0%. In conclusion, breast parenchymal enhancement may be associated with breast density and may be potential useful as an additional characteristic for assessing breast cancer risk.

Quality Assessments of Long-Term Quantitative Proteomic Analysis of Breast Cancer Xenograft Tissues

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Jian-Ying; Chen, Lijun; Zhang, Bai

The identification of protein biomarkers requires large-scale analysis of human specimens to achieve statistical significance. In this study, we evaluated the long-term reproducibility of an iTRAQ (isobaric tags for relative and absolute quantification) based quantitative proteomics strategy using one channel for universal normalization across all samples. A total of 307 liquid chromatography tandem mass spectrometric (LC-MS/MS) analyses were completed, generating 107 one-dimensional (1D) LC-MS/MS datasets and 8 offline two-dimensional (2D) LC-MS/MS datasets (25 fractions for each set) for human-in-mouse breast cancer xenograft tissues representative of basal and luminal subtypes. Such large-scale studies require the implementation of robust metrics to assessmore » the contributions of technical and biological variability in the qualitative and quantitative data. Accordingly, we developed a quantification confidence score based on the quality of each peptide-spectrum match (PSM) to remove quantification outliers from each analysis. After combining confidence score filtering and statistical analysis, reproducible protein identification and quantitative results were achieved from LC-MS/MS datasets collected over a 16 month period.« less

Mammographic Breast Density Assessment Using Automated Volumetric Software and Breast Imaging Reporting and Data System (BIRADS) Categorization by Expert Radiologists.

PubMed

Damases, Christine N; Brennan, Patrick C; Mello-Thoms, Claudia; McEntee, Mark F

2016-01-01

To investigate agreement on mammographic breast density (MD) assessment between automated volumetric software and Breast Imaging Reporting and Data System (BIRADS) categorization by expert radiologists. Forty cases of left craniocaudal and mediolateral oblique mammograms from 20 women were used. All images had their volumetric density classified using Volpara density grade (VDG) and average volumetric breast density percentage. The same images were then classified into BIRADS categories (I-IV) by 20 American Board of Radiology examiners. The results demonstrated a moderate agreement (κ = 0.537; 95% CI = 0.234-0.699) between VDG classification and radiologists' BIRADS density assessment. Interreader agreement using BIRADS also demonstrated moderate agreement (κ = 0.565; 95% CI = 0.519-0.610) ranging from 0.328 to 0.669. Radiologists' average BIRADS was lower than average VDG scores by 0.33, with their mean being 2.13, whereas the mean VDG was 2.48 (U = -3.742; P < 0.001). VDG and BIRADS showed a very strong positive correlation (ρ = 0.91; P < 0.001) as did BIRADS and average volumetric breast density percentage (ρ = 0.94; P < 0.001). Automated volumetric breast density assessment shows moderate agreement and very strong correlation with BIRADS; interreader variations still exist within BIRADS. Because of the increasing importance of MD measurement in clinical management of patients, widely accepted, reproducible, and accurate measures of MD are required. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Volumetric versus area-based density assessment: comparisons using automated quantitative measurements from a large screening cohort

NASA Astrophysics Data System (ADS)

Gastounioti, Aimilia; Hsieh, Meng-Kang; Pantalone, Lauren; Conant, Emily F.; Kontos, Despina

2018-03-01

Mammographic density is an established risk factor for breast cancer. However, area-based density (ABD) measured in 2D mammograms consider the projection, rather than the actual volume of dense tissue which may be an important limitation. With the increasing utilization of digital breast tomosynthesis (DBT) in screening, there's an opportunity to routinely estimate volumetric breast density (VBD). In this study, we investigate associations between DBT-VBD and ABD extracted from standard-dose mammography (DM) and synthetic 2D digital mammography (sDM) increasingly replacing DM. We retrospectively analyzed bilateral imaging data from a random sample of 1000 women, acquired over a transitional period at our institution when all women had DBT, sDM and DM acquired as part of their routine breast screening. For each exam, ABD was measured in DM and sDM images with the publicly available "LIBRA" software, while DBT-VBD was measured using a previously validated, fully-automated computer algorithm. Spearman correlation (r) was used to compare VBD to ABD measurements. For each density measure, we also estimated the within woman intraclass correlation (ICC) and finally, to compare to clinical assessments, we performed analysis of variance (ANOVA) to evaluate the variation to the assigned clinical BI-RADS breast density category for each woman. DBT-VBD was moderately correlated to ABD from DM (r=0.70) and sDM (r=0.66). All density measures had strong bilateral symmetry (ICC = [0.85, 0.95]), but were significantly different across BI-RADS density categories (ANOVA, p<0.001). Our results contribute to further elaborating the clinical implications of breast density measures estimated with DBT which may better capture the volumetric amount of dense tissue within the breast than area-based measures and visual assessment.

A Comparative Performance Analysis of Multispectral and RGB Imaging on HER2 Status Evaluation for the Prediction of Breast Cancer Prognosis.

PubMed

Liu, Wenlou; Wang, Linwei; Liu, Jiuyang; Yuan, Jingping; Chen, Jiamei; Wu, Han; Xiang, Qingming; Yang, Guifang; Li, Yan

2016-12-01

Despite the extensive application of multispectral imaging (MSI) in biomedical multidisciplinary researches, there is a paucity of data available regarding the implication of MSI in tumor prognosis prediction. We compared the behaviors of multispectral (MS) and conventional red-green-blue (RGB) images on assessment of human epidermal growth factor receptor 2 (HER2) immunohistochemistry to explore their impact on outcome in patients with invasive breast cancer (BC). Tissue microarrays containing 240 BC patients were introduced to compare the performance of MS and RGB imaging methods on the quantitative assessment of HER2 status and the prognostic value of 5-year disease-free survival (5-DFS). Both the total and average signal optical density values of HER2 MS and RGB images were analyzed, and all patients were divided into two groups based on the different 5-DFS. The quantification of HER2 MS images was negatively correlated with 5-DFS in lymph node-negative and -positive patients (P<.05), but RGB images were not in lymph node-positive patients (P=.101). Multivariate analysis indicated that the hazard ratio (HR) of HER2 MS was higher than that of HER2 RGB (HR=2.454; 95% confidence interval [CI], 1.636-3.681 vs HR=2.060; 95% CI, 1.361-3.119). Additionally, area under curve (AUC) by receiver operating characteristic analysis for HER2 MS was greater than that for HER2 RGB (AUC=0.649; 95% CI, 0.577-0.722 vs AUC=0.596; 95% CI, 0.522-0.670) in predicting the risk for recurrence. More importantly, the quantification of HER2 MS images has higher prediction accuracy than that of HER2 RGB images (69.6% vs 65.0%) on 5-DFS. Our study suggested that better information on BC prognosis could be obtained from the quantification of HER2 MS images and MS images might perform better in predicting BC prognosis than conventional RGB images. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Effect of Taxane-Based Neoadjuvant Chemotherapy on Fibroglandular Tissue Volume and Percent Breast Density in the Contralateral Normal Breast: Evaluated at 3T MR

PubMed Central

Chen, Jeon-Hor; Pan, Wei-Fan; Kao, Julian; Lu, Jocelyn; Chen, Li-Kuang; Kuo, Chih-Chen; Chang, Chih-Kai; Chen, Wen-Pin; McLaren, Christine E.; Bahri, Shadfar; Mehta, Rita S.; Su, Min-Ying

2013-01-01

The aim of this study was to evaluate the change of breast density in the normal breast of patients receiving neoadjuvant chemotherapy (NAC). Forty-four breast cancer patients were studied. MRI acquisition was performed before treatment (baseline), and 4 and 12 weeks after treatment. A computer algorithm-based program was used to segment breast tissue and calculate breast volume (BV), fibroglandular tissue volume (FV) and percent density (PD) (the ratio of FV over BV x100%). The reduction of FV and PD after treatment was compared to baseline using paired t-tests with a Bonferroni-Holm correction. The association of density reduction with age was analyzed. FV and PD after NAC showed significant decreases compared to the baseline. FV was 110.0ml (67.2, 189.8) (geometric mean (interquartile range)) at baseline, 104.3ml (66.6, 164.4) after 4 weeks (p< 0.0001), and 94.7ml (60.2, 144.4) after 12 weeks (comparison to baseline, p<0.0001; comparison to 4 weeks, p=0.016). PD was 11.2% (6.4, 22.4) at baseline, 10.6% (6.6, 20.3) after 4 weeks (p< 0.0001), and 9.7% (6.2, 17.9) after 12 weeks (comparison to baseline, p=0.0001; comparison to 4 weeks, p =0.018). Younger patients tended to show a higher density reduction, but overall correlation with age was only moderate (r=0.28 for FV, p=0.07 and r=0.52 for PD, p=0.0003). Our study showed that breast density measured from MR images acquired at 3T MR can be accurately quantified using a robust computer-aided algorithm based on nonparametric nonuniformity normalization (N3) and an adaptive fuzzy C-means algorithm. Similar to doxorubicin and cyclophosphamide regimens, the taxane-based NAC regimen also caused density atrophy in the normal breast and showed reduction in FV and PD. The effect of breast density reduction was age-related and duration-related. PMID:23940080

Quantitative breast MRI radiomics for cancer risk assessment and the monitoring of high-risk populations

NASA Astrophysics Data System (ADS)

Mendel, Kayla R.; Li, Hui; Giger, Maryellen L.

2016-03-01

Breast density is routinely assessed qualitatively in screening mammography. However, it is challenging to quantitatively determine a 3D density from a 2D image such as a mammogram. Furthermore, dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is used more frequently in the screening of high-risk populations. The purpose of our study is to segment parenchyma and to quantitatively determine volumetric breast density on pre-contrast axial DCE-MRI images (i.e., non-contrast) using a semi-automated quantitative approach. In this study, we retroactively examined 3D DCE-MRI images taken for breast cancer screening of a high-risk population. We analyzed 66 cases with ages between 28 and 76 (mean 48.8, standard deviation 10.8). DCE-MRIs were obtained on a Philips 3.0 T scanner. Our semi-automated DCE-MRI algorithm includes: (a) segmentation of breast tissue from non-breast tissue using fuzzy cmeans clustering (b) separation of dense and fatty tissues using Otsu's method, and (c) calculation of volumetric density as the ratio of dense voxels to total breast voxels. We examined the relationship between pre-contrast DCE-MRI density and clinical BI-RADS density obtained from radiology reports, and obtained a statistically significant correlation [Spearman ρ-value of 0.66 (p < 0.0001)]. Our method within precision medicine may be useful for monitoring high-risk populations.

Statistical analysis to assess automated level of suspicion scoring methods in breast ultrasound

NASA Astrophysics Data System (ADS)

Galperin, Michael

2003-05-01

A well-defined rule-based system has been developed for scoring 0-5 the Level of Suspicion (LOS) based on qualitative lexicon describing the ultrasound appearance of breast lesion. The purposes of the research are to asses and select one of the automated LOS scoring quantitative methods developed during preliminary studies in benign biopsies reduction. The study has used Computer Aided Imaging System (CAIS) to improve the uniformity and accuracy of applying the LOS scheme by automatically detecting, analyzing and comparing breast masses. The overall goal is to reduce biopsies on the masses with lower levels of suspicion, rather that increasing the accuracy of diagnosis of cancers (will require biopsy anyway). On complex cysts and fibroadenoma cases experienced radiologists were up to 50% less certain in true negatives than CAIS. Full correlation analysis was applied to determine which of the proposed LOS quantification methods serves CAIS accuracy the best. This paper presents current results of applying statistical analysis for automated LOS scoring quantification for breast masses with known biopsy results. It was found that First Order Ranking method yielded most the accurate results. The CAIS system (Image Companion, Data Companion software) is developed by Almen Laboratories and was used to achieve the results.

Hair product use, age at menarche and mammographic breast density in multiethnic urban women.

PubMed

McDonald, Jasmine A; Tehranifar, Parisa; Flom, Julie D; Terry, Mary Beth; James-Todd, Tamarra

2018-01-04

Select hair products contain endocrine disrupting chemicals (EDCs) that may affect breast cancer risk. We hypothesize that, if EDCs are related to breast cancer risk, then they may also affect two important breast cancer risk factors: age at menarche and mammographic breast density. In two urban female cohorts (N = 248): 1) the New York site of the National Collaborative Perinatal Project and 2) the New York City Multiethnic Breast Cancer Project, we measured childhood and adult use of hair oils, lotions, leave-in conditioners, root stimulators, perms/relaxers, and hair dyes using the same validated questionnaire. We used multivariable relative risk regression models to examine the association between childhood hair product use and early age at menarche (defined as <11 years of age) and multivariable linear regression models to examine the association between childhood and adult hair product use and adult mammographic breast density. Early menarche was associated with ever use of childhood hair products (RR 2.3, 95% CI 1.1, 4.8) and hair oil use (RR 2.5, 95% CI 1.2, 5.2); however, additional adjustment for race/ethnicity, attenuated associations (hair products RR 1.8, 95% CI 0.8, 4.1; hair oil use RR 2.3, 95% CI 1.0, 5.5). Breast density was not associated with adult or childhood hair product or hair oil use. If confirmed in larger prospective studies, these data suggest that exposure to EDCs through hair products in early life may affect breast cancer risk by altering timing of menarche, and may operate through a mechanism distinct from breast density.

Comparison of Breast Density Between Synthesized Versus Standard Digital Mammography.

PubMed

Haider, Irfanullah; Morgan, Matthew; McGow, Anna; Stein, Matthew; Rezvani, Maryam; Freer, Phoebe; Hu, Nan; Fajardo, Laurie; Winkler, Nicole

2018-06-12

To evaluate perceptual difference in breast density classification using synthesized mammography (SM) compared with standard or full-field digital mammography (FFDM) for screening. This institutional review board-approved, retrospective, multireader study evaluated breast density on 200 patients who underwent baseline screening mammogram during which both SM and FFDM were obtained contemporaneously from June 1, 2016, through November 30, 2016. Qualitative breast density was independently assigned by seven readers initially evaluating FFDM alone. Then, in a separate session, these same readers assigned breast density using synthetic views alone on the same 200 patients. The readers were again blinded to each other's assignment. Qualitative density assessment was based on BI-RADS fifth edition. Interreader agreement was evaluated with κ statistic using 95% confidence intervals. Testing for homogeneity in paired proportions was performed using McNemar's test with a level of significance of .05. For patients across the SM and standard 2-D data set, diagnostic testing with McNemar's test with P = 0.32 demonstrates that the minimal density transitions across FFDM and SM are not statistically significant density shifts. Taking clinical significance into account, only 8 of 200 (4%) patients had clinically significant transition (dense versus not dense). There was substantial interreader agreement with overall κ in FFDM of 0.71 (minimum 0.53, maximum 0.81) and overall SM κ average of 0.63 (minimum 0.56, maximum 0.87). Overall subjective breast density assignment by radiologists on SM is similar to density assignment on standard 2-D mammogram. Copyright © 2018 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Mammographic Breast Density in a Cohort of Medically Underserved Women

DTIC Science & Technology

2012-10-01

training, faculty from MMC and VUMC will conduct a case-control study of mammographic breast density to investigate its’ association with obesity and...hormones and growth factors, 4) to perform statistical analyses to determine the associations between obesity and insulin resistance and mammographic...on obesity and insulin resistance as they relate to mammographic breast density. We hypothesize that: 1) obesity and insulin resistance, defined

Identifying women with dense breasts at high risk for interval cancer: a cohort study.

PubMed

Kerlikowske, Karla; Zhu, Weiwei; Tosteson, Anna N A; Sprague, Brian L; Tice, Jeffrey A; Lehman, Constance D; Miglioretti, Diana L

2015-05-19

Twenty-one states have laws requiring that women be notified if they have dense breasts and that they be advised to discuss supplemental imaging with their provider. To better direct discussions of supplemental imaging by determining which combinations of breast cancer risk and Breast Imaging Reporting and Data System (BI-RADS) breast density categories are associated with high interval cancer rates. Prospective cohort. Breast Cancer Surveillance Consortium (BCSC) breast imaging facilities. 365,426 women aged 40 to 74 years who had 831,455 digital screening mammography examinations. BI-RADS breast density, BCSC 5-year breast cancer risk, and interval cancer rate (invasive cancer ≤12 months after a normal mammography result) per 1000 mammography examinations. High interval cancer rate was defined as more than 1 case per 1000 examinations. High interval cancer rates were observed for women with 5-year risk of 1.67% or greater and extremely dense breasts or 5-year risk of 2.50% or greater and heterogeneously dense breasts (24% of all women with dense breasts). The interval rate of advanced-stage disease was highest (>0.4 case per 1000 examinations) among women with 5-year risk of 2.50% or greater and heterogeneously or extremely dense breasts (21% of all women with dense breasts). Five-year risk was low to average (0% to 1.66%) for 51.0% of women with heterogeneously dense breasts and 52.5% with extremely dense breasts, with interval cancer rates of 0.58 to 0.63 and 0.72 to 0.89 case per 1000 examinations, respectively. The benefit of supplemental imaging was not assessed. Breast density should not be the sole criterion for deciding whether supplemental imaging is justified because not all women with dense breasts have high interval cancer rates. BCSC 5-year risk combined with BI-RADS breast density can identify women at high risk for interval cancer to inform patient-provider discussions about alternative screening strategies. National Cancer Institute.

Local breast density assessment using reacquired mammographic images.

PubMed

García, Eloy; Diaz, Oliver; Martí, Robert; Diez, Yago; Gubern-Mérida, Albert; Sentís, Melcior; Martí, Joan; Oliver, Arnau

2017-08-01

The aim of this paper is to evaluate the spatial glandular volumetric tissue distribution as well as the density measures provided by Volpara™ using a dataset composed of repeated pairs of mammograms, where each pair was acquired in a short time frame and in a slightly changed position of the breast. We conducted a retrospective analysis of 99 pairs of repeatedly acquired full-field digital mammograms from 99 different patients. The commercial software Volpara™ Density Maps (Volpara Solutions, Wellington, New Zealand) is used to estimate both the global and the local glandular tissue distribution in each image. The global measures provided by Volpara™, such as breast volume, volume of glandular tissue, and volumetric breast density are compared between the two acquisitions. The evaluation of the local glandular information is performed using histogram similarity metrics, such as intersection and correlation, and local measures, such as statistics from the difference image and local gradient correlation measures. Global measures showed a high correlation (breast volume R=0.99, volume of glandular tissue R=0.94, and volumetric breast density R=0.96) regardless the anode/filter material. Similarly, histogram intersection and correlation metric showed that, for each pair, the images share a high degree of information. Regarding the local distribution of glandular tissue, small changes in the angle of view do not yield significant differences in the glandular pattern, whilst changes in the breast thickness between both acquisition affect the spatial parenchymal distribution. This study indicates that Volpara™ Density Maps is reliable in estimating the local glandular tissue distribution and can be used for its assessment and follow-up. Volpara™ Density Maps is robust to small variations of the acquisition angle and to the beam energy, although divergences arise due to different breast compression conditions. Copyright © 2017 Elsevier B.V. All rights reserved.

Breast Cancer Risk and Mammographic Density Assessed with Semiautomated and Fully Automated Methods and BI-RADS.

PubMed

Jeffers, Abra M; Sieh, Weiva; Lipson, Jafi A; Rothstein, Joseph H; McGuire, Valerie; Whittemore, Alice S; Rubin, Daniel L

2017-02-01

Purpose To compare three metrics of breast density on full-field digital mammographic (FFDM) images as predictors of future breast cancer risk. Materials and Methods This institutional review board-approved study included 125 women with invasive breast cancer and 274 age- and race-matched control subjects who underwent screening FFDM during 2004-2013 and provided informed consent. The percentage of density and dense area were assessed semiautomatically with software (Cumulus 4.0; University of Toronto, Toronto, Canada), and volumetric percentage of density and dense volume were assessed automatically with software (Volpara; Volpara Solutions, Wellington, New Zealand). Clinical Breast Imaging Reporting and Data System (BI-RADS) classifications of breast density were extracted from mammography reports. Odds ratios and 95% confidence intervals (CIs) were estimated by using conditional logistic regression stratified according to age and race and adjusted for body mass index, parity, and menopausal status, and the area under the receiver operating characteristic curve (AUC) was computed. Results The adjusted odds ratios and 95% CIs for each standard deviation increment of the percentage of density, dense area, volumetric percentage of density, and dense volume were 1.61 (95% CI: 1.19, 2.19), 1.49 (95% CI: 1.15, 1.92), 1.54 (95% CI: 1.12, 2.10), and 1.41 (95% CI: 1.11, 1.80), respectively. Odds ratios for women with extremely dense breasts compared with those with scattered areas of fibroglandular density were 2.06 (95% CI: 0.85, 4.97) and 2.05 (95% CI: 0.90, 4.64) for BI-RADS and Volpara density classifications, respectively. Clinical BI-RADS was more accurate (AUC, 0.68; 95% CI: 0.63, 0.74) than Volpara (AUC, 0.64; 95% CI: 0.58, 0.70) and continuous measures of percentage of density (AUC, 0.66; 95% CI: 0.60, 0.72), dense area (AUC, 0.66; 95% CI: 0.60, 0.72), volumetric percentage of density (AUC, 0.64; 95% CI: 0.58, 0.70), and density volume (AUC, 0.65; 95% CI: 0.59, 0.71), although the AUC differences were not statistically significant. Conclusion Mammographic density on FFDM images was positively associated with breast cancer risk by using the computer assisted methods and BI-RADS. BI-RADS classification was as accurate as computer-assisted methods for discrimination of patients from control subjects. © RSNA, 2016.

Integrated PET/MR breast cancer imaging: Attenuation correction and implementation of a 16-channel RF coil

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oehmigen, Mark, E-mail: mark.oehmigen@uni-due.de

Purpose: This study aims to develop, implement, and evaluate a 16-channel radiofrequency (RF) coil for integrated positron emission tomography/magnetic resonance (PET/MR) imaging of breast cancer. The RF coil is designed for optimized MR imaging performance and PET transparency and attenuation correction (AC) is applied for accurate PET quantification. Methods: A 16-channel breast array RF coil was designed for integrated PET/MR hybrid imaging of breast cancer lesions. The RF coil features a lightweight rigid design and is positioned with a spacer at a defined position on the patient table of an integrated PET/MR system. Attenuation correction is performed by generating andmore » applying a dedicated 3D CT-based template attenuation map. Reposition accuracy of the RF coil on the system patient table while using the positioning frame was tested in repeated measurements using MR-visible markers. The MR, PET, and PET/MR imaging performances were systematically evaluated using modular breast phantoms. Attenuation correction of the RF coil was evaluated with difference measurements of the active breast phantoms filled with radiotracer in the PET detector with and without the RF coil in place, serving as a standard of reference measurement. The overall PET/MR imaging performance and PET quantification accuracy of the new 16-channel RF coil and its AC were then evaluated in first clinical examinations on ten patients with local breast cancer. Results: The RF breast array coil provides excellent signal-to-noise ratio and signal homogeneity across the volume of the breast phantoms in MR imaging and visualizes small structures in the phantoms down to 0.4 mm in plane. Difference measurements with PET revealed a global loss and thus attenuation of counts by 13% (mean value across the whole phantom volume) when the RF coil is placed in the PET detector. Local attenuation ranging from 0% in the middle of the phantoms up to 24% was detected in the peripheral regions of the phantoms at positions closer to attenuating hardware structures of the RF coil. The position accuracy of the RF coil on the patient table when using the positioning frame was determined well below 1 mm for all three spatial dimensions. This ensures perfect position match between the RF coil and its three-dimensional attenuation template during the PET data reconstruction process. When applying the CT-based AC of the RF coil, the global attenuation bias was mostly compensated to ±0.5% across the entire breast imaging volume. The patient study revealed high quality MR, PET, and combined PET/MR imaging of breast cancer. Quantitative activity measurements in all 11 breast cancer lesions of the ten patients resulted in increased mean difference values of SUV{sub max} 11.8% (minimum 3.2%; maximum 23.2%) between nonAC images and images when AC of the RF breast coil was applied. This supports the quantitative results of the phantom study as well as successful attenuation correction of the RF coil. Conclusions: A 16-channel breast RF coil was designed for optimized MR imaging performance and PET transparency and was successfully integrated with its dedicated attenuation correction template into a whole-body PET/MR system. Systematic PET/MR imaging evaluation with phantoms and an initial study on patients with breast cancer provided excellent MR and PET image quality and accurate PET quantification.« less

Prevalence of Mammographically Dense Breasts in the United States

PubMed Central

Gangnon, Ronald E.; Burt, Veronica; Trentham-Dietz, Amy; Hampton, John M.; Wellman, Robert D.; Kerlikowske, Karla; Miglioretti, Diana L.

2014-01-01

Background National legislation is under consideration that would require women with mammographically dense breasts to be informed of their breast density and encouraged to discuss supplemental breast cancer screening with their health care providers. The number of US women potentially affected by this legislation is unknown. Methods We determined the mammographic breast density distribution by age and body mass index (BMI) using data from 1518 599 mammograms conducted from 2007 through 2010 at mammography facilities in the Breast Cancer Surveillance Consortium (BCSC). We applied these breast density distributions to age- and BMI-specific counts of the US female population derived from the 2010 US Census and the National Health and Nutrition Examination Survey (NHANES) to estimate the number of US women with dense breasts. Results Overall, 43.3% (95% confidence interval [CI] = 43.1% to 43.4%) of women 40 to 74 years of age had heterogeneously or extremely dense breasts, and this proportion was inversely associated with age and BMI. Based on the age and BMI distribution of US women, we estimated that 27.6 million women (95% CI = 27.5 to 27.7 million) aged 40 to 74 years in the United States have heterogeneously or extremely dense breasts. Women aged 40 to 49 years (N = 12.3 million) accounted for 44.3% of this group. Conclusion The prevalence of dense breasts among US women of common breast cancer screening ages exceeds 25 million. Policymakers and healthcare providers should consider this large prevalence when debating breast density notification legislation and designing strategies to ensure that women who are notified have opportunities to evaluate breast cancer risk and discuss and pursue supplemental screening options if deemed appropriate. PMID:25217577

Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures

PubMed Central

Stone, Jennifer; Thompson, Deborah J.; dos-Santos-Silva, Isabel; Scott, Christopher; Tamimi, Rulla M.; Lindstrom, Sara; Kraft, Peter; Hazra, Aditi; Li, Jingmei; Eriksson, Louise; Czene, Kamila; Hall, Per; Jensen, Matt; Cunningham, Julie; Olson, Janet E.; Purrington, Kristen; Couch, Fergus J.; Brown, Judith; Leyland, Jean; Warren, Ruth M. L.; Luben, Robert N.; Khaw, Kay-Tee; Smith, Paula; Wareham, Nicholas J.; Jud, Sebastian M.; Heusinger, Katharina; Beckmann, Matthias W.; Douglas, Julie A.; Shah, Kaanan P.; Chan, Heang-Ping; Helvie, Mark A.; Le Marchand, Loic; Kolonel, Laurence N.; Woolcott, Christy; Maskarinec, Gertraud; Haiman, Christopher; Giles, Graham G.; Baglietto, Laura; Krishnan, Kavitha; Southey, Melissa C.; Apicella, Carmel; Andrulis, Irene L.; Knight, Julia A.; Ursin, Giske; Grenaker Alnaes, Grethe I.; Kristensen, Vessela N.; Borresen-Dale, Anne-Lise; Gram, Inger Torhild; Bolla, Manjeet K.; Wang, Qin; Michailidou, Kyriaki; Dennis, Joe; Simard, Jacques; Paroah, Paul; Dunning, Alison M.; Easton, Douglas F.; Fasching, Peter A.; Pankratz, V. Shane; Hopper, John; Vachon, Celine M.

2015-01-01

Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute non-dense area adjusted for study, age and BMI using mixed linear modeling. We found strong support for established associations between rs10995190 (in the region of ZNF365), rs2046210 (ESR1) and rs3817198 (LSP1) and adjusted absolute and percent dense areas (all p <10−5). Of 41 recently discovered breast cancer susceptibility variants, associations were found between rs1432679 (EBF1), rs17817449 (MIR1972-2: FTO), rs12710696 (2p24.1), and rs3757318 (ESR1) and adjusted absolute and percent dense areas, respectively. There were associations between rs6001930 (MKL1) and both adjusted absolute dense and non-dense areas, and between rs17356907 (NTN4) and adjusted absolute non-dense area. Trends in all but two associations were consistent with those for breast cancer risk. Results suggested that 18% of breast cancer susceptibility variants were associated with at least one mammographic density measure. Genetic variants at multiple loci were associated with both breast cancer risk and the mammographic density measures. Further understanding of the underlying mechanisms at these loci could help identify etiological pathways implicated in how mammographic density predicts breast cancer risk. PMID:25862352

Comparison of variability in breast density assessment by BI-RADS category according to the level of experience.

PubMed

Eom, Hye-Joung; Cha, Joo Hee; Kang, Ji-Won; Choi, Woo Jung; Kim, Han Jun; Go, EunChae

2018-05-01

Background Only few studies have assessed variability in the results obtained by the readers with different experience levels in comparison with automated volumetric breast density measurements. Purpose To examine the variations in breast density assessment according to BI-RADS categories among readers with different experience levels and to compare it with the results of automated quantitative measurements. Material and Methods Density assignment was done for 1000 screening mammograms by six readers with three different experience levels (breast-imaging experts, general radiologists, and students). Agreement level between the results obtained by the readers and the Volpara automated volumetric breast density measurements was assessed. The agreement analysis using two categories-non-dense and dense breast tissue-was also performed. Results Intra-reader agreement for experts, general radiologists, and students were almost perfect or substantial (k = 0.74-0.95). The agreement between visual assessments of the breast-imaging experts and volumetric assessments by Volpara was substantial (k = 0.77). The agreement was moderate between the experts and general radiologists (k = 0.67) and slight between the students and Volpara (k = 0.01). The agreement for the two category groups (nondense and dense) was almost perfect between the experts and Volpara (k = 0.83). The agreement was substantial between the experts and general radiologists (k = 0.78). Conclusion We observed similar high agreement levels between visual assessments of breast density performed by radiologists and the volumetric assessments. However, agreement levels were substantially lower for the untrained readers.

Simultaneous liquid chromatography-tandem mass spectrometry analysis of brominated flame retardants (tetrabromobisphenol A and hexabromocyclododecane diastereoisomers) in French breast milk.

PubMed

Inthavong, Chanthadary; Hommet, Frédéric; Bordet, François; Rigourd, Virginie; Guérin, Thierry; Dragacci, Sylviane

2017-11-01

TBBPA and HBCDs are the two classes of flame retardants that are still allowed for use by the European Commission. In May 2013, HBCDs were listed as Persistent Organic Pollutants under the Stockholm Convention, and they were banned with an exemption on EPS/XPS for cavity wall insulation. This study describes the development and optimisation of a rapid LC-ESI-MS/MS method using isotopic dilution quantification including a simplified extraction step using a mixture of solvents and sulphuric acid hydrolysis followed by the one-shot analysis of TBBPA and each of the α-, β- and γ-HBCD diastereoisomers. The limits of detection and quantification (LOD and LOQ) were 0.5 and 2.5 ng g -1 (lipid weight, lw) for TBBPA and HBCD diastereoisomers, respectively. The method was applied to analyse 106 samples of individual mature breast milk. TBBPA was quantified in 42% of these samples within a range of

Breast cancer screening in the era of density notification legislation: summary of 2014 Massachusetts experience and suggestion of an evidence-based management algorithm by multi-disciplinary expert panel.

PubMed

Freer, Phoebe E; Slanetz, Priscilla J; Haas, Jennifer S; Tung, Nadine M; Hughes, Kevin S; Armstrong, Katrina; Semine, A Alan; Troyan, Susan L; Birdwell, Robyn L

2015-09-01

Stemming from breast density notification legislation in Massachusetts effective 2015, we sought to develop a collaborative evidence-based approach to density notification that could be used by practitioners across the state. Our goal was to develop an evidence-based consensus management algorithm to help patients and health care providers follow best practices to implement a coordinated, evidence-based, cost-effective, sustainable practice and to standardize care in recommendations for supplemental screening. We formed the Massachusetts Breast Risk Education and Assessment Task Force (MA-BREAST) a multi-institutional, multi-disciplinary panel of expert radiologists, surgeons, primary care physicians, and oncologists to develop a collaborative approach to density notification legislation. Using evidence-based data from the Institute for Clinical and Economic Review, the Cochrane review, National Comprehensive Cancer Network guidelines, American Cancer Society recommendations, and American College of Radiology appropriateness criteria, the group collaboratively developed an evidence-based best-practices algorithm. The expert consensus algorithm uses breast density as one element in the risk stratification to determine the need for supplemental screening. Women with dense breasts and otherwise low risk (<15% lifetime risk), do not routinely require supplemental screening per the expert consensus. Women of high risk (>20% lifetime) should consider supplemental screening MRI in addition to routine mammography regardless of breast density. We report the development of the multi-disciplinary collaborative approach to density notification. We propose a risk stratification algorithm to assess personal level of risk to determine the need for supplemental screening for an individual woman.

Combined effects of endogenous sex hormone levels and mammographic density on postmenopausal breast cancer risk: results from the Breakthrough Generations Study

PubMed Central

Schoemaker, M J; Folkerd, E J; Jones, M E; Rae, M; Allen, S; Ashworth, A; Dowsett, M; Swerdlow, A J

2014-01-01

Background: Mammographic density and sex hormone levels are strong risk factors for breast cancer, but it is unclear whether they represent the same aetiological entity or are independent risk factors. Methods: Within the Breakthrough Generations Study cohort, we conducted a case–control study of 265 postmenopausal breast cancer cases and 343 controls with prediagnostic mammograms and blood samples. Plasma was assayed for oestradiol, testosterone and sex hormone-binding globulin (SHBG) concentrations and mammographic density assessed by Cumulus. Results: Oestradiol and testosterone were negatively and SHBG positively associated with percentage density and absolute dense area, but after adjusting for body mass index the associations remained significant only for SHBG. Breast cancer risk was independently and significantly positively associated with percentage density (P=0.002), oestradiol (P=0.002) and testosterone (P=0.007) levels. Women in the highest tertile of both density and sex hormone level were at greatest risk, with an odds ratio of 7.81 (95% confidence interval (CI): 2.89–21.1) for oestradiol and 4.57 (95% CI: 1.75–11.9) for testosterone and high density compared with those who were in the lowest tertiles. The cumulative risk of breast cancer in the highest oestradiol and density tertiles, representing 8% of controls, was estimated as 12.8% at ages 50–69 years and 19.4% at ages 20–79 years, and in the lowest tertiles was 1.7% and 4.3%, respectively. Associations of breast cancer risk with tertiles of mammographic dense area were less strong than for percentage density. Conclusions: Endogenous sex hormone levels and mammographic density are independent risk factors for postmenopausal breast cancer, which in combination can identify women who might benefit from increased frequency of screening and chemoprophylaxis. PMID:24518596

Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening

PubMed Central

Brandt, Kathleen R.; Scott, Christopher G.; Ma, Lin; Mahmoudzadeh, Amir P.; Jensen, Matthew R.; Whaley, Dana H.; Wu, Fang Fang; Malkov, Serghei; Hruska, Carrie B.; Norman, Aaron D.; Heine, John; Shepherd, John; Pankratz, V. Shane; Kerlikowske, Karla

2016-01-01

Purpose To compare the classification of breast density with two automated methods, Volpara (version 1.5.0; Matakina Technology, Wellington, New Zealand) and Quantra (version 2.0; Hologic, Bedford, Mass), with clinical Breast Imaging Reporting and Data System (BI-RADS) density classifications and to examine associations of these measures with breast cancer risk. Materials and Methods In this study, 1911 patients with breast cancer and 4170 control subjects matched for age, race, examination date, and mammography machine were evaluated. Participants underwent mammography at Mayo Clinic or one of four sites within the San Francisco Mammography Registry between 2006 and 2012 and provided informed consent or a waiver for research, in compliance with HIPAA regulations and institutional review board approval. Digital mammograms were retrieved a mean of 2.1 years (range, 6 months to 6 years) before cancer diagnosis, with the corresponding clinical BI-RADS density classifications, and Volpara and Quantra density estimates were generated. Agreement was assessed with weighted κ statistics among control subjects. Breast cancer associations were evaluated with conditional logistic regression, adjusted for age and body mass index. Odds ratios, C statistics, and 95% confidence intervals (CIs) were estimated. Results Agreement between clinical BI-RADS density classifications and Volpara and Quantra BI-RADS estimates was moderate, with κ values of 0.57 (95% CI: 0.55, 0.59) and 0.46 (95% CI: 0.44, 0.47), respectively. Differences of up to 14% in dense tissue classification were found, with Volpara classifying 51% of women as having dense breasts, Quantra classifying 37%, and clinical BI-RADS assessment used to classify 43%. Clinical and automated measures showed similar breast cancer associations; odds ratios for extremely dense breasts versus scattered fibroglandular densities were 1.8 (95% CI: 1.5, 2.2), 1.9 (95% CI: 1.5, 2.5), and 2.3 (95% CI: 1.9, 2.8) for Volpara, Quantra, and BI-RADS classifications, respectively. Clinical BI-RADS assessment showed better discrimination of case status (C = 0.60; 95% CI: 0.58, 0.61) than did Volpara (C = 0.58; 95% CI: 0.56, 0.59) and Quantra (C = 0.56; 95% CI: 0.54, 0.58) BI-RADS classifications. Conclusion Automated and clinical assessments of breast density are similarly associated with breast cancer risk but differ up to 14% in the classification of women with dense breasts. This could have substantial effects on clinical practice patterns. © RSNA, 2015 Online supplemental material is available for this article. PMID:26694052

Near-infrared spectral tomography integrated with digital breast tomosynthesis: Effects of tissue scattering on optical data acquisition design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Michaelsen, Kelly; Krishnaswamy, Venkat; Pogue, Brian W.

2012-07-15

Purpose: Design optimization and phantom validation of an integrated digital breast tomosynthesis (DBT) and near-infrared spectral tomography (NIRST) system targeting improvement in sensitivity and specificity of breast cancer detection is presented. Factors affecting instrumentation design include minimization of cost, complexity, and examination time while maintaining high fidelity NIRST measurements with sufficient information to recover accurate optical property maps. Methods: Reconstructed DBT slices from eight patients with abnormal mammograms provided anatomical information for the NIRST simulations. A limited frequency domain (FD) and extensive continuous wave (CW) NIRST system was modeled. The FD components provided tissue scattering estimations used in the reconstructionmore » of the CW data. Scattering estimates were perturbed to study the effects on hemoglobin recovery. Breast mimicking agar phantoms with inclusions were imaged using the combined DBT/NIRST system for comparison with simulation results. Results: Patient simulations derived from DBT images show successful reconstruction of both normal and malignant lesions in the breast. They also demonstrate the importance of accurately quantifying tissue scattering. Specifically, 20% errors in optical scattering resulted in 22.6% or 35.1% error in quantification of total hemoglobin concentrations, depending on whether scattering was over- or underestimated, respectively. Limited frequency-domain optical signal sampling provided two regions scattering estimates (for fat and fibroglandular tissues) that led to hemoglobin concentrations that reduced the error in the tumor region by 31% relative to when a single estimate of optical scattering was used throughout the breast volume of interest. Acquiring frequency-domain data with six wavelengths instead of three did not significantly improve the hemoglobin concentration estimates. Simulation results were confirmed through experiments in two-region breast mimicking gelatin phantoms. Conclusions: Accurate characterization of scattering is necessary for quantification of hemoglobin. Based on this study, a system design is described to optimally combine breast tomosynthesis with NIRST.« less

Segmentation of the pectoral muscle in breast MR images using structure tensor and deformable model

NASA Astrophysics Data System (ADS)

Lee, Myungeun; Kim, Jong Hyo

2012-02-01

Recently, breast MR images have been used in wider clinical area including diagnosis, treatment planning, and treatment response evaluation, which requests quantitative analysis and breast tissue segmentation. Although several methods have been proposed for segmenting MR images, segmenting out breast tissues robustly from surrounding structures in a wide range of anatomical diversity still remains challenging. Therefore, in this paper, we propose a practical and general-purpose approach for segmenting the pectoral muscle boundary based on the structure tensor and deformable model. The segmentation work flow comprises four key steps: preprocessing, detection of the region of interest (ROI) within the breast region, segmenting the pectoral muscle and finally extracting and refining the pectoral muscle boundary. From experimental results we show that the proposed method can segment the pectoral muscle robustly in diverse patient cases. In addition, the proposed method will allow the application of the quantification research for various breast images.

Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer.

PubMed

Wang, Jeff; Kato, Fumi; Yamashita, Hiroko; Baba, Motoi; Cui, Yi; Li, Ruijiang; Oyama-Manabe, Noriko; Shirato, Hiroki

2017-04-01

Breast cancer is the most common invasive cancer among women and its incidence is increasing. Risk assessment is valuable and recent methods are incorporating novel biomarkers such as mammographic density. Artificial neural networks (ANN) are adaptive algorithms capable of performing pattern-to-pattern learning and are well suited for medical applications. They are potentially useful for calibrating full-field digital mammography (FFDM) for quantitative analysis. This study uses ANN modeling to estimate volumetric breast density (VBD) from FFDM on Japanese women with and without breast cancer. ANN calibration of VBD was performed using phantom data for one FFDM system. Mammograms of 46 Japanese women diagnosed with invasive carcinoma and 53 with negative findings were analyzed using ANN models learned. ANN-estimated VBD was validated against phantom data, compared intra-patient, with qualitative composition scoring, with MRI VBD, and inter-patient with classical risk factors of breast cancer as well as cancer status. Phantom validations reached an R 2 of 0.993. Intra-patient validations ranged from R 2 of 0.789 with VBD to 0.908 with breast volume. ANN VBD agreed well with BI-RADS scoring and MRI VBD with R 2 ranging from 0.665 with VBD to 0.852 with breast volume. VBD was significantly higher in women with cancer. Associations with age, BMI, menopause, and cancer status previously reported were also confirmed. ANN modeling appears to produce reasonable measures of mammographic density validated with phantoms, with existing measures of breast density, and with classical biomarkers of breast cancer. FFDM VBD is significantly higher in Japanese women with cancer.

Red clover-derived isoflavones and mammographic breast density: a double-blind, randomized, placebo-controlled trial [ISRCTN42940165

PubMed Central

Atkinson, Charlotte; Warren, Ruth ML; Sala, Evis; Dowsett, Mitch; Dunning, Alison M; Healey, Catherine S; Runswick, Shirley; Day, Nicholas E; Bingham, Sheila A

2004-01-01

Introduction Isoflavones are hypothesized to protect against breast cancer, but it is not clear whether they act as oestrogens or anti-oestrogens in breast tissue. Our aim was to determine the effects of taking a red clover-derived isoflavone supplement daily for 1 year on mammographic breast density. Effects on oestradiol, follicle-stimulating hormone (FSH), luteinizing hormone (LH), lymphocyte tyrosine kinase activity and menopausal symptoms were also assessed. Methods A total of 205 women (age range 49–65 years) with Wolfe P2 or DY mammographic breast patterns were randomly assigned to receive either a red clover-derived isoflavone tablet (26 mg biochanin A, 16 mg formononetin, 1 mg genistein and 0.5 mg daidzein) or placebo. Change in mammographic breast density, serum oestradiol, FSH, LH, menopausal symptoms and lymphocyte tyrosine kinase activity from baseline to 12 months were assessed. Results A total of 177 women completed the trial. Mammographic breast density decreased in both groups but the difference between the treatment and placebo was not statistically significant. There was a significant interaction between treatment group and oestrogen receptor (ESR1) PvuII polymorphism for the change in estimated percentage breast density (mean ± standard deviation): TT isoflavone 1.4 ± 12.3% and TT placebo -9.6 ± 14.2%; CT isoflavone -5.2 ± 12.0% and CT placebo -2.8 ± 10.3%; and CC isoflavone -3.4 ± 9.7% and CC placebo -1.1 ± 9.5%. There were no statistically significant treatment effects on oestradiol, FSH, or LH (assessed only in postmenopausal women), or on lymphocyte tyrosine kinase activity. Baseline levels of menopausal symptoms were low, and there were no statistically significant treatment effects on frequency of hot flushes or other menopausal symptoms. Conclusion In contrast to studies showing that conventional hormone replacement therapies increase mammographic breast density, the isoflavone supplement did not increase mammographic breast density in this population of women. Furthermore, there were no effects on oestradiol, gonadotrophins, lymphocyte tyrosine kinase activity, or menopausal symptoms. PMID:15084240

Magnetic Resonance Spectroscopy: An Objective Technique for the Quantification of Prostate Cancer Pathologies

DTIC Science & Technology

2005-02-01

Holshouser BA, Burley T, Ashwal S . Predicting neuropsychologic outcome after traumatic brain injury in children . Pediatr Neurol 2003;28(2):104-114. 5...breast cancer tissue. NMR Biomed 2002;15(5):327-337. 25. Ala- Korpela M, Posio P, Mattila S , Korhonen A, Williams SR. Absolute quantification of...STATEMENT: Approved for Public Release; Distribution Unlimited The views, opinions and/or findings contained in this report are those of the author( s ) and

Common variants in ZNF365 are associated with both mammographic density and breast cancer risk.

PubMed

Lindström, Sara; Vachon, Celine M; Li, Jingmei; Varghese, Jajini; Thompson, Deborah; Warren, Ruth; Brown, Judith; Leyland, Jean; Audley, Tina; Wareham, Nicholas J; Loos, Ruth J F; Paterson, Andrew D; Rommens, Johanna; Waggott, Darryl; Martin, Lisa J; Scott, Christopher G; Pankratz, V Shane; Hankinson, Susan E; Hazra, Aditi; Hunter, David J; Hopper, John L; Southey, Melissa C; Chanock, Stephen J; Silva, Isabel dos Santos; Liu, JianJun; Eriksson, Louise; Couch, Fergus J; Stone, Jennifer; Apicella, Carmel; Czene, Kamila; Kraft, Peter; Hall, Per; Easton, Douglas F; Boyd, Norman F; Tamimi, Rulla M

2011-03-01

High-percent mammographic density adjusted for age and body mass index is one of the strongest risk factors for breast cancer. We conducted a meta analysis of five genome-wide association studies of percent mammographic density and report an association with rs10995190 in ZNF365 (combined P = 9.6 × 10(-10)). Common variants in ZNF365 have also recently been associated with susceptibility to breast cancer.

[Changes in mammographic features of breast cancer--comparison with previous films].

PubMed

Matsunaga, T; Hagiwara, K; Kimura, K; Kusama, M

1992-11-25

Mammographic features of 87 breast cancer patients were studied in comparison with their previous survey films. Changes in the mammographic features included microcalicification (28 cases), tumor shadow (35 cases) and intratumorous microcalicifications (6 cases). Seven cases had several extremely faint calcifications on the previous films, and three of six cases with clustered and scattered microcalcifications that extended over an entire breast quadrant had increased in number, density and extent. Eight cases in which clustered microcalcifications had increased in number, density and extent suggested a relationship between the increase in the extent of microcalcifications and length of time between visits. In most cases with tumor shadow, a slight localized increase in mammary gland density, irregular margins and straightened trabeculae were overlooked because of breast density.

Mammography

MedlinePlus

... Prior Mammograms Helps Radiologists Detect Breast Cancer MammographySavesLives.org A general information resource on breast imaging from ... doctors: Breast Density and Breast Cancer Screening RTAnswers.org Radiation Therapy for Breast Cancer MedLinePlus Mammography top ...

Dual Approach To Superquantile Estimation And Applications To Density Fitting

DTIC Science & Technology

2016-06-01

incorporate additional constraints to improve the fidelity of density estimates in tail regions. We limit our investigation to data with heavy tails, where...samples of various heavy -tailed distributions. 14. SUBJECT TERMS probability density estimation, epi-splines, optimization, risk quantification...limit our investigation to data with heavy tails, where risk quantification is typically the most difficult. Demonstrations are provided in the form of

Acoustic radiation force impulse imaging with Virtual Touch™ tissue quantification: mean shear wave velocity of malignant and benign breast masses.

PubMed

Wojcinski, Sebastian; Brandhorst, Kathrin; Sadigh, Gelareh; Hillemanns, Peter; Degenhardt, Friedrich

2013-01-01

Acoustic radiation force impulse imaging (ARFI) with Virtual Touch™ tissue quantification (VTTQ) enables the determination of shear wave velocity (SWV) in meters per second (m/s). The aim of our study was to describe the mean SWV in normal breast tissue and various breast masses. We performed measurements of SWV with ARFI VTTQ in 145 breast masses (57 malignant, 88 benign) and in the adjacent breast parenchyma and adipose tissue. The mean SWV as well as the rate of successful measurements were analyzed. The difference between adipose tissue and parenchyma was statistically significant (3.05 versus 3.65 m/s) (P < 0.001). Focusing on breast masses, numerous measurements exceeded the upper limit of possible measurement (≥9.10 m/s, indicated as "X.XX m/s"). Nevertheless, the difference between the malignant and benign masses was statistically significant (8.38 ± 1.99 m/s versus 5.39 ± 2.95 m/s) (P < 0.001). The best diagnostic accuracy (75.9%) was achieved when the cutoff point for malignancy was set to 9.10 m/s in ARFI VTTQ. This implies that the SWV was regarded as suspicious when the upper limit of possible measurement was exceeded and the machine returned the value X.XX m/s. In conclusion, ARFI VTTQ is a feasible method for measurement of SWV in a region of interest. Furthermore, we propose the event of a highly elevated SWV as a significant criterion for malignancy. However, the method is technically not yet fully developed, and the problem of unsuccessful measurements must still be solved.

Tumor characteristics and family history in relation to mammographic density and breast cancer: The French E3N cohort.

PubMed

Maskarinec, Gertraud; Dartois, Laureen; Delaloge, Suzette; Hopper, John; Clavel-Chapelon, Françoise; Baglietto, Laura

2017-08-01

Mammographic density is a known heritable risk factor for breast cancer, but reports how tumor characteristics and family history may modify this association are inconsistent. Dense and total breast areas were assessed using Cumulus™ from pre-diagnostic mammograms for 820 invasive breast cancer cases and 820 matched controls nested within the French E3N cohort study. To allow comparisons across models, percent mammographic density (PMD) was standardized to the distribution of the controls. Odds ratios (OR) and 95% confidence intervals (CI) of breast cancer risk for mammographic density were estimated by conditional logistic regression while adjusting for age and body mass index. Heterogeneity according to tumor characteristic and family history was assessed using stratified analyses. Overall, the OR per 1 SD for PMD was 1.50 (95% CI, 1.33-1.69). No evidence for significant heterogeneity by tumor size, lymph node status, grade, and hormone receptor status (estrogen, progesterone, and HER2) was detected. However, the association of PMD was stronger for women reporting a family history of breast cancer (OR 1SD =2.25; 95% CI, 1.67-3.04) than in women reporting none (OR 1SD =1.41; 95% CI, 1.24-1.60; p heterogeneity =0.002). Similarly, effect modification by FHBC was observed using categories of PMD (p heterogeneity =0.02) with respective ORs of 15.16 (95% CI, 4.23-54.28) vs. 3.14 (95% CI, 1.89-5.22) for ≥50% vs. <10% PMD. The stronger association between mammographic density and breast cancer risk with a family history supports the hypothesis of shared genetic factors responsible for familial aggregation of breast cancer and the heritable component of mammographic density. Copyright © 2017 Elsevier Ltd. All rights reserved.

Gene Methylation and Cytological Atypia in Random Fine Needle Aspirates for Assessment of Breast Cancer Risk

PubMed Central

Hafeez, Sidra; Bujanda, Zoila Lopez; Chatterton, Robert T.; Jacobs, Lisa K.; Khouri, Nagi F.; Ivancic, David; Kenney, Kara; Shehata, Christina; Jeter, Stacie C.; Wolfman, Judith A.; Zalles, Carola M.; Huang, Peng

2016-01-01

Methods to determine individualized breast cancer risk lack sufficient sensitivity to select women most likely to benefit from preventive strategies. Alterations in DNA methylation occur early in breast cancer. We hypothesized that cancer-specific methylation markers could enhance breast cancer risk assessment. We evaluated 380 women without a history of breast cancer. We determined their menopausal status or menstrual cycle phase, risk of developing breast cancer (Gail model), and breast density, and obtained random fine needle aspiration (rFNA) samples for assessment of cytopathology and cumulative methylation index (CMI). Eight methylated gene markers were identified through whole genome methylation analysis and included novel and previously established breast cancer detection genes. We performed correlative and multivariate linear regression analyses to evaluate DNA methylation of a gene panel as a function of clinical factors associated with breast cancer risk. CMI and individual gene methylation were independent of age, menopausal status or menstrual phase, lifetime Gail risk score, and breast density. CMI and individual gene methylation for the eight genes increased significantly (p<0.001) with increasing cytological atypia. The findings were verified with multivariate analyses correcting for age, log (Gail), log (percent density), rFNA cell number and BMI. Our results demonstrate a significant association between cytological atypia and high CMI, which does not vary with menstrual phase or menopause and is independent of Gail risk and mammographic density. Thus CMI is an excellent candidate breast cancer risk biomarker, warranting larger prospective studies to establish its utility for cancer risk assessment. PMID:27261491

Visual assessment of breast density using Visual Analogue Scales: observer variability, reader attributes and reading time

NASA Astrophysics Data System (ADS)

Ang, Teri; Harkness, Elaine F.; Maxwell, Anthony J.; Lim, Yit Y.; Emsley, Richard; Howell, Anthony; Evans, D. Gareth; Astley, Susan; Gadde, Soujanya

2017-03-01

Breast density is a strong risk factor for breast cancer and has potential use in breast cancer risk prediction, with subjective methods of density assessment providing a strong relationship with the development of breast cancer. This study aims to assess intra- and inter-observer variability in visual density assessment recorded on Visual Analogue Scales (VAS) among trained readers, and examine whether reader age, gender and experience are associated with assessed density. Eleven readers estimated the breast density of 120 mammograms on two occasions 3 years apart using VAS. Intra- and inter-observer agreement was assessed with Intraclass Correlation Coefficient (ICC) and variation between readers visualised on Bland-Altman plots. The mean scores of all mammograms per reader were used to analyse the effect of reader attributes on assessed density. Excellent intra-observer agreement (ICC>0.80) was found in the majority of the readers. All but one reader had a mean difference of <10 percentage points from the first to the second reading. Inter-observer agreement was excellent for consistency (ICC 0.82) and substantial for absolute agreement (ICC 0.69). However, the 95% limits of agreement for pairwise differences were -6.8 to 15.7 at the narrowest and 0.8 to 62.3 at the widest. No significant association was found between assessed density and reader age, experience or gender, or with reading time. Overall, the readers were consistent in their scores, although some large variations were observed. Reader evaluation and targeted training may alleviate this problem.

Breast Cancer Screening in the Era of Density Notification Legislation: Summary of 2014 Massachusetts Experience and Suggestion of An Evidence-Based Management Algorithm by Multi-disciplinary Expert Panel

PubMed Central

Freer, Phoebe E.; Slanetz, Priscilla J.; Haas, Jennifer S.; Tung, Nadine M.; Hughes, Kevin S.; Armstrong, Katrina; Semine, A. Alan; Troyan, Susan L.; Birdwell, Robyn L.

2015-01-01

Purpose Stemming from breast density notification legislation in Massachusetts effective 2015, we sought to develop a collaborative evidence-based approach to density notification that could be used by practitioners across the state. Our goal was to develop an evidence-based consensus management algorithm to help patients and health care providers follow best practices to implement a coordinated, evidence-based, cost-effective, sustainable practice and to standardize care in recommendations for supplemental screening. Methods We formed the Massachusetts Breast Risk Education and Assessment Task Force (MA-BREAST) a multi-institutional, multi-disciplinary panel of expert radiologists, surgeons, primary care physicians, and oncologists to develop a collaborative approach to density notification legislation. Using evidence-based data from the Institute for Clinical and Economic Review (ICER), the Cochrane review, National Comprehensive Cancer Network (NCCN) guidelines, American Cancer Society (ACS) recommendations, and American College of Radiology (ACR) appropriateness criteria, the group collaboratively developed an evidence-based best-practices algorithm. Results The expert consensus algorithm uses breast density as one element in the risk stratification to determine the need for supplemental screening. Women with dense breasts and otherwise low risk (<15% lifetime risk), do not routinely require supplemental screening per the expert consensus. Women of high risk (>20% lifetime) should consider supplemental screening MRI in addition to routine mammography regardless of breast density. Conclusion We report the development of the multi-disciplinary collaborative approach to density notification. We propose a risk stratification algorithm to assess personal level of risk to determine the need for supplemental screening for an individual woman. PMID:26290416

Improved correlation between CT emphysema quantification and pulmonary function test by density correction of volumetric CT data based on air and aortic density.

PubMed

Kim, Song Soo; Seo, Joon Beom; Kim, Namkug; Chae, Eun Jin; Lee, Young Kyung; Oh, Yeon Mok; Lee, Sang Do

2014-01-01

To determine the improvement of emphysema quantification with density correction and to determine the optimal site to use for air density correction on volumetric computed tomography (CT). Seventy-eight CT scans of COPD patients (GOLD II-IV, smoking history 39.2±25.3 pack-years) were obtained from several single-vendor 16-MDCT scanners. After density measurement of aorta, tracheal- and external air, volumetric CT density correction was conducted (two reference values: air, -1,000 HU/blood, +50 HU). Using in-house software, emphysema index (EI) and mean lung density (MLD) were calculated. Differences in air densities, MLD and EI prior to and after density correction were evaluated (paired t-test). Correlation between those parameters and FEV1 and FEV1/FVC were compared (age- and sex adjusted partial correlation analysis). Measured densities (HU) of tracheal- and external air differed significantly (-990 ± 14, -1016 ± 9, P<0.001). MLD and EI on original CT data, after density correction using tracheal- and external air also differed significantly (MLD: -874.9 ± 27.6 vs. -882.3 ± 24.9 vs. -860.5 ± 26.6; EI: 16.8 ± 13.4 vs. 21.1 ± 14.5 vs. 9.7 ± 10.5, respectively, P<0.001). The correlation coefficients between CT quantification indices and FEV1, and FEV1/FVC increased after density correction. The tracheal air correction showed better results than the external air correction. Density correction of volumetric CT data can improve correlations of emphysema quantification and PFT. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Sexual minority population density and incidence of lung, colorectal and female breast cancer in California.

PubMed

Boehmer, Ulrike; Miao, Xiaopeng; Maxwell, Nancy I; Ozonoff, Al

2014-03-26

Risk factors for breast, colorectal, and lung cancer are known to be more common among lesbian, gay, and bisexual (LGB) individuals, suggesting they may be more likely to develop these cancers. Our objective was to determine differences in cancer incidence by sexual orientation, using sexual orientation data aggregated at the county level. Data on cancer incidence were obtained from the California Cancer Registry and data on sexual orientation were obtained from the California Health Interview Survey, from which a measure of age-specific LGB population density by county was calculated. Using multivariable Poisson regression models, the association between the age-race-stratified incident rate of breast, lung and colorectal cancer in each county and LGB population density was examined, with race, age group and poverty as covariates. Among men, bisexual population density was associated with lower incidence of lung cancer and with higher incidence of colorectal cancer. Among women, lesbian population density was associated with lower incidence of lung and colorectal cancer and with higher incidence of breast cancer; bisexual population density was associated with higher incidence of lung and colorectal cancer and with lower incidence of breast cancer. These study findings clearly document links between county-level LGB population density and cancer incidence, illuminating an important public health disparity.

Magnetic levitation of single cells

PubMed Central

Durmus, Naside Gozde; Tekin, H. Cumhur; Guven, Sinan; Sridhar, Kaushik; Arslan Yildiz, Ahu; Calibasi, Gizem; Davis, Ronald W.; Steinmetz, Lars M.; Demirci, Utkan

2015-01-01

Several cellular events cause permanent or transient changes in inherent magnetic and density properties of cells. Characterizing these changes in cell populations is crucial to understand cellular heterogeneity in cancer, immune response, infectious diseases, drug resistance, and evolution. Although magnetic levitation has previously been used for macroscale objects, its use in life sciences has been hindered by the inability to levitate microscale objects and by the toxicity of metal salts previously applied for levitation. Here, we use magnetic levitation principles for biological characterization and monitoring of cells and cellular events. We demonstrate that each cell type (i.e., cancer, blood, bacteria, and yeast) has a characteristic levitation profile, which we distinguish at an unprecedented resolution of 1 × 10−4 g⋅mL−1. We have identified unique differences in levitation and density blueprints between breast, esophageal, colorectal, and nonsmall cell lung cancer cell lines, as well as heterogeneity within these seemingly homogenous cell populations. Furthermore, we demonstrate that changes in cellular density and levitation profiles can be monitored in real time at single-cell resolution, allowing quantification of heterogeneous temporal responses of each cell to environmental stressors. These data establish density as a powerful biomarker for investigating living systems and their responses. Thereby, our method enables rapid, density-based imaging and profiling of single cells with intriguing applications, such as label-free identification and monitoring of heterogeneous biological changes under various physiological conditions, including antibiotic or cancer treatment in personalized medicine. PMID:26124131

PET Quantification of the Norepinephrine Transporter in Human Brain with (S,S)-18F-FMeNER-D2.

PubMed

Moriguchi, Sho; Kimura, Yasuyuki; Ichise, Masanori; Arakawa, Ryosuke; Takano, Harumasa; Seki, Chie; Ikoma, Yoko; Takahata, Keisuke; Nagashima, Tomohisa; Yamada, Makiko; Mimura, Masaru; Suhara, Tetsuya

2017-07-01

Norepinephrine transporter (NET) in the brain plays important roles in human cognition and the pathophysiology of psychiatric disorders. Two radioligands, ( S , S )- 11 C-MRB and ( S , S )- 18 F-FMeNER-D 2 , have been used for imaging NETs in the thalamus and midbrain (including locus coeruleus) using PET in humans. However, NET density in the equally important cerebral cortex has not been well quantified because of unfavorable kinetics with ( S , S )- 11 C-MRB and defluorination with ( S , S )- 18 F-FMeNER-D 2 , which can complicate NET quantification in the cerebral cortex adjacent to the skull containing defluorinated 18 F radioactivity. In this study, we have established analysis methods of quantification of NET density in the brain including the cerebral cortex using ( S , S )- 18 F-FMeNER-D 2 PET. Methods: We analyzed our previous ( S , S )- 18 F-FMeNER-D 2 PET data of 10 healthy volunteers dynamically acquired for 240 min with arterial blood sampling. The effects of defluorination on the NET quantification in the superficial cerebral cortex was evaluated by establishing a time stability of NET density estimations with an arterial input 2-tissue-compartment model, which guided the less-invasive reference tissue model and area under the time-activity curve methods to accurately quantify NET density in all brain regions including the cerebral cortex. Results: Defluorination of ( S , S )- 18 F-FMeNER-D 2 became prominent toward the latter half of the 240-min scan. Total distribution volumes in the superficial cerebral cortex increased with the scan duration beyond 120 min. We verified that 90-min dynamic scans provided a sufficient amount of data for quantification of NET density unaffected by defluorination. Reference tissue model binding potential values from the 90-min scan data and area under the time-activity curve ratios of 70- to 90-min data allowed for the accurate quantification of NET density in the cerebral cortex. Conclusion: We have established methods of quantification of NET densities in the brain including the cerebral cortex unaffected by defluorination using ( S , S )- 18 F-FMeNER-D 2 These results suggest that we can accurately quantify NET density with a 90-min ( S , S )- 18 F-FMeNER-D 2 scan in broad brain areas. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Background risk of breast cancer and the association between physical activity and mammographic density.

PubMed

Trinh, Thang; Eriksson, Mikael; Darabi, Hatef; Bonn, Stephanie E; Brand, Judith S; Cuzick, Jack; Czene, Kamila; Sjölander, Arvid; Bälter, Katarina; Hall, Per

2015-04-02

High physical activity has been shown to decrease the risk of breast cancer, potentially by a mechanism that also reduces mammographic density. We tested the hypothesis that the risk of developing breast cancer in the next 10 years according to the Tyrer-Cuzick prediction model influences the association between physical activity and mammographic density. We conducted a population-based cross-sectional study of 38,913 Swedish women aged 40-74 years. Physical activity was assessed using the validated web-questionnaire Active-Q and mammographic density was measured by the fully automated volumetric Volpara method. The 10-year risk of breast cancer was estimated using the Tyrer-Cuzick (TC) prediction model. Linear regression analyses were performed to assess the association between physical activity and volumetric mammographic density and the potential interaction with the TC breast cancer risk. Overall, high physical activity was associated with lower absolute dense volume. As compared to women with the lowest total activity level (<40 metabolic equivalent hours [MET-h] per day), women with the highest total activity level (≥50 MET-h/day) had an estimated 3.4 cm(3) (95% confidence interval, 2.3-4.7) lower absolute dense volume. The inverse association was seen for any type of physical activity among women with <3.0% TC 10-year risk, but only for total and vigorous activities among women with 3.0-4.9% TC risk, and only for vigorous activity among women with ≥5.0% TC risk. The association between total activity and absolute dense volume was modified by the TC breast cancer risk (P interaction = 0.05). As anticipated, high physical activity was also associated with lower non-dense volume. No consistent association was found between physical activity and percent dense volume. Our results suggest that physical activity may decrease breast cancer risk through reducing mammographic density, and that the physical activity needed to reduce mammographic density may depend on background risk of breast cancer.

Implementation of several mathematical algorithms to breast tissue density classification

NASA Astrophysics Data System (ADS)

Quintana, C.; Redondo, M.; Tirao, G.

2014-02-01

The accuracy of mammographic abnormality detection methods is strongly dependent on breast tissue characteristics, where a dense breast tissue can hide lesions causing cancer to be detected at later stages. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. This paper presents the implementation and the performance of different mathematical algorithms designed to standardize the categorization of mammographic images, according to the American College of Radiology classifications. These mathematical techniques are based on intrinsic properties calculations and on comparison with an ideal homogeneous image (joint entropy, mutual information, normalized cross correlation and index Q) as categorization parameters. The algorithms evaluation was performed on 100 cases of the mammographic data sets provided by the Ministerio de Salud de la Provincia de Córdoba, Argentina—Programa de Prevención del Cáncer de Mama (Department of Public Health, Córdoba, Argentina, Breast Cancer Prevention Program). The obtained breast classifications were compared with the expert medical diagnostics, showing a good performance. The implemented algorithms revealed a high potentiality to classify breasts into tissue density categories.

Quantification of micro stickies

Treesearch

Mahendra Doshi; Jeffrey Dyer; Salman Aziz; Kristine Jackson; Said M. Abubakr

1997-01-01

The objective of this project was to compare the different methods for the quantification of micro stickies. The hydrophobic materials investigated in this project for the collection of micro stickies were Microfoam* (polypropylene packing material), low density polyethylene film (LDPE), high density polyethylene (HDPE; a flat piece from a square plastic bottle), paper...

Diagnostic value of virtual touch tissue imaging quantification for benign and malignant breast lesions with different sizes

PubMed Central

Liu, Hui; Zhao, Li-Xia; Xu, Guang; Yao, Ming-Hua; Zhang, Ai-Hong; Xu, Hui-Xiong; Wu, Rong

2015-01-01

The study was to explore diagnostic value of the virtual touch tissue imaging quantification (VTIQ) in distinguishing benign and malignant breast lesions of variable sizes. We performed conventional ultrasound and VTIQ in 139 breast lesions. The lesions were categorized into three groups according to size (group 1, ≤ 10 mm; group 2, 10-20 mm; and group 3, > 20 mm), and their mean, min, and max shear wave velocities (SWVs) were measured. Diagnoses were confirmed by pathological examination after surgery or needle biopsy. Receiver-operating characteristic curves (ROC) were constructed to determine the optimum cut-off values, calculate the area under curve (AUC), the sensitivity, specificity and accuracy for each velocity. For all groups, the mean, min, and max SWVs of malignant lesions were significantly higher than those of benign lesions (P < 0.05). The cut-off values of mean, min, and max SWVs were not significantly different among the three groups. In addition, the diagnostic performance of mean, min, and max SWV values is analogous, regardless of lesion size. In conclusion, VTIQ is a strong complement to conventional ultrasound, which is a promising method in the differential diagnosis of the breast lesions with different sizes. Further studies validate our results as well as reduce the number of unnecessary biopsies, regardless of size is warranted. PMID:26550234

The Relationship Between Geographic Access to Plastic Surgeons and Breast Reconstruction Rates Among Women Undergoing Mastectomy for Cancer.

PubMed

Bauder, Andrew R; Gross, Cary P; Killelea, Brigid K; Butler, Paris D; Kovach, Stephen J; Fox, Justin P

2017-03-01

Despite a national health care policy requiring payers to cover breast reconstruction, rates of postmastectomy reconstruction are low, particularly among minority populations. We conducted this study to determine if geographic access to a plastic surgeon impacts breast reconstruction rates. Using 2010 inpatient and ambulatory surgery data from 10 states, we identified adult women who underwent mastectomy for breast cancer. Data were aggregated to the health service area (HSA) level and hierarchical generalized linear models were used to risk-standardize breast reconstruction rates (RSRR) across HSAs. The relationship between an HSA's RSRR and plastic surgeon density (surgeons/100,000 population) was quantified using correlation coefficients. The final cohort included 22,997 patients across 134 HSAs. There was substantial variation in plastic surgeon density (median, 1.4 surgeons/100,000; interquartile range, [0.0-2.6]/100,000) and the use of breast reconstruction (median RSRR, 43.0%; interquartile range, [29.9%-62.8%]) across HSAs. Higher plastic surgeon density was positively correlated with breast reconstruction rates (correlation coefficient = 0.66, P < 0.001) and inversely related to time between mastectomy and reconstruction (correlation coefficient = -0.19, P < 0.001). Non-white and publicly insured women were least likely to undergo breast reconstruction overall. Among privately insured patients, racial disparities were noted in high surgeon density areas (white = 79.0% vs. non-white = 63.3%; P < 0.001) but not in low surgeon density areas (34.4% vs 36.5%; P = 0.70). The lack of geographic access to a plastic surgeon serves as a barrier to breast reconstruction and may compound disparities in care associated with race and insurance status. Future efforts to improve equitable access should consider strategies to ensure access to appropriate clinical expertise.

Automated breast tissue density assessment using high order regional texture descriptors in mammography

NASA Astrophysics Data System (ADS)

Law, Yan Nei; Lieng, Monica Keiko; Li, Jingmei; Khoo, David Aik-Aun

2014-03-01

Breast cancer is the most common cancer and second leading cause of cancer death among women in the US. The relative survival rate is lower among women with a more advanced stage at diagnosis. Early detection through screening is vital. Mammography is the most widely used and only proven screening method for reliably and effectively detecting abnormal breast tissues. In particular, mammographic density is one of the strongest breast cancer risk factors, after age and gender, and can be used to assess the future risk of disease before individuals become symptomatic. A reliable method for automatic density assessment would be beneficial and could assist radiologists in the evaluation of mammograms. To address this problem, we propose a density classification method which uses statistical features from different parts of the breast. Our method is composed of three parts: breast region identification, feature extraction and building ensemble classifiers for density assessment. It explores the potential of the features extracted from second and higher order statistical information for mammographic density classification. We further investigate the registration of bilateral pairs and time-series of mammograms. The experimental results on 322 mammograms demonstrate that (1) a classifier using features from dense regions has higher discriminative power than a classifier using only features from the whole breast region; (2) these high-order features can be effectively combined to boost the classification accuracy; (3) a classifier using these statistical features from dense regions achieves 75% accuracy, which is a significant improvement from 70% accuracy obtained by the existing approaches.

The relationship between bone mineral density and mammographic density in Korean women: the Healthy Twin study.

PubMed

Sung, Joohon; Song, Yun-Mi; Stone, Jennifer; Lee, Kayoung

2011-09-01

Mammographic density is one of the strong risk factors for breast cancer. A potential mechanism for this association is that cumulative exposure to mammographic density may reflect cumulative exposure to hormones that stimulate cell division in breast stroma and epithelium, which may have corresponding effects on breast cancer development. Bone mineral density (BMD), a marker of lifetime estrogen exposure, has been found to be associated with breast cancer. We examined the association between BMD and mammographic density in a Korean population. Study subjects were 730 Korean women selected from the Healthy Twin study. BMD (g/cm(2)) was measured with dual-energy X-ray absorptiometry. Mammographic density was measured from digital mammograms using a computer-assisted thresholding method. Linear mixed model considering familial correlations and a wide range of covariates was used for analyses. Quantitative genetic analysis was completed using SOLAR. In premenopausal women, positive associations existed between absolute dense area and BMD at ribs, pelvis, and legs, and between percent dense area and BMD at pelvis and legs. However, in postmenopausal women, there was no association between BMD at any site and mammographic density measures. An evaluation of additive genetic cross-trait correlation showed that absolute dense area had a weak-positive additive genetic cross-trait correlation with BMD at ribs and spines after full adjustment of covariates. This finding suggests that the association between mammographic density and breast cancer could, at least in part, be attributable to an estrogen-related hormonal mechanism.

New Embedded Denotes Fuzzy C-Mean Application for Breast Cancer Density Segmentation in Digital Mammograms

NASA Astrophysics Data System (ADS)

Othman, Khairulnizam; Ahmad, Afandi

2016-11-01

In this research we explore the application of normalize denoted new techniques in advance fast c-mean in to the problem of finding the segment of different breast tissue regions in mammograms. The goal of the segmentation algorithm is to see if new denotes fuzzy c- mean algorithm could separate different densities for the different breast patterns. The new density segmentation is applied with multi-selection of seeds label to provide the hard constraint, whereas the seeds labels are selected based on user defined. New denotes fuzzy c- mean have been explored on images of various imaging modalities but not on huge format digital mammograms just yet. Therefore, this project is mainly focused on using normalize denoted new techniques employed in fuzzy c-mean to perform segmentation to increase visibility of different breast densities in mammography images. Segmentation of the mammogram into different mammographic densities is useful for risk assessment and quantitative evaluation of density changes. Our proposed methodology for the segmentation of mammograms on the basis of their region into different densities based categories has been tested on MIAS database and Trueta Database.

Total Xenoestrogen Body Burden in Relation to Mammographic Density, a Marker of Breast Cancer Risk

DTIC Science & Technology

2008-10-01

average, how often do you eat a serving of meat, including beef , chicken, lamb, or pork? Never or less than once per month 1-3 servings per...has been obtained from the Susan Komen Foundation for an ancillary study of sex hormones and breast density. The Komen Foundation is providing funds...to analyze sex hormone levels in the blood samples obtained in this study. The relation between sex hormone levels and mammographic breast density

Quantification of Estrogen Receptor Expression in Normal Breast Tissue in Postmenopausal Women With Breast Cancer and Association With Tumor Subtypes.

PubMed

Gulbahce, H Evin; Blair, Cindy K; Sweeney, Carol; Salama, Mohamed E

2017-09-01

Estrogen exposure is important in the pathogenesis of breast cancer and is a contributing risk factor. In this study we quantified estrogen receptor (ER) alpha expression in normal breast epithelium (NBR) in women with breast cancer and correlated it with breast cancer subtypes. Tissue microarrays were constructed from 204 breast cancer patients for whom normal breast tissue away from tumor was available. Slides stained with ER were scanned and expression in normal terminal duct lobular epithelium was quantitated using computer-assisted image analysis. ER expression in normal terminal duct lobular epithelium of postmenopausal women with breast cancer was significantly associated with estrogen and triple (estrogen, progesterone receptors, and HER2) negative phenotypes. Also increased age at diagnosis was significantly associated with ER expression in NBR. ER positivity in normal epithelium did not vary by tumor size, lymph node status, tumor grade, or stage. On the basis of quantitative image analysis, we confirm that ER expression in NBR increases with age in women with breast cancer, and report for the first time, a significant association between ER expression in NBR with ER-negative and triple-negative cancers in postmenopausal women.

Optical tomographic imaging for breast cancer detection

NASA Astrophysics Data System (ADS)

Cong, Wenxiang; Intes, Xavier; Wang, Ge

2017-09-01

Diffuse optical breast imaging utilizes near-infrared (NIR) light propagation through tissues to assess the optical properties of tissues for the identification of abnormal tissue. This optical imaging approach is sensitive, cost-effective, and does not involve any ionizing radiation. However, the image reconstruction of diffuse optical tomography (DOT) is a nonlinear inverse problem and suffers from severe illposedness due to data noise, NIR light scattering, and measurement incompleteness. An image reconstruction method is proposed for the detection of breast cancer. This method splits the image reconstruction problem into the localization of abnormal tissues and quantification of absorption variations. The localization of abnormal tissues is performed based on a well-posed optimization model, which can be solved via a differential evolution optimization method to achieve a stable reconstruction. The quantification of abnormal absorption is then determined in localized regions of relatively small extents, in which a potential tumor might be. Consequently, the number of unknown absorption variables can be greatly reduced to overcome the underdetermined nature of DOT. Numerical simulation experiments are performed to verify merits of the proposed method, and the results show that the image reconstruction method is stable and accurate for the identification of abnormal tissues, and robust against the measurement noise of data.

Shear wave velocity measurements for differential diagnosis of solid breast masses: a comparison between virtual touch quantification and virtual touch IQ.

PubMed

Tozaki, Mitsuhiro; Saito, Masahiro; Benson, John; Fan, Liexiang; Isobe, Sachiko

2013-12-01

This study compared the diagnostic performance of two shear wave speed measurement techniques in 81 patients with 83 solid breast lesions. Virtual Touch Quantification, which provides single-point shear wave speed measurement capability (SP-SWS), was compared with Virtual Touch IQ, a new 2-D shear wave imaging technique with multi-point shear wave speed measurement capability (2D-SWS). With SP-SWS, shear wave velocity was measured within the lesion ("internal" value) and the marginal areas ("marginal" value). With 2D-SWS, the highest velocity was measured. The marginal values obtained with the SP-SWS and 2D-SWS methods were significantly higher for malignant lesions and benign lesions, respectively (p < 0.0001). Sensitivity, specificity and accuracy were 86% (36/42), 90% (37/41) and 88% (73/83), respectively, for SP-SWS, and 88% (37/42), 93% (38/41) and 90% (75/83), respectively, for 2D-SWS. It is concluded that 2D-SWS is a useful diagnostic tool for differentiating malignant from benign solid breast masses. Copyright © 2013 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Mammographic breast density and breast cancer: evidence of a shared genetic basis.

PubMed

Varghese, Jajini S; Thompson, Deborah J; Michailidou, Kyriaki; Lindström, Sara; Turnbull, Clare; Brown, Judith; Leyland, Jean; Warren, Ruth M L; Luben, Robert N; Loos, Ruth J; Wareham, Nicholas J; Rommens, Johanna; Paterson, Andrew D; Martin, Lisa J; Vachon, Celine M; Scott, Christopher G; Atkinson, Elizabeth J; Couch, Fergus J; Apicella, Carmel; Southey, Melissa C; Stone, Jennifer; Li, Jingmei; Eriksson, Louise; Czene, Kamila; Boyd, Norman F; Hall, Per; Hopper, John L; Tamimi, Rulla M; Rahman, Nazneen; Easton, Douglas F

2012-03-15

Percent mammographic breast density (PMD) is a strong heritable risk factor for breast cancer. However, the pathways through which this risk is mediated are still unclear. To explore whether PMD and breast cancer have a shared genetic basis, we identified genetic variants most strongly associated with PMD in a published meta-analysis of five genome-wide association studies (GWAS) and used these to construct risk scores for 3,628 breast cancer cases and 5,190 controls from the UK2 GWAS of breast cancer. The signed per-allele effect estimates of single-nucleotide polymorphisms (SNP) were multiplied with the respective allele counts in the individual and summed over all SNPs to derive the risk score for an individual. These scores were included as the exposure variable in a logistic regression model with breast cancer case-control status as the outcome. This analysis was repeated using 10 different cutoff points for the most significant density SNPs (1%-10% representing 5,222-50,899 SNPs). Permutation analysis was also conducted across all 10 cutoff points. The association between risk score and breast cancer was significant for all cutoff points from 3% to 10% of top density SNPs, being most significant for the 6% (2-sided P = 0.002) to 10% (P = 0.001) cutoff points (overall permutation P = 0.003). Women in the top 10% of the risk score distribution had a 31% increased risk of breast cancer [OR = 1.31; 95% confidence interval (CI), 1.08-1.59] compared with women in the bottom 10%. Together, our results show that PMD and breast cancer have a shared genetic basis that is mediated through a large number of common variants.

Mammographic breast density and breast cancer: evidence of a shared genetic basis

PubMed Central

Varghese, Jajini S; Thompson, Deborah J; Michailidou, Kyriaki; Lindström, Sara; Turnbull, Clare; Brown, Judith; Leyland, Jean; Warren, Ruth ML; Luben, Robert N; Loos, Ruth J; Wareham, Nicholas J; Rommens, Johanna; Paterson, Andrew D; Martin, Lisa J; Vachon, Celine M; Scott, Christopher G; Atkinson, Elizabeth J; Couch, Fergus J; Apicella, Carmel; Southey, Melissa C; Stone, Jennifer; Li, Jingmei; Eriksson, Louise; Czene, Kamila; Boyd, Norman F; Hall, Per; Hopper, John L; Tamimi, Rulla M; Rahman, Nazneen; Easton, Douglas F

2012-01-01

Percent mammographic breast density (PMD) is a strong heritable risk factor for breast cancer. However, the pathways through which this risk is mediated are still unclear. To explore whether PMD and breast cancer have a shared genetic basis, we identified genetic variants most strongly associated with PMD in a published meta-analysis of five genome-wide association studies (GWAS) and used these to construct risk scores for 3628 breast cancer cases and 5190 controls from the UK2 GWAS of breast cancer. The signed per-allele effect estimates of SNPs were multiplied with the respective allele counts in the individual and summed over all SNPs to derive the risk score for an individual. These scores were included as the exposure variable in a logistic regression model with breast cancer case-control status as the outcome. This analysis was repeated using ten different cut-offs for the most significant density SNPs (1-10% representing 5,222-50,899 SNPs). Permutation analysis was also performed across all 10 cut-offs. The association between risk score and breast cancer was significant for all cut-offs from 3-10% of top density SNPs, being most significant for the 6% (2-sided P=0.002) to 10% (P=0.001) cut-offs (overall permutation P=0.003). Women in the top 10% of the risk score distribution had a 31% increased risk of breast cancer [OR= 1.31 (95%CI 1.08-1.59)] compared to women in the bottom 10%. Together, our results demonstrate that PMD and breast cancer have a shared genetic basis that is mediated through a large number of common variants. PMID:22266113

Gene Methylation and Cytological Atypia in Random Fine-Needle Aspirates for Assessment of Breast Cancer Risk.

PubMed

Stearns, Vered; Fackler, Mary Jo; Hafeez, Sidra; Bujanda, Zoila Lopez; Chatterton, Robert T; Jacobs, Lisa K; Khouri, Nagi F; Ivancic, David; Kenney, Kara; Shehata, Christina; Jeter, Stacie C; Wolfman, Judith A; Zalles, Carola M; Huang, Peng; Khan, Seema A; Sukumar, Saraswati

2016-08-01

Methods to determine individualized breast cancer risk lack sufficient sensitivity to select women most likely to benefit from preventive strategies. Alterations in DNA methylation occur early in breast cancer. We hypothesized that cancer-specific methylation markers could enhance breast cancer risk assessment. We evaluated 380 women without a history of breast cancer. We determined their menopausal status or menstrual cycle phase, risk of developing breast cancer (Gail model), and breast density and obtained random fine-needle aspiration (rFNA) samples for assessment of cytopathology and cumulative methylation index (CMI). Eight methylated gene markers were identified through whole-genome methylation analysis and included novel and previously established breast cancer detection genes. We performed correlative and multivariate linear regression analyses to evaluate DNA methylation of a gene panel as a function of clinical factors associated with breast cancer risk. CMI and individual gene methylation were independent of age, menopausal status or menstrual phase, lifetime Gail risk score, and breast density. CMI and individual gene methylation for the eight genes increased significantly (P < 0.001) with increasing cytological atypia. The findings were verified with multivariate analyses correcting for age, log (Gail), log (percent density), rFNA cell number, and body mass index. Our results demonstrate a significant association between cytological atypia and high CMI, which does not vary with menstrual phase or menopause and is independent of Gail risk and mammographic density. Thus, CMI is an excellent candidate breast cancer risk biomarker, warranting larger prospective studies to establish its utility for cancer risk assessment. Cancer Prev Res; 9(8); 673-82. ©2016 AACR. ©2016 American Association for Cancer Research.

Endogenous sex hormones and breast density in young women.

PubMed

Jung, Seungyoun; Stanczyk, Frank Z; Egleston, Brian L; Snetselaar, Linda G; Stevens, Victor J; Shepherd, John A; Van Horn, Linda; LeBlanc, Erin S; Paris, Kenneth; Klifa, Catherine; Dorgan, Joanne F

2015-02-01

Breast density is a strong risk factor for breast cancer and reflects epithelial and stromal content. Breast tissue is particularly sensitive to hormonal stimuli before it fully differentiates following the first full-term pregnancy. Few studies have examined associations between sex hormones and breast density among young women. We conducted a cross-sectional study among 180 women ages 25 to 29 years old who participated in the Dietary Intervention Study in Children 2006 Follow-up Study. Eighty-five percent of participants attended a clinic visit during their luteal phase of menstrual cycle. Magnetic resonance imaging measured the percentage of dense breast volume (%DBV), absolute dense breast volume (ADBV), and absolute nondense breast volume (ANDBV). Multiple-linear mixed-effect regression models were used to evaluate the association of sex hormones and sex hormone-binding globulin (SHBG) with %DBV, ADBV, and ANDBV. Testosterone was significantly positively associated with %DBV and ADBV. The multivariable geometric mean of %DBV and ADBV across testosterone quartiles increased from 16.5% to 20.3% and from 68.6 to 82.3 cm(3), respectively (Ptrend ≤ 0.03). There was no association of %DBV or ADBV with estrogens, progesterone, non-SHBG-bound testosterone, or SHBG (Ptrend ≥ 0.27). Neither sex hormones nor SHBG was associated with ANDBV except progesterone; however, the progesterone result was nonsignificant in analysis restricted to women in the luteal phase. These findings suggest a modest positive association between testosterone and breast density in young women. Hormonal influences at critical periods may contribute to morphologic differences in the breast associated with breast cancer risk later in life. ©2014 American Association for Cancer Research.

Detection of the Peanut Allergens Ara h 2 and Ara h 6 in Human Breast Milk: Development of 2 Sensitive and Specific Sandwich ELISA Assays.

PubMed

Schocker, Frauke; Scharf, Alexandra; Kull, Skadi; Jappe, Uta

2017-01-01

Little is known about breast milk as a vehicle for tolerance development or sensitization to peanuts very early in life. Thus, well-characterized and highly sensitive detection systems for the reliable determination of peanut allergens in breast milk are mandatory. For the quantification of the marker allergens Ara h 2 and Ara h 6 in the low nanogram per milliliter range in breast milk samples of a German cohort, sensitive and highly specific sandwich ELISAs were optimized and validated. The Ara h 2 ELISA revealed a limit of detection (LOD) of 1.3 ng Ara h 2/mL and a quantification range of 2.3-250 ng/mL, the Ara h 6 ELISA showed an LOD of 0.7 ng/mL and a working range of 1.1-14.4 ng/mL. The assays showed no relevant cross-reactivity against other potentially cross-reactive legume, seed, and tree nut extracts (<0.01%, except for Ara h 1 in the Ara h 2 ELISA <0.1%). Ara h 2 was detectable in breast milk samples from 14/40 (35%) of the participants in concentrations from 2.3 to 184 ng/mL, Ara h 6 appeared in 9/40 (22.5%) of the lactating mothers between 1.1 and 9.7 ng/mL, and 1 highly positive sample with 79 ng/mL. Both allergens appeared at the same time points, but Ara h 6 in lower concentrations than Ara h 2. Sensitive and specific diagnostic tools for the determination of Ara h 2 and Ara h 6 in human breast milk were established. The kinetics of secreted Ara h 2 and Ara h 6 seem to be similar but with a difference in concentration. Follow-up investigations on their tolerogenic or sensitizing properties in breast milk become now accessible. © 2017 S. Karger AG, Basel.

Association between power law coefficients of the anatomical noise power spectrum and lesion detectability in breast imaging modalities

NASA Astrophysics Data System (ADS)

Chen, Lin; Abbey, Craig K.; Boone, John M.

2013-03-01

Previous research has demonstrated that a parameter extracted from a power function fit to the anatomical noise power spectrum, β, may be predictive of breast mass lesion detectability in x-ray based medical images of the breast. In this investigation, the value of β was compared with a number of other more widely used parameters, in order to determine the relationship between β and these other parameters. This study made use of breast CT data sets, acquired on two breast CT systems developed in our laboratory. A total of 185 breast data sets in 183 women were used, and only the unaffected breast was used (where no lesion was suspected). The anatomical noise power spectrum computed from two-dimensional region of interests (ROIs), was fit to a power function (NPS(f) = α f-β), and the exponent parameter (β) was determined using log/log linear regression. Breast density for each of the volume data sets was characterized in previous work. The breast CT data sets analyzed in this study were part of a previous study which evaluated the receiver operating characteristic (ROC) curve performance using simulated spherical lesions and a pre-whitened matched filter computer observer. This ROC information was used to compute the detectability index as well as the sensitivity at 95% specificity. The fractal dimension was computed from the same ROIs which were used for the assessment of β. The value of β was compared to breast density, detectability index, sensitivity, and fractal dimension, and the slope of these relationships was investigated to assess statistical significance from zero slope. A statistically significant non-zero slope was considered to be a positive association in this investigation. All comparisons between β and breast density, detectability index, sensitivity at 95% specificity, and fractal dimension demonstrated statistically significant association with p < 0.001 in all cases. The value of β was also found to be associated with patient age and breast diameter, parameters both related to breast density. In all associations between other parameters, lower values of β were associated with increased breast cancer detection performance. Specifically, lower values of β were associated with lower breast density, higher detectability index, higher sensitivity, and lower fractal dimension values. While causality was not and probably cannot be demonstrated, the strong, statistically significant association between the β metric and the other more widely used parameters suggest that β may be considered as a surrogate measure for breast cancer detection performance. These findings are specific to breast parenchymal patterns and mass lesions only.

Exploring a new bilateral focal density asymmetry based image marker to predict breast cancer risk

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Mirniaharikandehei, Seyedehnafiseh; Hollingsworth, Alan B.; Wang, Yunzhi; Qiu, Yuchen; Liu, Hong; Zheng, Bin

2017-03-01

Although breast density has been widely considered an important breast cancer risk factor, it is not very effective to predict risk of developing breast cancer in a short-term or harboring cancer in mammograms. Based on our recent studies to build short-term breast cancer risk stratification models based on bilateral mammographic density asymmetry, we in this study explored a new quantitative image marker based on bilateral focal density asymmetry to predict the risk of harboring cancers in mammograms. For this purpose, we assembled a testing dataset involving 100 positive and 100 negative cases. In each of positive case, no any solid masses are visible on mammograms. We developed a computer-aided detection (CAD) scheme to automatically detect focal dense regions depicting on two bilateral mammograms of left and right breasts. CAD selects one focal dense region with the maximum size on each image and computes its asymmetrical ratio. We used this focal density asymmetry as a new imaging marker to divide testing cases into two groups of higher and lower focal density asymmetry. The first group included 70 cases in which 62.9% are positive, while the second group included 130 cases in which 43.1% are positive. The odds ratio is 2.24. As a result, this preliminary study supported the feasibility of applying a new focal density asymmetry based imaging marker to predict the risk of having mammography-occult cancers. The goal is to assist radiologists more effectively and accurately detect early subtle cancers using mammography and/or other adjunctive imaging modalities in the future.

Menstrual and reproductive factors in relation to mammographic density: the Study of Women’s Health Across the Nation (SWAN)

PubMed Central

Butler, Lesley M.; Gold, Ellen B.; Greendale, Gail A.; Crandall, Carolyn J.; Modugno, Francesmary; Oestreicher, Nina; Quesenberry, Charles P.; Habel, Laurel A.

2009-01-01

Menstrual and reproductive factors may increase breast cancer risk through a pathway that includes increased mammographic density. We assessed whether known or suspected menstrual and reproductive breast cancer risk factors were cross-sectionally associated with mammographic density, by measuring area of radiographic density and total breast area on mammograms from 801 participants in the Study of Women’s Health Across the Nation (SWAN), a multi-ethnic cohort of pre- and early perimenopausal women. From multivariable linear regression, the following menstrual or reproductive factors were independently associated with percent mammographic density (area of dense breast/breast area): older age at menarche (β = 10.3, P < 0.01, for >13 vs. <12 years), premenstrual cravings and bloating (β = −3.36, P = 0.02), younger age at first full-term birth (β = −8.12, P < 0.01 for ≤23 years versus no births), greater number of births (β = −6.80, P < 0.01 for ≥3 births versus no births), and premenopausal status (β = 3.78, P < 0.01 versus early perimenopausal). Only number of births remained associated with percent density after adjustment for age, race/ethnicity, study site, body mass index (BMI), and smoking. In addition, stratified analyses revealed that the association with number of births was confined to women within the lowest BMI tertile (β = −12.2, P < 0.01 for ≥3 births versus no births). Our data support a mechanism for parity and breast cancer that involves mammographic density among pre- and early perimenopausal women that may be modified by body size. PMID:18066689

Quantitative 3D breast magnetic resonance imaging fibroglandular tissue analysis and correlation with qualitative assessments: a feasibility study.

PubMed

Ha, Richard; Mema, Eralda; Guo, Xiaotao; Mango, Victoria; Desperito, Elise; Ha, Jason; Wynn, Ralph; Zhao, Binsheng

2016-04-01

The amount of fibroglandular tissue (FGT) has been linked to breast cancer risk based on mammographic density studies. Currently, the qualitative assessment of FGT on mammogram (MG) and magnetic resonance imaging (MRI) is prone to intra and inter-observer variability. The purpose of this study is to develop an objective quantitative FGT measurement tool for breast MRI that could provide significant clinical value. An IRB approved study was performed. Sixty breast MRI cases with qualitative assessment of mammographic breast density and MRI FGT were randomly selected for quantitative analysis from routine breast MRIs performed at our institution from 1/2013 to 12/2014. Blinded to the qualitative data, whole breast and FGT contours were delineated on T1-weighted pre contrast sagittal images using an in-house, proprietary segmentation algorithm which combines the region-based active contours and a level set approach. FGT (%) was calculated by: [segmented volume of FGT (mm(3))/(segmented volume of whole breast (mm(3))] ×100. Statistical correlation analysis was performed between quantified FGT (%) on MRI and qualitative assessments of mammographic breast density and MRI FGT. There was a significant positive correlation between quantitative MRI FGT assessment and qualitative MRI FGT (r=0.809, n=60, P<0.001) and mammographic density assessment (r=0.805, n=60, P<0.001). There was a significant correlation between qualitative MRI FGT assessment and mammographic density assessment (r=0.725, n=60, P<0.001). The four qualitative assessment categories of FGT correlated with the calculated mean quantitative FGT (%) of 4.61% (95% CI, 0-12.3%), 8.74% (7.3-10.2%), 18.1% (15.1-21.1%), 37.4% (29.5-45.3%). Quantitative measures of FGT (%) were computed with data derived from breast MRI and correlated significantly with conventional qualitative assessments. This quantitative technique may prove to be a valuable tool in clinical use by providing computer generated standardized measurements with limited intra or inter-observer variability.

The combined effect of mammographic texture and density on breast cancer risk: a cohort study.

PubMed

Wanders, Johanna O P; van Gils, Carla H; Karssemeijer, Nico; Holland, Katharina; Kallenberg, Michiel; Peeters, Petra H M; Nielsen, Mads; Lillholm, Martin

2018-05-02

Texture patterns have been shown to improve breast cancer risk segregation in addition to area-based mammographic density. The additional value of texture pattern scores on top of volumetric mammographic density measures in a large screening cohort has never been studied. Volumetric mammographic density and texture pattern scores were assessed automatically for the first available digital mammography (DM) screening examination of 51,400 women (50-75 years of age) participating in the Dutch biennial breast cancer screening program between 2003 and 2011. The texture assessment method was developed in a previous study and validated in the current study. Breast cancer information was obtained from the screening registration system and through linkage with the Netherlands Cancer Registry. All screen-detected breast cancers diagnosed at the first available digital screening examination were excluded. During a median follow-up period of 4.2 (interquartile range (IQR) 2.0-6.2) years, 301 women were diagnosed with breast cancer. The associations between texture pattern scores, volumetric breast density measures and breast cancer risk were determined using Cox proportional hazard analyses. Discriminatory performance was assessed using c-indices. The median age of the women at the time of the first available digital mammography examination was 56 years (IQR 51-63). Texture pattern scores were positively associated with breast cancer risk (hazard ratio (HR) 3.16 (95% CI 2.16-4.62) (p value for trend <0.001), for quartile (Q) 4 compared to Q1). The c-index of texture was 0.61 (95% CI 0.57-0.64). Dense volume and percentage dense volume showed positive associations with breast cancer risk (HR 1.85 (95% CI 1.32-2.59) (p value for trend <0.001) and HR 2.17 (95% CI 1.51-3.12) (p value for trend <0.001), respectively, for Q4 compared to Q1). When adding texture measures to models with dense volume or percentage dense volume, c-indices increased from 0.56 (95% CI 0.53-0.59) to 0.62 (95% CI 0.58-0.65) (p < 0.001) and from 0.58 (95% CI 0.54-0.61) to 0.60 (95% CI 0.57-0.63) (p = 0.054), respectively. Deep-learning-based texture pattern scores, measured automatically on digital mammograms, are associated with breast cancer risk, independently of volumetric mammographic density, and augment the capacity to discriminate between future breast cancer and non-breast cancer cases.

The SDF1-CXCR4 Axis Functions through p38-MAPK Signaling to Drive Breast Cancer Progression and Metastasis

DTIC Science & Technology

2008-09-01

with breast cancer cells (MCF7 cell line) could induce proliferation and lead to hormone independent tumors in vivo. Upon analysis of these tumors by...1-0694 4.B MCS induce gene expression of ER mediated genes. Endpoint tumors from above studies were harvested for use in Real-time PCR analysis ...Total RNA was isolated from tumors, reverse transcribed into cDNA and subjected to real-time PCR analysis for quantification. A. Real time PCR results

The SDF1-CXCR4 Axis Functions through p38-MAPK Signaling to Drive Breast Cancer Progression and Metastasis

DTIC Science & Technology

2008-09-01

with breast cancer cells (MCF7 cell line) could induce proliferation and lead to hormone independent tumors in vivo. Upon analysis of these tumors by...S.5 MCS induce gene expression of ER mediated genes. Endpoint tumors from above studies were harvested for use in Real-time PCR analysis . As...subjected to real-time PCR analysis for quantification. A. Real time PCR results from matrigel + estrogen tumor samples. MCF7 + E2 control tumors are

The proteomic landscape of triple-negative breast cancer.

PubMed

Lawrence, Robert T; Perez, Elizabeth M; Hernández, Daniel; Miller, Chris P; Haas, Kelsey M; Irie, Hanna Y; Lee, Su-In; Blau, C Anthony; Villén, Judit

2015-04-28

Triple-negative breast cancer is a heterogeneous disease characterized by poor clinical outcomes and a shortage of targeted treatment options. To discover molecular features of triple-negative breast cancer, we performed quantitative proteomics analysis of twenty human-derived breast cell lines and four primary breast tumors to a depth of more than 12,000 distinct proteins. We used this data to identify breast cancer subtypes at the protein level and demonstrate the precise quantification of biomarkers, signaling proteins, and biological pathways by mass spectrometry. We integrated proteomics data with exome sequence resources to identify genomic aberrations that affect protein expression. We performed a high-throughput drug screen to identify protein markers of drug sensitivity and understand the mechanisms of drug resistance. The genome and proteome provide complementary information that, when combined, yield a powerful engine for therapeutic discovery. This resource is available to the cancer research community to catalyze further analysis and investigation. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Correlation of white female breast cancer incidence trends with nitrogen dioxide emission levels and motor vehicle density patterns.

PubMed

Chen, Fan; Bina, William F

2012-02-01

The long-term trend of female breast cancer incidence rates in the United States and some European countries demonstrates a similar pattern: an increasing trend in the last century followed by a declining trend in this century. The well-known risk factors cannot explain this trend. We compared the breast cancer incidence trends obtained from SEER data with the trend of nitrogen dioxides (NOx) emission and monitoring data as well as motor vehicle density data. The upward followed by downward trend of NOx is similar to the breast cancer incidence trend but with an offset of 20 years earlier. Motor vehicles are the major source of NOx emissions. The geographic distribution of motor vehicles density in 1970 in the observed US counties is positively correlated with breast cancer incidence rates (R(2) 0.8418, the correlation coefficient = 0.9175) in 1980-1995. Because both the time trend and geographic pattern are associated with breast cancer incidence rates, further studies on the relationship between breast cancer and air pollution are needed.

Characterization of invisible breast cancers in digital mammography and tomosynthesis: radio-pathological correlation.

PubMed

Aguilar Angulo, P M; Romero Castellano, C; Ruiz Martín, J; Sánchez-Camacho González-Carrato, M P; Cruz Hernández, L M

To review the radio-pathologic features of symptomatic breast cancers not detected at digital mammography (DM) and digital breast tomosynthesis (DBT). Retrospective analysis of 169 lesions from symptomatic patients with breast cancer that were studied with DM, DBT, ultrasound (US) and magnetic resonance (MR). We identified occult lesions (true false negatives) in DM and DBT. Clinical data, density, US and MR findings were analyzed as well as histopathological results. We identified seven occult lesions in DM and DBT. 57% (4/7) of the lesions were identified in high-density breasts (type c and d), and the rest of them in breasts of density type b. Six carcinomas were identified at US and MR (BI-RADS 4 masses); the remaining lesion was only identified at MR. The tumor size was larger than 3cm at MRI in 57% of the lesions. All tumors were ductal infiltrating carcinomas, six of them with high stromal proportion. According to molecular classification, we found only one triple-negative breast cancer, the other lesions were luminal-type. We analyzed the tumor margins of two resected carcinomas that were not treated with neoadjuvant chemotherapy, both lesions presented margins that displaced the adjacent parenchyma without infiltrating it. Occult breast carcinomas in DM and DBT accounted for 4% of lesions detected in patients with symptoms. They were mostly masses, all of them presented the diagnosis of infiltrating ductal carcinoma (with predominance of the luminal immunophenotype) and were detected in breasts of density type b, c and d. Copyright © 2017 SERAM. Publicado por Elsevier España, S.L.U. All rights reserved.

Birth weight, childhood body mass index, and height in relation to mammographic density and breast cancer: a register-based cohort study.

PubMed

Andersen, Zorana J; Baker, Jennifer L; Bihrmann, Kristine; Vejborg, Ilse; Sørensen, Thorkild I A; Lynge, Elsebeth

2014-01-20

High breast density, a strong predictor of breast cancer may be determined early in life. Childhood anthropometric factors have been related to breast cancer and breast density, but rarely simultaneously. We examined whether mammographic density (MD) mediates an association of birth weight, childhood body mass index (BMI), and height with the risk of breast cancer. 13,572 women (50 to 69 years) in the Copenhagen mammography screening program (1991 through 2001) with childhood anthropometric measurements in the Copenhagen School Health Records Register were followed for breast cancer until 2010. With logistic and Cox regression models, we investigated associations among birth weight, height, and BMI at ages 7 to 13 years with MD (mixed/dense or fatty) and breast cancer, respectively. 8,194 (60.4%) women had mixed/dense breasts, and 716 (5.3%) developed breast cancer. Childhood BMI was significantly inversely related to having mixed/dense breasts at all ages, with odds ratios (95% confidence intervals) ranging from 0.69 (0.66 to 0.72) at age 7 to 0.56 (0.53 to 0.58) at age 13, per one-unit increase in z-score. No statistically significant associations were detected between birth weight and MD, height and MD, or birth weight and breast cancer risk. BMI was inversely associated with breast cancer, with hazard ratios of 0.91 (0.83 to 0.99) at age 7 and 0.92 (0.84 to 1.00) at age 13, whereas height was positively associated with breast cancer risk (age 7, 1.06 (0.98 to 1.14) and age 13, 1.08 (1.00 to 1.16)). After additional adjustment for MD, associations of BMI with breast cancer diminished (age 7, 0.97 (0.88 to 1.06) and age 13, 1.01 (0.93 to 1.11)), but remained with height (age 7, 1.06 (0.99 to 1.15) and age 13, 1.09 (1.01 to 1.17)). Among women 50 years and older, childhood body fatness was inversely associated with the breast cancer risk, possibly via a mechanism mediated by MD, at least partially. Childhood tallness was positively associated with breast cancer risk, seemingly via a pathway independent of MD. Birth weight was not associated with MD or breast cancer in this age group.

Common variants in ZNF365 are associated with both mammographic density and breast cancer risk

PubMed Central

Lindström, Sara; Vachon, Celine M.; Li, Jingmei; Varghese, Jajini; Thompson, Deborah; Warren, Ruth; Brown, Judith; Leyland, Jean; Audley, Tina; Wareham, Nicholas J.; Loos, Ruth J.F.; Paterson, Andrew D.; Waggott, Darryl; Martin, Lisa J.; Scott, Christopher G.; Pankratz, V. Shane; Hankinson, Susan E.; Hazra, Aditi; Hunter, David J.; Hopper, John L.; Southey, Melissa C.; Chanock, Stephen J.; Silva, Isabel dos Santos; Liu, JianJun; Eriksson, Louise; Couch, Fergus J.; Stone, Jennifer; Apicella, Carmel; Czene, Kamila; Kraft, Peter; Hall, Per; Easton, Douglas F.; Boyd, Norman F.; Tamimi, Rulla M.

2011-01-01

High percent mammographic density adjusted for age and body mass index (BMI) is one of the strongest risk factors for breast cancer. We conducted a meta-analysis of five genome-wide association studies of percent mammographic density and report an association with rs10995190 in ZNF365 (combined P=9×6·10−10). This finding might partly explain the underlying biology of the recently discovered association between common variants in ZNF365 and breast cancer risk. PMID:21278746

Radiographic evaluation of vessel count and density with quantitative magnetic resonance imaging during external breast expansion in Asian women: A prospective clinical trial.

PubMed

Myung, Yujin; Kwon, Heeyeon; Pak, Changsik; Lee, Hobin; Jeong, Jae Hoon; Heo, Chan Yeong

2016-12-01

Breast augmentation with fat transfer does not bear the risks associated with silicone implantation. The method can potentially be especially useful in Asian women, who often reject augmentation mammoplasty with implants. This prospective clinical trial evaluated the effects of external breast expansion on breast density and vessel count using magnetic resonance imaging. Thirty-four enrolled patients were instructed to apply one of two devices, the conventional BRAVA device (used in the AESTES trial) or a novel external expansion device (EVERA) designed for Asian women, continuously for 8 h per day for 12 weeks. For external expansion, the pressure was set to 25 mmHg. Follow-up examinations were performed for 4 weeks after completion of the expansion. The ratio between the fibroglandular and adipose tissues of the breast was measured using T1-weighted MRI, and the number of vessels in the breast tissue was determined before and after the treatment by contrast MRI. Additionally, the volume of the breast was measured by laser scanning before, during, and after the device application. The obtained measurements were compared within and between the groups at different time points. Six patients dropped out, while 28 completed the trial without major side effects or adverse events. External expansion significantly increased breast vessel count in both the EVERA and AESTES groups (p = 0.019, p = 0.022). However, it did not significantly change breast density in either group (p = 0.186, p = 0.638). No significant intergroup differences were noted in vessel count (p = 0.874) or density (p = 0.482). Breast volume increases after 12 weeks of application were statistically significant in both groups, with mean changes of 81 ± 22 cc (AESTES) and 98 ± 30 cc (EVERA) (p < 0.001 in both cases). External expansion resulted in a marked increase in breast vessel count but did not affect breast density. The observed increase in breast volume can be considered substantial for Asian women. Level II, therapeutic study. Copyright © 2016 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

Computer Simulation of Breast Cancer Screening

DTIC Science & Technology

1999-07-01

techniques for evaluating the screening efficacy of mammography. Breast cancer growth rates, incidence rates, multiracial population demographics, death ... rates , breast cancer prognosis factors, breast density considerations, detection versus diameter probabilities, and other pertinent data have been

Differences in mammographic density between Asian and Caucasian populations: a comparative analysis.

PubMed

Rajaram, Nadia; Mariapun, Shivaani; Eriksson, Mikael; Tapia, Jose; Kwan, Pui Yoke; Ho, Weang Kee; Harun, Faizah; Rahmat, Kartini; Czene, Kamila; Taib, Nur Aishah Mohd; Hall, Per; Teo, Soo Hwang

2017-01-01

Mammographic density is a measurable and modifiable biomarker that is strongly and independently associated with breast cancer risk. Paradoxically, although Asian women have lower risk of breast cancer, studies of minority Asian women in predominantly Caucasian populations have found that Asian women have higher percent density. In this cross-sectional study, we compared the distribution of mammographic density for a matched cohort of Asian women from Malaysia and Caucasian women from Sweden, and determined if variations in mammographic density could be attributed to population differences in breast cancer risk factors. Volumetric mammographic density was compared for 1501 Malaysian and 4501 Swedish healthy women, matched on age and body mass index. We used multivariable log-linear regression to determine the risk factors associated with mammographic density and mediation analysis to identify factors that account for differences in mammographic density between the two cohorts. Compared to Caucasian women, percent density was 2.0% higher among Asian women (p < 0.001), and dense volume was 5.7 cm 3 higher among pre-menopausal Asian women (p < 0.001). Dense volume was 3.0 cm 3 lower among post-menopausal Asian women (p = 0.009) compared to post-menopausal Caucasian women, and this difference was attributed to population differences in height, weight, and parity (p < 0.001). Our analysis suggests that among post-menopausal women, population differences in mammographic density and risk to breast cancer may be accounted for by height, weight, and parity. Given that pre-menopausal Asian and Caucasian women have similar population risk to breast cancer but different dense volume, development of more appropriate biomarkers of risk in pre-menopausal women is required.

Population-Attributable Risk Proportion of Clinical Risk Factors for Breast Cancer.

PubMed

Engmann, Natalie J; Golmakani, Marzieh K; Miglioretti, Diana L; Sprague, Brian L; Kerlikowske, Karla

2017-09-01

Many established breast cancer risk factors are used in clinical risk prediction models, although the proportion of breast cancers explained by these factors is unknown. To determine the population-attributable risk proportion (PARP) for breast cancer associated with clinical breast cancer risk factors among premenopausal and postmenopausal women. Case-control study with 1:10 matching on age, year of risk factor assessment, and Breast Cancer Surveillance Consortium (BCSC) registry. Risk factor data were collected prospectively from January 1, 1996, through October 31, 2012, from BCSC community-based breast imaging facilities. A total of 18 437 women with invasive breast cancer or ductal carcinoma in situ were enrolled as cases and matched to 184 309 women without breast cancer, with a total of 58 146 premenopausal and 144 600 postmenopausal women enrolled in the study. Breast Imaging Reporting and Data System (BI-RADS) breast density (heterogeneously or extremely dense vs scattered fibroglandular densities), first-degree family history of breast cancer, body mass index (>25 vs 18.5-25), history of benign breast biopsy, and nulliparity or age at first birth (≥30 years vs <30 years). Population-attributable risk proportion of breast cancer. Of the 18 437 women with breast cancer, the mean (SD) age was 46.3 (3.7) years among premenopausal women and 61.7 (7.2) years among the postmenopausal women. Overall, 4747 (89.8%) premenopausal and 12 502 (95.1%) postmenopausal women with breast cancer had at least 1 breast cancer risk factor. The combined PARP of all risk factors was 52.7% (95% CI, 49.1%-56.3%) among premenopausal women and 54.7% (95% CI, 46.5%-54.7%) among postmenopausal women. Breast density was the most prevalent risk factor for both premenopausal and postmenopausal women and had the largest effect on the PARP; 39.3% (95% CI, 36.6%-42.0%) of premenopausal and 26.2% (95% CI, 24.4%-28.0%) of postmenopausal breast cancers could potentially be averted if all women with heterogeneously or extremely dense breasts shifted to scattered fibroglandular breast density. Among postmenopausal women, 22.8% (95% CI, 18.3%-27.3%) of breast cancers could potentially be averted if all overweight and obese women attained a body mass index of less than 25. Most women with breast cancer have at least 1 breast cancer risk factor routinely documented at the time of mammography, and more than half of premenopausal and postmenopausal breast cancers are explained by these factors. These easily assessed risk factors should be incorporated into risk prediction models to stratify breast cancer risk and promote risk-based screening and targeted prevention efforts.

Analysis of percent density estimates from digital breast tomosynthesis projection images

NASA Astrophysics Data System (ADS)

Bakic, Predrag R.; Kontos, Despina; Zhang, Cuiping; Yaffe, Martin J.; Maidment, Andrew D. A.

2007-03-01

Women with dense breasts have an increased risk of breast cancer. Breast density is typically measured as the percent density (PD), the percentage of non-fatty (i.e., dense) tissue in breast images. Mammographic PD estimates vary, in part, due to the projective nature of mammograms. Digital breast tomosynthesis (DBT) is a novel radiographic method in which 3D images of the breast are reconstructed from a small number of projection (source) images, acquired at different positions of the x-ray focus. DBT provides superior visualization of breast tissue and has improved sensitivity and specificity as compared to mammography. Our long-term goal is to test the hypothesis that PD obtained from DBT is superior in estimating cancer risk compared with other modalities. As a first step, we have analyzed the PD estimates from DBT source projections since the results would be independent of the reconstruction method. We estimated PD from MLO mammograms (PD M) and from individual DBT projections (PD T). We observed good agreement between PD M and PD T from the central projection images of 40 women. This suggests that variations in breast positioning, dose, and scatter between mammography and DBT do not negatively affect PD estimation. The PD T estimated from individual DBT projections of nine women varied with the angle between the projections. This variation is caused by the 3D arrangement of the breast dense tissue and the acquisition geometry.

A comparison of five methods of measuring mammographic density: a case-control study.

PubMed

Astley, Susan M; Harkness, Elaine F; Sergeant, Jamie C; Warwick, Jane; Stavrinos, Paula; Warren, Ruth; Wilson, Mary; Beetles, Ursula; Gadde, Soujanya; Lim, Yit; Jain, Anil; Bundred, Sara; Barr, Nicola; Reece, Valerie; Brentnall, Adam R; Cuzick, Jack; Howell, Tony; Evans, D Gareth

2018-02-05

High mammographic density is associated with both risk of cancers being missed at mammography, and increased risk of developing breast cancer. Stratification of breast cancer prevention and screening requires mammographic density measures predictive of cancer. This study compares five mammographic density measures to determine the association with subsequent diagnosis of breast cancer and the presence of breast cancer at screening. Women participating in the "Predicting Risk Of Cancer At Screening" (PROCAS) study, a study of cancer risk, completed questionnaires to provide personal information to enable computation of the Tyrer-Cuzick risk score. Mammographic density was assessed by visual analogue scale (VAS), thresholding (Cumulus) and fully-automated methods (Densitas, Quantra, Volpara) in contralateral breasts of 366 women with unilateral breast cancer (cases) detected at screening on entry to the study (Cumulus 311/366) and in 338 women with cancer detected subsequently. Three controls per case were matched using age, body mass index category, hormone replacement therapy use and menopausal status. Odds ratios (OR) between the highest and lowest quintile, based on the density distribution in controls, for each density measure were estimated by conditional logistic regression, adjusting for classic risk factors. The strongest predictor of screen-detected cancer at study entry was VAS, OR 4.37 (95% CI 2.72-7.03) in the highest vs lowest quintile of percent density after adjustment for classical risk factors. Volpara, Densitas and Cumulus gave ORs for the highest vs lowest quintile of 2.42 (95% CI 1.56-3.78), 2.17 (95% CI 1.41-3.33) and 2.12 (95% CI 1.30-3.45), respectively. Quantra was not significantly associated with breast cancer (OR 1.02, 95% CI 0.67-1.54). Similar results were found for subsequent cancers, with ORs of 4.48 (95% CI 2.79-7.18), 2.87 (95% CI 1.77-4.64) and 2.34 (95% CI 1.50-3.68) in highest vs lowest quintiles of VAS, Volpara and Densitas, respectively. Quantra gave an OR in the highest vs lowest quintile of 1.32 (95% CI 0.85-2.05). Visual density assessment demonstrated a strong relationship with cancer, despite known inter-observer variability; however, it is impractical for population-based screening. Percentage density measured by Volpara and Densitas also had a strong association with breast cancer risk, amongst the automated measures evaluated, providing practical automated methods for risk stratification.

Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring.

PubMed

Kallenberg, Michiel; Petersen, Kersten; Nielsen, Mads; Ng, Andrew Y; Pengfei Diao; Igel, Christian; Vachon, Celine M; Holland, Katharina; Winkel, Rikke Rass; Karssemeijer, Nico; Lillholm, Martin

2016-05-01

Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlabeled data. When the learned features are used as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentation, and ii) scoring of mammographic texture. The proposed model learns features at multiple scales. To control the models capacity a novel sparsity regularizer is introduced that incorporates both lifetime and population sparsity. We evaluated our method on three different clinical datasets. Our state-of-the-art results show that the learned breast density scores have a very strong positive relationship with manual ones, and that the learned texture scores are predictive of breast cancer. The model is easy to apply and generalizes to many other segmentation and scoring problems.

Cytochrome P450 1A2 (CYP1A2) activity, mammographic density, and oxidative stress: a cross-sectional study

PubMed Central

Hong, Chi-Chen; Tang, Bing-Kou; Rao, Venketeshwer; Agarwal, Sanjiv; Martin, Lisa; Tritchler, David; Yaffe, Martin; Boyd, Norman F

2004-01-01

Introduction Mammographically dense breast tissue is a strong predictor of breast cancer risk, and is influenced by both mitogens and mutagens. One enzyme that is able to affect both the mitogenic and mutagenic characteristics of estrogens is cytochrome P450 1A2 (CYP1A2), which is principally responsible for the metabolism of 17β-estradiol. Methods In a cross-sectional study of 146 premenopausal and 149 postmenopausal women, we examined the relationships between CYP1A2 activity, malondialdehyde (MDA) levels, and mammographic density. In vivo CYP1A2 activity was assessed by measuring caffeine metabolites in urine. Levels of serum and urinary MDA, and MDA–deoxyguanosine adducts in DNA were measured. Mammograms were digitized and measured using a computer-assisted method. Results CYP1A2 activity in postmenopausal women, but not in premenopausal women, was positively associated with mammographic density, suggesting that increased CYP1A2 activity after the menopause is a risk factor for breast cancer. In premenopausal women, but not in postmenopausal women, CYP1A2 activity was positively associated with serum and urinary MDA levels; there was also some evidence that CYP1A2 activity was more positively associated with percentage breast density when MDA levels were high, and more negatively associated with percentage breast density when MDA levels were low. Conclusion These findings provide further evidence that variation in the activity level of enzymes involved in estrogen metabolism is related to levels of mammographic density and potentially to breast cancer risk. PMID:15217501

Breast cancer patients with dense breasts do not have increased death risk

Cancer.gov

High mammographic breast density, which is a marker of increased risk of developing breast cancer, does not seem to increase the risk of death among breast cancer patients, according to a study led by Gretchen L. Gierach, Ph.D., NCI. Image shows physician

[Effect of elastic strain rate ratio method and virtual touch tissue quantification on the diagnosis of breast masses].

PubMed

Gong, LiJie; He, Yan; Tian, Peng; Yan, Yan

2016-07-01

To determine the effect of elastic strain rate ratio method and virtual touch tissue quantification (VTQ) on the diagnosis of breast masses.
 Sixty female patients with breast cancer, who received surgical treatment in Daqing Oilfield General Hospital, were enrolled. All patients signed the informed consent paperwork and they were treated by routine ultrasound examination, compression elastography (CE) examination, and VTQ examination in turn. Strain ratio (SR) was checked by CE and shear wave velocity (SWV) value was measured by VTQ. The diagnostic values of different methods were evaluated by receiver operating characteristic (ROC) curves in the diagnosis of benign and malignant breast tumors.
 The maximum diameter and SWV value of the benign tumors were lower than those of the malignant tumors, and the SR ratio of benign masses was higher than that of malignant tumors (P<0.01). The AUC, sensitivity and specificity for elastic strain rate and VTQ for single or combined use were higher than those of conventional ultrasound (0.904, 97.5%, 69.2%; 0.946, 87.5%, 87.2%; 0.976, 90%, 97.4% vs 0.783, 85%, 61.5%). The AUC and specificity of VTQ were higher than those of the elastic strain rate (0.946, 87.2% vs 0.904, 69.2%), but the sensitivity of VTQ was higher than that of the latter (87.5% vs 97.5%). The AUC and specificity for combination of both methods were higher than those of single method, but the sensitivity was lower than that of the elastic strain rate. 
 Combination of elastic strain rate ratio method with VTQ possesses the best diagnostic value and the highest diagnostic accuracy in the diagnosis of breast mass than that used alone.

Comparative prognostic relevance of breast intra-tumoral microvessel density evaluated by CD105 and CD146: A pilot study of 42 cases.

PubMed

Martinez, Leandro Marcelo; Labovsky, Vivian; Calcagno, María de Luján; Davies, Kevin Mauro; Rivello, Hernán Garcia; Wernicke, Alejandra; Calvo, Juan Carlos; Chasseing, Norma Alejandra

2016-04-01

Angiogenesis is a key process for metastatic progression. While it has been established that the evaluation of breast tumoral microvessel density by CD105 marker is a potential prognostic parameter, its evaluation by CD146 marker has been poorly studied. The purpose of this study was to compare the prognostic value of intra-tumoral microvessel density assayed by CD105 and CD146 in early breast cancer patients. 42 women with breast infiltrative ductal carcinoma (I and II-stages) were retrospectively reviewed. Intra-tumoral microvessel density was immunohistochemically examined using antibodies anti-CD105 and CD146 in paraffin-embedded tissues, and their association with classical prognostic-markers, metastatic recurrence, metastasis-free survival and overall survival was analyzed. High microvessel density assessed by CD146 was significantly associated with a higher risk of developing metastasis (p=0.0310) and a shorter metastasis-free survival (p=0.0197). In contrast, when we used the CD105-antibody, we did not find any significant association. Finally, CD146 showed to be an independent predictive indicator for metastasis-free survival (p=0.0055). Our data suggest that the intra-tumoral microvessel density evaluated by CD146 may be a more suitable predictor of metastatic development than that evaluated by CD105 in early breast cancer. Copyright © 2016 Elsevier GmbH. All rights reserved.

The contributions of breast density and common genetic variation to breast cancer risk.

PubMed

Vachon, Celine M; Pankratz, V Shane; Scott, Christopher G; Haeberle, Lothar; Ziv, Elad; Jensen, Matthew R; Brandt, Kathleen R; Whaley, Dana H; Olson, Janet E; Heusinger, Katharina; Hack, Carolin C; Jud, Sebastian M; Beckmann, Matthias W; Schulz-Wendtland, Ruediger; Tice, Jeffrey A; Norman, Aaron D; Cunningham, Julie M; Purrington, Kristen S; Easton, Douglas F; Sellers, Thomas A; Kerlikowske, Karla; Fasching, Peter A; Couch, Fergus J

2015-05-01

We evaluated whether a 76-locus polygenic risk score (PRS) and Breast Imaging Reporting and Data System (BI-RADS) breast density were independent risk factors within three studies (1643 case patients, 2397 control patients) using logistic regression models. We incorporated the PRS odds ratio (OR) into the Breast Cancer Surveillance Consortium (BCSC) risk-prediction model while accounting for its attributable risk and compared five-year absolute risk predictions between models using area under the curve (AUC) statistics. All statistical tests were two-sided. BI-RADS density and PRS were independent risk factors across all three studies (P interaction = .23). Relative to those with scattered fibroglandular densities and average PRS (2(nd) quartile), women with extreme density and highest quartile PRS had 2.7-fold (95% confidence interval [CI] = 1.74 to 4.12) increased risk, while those with low density and PRS had reduced risk (OR = 0.30, 95% CI = 0.18 to 0.51). PRS added independent information (P < .001) to the BCSC model and improved discriminatory accuracy from AUC = 0.66 to AUC = 0.69. Although the BCSC-PRS model was well calibrated in case-control data, independent cohort data are needed to test calibration in the general population. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Comparison of breast percent density estimation from raw versus processed digital mammograms

NASA Astrophysics Data System (ADS)

Li, Diane; Gavenonis, Sara; Conant, Emily; Kontos, Despina

2011-03-01

We compared breast percent density (PD%) measures obtained from raw and post-processed digital mammographic (DM) images. Bilateral raw and post-processed medio-lateral oblique (MLO) images from 81 screening studies were retrospectively analyzed. Image acquisition was performed with a GE Healthcare DS full-field DM system. Image post-processing was performed using the PremiumViewTM algorithm (GE Healthcare). Area-based breast PD% was estimated by a radiologist using a semi-automated image thresholding technique (Cumulus, Univ. Toronto). Comparison of breast PD% between raw and post-processed DM images was performed using the Pearson correlation (r), linear regression, and Student's t-test. Intra-reader variability was assessed with a repeat read on the same data-set. Our results show that breast PD% measurements from raw and post-processed DM images have a high correlation (r=0.98, R2=0.95, p<0.001). Paired t-test comparison of breast PD% between the raw and the post-processed images showed a statistically significant difference equal to 1.2% (p = 0.006). Our results suggest that the relatively small magnitude of the absolute difference in PD% between raw and post-processed DM images is unlikely to be clinically significant in breast cancer risk stratification. Therefore, it may be feasible to use post-processed DM images for breast PD% estimation in clinical settings. Since most breast imaging clinics routinely use and store only the post-processed DM images, breast PD% estimation from post-processed data may accelerate the integration of breast density in breast cancer risk assessment models used in clinical practice.

Circulating insulin-like growth factor-I, insulin-like growth factor binding protein-3 and terminal duct lobular unit involution of the breast: a cross-sectional study of women with benign breast disease.

PubMed

Horne, Hisani N; Sherman, Mark E; Pfeiffer, Ruth M; Figueroa, Jonine D; Khodr, Zeina G; Falk, Roni T; Pollak, Michael; Patel, Deesha A; Palakal, Maya M; Linville, Laura; Papathomas, Daphne; Geller, Berta; Vacek, Pamela M; Weaver, Donald L; Chicoine, Rachael; Shepherd, John; Mahmoudzadeh, Amir Pasha; Wang, Jeff; Fan, Bo; Malkov, Serghei; Herschorn, Sally; Hewitt, Stephen M; Brinton, Louise A; Gierach, Gretchen L

2016-02-18

Terminal duct lobular units (TDLUs) are the primary structures from which breast cancers and their precursors arise. Decreased age-related TDLU involution and elevated mammographic density are both correlated and independently associated with increased breast cancer risk, suggesting that these characteristics of breast parenchyma might be linked to a common factor. Given data suggesting that increased circulating levels of insulin-like growth factors (IGFs) factors are related to reduced TDLU involution and increased mammographic density, we assessed these relationships using validated quantitative methods in a cross-sectional study of women with benign breast disease. Serum IGF-I, IGFBP-3 and IGF-I:IGFBP-3 molar ratios were measured in 228 women, ages 40-64, who underwent diagnostic breast biopsies yielding benign diagnoses at University of Vermont affiliated centers. Biopsies were assessed for three separate measures inversely related to TDLU involution: numbers of TDLUs per unit of tissue area ("TDLU count"), median TDLU diameter ("TDLU span"), and number of acini per TDLU ("acini count"). Regression models, stratified by menopausal status and adjusted for potential confounders, were used to assess the associations of TDLU count, median TDLU span and median acini count per TDLU with tertiles of circulating IGFs. Given that mammographic density is associated with both IGF levels and breast cancer risk, we also stratified these associations by mammographic density. Higher IGF-I levels among postmenopausal women and an elevated IGF-I:IGFBP-3 ratio among all women were associated with higher TDLU counts, a marker of decreased lobular involution (P-trend = 0.009 and <0.0001, respectively); these associations were strongest among women with elevated mammographic density (P-interaction <0.01). Circulating IGF levels were not significantly associated with TDLU span or acini count per TDLU. These results suggest that elevated IGF levels may define a sub-group of women with high mammographic density and limited TDLU involution, two markers that have been related to increased breast cancer risk. If confirmed in prospective studies with cancer endpoints, these data may suggest that evaluation of IGF signaling and its downstream effects may have value for risk prediction and suggest strategies for breast cancer chemoprevention through inhibition of the IGF system.

Least squares parameter estimation methods for material decomposition with energy discriminating detectors

PubMed Central

Le, Huy Q.; Molloi, Sabee

2011-01-01

Purpose: Energy resolving detectors provide more than one spectral measurement in one image acquisition. The purpose of this study is to investigate, with simulation, the ability to decompose four materials using energy discriminating detectors and least squares minimization techniques. Methods: Three least squares parameter estimation decomposition techniques were investigated for four-material breast imaging tasks in the image domain. The first technique treats the voxel as if it consisted of fractions of all the materials. The second method assumes that a voxel primarily contains one material and divides the decomposition process into segmentation and quantification tasks. The third is similar to the second method but a calibration was used. The simulated computed tomography (CT) system consisted of an 80 kVp spectrum and a CdZnTe (CZT) detector that could resolve the x-ray spectrum into five energy bins. A postmortem breast specimen was imaged with flat panel CT to provide a model for the digital phantoms. Hydroxyapatite (HA) (50, 150, 250, 350, 450, and 550 mg∕ml) and iodine (4, 12, 20, 28, 36, and 44 mg∕ml) contrast elements were embedded into the glandular region of the phantoms. Calibration phantoms consisted of a 30∕70 glandular-to-adipose tissue ratio with embedded HA (100, 200, 300, 400, and 500 mg∕ml) and iodine (5, 15, 25, 35, and 45 mg∕ml). The x-ray transport process was simulated where the Beer–Lambert law, Poisson process, and CZT absorption efficiency were applied. Qualitative and quantitative evaluations of the decomposition techniques were performed and compared. The effect of breast size was also investigated. Results: The first technique decomposed iodine adequately but failed for other materials. The second method separated the materials but was unable to quantify the materials. With the addition of a calibration, the third technique provided good separation and quantification of hydroxyapatite, iodine, glandular, and adipose tissues. Quantification with this technique was accurate with errors of 9.83% and 6.61% for HA and iodine, respectively. Calibration at one point (one breast size) showed increased errors as the mismatch in breast diameters between calibration and measurement increased. A four-point calibration successfully decomposed breast diameter spanning the entire range from 8 to 20 cm. For a 14 cm breast, errors were reduced from 5.44% to 1.75% and from 6.17% to 3.27% with the multipoint calibration for HA and iodine, respectively. Conclusions: The results of the simulation study showed that a CT system based on CZT detectors in conjunction with least squares minimization technique can be used to decompose four materials. The calibrated least squares parameter estimation decomposition technique performed the best, separating and accurately quantifying the concentrations of hydroxyapatite and iodine. PMID:21361193

DOE Office of Scientific and Technical Information (OSTI.GOV)

Le, Huy Q.; Molloi, Sabee

Purpose: Energy resolving detectors provide more than one spectral measurement in one image acquisition. The purpose of this study is to investigate, with simulation, the ability to decompose four materials using energy discriminating detectors and least squares minimization techniques. Methods: Three least squares parameter estimation decomposition techniques were investigated for four-material breast imaging tasks in the image domain. The first technique treats the voxel as if it consisted of fractions of all the materials. The second method assumes that a voxel primarily contains one material and divides the decomposition process into segmentation and quantification tasks. The third is similar tomore » the second method but a calibration was used. The simulated computed tomography (CT) system consisted of an 80 kVp spectrum and a CdZnTe (CZT) detector that could resolve the x-ray spectrum into five energy bins. A postmortem breast specimen was imaged with flat panel CT to provide a model for the digital phantoms. Hydroxyapatite (HA) (50, 150, 250, 350, 450, and 550 mg/ml) and iodine (4, 12, 20, 28, 36, and 44 mg/ml) contrast elements were embedded into the glandular region of the phantoms. Calibration phantoms consisted of a 30/70 glandular-to-adipose tissue ratio with embedded HA (100, 200, 300, 400, and 500 mg/ml) and iodine (5, 15, 25, 35, and 45 mg/ml). The x-ray transport process was simulated where the Beer-Lambert law, Poisson process, and CZT absorption efficiency were applied. Qualitative and quantitative evaluations of the decomposition techniques were performed and compared. The effect of breast size was also investigated. Results: The first technique decomposed iodine adequately but failed for other materials. The second method separated the materials but was unable to quantify the materials. With the addition of a calibration, the third technique provided good separation and quantification of hydroxyapatite, iodine, glandular, and adipose tissues. Quantification with this technique was accurate with errors of 9.83% and 6.61% for HA and iodine, respectively. Calibration at one point (one breast size) showed increased errors as the mismatch in breast diameters between calibration and measurement increased. A four-point calibration successfully decomposed breast diameter spanning the entire range from 8 to 20 cm. For a 14 cm breast, errors were reduced from 5.44% to 1.75% and from 6.17% to 3.27% with the multipoint calibration for HA and iodine, respectively. Conclusions: The results of the simulation study showed that a CT system based on CZT detectors in conjunction with least squares minimization technique can be used to decompose four materials. The calibrated least squares parameter estimation decomposition technique performed the best, separating and accurately quantifying the concentrations of hydroxyapatite and iodine.« less

Novel approach to quantitative polymerase chain reaction using real-time detection: application to the detection of gene amplification in breast cancer.

PubMed

Bièche, I; Olivi, M; Champème, M H; Vidaud, D; Lidereau, R; Vidaud, M

1998-11-23

Gene amplification is a common event in the progression of human cancers, and amplified oncogenes have been shown to have diagnostic, prognostic and therapeutic relevance. A kinetic quantitative polymerase-chain-reaction (PCR) method, based on fluorescent TaqMan methodology and a new instrument (ABI Prism 7700 Sequence Detection System) capable of measuring fluorescence in real-time, was used to quantify gene amplification in tumor DNA. Reactions are characterized by the point during cycling when PCR amplification is still in the exponential phase, rather than the amount of PCR product accumulated after a fixed number of cycles. None of the reaction components is limited during the exponential phase, meaning that values are highly reproducible in reactions starting with the same copy number. This greatly improves the precision of DNA quantification. Moreover, real-time PCR does not require post-PCR sample handling, thereby preventing potential PCR-product carry-over contamination; it possesses a wide dynamic range of quantification and results in much faster and higher sample throughput. The real-time PCR method, was used to develop and validate a simple and rapid assay for the detection and quantification of the 3 most frequently amplified genes (myc, ccndl and erbB2) in breast tumors. Extra copies of myc, ccndl and erbB2 were observed in 10, 23 and 15%, respectively, of 108 breast-tumor DNA; the largest observed numbers of gene copies were 4.6, 18.6 and 15.1, respectively. These results correlated well with those of Southern blotting. The use of this new semi-automated technique will make molecular analysis of human cancers simpler and more reliable, and should find broad applications in clinical and research settings.

Mammographic density and breast cancer risk: current understanding and future prospects

PubMed Central

2011-01-01

Variations in percent mammographic density (PMD) reflect variations in the amounts of collagen and number of epithelial and non-epithelial cells in the breast. Extensive PMD is associated with a markedly increased risk of invasive breast cancer. The PMD phenotype is important in the context of breast cancer prevention because extensive PMD is common in the population, is strongly associated with risk of the disease, and, unlike most breast cancer risk factors, can be changed. Work now in progress makes it likely that measurement of PMD will be improved in the near future and that understanding of the genetics and biological basis of the association of PMD with breast cancer risk will also improve. Future prospects for the application of PMD include mammographic screening, risk prediction in individuals, breast cancer prevention research, and clinical decision making. PMID:22114898

Volumetric breast density affects performance of digital screening mammography.

PubMed

Wanders, Johanna O P; Holland, Katharina; Veldhuis, Wouter B; Mann, Ritse M; Pijnappel, Ruud M; Peeters, Petra H M; van Gils, Carla H; Karssemeijer, Nico

2017-02-01

To determine to what extent automatically measured volumetric mammographic density influences screening performance when using digital mammography (DM). We collected a consecutive series of 111,898 DM examinations (2003-2011) from one screening unit of the Dutch biennial screening program (age 50-75 years). Volumetric mammographic density was automatically assessed using Volpara. We determined screening performance measures for four density categories comparable to the American College of Radiology (ACR) breast density categories. Of all the examinations, 21.6% were categorized as density category 1 ('almost entirely fatty') and 41.5, 28.9, and 8.0% as category 2-4 ('extremely dense'), respectively. We identified 667 screen-detected and 234 interval cancers. Interval cancer rates were 0.7, 1.9, 2.9, and 4.4‰ and false positive rates were 11.2, 15.1, 18.2, and 23.8‰ for categories 1-4, respectively (both p-trend < 0.001). The screening sensitivity, calculated as the proportion of screen-detected among the total of screen-detected and interval tumors, was lower in higher density categories: 85.7, 77.6, 69.5, and 61.0% for categories 1-4, respectively (p-trend < 0.001). Volumetric mammographic density, automatically measured on digital mammograms, impacts screening performance measures along the same patterns as established with ACR breast density categories. Since measuring breast density fully automatically has much higher reproducibility than visual assessment, this automatic method could help with implementing density-based supplemental screening.

Increased extracellular matrix density decreases MCF10A breast cell acinus formation in 3D culture conditions.

PubMed

Lance, Amanda; Yang, Chih-Chao; Swamydas, Muthulekha; Dean, Delphine; Deitch, Sandy; Burg, Karen J L; Dréau, Didier

2016-01-01

The extracellular matrix (ECM) contributes to the generation and dynamic of normal breast tissue, in particular to the generation of polarized acinar and ductal structures. In vitro 3D culture conditions, including variations in the composition of the ECM, have been shown to directly influence the formation and organization of acinus-like and duct-like structures. Furthermore, the density of the ECM appears to also play a role in the normal mammary tissue and tumour formation. Here we show that the density of the ECM directly influences the number, organization and function of breast acini. Briefly, non-malignant human breast MCF10A cells were incubated in increasing densities of a Matrigel®-collagen I matrix. Elastic moduli near and distant to the acinus structures were measured by atomic force microscopy, and the number of acinus structures was determined. Immunochemistry was used to investigate the expression levels of E-cadherin, laminin, matrix metalloproteinase-14 and ß-casein in MCF10A cells. The modulus of the ECM was significantly increased near the acinus structures and the number of acinus structures decreased with the increase in Matrigel-collagen I density. As evaluated by the expression of laminin, the organization of the acinus structures present was altered as the density of the ECM increased. Increases in both E-cadherin and MMP14 expression by MCF10A cells as ECM density increased were also observed. In contrast, MCF10A cells expressed lower ß-casein levels as the ECM density increased. Taken together, these observations highlight the key role of ECM density in modulating the number, organization and function of breast acini. Copyright © 2013 John Wiley & Sons, Ltd.

The Effect of California's Breast Density Notification Legislation on Breast Cancer Screening.

PubMed

Chau, Stephanie Lynn; Alabaster, Amy; Luikart, Karin; Brenman, Leslie Manace; Habel, Laurel A

2017-04-01

Half of US states mandate women be notified if they have dense breasts on their mammogram, yet guidelines and data on supplemental screening modalities are limited. Breast density (BD) refers to the extent that breast tissue appears radiographically dense on mammograms. High BD reduces the sensitivity of screening mammography and increases breast cancer risk. The aim of this study was to determine the potential impact of California's 2013 BD notification legislation on breast cancer screening patterns. We conducted a cohort study of women aged 40 to 74 years who were members of a large Northern California integrated health plan (approximately 3.9 million members) in 2011-2015. We calculated pre- and post-legislation rates of screening mammography and magnetic resonance imaging (MRI). We also examined whether women with dense breasts (defined as BI-RADS density c or d) had higher MRI rates than women with nondense breasts (defined as BI-RADS density a or b). After adjustment for race/ethnicity, age, body mass index, medical facility, neighborhood median income, and cancer history, there was a relative 6.6% decrease (relative risk [RR] 0.934, confidence interval [CI] 0.92-0.95) in the rate of screening mammography, largely driven by a decrease among women <50 years. While infrequent, there was a relative 16% increase (RR 1.16, CI 1.07-1.25) in the rate of screening MRI, with the greatest increase among the youngest women. In the postlegislation period, women with extremely dense breasts (BI-RADS d) had 2.77 times (CI 1.93-3.95) the odds of a MRI within 9 months of a screening mammogram compared with women with nondense breasts (BI-RADS b). In this setting, MRI rates increased in the postlegislation period. In addition, women with higher BD were more likely to have supplementary MRI. The decrease in mammography rates seen primarily among younger women may have been due to changes in national screening guidelines.

Tumor phenotype and breast density in distinct categories of interval cancer: results of population-based mammography screening in Spain

PubMed Central

2014-01-01

Introduction Interval cancers are tumors arising after a negative screening episode and before the next screening invitation. They can be classified into true interval cancers, false-negatives, minimal-sign cancers, and occult tumors based on mammographic findings in screening and diagnostic mammograms. This study aimed to describe tumor-related characteristics and the association of breast density and tumor phenotype within four interval cancer categories. Methods We included 2,245 invasive tumors (1,297 screening-detected and 948 interval cancers) diagnosed from 2000 to 2009 among 645,764 women aged 45 to 69 who underwent biennial screening in Spain. Interval cancers were classified by a semi-informed retrospective review into true interval cancers (n = 455), false-negatives (n = 224), minimal-sign (n = 166), and occult tumors (n = 103). Breast density was evaluated using Boyd’s scale and was conflated into: <25%; 25 to 50%; 50 to 75%; >75%. Tumor-related information was obtained from cancer registries and clinical records. Tumor phenotype was defined as follows: luminal A: ER+/HER2- or PR+/HER2-; luminal B: ER+/HER2+ or PR+/HER2+; HER2: ER-/PR-/HER2+; triple-negative: ER-/PR-/HER2-. The association of tumor phenotype and breast density was assessed using a multinomial logistic regression model. Adjusted odds ratios (OR) and 95% confidence intervals (95% CI) were calculated. All statistical tests were two-sided. Results Forty-eight percent of interval cancers were true interval cancers and 23.6% false-negatives. True interval cancers were associated with HER2 and triple-negative phenotypes (OR = 1.91 (95% CI:1.22-2.96), OR = 2.07 (95% CI:1.42-3.01), respectively) and extremely dense breasts (>75%) (OR = 1.67 (95% CI:1.08-2.56)). However, among true interval cancers a higher proportion of triple-negative tumors was observed in predominantly fatty breasts (<25%) than in denser breasts (28.7%, 21.4%, 11.3% and 14.3%, respectively; <0.001). False-negatives and occult tumors had similar phenotypic characteristics to screening-detected cancers, extreme breast density being strongly associated with occult tumors (OR = 6.23 (95% CI:2.65-14.66)). Minimal-sign cancers were biologically close to true interval cancers but showed no association with breast density. Conclusions Our findings revealed that both the distribution of tumor phenotype and breast density play specific and independent roles in each category of interval cancer. Further research is needed to understand the biological basis of the overrepresentation of triple-negative phenotype among predominantly fatty breasts in true interval cancers. PMID:24410848

Bioimpedence to Assess Breast Density as a Risk Factor for Breast Cancer in Adult Women and Adolescent Girls.

PubMed

Maskarinec, Gertraud; Morimoto, Yukiko; Laguana, Michelle B; Novotny, Rachel; Leon Guerrero, Rachael T

2016-01-01

Although high mammographic density is one of the strongest predictors of breast cancer risk, X-ray based mammography cannot be performed before the recommended screening age, especially not in adolescents and young women. Therefore, new techniques for breast density measurement are of interest. In this pilot study in Guam and Hawaii, we evaluated a radiation-free, bioimpedance device called Electrical Breast DensitometerTM (EBD; senoSENSE Medical Systems, Inc., Ontario, Canada) for measuring breast density in 95 women aged 31-82 years and 41 girls aged 8-18 years. Percent density (PD) was estimated in the women's most recent mammogram using a computer-assisted method. Correlation coefficients and linear regression were applied for statistical analysis. In adult women, mean EBD and PD values of the left and right breasts were 230±52 and 226±50 Ω and 23.7±15.1 and 24.2±15.2%, respectively. The EBD measurements were inversely correlated with PD (rSpearman=-0.52, p<0.0001); the correlation was stronger in Caucasians (rSpearman=-0.70, p<0.0001) than Asians (rSpearman=-0.54, p<0.01) and Native Hawaiian/Chamorro/Pacific Islanders (rSpearman=-0.34, p=0.06). Using 4 categories of PD (<10, 10-25, 26-50, 51-75%), the respective mean EBD values were 256±32, 249±41, 202±46, and 178±43 Ω (p<0.0001). In girls, the mean EBD values in the left and right breast were 148±40 and 155±54 Ω; EBD values decreased from Tanner stages 1 to 4 (204±14, 154±79, 136±43, and 119±16 Ω for stages 1-4, respectively) but were higher at Tanner stage 5 (165±30 Ω). With further development, this bioimpedance method may allow for investigations of breast development among adolescent, as well as assessment of breast cancer risk early in life and in populations without access to mammography.

Pilot study of quantitative analysis of background enhancement on breast MR images: association with menstrual cycle and mammographic breast density.

PubMed

Scaranelo, Anabel M; Carrillo, Maria Claudia; Fleming, Rachel; Jacks, Lindsay M; Kulkarni, Supriya R; Crystal, Pavel

2013-06-01

To perform semiautomated quantitative analysis of the background enhancement (BE) in a cohort of patients with newly diagnosed breast cancer and to correlate it with mammographic breast density and menstrual cycle. Informed consent was waived after the research ethics board approved this study. Results of 177 consecutive preoperative breast magnetic resonance (MR) examinations performed from February to December 2009 were reviewed; 147 female patients (median age, 48 years; range, 26-86 years) were included. Ordinal values of BE and breast density were described by two independent readers by using the Breast Imaging Reporting and Data System lexicon. The BE coefficient (BEC) was calculated thus: (SI2 · 100/SI1) - 100, where SI is signal intensity, SI2 is the SI enhancement measured in the largest anteroposterior dimension in the axial plane 1 minute after the contrast agent injection, and SI1is the SI before contrast agent injection. BEC was used for the quantitative analysis of BE. Menstrual cycle status was based on the last menstrual period. The Wilcoxon rank-sum or Kruskal-Wallis test was used to compare quantitative assessment groups. Cohen weighted κ was used to evaluate agreement. Of 147 patients, 68 (46%) were premenopausal and 79 (54%) were postmenopausal. The quantitative BEC was associated with the menstrual status (BEC in premenopausal women, 31.48 ± 20.68 [standard deviation]; BEC in postmenopausal women, 25.65 ± 16.74; P = .02). The percentage of overall BE was higher when the MR imaging was performed in women in the inadequate phase of the cycle (<35 days, not 7-14 days; mean BEC, 35.7) compared with women in the postmenopausal group (P = .001). Premenopausal women had significantly higher BEC when compared with postmenopausal women (P = .03). There was no significant difference in the percentage of BE between breast density groups. Premenopausal women with breast cancer, and specifically women in the inadequate phase of the cycle, presented with higher quantitative BE than postmenopausal women. No association was found between BE and breast density.

Supplemental Screening for Breast Cancer in Women With Dense Breasts: A Systematic Review for the U.S. Preventive Services Task Force.

PubMed

Melnikow, Joy; Fenton, Joshua J; Whitlock, Evelyn P; Miglioretti, Diana L; Weyrich, Meghan S; Thompson, Jamie H; Shah, Kunal

2016-02-16

Screening mammography has lower sensitivity and specificity in women with dense breasts, who experience higher breast cancer risk. To perform a systematic review of reproducibility of Breast Imaging Reporting and Data System (BI-RADS) density categorization and test performance and clinical outcomes of supplemental screening with breast ultrasonography, magnetic resonance imaging (MRI), and digital breast tomosynthesis (DBT) in women with dense breasts and negative mammography results. MEDLINE, PubMed, EMBASE, and Cochrane database from January 2000 to July 2015. Studies reporting BI-RADS density reproducibility or supplemental screening results for women with dense breasts. Quality assessment and abstraction of 24 studies from 7 countries; 6 studies were good-quality. Three good-quality studies reported reproducibility of BI-RADS density; 13% to 19% of women were recategorized between "dense" and "nondense" at subsequent screening. Two good-quality studies reported that sensitivity of ultrasonography for women with negative mammography results ranged from 80% to 83%; specificity, from 86% to 94%; and positive predictive value (PPV), from 3% to 8%. The sensitivity of MRI ranged from 75% to 100%; specificity, from 78% to 94%; and PPV, from 3% to 33% (3 studies). Rates of additional cancer detection with ultrasonography were 4.4 per 1000 examinations (89% to 93% invasive); recall rates were 14%. Use of MRI detected 3.5 to 28.6 additional cancer cases per 1000 examinations (34% to 86% invasive); recall rates were 12% to 24%. Rates of cancer detection with DBT increased by 1.4 to 2.5 per 1000 examinations compared with mammography alone (3 studies). Recall rates ranged from 7% to 11%, compared with 7% to 17% with mammography alone. No studies examined breast cancer outcomes. Good-quality evidence was sparse. Studies were small and CIs were wide. Definitions of recall were absent or inconsistent. Density ratings may be recategorized on serial screening mammography. Supplemental screening of women with dense breasts finds additional breast cancer but increases false-positive results. Use of DBT may reduce recall rates. Effects of supplemental screening on breast cancer outcomes remain unclear. Agency for Healthcare Research and Quality.

Supine breast US: how to correlate breast lesions from prone MRI

PubMed Central

Telegrafo, Michele; Rella, Leonarda; Stabile Ianora, Amato A; Angelelli, Giuseppe

2016-01-01

Objective: To evaluate spatial displacement of breast lesions from prone MR to supine ultrasound positions, and to determine whether the degree of displacement may be associated with breast density and lesion histotype. Methods: 380 patients underwent breast MR and second-look ultrasound. The MR and ultrasound lesion location within the breast gland, distances from anatomical landmarks (nipple, skin and pectoral muscle), spatial displacement (distance differences from the landmarks within the same breast region) and region displacement (breast region change) were prospectively evaluated. Differences between MR and ultrasound measurements, association between the degree of spatial displacement and both breast density and lesion histotypes were calculated. Results: In 290/380 (76%) patients, 300 MR lesions were detected. 285/300 (95%) lesions were recognized on ultrasound. By comparing MR and ultrasound, spatial displacement occurred in 183/285 (64.3%) cases while region displacement in 102/285 (35.7%) cases with a circumferential movement along an arc centred on the nipple, having supine ultrasound as the reference standard. A significant association between the degree of lesion displacement and breast density was found (p < 0.00001) with a significant higher displacement in case of fatty breasts. No significant association between the degree of displacement and lesion histotype was found (p = 0.1). Conclusion: Lesion spatial displacement from MRI to ultrasound may occur especially in adipose breasts. Lesion–nipple distance and circumferential displacement from the nipple need to be considered for ultrasound lesion detection. Advances in knowledge: Second-look ultrasound breast lesion detection could be improved by calculating the lesion–nipple distance and considering that spatial displacement from MRI occurs with a circumferential movement along an arc centred on the nipple. PMID:26689093

Supine breast US: how to correlate breast lesions from prone MRI.

PubMed

Telegrafo, Michele; Rella, Leonarda; Stabile Ianora, Amato A; Angelelli, Giuseppe; Moschetta, Marco

2016-01-01

To evaluate spatial displacement of breast lesions from prone MR to supine ultrasound positions, and to determine whether the degree of displacement may be associated with breast density and lesion histotype. 380 patients underwent breast MR and second-look ultrasound. The MR and ultrasound lesion location within the breast gland, distances from anatomical landmarks (nipple, skin and pectoral muscle), spatial displacement (distance differences from the landmarks within the same breast region) and region displacement (breast region change) were prospectively evaluated. Differences between MR and ultrasound measurements, association between the degree of spatial displacement and both breast density and lesion histotypes were calculated. In 290/380 (76%) patients, 300 MR lesions were detected. 285/300 (95%) lesions were recognized on ultrasound. By comparing MR and ultrasound, spatial displacement occurred in 183/285 (64.3%) cases while region displacement in 102/285 (35.7%) cases with a circumferential movement along an arc centred on the nipple, having supine ultrasound as the reference standard. A significant association between the degree of lesion displacement and breast density was found (p < 0.00001) with a significant higher displacement in case of fatty breasts. No significant association between the degree of displacement and lesion histotype was found (p = 0.1). Lesion spatial displacement from MRI to ultrasound may occur especially in adipose breasts. Lesion-nipple distance and circumferential displacement from the nipple need to be considered for ultrasound lesion detection. Second-look ultrasound breast lesion detection could be improved by calculating the lesion-nipple distance and considering that spatial displacement from MRI occurs with a circumferential movement along an arc centred on the nipple.

A novel deep learning-based approach to high accuracy breast density estimation in digital mammography

NASA Astrophysics Data System (ADS)

Ahn, Chul Kyun; Heo, Changyong; Jin, Heongmin; Kim, Jong Hyo

2017-03-01

Mammographic breast density is a well-established marker for breast cancer risk. However, accurate measurement of dense tissue is a difficult task due to faint contrast and significant variations in background fatty tissue. This study presents a novel method for automated mammographic density estimation based on Convolutional Neural Network (CNN). A total of 397 full-field digital mammograms were selected from Seoul National University Hospital. Among them, 297 mammograms were randomly selected as a training set and the rest 100 mammograms were used for a test set. We designed a CNN architecture suitable to learn the imaging characteristic from a multitudes of sub-images and classify them into dense and fatty tissues. To train the CNN, not only local statistics but also global statistics extracted from an image set were used. The image set was composed of original mammogram and eigen-image which was able to capture the X-ray characteristics in despite of the fact that CNN is well known to effectively extract features on original image. The 100 test images which was not used in training the CNN was used to validate the performance. The correlation coefficient between the breast estimates by the CNN and those by the expert's manual measurement was 0.96. Our study demonstrated the feasibility of incorporating the deep learning technology into radiology practice, especially for breast density estimation. The proposed method has a potential to be used as an automated and quantitative assessment tool for mammographic breast density in routine practice.

Model-based estimation of breast percent density in raw and processed full-field digital mammography images from image-acquisition physics and patient-image characteristics

NASA Astrophysics Data System (ADS)

Keller, Brad M.; Nathan, Diane L.; Conant, Emily F.; Kontos, Despina

2012-03-01

Breast percent density (PD%), as measured mammographically, is one of the strongest known risk factors for breast cancer. While the majority of studies to date have focused on PD% assessment from digitized film mammograms, digital mammography (DM) is becoming increasingly common, and allows for direct PD% assessment at the time of imaging. This work investigates the accuracy of a generalized linear model-based (GLM) estimation of PD% from raw and postprocessed digital mammograms, utilizing image acquisition physics, patient characteristics and gray-level intensity features of the specific image. The model is trained in a leave-one-woman-out fashion on a series of 81 cases for which bilateral, mediolateral-oblique DM images were available in both raw and post-processed format. Baseline continuous and categorical density estimates were provided by a trained breast-imaging radiologist. Regression analysis is performed and Pearson's correlation, r, and Cohen's kappa, κ, are computed. The GLM PD% estimation model performed well on both processed (r=0.89, p<0.001) and raw (r=0.75, p<0.001) images. Model agreement with radiologist assigned density categories was also high for processed (κ=0.79, p<0.001) and raw (κ=0.76, p<0.001) images. Model-based prediction of breast PD% could allow for a reproducible estimation of breast density, providing a rapid risk assessment tool for clinical practice.

An ultra performance liquid chromatography-tandem MS assay for tamoxifen metabolites profiling in plasma: first evidence of 4'-hydroxylated metabolites in breast cancer patients.

PubMed

Dahmane, E; Mercier, T; Zanolari, B; Cruchon, S; Guignard, N; Buclin, T; Leyvraz, S; Zaman, K; Csajka, C; Decosterd, L A

2010-12-15

There is increasing evidence that the clinical efficacy of tamoxifen, the first and most widely used targeted therapy for estrogen-sensitive breast cancer, depends on the formation of the active metabolites 4-hydroxy-tamoxifen and 4-hydroxy-N-desmethyl-tamoxifen (endoxifen). Large inter-individual variability in endoxifen plasma concentrations has been observed and related both to genetic and environmental (i.e. drug-induced) factors altering CYP450s metabolizing enzymes activity. In this context, we have developed an ultra performance liquid chromatography-tandem mass spectrometry method (UPLC-MS/MS) requiring 100 μL of plasma for the quantification of tamoxifen and three of its major metabolites in breast cancer patients. Plasma is purified by a combination of protein precipitation, evaporation at room temperature under nitrogen, and reconstitution in methanol/20 mM ammonium formate 1:1 (v/v), adjusted to pH 2.9 with formic acid. Reverse-phase chromatographic separation of tamoxifen, N-desmethyl-tamoxifen, 4-hydroxy-tamoxifen and 4-hydroxy-N-desmethyl-tamoxifen is performed within 13 min using elution with a gradient of 10 mM ammonium formate and acetonitrile, both containing 0.1% formic acid. Analytes quantification, using matrix-matched calibration samples spiked with their respective deuterated internal standards, is performed by electrospray ionization-triple quadrupole mass spectrometry using selected reaction monitoring detection in the positive mode. The method was validated according to FDA recommendations, including assessment of relative matrix effects variability, as well as tamoxifen and metabolites short-term stability in plasma and whole blood. The method is precise (inter-day CV%: 2.5-7.8%), accurate (-1.4 to +5.8%) and sensitive (lower limits of quantification comprised between 0.4 and 2.0 ng/mL). Application of this method to patients' samples has made possible the identification of two further metabolites, 4'-hydroxy-tamoxifen and 4'-hydroxy-N-desmethyl-tamoxifen, described for the first time in breast cancer patients. This UPLC-MS/MS assay is currently applied for monitoring plasma levels of tamoxifen and its metabolites in breast cancer patients within the frame of a clinical trial aiming to assess the impact of dose increase on tamoxifen and endoxifen exposure. Copyright © 2010 Elsevier B.V. All rights reserved.

Using deep learning to segment breast and fibroglandular tissue in MRI volumes.

PubMed

Dalmış, Mehmet Ufuk; Litjens, Geert; Holland, Katharina; Setio, Arnaud; Mann, Ritse; Karssemeijer, Nico; Gubern-Mérida, Albert

2017-02-01

Automated segmentation of breast and fibroglandular tissue (FGT) is required for various computer-aided applications of breast MRI. Traditional image analysis and computer vision techniques, such atlas, template matching, or, edge and surface detection, have been applied to solve this task. However, applicability of these methods is usually limited by the characteristics of the images used in the study datasets, while breast MRI varies with respect to the different MRI protocols used, in addition to the variability in breast shapes. All this variability, in addition to various MRI artifacts, makes it a challenging task to develop a robust breast and FGT segmentation method using traditional approaches. Therefore, in this study, we investigated the use of a deep-learning approach known as "U-net." We used a dataset of 66 breast MRI's randomly selected from our scientific archive, which includes five different MRI acquisition protocols and breasts from four breast density categories in a balanced distribution. To prepare reference segmentations, we manually segmented breast and FGT for all images using an in-house developed workstation. We experimented with the application of U-net in two different ways for breast and FGT segmentation. In the first method, following the same pipeline used in traditional approaches, we trained two consecutive (2C) U-nets: first for segmenting the breast in the whole MRI volume and the second for segmenting FGT inside the segmented breast. In the second method, we used a single 3-class (3C) U-net, which performs both tasks simultaneously by segmenting the volume into three regions: nonbreast, fat inside the breast, and FGT inside the breast. For comparison, we applied two existing and published methods to our dataset: an atlas-based method and a sheetness-based method. We used Dice Similarity Coefficient (DSC) to measure the performances of the automated methods, with respect to the manual segmentations. Additionally, we computed Pearson's correlation between the breast density values computed based on manual and automated segmentations. The average DSC values for breast segmentation were 0.933, 0.944, 0.863, and 0.848 obtained from 3C U-net, 2C U-nets, atlas-based method, and sheetness-based method, respectively. The average DSC values for FGT segmentation obtained from 3C U-net, 2C U-nets, and atlas-based methods were 0.850, 0.811, and 0.671, respectively. The correlation between breast density values based on 3C U-net and manual segmentations was 0.974. This value was significantly higher than 0.957 as obtained from 2C U-nets (P < 0.0001, Steiger's Z-test with Bonferoni correction) and 0.938 as obtained from atlas-based method (P = 0.0016). In conclusion, we applied a deep-learning method, U-net, for segmenting breast and FGT in MRI in a dataset that includes a variety of MRI protocols and breast densities. Our results showed that U-net-based methods significantly outperformed the existing algorithms and resulted in significantly more accurate breast density computation. © 2016 American Association of Physicists in Medicine.

Determinants of the reliability of ultrasound tomography sound speed estimates as a surrogate for volumetric breast density

PubMed Central

Khodr, Zeina G.; Sak, Mark A.; Pfeiffer, Ruth M.; Duric, Nebojsa; Littrup, Peter; Bey-Knight, Lisa; Ali, Haythem; Vallieres, Patricia; Sherman, Mark E.; Gierach, Gretchen L.

2015-01-01

Purpose: High breast density, as measured by mammography, is associated with increased breast cancer risk, but standard methods of assessment have limitations including 2D representation of breast tissue, distortion due to breast compression, and use of ionizing radiation. Ultrasound tomography (UST) is a novel imaging method that averts these limitations and uses sound speed measures rather than x-ray imaging to estimate breast density. The authors evaluated the reproducibility of measures of speed of sound and changes in this parameter using UST. Methods: One experienced and five newly trained raters measured sound speed in serial UST scans for 22 women (two scans per person) to assess inter-rater reliability. Intrarater reliability was assessed for four raters. A random effects model was used to calculate the percent variation in sound speed and change in sound speed attributable to subject, scan, rater, and repeat reads. The authors estimated the intraclass correlation coefficients (ICCs) for these measures based on data from the authors’ experienced rater. Results: Median (range) time between baseline and follow-up UST scans was five (1–13) months. Contributions of factors to sound speed variance were differences between subjects (86.0%), baseline versus follow-up scans (7.5%), inter-rater evaluations (1.1%), and intrarater reproducibility (∼0%). When evaluating change in sound speed between scans, 2.7% and ∼0% of variation were attributed to inter- and intrarater variation, respectively. For the experienced rater’s repeat reads, agreement for sound speed was excellent (ICC = 93.4%) and for change in sound speed substantial (ICC = 70.4%), indicating very good reproducibility of these measures. Conclusions: UST provided highly reproducible sound speed measurements, which reflect breast density, suggesting that UST has utility in sensitively assessing change in density. PMID:26429241

Determinants of the reliability of ultrasound tomography sound speed estimates as a surrogate for volumetric breast density

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khodr, Zeina G.; Pfeiffer, Ruth M.; Gierach, Gretchen L., E-mail: GierachG@mail.nih.gov

Purpose: High breast density, as measured by mammography, is associated with increased breast cancer risk, but standard methods of assessment have limitations including 2D representation of breast tissue, distortion due to breast compression, and use of ionizing radiation. Ultrasound tomography (UST) is a novel imaging method that averts these limitations and uses sound speed measures rather than x-ray imaging to estimate breast density. The authors evaluated the reproducibility of measures of speed of sound and changes in this parameter using UST. Methods: One experienced and five newly trained raters measured sound speed in serial UST scans for 22 women (twomore » scans per person) to assess inter-rater reliability. Intrarater reliability was assessed for four raters. A random effects model was used to calculate the percent variation in sound speed and change in sound speed attributable to subject, scan, rater, and repeat reads. The authors estimated the intraclass correlation coefficients (ICCs) for these measures based on data from the authors’ experienced rater. Results: Median (range) time between baseline and follow-up UST scans was five (1–13) months. Contributions of factors to sound speed variance were differences between subjects (86.0%), baseline versus follow-up scans (7.5%), inter-rater evaluations (1.1%), and intrarater reproducibility (∼0%). When evaluating change in sound speed between scans, 2.7% and ∼0% of variation were attributed to inter- and intrarater variation, respectively. For the experienced rater’s repeat reads, agreement for sound speed was excellent (ICC = 93.4%) and for change in sound speed substantial (ICC = 70.4%), indicating very good reproducibility of these measures. Conclusions: UST provided highly reproducible sound speed measurements, which reflect breast density, suggesting that UST has utility in sensitively assessing change in density.« less

Adolescent intake of animal fat and red meat in relation to premenopausal mammographic density

PubMed Central

Bertrand, Kimberly A.; Burian, Rosemarie A.; Eliassen, A. Heather; Willett, Walter C.; Tamimi, Rulla M.

2016-01-01

Purpose Adolescence is hypothesized to be a time period of particular susceptibility to breast cancer risk factors. Red meat and fat intake during high school was positively associated with risk of breast cancer among premenopausal women in the Nurses’ Health Study II (NHSII). High mammographic density is a strong predictor of breast cancer risk but there is limited research on dietary factors associated with breast density. To test the hypothesis that high intake of animal fat or red meat during adolescence is associated with mammographic density, we analyzed data from premenopausal women in the NHSII. Methods Participants recalled adolescent diet on a high school food frequency questionnaire. We assessed absolute and percent mammographic density on digitized analog film mammograms for 687 premenopausal women with no history of cancer. We used generalized linear regression to quantify associations of adolescent animal fat and red meat intake with mammographic density, adjusting for age, body mass index, and other predictors of mammographic density. Results Adolescent animal fat intake was significantly positively associated with premenopausal mammographic density, with a mean percent density of 39.2% in the lowest quartile of adolescent animal fat intake vs. 43.1% in the highest quartile (p-trend: 0.03). A non-significant positive association was also observed for adolescent red meat intake (p-trend: 0.14). Conclusions These findings suggest that higher adolescent animal fat intake is weakly associated with percent mammographic density in premenopausal women. PMID:26791521

Radiomic modeling of BI-RADS density categories

NASA Astrophysics Data System (ADS)

Wei, Jun; Chan, Heang-Ping; Helvie, Mark A.; Roubidoux, Marilyn A.; Zhou, Chuan; Hadjiiski, Lubomir

2017-03-01

Screening mammography is the most effective and low-cost method to date for early cancer detection. Mammographic breast density has been shown to be highly correlated with breast cancer risk. We are developing a radiomic model for BI-RADS density categorization on digital mammography (FFDM) with a supervised machine learning approach. With IRB approval, we retrospectively collected 478 FFDMs from 478 women. As a gold standard, breast density was assessed by an MQSA radiologist based on BI-RADS categories. The raw FFDMs were used for computerized density assessment. The raw FFDM first underwent log-transform to approximate the x-ray sensitometric response, followed by multiscale processing to enhance the fibroglandular densities and parenchymal patterns. Three ROIs were automatically identified based on the keypoint distribution, where the keypoints were obtained as the extrema in the image Gaussian scale-space. A total of 73 features, including intensity and texture features that describe the density and the parenchymal pattern, were extracted from each breast. Our BI-RADS density estimator was constructed by using a random forest classifier. We used a 10-fold cross validation resampling approach to estimate the errors. With the random forest classifier, computerized density categories for 412 of the 478 cases agree with radiologist's assessment (weighted kappa = 0.93). The machine learning method with radiomic features as predictors demonstrated a high accuracy in classifying FFDMs into BI-RADS density categories. Further work is underway to improve our system performance as well as to perform an independent testing using a large unseen FFDM set.

Influence of lifestyle factors on mammographic density in postmenopausal women.

PubMed

Brand, Judith S; Czene, Kamila; Eriksson, Louise; Trinh, Thang; Bhoo-Pathy, Nirmala; Hall, Per; Celebioglu, Fuat

2013-01-01

Mammographic density is a strong risk factor for breast cancer. Apart from hormone replacement therapy (HRT), little is known about lifestyle factors that influence breast density. We examined the effect of smoking, alcohol and physical activity on mammographic density in a population-based sample of postmenopausal women without breast cancer. Lifestyle factors were assessed by a questionnaire and percentage and area measures of mammographic density were measured using computer-assisted software. General linear models were used to assess the association between lifestyle factors and mammographic density and effect modification by body mass index (BMI) and HRT was studied. Overall, alcohol intake was positively associated with percent mammographic density (P trend  = 0.07). This association was modified by HRT use (P interaction  = 0.06): increasing alcohol intake was associated with increasing percent density in current HRT users (P trend  = 0.01) but not in non-current users (P trend  = 0.82). A similar interaction between alcohol and HRT was found for the absolute dense area, with a positive association being present in current HRT users only (P interaction  = 0.04). No differences in mammographic density were observed across categories of smoking and physical activity, neither overall nor in stratified analyses by BMI and HRT use. Increasing alcohol intake is associated with an increase in mammography density, whereas smoking and physical activity do not seem to influence density. The observed interaction between alcohol and HRT may pose an opportunity for HRT users to lower their mammographic density and breast cancer risk.

Influence of Lifestyle Factors on Mammographic Density in Postmenopausal Women

PubMed Central

Brand, Judith S.; Czene, Kamila; Eriksson, Louise; Trinh, Thang; Bhoo-Pathy, Nirmala; Hall, Per; Celebioglu, Fuat

2013-01-01

Background Mammographic density is a strong risk factor for breast cancer. Apart from hormone replacement therapy (HRT), little is known about lifestyle factors that influence breast density. Methods We examined the effect of smoking, alcohol and physical activity on mammographic density in a population-based sample of postmenopausal women without breast cancer. Lifestyle factors were assessed by a questionnaire and percentage and area measures of mammographic density were measured using computer-assisted software. General linear models were used to assess the association between lifestyle factors and mammographic density and effect modification by body mass index (BMI) and HRT was studied. Results Overall, alcohol intake was positively associated with percent mammographic density (P trend  = 0.07). This association was modified by HRT use (P interaction  = 0.06): increasing alcohol intake was associated with increasing percent density in current HRT users (P trend  = 0.01) but not in non-current users (P trend  = 0.82). A similar interaction between alcohol and HRT was found for the absolute dense area, with a positive association being present in current HRT users only (P interaction  = 0.04). No differences in mammographic density were observed across categories of smoking and physical activity, neither overall nor in stratified analyses by BMI and HRT use. Conclusions Increasing alcohol intake is associated with an increase in mammography density, whereas smoking and physical activity do not seem to influence density. The observed interaction between alcohol and HRT may pose an opportunity for HRT users to lower their mammographic density and breast cancer risk. PMID:24349146

SU-E-T-129: Dosimetric Evaluation of the Impact of Density Correction On Dose Calculation of Breast Cancer Treatment: A Study Based On RTOG 1005 Cases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, J; Yu, Y

Purpose: RTOG 1005 requires density correction in the dose calculation of breast cancer radiation treatment. The aim of the study was to evaluate the impact of density correction on the dose calculation. Methods: Eight cases were studied, which were planned on an XiO treatment planning system with pixel-by-pixel density correction using a superposition algorithm, following RTOG 1005 protocol requirements. Four were protocol Arm 1 (standard whole breast irradiation with sequential boost) cases and four were Arm 2 (hypofractionated whole breast irradiation with concurrent boost) cases. The plans were recalculated with the same monitor units without density correction. Dose calculations withmore » and without density correction were compared. Results: Results of Arm 1 and Arm 2 cases showed similar trends in the comparison. The average differences between the calculations with and without density correction (difference = Without - With) among all the cases were: -0.82 Gy (range: -2.65∼−0.18 Gy) in breast PTV Eval D95, −0.75 Gy (range: −1.23∼0.26 Gy) in breast PTV Eval D90, −1.00 Gy (range: −2.46∼−0.29 Gy) in lumpectomy PTV Eval D95, −0.78 Gy (range: −1.30∼0.11 Gy) in lumpectomy PTV Eval D90, −0.43% (range: −0.95∼−0.14%) in ipsilateral lung V20, −0.81% (range: −1.62∼−0.26%) in V16, −1.95% (range: −4.13∼−0.84%) in V10, −2.64% (−5.55∼−1.04%) in V8, −4.19% (range: −6.92∼−1.81%) in V5, and −4.95% (range: −7.49∼−2.01%) in V4, respectively. The differences in other normal tissues were minimal. Conclusion: The effect of density correction was observed in breast target doses (an average increase of ∼1 Gy in D95 and D90, compared to the calculation without density correction) and exposed ipsilateral lung volumes in low dose region (average increases of ∼4% and ∼5% in V5 and V4, respectively)« less

A new bias field correction method combining N3 and FCM for improved segmentation of breast density on MRI.

PubMed

Lin, Muqing; Chan, Siwa; Chen, Jeon-Hor; Chang, Daniel; Nie, Ke; Chen, Shih-Ting; Lin, Cheng-Ju; Shih, Tzu-Ching; Nalcioglu, Orhan; Su, Min-Ying

2011-01-01

Quantitative breast density is known as a strong risk factor associated with the development of breast cancer. Measurement of breast density based on three-dimensional breast MRI may provide very useful information. One important step for quantitative analysis of breast density on MRI is the correction of field inhomogeneity to allow an accurate segmentation of the fibroglandular tissue (dense tissue). A new bias field correction method by combining the nonparametric nonuniformity normalization (N3) algorithm and fuzzy-C-means (FCM)-based inhomogeneity correction algorithm is developed in this work. The analysis is performed on non-fat-sat T1-weighted images acquired using a 1.5 T MRI scanner. A total of 60 breasts from 30 healthy volunteers was analyzed. N3 is known as a robust correction method, but it cannot correct a strong bias field on a large area. FCM-based algorithm can correct the bias field on a large area, but it may change the tissue contrast and affect the segmentation quality. The proposed algorithm applies N3 first, followed by FCM, and then the generated bias field is smoothed using Gaussian kernal and B-spline surface fitting to minimize the problem of mistakenly changed tissue contrast. The segmentation results based on the N3+FCM corrected images were compared to the N3 and FCM alone corrected images and another method, coherent local intensity clustering (CLIC), corrected images. The segmentation quality based on different correction methods were evaluated by a radiologist and ranked. The authors demonstrated that the iterative N3+FCM correction method brightens the signal intensity of fatty tissues and that separates the histogram peaks between the fibroglandular and fatty tissues to allow an accurate segmentation between them. In the first reading session, the radiologist found (N3+FCM > N3 > FCM) ranking in 17 breasts, (N3+FCM > N3 = FCM) ranking in 7 breasts, (N3+FCM = N3 > FCM) in 32 breasts, (N3+FCM = N3 = FCM) in 2 breasts, and (N3 > N3+FCM > FCM) in 2 breasts. The results of the second reading session were similar. The performance in each pairwise Wilcoxon signed-rank test is significant, showing N3+FCM superior to both N3 and FCM, and N3 superior to FCM. The performance of the new N3+FCM algorithm was comparable to that of CLIC, showing equivalent quality in 57/60 breasts. Choosing an appropriate bias field correction method is a very important preprocessing step to allow an accurate segmentation of fibroglandular tissues based on breast MRI for quantitative measurement of breast density. The proposed algorithm combining N3+FCM and CLIC both yield satisfactory results.

Mammographic Breast Density in a Cohort of Medically Underserved Women

DTIC Science & Technology

2015-12-01

Health Center, a public facility serving medically indigent and underserved women. Dietary and total ( dietary plus supplements ) vitamin D and calcium...Cancer Study Questionnaire [18] were used to categorize dietary intake and supplement use of vitamin D and calcium into tertiles. The Harvard African...Byrne C, Evers KA, Daly MB. Dietary intake and breast density in high- risk women: a cross-sectional study. Breast Cancer Res 2007;9:R72. [4] Yaghjyan L

Building a Better Model: A Comprehensive Breast Cancer Risk Model Incorporating Breast Density to Stratify Risk and Apply Resources

DTIC Science & Technology

2014-10-01

variability with well trained readers. Figure 7: comparison between the PD (percent density using Cumulus area) and the automatic PD. The...evaluation of outlier correction, comparison of several different software methods, precision measurement, and evaluation of variation by mammography...chart review for selected cases (month 4-6). Comparison of information from the Breast Cancer Database and medical records showed good consistency

Postmenopausal hormone therapy and changes in mammographic density.

PubMed

van Duijnhoven, Fränzel J B; Peeters, Petra H M; Warren, Ruth M L; Bingham, Sheila A; van Noord, Paulus A H; Monninkhof, Evelyn M; Grobbee, Diederick E; van Gils, Carla H

2007-04-10

Hormone therapy (HT) use has been associated with an increased breast cancer risk. We explored the underlying mechanism further by determining the effects of HT on mammographic density, a measure of dense tissue in the breast and a consistent breast cancer risk factor. A total of 620 HT users and 620 never users from the Dutch Prospect-European Prospective Investigation into Cancer and Nutrition (EPIC) cohort and 175 HT users and 161 never users from the United Kingdom EPIC-Norfolk cohort were included. For HT users, one mammogram before and one mammogram during HT use was included. For never users, mammograms with similar time intervals were included. Mammographic density was assessed using a computer-assisted method. Changes in density were analyzed using linear regression. The median time between mammograms was 3.0 years and the median duration of HT use was 1 year. The absolute mean decline in percent density was larger in never users (7.3%) than in estrogen therapy users (6.4%; P = .22) and combined HT users (3.5%; P < .01). The effect of HT appeared to be high in a small number of women, whereas most women were unaffected. Our results suggest that HT use, and especially estrogen and progestin use, slows the changes from dense patterns to more fatty patterns that are normally seen in women with increasing age. Given that it is postulated that lifetime cumulative exposure to high density may be related to breast cancer risk, a delay in density decline in HT users potentially could explain their increased breast cancer risk.

Retrospective and comparative analysis of (99m)Tc-Sestamibi breast specific gamma imaging versus mammography, ultrasound, and magnetic resonance imaging for the detection of breast cancer in Chinese women.

PubMed

Yu, Xiuyan; Hu, Guoming; Zhang, Zhigang; Qiu, Fuming; Shao, Xuan; Wang, Xiaochen; Zhan, Hongwei; Chen, Yiding; Deng, Yongchuan; Huang, Jian

2016-07-11

Diagnosing breast cancer during the early stage may be helpful for decreasing cancer-related mortality. In Western developed countries, mammographies have been the gold standard for breast cancer detection. However, Chinese women usually have denser and smaller-sized breasts compared to Caucasian women, which decreases the diagnostic accuracy of mammography. However, breast specific gamma imaging, a type of molecular functional breast imaging, has been used for the accurate diagnosis of breast cancer and is not influenced by breast density. Our objective was to analyze the breast specific gamma imaging (BSGI) diagnostic value for Chinese women. During a 2-year period, 357 women were diagnosed and treated at our oncology department and received BSGI in addition to mammography (MMG), ultrasound (US) and magnetic resonance imaging (MRI) for diagnostic assessment. We investigated the sensitivity and specificity of each method of detection and compared the biological profiles of the four imaging methods. A total of 357 women received a final surgical pathology diagnosis, with 168 malignant diseases (58.5 %) and 119 benign diseases (41.5 %). Of these, 166 underwent the four imaging tests preoperatively. The sensitivity of BSGI was 80.35 and 82.14 % by US, 75.6 % by MMG, and 94.06 % by MRI. Furthermore, the breast cancer diagnosis specificity of BSGI was high (83.19 % vs. 77.31 % vs. 66.39 % vs. 67.69 %, respectively). The BSGI diagnostic sensitivity for mammographic breast density in women was superior to mammography and more sensitive for non-luminal A subtypes (luminal A vs. non-luminal A, 68.63 % vs. 88.30 %). BSGI may help improve the ability to diagnose early stage breast cancer for Chinese women, particularly for ductal carcinoma in situ (DCIS), mammographic breast density and non-luminal A breast cancer.

Cross-sectional study to assess the association of population density with predicted breast cancer risk.

PubMed

Lee, Jeannette Y; Klimberg, Suzanne; Bondurant, Kristina L; Phillips, Martha M; Kadlubar, Susan A

2014-01-01

The Gail and CARE models estimate breast cancer risk for white and African-American (AA) women, respectively. The aims of this study were to compare metropolitan and nonmetropolitan women with respect to predicted breast cancer risks based on known risk factors, and to determine if population density was an independent risk factor for breast cancer risk. A cross-sectional survey was completed by 15,582 women between 35 and 85 years of age with no history of breast cancer. Metropolitan and nonmetropolitan women were compared with respect to risk factors, and breast cancer risk estimates, using general linear models adjusted for age. For both white and AA women, tisk factors used to estimate breast cancer risk included age at menarche, history of breast biopsies, and family history. For white women, age at first childbirth was an additional risk factor. In comparison to their nonmetropolitan counterparts, metropolitan white women were more likely to report having a breast biopsy, have family history of breast cancer, and delay childbirth. Among white metropolitan and nonmetropolitan women, mean estimated 5-year risks were 1.44% and 1.32% (p < 0.001), and lifetime risks of breast cancer were 10.81% and 10.01% (p < 0.001), respectively. AA metropolitan residents were more likely than those from nonmetropolitan areas to have had a breast biopsy. Among AA metropolitan and nonmetropolitan women, mean estimated 5-year risks were 1.16% and 1.12% (p = 0.039) and lifetime risks were 8.94%, and 8.85% (p = 0.344). Metropolitan residence was associated with higher predicted breast cancer risks for white women. Among AA women, metropolitan residence was associated with a higher predicted breast cancer risk at 5 years, but not over a lifetime. Population density was not an independent risk factor for breast cancer. © 2014 Wiley Periodicals, Inc.

Are Qualitative Assessments of Background Parenchymal Enhancement, Amount of Fibroglandular Tissue on MR Images, and Mammographic Density Associated with Breast Cancer Risk?

PubMed Central

Dontchos, Brian N.; Partridge, Savannah C.; Korde, Larissa A.; Lam, Diana L.; Scheel, John R.; Peacock, Sue; Lehman, Constance D.

2015-01-01

Purpose To investigate whether qualitative magnetic resonance (MR) imaging assessments of background parenchymal enhancement (BPE), amount of fibroglandular tissue (FGT), and mammographic density are associated with risk of developing breast cancer in women who are at high risk. Materials and Methods In this institutional review board–approved HIPAA-compliant retrospective study, all screening breast MR images obtained from January 2006 to December 2011 in women aged 18 years or older and at high risk for but without a history of breast cancer were identified. Women in whom breast cancer was diagnosed after index MR imaging comprised the cancer cohort, and one-to-one matching (age and BRCA status) of each woman with breast cancer to a control subject was performed by using MR images obtained in women who did not develop breast cancer with follow-up time maximized. Amount of BPE, BPE pattern (peripheral vs central), amount of FGT at MR imaging, and mammographic density were assessed on index images. Imaging features were compared between cancer and control cohorts by using conditional logistic regression. Results Twenty-three women at high risk (mean age, 47 years ± 10 [standard deviation]; six women had BRCA mutations) with no history of breast cancer underwent screening breast MR imaging; in these women, a diagnosis of breast cancer (invasive, n = 12; in situ, n = 11) was made during the follow-up interval. Women with mild, moderate, or marked BPE were nine times more likely to receive a diagnosis of breast cancer during the follow-up interval than were those with minimal BPE (P = .007; odds ratio = 9.0; 95% confidence interval: 1.1, 71.0). BPE pattern, MR imaging amount of FGT, and mammographic density were not significantly different between the cohorts (P = .5, P = .5, and P = .4, respectively). Conclusion Greater BPE was associated with a higher probability of developing breast cancer in women at high risk for cancer and warrants further study. © RSNA, 2015 Online supplemental material is available for this article. PMID:25965809

Supplemental Screening for Breast Cancer in Women with Dense Breasts: A Systematic Review for the U.S. Preventive Services Task Force

PubMed Central

Melnikow, Joy; Fenton, Joshua J.; Whitlock, Evelyn P.; Miglioretti, Diana L.; Weyrich, Meghan S.; Thompson, Jamie H.; Shah, Kunal

2016-01-01

Background Screening mammography has lower sensitivity and specificity in women with dense breasts, who experience higher breast cancer risk. Purpose Systematic review of: reproducibility of BI-RADS density categorization; test performance and clinical outcomes of supplemental screening with breast ultrasound, magnetic resonance imaging (MRI), and digital breast tomosynthesis (DBT) in women with dense breasts and negative mammography. Data Sources MEDLINE, PubMed, Embase, and Cochrane January 2000–July 2015. Study Selection Studies reporting BI-RADS density reproducibility or supplemental screening results for women with dense breasts. Data Extraction Quality assessment and abstraction of twenty-four studies from seven countries; six were good quality. Data Synthesis Three good-quality studies reported reproducibility of BI-RADS density; 13–19% of women were re-categorized between “dense” and “non-dense” at subsequent screening. Two good-quality studies reported ultrasound sensitivity for women with negative mammography ranging from 80–83%; specificity 86–94%; and positive predictive value (PPV) 3–8%. MRI sensitivity ranged from 75–100%, specificity 78–94%, and PPV 3–33% (3 studies). Ultrasound additional cancer detection rates were 4.4 per 1,000 exams (89–93% invasive); recall rates were 14%. MRI detected 3.5–28.6 additional cancers per 1,000 exams (34–86% invasive); recall rates were 12–24 %. DBT cancer detection rates increased by 1.4–2.5 per 1000 exams compared to mammography alone (3 studies). Recall rates ranged from 7–11%, compared to 7–17% with mammography alone. No studies examined breast cancer outcomes. Limitations Good quality evidence was sparse. Studies were small and confidence intervals were wide. Definitions of recall were absent or inconsistent. Conclusions Density ratings may be re-categorized on serial screening mammograms. Supplemental screening of women with dense breasts finds additional breast cancers, but increases false-positives. DBT may reduce recall rates. Supplemental screening impacts on breast cancer outcomes remain unclear. Primary Funding Source Agency for Healthcare Research and Quality PMID:26757021

Spatial recurrence analysis: A sensitive and fast detection tool in digital mammography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prado, T. L.; Galuzio, P. P.; Lopes, S. R.

Efficient diagnostics of breast cancer requires fast digital mammographic image processing. Many breast lesions, both benign and malignant, are barely visible to the untrained eye and requires accurate and reliable methods of image processing. We propose a new method of digital mammographic image analysis that meets both needs. It uses the concept of spatial recurrence as the basis of a spatial recurrence quantification analysis, which is the spatial extension of the well-known time recurrence analysis. The recurrence-based quantifiers are able to evidence breast lesions in a way as good as the best standard image processing methods available, but with amore » better control over the spurious fragments in the image.« less

Calibration and analysis of a multimodal micro-CT and structured light imaging system for the evaluation of excised breast tissue

NASA Astrophysics Data System (ADS)

McClatchy, David M., III; Rizzo, Elizabeth J.; Meganck, Jeff; Kempner, Josh; Vicory, Jared; Wells, Wendy A.; Paulsen, Keith D.; Pogue, Brian W.

2017-12-01

A multimodal micro-computed tomography (CT) and multi-spectral structured light imaging (SLI) system is introduced and systematically analyzed to test its feasibility to aid in margin delineation during breast conserving surgery (BCS). Phantom analysis of the micro-CT yielded a signal-to-noise ratio of 34, a contrast of 1.64, and a minimum detectable resolution of 240 μm for a 1.2 min scan. The SLI system, spanning wavelengths 490 nm to 800 nm and spatial frequencies up to 1.37 mm-1 , was evaluated with aqueous tissue simulating phantoms having variations in particle size distribution, scatter density, and blood volume fraction. The reduced scattering coefficient, μs\\prime and phase function parameter, γ, were accurately recovered over all wavelengths independent of blood volume fractions from 0% to 4%, assuming a flat sample geometry perpendicular to the imaging plane. The resolution of the optical system was tested with a step phantom, from which the modulation transfer function was calculated yielding a maximum resolution of 3.78 cycles per mm. The three dimensional spatial co-registration between the CT and optical imaging space was tested and shown to be accurate within 0.7 mm. A freshly resected breast specimen, with lobular carcinoma, fibrocystic disease, and adipose, was imaged with the system. The micro-CT provided visualization of the tumor mass and its spiculations, and SLI yielded superficial quantification of light scattering parameters for the malignant and benign tissue types. These results appear to be the first demonstration of SLI combined with standard medical tomography for imaging excised tumor specimens. While further investigations are needed to determine and test the spectral, spatial, and CT features required to classify tissue, this study demonstrates the ability of multimodal CT/SLI to quantify, visualize, and spatially navigate breast tumor specimens, which could potentially aid in the assessment of tumor margin status during BCS.

Comparison of digital breast tomosynthesis and 2D digital mammography using a hybrid performance test

NASA Astrophysics Data System (ADS)

Cockmartin, Lesley; Marshall, Nicholas W.; Van Ongeval, Chantal; Aerts, Gwen; Stalmans, Davina; Zanca, Federica; Shaheen, Eman; De Keyzer, Frederik; Dance, David R.; Young, Kenneth C.; Bosmans, Hilde

2015-05-01

This paper introduces a hybrid method for performing detection studies in projection image based modalities, based on image acquisitions of target objects and patients. The method was used to compare 2D mammography and digital breast tomosynthesis (DBT) in terms of the detection performance of spherical densities and microcalcifications. The method starts with the acquisition of spheres of different glandular equivalent densities and microcalcifications of different sizes immersed in a homogeneous breast tissue simulating medium. These target objects are then segmented and the subsequent templates are fused in projection images of patients and processed or reconstructed. This results in hybrid images with true mammographic anatomy and clinically relevant target objects, ready for use in observer studies. The detection study of spherical densities used 108 normal and 178 hybrid 2D and DBT images; 156 normal and 321 hybrid images were used for the microcalcifications. Seven observers scored the presence/absence of the spheres/microcalcifications in a square region via a 5-point confidence rating scale. Detection performance in 2D and DBT was compared via ROC analysis with sub-analyses for the density of the spheres, microcalcification size, breast thickness and z-position. The study was performed on a Siemens Inspiration tomosynthesis system using patient acquisitions with an average age of 58 years and an average breast thickness of 53 mm providing mean glandular doses of 1.06 mGy (2D) and 2.39 mGy (DBT). Study results showed that breast tomosynthesis (AUC = 0.973) outperformed 2D (AUC = 0.831) for the detection of spheres (p  <  0.0001) and this applied for all spherical densities and breast thicknesses. By way of contrast, DBT was worse than 2D for microcalcification detection (AUC2D = 0.974, AUCDBT = 0.838, p  <  0.0001), with significant differences found for all sizes (150-354 µm), for breast thicknesses above 40 mm and for heights above the detector of 20 mm and above. In conclusion, the hybrid method was successfully used to produce images for a detection study; results showed breast tomosynthesis outperformed 2D for spherical densities while further optimization of DBT for microcalcifications is suggested.

Stavudine (d4T) concentrations in women receiving post-partum antiretroviral treatment and their breastfeeding infants

PubMed Central

Fogel, Jessica M.; Taha, Taha E.; Sun, Jin; Hoover, Donald R.; Parsons, Teresa L.; Kumwenda, Johnstone J.; Mofenson, Lynne M.; Fowler, Mary Glenn; Hendrix, Craig W.; Kumwenda, Newton I.; Eshleman, Susan H.; Mirochnick, Mark

2012-01-01

First-line antiretroviral treatment regimens in resource-limited settings used in breastfeeding mothers often include stavudine (d4T). Limited data describing d4T concentrations in breast milk are available. We analyzed d4T concentrations in 52 mother-infant pairs using ultra-performance liquid chromatography-tandem mass spectrometry (lower limit of quantification: 5 ng/ml in plasma, 20 ng/ml in breast milk). Median (interquartile range) d4T concentrations were 86 (36–191) ng/ml in maternal plasma, 151 (48–259) ng/ml in whole milk, 190 (58–296) ng/ml in skim milk, and <5 (<5-<5) ng/ml in infant plasma. While d4T is concentrated in breast milk relative to maternal plasma, the infant d4T dose received from breast milk is very small and not clinically significant. PMID:22614899

Stavudine concentrations in women receiving postpartum antiretroviral treatment and their breastfeeding infants.

PubMed

Fogel, Jessica M; Taha, Taha E; Sun, Jin; Hoover, Donald R; Parsons, Teresa L; Kumwenda, Johnstone J; Mofenson, Lynne M; Fowler, Mary Glenn; Hendrix, Craig W; Kumwenda, Newton I; Eshleman, Susan H; Mirochnick, Mark

2012-08-15

First-line antiretroviral treatment regimens in resource-limited settings used in breastfeeding mothers often include stavudine (d4T). Limited data describing d4T concentrations in breast milk are available. We analyzed d4T concentrations in 52 mother-infant pairs using ultra-performance liquid chromatography-tandem mass spectrometry (lower limit of quantification: 5 ng/mL in plasma, 20 ng/mL in breast milk). Median (interquartile range) d4T concentrations were 86 (36-191) ng/mL in maternal plasma, 151 (48-259) ng/mL in whole milk, 190 (58-296) ng/mL in skim milk, and <5 (<5 to <5) ng/mL in infant plasma. Although d4T is concentrated in breast milk relative to maternal plasma, the infant d4T dose received from breast milk is very small and not clinically significant.

Digital histologic analysis reveals morphometric patterns of age-related involution in breast epithelium and stroma.

PubMed

Sandhu, Rupninder; Chollet-Hinton, Lynn; Kirk, Erin L; Midkiff, Bentley; Troester, Melissa A

2016-02-01

Complete age-related regression of mammary epithelium, often termed postmenopausal involution, is associated with decreased breast cancer risk. However, most studies have qualitatively assessed involution. We quantitatively analyzed epithelium, stroma, and adipose tissue from histologically normal breast tissue of 454 patients in the Normal Breast Study. High-resolution digital images of normal breast hematoxylin and eosin-stained slides were partitioned into epithelium, adipose tissue, and nonfatty stroma. Percentage area and nuclei per unit area (nuclear density) were calculated for each component. Quantitative data were evaluated in association with age using linear regression and cubic spline models. Stromal area decreased (P = 0.0002), and adipose tissue area increased (P < 0.0001), with an approximate 0.7% change in area for each component, until age 55 years when these area measures reached a steady state. Although epithelial area did not show linear changes with age, epithelial nuclear density decreased linearly beginning in the third decade of life. No significant age-related trends were observed for stromal or adipose nuclear density. Digital image analysis offers a high-throughput method for quantitatively measuring tissue morphometry and for objectively assessing age-related changes in adipose tissue, stroma, and epithelium. Epithelial nuclear density is a quantitative measure of age-related breast involution that begins to decline in the early premenopausal period. Copyright © 2015 Elsevier Inc. All rights reserved.

Breast Cancer Prevention (PDQ®)—Health Professional Version

Cancer.gov

Risk factors for breast cancer are female sex and advancing age, inherited risk, breast density, obesity, alcohol consumption, and exposure to ionizing radiation. Interventions to prevent breast cancer include chemoprevention (e.g. SERMs, AIs), risk-reducing surgery (e.g. mastectomy, oophorectomy). Review the evidence on risk factors and interventions to prevent breast cancer in this expert-reviewed summary.

Impact of positional difference on the measurement of breast density using MRI.

PubMed

Chen, Jeon-Hor; Chan, Siwa; Tang, Yi-Ting; Hon, Jia Shen; Tseng, Po-Chuan; Cheriyan, Angela T; Shah, Nikita Rakesh; Yeh, Dah-Cherng; Lee, San-Kan; Chen, Wen-Pin; McLaren, Christine E; Su, Min-Ying

2015-05-01

This study investigated the impact of arms/hands and body position on the measurement of breast density using MRI. Noncontrast-enhanced T1-weighted images were acquired from 32 healthy women. Each subject received four MR scans using different experimental settings, including a high resolution hands-up, a low resolution hands-up, a high resolution hands-down, and finally, another high resolution hands-up after repositioning. The breast segmentation was performed using a fully automatic chest template-based method. The breast volume (BV), fibroglandular tissue volume (FV), and percent density (PD) measured from the four MR scan settings were analyzed. A high correlation of BV, FV, and PD between any pair of the four MR scans was noted (r > 0.98 for all). Using the generalized estimating equation method, a statistically significant difference in mean BV among four settings was noted (left breast, score test p = 0.0056; right breast, score test p = 0.0016), adjusted for age and body mass index. Despite differences in BV, there were no statistically significant differences in the mean PDs among the four settings (p > 0.10 for left and right breasts). Using Bland-Altman plots, the smallest mean difference/bias and standard deviations for BV, FV, and PD were noted when comparing hands-up high vs low resolution when the breast positions were exactly the same. The authors' study showed that BV, FV, and PD measurements from MRI of different positions were highly correlated. BV may vary with positions but the measured PD did not differ significantly between positions. The study suggested that the percent density analyzed from MRI studies acquired using different arms/hands and body positions from multiple centers can be combined for analysis.

Evaluation of the association between quantitative mammographic density and breast cancer occurred in different quadrants.

PubMed

Chan, Siwa; Chen, Jeon-Hor; Li, Shunshan; Chang, Rita; Yeh, Darh-Cherng; Chang, Ruey-Feng; Yeh, Lee-Ren; Kwong, Jessica; Su, Min-Ying

2017-04-17

To investigate the relationship between mammographic density measured in four quadrants of a breast with the location of the occurred cancer. One hundred and ten women diagnosed with unilateral breast cancer that could be determined in one specific breast quadrant were retrospectively studied. Women with previous cancer/breast surgery were excluded. The craniocaudal (CC) and mediolateral oblique (MLO) mammography of the contralateral normal breast were used to separate a breast into 4 quadrants: Upper-Outer (UO), Upper-Inner (UI), Lower-Outer (LO), and Lower-Inner (LI). The breast area (BA), dense area (DA), and percent density (PD) in each quadrant were measured by using the fuzzy-C-means segmentation. The BA, DA, and PD were compared between patients who had cancer occurring in different quadrants. The upper-outer quadrant had the highest BA (37 ± 15 cm 2 ) and DA (7.1 ± 2.9 cm 2 ), with PD = 20.0 ± 5.8%. The order of BA and DA in the 4 separated quadrants were: UO > UI > LO > LI, and almost all pair-wise comparisons showed significant differences. For tumor location, 67 women (60.9%) had tumor in UO, 16 (14.5%) in UI, 7 (6.4%) in LO, and 20 (18.2%) in LI quadrant, respectively. The estimated odds and the 95% confidence limits of tumor development in the UO, UI, LO and LI quadrants were 1.56 (1.06, 2.29), 0.17 (0.10, 0.29), 0.07 (0.03, 0.15), and 0.22 (0.14, 0.36), respectively. In these 4 groups of women, the order of quadrant BA and DA were all the same (UO > UI > LO > LI), and there was no significant difference in BA, DA or PD among them (all p > 0.05). Breast cancer was most likely to occur in the UO quadrant, which was also the quadrant with highest BA and DA; but for women with tumors in other quadrants, the density in that quadrant was not the highest. Therefore, there was no direct association between quadrant density and tumor occurrence.

Development of a Framework for Model-Based Analysis, Uncertainty Quantification, and Robust Control Design of Nonlinear Smart Composite Systems

DTIC Science & Technology

2015-06-04

control, vibration and noise control, health monitoring, and energy harvesting . However, these advantages come at the cost of rate-dependent hysteresis...configuration used for energy harvesting . Uncertainty Quantification Uncertainty quantification is pursued in two steps: (i) determination of densities...Crews and R.C. Smith, “Quantification of parameter and model uncertainty for shape mem- ory alloy bending actuators,” Journal of Intelligent material

Predicting chemotherapy response to paclitaxel with 18F-Fluoropaclitaxel and PET.

PubMed

Hsueh, Wei-Ann; Kesner, Amanda L; Gangloff, Anne; Pegram, Mark D; Beryt, Malgorzata; Czernin, Johannes; Phelps, Michael E; Silverman, Daniel H S

2006-12-01

Paclitaxel is used as a chemotherapy drug for the treatment of various malignancies, including breast, ovarian, and lung cancers. To evaluate the potential of a noninvasive prognostic tool for specifically predicting the resistance of tumors to paclitaxel therapy, we examined the tumoral uptake of (18)F-fluoropaclitaxel ((18)F-FPAC) in mice bearing human breast cancer xenografts by using small-animal-dedicated PET and compared (18)F-FPAC uptake with the tumor response to paclitaxel treatment. PET data were acquired after tail vein injection of approximately 9 MBq of (18)F-FPAC in anesthetized nude mice bearing breast cancer xenografts. Tracer uptake in reconstructed images was quantified by region-of-interest analyses and compared with the tumor response, as measured by changes in tumor volume, after treatment with paclitaxel. Mice with tumors that progressed demonstrated lower tumoral uptake of (18)F-FPAC than mice with tumors that did not progress or that regressed (r = 0.55, P < 0.02; n = 19), indicating that low (18)F-FPAC uptake was a significant predictor of chemoresistance. Conversely, high (18)F-FPAC uptake predicted tumor regression. This relationship was found for mice bearing xenografts from cell lines selected to be either sensitive or intrinsically resistant to paclitaxel in vitro. PET data acquired with (18)F-FPAC suggest that this tracer holds promise for the noninvasive quantification of its distribution in vivo in a straightforward manner. In combination with approaches for examining other aspects of resistance, such quantification could prove useful in helping to predict subsequent resistance to paclitaxel chemotherapy of breast cancer.

Evidence that breast tissue stiffness is associated with risk of breast cancer.

PubMed

Boyd, Norman F; Li, Qing; Melnichouk, Olga; Huszti, Ella; Martin, Lisa J; Gunasekara, Anoma; Mawdsley, Gord; Yaffe, Martin J; Minkin, Salomon

2014-01-01

Evidence from animal models shows that tissue stiffness increases the invasion and progression of cancers, including mammary cancer. We here use measurements of the volume and the projected area of the compressed breast during mammography to derive estimates of breast tissue stiffness and examine the relationship of stiffness to risk of breast cancer. Mammograms were used to measure the volume and projected areas of total and radiologically dense breast tissue in the unaffected breasts of 362 women with newly diagnosed breast cancer (cases) and 656 women of the same age who did not have breast cancer (controls). Measures of breast tissue volume and the projected area of the compressed breast during mammography were used to calculate the deformation of the breast during compression and, with the recorded compression force, to estimate the stiffness of breast tissue. Stiffness was compared in cases and controls, and associations with breast cancer risk examined after adjustment for other risk factors. After adjustment for percent mammographic density by area measurements, and other risk factors, our estimate of breast tissue stiffness was significantly associated with breast cancer (odds ratio = 1.21, 95% confidence interval = 1.03, 1.43, p = 0.02) and improved breast cancer risk prediction in models with percent mammographic density, by both area and volume measurements. An estimate of breast tissue stiffness was associated with breast cancer risk and improved risk prediction based on mammographic measures and other risk factors. Stiffness may provide an additional mechanism by which breast tissue composition is associated with risk of breast cancer and merits examination using more direct methods of measurement.

A comprehensive tool for measuring mammographic density changes over time.

PubMed

Eriksson, Mikael; Li, Jingmei; Leifland, Karin; Czene, Kamila; Hall, Per

2018-06-01

Mammographic density is a marker of breast cancer risk and diagnostics accuracy. Density change over time is a strong proxy for response to endocrine treatment and potentially a stronger predictor of breast cancer incidence. We developed STRATUS to analyse digital and analogue images and enable automated measurements of density changes over time. Raw and processed images from the same mammogram were randomly sampled from 41,353 healthy women. Measurements from raw images (using FDA approved software iCAD) were used as templates for STRATUS to measure density on processed images through machine learning. A similar two-step design was used to train density measures in analogue images. Relative risks of breast cancer were estimated in three unique datasets. An alignment protocol was developed using images from 11,409 women to reduce non-biological variability in density change. The protocol was evaluated in 55,073 women having two regular mammography screens. Differences and variances in densities were compared before and after image alignment. The average relative risk of breast cancer in the three datasets was 1.6 [95% confidence interval (CI) 1.3-1.8] per standard deviation of percent mammographic density. The discrimination was AUC 0.62 (CI 0.60-0.64). The type of image did not significantly influence the risk associations. Alignment decreased the non-biological variability in density change and re-estimated the yearly overall percent density decrease from 1.5 to 0.9%, p < 0.001. The quality of STRATUS density measures was not influenced by mammogram type. The alignment protocol reduced the non-biological variability between images over time. STRATUS has the potential to become a useful tool for epidemiological studies and clinical follow-up.

Percent Mammographic Density and Dense Area as Risk Factors for Breast Cancer.

PubMed

Rauh, C; Hack, C C; Häberle, L; Hein, A; Engel, A; Schrauder, M G; Fasching, P A; Jud, S M; Ekici, A B; Loehberg, C R; Meier-Meitinger, M; Ozan, S; Schulz-Wendtland, R; Uder, M; Hartmann, A; Wachter, D L; Beckmann, M W; Heusinger, K

2012-08-01

Purpose: Mammographic characteristics are known to be correlated to breast cancer risk. Percent mammographic density (PMD), as assessed by computer-assisted methods, is an established risk factor for breast cancer. Along with this assessment the absolute dense area (DA) of the breast is reported as well. Aim of this study was to assess the predictive value of DA concerning breast cancer risk in addition to other risk factors and in addition to PMD. Methods: We conducted a case control study with hospital-based patients with a diagnosis of invasive breast cancer and healthy women as controls. A total of 561 patients and 376 controls with available mammographic density were included into this study. We describe the differences concerning the common risk factors BMI, parital status, use of hormone replacement therapy (HRT) and menopause between cases and controls and estimate the odds ratios for PMD and DA, adjusted for the mentioned risk factors. Furthermore we compare the prediction models with each other to find out whether the addition of DA improves the model. Results: Mammographic density and DA were highly correlated with each other. Both variables were as well correlated to the commonly known risk factors with an expected direction and strength, however PMD (ρ = -0.56) was stronger correlated to BMI than DA (ρ = -0.11). The group of women within the highest quartil of PMD had an OR of 2.12 (95 % CI: 1.25-3.62). This could not be seen for the fourth quartile concerning DA. However the assessment of breast cancer risk could be improved by including DA in a prediction model in addition to common risk factors and PMD. Conclusions: The inclusion of the parameter DA into a prediction model for breast cancer in addition to established risk factors and PMD could improve the breast cancer risk assessment. As DA is measured together with PMD in the process of computer-assisted assessment of PMD it might be considered to include it as one additional breast cancer risk factor that is obtained from breast imaging.

Tailored breast cancer screening program with microdose mammography, US, and MR Imaging: short-term results of a pilot study in 40-49-year-old women.

PubMed

Venturini, Elena; Losio, Claudio; Panizza, Pietro; Rodighiero, Maria Grazia; Fedele, Isabella; Tacchini, Simona; Schiani, Elena; Ravelli, Silvia; Cristel, Giulia; Panzeri, Marta Maria; De Cobelli, Francesco; Del Maschio, Alessandro

2013-08-01

To evaluate the feasibility, performance, and cost of a breast cancer screening program aimed at 40-49-year-old women and tailored to their risk profile with supplemental ultrasonography (US) and magnetic resonance (MR) imaging. The institutional review board approved this study, and informed written consent was obtained. A total of 3017 40-49-year-old women were invited to participate. The screening program was tailored to lifetime risk (Gail test) and mammographic density (according to Breast Imaging Reporting and Data Systems [BI-RADS] criteria) with supplemental US or MR imaging and bilateral two-view microdose mammography. The indicators suggested by European guidelines, US incremental cancer detection rate (CDR), and estimated costs were evaluated. A total of 1666 women (67.5% participation rate) were recruited. The average lifetime risk of breast cancer was 11.6%, and nine women had a high risk of breast cancer; 917 women (55.0%) had a high density score (BI-RADS density category 3 or 4). The average glandular dose for screening examinations was 1.49 mGy. Screening US was performed in 835 study participants (50.1%), mostly due to high breast density (800 of 1666 women [48.0%]). Screening MR imaging was performed in nine women (0.5%) at high risk for breast cancer. Breast cancer was diagnosed in 14 women (8.4 cases per 1000 women). Twelve diagnoses were made with microdose mammography, and two were made with supplemental US in dense breasts (2.4 cases per 1000 women). All patients were submitted for surgery, and 10 underwent breast-conserving surgery. The sentinel lymph node was evaluated in 11 patients, resulting in negative findings in six. Pathologic analysis resulted in the diagnosis of four ductal carcinomas in situ and 10 invasive carcinomas (five at stage I). A tailored breast cancer screening program in 40-49-year-old women yielded a greater-than-expected number of cancers, most of which were low-stage disease.

Mammographic Density Reduction as a Prognostic Marker for Postmenopausal Breast Cancer: Results Using a Joint Longitudinal-Survival Modeling Approach.

PubMed

Andersson, Therese M-L; Crowther, Michael J; Czene, Kamila; Hall, Per; Humphreys, Keith

2017-11-01

Previous studies have linked reductions in mammographic density after a breast cancer diagnosis to an improved prognosis. These studies focused on short-term change, using a 2-stage process, treating estimated change as a fixed covariate in a survival model. We propose the use of a joint longitudinal-survival model. This enables us to model long-term trends in density while accounting for dropout as well as for measurement error. We studied the change in mammographic density after a breast cancer diagnosis and its association with prognosis (measured by cause-specific mortality), overall and with respect to hormone replacement therapy and tamoxifen treatment. We included 1,740 women aged 50-74 years, diagnosed with breast cancer in Sweden during 1993-1995, with follow-up until 2008. They had a total of 6,317 mammographic density measures available from the first 5 years of follow-up, including baseline measures. We found that the impact of the withdrawal of hormone replacement therapy on density reduction was larger than that of tamoxifen treatment. Unlike previous studies, we found that there was an association between density reduction and survival, both for tamoxifen-treated women and women who were not treated with tamoxifen. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.

Population of 224 realistic human subject-based computational breast phantoms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Erickson, David W.; Wells, Jered R., E-mail: jered.wells@duke.edu; Sturgeon, Gregory M.

Purpose: To create a database of highly realistic and anatomically variable 3D virtual breast phantoms based on dedicated breast computed tomography (bCT) data. Methods: A tissue classification and segmentation algorithm was used to create realistic and detailed 3D computational breast phantoms based on 230 + dedicated bCT datasets from normal human subjects. The breast volume was identified using a coarse three-class fuzzy C-means segmentation algorithm which accounted for and removed motion blur at the breast periphery. Noise in the bCT data was reduced through application of a postreconstruction 3D bilateral filter. A 3D adipose nonuniformity (bias field) correction was thenmore » applied followed by glandular segmentation using a 3D bias-corrected fuzzy C-means algorithm. Multiple tissue classes were defined including skin, adipose, and several fractional glandular densities. Following segmentation, a skin mask was produced which preserved the interdigitated skin, adipose, and glandular boundaries of the skin interior. Finally, surface modeling was used to produce digital phantoms with methods complementary to the XCAT suite of digital human phantoms. Results: After rejecting some datasets due to artifacts, 224 virtual breast phantoms were created which emulate the complex breast parenchyma of actual human subjects. The volume breast density (with skin) ranged from 5.5% to 66.3% with a mean value of 25.3% ± 13.2%. Breast volumes ranged from 25.0 to 2099.6 ml with a mean value of 716.3 ± 386.5 ml. Three breast phantoms were selected for imaging with digital compression (using finite element modeling) and simple ray-tracing, and the results show promise in their potential to produce realistic simulated mammograms. Conclusions: This work provides a new population of 224 breast phantoms based on in vivo bCT data for imaging research. Compared to previous studies based on only a few prototype cases, this dataset provides a rich source of new cases spanning a wide range of breast types, volumes, densities, and parenchymal patterns.« less

Population of 224 realistic human subject-based computational breast phantoms

PubMed Central

Erickson, David W.; Wells, Jered R.; Sturgeon, Gregory M.; Dobbins, James T.; Segars, W. Paul; Lo, Joseph Y.

2016-01-01

Purpose: To create a database of highly realistic and anatomically variable 3D virtual breast phantoms based on dedicated breast computed tomography (bCT) data. Methods: A tissue classification and segmentation algorithm was used to create realistic and detailed 3D computational breast phantoms based on 230 + dedicated bCT datasets from normal human subjects. The breast volume was identified using a coarse three-class fuzzy C-means segmentation algorithm which accounted for and removed motion blur at the breast periphery. Noise in the bCT data was reduced through application of a postreconstruction 3D bilateral filter. A 3D adipose nonuniformity (bias field) correction was then applied followed by glandular segmentation using a 3D bias-corrected fuzzy C-means algorithm. Multiple tissue classes were defined including skin, adipose, and several fractional glandular densities. Following segmentation, a skin mask was produced which preserved the interdigitated skin, adipose, and glandular boundaries of the skin interior. Finally, surface modeling was used to produce digital phantoms with methods complementary to the XCAT suite of digital human phantoms. Results: After rejecting some datasets due to artifacts, 224 virtual breast phantoms were created which emulate the complex breast parenchyma of actual human subjects. The volume breast density (with skin) ranged from 5.5% to 66.3% with a mean value of 25.3% ± 13.2%. Breast volumes ranged from 25.0 to 2099.6 ml with a mean value of 716.3 ± 386.5 ml. Three breast phantoms were selected for imaging with digital compression (using finite element modeling) and simple ray-tracing, and the results show promise in their potential to produce realistic simulated mammograms. Conclusions: This work provides a new population of 224 breast phantoms based on in vivo bCT data for imaging research. Compared to previous studies based on only a few prototype cases, this dataset provides a rich source of new cases spanning a wide range of breast types, volumes, densities, and parenchymal patterns. PMID:26745896

Association of Vascular Endothelial Growth Factor Expression with Tumor Angiogenesis and with Early Relapse in Primary Breast Cancer

PubMed Central

Hoshina, Seigo; Takayanagi, Toshiaki; Tominaga, Takeshi

1994-01-01

Angiogenesis is an independent prognostic indicator in breast cancer. In this report, the relationship between expression of vascular endothclial growth factor (VEGF; a selective mitogen for endothelial cells) and the microvessel density was examined in 103 primary breast cancers. The expression of VEGF was evaluated by immunocytochemical staining using anti‐VEGF antibody. The microvessel density, which was determined by immunostaining for factor VIII antigen, in VEGF‐rich tumors was clearly higher than that in VEGF‐poor tumors (P<0.01). There was a good correlation between VEGF expression and the increment of microvessel density. Furthermore, postoperative survey demonstrated that the relapse‐free survival rate of VEGF‐rich tumors was significantly worse than that of VEGF‐poor tumors. It was suggested that the expression of VEGF is closely associated with the promotion of angiogenesis and with early relapse in primary breast cancer. PMID:7525523

Automated Percentage of Breast Density Measurements for Full-field Digital Mammography Applications.

PubMed

Fowler, Erin E E; Vachon, Celine M; Scott, Christopher G; Sellers, Thomas A; Heine, John J

2014-08-01

Increased mammographic breast density is a significant risk factor for breast cancer. A reproducible, accurate, and automated breast density measurement is required for full-field digital mammography (FFDM) to support clinical applications. We evaluated a novel automated percentage of breast density measure (PDa) and made comparisons with the standard operator-assisted measure (PD) using FFDM data. We used a nested breast cancer case-control study matched on age, year of mammogram and diagnosis with images acquired from a specific direct x-ray conversion FFDM technology. PDa was applied to the raw and clinical display (or processed) representation images. We evaluated the transformation (pixel mapping) of the raw image, giving a third representation (raw-transformed), to improve the PDa performance using differential evolution optimization. We applied PD to the raw and clinical display images as a standard for measurement comparison. Conditional logistic regression was used to estimate the odd ratios (ORs) for breast cancer with 95% confidence intervals (CI) for all measurements; analyses were adjusted for body mass index. PDa operates by evaluating signal-dependent noise (SDN), captured as local signal variation. Therefore, we characterized the SDN relationship to understand the PDa performance as a function of data representation and investigated a variation analysis of the transformation. The associations of the quartiles of operator-assisted PD with breast cancer were similar for the raw (OR: 1.00 [ref.]; 1.59 [95% CI, 0.93-2.70]; 1.70 [95% CI, 0.95-3.04]; 2.04 [95% CI, 1.13-3.67]) and clinical display (OR: 1.00 [ref.]; 1.31 [95% CI, 0.79-2.18]; 1.14 [95% CI, 0.65-1.98]; 1.95 [95% CI, 1.09-3.47]) images. PDa could not be assessed on the raw images without preprocessing. However, PDa had similar associations with breast cancer when assessed on 1) raw-transformed (OR: 1.00 [ref.]; 1.27 [95% CI, 0.74-2.19]; 1.86 [95% CI, 1.05-3.28]; 3.00 [95% CI, 1.67-5.38]) and 2) clinical display (OR: 1.00 [ref.]; 1.79 [95% CI, 1.04-3.11]; 1.61 [95% CI, 0.90-2.88]; 2.94 [95% CI, 1.66-5.19]) images. The SDN analysis showed that a nonlinear relationship between the mammographic signal and its variation (ie, the biomarker for the breast density) is required for PDa. Although variability in the transform influenced the respective PDa distribution, it did not affect the measurement's association with breast cancer. PDa assessed on either raw-transformed or clinical display images is a valid automated breast density measurement for a specific FFDM technology and compares well against PD. Further work is required for measurement generalization. Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

Occupation and mammographic density: A population-based study (DDM-Occup).

PubMed

García-Pérez, Javier; Pollán, Marina; Pérez-Gómez, Beatriz; González-Sánchez, Mario; Cortés Barragán, Rosa Ana; Maqueda Blasco, Jerónimo; González-Galarzo, María Carmen; Alba, Miguel Ángel; van der Haar, Rudolf; Casas, Silvia; Vicente, Cándida; Medina, Pilar; Ederra, María; Santamariña, Carmen; Moreno, María Pilar; Casanova, Francisco; Pedraz-Pingarrón, Carmen; Moreo, Pilar; Ascunce, Nieves; García, Montse; Salas-Trejo, Dolores; Sánchez-Contador, Carmen; Llobet, Rafael; Lope, Virginia

2017-11-01

High mammographic density is one of the main risk factors for breast cancer. Although several occupations have been associated with breast cancer, there are no previous occupational studies exploring the association with mammographic density. Our objective was to identify occupations associated with high mammographic density in Spanish female workers. We conducted a population-based cross-sectional study of occupational determinants of high mammographic density in Spain, based on 1476 women, aged 45-68 years, recruited from seven screening centers within the Spanish Breast Cancer Screening Program network. Reproductive, family, personal, and occupational history data were collected. The latest occupation of each woman was collected and coded according to the 1994 National Classification of Occupations. Mammographic density was assessed from the cranio-caudal mammogram of the left breast using a semi-automated computer-assisted tool. Association between mammographic density and occupation was evaluated by using mixed linear regression models, using log-transformed percentage of mammographic density as dependent variable. Models were adjusted for age, body mass index, menopausal status, parity, smoking, alcohol intake, educational level, type of mammography, first-degree relative with breast cancer, and hormonal replacement therapy use. Screening center and professional reader were included as random effects terms. Mammographic density was higher, although non-statistically significant, among secondary school teachers (e β = 1.41; 95%CI = 0.98-2.03) and nurses (e β = 1.23; 95%CI = 0.96-1.59), whereas workers engaged in the care of people (e β = 0.81; 95%CI = 0.66-1.00) and housewives (e β = 0.87; 95%CI = 0.79-0.95) showed an inverse association with mammographic density. A positive trend for every 5 years working as secondary school teachers was also detected (p-value = 0.035). Nurses and secondary school teachers were the occupations with the highest mammographic density in our study, showing the latter a positive trend with duration of employment. Future studies are necessary to confirm if these results are due to chance or are the result of a true association whose causal hypothesis is, for the moment, unknown. Copyright © 2017 Elsevier Inc. All rights reserved.

Interaction of mammographic breast density with menopausal status and postmenopausal hormone use in relation to the risk of aggressive breast cancer subtypes.

PubMed

Yaghjyan, Lusine; Tamimi, Rulla M; Bertrand, Kimberly A; Scott, Christopher G; Jensen, Matthew R; Pankratz, V Shane; Brandt, Kathy; Visscher, Daniel; Norman, Aaron; Couch, Fergus; Shepherd, John; Fan, Bo; Chen, Yunn-Yi; Ma, Lin; Beck, Andrew H; Cummings, Steven R; Kerlikowske, Karla; Vachon, Celine M

2017-09-01

We examined the associations of mammographic breast density with breast cancer risk by tumor aggressiveness and by menopausal status and current postmenopausal hormone therapy. This study included 2596 invasive breast cancer cases and 4059 controls selected from participants of four nested case-control studies within four established cohorts: the Mayo Mammography Health Study, the Nurses' Health Study, Nurses' Health Study II, and San Francisco Mammography Registry. Percent breast density (PD), absolute dense (DA), and non-dense areas (NDA) were assessed from digitized film-screen mammograms using a computer-assisted threshold technique and standardized across studies. We used polytomous logistic regression to quantify the associations of breast density with breast cancer risk by tumor aggressiveness (defined as presence of at least two of the following tumor characteristics: size ≥2 cm, grade 2/3, ER-negative status, or positive nodes), stratified by menopausal status and current hormone therapy. Overall, the positive association of PD and borderline inverse association of NDA with breast cancer risk was stronger in aggressive vs. non-aggressive tumors (≥51 vs. 11-25% OR 2.50, 95% CI 1.94-3.22 vs. OR 2.03, 95% CI 1.70-2.43, p-heterogeneity = 0.03; NDA 4th vs. 2nd quartile OR 0.54, 95% CI 0.41-0.70 vs. OR 0.71, 95% CI 0.59-0.85, p-heterogeneity = 0.07). However, there were no differences in the association of DA with breast cancer by aggressive status. In the stratified analysis, there was also evidence of a stronger association of PD and NDA with aggressive tumors among postmenopausal women and, in particular, current estrogen+progesterone users (≥51 vs. 11-25% OR 3.24, 95% CI 1.75-6.00 vs. OR 1.93, 95% CI 1.25-2.98, p-heterogeneity = 0.01; NDA 4th vs. 2nd quartile OR 0.43, 95% CI 0.21-0.85 vs. OR 0.56, 95% CI 0.35-0.89, p-heterogeneity = 0.01), even though the interaction was not significant. Our findings suggest that associations of mammographic density with breast cancer risk differ by tumor aggressiveness. While there was no strong evidence that these associations differed by menopausal status or hormone therapy, they did appear more prominent among current estrogen+progesterone users.

Evidence That Breast Tissue Stiffness Is Associated with Risk of Breast Cancer

PubMed Central

Boyd, Norman F.; Li, Qing; Melnichouk, Olga; Huszti, Ella; Martin, Lisa J.; Gunasekara, Anoma; Mawdsley, Gord; Yaffe, Martin J.; Minkin, Salomon

2014-01-01

Background Evidence from animal models shows that tissue stiffness increases the invasion and progression of cancers, including mammary cancer. We here use measurements of the volume and the projected area of the compressed breast during mammography to derive estimates of breast tissue stiffness and examine the relationship of stiffness to risk of breast cancer. Methods Mammograms were used to measure the volume and projected areas of total and radiologically dense breast tissue in the unaffected breasts of 362 women with newly diagnosed breast cancer (cases) and 656 women of the same age who did not have breast cancer (controls). Measures of breast tissue volume and the projected area of the compressed breast during mammography were used to calculate the deformation of the breast during compression and, with the recorded compression force, to estimate the stiffness of breast tissue. Stiffness was compared in cases and controls, and associations with breast cancer risk examined after adjustment for other risk factors. Results After adjustment for percent mammographic density by area measurements, and other risk factors, our estimate of breast tissue stiffness was significantly associated with breast cancer (odds ratio = 1.21, 95% confidence interval = 1.03, 1.43, p = 0.02) and improved breast cancer risk prediction in models with percent mammographic density, by both area and volume measurements. Conclusion An estimate of breast tissue stiffness was associated with breast cancer risk and improved risk prediction based on mammographic measures and other risk factors. Stiffness may provide an additional mechanism by which breast tissue composition is associated with risk of breast cancer and merits examination using more direct methods of measurement. PMID:25010427

Neighbourhoods matter too: the association between neighbourhood socioeconomic position, population density and breast, prostate and lung cancer incidence in Denmark between 2004 and 2008.

PubMed

Meijer, Mathias; Bloomfield, Kim; Engholm, Gerda

2013-01-01

Previous studies have shown that cancer incidence is related to a number of individual factors, including socioeconomic status. The aim of this study was to refine the current knowledge about indicators associated with cancer incidence by evaluating the influence of neighbourhood characteristics on breast, prostate and lung cancer incidence in Denmark. All women aged 30-83 years were followed for breast cancer between 2004 and 2008, men between 50 and 83 years were followed for prostate cancer and both sexes between ages 50 and 83 were followed for lung cancer. Registry data obtained from Statistics Denmark included age, sex, availability of breast cancer screening, marital status, education, disposable income and occupational socioeconomic status on the individual level and population density and neighbourhood socioeconomic status (the proportion of unemployed) on the parish level. Frailty modelling with individuals on the first level and parishes on the second level was conducted. A significantly lower HR of breast cancer was found in areas with low population density (HR=0.93; CI 0.88 to 0.99), while neighbourhood unemployment had no effect. Inhabitants of lower unemployment areas had a higher risk of prostate cancer (HR=1.14; CI 1.08 to 1.21) compared with those in higher unemployment areas, whereas population density had no effect. Risk of lung cancer was lower in areas with lowest population density (HR=0.80; CI 0.74 to 0.85) and lowest in areas with lowest unemployment (HR=0.88; CI 0.84 to 0.92). In addition to individual-level factors, characteristics on the neighbourhood level also have an influence on breast, prostate and lung cancer incidence.

Interactions of alcohol and postmenopausal hormone use in regards to mammographic breast density.

PubMed

Yaghjyan, Lusine; Colditz, Graham; Eliassen, Heather; Rosner, Bernard; Gasparova, Aleksandra; Tamimi, Rulla M

2018-06-25

We investigated the association of alcohol intake with mammographic breast density in postmenopausal women by their hormone therapy (HT) status. This study included 2,100 cancer-free postmenopausal women within the Nurses' Health Study and Nurses' Health Study II cohorts. Percent breast density (PD), absolute dense (DA), and non-dense areas (NDA) were measured from digitized film mammograms using a computer-assisted thresholding technique; all measures were square root transformed. Alcohol consumption was assessed with a food frequency questionnaire (0, < 5, and ≥ 5 g/day). Information regarding breast cancer risk factors was obtained from baseline or biennial questionnaires closest to the mammogram date. We used generalized linear regression to examine associations between alcohol and breast density measures in women with no HT history, current, and past HT users. In multivariable analyses, we found no associations of alcohol consumption with PD (p trend = 0.32) and DA (p trend = 0.53) and an inverse association with NDA (β = - 0.41, 95% CI - 0.73, - 0.09 for ≥ 5 g/day, p trend < 0.01). In the stratified analysis by HT status, alcohol was not associated with PD in any of the strata. We found a significant inverse association of alcohol with NDA among past HT users (β = - 0.79, 95% CI - 1.51, - 0.07 for ≥ 5 g/day, p trend = 0.02). There were no significant interactions between alcohol and HT in relation to PD, DA, and NDA (p interaction = 0.19, 0.42, and 0.43, respectively). Our findings suggest that associations of alcohol with breast density do not vary by HT status.

TU-CD-207-01: Characterization of Breast Tissue Composition Using Spectral Mammography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ding, H; Cho, H; Kumar, N

Purpose: To investigate the feasibility of characterizing the chemical composition of breast tissue, in terms of water and lipid, by using spectral mammography in simulation and postmortem studies. Methods: Analytical simulations were performed to obtain low- and high-energy signals of breast tissue based on previously reported water, lipid, and protein contents. Dual-energy decomposition was used to characterize the simulated breast tissue into water and lipid basis materials and the measured water density was compared to the known value. In experimental studies, postmortem breasts were imaged with a spectral mammography system based on a scanning multi-slit Si strip photon-counting detector. Low-more » and high-energy images were acquired simultaneously from a single exposure by sorting the recorded photons into the corresponding energy bins. Dual-energy material decomposition of the low- and high-energy images yielded individual pixel measurements of breast tissue composition in terms of water and lipid thicknesses. After imaging, each postmortem breast was chemically decomposed into water, lipid and protein. The water density calculated from chemical analysis was used as the reference gold standard. Correlation of the water density measurements between spectral mammography and chemical analysis was analyzed using linear regression. Results: Both simulation and postmortem studies showed good linear correlation between the decomposed water thickness using spectral mammography and chemical analysis. The slope of the linear fitting function in the simulation and postmortem studies were 1.15 and 1.21, respectively. Conclusion: The results indicate that breast tissue composition, in terms of water and lipid, can be accurately measured using spectral mammography. Quantitative breast tissue composition can potentially be used to stratify patients according to their breast cancer risk.« less

Increased COX-2 expression in epithelial and stromal cells of high mammographic density tissues and in a xenograft model of mammographic density.

PubMed

Chew, G L; Huo, C W; Huang, D; Hill, P; Cawson, J; Frazer, H; Hopper, J L; Haviv, I; Henderson, M A; Britt, K; Thompson, E W

2015-08-01

Mammographic density (MD) adjusted for age and body mass index is one of the strongest known risk factors for breast cancer. Given the high attributable risk of MD for breast cancer, chemoprevention with a safe and available agent that reduces MD and breast cancer risk would be beneficial. Cox-2 has been implicated in MD-related breast cancer risk, and was increased in stromal cells in high MD tissues in one study. Our study assessed differential Cox-2 expression in epithelial and stromal cells in paired samples of high and low MD human breast tissue, and in a validated xenograft biochamber model of MD. We also examined the effects of endocrine treatment upon Cox-2 expression in high and low MD tissues in the MD xenograft model. Paired high and low MD human breast tissue samples were immunostained for Cox-2, then assessed for differential expression and staining intensity in epithelial and stromal cells. High and low MD human breast tissues were separately maintained in biochambers in mice treated with Tamoxifen, oestrogen or placebo implants, then assessed for percentage Cox-2 staining in epithelial and stromal cells. Percentage Cox-2 staining was greater for both epithelial (p = 0.01) and stromal cells (p < 0.0001) of high compared with low MD breast tissues. In high MD biochamber tissues, percentage Cox-2 staining was greater in stromal cells of oestrogen-treated versus placebo-treated tissues (p = 0.05).

The effect of weight change on changes in breast density measures over menopause in a breast cancer screening cohort.

PubMed

Wanders, Johanna Olga Pauline; Bakker, Marije Fokje; Veldhuis, Wouter Bernard; Peeters, Petra Huberdina Maria; van Gils, Carla Henrica

2015-05-30

High weight and high percentage mammographic breast density are both breast cancer risk factors but are negatively correlated. Therefore, we wanted to obtain more insight into this apparent paradox. We investigated in a longitudinal study how weight change over menopause is related to changes in mammographic breast features. Five hundred ninety-one participants of the EPIC-NL cohort were divided into three groups according to their prospectively measured weight change over menopause: (1) weight loss (more than -3.0 %), (2) stable weight (between -3.0 % and +3.0 %), and (3) weight gain (more than 3.0 %). SPSS GLM univariate analysis was used to determine both the mean breast measure changes in, and the trend over, the weight change groups. Over a median period of 5 years, the mean changes in percent density in these groups were -5.0 % (95 % confidence interval (CI) -8.0; -2.1), -6.8 % (95 % CI -9.0; -4.5), and -10.2 % (95 % CI -12.5; -7.9), respectively (P-trend = 0.001). The mean changes in dense area were -16.7 cm(2) (95 % CI -20.1; -13.4), -16.4 cm(2) (95 % CI -18.9; -13.9), and -18.1 cm(2) (95 % CI -20.6; -15.5), respectively (P-trend = 0.437). Finally, the mean changes in nondense area were -6.1 cm(2) (95 % CI -11.9; -0.4), -0.6 cm(2) (95 % CI -4.9; 3.8), and 5.3 cm(2) (95 % CI 0.9; 9.8), respectively (P-trend < 0.001). Going through menopause is associated with a decrease in both percent density and dense area. Owing to an increase in the nondense tissue, the decrease in percent density is largest in women who gain weight. The decrease in dense area is not related to weight change. So the fact that both high percent density and high weight or weight gain are associated with high postmenopausal breast cancer risk can probably not be explained by an increase (or slower decrease) of dense area in women gaining weight compared with women losing weight or maintaining a stable weight. These results suggest that weight and dense area are presumably two independent postmenopausal breast cancer risk factors.

Long-term results of conservative surgery and radiotherapy for ductal carcinoma in situ using lung density correction: the University of Michigan experience.

PubMed

Ben-David, Merav A; Sturtz, David E; Griffith, Kent A; Douglas, Kathye R; Hayman, James A; Lichter, Allen S; Pierce, Lori J

2007-01-01

The purpose of the study was to review the treatment outcomes of 198 patients treated with breast-conserving surgery (BCS) and whole breast radiation therapy using lung density correction for ductal carcinoma in situ (DCIS). Between April 1985 and December 2002, 198 patients with 200 lesions diagnosed as DCIS (AJCC stage 0) were treated at the University of Michigan. All underwent BCS and whole breast radiotherapy. Median total follow-up was 6.2 years (range: 0.8-18.2). The 5- and 10-year cumulative rates of in-breast only failure were 5.9% (95% CI: 2.6-9.3%) and 9.8% (95% CI: 5.2-14.4%), respectively. Factors that significantly predicted for an increased risk of local failure were family history of breast cancer, positive or close surgical margins and age

Assessment of Interradiologist Agreement Regarding Mammographic Breast Density Classification Using the Fifth Edition of the BI-RADS Atlas.

PubMed

Ekpo, Ernest U; Ujong, Ujong Peter; Mello-Thoms, Claudia; McEntee, Mark F

2016-05-01

The objective of the present study was to assess interradiologist agreement regarding mammographic breast density assessment performed using the rating scale outlined in the fifth edition of the BI-RADS atlas of the American College of Radiology. Breast density assessments of 1000 cases were conducted by five radiologists from the same institution who together had recently undergone retraining in mammographic breast density classification based on the fifth edition of BI-RADS. The readers assigned breast density grades (A-D) on the basis of the BI-RADS classification scheme. Repeat assessment of 100 cases was performed by all readers 1 month after the initial assessment. A weighted kappa was used to calculate intrareader and interreader agreement. Intrareader agreement ranged from a kappa value of 0.86 (95% CI, 0.77-0.93) to 0.89 (95% CI, 0.81-0.95) on a four-category scale (categories A-D) and from 0.89 (95% CI, 0.86-0.92) to 0.94 (95% CI, 0.89-0.97) on a two-category scale (category A-B vs category C-D). Interreader agreement ranged from substantial (κ = 0.76; 95% CI, 0.73-0.78) to almost perfect (κ = 0.87; 95% CI, 0.86-0.89) on a four-category scale, and the overall weighted kappa value was substantial (0.79; 95% CI, 0.78-0.83). Interreader agreement on a two-category scale ranged from a kappa value of 0.85 (95% CI, 0.83-0.86) to 0.91 (95% CI, 0.90-0.92), and the overall weighted kappa was 0.88 (95% CI, 0.87-0.89). Overall, with regard to mammographic breast density classification, radiologists had substantial interreader agreement when a four-category scale was used and almost perfect interreader agreement when a dichotomous scale was used.

The effect of preoperative serum triglycerides and high-density lipoprotein-cholesterol levels on the prognosis of breast cancer.

PubMed

Li, Xing; Tang, Hailin; Wang, Jin; Xie, Xinhua; Liu, Peng; Kong, Yanan; Ye, Feng; Shuang, Zeyu; Xie, Zeming; Xie, Xiaoming

2017-04-01

Although dyslipidemia has been documented to be associated with several types of cancer including breast cancer, it remains uncertainty the prognostic value of serum lipid in breast cancer. The purpose of this study is to evaluate the association between the preoperative plasma lipid profile and the prognostic of breast cancer patients. The levels of preoperative serum lipid profile (including cholesterol [CHO], Triglycerides [TG], high-density lipoprotein-cholesterol [HDL-C], low-density lipoprotein-cholesterol [LDL-C], apolipoprotein A-I [ApoAI], and apolipoprotein B [ApoB]) and the clinical data were retrospectively collected and reviewed in 1044 breast cancer patients undergoing operation. Kaplan-Meier method and the Cox proportional hazards regression model were used in analyzing the overall survival [OS] and disease-free survival [DFS]. Combining the receiver-operating characteristic and Kaplan-Meier analysis, we found that preoperative lower TG and HDL-C level were risk factors of breast cancer patients. In multivariate analyses, a decreased HDL-C level showed significant association with worse OS (HR: 0.528; 95% CI: 0.302-0.923; P = 0.025), whereas a decreased TG level showed significant association with worse DFS (HR: 0.569; 95% CI: 0.370-0.873; P = 0.010). Preoperative serum levels of TG and HDL-C may be independent factor to predict outcome in breast cancer patient. Copyright © 2016 Elsevier Ltd. All rights reserved.

Role of multidetector computed tomography in evaluating incidentally detected breast lesions.

PubMed

Moschetta, Marco; Scardapane, Arnaldo; Lorusso, Valentina; Rella, Leonarda; Telegrafo, Michele; Serio, Gabriella; Angelelli, Giuseppe; Ianora, Amato Antonio Stabile

2015-01-01

Computed tomography (CT) does not represent the primary method for the evaluation of breast lesions; however, it can detect breast abnormalities, even when performed for other reasons related to thoracic structures. The aim of this study is to evaluate the potential benefits of 320-row multidetector CT (MDCT) in evaluating and differentiating incidentally detected breast lesions by using vessel probe and 3D analysis software with net enhancement value. Sixty-two breast lesions in 46 patients who underwent 320-row chest CT examination were retrospectively evaluated. CT scans were assessed searching for the presence, location, number, morphological features, and density of breast nodules. Net enhancement was calculated by subtracting precontrast density from the density obtained by postcontrast values. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of CT were calculated for morphological features and net enhancement. Thirty of 62 lesions were found to be malignant at histological examination and 32 were found to be benign. When morphological features were considered, the sensitivity, specificity, accuracy, PPV, and NPV of CT were 87%, 100%, 88%, 100%, and 50%, respectively. Based on net enhancement, CT reached a sensitivity, specificity, accuracy, PPV, and NPV of 100%, 94%, 97%, 94%, and 100%, respectively. MDCT allows to recognize and characterize breast lesions based on morphological features. Net enhancement can be proposed as an additional accurate feature of CT.

Update on breast cancer risk prediction and prevention.

PubMed

Sestak, Ivana; Cuzick, Jack

2015-02-01

Breast cancer is the most common cancer in women worldwide. This review will focus on current prevention strategies for women at high risk. The identification of women who are at high risk of developing breast cancer is key to breast cancer prevention. Recent findings have shown that the inclusion of breast density and a panel of low-penetrance genetic polymorphisms can improve risk estimation compared with previous models. Preventive therapy with aromatase inhibitors has produced large reductions in breast cancer incidence in postmenopausal women. Tamoxifen confers long-term protection and is the only proven preventive treatment for premenopausal women. Several other agents, including metformin, bisphosphonates, aspirin and statins, have been found to be effective in nonrandomized settings. There are many options for the prevention of oestrogen-positive breast cancer, in postmenopausal women who can be given a selective oestrogen receptor modulator or an aromatase inhibitor. It still remains unclear how to prevent oestrogen-negative breast cancer, which occurs more often in premenopausal women. Identification of women at high risk of the disease is crucial, and the inclusion of breast density and a panel of genetic polymorphisms, which individually have low penetrance, can improve risk assessment.

The Impact of a Common MDM2 SNP on the Sensitivity of Breast Cancer to Treatment

DTIC Science & Technology

2011-06-01

Kirchhoff T, Alexe G, Bond EE, Robins H, Bartel F, Taubert H, Wuerl P, Hait W, Toppmeyer D, Offit K, and Levine A. MDM2 SNP309 accelerates tumor...the Western blot analysis corresponding to the quantification in the upper graphs . 29 Figure 5. Effect of

Mammographic density changes following discontinuation of tamoxifen in premenopausal women with oestrogen receptor-positive breast cancer.

PubMed

Kim, Won Hwa; Cho, Nariya; Kim, Young-Seon; Yi, Ann

2018-04-06

To evaluate the changes in mammographic density after tamoxifen discontinuation in premenopausal women with oestrogen receptor-positive breast cancers and the underlying factors METHODS: A total of 213 consecutive premenopausal women with breast cancer who received tamoxifen treatment after curative surgery and underwent three mammograms (baseline, after tamoxifen treatment, after tamoxifen discontinuation) were included. Changes in mammographic density after tamoxifen discontinuation were assessed qualitatively (decrease, no change, or increase) by two readers and measured quantitatively by semi-automated software. The association between % density change and clinicopathological factors was evaluated using univariate and multivariate regression analyses. After tamoxifen discontinuation, a mammographic density increase was observed in 31.9% (68/213, reader 1) to 22.1% (47/213, reader 2) by qualitative assessment, with a mean density increase of 1.8% by quantitative assessment compared to density before tamoxifen discontinuation. In multivariate analysis, younger age (≤ 39 years) and greater % density decline after tamoxifen treatment (≥ 17.0%) were independent factors associated with density change after tamoxifen discontinuation (p < .001 and p = .003, respectively). Tamoxifen discontinuation was associated with mammographic density change with a mean density increase of 1.8%, which was associated with younger age and greater density change after tamoxifen treatment. • Increased mammographic density after tamoxifen discontinuation can occur in premenopausal women. • Mean density increase after tamoxifen discontinuation was 1.8%. • Density increase is associated with age and density decrease after tamoxifen.

An inter-observer Ki67 reproducibility study applying two different assessment methods: on behalf of the Danish Scientific Committee of Pathology, Danish breast cancer cooperative group (DBCG).

PubMed

Laenkholm, Anne-Vibeke; Grabau, Dorthe; Møller Talman, Maj-Lis; Balslev, Eva; Bak Jylling, Anne Marie; Tabor, Tomasz Piotr; Johansen, Morten; Brügmann, Anja; Lelkaitis, Giedrius; Di Caterino, Tina; Mygind, Henrik; Poulsen, Thomas; Mertz, Henrik; Søndergaard, Gorm; Bruun Rasmussen, Birgitte

2018-01-01

In 2011, the St. Gallen Consensus Conference introduced the use of pathology to define the intrinsic breast cancer subtypes by application of immunohistochemical (IHC) surrogate markers ER, PR, HER2 and Ki67 with a specified Ki67 cutoff (>14%) for luminal B-like definition. Reports concerning impaired reproducibility of Ki67 estimation and threshold inconsistency led to the initiation of this quality assurance study (2013-2015). The aim of the study was to investigate inter-observer variation for Ki67 estimation in malignant breast tumors by two different quantification methods (assessment method and count method) including measure of agreement between methods. Fourteen experienced breast pathologists from 12 pathology departments evaluated 118 slides from a consecutive series of malignant breast tumors. The staining interpretation was performed according to both the Danish and Swedish guidelines. Reproducibility was quantified by intra-class correlation coefficient (ICC) and Lights Kappa with dichotomization of observations at the larger than (>) 20% threshold. The agreement between observations by the two quantification methods was evaluated by Bland-Altman plot. For the fourteen raters the median ranged from 20% to 40% by the assessment method and from 22.5% to 36.5% by the count method. Light's Kappa was 0.664 for observation by the assessment method and 0.649 by the count method. The ICC was 0.82 (95% CI: 0.77-0.86) by the assessment method vs. 0.84 (95% CI: 0.80-0.87) by the count method. Although the study in general showed a moderate to good inter-observer agreement according to both ICC and Lights Kappa, still major discrepancies were identified in especially the mid-range of observations. Consequently, for now Ki67 estimation is not implemented in the DBCG treatment algorithm.

The role of chemoprevention in modifying the risk of breast cancer in women with atypical breast lesions.

PubMed

Coopey, Suzanne B; Mazzola, Emanuele; Buckley, Julliette M; Sharko, John; Belli, Ahmet K; Kim, Elizabeth M H; Polubriaginof, Fernanda; Parmigiani, Giovanni; Garber, Judy E; Smith, Barbara L; Gadd, Michele A; Specht, Michelle C; Guidi, Anthony J; Roche, Constance A; Hughes, Kevin S

2012-12-01

Women with atypical ductal hyperplasia (ADH), atypical lobular hyperplasia (ALH), lobular carcinoma in situ (LCIS), and severe ADH are at increased risk of breast cancer, but a systematic quantification of this risk and the efficacy of chemoprevention in the clinical setting is still lacking. The objective of this study is to evaluate a woman's risk of breast cancer based on atypia type and to determine the effect of chemoprevention in decreasing this risk. Review of 76,333 breast pathology reports from three institutions within Partners Healthcare System, Boston, from 1987 to 2010 using natural language processing was carried out. This approach identified 2,938 women diagnosed with atypical breast lesions. The main outcome of this study is breast cancer occurrence. Of the 2,938 patients with atypical breast lesions, 1,658 were documented to have received no chemoprevention, and 184/1,658 (11.1 %) developed breast cancer at a mean follow-up of 68 months. Estimated 10-year cancer risks were 17.3 % with ADH, 20.7 % with ALH, 23.7 % with LCIS, and 26.0 % with severe ADH. In a subset of patients treated from 1999 on (the chemoprevention era), those who received no chemoprevention had an estimated 10-year breast cancer risk of 21.3 %, whereas those treated with chemoprevention had a 10-year risk of 7.5 % (p < 0.001). Chemoprevention use significantly reduced breast cancer risk for all atypia types (p < 0.05). The risk of breast cancer with atypical breast lesions is substantial. Physicians should counsel patients with ADH, ALH, LCIS, and severe ADH about the benefit of chemoprevention in decreasing their breast cancer risk.

Energy intake and dietary patterns in childhood and throughout adulthood and mammographic density: results from a British prospective cohort.

PubMed

Mishra, Gita D; dos Santos Silva, Isabel; McNaughton, Sarah A; Stephen, Alison; Kuh, Diana

2011-02-01

To examine the role of energy intake and dietary patterns in childhood and throughout adulthood on subsequent mammographic density. Prospective data were available from a cohort of 1161 British women followed up since their birth in 1946. Dietary intakes at age 4 years were determined by 24-hour recalls and during adulthood, average food consumed at ages 36 and 43 years by 5-day food records. Dietary patterns were determined by factor analysis. Associations between energy intake, dietary patterns, and percent breast density were investigated using regression analysis. During adulthood, energy intake was positively associated with percent breast density (adjusted regression coefficient [per SD) (95% CI): 0.12 (0.01, 0.23)]. The effect of the high fat and sugar dietary pattern remained similar when adjusted for total energy intake [0.06 (-0.01, 0.13)]. There was no evidence of an associations for the patterns low fat, high fiber pattern 0.03 (-0.04, 0.11); the alcohol and fish -0.02 (-0.13, 0.17); meat, potatoes, and vegetables -0.03 (-0.10, 0.04). No association was found for dietary pattern at age 4 and percent breast density. This study supports the hypothesis that overall energy intake during middle life is a determinant of subsequent mammographic breast density measured 15 years later.

Maternal Anthropometry and Mammographic Density in Adult Daughters.

PubMed

Michels, Karin B; Cohn, Barbara A; Goldberg, Mandy; Flom, Julie D; Dougan, Marcelle; Terry, Mary Beth

2016-11-01

We examined the relation between maternal anthropometry and mammographic density in the adult daughter using prospectively collected data. Our study included a total of 700 mother-daughter dyads participating in an adult follow-up of women born in 2 US birth cohorts: the Child Health and Development Study and the Boston, Massachusetts, and Providence, Rhode Island sites of the National Collaborative Perinatal Project. We observed an increased percent breast density at a mean age of 43.1 years in the daughters of mothers who gained 5 kg or less during pregnancy compared with mother-daughter pairs in which the mother gained 5 to 10 kg (β = 4.8, 95% confidence interval: 1.0 to 8.6). The daughters of mothers who were overweight at the time of conception (prepregnancy BMI ≥25) and who gained >5 kg during pregnancy had a lower percent density (β = -3.2, 95% confidence interval: -6.2 to -0.2) compared with mothers with a BMI <25 at conception who gained >5 kg. We did not find any strong and consistent patterns between maternal anthropometry and the daughter's breast density, a strong predictor of breast cancer risk. A modest association between low gestational weight gain and increased breast density 40 years later in the daughter was observed, even after accounting for adult body size, and if confirmed, possible mechanisms need to be further elucidated. Copyright © 2016 by the American Academy of Pediatrics.

Mammographic density and ageing: A collaborative pooled analysis of cross-sectional data from 22 countries worldwide

PubMed Central

Maskarinec, Gertraud; Perez-Gomez, Beatriz; Vachon, Celine; Miao, Hui; Lajous, Martín; López-Ridaura, Ruy; Rice, Megan; Pereira, Ana; Garmendia, Maria Luisa; Tamimi, Rulla M.; Bertrand, Kimberly; Kwong, Ava; Ursin, Giske; Lee, Eunjung; Qureshi, Samera A.; Ma, Huiyan; Moss, Sue; Allen, Steve; Ndumia, Rose; Vinayak, Sudhir; Teo, Soo-Hwang; Mariapun, Shivaani; Fadzli, Farhana; Bukowska, Agnieszka; Nagata, Chisato; Stone, Jennifer; Ozmen, Vahit; Aribal, Mustafa Erkin; Schüz, Joachim; Wanders, Johanna O. P.; Sirous, Reza; Sirous, Mehri; Kim, Jisun; Lee, Jong Won; Dickens, Caroline; Hartman, Mikael; Chia, Kee-Seng; Chiarelli, Anna M.; Linton, Linda; Pollan, Marina; Flugelman, Anath Arzee; Salem, Dorria; Kamal, Rasha; Boyd, Norman; dos-Santos-Silva, Isabel; McCormack, Valerie

2017-01-01

Background Mammographic density (MD) is one of the strongest breast cancer risk factors. Its age-related characteristics have been studied in women in western countries, but whether these associations apply to women worldwide is not known. Methods and findings We examined cross-sectional differences in MD by age and menopausal status in over 11,000 breast-cancer-free women aged 35–85 years, from 40 ethnicity- and location-specific population groups across 22 countries in the International Consortium on Mammographic Density (ICMD). MD was read centrally using a quantitative method (Cumulus) and its square-root metrics were analysed using meta-analysis of group-level estimates and linear regression models of pooled data, adjusted for body mass index, reproductive factors, mammogram view, image type, and reader. In all, 4,534 women were premenopausal, and 6,481 postmenopausal, at the time of mammography. A large age-adjusted difference in percent MD (PD) between post- and premenopausal women was apparent (–0.46 cm [95% CI: −0.53, −0.39]) and appeared greater in women with lower breast cancer risk profiles; variation across population groups due to heterogeneity (I2) was 16.5%. Among premenopausal women, the √PD difference per 10-year increase in age was −0.24 cm (95% CI: −0.34, −0.14; I2 = 30%), reflecting a compositional change (lower dense area and higher non-dense area, with no difference in breast area). In postmenopausal women, the corresponding difference in √PD (−0.38 cm [95% CI: −0.44, −0.33]; I2 = 30%) was additionally driven by increasing breast area. The study is limited by different mammography systems and its cross-sectional rather than longitudinal nature. Conclusions Declines in MD with increasing age are present premenopausally, continue postmenopausally, and are most pronounced over the menopausal transition. These effects were highly consistent across diverse groups of women worldwide, suggesting that they result from an intrinsic biological, likely hormonal, mechanism common to women. If cumulative breast density is a key determinant of breast cancer risk, younger ages may be the more critical periods for lifestyle modifications aimed at breast density and breast cancer risk reduction. PMID:28666001

Quantitative background parenchymal uptake on molecular breast imaging and breast cancer risk: a case-control study.

PubMed

Hruska, Carrie B; Geske, Jennifer R; Swanson, Tiffinee N; Mammel, Alyssa N; Lake, David S; Manduca, Armando; Conners, Amy Lynn; Whaley, Dana H; Scott, Christopher G; Carter, Rickey E; Rhodes, Deborah J; O'Connor, Michael K; Vachon, Celine M

2018-06-05

Background parenchymal uptake (BPU), which refers to the level of Tc-99m sestamibi uptake within normal fibroglandular tissue on molecular breast imaging (MBI), has been identified as a breast cancer risk factor, independent of mammographic density. Prior analyses have used subjective categories to describe BPU. We evaluate a new quantitative method for assessing BPU by testing its reproducibility, comparing quantitative results with previously established subjective BPU categories, and determining the association of quantitative BPU with breast cancer risk. Two nonradiologist operators independently performed region-of-interest analysis on MBI images viewed in conjunction with corresponding digital mammograms. Quantitative BPU was defined as a unitless ratio of the average pixel intensity (counts/pixel) within the fibroglandular tissue versus the average pixel intensity in fat. Operator agreement and the correlation of quantitative BPU measures with subjective BPU categories assessed by expert radiologists were determined. Percent density on mammograms was estimated using Cumulus. The association of quantitative BPU with breast cancer (per one unit BPU) was examined within an established case-control study of 62 incident breast cancer cases and 177 matched controls. Quantitative BPU ranged from 0.4 to 3.2 across all subjects and was on average higher in cases compared to controls (1.4 versus 1.2, p < 0.007 for both operators). Quantitative BPU was strongly correlated with subjective BPU categories (Spearman's r = 0.59 to 0.69, p < 0.0001, for each paired combination of two operators and two radiologists). Interoperator and intraoperator agreement in the quantitative BPU measure, assessed by intraclass correlation, was 0.92 and 0.98, respectively. Quantitative BPU measures showed either no correlation or weak negative correlation with mammographic percent density. In a model adjusted for body mass index and percent density, higher quantitative BPU was associated with increased risk of breast cancer for both operators (OR = 4.0, 95% confidence interval (CI) 1.6-10.1, and 2.4, 95% CI 1.2-4.7). Quantitative measurement of BPU, defined as the ratio of average counts in fibroglandular tissue relative to that in fat, can be reliably performed by nonradiologist operators with a simple region-of-interest analysis tool. Similar to results obtained with subjective BPU categories, quantitative BPU is a functional imaging biomarker of breast cancer risk, independent of mammographic density and hormonal factors.

Building a Better Model: A Personalized Breast Cancer Risk Model Incorporating Breast Density to Stratify Risk and Improve Application of Resources

DTIC Science & Technology

2014-10-01

density using automated methods will be optimized during this study through the evaluation of outlier correction, comparison of several different...7 VBD comparison y = 1.3477x - 1.3764 R2 = 0.8213 0 10 20 30 40 50 60 70 0 10 20 30 40 50 VBD volpara [%] VB D cu m ul us [% ] VBD cumulus...Access database and chart review. 5c. Conduct chart review for selected cases (month 4-6). Comparison of information from the Breast Cancer

The effect of change in body mass index on volumetric measures of mammographic density

PubMed Central

Hart, Vicki; Reeves, Katherine W.; Sturgeon, Susan R.; Reich, Nicholas G.; Sievert, Lynnette Leidy; Kerlikowske, Karla; Ma, Lin; Shepherd, John; Tice, Jeffrey A.; Mahmoudzadeh, Amir Pasha; Malkov, Serghei; Sprague, Brian L.

2015-01-01

Background Understanding how changes in body mass index (BMI) relate to changes in mammographic density is necessary to evaluate adjustment for BMI gain/loss in studies of change in density and breast cancer risk. Increase in BMI has been associated with a decrease in percent density, but the effect on change in absolute dense area or volume is unclear. Methods We examined the association between change in BMI and change in volumetric breast density among 24,556 women in the San Francisco Mammography Registry from 2007-2013. Height and weight were self-reported at the time of mammography. Breast density was assessed using single x-ray absorptiometry measurements. Cross-sectional and longitudinal associations between BMI and dense volume (DV), non-dense volume (NDV) and percent dense volume (PDV) were assessed using multivariable linear regression models, adjusted for demographics, risk factors, and reproductive history. Results In cross-sectional analysis, BMI was positively associated with DV (β=2.95 cm3, 95% CI 2.69, 3.21) and inversely associated with PDV (β=-2.03%, 95% CI -2.09, -1.98). In contrast, increasing BMI was longitudinally associated with a decrease in both DV (β=-1.01 cm3, 95% CI -1.59, -0.42) and PDV (β=-1.17%, 95% CI -1.31, -1.04). These findings were consistent for both pre- and postmenopausal women. Conclusion Our findings support an inverse association between change in BMI and change in PDV. The association between increasing BMI and decreasing DV requires confirmation. Impact Longitudinal studies of PDV and breast cancer risk, or those using PDV as an indicator of breast cancer risk, should evaluate adjustment for change in BMI. PMID:26315554

Collagen Matrix Density Drives the Metabolic Shift in Breast Cancer Cells.

PubMed

Morris, Brett A; Burkel, Brian; Ponik, Suzanne M; Fan, Jing; Condeelis, John S; Aguirre-Ghiso, Julio A; Castracane, James; Denu, John M; Keely, Patricia J

2016-11-01

Increased breast density attributed to collagen I deposition is associated with a 4-6 fold increased risk of developing breast cancer. Here, we assessed cellular metabolic reprogramming of mammary carcinoma cells in response to increased collagen matrix density using an in vitro 3D model. Our initial observations demonstrated changes in functional metabolism in both normal mammary epithelial cells and mammary carcinoma cells in response to changes in matrix density. Further, mammary carcinoma cells grown in high density collagen matrices displayed decreased oxygen consumption and glucose metabolism via the tricarboxylic acid (TCA) cycle compared to cells cultured in low density matrices. Despite decreased glucose entry into the TCA cycle, levels of glucose uptake, cell viability, and ROS were not different between high and low density matrices. Interestingly, under high density conditions the contribution of glutamine as a fuel source to drive the TCA cycle was significantly enhanced. These alterations in functional metabolism mirrored significant changes in the expression of metabolic genes involved in glycolysis, oxidative phosphorylation, and the serine synthesis pathway. This study highlights the broad importance of the collagen microenvironment to cellular expression profiles, and shows that changes in density of the collagen microenvironment can modulate metabolic shifts of cancer cells. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Habitat use of woodpeckers in the Big Woods of eastern Arkansas

USGS Publications Warehouse

Krementz, David G.; Lehnen, Sarah E.; Luscier, J.D.

2012-01-01

The Big Woods of eastern Arkansas contain some of the highest densities of woodpeckers recorded within bottomland hardwood forests of the southeastern United States. A better understanding of habitat use patterns by these woodpeckers is a priority for conservationists seeking to maintain these high densities in the Big Woods and the Lower Mississippi Alluvial Valley as a whole. Hence, we used linear mixed-effects and linear models to estimate the importance of habitat characteristics to woodpecker density in the Big Woods during the breeding seasons of 2006 and 2007 and the winter of 2007. Northern flicker Colaptes auratus density was negatively related to tree density both for moderate (. 25 cm diameter at breast height) and larger trees (>61 cm diameter at breast height). Red-headed woodpeckers Melanerpes erythrocephalus also had a negative relationship with density of large (. 61 cm diameter at breast height) trees. Bark disfiguration (an index of tree health) was negatively related to red-bellied woodpecker Melanerpes carolinus and yellow-bellied sapsucker Sphyrapicus varius densities. No measured habitat variables explained pileated woodpecker Dryocopus pileatus density. Overall, the high densities of woodpeckers observed in our study suggest that the current forest management of the Big Woods of Arkansas is meeting the nesting, roosting, and foraging requirements for these birds.

Childhood factors associated with mammographic density in adult women.

PubMed

Lope, Virginia; Pérez-Gómez, Beatriz; Moreno, María Pilar; Vidal, Carmen; Salas-Trejo, Dolores; Ascunce, Nieves; Román, Isabel González; Sánchez-Contador, Carmen; Santamariña, María Carmen; Carrete, Jose Antonio Vázquez; Collado-García, Francisca; Pedraz-Pingarrón, Carmen; Ederra, María; Ruiz-Perales, Francisco; Peris, Mercé; Abad, Soledad; Cabanes, Anna; Pollán, Marina

2011-12-01

Growth and development factors could contribute to the development of breast cancer associated with an increase in mammographic density. This study examines the influence of certain childhood-related, socio-demographic and anthropometric variables on mammographic density in adult woman. The study covered 3574 women aged 45-68 years, participating in breast cancer-screening programmes in seven Spanish cities. Based on a craniocaudal mammogram, blind, anonymous measurement of mammographic density was made by a single radiologist, using Boyd's semiquantitative scale. Data associated with the early stages of life were obtained from a direct survey. Ordinal logistic regression and generalised linear models were employed to estimate the association between mammographic density and the variables covered by the questionnaire. Screening programme was introduced as a random effects term. Age, number of children, body mass index (BMI) and other childhood-related variables were used as adjustment variables, and stratified by menopausal status. A total of 811 women (23%) presented mammographic density of over 50%, and 5% of densities exceeded 75%. Our results show a greater prevalence of high mammographic density in women with low prepubertal weight (OR: 1.18; 95% CI: 1.02-1.36); marked prepubertal height (OR: 1.25; 95% CI: 0.97-1.60) and advanced age of their mothers at their birth (>39 years: OR: 1.28; 95% CI: 1.03-1.60); and a lower prevalence of high mammographic density in women with higher prepubertal weight, low birth weight and earlier menarche. The influence of these early-life factors may be explained by greater exposure to hormones and growth factors during the development of the breast gland, when breast tissue would be particularly susceptible to proliferative and carcinogenic stimulus.

Building a Better Model: A Personalized Breast Cancer Risk Model Incorporating Breast Density to Stratify Risk and Improve Application of Resources

DTIC Science & Technology

2013-10-01

MAMMOGRAM 5 OTHER BREAST IMAGING TEST (E.G., MRI , ULTRASOUND) 6 THEMOGRAPHY 7 NOTHING 8 DK/REF PROBE: ANYTHING ELSE? IF NECESSARY: JUST THE...SKIP IF HOWCHECK = 5] Have you ever had any other breast imaging procedure designed to detect breast cancer (for example, an MRI or ultrasound? 1 YES...SELECT ALL THAT APPLY) 1 Another mammogram 2 Ultrasound of the breast 3 MRI of the breast 4 OTHER [specify:] 31 5 NONE 6 DK/REF {Q: ADDSURG

Comparison of sound speed measurements on two different ultrasound tomography devices

NASA Astrophysics Data System (ADS)

Sak, Mark; Duric, Neb; Littrup, Peter; Bey-Knight, Lisa; Sherman, Mark; Gierach, Gretchen; Malyarenko, Antonina

2014-03-01

Ultrasound tomography (UST) employs sound waves to produce three-dimensional images of breast tissue and precisely measures the attenuation of sound speed secondary to breast tissue composition. High breast density is a strong breast cancer risk factor and sound speed is directly proportional to breast density. UST provides a quantitative measure of breast density based on three-dimensional imaging without compression, thereby overcoming the shortcomings of many other imaging modalities. The quantitative nature of the UST breast density measures are tied to an external standard, so sound speed measurement in breast tissue should be independent of specific hardware. The work presented here compares breast sound speed measurement obtained with two different UST devices. The Computerized Ultrasound Risk Evaluation (CURE) system located at the Karmanos Cancer Institute in Detroit, Michigan was recently replaced with the SoftVue ultrasound tomographic device. Ongoing clinical trials have used images generated from both sets of hardware, so maintaining consistency in sound speed measurements is important. During an overlap period when both systems were in the same exam room, a total of 12 patients had one or both of their breasts imaged on both systems on the same day. There were 22 sound speed scans analyzed from each system and the average breast sound speeds were compared. Images were either reconstructed using saved raw data (for both CURE and SoftVue) or were created during the image acquisition (saved in DICOM format for SoftVue scans only). The sound speed measurements from each system were strongly and positively correlated with each other. The average difference in sound speed between the two sets of data was on the order of 1-2 m/s and this result was not statistically significant. The only sets of images that showed a statistical difference were the DICOM images created during the SoftVue scan compared to the SoftVue images reconstructed from the raw data. However, the discrepancy between the sound speed values could be easily handled by uniformly increasing the DICOM sound speed by approximately 0.5 m/s. These results suggest that there is no fundamental difference in sound speed measurement for the two systems and support combining data generated with these instruments in future studies.

Estrogens and women's health: interrelation of coronary heart disease, breast cancer and osteoporosis.

PubMed

Kuller, L H; Matthews, K A; Meilahn, E N

2000-11-30

The determinants of blood levels of estrogen, estrogen metabolites, and relation to receptors and post-transitional effects are the likely primary cause of breast cancer. Very high risk women for breast cancer can now be identified by measuring bone mineral density and hormone levels. These high risk women have rates of breast cancer similar to risk of myocardial infarction. They are candidates for SERM therapies to reduce risk of breast cancer. The completion of the Women's Health Initiative and other such trials will likely provide a definite association of risk and benefit of both estrogen alone and estrogen-progesterone therapy, coronary heart disease, osteoporotic fracture, and breast cancer. The potential intervention of hormone replacement therapy, obesity, or weight gain and increased atherogenic lipoproteinemia may be of concern and confound the results of clinical trials. Estrogens, clearly, are important in the risk of bone loss and osteoporotic fracture. Obesity is the primary determinant of postmenopausal estrogen levels and reduced risk of fracture. Weight reduction may increase rates of bone loss and fracture. Clinical trials that evaluate weight loss should monitor effects on bone. The beneficial addition of increased physical activity, higher dose of calcium or vitamin D, or use of bone reabsorption drugs in coordination with weight loss should be evaluated. Any therapy that raises blood estrogen or metabolite activity and decreases bone loss may increase risk of breast cancer. Future clinical trials must evaluate multiple endpoints such as CHD, osteoporosis, and breast cancer within the study. The use of surrogate markers such as bone mineral density, coronary calcium, carotid intimal medial thickness and plaque, endothelial function, breast density, hormone levels and metabolites could enhance the evaluation of risk factors, genetic-environmental intervention, and new therapies.

Gene expression profiling of human breast tissue samples using SAGE-Seq.

PubMed

Wu, Zhenhua Jeremy; Meyer, Clifford A; Choudhury, Sibgat; Shipitsin, Michail; Maruyama, Reo; Bessarabova, Marina; Nikolskaya, Tatiana; Sukumar, Saraswati; Schwartzman, Armin; Liu, Jun S; Polyak, Kornelia; Liu, X Shirley

2010-12-01

We present a powerful application of ultra high-throughput sequencing, SAGE-Seq, for the accurate quantification of normal and neoplastic mammary epithelial cell transcriptomes. We develop data analysis pipelines that allow the mapping of sense and antisense strands of mitochondrial and RefSeq genes, the normalization between libraries, and the identification of differentially expressed genes. We find that the diversity of cancer transcriptomes is significantly higher than that of normal cells. Our analysis indicates that transcript discovery plateaus at 10 million reads/sample, and suggests a minimum desired sequencing depth around five million reads. Comparison of SAGE-Seq and traditional SAGE on normal and cancerous breast tissues reveals higher sensitivity of SAGE-Seq to detect less-abundant genes, including those encoding for known breast cancer-related transcription factors and G protein-coupled receptors (GPCRs). SAGE-Seq is able to identify genes and pathways abnormally activated in breast cancer that traditional SAGE failed to call. SAGE-Seq is a powerful method for the identification of biomarkers and therapeutic targets in human disease.

Segmentation of the whole breast from low-dose chest CT images

NASA Astrophysics Data System (ADS)

Liu, Shuang; Salvatore, Mary; Yankelevitz, David F.; Henschke, Claudia I.; Reeves, Anthony P.

2015-03-01

The segmentation of whole breast serves as the first step towards automated breast lesion detection. It is also necessary for automatically assessing the breast density, which is considered to be an important risk factor for breast cancer. In this paper we present a fully automated algorithm to segment the whole breast in low-dose chest CT images (LDCT), which has been recommended as an annual lung cancer screening test. The automated whole breast segmentation and potential breast density readings as well as lesion detection in LDCT will provide useful information for women who have received LDCT screening, especially the ones who have not undergone mammographic screening, by providing them additional risk indicators for breast cancer with no additional radiation exposure. The two main challenges to be addressed are significant range of variations in terms of the shape and location of the breast in LDCT and the separation of pectoral muscles from the glandular tissues. The presented algorithm achieves robust whole breast segmentation using an anatomy directed rule-based method. The evaluation is performed on 20 LDCT scans by comparing the segmentation with ground truth manually annotated by a radiologist on one axial slice and two sagittal slices for each scan. The resulting average Dice coefficient is 0.880 with a standard deviation of 0.058, demonstrating that the automated segmentation algorithm achieves results consistent with manual annotations of a radiologist.

Stereotactic core needle breast biopsy marker migration: An analysis of factors contributing to immediate marker migration.

PubMed

Jain, Ashali; Khalid, Maria; Qureshi, Muhammad M; Georgian-Smith, Dianne; Kaplan, Jonah A; Buch, Karen; Grinstaff, Mark W; Hirsch, Ariel E; Hines, Neely L; Anderson, Stephan W; Gallagher, Katherine M; Bates, David D B; Bloch, B Nicolas

2017-11-01

To evaluate breast biopsy marker migration in stereotactic core needle biopsy procedures and identify contributing factors. This retrospective study analyzed 268 stereotactic biopsy markers placed in 263 consecutive patients undergoing stereotactic biopsies using 9G vacuum-assisted devices from August 2010-July 2013. Mammograms were reviewed and factors contributing to marker migration were evaluated. Basic descriptive statistics were calculated and comparisons were performed based on radiographically-confirmed marker migration. Of the 268 placed stereotactic biopsy markers, 35 (13.1%) migrated ≥1 cm from their biopsy cavity. Range: 1-6 cm; mean (± SD): 2.35 ± 1.22 cm. Of the 35 migrated biopsy markers, 9 (25.7%) migrated ≥3.5 cm. Patient age, biopsy pathology, number of cores, and left versus right breast were not associated with migration status (P> 0.10). Global fatty breast density (P= 0.025) and biopsy in the inner region of breast (P = 0.031) were associated with marker migration. Superior biopsy approach (P= 0.025), locally heterogeneous breast density, and t-shaped biopsy markers (P= 0.035) were significant for no marker migration. Multiple factors were found to influence marker migration. An overall migration rate of 13% supports endeavors of research groups actively developing new biopsy marker designs for improved resistance to migration. • Breast biopsy marker migration is documented in 13% of 268 procedures. • Marker migration is affected by physical, biological, and pathological factors. • Breast density, marker shape, needle approach etc. affect migration. • Study demonstrates marker migration prevalence; marker design improvements are needed.

Feasibility of anomaly detection and characterization using trans-admittance mammography with 60 × 60 electrode array

NASA Astrophysics Data System (ADS)

Zhao, Mingkang; Wi, Hun; Lee, Eun Jung; Woo, Eung Je; In Oh, Tong

2014-10-01

Electrical impedance imaging has the potential to detect an early stage of breast cancer due to higher admittivity values compared with those of normal breast tissues. The tumor size and extent of axillary lymph node involvement are important parameters to evaluate the breast cancer survival rate. Additionally, the anomaly characterization is required to distinguish a malignant tumor from a benign tumor. In order to overcome the limitation of breast cancer detection using impedance measurement probes, we developed the high density trans-admittance mammography (TAM) system with 60 × 60 electrode array and produced trans-admittance maps obtained at several frequency pairs. We applied the anomaly detection algorithm to the high density TAM system for estimating the volume and position of breast tumor. We tested four different sizes of anomaly with three different conductivity contrasts at four different depths. From multifrequency trans-admittance maps, we can readily observe the transversal position and estimate its volume and depth. Specially, the depth estimated values were obtained accurately, which were independent to the size and conductivity contrast when applying the new formula using Laplacian of trans-admittance map. The volume estimation was dependent on the conductivity contrast between anomaly and background in the breast phantom. We characterized two testing anomalies using frequency difference trans-admittance data to eliminate the dependency of anomaly position and size. We confirmed the anomaly detection and characterization algorithm with the high density TAM system on bovine breast tissue. Both results showed the feasibility of detecting the size and position of anomaly and tissue characterization for screening the breast cancer.

Prediction of near-term breast cancer risk using a Bayesian belief network

NASA Astrophysics Data System (ADS)

Zheng, Bin; Ramalingam, Pandiyarajan; Hariharan, Harishwaran; Leader, Joseph K.; Gur, David

2013-03-01

Accurately predicting near-term breast cancer risk is an important prerequisite for establishing an optimal personalized breast cancer screening paradigm. In previous studies, we investigated and tested the feasibility of developing a unique near-term breast cancer risk prediction model based on a new risk factor associated with bilateral mammographic density asymmetry between the left and right breasts of a woman using a single feature. In this study we developed a multi-feature based Bayesian belief network (BBN) that combines bilateral mammographic density asymmetry with three other popular risk factors, namely (1) age, (2) family history, and (3) average breast density, to further increase the discriminatory power of our cancer risk model. A dataset involving "prior" negative mammography examinations of 348 women was used in the study. Among these women, 174 had breast cancer detected and verified in the next sequential screening examinations, and 174 remained negative (cancer-free). A BBN was applied to predict the risk of each woman having cancer detected six to 18 months later following the negative screening mammography. The prediction results were compared with those using single features. The prediction accuracy was significantly increased when using the BBN. The area under the ROC curve increased from an AUC=0.70 to 0.84 (p<0.01), while the positive predictive value (PPV) and negative predictive value (NPV) also increased from a PPV=0.61 to 0.78 and an NPV=0.65 to 0.75, respectively. This study demonstrates that a multi-feature based BBN can more accurately predict the near-term breast cancer risk than with a single feature.

Calcium and Vitamin D Supplementation and Loss of Bone Mineral Density in Women Undergoing Breast Cancer Therapy

PubMed Central

Datta, Mridul; Schwartz, Gary G.

2013-01-01

An unintended consequence of breast cancer therapies is an increased risk of osteoporosis due to accelerated bone loss. We conducted a systematic review of calcium and/or vitamin D (Ca±D) supplementation trials for maintaining bone mineral density (BMD) in women with breast cancer using the “before-after” data from the Ca±D supplemented comparison group of trials evaluating the effect of drugs such as bisphosphonates on BMD. Whether Ca±D supplements increase BMD in women undergoing breast cancer therapy has never been tested against an unsupplemented control group. However, results from 16 trials indicate that the Ca±D doses tested (500-1500 mg calcium; 200-1000 IU vitamin D) were inadequate to prevent BMD loss in these women. Cardiovascular disease is the main cause of mortality in women with breast cancer. Because calcium supplements may increase cardiovascular disease risk, future trials should evaluate the safety and efficacy of Ca±D supplementation in women undergoing breast cancer therapy. PMID:23932583

What we need to know about dense breasts: implications for breast cancer screening.

PubMed

Carreira Gómez, M C; Estrada Blan, M C

High breast density and its relationship to the risk of breast cancer has become a hot topic in the medical literature and in the lay press, especially in the United States, where it has brought about changes in the legal framework that require radiologists to inform clinicians and patients about breast density. Radiologists, who are mainly responsible for this information, need to know the scientific evidence and controversies regarding this subject. The discussion is centered on the real importance of the risk, the limitation that not having standardized methods of measurement represents, and the possible application of complementary screening techniques (ultrasound, magnetic resonance imaging, or tomosynthesis) for which clear recommendations have yet to be established. We need controlled studies that evaluate the application of these techniques in women with dense breasts, including the possibility that they can lead to overdiagnosis. Copyright © 2016 SERAM. Publicado por Elsevier España, S.L.U. All rights reserved.

Ultrasound tomography imaging with waveform sound speed: parenchymal changes in women undergoing tamoxifen therapy

NASA Astrophysics Data System (ADS)

Sak, Mark; Duric, Neb; Littrup, Peter; Sherman, Mark; Gierach, Gretchen

2017-03-01

Ultrasound tomography (UST) is an emerging modality that can offer quantitative measurements of breast density. Recent breakthroughs in UST image reconstruction involve the use of a waveform reconstruction as opposed to a raybased reconstruction. The sound speed (SS) images that are created using the waveform reconstruction have a much higher image quality. These waveform images offer improved resolution and contrasts between regions of dense and fatty tissues. As part of a study that was designed to assess breast density changes using UST sound speed imaging among women undergoing tamoxifen therapy, UST waveform sound speed images were then reconstructed for a subset of participants. These initial results show that changes to the parenchymal tissue can more clearly be visualized when using the waveform sound speed images. Additional quantitative testing of the waveform images was also started to test the hypothesis that waveform sound speed images are a more robust measure of breast density than ray-based reconstructions. Further analysis is still needed to better understand how tamoxifen affects breast tissue.

Association between mammographic density and pregnancies relative to age and BMI: a breast cancer case-only analysis.

PubMed

Hack, Carolin C; Emons, Julius; Jud, Sebastian M; Heusinger, Katharina; Adler, Werner; Gass, Paul; Haeberle, Lothar; Heindl, Felix; Hein, Alexander; Schulz-Wendtland, Rüdiger; Uder, Michael; Hartmann, Arndt; Beckmann, Matthias W; Fasching, Peter A; Pöhls, Uwe G

2017-12-01

Percentage mammographic density (PMD) is a major risk factor for breast cancer (BC). It is strongly associated with body mass index (BMI) and age, which are themselves risk factors for breast cancer. This analysis investigated the association between the number of full-term pregnancies and PMD in different subgroups relative to age and BMI. Patients were identified in the breast cancer database of the University Breast Center for Franconia. A total of 2410 patients were identified, for whom information on parity, age, and BMI, and a mammogram from the time of first diagnosis were available for assessing PMD. Linear regression analyses were conducted to investigate the influence on PMD of the number of full-term pregnancies (FTPs), age, BMI, and interaction terms between them. As in previous studies, age, number of FTPs, and BMI were found to be associated with PMD in the expected direction. However, including the respective interaction terms improved the prediction of PMD even further. Specifically, the association between PMD and the number of FTPs differed in young patients under the age of 45 (mean decrease of 0.37 PMD units per pregnancy) from the association in older age groups (mean decrease between 2.29 and 2.39 PMD units). BMI did not alter the association between PMD and the number of FTPs. The effect of pregnancies on mammographic density does not appear to become apparent before the age of menopause. The mechanism that drives the effect of pregnancies on mammographic density appears to be counter-regulated by other influences on mammographic density in younger patients.

Lymphocyte density determined by computational pathology validated as a predictor of response to neoadjuvant chemotherapy in breast cancer: secondary analysis of the ARTemis trial.

PubMed

Ali, H R; Dariush, A; Thomas, J; Provenzano, E; Dunn, J; Hiller, L; Vallier, A-L; Abraham, J; Piper, T; Bartlett, J M S; Cameron, D A; Hayward, L; Brenton, J D; Pharoah, P D P; Irwin, M J; Walton, N A; Earl, H M; Caldas, C

2017-08-01

We have previously shown lymphocyte density, measured using computational pathology, is associated with pathological complete response (pCR) in breast cancer. The clinical validity of this finding in independent studies, among patients receiving different chemotherapy, is unknown. The ARTemis trial randomly assigned 800 women with early stage breast cancer between May 2009 and January 2013 to three cycles of docetaxel, followed by three cycles of fluorouracil, epirubicin and cyclophosphamide once every 21 days with or without four cycles of bevacizumab. The primary endpoint was pCR (absence of invasive cancer in the breast and lymph nodes). We quantified lymphocyte density within haematoxylin and eosin (H&E) whole slide images using our previously described computational pathology approach: for every detected lymphocyte the average distance to the nearest 50 lymphocytes was calculated and the density derived from this statistic. We analyzed both pre-treatment biopsies and post-treatment surgical samples of the tumour bed. Of the 781 patients originally included in the primary endpoint analysis of the trial, 609 (78%) were included for baseline lymphocyte density analyses and a subset of 383 (49% of 781) for analyses of change in lymphocyte density. The main reason for loss of patients was the availability of digitized whole slide images. Pre-treatment lymphocyte density modelled as a continuous variable was associated with pCR on univariate analysis (odds ratio [OR], 2.92; 95% CI, 1.78-4.85; P < 0.001) and after adjustment for clinical covariates (OR, 2.13; 95% CI, 1.24-3.67; P = 0.006). Increased pre- to post-treatment lymphocyte density showed an independent inverse association with pCR (adjusted OR, 0.1; 95% CI, 0.033-0.31; P < 0.001). Lymphocyte density in pre-treatment biopsies was validated as an independent predictor of pCR in breast cancer. Computational pathology is emerging as a viable and objective means of identifying predictive biomarkers for cancer patients. NCT01093235. © The Author 2017. Published by Oxford University Press on behalf of the European Society for Medical Oncology.

TU-AB-201-11: A Novel Theoretical Framework for MRI-Only Image Guided LDR Prostate and Breast Brachytherapy Implant Dosimetry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Soliman, A; Elzibak, A; Fatemi, A

Purpose: To propose a novel framework for accurate model-based dose calculations using only MR images for LDR prostate and breast seed implant brachytherapy. Methods: Model-based dose calculation methodologies recommended by TG-186 require further knowledge about specific tissue composition, which is challenging with MRI. However, relying on MRI-only for implant dosimetry would reduce the soft tissue delineation uncertainty, costs, and uncertainties associated with multi-modality registration and fusion processes. We propose a novel framework to address this problem using quantitative MRI acquisitions and reconstruction techniques. The framework includes three steps: (1) Identify the locations of seeds(2) Identify the presence (or absence) ofmore » calcification(s)(3) Quantify the water and fat content in the underlying tissueSteps (1) and (2) consider the sources that limit patient dosimetry, particularly the inter-seed attenuation and the calcified regions; while step (3) targets the quantification of the tissue composition to consider the heterogeneities in the medium. Our preliminary work has shown that the seeds and the calcifications can be identified with MRI using both the magnitude and the phase images. By employing susceptibility-weighted imaging with specific post-processing techniques, the phase images can be further explored to distinguish the seeds from the calcifications. Absolute quantification of tissue, water, and fat content is feasible and was previously demonstrated in phantoms and in-vivo applications, particularly for brain diseases. The approach relies on the proportionality of the MR signal to the number of protons in an image volume. By employing appropriate correction algorithms for T1 - and T2*-related biases, B1 transmit and receive field inhomogeneities, absolute water/fat content can be determined. Results: By considering calcification and interseed attenuation, and through the knowledge of water and fat mass density, accurate patient-specific implant dosimetry can be achieved with MRI-only. Conclusion: The proposed framework showed that model-based dose calculation is feasible using MRI-only state-of-the-art techniques.« less

Mammographic Breast Density Evaluation in Korean Women Using Fully Automated Volumetric Assessment

PubMed Central

2016-01-01

The purpose was to present mean breast density of Korean women according to age using fully automated volumetric assessment. This study included 5,967 screening normal or benign mammograms (mean age, 46.2 ± 9.7; range, 30–89 years), from cancer-screening program. We evaluated mean fibroglandular tissue volume, breast tissue volume, volumetric breast density (VBD), and the results were 53.7 ± 30.8 cm3, 383.8 ± 205.2 cm3, and 15.8% ± 7.3%. The frequency of dense breasts and mean VBD by age group were 94.3% and 19.1% ± 6.7% for the 30s (n = 1,484), 91.4% and 17.2% ± 6.8% for the 40s (n = 2,706), 72.2% and 12.4% ± 6.2% for the 50s (n = 1,138), 44.0% and 8.6% ± 4.3% for the 60s (n = 89), 39.1% and 8.0% ± 3.8% for the 70s (n = 138), and 39.1% and 8.0% ± 3.5% for the 80s (n = 12). The frequency of dense breasts was higher in younger women (n = 4,313, 92.3%) than older women (n = 1,654, 59.8%). Mean VBD decreased with aging or menopause, and was about 16% for 46-year-old-Korean women, much higher than in other countries. The proportion of dense breasts sharply decreases in Korean women between 40 and 69 years of age. PMID:26955249

SU-E-I-59: Investigation of the Usefulness of a Standard Deviation and Mammary Gland Density as Indexes for Mammogram Classification.

PubMed

Takarabe, S; Yabuuchi, H; Morishita, J

2012-06-01

To investigate the usefulness of the standard deviation of pixel values in a whole mammary glands region and the percentage of a high- density mammary glands region to a whole mammary glands region as features for classification of mammograms into four categories based on the ACR BI-RADS breast composition. We used 36 digital mediolateral oblique view mammograms (18 patients) approved by our IRB. These images were classified into the four categories of breast compositions by an experienced breast radiologist and the results of the classification were regarded as a gold standard. First, a whole mammary region in a breast was divided into two regions such as a high-density mammary glands region and a low/iso-density mammary glands region by using a threshold value that was obtained from the pixel values corresponding to a pectoral muscle region. Then the percentage of a high-density mammary glands region to a whole mammary glands region was calculated. In addition, as a new method, the standard deviation of pixel values in a whole mammary glands region was calculated as an index based on the intermingling of mammary glands and fats. Finally, all mammograms were classified by using the combination of the percentage of a high-density mammary glands region and the standard deviation of each image. The agreement rates of the classification between our proposed method and gold standard was 86% (31/36). This result signified that our method has the potential to classify mammograms. The combination of the standard deviation of pixel values in a whole mammary glands region and the percentage of a high-density mammary glands region to a whole mammary glands region was available as features to classify mammograms based on the ACR BI- RADS breast composition. © 2012 American Association of Physicists in Medicine.

Volume and tissue composition preserving deformation of breast CT images to simulate breast compression in mammographic imaging

NASA Astrophysics Data System (ADS)

Han, Tao; Chen, Lingyun; Lai, Chao-Jen; Liu, Xinming; Shen, Youtao; Zhong, Yuncheng; Ge, Shuaiping; Yi, Ying; Wang, Tianpeng; Shaw, Chris C.

2009-02-01

Images of mastectomy breast specimens have been acquired with a bench top experimental Cone beam CT (CBCT) system. The resulting images have been segmented to model an uncompressed breast for simulation of various CBCT techniques. To further simulate conventional or tomosynthesis mammographic imaging for comparison with the CBCT technique, a deformation technique was developed to convert the CT data for an uncompressed breast to a compressed breast without altering the breast volume or regional breast density. With this technique, 3D breast deformation is separated into two 2D deformations in coronal and axial views. To preserve the total breast volume and regional tissue composition, each 2D deformation step was achieved by altering the square pixels into rectangular ones with the pixel areas unchanged and resampling with the original square pixels using bilinear interpolation. The compression was modeled by first stretching the breast in the superior-inferior direction in the coronal view. The image data were first deformed by distorting the voxels with a uniform distortion ratio. These deformed data were then deformed again using distortion ratios varying with the breast thickness and re-sampled. The deformation procedures were applied in the axial view to stretch the breast in the chest wall to nipple direction while shrinking it in the mediolateral to lateral direction re-sampled and converted into data for uniform cubic voxels. Threshold segmentation was applied to the final deformed image data to obtain the 3D compressed breast model. Our results show that the original segmented CBCT image data were successfully converted into those for a compressed breast with the same volume and regional density preserved. Using this compressed breast model, conventional and tomosynthesis mammograms were simulated for comparison with CBCT.

tRNAs as Biomarkers and Regulators for Breast Cancer

DTIC Science & Technology

2009-08-01

codon usage and tRNA genes of 18 unicellular organisms and quantification of Bacillus subtilis tRNAs: gene expression level and species-specific...CONTRACTING ORGANIZATION : The University of Chicago Chicago, IL 60637 REPORT...Geslain, Q. Dai. 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT

Feasibility of generating quantitative composition images in dual energy mammography: a simulation study

NASA Astrophysics Data System (ADS)

Lee, Donghoon; Kim, Ye-seul; Choi, Sunghoon; Lee, Haenghwa; Choi, Seungyeon; Kim, Hee-Joung

2016-03-01

Breast cancer is one of the most common malignancies in women. For years, mammography has been used as the gold standard for localizing breast cancer, despite its limitation in determining cancer composition. Therefore, the purpose of this simulation study is to confirm the feasibility of obtaining tumor composition using dual energy digital mammography. To generate X-ray sources for dual energy mammography, 26 kVp and 39 kVp voltages were generated for low and high energy beams, respectively. Additionally, the energy subtraction and inverse mapping functions were applied to provide compositional images. The resultant images showed that the breast composition obtained by the inverse mapping function with cubic fitting achieved the highest accuracy and least noise. Furthermore, breast density analysis with cubic fitting showed less than 10% error compare to true values. In conclusion, this study demonstrated the feasibility of creating individual compositional images and capability of analyzing breast density effectively.

Computer-aided Assessment of Regional Abdominal Fat with Food Residue Removal in CT

PubMed Central

Makrogiannis, Sokratis; Caturegli, Giorgio; Davatzikos, Christos; Ferrucci, Luigi

2014-01-01

Rationale and Objectives Separate quantification of abdominal subcutaneous and visceral fat regions is essential to understand the role of regional adiposity as risk factor in epidemiological studies. Fat quantification is often based on computed tomography (CT) because fat density is distinct from other tissue densities in the abdomen. However, the presence of intestinal food residues with densities similar to fat may reduce fat quantification accuracy. We introduce an abdominal fat quantification method in CT with interest in food residue removal. Materials and Methods Total fat was identified in the feature space of Hounsfield units and divided into subcutaneous and visceral components using model-based segmentation. Regions of food residues were identified and removed from visceral fat using a machine learning method integrating intensity, texture, and spatial information. Cost-weighting and bagging techniques were investigated to address class imbalance. Results We validated our automated food residue removal technique against semimanual quantifications. Our feature selection experiments indicated that joint intensity and texture features produce the highest classification accuracy at 95%. We explored generalization capability using k-fold cross-validation and receiver operating characteristic (ROC) analysis with variable k. Losses in accuracy and area under ROC curve between maximum and minimum k were limited to 0.1% and 0.3%. We validated tissue segmentation against reference semimanual delineations. The Dice similarity scores were as high as 93.1 for subcutaneous fat and 85.6 for visceral fat. Conclusions Computer-aided regional abdominal fat quantification is a reliable computational tool for large-scale epidemiological studies. Our proposed intestinal food residue reduction scheme is an original contribution of this work. Validation experiments indicate very good accuracy and generalization capability. PMID:24119354

Computer-aided assessment of regional abdominal fat with food residue removal in CT.

PubMed

Makrogiannis, Sokratis; Caturegli, Giorgio; Davatzikos, Christos; Ferrucci, Luigi

2013-11-01

Separate quantification of abdominal subcutaneous and visceral fat regions is essential to understand the role of regional adiposity as risk factor in epidemiological studies. Fat quantification is often based on computed tomography (CT) because fat density is distinct from other tissue densities in the abdomen. However, the presence of intestinal food residues with densities similar to fat may reduce fat quantification accuracy. We introduce an abdominal fat quantification method in CT with interest in food residue removal. Total fat was identified in the feature space of Hounsfield units and divided into subcutaneous and visceral components using model-based segmentation. Regions of food residues were identified and removed from visceral fat using a machine learning method integrating intensity, texture, and spatial information. Cost-weighting and bagging techniques were investigated to address class imbalance. We validated our automated food residue removal technique against semimanual quantifications. Our feature selection experiments indicated that joint intensity and texture features produce the highest classification accuracy at 95%. We explored generalization capability using k-fold cross-validation and receiver operating characteristic (ROC) analysis with variable k. Losses in accuracy and area under ROC curve between maximum and minimum k were limited to 0.1% and 0.3%. We validated tissue segmentation against reference semimanual delineations. The Dice similarity scores were as high as 93.1 for subcutaneous fat and 85.6 for visceral fat. Computer-aided regional abdominal fat quantification is a reliable computational tool for large-scale epidemiological studies. Our proposed intestinal food residue reduction scheme is an original contribution of this work. Validation experiments indicate very good accuracy and generalization capability. Published by Elsevier Inc.

[Determination of triclosan and triclocarban in human breast milk by solid-phase extraction and ultra performance liquid chromatography-tandem mass spectrometry].

PubMed

Zhang, Pin; Zhang, Jing; Shi, Ying; Shao, Bing

2015-03-01

An analytical method was developed to simultaneously detect triclosan (TCS) and triclocarban (TCC) in human breast milk using solid-phase extraction (SPE) with ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Samples were extracted by acetonitrile and purified with C -18 SPE cartridge after enzymolysis with β-glucuronidase/arylsulfatase. The chromatographic separation was performed on a Waters ACQUITY UPLC™ HSS T3 column (100 mm x 2. 1 mm, 1. 8 µm) with gradient elution using methanol and water at a flow rate of 0. 3 ml/min. The target analytes were assayed by triple quadrupole mass spectrometer operating in the negative ion mode. Quantification was performed by isotopic internal standard calibration. Satisfactory linearity (r2 > 0. 999) was obtained over the range of 0. 2 - 20. 0 µg/L and 0. 02 - 2. 0 µg/L for triclosan and triclocarban, respectively, with the limits of quantifications (LOQs) of 0. 41 and 0. 03 µg/kg. Average recoveries of two target compounds (spiked at three concentration levels) ranged from 100. 2% to 119. 3%, with the relative standard deviations (RSDs) between 5. 91% and 11. 31% (n =6). Twenty-five real samples (n = 25) were detected containing TCS and TCC at concentrations of < LOQ - 0. 77 µg/kg and < LOQ - 4. 28 µg/kg, respectively. Due to its high sensitivity and good reproductivity, this method can be applied to analyze TCS and TCC in human breast milk.

Digital Breast Tomosynthesis guided Near Infrared Spectroscopy: Volumetric estimates of fibroglandular fraction and breast density from tomosynthesis reconstructions

PubMed Central

Vedantham, Srinivasan; Shi, Linxi; Michaelsen, Kelly E.; Krishnaswamy, Venkataramanan; Pogue, Brian W.; Poplack, Steven P.; Karellas, Andrew; Paulsen, Keith D.

2016-01-01

A multimodality system combining a clinical prototype digital breast tomosynthesis with its imaging geometry modified to facilitate near-infrared spectroscopic imaging has been developed. The accuracy of parameters recovered from near-infrared spectroscopy is dependent on fibroglandular tissue content. Hence, in this study, volumetric estimates of fibroglandular tissue from tomosynthesis reconstructions were determined. A kernel-based fuzzy c-means algorithm was implemented to segment tomosynthesis reconstructed slices in order to estimate fibroglandular content and to provide anatomic priors for near-infrared spectroscopy. This algorithm was used to determine volumetric breast density (VBD), defined as the ratio of fibroglandular tissue volume to the total breast volume, expressed as percentage, from 62 tomosynthesis reconstructions of 34 study participants. For a subset of study participants who subsequently underwent mammography, VBD from mammography matched for subject, breast laterality and mammographic view was quantified using commercial software and statistically analyzed to determine if it differed from tomosynthesis. Summary statistics of the VBD from all study participants were compared with prior independent studies. The fibroglandular volume from tomosynthesis and mammography were not statistically different (p=0.211, paired t-test). After accounting for the compressed breast thickness, which were different between tomosynthesis and mammography, the VBD from tomosynthesis was correlated with (r =0.809, p<0.001), did not statistically differ from (p>0.99, paired t-test), and was linearly related to, the VBD from mammography. Summary statistics of the VBD from tomosynthesis were not statistically different from prior studies using high-resolution dedicated breast computed tomography. The observation of correlation and linear association in VBD between mammography and tomosynthesis suggests that breast density associated risk measures determined for mammography are translatable to tomosynthesis. Accounting for compressed breast thickness is important when it differs between the two modalities. The fibroglandular volume from tomosynthesis reconstructions is similar to mammography indicating suitability for use during near-infrared spectroscopy. PMID:26941961

Expression levels of uridine 5'-diphospho-glucuronosyltransferase genes in breast tissue from healthy women are associated with mammographic density.

PubMed

Haakensen, Vilde D; Biong, Margarethe; Lingjærde, Ole Christian; Holmen, Marit Muri; Frantzen, Jan Ole; Chen, Ying; Navjord, Dina; Romundstad, Linda; Lüders, Torben; Bukholm, Ida K; Solvang, Hiroko K; Kristensen, Vessela N; Ursin, Giske; Børresen-Dale, Anne-Lise; Helland, Aslaug

2010-01-01

Mammographic density (MD), as assessed from film screen mammograms, is determined by the relative content of adipose, connective and epithelial tissue in the female breast. In epidemiological studies, a high percentage of MD confers a four to six fold risk elevation of developing breast cancer, even after adjustment for other known breast cancer risk factors. However, the biologic correlates of density are little known. Gene expression analysis using whole genome arrays was performed on breast biopsies from 143 women; 79 women with no malignancy (healthy women) and 64 newly diagnosed breast cancer patients, both included from mammographic centres. Percent MD was determined using a previously validated, computerized method on scanned mammograms. Significance analysis of microarrays (SAM) was performed to identify genes influencing MD and a linear regression model was used to assess the independent contribution from different variables to MD. SAM-analysis identified 24 genes differentially expressed between samples from breasts with high and low MD. These genes included three uridine 5'-diphospho-glucuronosyltransferase (UGT) genes and the oestrogen receptor gene (ESR1). These genes were down-regulated in samples with high MD compared to those with low MD. The UGT gene products, which are known to inactivate oestrogen metabolites, were also down-regulated in tumour samples compared to samples from healthy individuals. Several single nucleotide polymorphisms (SNPs) in the UGT genes associated with the expression of UGT and other genes in their vicinity were identified. Three UGT enzymes were lower expressed both in breast tissue biopsies from healthy women with high MD and in biopsies from newly diagnosed breast cancers. The association was strongest amongst young women and women using hormonal therapy. UGT2B10 predicts MD independently of age, hormone therapy and parity. Our results indicate that down-regulation of UGT genes in women exposed to female sex hormones is associated with high MD and might increase the risk of breast cancer.

Expression levels of uridine 5'-diphospho-glucuronosyltransferase genes in breast tissue from healthy women are associated with mammographic density

PubMed Central

2010-01-01

Introduction Mammographic density (MD), as assessed from film screen mammograms, is determined by the relative content of adipose, connective and epithelial tissue in the female breast. In epidemiological studies, a high percentage of MD confers a four to six fold risk elevation of developing breast cancer, even after adjustment for other known breast cancer risk factors. However, the biologic correlates of density are little known. Methods Gene expression analysis using whole genome arrays was performed on breast biopsies from 143 women; 79 women with no malignancy (healthy women) and 64 newly diagnosed breast cancer patients, both included from mammographic centres. Percent MD was determined using a previously validated, computerized method on scanned mammograms. Significance analysis of microarrays (SAM) was performed to identify genes influencing MD and a linear regression model was used to assess the independent contribution from different variables to MD. Results SAM-analysis identified 24 genes differentially expressed between samples from breasts with high and low MD. These genes included three uridine 5'-diphospho-glucuronosyltransferase (UGT) genes and the oestrogen receptor gene (ESR1). These genes were down-regulated in samples with high MD compared to those with low MD. The UGT gene products, which are known to inactivate oestrogen metabolites, were also down-regulated in tumour samples compared to samples from healthy individuals. Several single nucleotide polymorphisms (SNPs) in the UGT genes associated with the expression of UGT and other genes in their vicinity were identified. Conclusions Three UGT enzymes were lower expressed both in breast tissue biopsies from healthy women with high MD and in biopsies from newly diagnosed breast cancers. The association was strongest amongst young women and women using hormonal therapy. UGT2B10 predicts MD independently of age, hormone therapy and parity. Our results indicate that down-regulation of UGT genes in women exposed to female sex hormones is associated with high MD and might increase the risk of breast cancer. PMID:20799965

Mammographic evidence of microenvironment changes in tumorous breasts.

PubMed

Marin, Zach; Batchelder, Kendra A; Toner, Brian C; Guimond, Lyne; Gerasimova-Chechkina, Evgeniya; Harrow, Amy R; Arneodo, Alain; Khalil, Andre

2017-04-01

The microenvironment of breast tumors plays a critical role in tumorigenesis. As long as the structural integrity of the microenvironment is upheld, the tumor is suppressed. If tissue structure is lost through disruptions in the normal cell cycle, the microenvironment may act as a tumor promoter. Therefore, the properties that distinguish between healthy and tumorous tissues may not be solely in the tumor characteristics but rather in surrounding non-tumor tissue. The goal of this paper was to show preliminary evidence that tissue disruption and loss of homeostasis in breast tissue microenvironment and breast bilateral asymmetry can be quantitatively and objectively assessed from mammography via a localized, wavelet-based analysis of the whole breast. A wavelet-based multifractal formalism called the 2D Wavelet Transform Modulus Maxima (WTMM) method was used to quantitate density fluctuations from mammographic breast tissue via the Hurst exponent (H). Each entire mammogram was cut in hundreds of 360 × 360 pixel subregions in a gridding scheme of overlapping sliding windows, with each window boundary separated by 32 pixels. The 2D WTMM method was applied to each subregion individually. A data mining approach was set up to determine which metrics best discriminated between normal vs. cancer cases. These same metrics were then used, without modification, to discriminate between normal vs. benign and benign vs. cancer cases. The density fluctuations in healthy mammographic breast tissue are either monofractal anti-correlated (H < 1/2) for fatty tissue or monofractal long-range correlated (H>1/2) for dense tissue. However, tissue regions with H~1/2, as well as left vs. right breast asymetries, were found preferably in tumorous (benign or cancer) breasts vs. normal breasts, as quantified via a combination metric yielding a P-value ~ 0.0006. No metric considered showed significant differences between cancer vs. benign breasts. Since mammographic tissue regions associated with uncorrelated (H~1/2) density fluctuations were predominantly in tumorous breasts, and since the underlying physical processes associated with a H~1/2 signature are those of randomness, lack of spatial correlation, and free diffusion, it is hypothesized that this signature is also associated with tissue disruption and loss of tissue homeostasis. © 2017 American Association of Physicists in Medicine.

Polymeric micelles as a diagnostic tool for image-guided drug delivery and radiotherapy of HER2 overexpressing breast cancer

NASA Astrophysics Data System (ADS)

Hoang, Nu Bryan

Block copolymer micelles have emerged as a viable formulation strategy with several drugs relying on this technology in clinical evaluation. To date, information on the tumor penetration and intratumoral distribution of block copolymer micelles (BCM) has been quite limited. Thus, there is impetus to develop a radiolabeled formulation that can be used to gain invaluable insight into the intratumoral distribution of the BCMs. This information could then be used to direct formulation strategies as a means to optimize treatment outcomes. This thesis describes the synthesis and characterization of a targeted block copolymer micelle system based on poly(ethylene glycol)-block -poly(epsilon-caprolactone) labeled with the radionuclide Indium-111 (111In). The incorporation of the imageable component, 111In permits pursuit of image-guided drug delivery for real-time monitoring of tumor localization and intratumoral distribution. Intracellular trafficking of drugs and therapies such as Auger electron emitting radionuclides to perinuclear and nuclear regions of cells is critical to realizing their full therapeutic potential. HER2 specific antibodies (trastuzumab fab fragments) and nuclear localization signal peptides were conjugated to the surface of the BCMs to direct uptake in HER2 expressing cells and subsequent localization in the cell nucleus. Cell uptake was HER2 density dependent, confirming receptor-mediated internalization of the BCMs. Importantly, conjugation of NLS resulted in a significant increase in nuclear uptake of the radionuclide 111In. Successful nuclear targeting was shown to improve the antiproliferative effect of the Auger electrons. In addition, a significant radiation enhancement effect was observed by concurrent delivery of low-dose MTX and 111In in all breast cancer cell lines evaluated. Imaging enabled the accurate quantification of the specific tumor uptake of the micelles and visualization of their degree of tumor penetration in relation to microvessel density. Ultimately, the 111In-micelles could be used for such diverse applications as detection of malignancies, molecular characterization of tumors, improved therapy guidance and targeted anti-cancer treatment.

Semiautomatic estimation of breast density with DM-Scan software.

PubMed

Martínez Gómez, I; Casals El Busto, M; Antón Guirao, J; Ruiz Perales, F; Llobet Azpitarte, R

2014-01-01

To evaluate the reproducibility of the calculation of breast density with DM-Scan software, which is based on the semiautomatic segmentation of fibroglandular tissue, and to compare it with the reproducibility of estimation by visual inspection. The study included 655 direct digital mammograms acquired using craniocaudal projections. Three experienced radiologists analyzed the density of the mammograms using DM-Scan, and the inter- and intra-observer agreement between pairs of radiologists for the Boyd and BI-RADS® scales were calculated using the intraclass correlation coefficient. The Kappa index was used to compare the inter- and intra-observer agreements with those obtained previously for visual inspection in the same set of images. For visual inspection, the mean interobserver agreement was 0,876 (95% CI: 0,873-0,879) on the Boyd scale and 0,823 (95% CI: 0,818-0,829) on the BI-RADS® scale. The mean intraobserver agreement was 0,813 (95% CI: 0,796-0,829) on the Boyd scale and 0,770 (95% CI: 0,742-0,797) on the BI-RADS® scale. For DM-Scan, the mean inter- and intra-observer agreement was 0,92, considerably higher than the agreement for visual inspection. The semiautomatic calculation of breast density using DM-Scan software is more reliable and reproducible than visual estimation and reduces the subjectivity and variability in determining breast density. Copyright © 2012 SERAM. Published by Elsevier Espana. All rights reserved.

Effects of varied energy density of complementary foods on breast-milk intakes and total energy consumption by healthy, breastfed Bangladeshi children.

PubMed

Islam, M Munirul; Peerson, Janet M; Ahmed, Tahmeed; Dewey, Kathryn G; Brown, Kenneth H

2006-04-01

Information is needed to design studies of the effects of complementary feeding regimens on children's intakes of complementary foods (CFs) and breast milk. We evaluated the effects of varied energy density of CFs on the time until stabilization of dietary intakes and on total daily energy intakes (EIs) and breast-milk intakes. CFs with low [0.4 kcal/g (LD)] and high [1.5 kcal/g (HD)] energy density were fed 3 times/d to 10 children (aged 9-18 mo) during 2 randomly assigned sequences of three 8-d diet periods (HD-LD-HD or LD-HD-LD) along with ad libitum breastfeeding. CF and breast-milk intakes were measured. Intakes of the HD diet and breast milk did not vary by day of period, but intake of the LD diet increased progressively. During days 5-7 of the last 2 diet periods in each sequence, more of the LD than of the HD diet was consumed (752 +/- 252 and 439 +/- 111 g/d, respectively; P < 0.001), but EIs from CFs were greater with the HD diet. Breast-milk consumption was slightly less (192 +/- 115 and 234 +/- 121 g/d, respectively; P = 0.03) but total daily EI was greater (774 +/- 175 and 441 +/- 85 kcal/d, respectively; P < 0.001) during the HD than during the LD diet period. New information on the effects of newly introduced diets on daily intakes of these diets and of breast milk can be used to design future studies. Total daily EIs were greater with the HD diet despite its negative effects on breast-milk intakes.

Reader performance in visual assessment of breast density using visual analogue scales: Are some readers more predictive of breast cancer?

NASA Astrophysics Data System (ADS)

Rayner, Millicent; Harkness, Elaine F.; Foden, Philip; Wilson, Mary; Gadde, Soujanya; Beetles, Ursula; Lim, Yit Y.; Jain, Anil; Bundred, Sally; Barr, Nicky; Evans, D. Gareth; Howell, Anthony; Maxwell, Anthony; Astley, Susan M.

2018-03-01

Mammographic breast density is one of the strongest risk factors for breast cancer, and is used in risk prediction and for deciding appropriate imaging strategies. In the Predicting Risk Of Cancer At Screening (PROCAS) study, percent density estimated by two readers on Visual Analogue Scales (VAS) has shown a strong relationship with breast cancer risk when assessed against automated methods. However, this method suffers from reader variability. This study aimed to assess the performance of PROCAS readers using VAS, and to identify those most predictive of breast cancer. We selected the seven readers who had estimated density on over 6,500 women including at least 100 cancer cases, analysing their performance using multivariable logistic regression and Receiver Operator Characteristic (ROC) analysis. All seven readers showed statistically significant odds ratios (OR) for cancer risk according to VAS score after adjusting for classical risk factors. The OR was greatest for reader 18 at 1.026 (95% Cl 1.018-1.034). Adjusted Area Under the ROC Curves (AUCs) were statistically significant for all readers, but greatest for reader 14 at 0.639. Further analysis of the VAS scores for these two readers showed reader 14 had higher sensitivity (78.0% versus 42.2%), whereas reader 18 had higher specificity (78.0% versus 46.0%). Our results demonstrate individual differences when assigning VAS scores; one better identified those with increased risk, whereas another better identified low risk individuals. However, despite their different strengths, both readers showed similar predictive abilities overall. Standardised training for VAS may improve reader variability and consistency of VAS scoring.

RCL2, a New Fixative, Preserves Morphology and Nucleic Acid Integrity in Paraffin-Embedded Breast Carcinoma and Microdissected Breast Tumor Cells

PubMed Central

Delfour, Christophe; Roger, Pascal; Bret, Caroline; Berthe, Marie-Laurence; Rochaix, Philippe; Kalfa, Nicolas; Raynaud, Pierre; Bibeau, Frédéric; Maudelonde, Thierry; Boulle, Nathalie

2006-01-01

Methacarn and RCL2, a new noncrosslinking fixative, were compared to formalin-fixed or frozen tissue samples of the same invasive breast carcinoma and were evaluated for their effects on tissue morphology and immunohistochemistry as well as DNA and RNA integrity. The histomorphology of methacarn- or RCL2-fixed paraffin-embedded tumors was similar to that observed with the matched formalin-fixed tissues. Immunohistochemistry using various antibodies showed comparable results with either fixative, leading to accurate breast tumor diagnosis and determination of estrogen and progesterone receptors, and HER2 status. Methacarn and RCL2 fixation preserved DNA integrity as demonstrated by successful amplification and sequencing of large DNA amplicons. Similarly, high-quality RNA could be extracted from methacarn- or RCL2-fixed paraffin-embedded MCF-7 cells, whole breast tumor tissues, or microdissected breast tumor cells, as assessed by electropherogram profiles and real-time reverse transcriptase-polymerase chain reaction quantification of various genes. Moreover, tissue morphology and RNA integrity were preserved after 8 months of storage. Altogether, these results indicate that methacarn, as previously shown, and RCL2, a promising new fixative, have great potential for performing both morphological and molecular analyses on the same fixed tissue sample, even after laser-capture microdissection, and can open new doors for investigating small target lesions such as premalignant breast lesions. PMID:16645201

Relationship of mast cell density with lymphangiogenesis and prognostic parameters in breast carcinoma.

PubMed

Keser, Sevinc H; Kandemir, Nilufer O; Ece, Dilek; Gecmen, Gonca G; Gul, Aylin E; Barisik, Nagehan O; Sensu, Sibel; Buyukuysal, Cagatay; Barut, Figen

2017-04-01

In many cancers, mast cell density (MCD) in the tumor microenvironment is associated with tumor progression and, to a greater extent, angiogenesis. Our study was designed to investigate the correlation between MCD, tumor lymphangiogenesis, and several well-established prognostic parameters in breast cancer. One hundred and four cases of invasive breast carcinoma diagnosed in our clinic between 2007 and 2011 were included. Mast cells and lymphatic vessels were stained with toluidine blue and D2-40, respectively, and their densities were calculated in various areas of tumors and lymph nodes. The variables of MCD and lymphatic vessel density (LVD) were compared using prognostic parameters as well as with each other. As tumor size and volume increased, MCD increased comparably in metastatic lymph nodes; intratumoral and peritumoral LVD also increased. Lymphovascular invasion, lymphatic invasion, perineural invasion, and estrogen receptor positivity were positively related to intratumoral MCD. The relationship between peritumoral MCD and nontumoral breast tissue MCD was statistically significant. Stage was correlated with MCD in metastatic lymph nodes. Metastatic lymph node MCD and intratumoral MCD were also significantly related. Stage, lymphatic invasion, perineural invasion, lymphovascular invasion, and metastatic lymph node MCD were all correlated with intratumoral and/or peritumoral LVD. As nuclear grade increased, intratumoral LVD became higher. In breast carcinoma, MCD, depending on its location, was related to several prognostic parameters. Notably, mast cells may have at least some effect on lymphangiogenesis, which appears to be a predictor of tumor progression. Copyright © 2017. Published by Elsevier Taiwan.

Methodological considerations in estrogen assays of breast fluid and breast tissue.

PubMed

Chatterton, Robert T; Muzzio, Miguel; Heinz, Richard; Gann, Peter H; Khan, Seema A

2015-07-01

Estradiol (E2) in nipple aspirate fluid (NAF), ductal lavage fluid (DLF), and random fine needle aspirates (rFNA) are compared. Quantification was by immunoassay or tandem MS. The percent of women yielding NAF varied between 24% and 48% and for DLF was 86.3%. Variation between ducts within a breast was not less than variation between breasts within women but variation between breasts and within women over time was significantly less than variation between women. Serum E2 was highly significantly different among phases of the menstrual cycle but NAF E2 was not different. The correlation between serum and breast fluid E2 concentrations in premenopausal women had coefficients of determination of less than 15%. The correlation between serum and NAF in studies of postmenopausal women varied greatly and may depend on patient selection. The difference between NAF E2 between pre- and postmenopausal women was only 22%; for rFNA it was non-significantly 44% lower in a similar group of postmenopausal women. Progesterone was 96% and 98% lower in postmenopausal NAF and rFNA samples, respectively. Measurements of E2 in breast fluid or breast tissue appears to provide similar estimates of E2 exposure. E2 levels in breast fluid do not reflect the rapid changes that occur in serum and, thus, serum availability of E2 is only one factor determining its levels in the breast. The similarity of levels between breasts and between ducts suggests that estimates of estrogen exposure does not require multiple samples, however, unavailability of fluid may require rFNA in some cases. Copyright © 2014 Elsevier Inc. All rights reserved.

A minimum spanning forest based classification method for dedicated breast CT images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pike, Robert; Sechopoulos, Ioannis; Fei, Baowei, E-mail: bfei@emory.edu

Purpose: To develop and test an automated algorithm to classify different types of tissue in dedicated breast CT images. Methods: Images of a single breast of five different patients were acquired with a dedicated breast CT clinical prototype. The breast CT images were processed by a multiscale bilateral filter to reduce noise while keeping edge information and were corrected to overcome cupping artifacts. As skin and glandular tissue have similar CT values on breast CT images, morphologic processing is used to identify the skin based on its position information. A support vector machine (SVM) is trained and the resulting modelmore » used to create a pixelwise classification map of fat and glandular tissue. By combining the results of the skin mask with the SVM results, the breast tissue is classified as skin, fat, and glandular tissue. This map is then used to identify markers for a minimum spanning forest that is grown to segment the image using spatial and intensity information. To evaluate the authors’ classification method, they use DICE overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on five patient images. Results: Comparison between the automatic and the manual segmentation shows that the minimum spanning forest based classification method was able to successfully classify dedicated breast CT image with average DICE ratios of 96.9%, 89.8%, and 89.5% for fat, glandular, and skin tissue, respectively. Conclusions: A 2D minimum spanning forest based classification method was proposed and evaluated for classifying the fat, skin, and glandular tissue in dedicated breast CT images. The classification method can be used for dense breast tissue quantification, radiation dose assessment, and other applications in breast imaging.« less

Phase Contrast Microscopy Analysis of Breast Tissue

PubMed Central

Wells, Wendy A.; Wang, Xin; Daghlian, Charles P.; Paulsen, Keith D.; Pogue, Brian W.

2010-01-01

OBJECTIVE To assess how optical scatter properties in breast tissue, as measured by phase contrast microscopy and interpreted pathophysiologically, might be exploited as a diagnostic tool to differentiate cancer from benign tissue. STUDY DESIGN We evaluated frozen human breast tissue sections of adipose tissue, normal breast parenchyma, benign fibroadenoma tumors and noninvasive and invasive malignant cancers by phase contrast microscopy through quantification of grayscale values, using multiple regions of interest (ROI). Student’s t tests were performed on phase contrast measures across diagnostic categories testing data from individual cases; all ROI data were used as separate measures. RESULTS Stroma demonstrated significantly higher scatter intensity than did epithelium, with lower scattering in tumor-associated stroma as compared with normal or benign-associated stroma. Measures were comparable for invasive and noninvasive malignant tumors but were higher than those found in benign tumors and were lowest in adipose tissue. CONCLUSION Significant differences were found in scatter coefficient properties of epithelium and stroma across diagnostic categories of breast tissue, particularly between benign and malignant-associated stroma. Improved understanding of how scatter properties correlate with morphologic criteria used in routine pathologic diagnoses could have a significant clinical impact as developing optical technology allows macroscopic in situ phase contrast imaging. PMID:19736867

TaqMan RT-PCR and VERSANT HIV-1 RNA 3.0 (bDNA) assay Quantification of HIV-1 RNA viral load in breast milk.

PubMed

Israel-Ballard, Kiersten; Ziermann, Rainer; Leutenegger, Christian; Di Canzio, James; Leung, Kimmy; Strom, Lynn; Abrams, Barbara; Chantry, Caroline

2005-12-01

Transmission of HIV via breast milk is a primary cause of pediatric HIV infection in developing countries. Reliable methods to detect breast milk viral load are important. To correlate the ability of the VERSANT HIV 3.0 (bDNA) assay to real-time (RT) TaqMan PCR in quantifying breast milk HIV-1 RNA. Forty-six breast milk samples that had been spiked with cell-free HIV-1 and eight samples spiked with cell-associated HIV-1 were assayed for HIV-1 RNA by both VERSANT HIV 3.0 and TaqMan RNA assays. Only assays on the cell-free samples were statistically compared. Both a Deming regression slope and a Bland-Altman slope indicated a linear relationship between the two assays. TaqMan quantitations were on average 2.6 times higher than those of HIV 3.0. A linear relationship was observed between serial dilutions of spiked cell-free HIV-1 and both the VERSANT HIV 3.0 and the TaqMan RNA assays. The two methods correlated well although the VERSANT HIV 3.0 research protocol quantified HIV-1 RNA slightly lower than TaqMan.

Basic physics and doubts about relationship between mammographically determined tissue density and breast cancer risk.

PubMed

Kopans, Daniel B

2008-02-01

Numerous studies have suggested a link between breast tissue patterns, as defined with mammography, and risk for breast cancer. There may be a relationship, but the author believes all of these studies have methodological flaws. It is impossible, with the parameters used in these studies, to accurately measure the percentage of tissues by volume when two-dimensional x-ray mammographic images are used. Without exposure values, half-value layer information, and knowledge of the compressed thickness of the breast, an accurate volume of tissue cannot be calculated. The great variability in positioning the breast for a mammogram is also an uncontrollable factor in measuring tissue density. Computerized segmentation algorithms can accurately assess the percentage of the x-ray image that is "dense," but this does not accurately measure the true volume of tissue. Since the percentage of dense tissue is ultimately measured in relation to the complete volume of the breast, defining the true boundaries of the breast is also a problem. Studies that purport to show small percentage differences between groups are likely inaccurate. Future investigations need to use three-dimensional information. (c) RSNA, 2008.

CADx Mammography

NASA Astrophysics Data System (ADS)

Costaridou, Lena

Although a wide variety of Computer-Aided Diagnosis (CADx) schemes have been proposed across breast imaging modalities, and especially in mammography, research is still ongoing to meet the high performance CADx requirements. In this chapter, methodological contributions to CADx in mammography and adjunct breast imaging modalities are reviewed, as they play a major role in early detection, diagnosis and clinical management of breast cancer. At first, basic terms and definitions are provided. Then, emphasis is given to lesion content derivation, both anatomical and functional, considering only quantitative image features of micro-calcification clusters and masses across modalities. Additionally, two CADx application examples are provided. The first example investigates the effect of segmentation accuracy on micro-calcification cluster morphology derivation in X-ray mammography. The second one demonstrates the efficiency of texture analysis in quantification of enhancement kinetics, related to vascular heterogeneity, for mass classification in dynamic contrast-enhanced magnetic resonance imaging.

Rapid quantification of soilborne pathogen communities in wheat-based long-term field experiments

USDA-ARS?s Scientific Manuscript database

Traditional isolation and quantification of inoculum density is difficult for most soilborne pathogens. Quantitative PCR methods have been developed to rapidly identify and quantify many of these pathogens using a single DNA extract from soil. Rainfed experiments operated continuously for up to 84 y...

The ANDROMEDA prospective cohort study: predictive value of combined criteria to tailor breast cancer screening and new opportunities from circulating markers: study protocol.

PubMed

Giordano, Livia; Gallo, Federica; Petracci, Elisabetta; Chiorino, Giovanna; Segnan, Nereo

2017-11-22

In recent years growing interest has been posed on alternative ways to screen women for breast cancer involving different imaging techniques or adjusting screening interval by breast cancer risk estimates. A new research area is studying circulating microRNAs as molecular biomarkers potentially useful for non invasive early detection together with the analysis of single-nucleotide polymorphisms (SNPs). The Andromeda study is a prospective cohort study on women attending breast cancer screening in a northern Italian area. The aims of the study are: 1) to define appropriate women risk-based stratifications for personalized screening considering different factors (reproductive, family and biopsy history, breast density, lifestyle habits); 2) to evaluate the diagnostic accuracy of selected circulating microRNAs in a case-control study nested within the above mentioned cohort. About 21,000 women aged 46-67 years compliant to screening mammography are expected to be enrolled. At enrolment, information on well-known breast cancer risk factors and life-styles habits are collected through self-admistered questionnaires. Information on breast density and anthropometric measurements (height, weight, body composition, and waist circumference) are recorded. In addition, women are requested to provide a blood sample for serum, plasma and buffy-coat storing for subsequent molecular analyses within the nested case-control study. This investigation will be performed on approximately 233 cases (screen-detected) and 699 matched controls to evaluate SNPs and circulating microRNAs. The whole study will last three years and the cohort will be followed up for ten years to observe the onset of new breast cancer cases. Nowadays women undergo the same screening protocol, independently of their breast density and their individual risk to develop breast cancer. New criteria to better stratify women in risk groups could enable the screening strategies to target high-risk women while reducing interventions in those at low-risk. In this frame the present study will contribute in identifying the feasibility and impact of implementing personalized breast cancer screening. NCT02618538 (retrospectively registered on 27-11-2015.).

Beyond sarcopenia: Characterization and integration of skeletal muscle quantity and radiodensity in a curable breast cancer population.

PubMed

Weinberg, Marc S; Shachar, Shlomit S; Muss, Hyman B; Deal, Allison M; Popuri, Karteek; Yu, Hyeon; Nyrop, Kirsten A; Alston, Shani M; Williams, Grant R

2018-05-01

Skeletal muscle loss, commonly known as sarcopenia, is highly prevalent and prognostic of adverse outcomes in oncology. However, there is limited information on adults with early breast cancer and examination of other skeletal muscle indices, despite the potential prognostic importance. This study characterizes and examines age-related changes in body composition of adults with early breast cancer and describes the creation of a novel integrated muscle measure. Female patients diagnosed with stage I-III breast cancer with abdominal computerized tomography (CT) scans within 12 weeks from diagnosis were identified from local tumor registry (N = 241). Skeletal muscle index (muscle area per height [cm 2 /m 2 ]), skeletal muscle density, and subcutaneous and visceral adipose tissue areas, were determined from CT L3 lumbar segments. We calculated a novel integrated skeletal measure, skeletal muscle gauge, which combines skeletal muscle index and density (SMI × SMD). 241 patients were identified with available CT imaging. Median age 52 years and range of 23-87. Skeletal muscle index and density significantly decreased with age. Using literature based cut-points, older adults (≥65 years) had significantly higher proportions of sarcopenia (63 vs 28%) and myosteatosis (90 vs 11%) compared to younger adults (<50 years). Body mass index was positively correlated with skeletal muscle index and negatively correlated with muscle density. Skeletal muscle gauge correlated better with increasing age (ρ = 0.52) than with either skeletal muscle index (ρ = 0.20) or density (ρ = 0.46). Wide variations and age-related changes in body composition metrics were found using routinely obtained abdominal CT imaging. Skeletal muscle index and density provide independent, complementary information, and the product of the two metrics, skeletal muscle gauge, requires further research to explore its impact on outcomes in women with curable breast cancer. © 2017 Wiley Periodicals, Inc.

Associations of coffee consumption and caffeine intake with mammographic breast density.

PubMed

Yaghjyan, Lusine; Colditz, Graham; Rosner, Bernard; Gasparova, Aleksandra; Tamimi, Rulla M

2018-05-01

Previous studies suggest that coffee and caffeine intake may be associated with reduced breast cancer risk. We investigated the association of coffee and caffeine intake with mammographic breast density by woman's menopausal status and, in postmenopausal women, by hormone therapy (HT). This study included 4130 cancer-free women within the Nurses' Health Study and Nurses' Health Study II cohorts. Percent breast density (PD) was measured from digitized film mammograms using a computer-assisted thresholding technique and square root-transformed for the analysis. Average cumulative coffee/caffeine consumption was calculated using data from all food frequency questionnaires preceding the mammogram date. Information regarding breast cancer risk factors was obtained from questionnaires closest to the mammogram date. We used generalized linear regression to quantify associations of regular, decaffeinated, and total coffee, and energy-adjusted caffeine intake with percent density. In multivariable analyses, decaffeinated coffee was positively associated with PD in premenopausal women (2+ cups/day: β = 0.23, p trend = 0.03). In postmenopausal women, decaffeinated and total coffee were inversely associated with PD (decaffeinated 2+ cups/day: β = - 0.24, p trend = 0.04; total 4+ cups/day: β = - 0.16, p trend = 0.02). Interaction of decaffeinated coffee with menopausal status was significant (p-interaction < 0.001). Among current HT users, regular coffee and caffeine were inversely associated with PD (regular coffee 4+ cups/day: β = - 0.29, p trend = 0.01; caffeine 4th vs. 1st quartile: β = - 0.32, p trend = 0.01). Among past users, decaffeinated coffee was inversely associated with PD (2+ cups/day β = - 0.70, p trend = 0.02). Associations of decaffeinated coffee with percent density differ by woman's menopausal status. Associations of regular coffee and caffeine with percent density may differ by HT status.

RAZOR: A Phase II Open Randomized Trial of Screening Plus Goserelin and Raloxifene Versus Screening Alone in Premenopausal Women at Increased Risk of Breast Cancer.

PubMed

Howell, Anthony; Ashcroft, Linda; Fallowfield, Lesley; Eccles, Diana M; Eeles, Rosalind A; Ward, Ann; Brentnall, Adam R; Dowsett, Mitchell; Cuzick, Jack M; Greenhalgh, Rosemary; Boggis, Caroline; Motion, Jamie; Sergeant, Jamie C; Adams, Judith; Evans, D Gareth

2018-01-01

Background: Ovarian suppression in premenopausal women is known to reduce breast cancer risk. This study aimed to assess uptake and compliance with ovarian suppression using the luteinizing hormone releasing hormone (LHRH) analogue, goserelin, with add-back raloxifene, as a potential regimen for breast cancer prevention. Methods: Women at ≥30% lifetime risk breast cancer were approached and randomized to mammographic screening alone (C-Control) or screening in addition to monthly subcutaneous injections of 3.6 mg goserelin and continuous 60 mg raloxifene daily orally (T-Treated) for 2 years. The primary endpoint was therapy adherence. Secondary endpoints were toxicity/quality of life, change in bone density, and mammographic density. Results: A total of 75/950 (7.9%) women approached agreed to randomization. In the T-arm, 20 of 38 (52%) of women completed the 2-year period of study compared with the C-arm (27/37, 73.0%). Dropouts were related to toxicity but also the wish to have established risk-reducing procedures and proven chemoprevention. As relatively few women completed the study, data are limited, but those in the T-arm reported significant increases in toxicity and sexual problems, no change in anxiety, and less cancer worry. Lumbar spine bone density declined by 7.0% and visually assessed mammographic density by 4.7% over the 2-year treatment period. Conclusions: Uptake is somewhat lower than comparable studies with tamoxifen for prevention with higher dropout rates. Raloxifene may preserve bone density, but reduction in mammographic density reversed after treatment was completed. Impact: This study indicates that breast cancer risk reduction may be possible using LHRH agonists, but reducing toxicity and preventing bone changes would make this a more attractive option. Cancer Epidemiol Biomarkers Prev; 27(1); 58-66. ©2017 AACR . ©2017 American Association for Cancer Research.

Obesity and menopause modify the epigenomic profile of breast cancer.

PubMed

Crujeiras, Ana B; Diaz-Lagares, Angel; Stefansson, Olafur A; Macias-Gonzalez, Manuel; Sandoval, Juan; Cueva, Juan; Lopez-Lopez, Rafael; Moran, Sebastian; Jonasson, Jon G; Tryggvadottir, Laufey; Olafsdottir, Elinborg; Tinahones, Francisco J; Carreira, Marcos C; Casanueva, Felipe F; Esteller, Manel

2017-07-01

Obesity is a high risk factor for breast cancer. This relationship could be marked by a specific methylome. The current work was aimed to explore the impact of obesity and menopausal status on variation in breast cancer methylomes. Data from Infinium 450K array-based methylomes of 64 breast tumors were coupled with information on BMI and menopausal status. Additionally, DNA methylation results were validated in 18 non-tumor and 81 tumor breast samples. Breast tumors arising in either pre- or postmenopausal women stratified by BMI or menopausal status alone were not associated with a specific DNA methylation pattern. Intriguingly, the DNA methylation pattern identified in association with the high-risk group (postmenopausal women with high BMI (>25) and premenopausal women with normal or low BMI < 25) exclusively characterized by hypermethylation of 1287 CpG sites as compared with the low-risk group. These CpG sites included the promoter region of fourteen protein-coding genes of which CpG methylation over the ZNF577 promoter region represents the top scoring associated event. In an independent cohort, the ZNF577 promoter methylation remained statistically significant in association with the high-risk group. Additionally, the impact of ZNF577 promoter methylation on mRNA expression levels was demonstrated in breast cancer cell lines after treatment with a demethylating agent (5-azacytidine). In conclusion, the epigenome of breast tumors is affected by a complex interaction between BMI and menopausal status. The ZNF577 methylation quantification is clearly relevant for the development of novel biomarkers of precision therapy in breast cancer. © 2017 Society for Endocrinology.

Approximation of the breast height diameter distribution of two-cohort stands by mixture models III Kernel density estimators vs mixture models

Treesearch

Rafal Podlaski; Francis A. Roesch

2014-01-01

Two-component mixtures of either the Weibull distribution or the gamma distribution and the kernel density estimator were used for describing the diameter at breast height (dbh) empirical distributions of two-cohort stands. The data consisted of study plots from the Å wietokrzyski National Park (central Poland) and areas close to and including the North Carolina section...

A Physical Mechanism and Global Quantification of Breast Cancer

PubMed Central

Yu, Chong; Wang, Jin

2016-01-01

Initiation and progression of cancer depend on many factors. Those on the genetic level are often considered crucial. To gain insight into the physical mechanisms of breast cancer, we construct a gene regulatory network (GRN) which reflects both genetic and environmental aspects of breast cancer. The construction of the GRN is based on available experimental data. Three basins of attraction, representing the normal, premalignant and cancer states respectively, were found on the phenotypic landscape. The progression of breast cancer can be seen as switching transitions between different state basins. We quantified the stabilities and kinetic paths of the three state basins to uncover the biological process of breast cancer formation. The gene expression levels at each state were obtained, which can be tested directly in experiments. Furthermore, by performing global sensitivity analysis on the landscape topography, six key genes (HER2, MDM2, TP53, BRCA1, ATM, CDK2) and four regulations (HER2⊣TP53, CDK2⊣BRCA1, ATM→MDM2, TP53→ATM) were identified as being critical for breast cancer. Interestingly, HER2 and MDM2 are the most popular targets for treating breast cancer. BRCA1 and TP53 are the most important oncogene of breast cancer and tumor suppressor gene, respectively. This further validates the feasibility of our model and the reliability of our prediction results. The regulation ATM→MDM2 has been extensive studied on DNA damage but not on breast cancer. We notice the importance of ATM→MDM2 on breast cancer. Previous studies of breast cancer have often focused on individual genes and the anti-cancer drugs are mainly used to target the individual genes. Our results show that the network-based strategy is more effective on treating breast cancer. The landscape approach serves as a new strategy for analyzing breast cancer on both the genetic and epigenetic levels and can help on designing network based medicine for breast cancer. PMID:27410227

Mammographic breast density patterns in asymptomatic mexican women.

PubMed

Calderón-Garcidueñas, Ana Laura; Sanabria-Mondragón, Mónica; Hernández-Beltrán, Lourdes; López-Amador, Noé; Cerda-Flores, Ricardo M

2012-01-01

Breast density (BD) is a risk factor for breast cancer. Aims. To describe BD patterns in asymptomatic Mexican women and the pathological mammographic findings. Methods and Material. Prospective, descriptive, and comparative study. Women answered a questionnaire and their mammograms were analyzed according to BI-RADS. Univariate (χ(2)) and conditional logistic regression analyses were performed. Results. In 300 women studied the BD patterns were fat 56.7% (170), fibroglandular 29% (87), heterogeneously dense 5.7% (17), and dense pattern 8.6% (26). Prevalence of fat pattern was significantly different in women under 50 years (37.6%, 44/117) and older than 50 (68.8%, 126/183). Patterns of high breast density (BD) (dense + heterogeneously dense) were observed in 25.6% (30/117) of women ≤50 years and 7.1% (13/183) of women >50. Asymmetry in BD was observed in 22% (66/300). Compression cone ruled out underlying disease in 56 cases. In the remaining 10, biopsy revealed one fibroadenoma, one complex cyst, and 6 invasive and 2 intraductal carcinomas. 2.6% (8/300) of patients had non-palpable carcinomas. Benign lesions were observed in 63.3% (190/300) of cases, vascular calcification in 150 cases (78.9%), and fat necrosis in 38 cases (20%). Conclusions. Mexican women have a low percentage of high-density patterns.

Possible effects of insulin-like growth factor-I, IGF-binding protein-3 and IGF-1/IGFBP-3 molar ratio on mammographic density: a cross-sectional study.

PubMed

Meggiorini, M L; Cipolla, V; Borgoni, G; Nofroni, I; Pala, A; de Felice, C

2012-01-01

The purpose of this study was to examine the possible effects of IGF-1, IGFBP-3 and IGF-1/IGFBP-3 molar ratio on mammographic density and assess whether this relationship was similar in subgroups of pre- and postmenopausal women. A group of 341 Italian women of childbearing age or naturally postmenopausal who had performed mammographic examination at the section of radiology of our department a maximum three months prior to recruitment were enrolled. A blood sample was drawn for determination of IGF-1, IGFBP-3 levels and IGF-1/IGFBP-3 molar ratio was calculated. On the basis of recent mammograms the women were divided into two groups: dense breast (DB) and non-dense breast (NDB). To assess the association between mammographic density and IGF-1, IGFBP-3 and Molar ratio Student's t-test was employed before and after stratified by menopausal status. The analysis of the relationship between mammographic density and plasma levels of IGF-1, IGFBP-3 and IGF-1/IGFBP-3 molar ratio showed that IGF-1 levels and molar ratio varied in the two groups resulting in higher mean values in the DB group whereas IGFBP-3 showed similar values in both groups (DB and NDB). After stratification of the study population by menopausal status, no association was found. Our study provides strong evidence of a crude association between breast density, and plasma levels of IGF-1 and molar ratio. IGF-1 and molar ratio might increase mammographic density and thus the risk of developing breast cancer.

Tradeoffs between hydraulic and mechanical stress responses of mature Norway spruce trunk wood.

PubMed

Rosner, Sabine; Klein, Andrea; Müller, Ulrich; Karlsson, Bo

2008-08-01

We tested the effects of growth characteristics and basic density on hydraulic and mechanical properties of mature Norway spruce (Picea abies (L.) Karst.) wood from six 24-year-old clones, grown on two sites in southern Sweden differing in water availability. Hydraulic parameters assessed were specific hydraulic conductivity at full saturation (ks100) and vulnerability to cavitation (Psi50), mechanical parameters included bending strength (sigma b), modulus of elasticity (MOE), compression strength (sigma a) and Young's modulus (E). Basic density, diameter at breast height, tree height, and hydraulic and mechanical parameters varied considerably among clones. Clonal means of hydraulic and mechanical properties were strongly related to basic density and to growth parameters across sites, especially to diameter at breast height. Compared with stem wood of slower growing clones, stem wood of rapidly growing clones had significantly lower basic density, lower sigma b, MOE, sigma a and E, was more vulnerable to cavitation, but had higher ks100. Basic density was negatively correlated to Psi50 and ks100. We therefore found a tradeoff between Psi50 and ks100. Clones with high basic density had significantly lower hydraulic vulnerability, but also lower hydraulic conductivity at full saturation and thus less rapid growth than clones with low basic density. This tradeoff involved a negative relationship between Psi50 and sigma b as well as MOE, and between ks100 and sigma b, MOE and sigma a. Basic density and Psi50 showed no site-specific differences, but tree height, diameter at breast height, ks100 and mechanical strength and stiffness were significantly lower at the drier site. Basic density had no influence on the site-dependent differences in hydraulic and mechanical properties, but was strongly negatively related to diameter at breast height. Selecting for growth may thus lead not only to a reduction in mechanical strength and stiffness but also to a reduction in hydraulic safety.

Inconsistencies of Breast Cancer Risk Factors between the Northern and Southern Regions of Vietnam

PubMed

Trieu, Phuong Dung (Yun); Mello-Thoms, Claudia; Peat, Jennifer K; Do, Thuan Doan; Brennan, Patrick C

2017-10-26

Background: In recent decades the amount of new breast cancer cases in the southern region has been reported to increase more rapidly than in the northernVietnam. The aim of this study is to compare breast cancer risk factors between the two regions and establish if westernized influences have an impact on any reported differences. Method: Data was collected from the two largest oncology hospitals in the north and the south of Vietnam in 2015. Breast density, demographic, reproductive and lifestyle data of 127 cases and 269 controls were collected in the north and 141 cases and 250 controls were gathered from the south. Baseline differences in factors between cases and age-matched controls in each region were assessed using chi-square tests and independent t-tests. Odds ratios (OR) for independent risk factors for breast cancer were obtained from conditional logistic regression. Results: In northern Vietnam significantly increased risks in developing breast cancer were observed for women with age at first menstrual period less than 14 years old (OR=2.1; P<0.05), post-menopausal status (OR=2.6; P<0.0001), having less than 2 babies (OR=2.1; P<0.05). Southern Vietnamese women having a breast density of more than 75% (OR=2.1; P<0.01), experiencing post-menopause (OR=1.6; P<0.05), having a history of less than 3 pregnancies (OR=2.6; P<0.0001) and drinking more than a cup of coffee per day (OR=1.9; P<0.05) were more likely to be diagnosed with breast cancer. Conclusion: We found that women living in the south had some breast cancer associations, such as increased mammographic density and coffee consumption, which are closer to the risks in westernized populations than women in the north. Creative Commons Attribution License

Family History and Breast Cancer Risk Among Older Women in the Breast Cancer Surveillance Consortium Cohort.

PubMed

Braithwaite, Dejana; Miglioretti, Diana L; Zhu, Weiwei; Demb, Joshua; Trentham-Dietz, Amy; Sprague, Brian; Tice, Jeffrey A; Onega, Tracy; Henderson, Louise M; Buist, Diana S M; Ziv, Elad; Walter, Louise C; Kerlikowske, Karla

2018-04-01

First-degree family history is a strong risk factor for breast cancer, but controversy exists about the magnitude of the association among older women. To determine whether first-degree family history is associated with increased risk of breast cancer among older women, and identify whether the association varies by breast density. Prospective cohort study between 1996 and 2012 from 7 Breast Cancer Surveillance Consortium (BCSC) registries located in New Hampshire, North Carolina, San Francisco Bay area, western Washington state, New Mexico, Colorado, and Vermont. During a mean (SD) follow-up of 6.3 (3.2) years, 10 929 invasive breast cancers were diagnosed in a cohort of 403 268 women 65 years and older with data from 472 220 mammography examinations. We estimated the 5-year cumulative incidence of invasive breast cancer by first-degree family history, breast density, and age groups. Cox proportional hazards models were fit to estimate the association of first-degree family history with risk of invasive breast cancer (after adjustment for breast density, BCSC registry, race/ethnicity, body mass index, postmenopausal hormone therapy use, and benign breast disease for age groups 65 to 74 years and 75 years and older, separately). Data analyses were performed between June 2016 and June 2017. First-degree family history of breast cancer. Incident breast cancer. In 403 268 women 65 years and older, first-degree family history was associated with an increased risk of breast cancer among women ages 65 to 74 years (hazard ratio [HR], 1.48; 95% CI, 1.35-1.61) and 75 years and older (HR, 1.44; 95% CI, 1.28-1.62). Estimates were similar for women 65 to 74 years with first-degree relative's diagnosis age younger than 50 years (HR, 1.47; 95% CI, 1.25-1.73) vs 50 years and older (HR, 1.33; 95% CI, 1.17-1.51) and for women ages 75 years and older with the relative's diagnosis age younger than 50 years (HR, 1.31; 95% CI, 1.05-1.63) vs 50 years and older (HR, 1.55; 95% CI, 1.33-1.81). Among women ages 65 to 74 years, the risk associated with first-degree family history was highest among those with fatty breasts (HR, 1.67; 95% CI, 1.27-2.21), whereas in women 75 years and older the risk associated with family history was highest among those with dense breasts (HR, 1.55; 95% CI, 1.29-1.87). First-degree family history was associated with increased risk of invasive breast cancer in all subgroups of older women irrespective of a relative's age at diagnosis.

Lymphatic and blood vessels in male breast cancer.

PubMed

Niemiec, Joanna; Sas-Korczynska, Beata; Harazin-Lechowska, Agnieszka; Martynow, Dariusz; Adamczyk, Agnieszka

2015-02-01

It is assumed that lymphatic vessels are responsible for breast cancer dissemination. In 32 male breast carcinomas we evaluated the correlation between: (i) lymphatic vessel density (LVD), distribution of podoplanin-immunostained vessels (DPV), blood vessel density (BVD), infiltration of immune cells and (ii) known clinicopathological parameters. Lymphatic and blood vessels were found in 77.8% and 100% of breast carcinomas, respectively. Double-negative estrogen and progesterone receptor tumors (ER-/PR-) presented significantly higher LVD than ER/PR positive cases, while high-grade tumors exhibited significantly higher DPV than low-grade carcinomas. We detected significantly higher frequency of vascular invasion in high-grade and double-negative carcinomas than in low-grade and ER/PR-positive ones, respectively. The relationship between high number of lymphatic vessels and high tumor grade or steroid receptor negativity might confirm the hypothesis regarding the influence of lymphangiogenesis on the formation of a more aggressive phenotype in male breast cancer. Copyright© 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Comparative membrane proteomics analyses of breast cancer cell lines to understand the molecular mechanism of breast cancer brain metastasis.

PubMed

Peng, Wenjing; Zhang, Yu; Zhu, Rui; Mechref, Yehia

2017-09-01

Breast cancer is the leading type of cancer in women. Breast cancer brain metastasis is currently considered an issue of concern among breast cancer patients. Membrane proteins play important roles in breast cancer brain metastasis, involving cell adhesion and penetration of blood-brain barrier. To understand the mechanism of breast cancer brain metastasis, liquid chromatography-tandem mass spectrometry (LC-MS/MS) was employed in conjunction with enrichment of membrane proteins to analyze the proteomes from five different breast cancer and a brain cancer cell lines. Quantitative proteomic data of all cell lines were compared with MDA-MB-231BR which is a brain seeking breast cancer cell line, thus representing brain metastasis characteristics. Label-free proteomics of the six cell lines facilitates the identification of 1238 proteins and the quantification of 899 proteins of which more than 70% were membrane proteins. Unsupervised principal component analysis (PCA) of the label-free proteomics data resulted in a distinct clustering of cell lines, suggesting quantitative differences in the expression of several proteins among the different cell lines. Unique protein expressions in 231BR were observed for 28 proteins. The up-regulation of STAU1, AT1B3, NPM1, hnRNP Q, and hnRNP K and the down-regulation of TUBB4B and TUBB5 were noted in 231BR relative to 231 (precursor cell lines from which 231BR is derived). These proteins might contribute to the breast cancer brain metastasis. Ingenuity pathway analysis (IPA) supported the great brain metastatic propensity of 231BR and suggested the importance of the up-regulation of integrin proteins and down-regulation of EPHA2 in brain metastasis. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

[Contrastive study on conventional ultrasound, compression elastography and acoustic radiation force impulse imaging in the differential diagnosis of benign and malignant breast tumors].

PubMed

Zhang, Lu; Zhou, Ping; Deng, Jin; Tian, Shuangming; Qian, Ying; Wu, Xiaomin; Ma, Shuhua; Li, Jiale

2014-12-01

To evaluate the diagnostic performance of conventional ultrasound, compression elastography (CE) and acoustic radiation force impulse imaging (ARFI) in differential diagnosis of benign and malignant breast tumors. A total of 98 patients with liver lesions were included in the study. The images of conventional ultrasound, CE and the values of virtual touch tissue quantification (VTQ) of breast lesions were obtained. The diagnostic performance of conventional ultrasound, CE and ARFI were assessed by using pathology as the gold standard, and then evaluate the diagnosis efficiency of these three approaches in differential diagnosing benign and malignant breast tumors. The specificity, sensitivity and accuracy in the diagnosis of malignant breast tumors for conventional ultrasound were 80.0%, 81.1% and 81.7%, respectively, whereas for CE elastic score were 85.7%, 86.7% and 86.3%, respectively. With a cutoff value of 3.71 for the SR, the sensitivity, specificity, accuracy in diagnosis of malignant breast tumors were 97.1%, 83.3% and 88.4%, respectively. With a cutoff value of 3.78 m/s for VTQ, the sensitivity, specificity, accuracy in diagnosis of malignant breast tumors were 94.3%, 91.7% and 92.6%, respectively. The difference in diagnosis efficiency among ARFI, CE and conventional ultrasound in differential diagnosis of benign and malignant breast tumors was significant (P< 0.05). Conventional ultrasound, CE and ARFI are all useful for the differential diagnosis of benign and malignant breast tumors. But the diagnosis efficiency of ARFI is superior to CE and conventional ultrasound. The three approaches can help each other in differential diagnosis of benign and malignant breast tumors.

Differential Expression of MicroRNAs in Breast Cancers from Four Different Ethnicities.

PubMed

Pollard, Jennifer; Burns, Phil A; Hughes, Tom A; Ho-Yen, Colan; Jones, J Louise; Mukherjee, Geetashree; Omoniyi-Esan, Ganiat O; Titloye, Nicholas Akinwale; Speirs, Valerie; Shaaban, Abeer M

2018-05-23

Breast cancer outcomes vary across different ethnic groups. MicroRNAs (miRs) are small non-coding RNA molecules that regulate gene expression across a range of pathologies, including breast cancer. The aim of this study was to evaluate the presence and expression of miRs in breast cancer samples from different ethnic groups. Breast cancer tissue from 4 ethnic groups, i.e., British Caucasian, British Black, Nigerian, and Indian, were identified and matched for patients' age, tumour grade/type, and 10 × 10 µm sections taken. Tumour areas were macrodissected, total RNA was extracted, and cDNA was synthesised. cDNA was applied to human miScript PCR arrays allowing the quantification of 84 of the most abundantly expressed/best-characterised miRs. Differential expression of 9 miRs was seen across the 4 groups. Significantly higher levels of miR-140-5p, miR-194 and miR-423-5p (the last of which harbours the single-nucleotide polymorphism rs6505162) were seen in the breast tumours of Nigerian patients when compared with other ethnic groups (all p < 0.0001). miR-101 was overexpressed in breast cancers in the Indian patients. An in silico analysis of miR-423-5p showed that the AC genotype is mainly associated with Europeans (57%), while Asians display mostly CC (approx. 60%), and Africans mainly AA (approx. 60%). This study shows divergence in miR expression in breast cancers from different ethnic groups, and suggests that specific genetic variants in miR genes may affect breast cancer risk in these groups. Predicted targets of these miRs may uncover useful biomarkers that could have clinical value in breast cancers in different ethnic groups. © 2018 S. Karger AG, Basel.

Lymphocytic mastopathy mimicking breast malignancy: a case report*

PubMed Central

Campos, Gabriela Couto Possati; Castro, Melissa Vieira Koch e; de Mattos, Viviane Ferreira Esteves; Pinto, Laura Zaiden Ferreira e; Boechat, Marcia Cristina Bastos; dos Santos, Alair Augusto Sarmet Moreira Damas

2014-01-01

Lymphocytic mastopathy affects both young and middle-aged women and is frequently associated with autoimmune diseases. Diagnosis is done by associating clinical (breast tissue thickening or hardened breast lump), radiological (increased breast density, presence of mass and calcifications), sonographic (nodule with posterior acoustic shadowing), histopathological (fibrosis and lymphocytic infiltrate) and immunohistochemical findings. Lymphocytic mastopathy is a benign entity that may mimic carcinoma. The authors report the case of a patient with lymphocytic mastopathy. PMID:25741094

Update on new technologies in digital mammography

PubMed Central

Patterson, Stephanie K; Roubidoux, Marilyn A

2014-01-01

Despite controversy regarding mammography’s efficacy, it continues to be the most commonly used breast cancer-screening modality. With the development of digital mammography, some improved benefit has been shown in women with dense breast tissue. However, the density of breast tissue continues to limit the sensitivity of conventional mammography. We discuss the development of some derivative digital technologies, primarily digital breast tomosynthesis, and their strengths, weaknesses, and potential patient impact. PMID:25152634

Novel image analysis approach for quantifying expression of nuclear proteins assessed by immunohistochemistry: application to measurement of oestrogen and progesterone receptor levels in breast cancer.

PubMed

Rexhepaj, Elton; Brennan, Donal J; Holloway, Peter; Kay, Elaine W; McCann, Amanda H; Landberg, Goran; Duffy, Michael J; Jirstrom, Karin; Gallagher, William M

2008-01-01

Manual interpretation of immunohistochemistry (IHC) is a subjective, time-consuming and variable process, with an inherent intra-observer and inter-observer variability. Automated image analysis approaches offer the possibility of developing rapid, uniform indicators of IHC staining. In the present article we describe the development of a novel approach for automatically quantifying oestrogen receptor (ER) and progesterone receptor (PR) protein expression assessed by IHC in primary breast cancer. Two cohorts of breast cancer patients (n = 743) were used in the study. Digital images of breast cancer tissue microarrays were captured using the Aperio ScanScope XT slide scanner (Aperio Technologies, Vista, CA, USA). Image analysis algorithms were developed using MatLab 7 (MathWorks, Apple Hill Drive, MA, USA). A fully automated nuclear algorithm was developed to discriminate tumour from normal tissue and to quantify ER and PR expression in both cohorts. Random forest clustering was employed to identify optimum thresholds for survival analysis. The accuracy of the nuclear algorithm was initially confirmed by a histopathologist, who validated the output in 18 representative images. In these 18 samples, an excellent correlation was evident between the results obtained by manual and automated analysis (Spearman's rho = 0.9, P < 0.001). Optimum thresholds for survival analysis were identified using random forest clustering. This revealed 7% positive tumour cells as the optimum threshold for the ER and 5% positive tumour cells for the PR. Moreover, a 7% cutoff level for the ER predicted a better response to tamoxifen than the currently used 10% threshold. Finally, linear regression was employed to demonstrate a more homogeneous pattern of expression for the ER (R = 0.860) than for the PR (R = 0.681). In summary, we present data on the automated quantification of the ER and the PR in 743 primary breast tumours using a novel unsupervised image analysis algorithm. This novel approach provides a useful tool for the quantification of biomarkers on tissue specimens, as well as for objective identification of appropriate cutoff thresholds for biomarker positivity. It also offers the potential to identify proteins with a homogeneous pattern of expression.

18F-Fluoride PET/CT tumor burden quantification predicts survival in breast cancer.

PubMed

Brito, Ana E; Santos, Allan; Sasse, André Deeke; Cabello, Cesar; Oliveira, Paulo; Mosci, Camila; Souza, Tiago; Amorim, Barbara; Lima, Mariana; Ramos, Celso D; Etchebehere, Elba

2017-05-30

In bone-metastatic breast cancer patients, there are no current imaging biomarkers to identify which patients have worst prognosis. The purpose of our study was to investigate if skeletal tumor burden determined by 18F-Fluoride PET/CT correlates with clinical outcomes and may help define prognosis throughout the course of the disease. Bone metastases were present in 49 patients. On multivariable analysis, skeletal tumor burden was significantly and independently associated with overall survival (p < 0.0001) and progression free-survival (p < 0.0001). The simple presence of bone metastases was associated with time to bone event (p = 0.0448). We quantified the skeletal tumor burden on 18F-Fluoride PET/CT images of 107 female breast cancer patients (40 for primary staging and the remainder for restaging after therapy). Clinical parameters, primary tumor characteristics and skeletal tumor burden were correlated to overall survival, progression free-survival and time to bone event. The median follow-up time was 19.5 months. 18F-Fluoride PET/CT skeletal tumor burden is a strong independent prognostic imaging biomarker in breast cancer patients.

Simultaneous LC-MS/MS determination of 40 legal and illegal psychoactive drugs in breast and bovine milk.

PubMed

López-García, Ester; Mastroianni, Nicola; Postigo, Cristina; Valcárcel, Yolanda; González-Alonso, Silvia; Barceló, Damia; López de Alda, Miren

2018-04-15

This work presents a fast, sensitive and reliable multi-residue methodology based on fat and protein precipitation and liquid chromatography-tandem mass spectrometry for the determination of common legal and illegal psychoactive drugs, and major metabolites, in breast milk. One-fourth of the 40 target analytes is investigated for the first time in this biological matrix. The method was validated in breast milk and also in various types of bovine milk, as tranquilizers are occasionally administered to food-producing animals. Absolute recoveries were satisfactory for 75% of the target analytes. The use of isotopically labeled compounds assisted in correcting analyte losses due to ionization suppression matrix effects (higher in whole milk than in the other investigated milk matrices) and ensured the reliability of the results. Average method limits of quantification ranged between 0.4 and 6.8 ng/mL. Application of the developed method showed the presence of caffeine in breast milk samples (12-179 ng/mL). Copyright © 2017 Elsevier Ltd. All rights reserved.

Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer.

PubMed

Ali, H Raza; Dariush, Aliakbar; Provenzano, Elena; Bardwell, Helen; Abraham, Jean E; Iddawela, Mahesh; Vallier, Anne-Laure; Hiller, Louise; Dunn, Janet A; Bowden, Sarah J; Hickish, Tamas; McAdam, Karen; Houston, Stephen; Irwin, Mike J; Pharoah, Paul D P; Brenton, James D; Walton, Nicholas A; Earl, Helena M; Caldas, Carlos

2016-02-16

There is a need to improve prediction of response to chemotherapy in breast cancer in order to improve clinical management and this may be achieved by harnessing computational metrics of tissue pathology. We investigated the association between quantitative image metrics derived from computational analysis of digital pathology slides and response to chemotherapy in women with breast cancer who received neoadjuvant chemotherapy. We digitised tissue sections of both diagnostic and surgical samples of breast tumours from 768 patients enrolled in the Neo-tAnGo randomized controlled trial. We subjected digital images to systematic analysis optimised for detection of single cells. Machine-learning methods were used to classify cells as cancer, stromal or lymphocyte and we computed estimates of absolute numbers, relative fractions and cell densities using these data. Pathological complete response (pCR), a histological indicator of chemotherapy response, was the primary endpoint. Fifteen image metrics were tested for their association with pCR using univariate and multivariate logistic regression. Median lymphocyte density proved most strongly associated with pCR on univariate analysis (OR 4.46, 95 % CI 2.34-8.50, p < 0.0001; observations = 614) and on multivariate analysis (OR 2.42, 95 % CI 1.08-5.40, p = 0.03; observations = 406) after adjustment for clinical factors. Further exploratory analyses revealed that in approximately one quarter of cases there was an increase in lymphocyte density in the tumour removed at surgery compared to diagnostic biopsies. A reduction in lymphocyte density at surgery was strongly associated with pCR (OR 0.28, 95 % CI 0.17-0.47, p < 0.0001; observations = 553). A data-driven analysis of computational pathology reveals lymphocyte density as an independent predictor of pCR. Paradoxically an increase in lymphocyte density, following exposure to chemotherapy, is associated with a lack of pCR. Computational pathology can provide objective, quantitative and reproducible tissue metrics and represents a viable means of outcome prediction in breast cancer. ClinicalTrials.gov NCT00070278 ; 03/10/2003.

Mammographic Breast Density in a Cohort of Medically Underserved Women

DTIC Science & Technology

2013-10-01

94http : / / www.elsev ier .com/ locate / jeghPerinatal factors and breast cancer risk among HispanicsMaureen Sanderson a,b,*, Adriana Pérez c,d, Mirabel...risk of breast cancer: a systematic review and meta-analysis of current evidence. Lancet Oncol 2007;8:1088–100. [3] Park SK, Kang D, McGlynn KA

Quantification of differences in the effective atomic numbers of healthy and cancerous tissues: A discussion in the context of diagnostics and dosimetry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Taylor, M. L.; Physical Sciences, Peter MacCallum Cancer Centre, East Melbourne 3001

Purpose: There are a range of genetic and nongenetic factors influencing the elemental composition of different human tissues. The elemental composition of cancerous tissues frequently differs from healthy tissue of the same organ, particularly in high-Z trace element concentrations. For this reason, one could suggest that this may be exploited in diagnostics and perhaps even influence dosimetry. Methods: In this work, for the first time, effective atomic numbers are computed for common cancerous and healthy tissues using a robust, energy-dependent approach between 10 keV and 100 MeV. These are then quantitatively compared within the context of diagnostics and dosimetry. Results:more » Differences between effective atomic numbers of healthy and diseased tissues are found to be typically less than 10%. Fibrotic tissues and calcifications of the breast exhibit substantial (tens to hundreds of percent) differences to healthy tissue. Expectedly, differences are most pronounced in the photoelectric regime and consequently most relevant for kV imaging/therapy and radionuclides with prominent low-energy peaks. Cancerous tissue of the testes and stomach have lower effective atomic numbers than corresponding healthy tissues, while diseased tissues of the other organ sites typically have higher values. Conclusions: As dose calculation approaches improve in accuracy, there may be an argument for the explicit inclusion of pathologies. This is more the case for breast, penile, prostate, nasopharyngeal, and stomach cancer, less so for testicular and kidney cancer. The calculated data suggest dual-energy computed tomography could potentially improve lesion identification in the aforementioned organs (with the exception of testicular cancer), with most import in breast imaging. Ultimately, however, the differences are very small. It is likely that the assumption of a generic 'tissue ramp' in planning will be sufficient for the foreseeable future, and that the Z differences do not notably aid lesion detection beyond that already facilitated by differences in mass density.« less

Electromechanical Coupling Factor of Breast Tissue as a Biomarker for Breast Cancer.

PubMed

Park, Kihan; Chen, Wenjin; Chekmareva, Marina A; Foran, David J; Desai, Jaydev P

2018-01-01

This research aims to validate a new biomarker of breast cancer by introducing electromechanical coupling factor of breast tissue samples as a possible additional indicator of breast cancer. Since collagen fibril exhibits a structural organization that gives rise to a piezoelectric effect, the difference in collagen density between normal and cancerous tissue can be captured by identifying the corresponding electromechanical coupling factor. The design of a portable diagnostic tool and a microelectromechanical systems (MEMS)-based biochip, which is integrated with a piezoresistive sensing layer for measuring the reaction force as well as a microheater for temperature control, is introduced. To verify that electromechanical coupling factor can be used as a biomarker for breast cancer, the piezoelectric model for breast tissue is described with preliminary experimental results on five sets of normal and invasive ductal carcinoma (IDC) samples in the 25-45 temperature range. While the stiffness of breast tissues can be captured as a representative mechanical signature which allows one to discriminate among tissue types especially in the higher strain region, the electromechanical coupling factor shows more distinct differences between the normal and IDC groups over the entire strain region than the mechanical signature. From the two-sample -test, the electromechanical coupling factor under compression shows statistically significant differences ( 0.0039) between the two groups. The increase in collagen density in breast tissue is an objective and reproducible characteristic of breast cancer. Although characterization of mechanical tissue property has been shown to be useful for differentiating cancerous tissue from normal tissue, using a single parameter may not be sufficient for practical usage due to inherent variation among biological samples. The portable breast cancer diagnostic tool reported in this manuscript shows the feasibility of measuring multiple parameters of breast tissue allowing for practical application.

Robust estimation of mammographic breast density: a patient-based approach

NASA Astrophysics Data System (ADS)

Heese, Harald S.; Erhard, Klaus; Gooßen, Andre; Bulow, Thomas

2012-02-01

Breast density has become an established risk indicator for developing breast cancer. Current clinical practice reflects this by grading mammograms patient-wise as entirely fat, scattered fibroglandular, heterogeneously dense, or extremely dense based on visual perception. Existing (semi-) automated methods work on a per-image basis and mimic clinical practice by calculating an area fraction of fibroglandular tissue (mammographic percent density). We suggest a method that follows clinical practice more strictly by segmenting the fibroglandular tissue portion directly from the joint data of all four available mammographic views (cranio-caudal and medio-lateral oblique, left and right), and by subsequently calculating a consistently patient-based mammographic percent density estimate. In particular, each mammographic view is first processed separately to determine a region of interest (ROI) for segmentation into fibroglandular and adipose tissue. ROI determination includes breast outline detection via edge-based methods, peripheral tissue suppression via geometric breast height modeling, and - for medio-lateral oblique views only - pectoral muscle outline detection based on optimizing a three-parameter analytic curve with respect to local appearance. Intensity harmonization based on separately acquired calibration data is performed with respect to compression height and tube voltage to facilitate joint segmentation of available mammographic views. A Gaussian mixture model (GMM) on the joint histogram data with a posteriori calibration guided plausibility correction is finally employed for tissue separation. The proposed method was tested on patient data from 82 subjects. Results show excellent correlation (r = 0.86) to radiologist's grading with deviations ranging between -28%, (q = 0.025) and +16%, (q = 0.975).

Relationship of Terminal Duct Lobular Unit Involution of the Breast with Area and Volume Mammographic Densities

PubMed Central

Gierach, Gretchen L.; Patel, Deesha A.; Pfeiffer, Ruth M.; Figueroa, Jonine D.; Linville, Laura; Papathomas, Daphne; Johnson, Jason M.; Chicoine, Rachael E.; Herschorn, Sally D.; Shepherd, John A.; Wang, Jeff; Malkov, Serghei; Vacek, Pamela M.; Weaver, Donald L.; Fan, Bo; Mahmoudzadeh, Amir Pasha; Palakal, Maya; Xiang, Jackie; Oh, Hannah; Horne, Hisani N.; Sprague, Brian L.; Hewitt, Stephen M.; Brinton, Louise A.; Sherman, Mark E.

2016-01-01

Elevated mammographic density (MD) is an established breast cancer risk factor. Reduced involution of terminal duct lobular units (TDLUs), the histologic source of most breast cancers, has been associated with higher MD and breast cancer risk. We investigated relationships of TDLU involution with area and volumetric MD, measured throughout the breast and surrounding biopsy targets (peri-lesional). Three measures inversely related to TDLU involution (TDLU count/mm2, median TDLU span, median acini count/TDLU) assessed in benign diagnostic biopsies from 348 women, ages 40–65, were related to MD area (quantified with thresholding software) and volume (assessed with a density phantom) by analysis of covariance, stratified by menopausal status and adjusted for confounders. Among premenopausal women, TDLU count was directly associated with percent peri-lesional MD (P-trend=0.03), but not with absolute dense area/volume. Greater TDLU span was associated with elevated percent dense area/volume (P-trend<0.05) and absolute peri-lesional MD (P=0.003). Acini count was directly associated with absolute peri-lesional MD (P=0.02). Greater TDLU involution (all metrics) was associated with increased nondense area/volume (P-trend≤0.04). Among postmenopausal women, TDLU measures were not significantly associated with MD. Among premenopausal women, reduced TDLU involution was associated with higher area and volumetric MD, particularly in peri-lesional parenchyma. Data indicating that TDLU involution and MD are correlated markers of breast cancer risk suggest that associations of MD with breast cancer may partly reflect amounts of at-risk epithelium. If confirmed, these results could suggest a prevention paradigm based on enhancing TDLU involution and monitoring efficacy by assessing MD reduction. PMID:26645278

Quantification of prairie restoration for phytostability at a remediated defense plant.

PubMed

Franson, Raymond L; Scholes, Chad M

2011-01-01

In June 2008 and 2009, cover, density, and species diversity were measured on two areas of the prairie at the U. S. Department of Energy Weldon Spring Site to begin quantification of the prairie establishment and the effects of a prairie burn. Sampling began by testing for the most appropriate transect length (cover) and quadrat size (density) for quantification of vegetation. Total cover increased in the first growing season after burning. Conversely, total cover decreased in the unburned area in one year. The trend in litter cover is the opposite with litter decreasing after burning, but increasing in one year in the unburned area. Bare ground decreased in one year in the unburned area, but was unchanged after burning. Species diversity tripled after fire, but was unchanged in one year in the unburned area. The results show that litter and fire both affect plant cover. If land reclamation activities are to be an integral part of hazardous waste remediation at contaminated sites, then the success of reclamation efforts needs to be quantified along with success criteria for waste remediation of the sites. The results show that plant cover can be easily quantified, but that density measures are more biased which makes it more difficult to achieve adequate sample size for plant density.

Phase II study of metformin for reduction of obesity-associated breast cancer risk: a randomized controlled trial protocol.

PubMed

Martinez, Jessica A; Chalasani, Pavani; Thomson, Cynthia A; Roe, Denise; Altbach, Maria; Galons, Jean-Philippe; Stopeck, Alison; Thompson, Patricia A; Villa-Guillen, Diana Evelyn; Chow, H-H Sherry

2016-07-19

Two-thirds of U.S. adult women are overweight or obese. High body mass index (BMI) and adult weight gain are risk factors for a number of chronic diseases, including postmenopausal breast cancer. The higher postmenopausal breast cancer risk in women with elevated BMI is likely to be attributable to related metabolic disturbances including altered circulating sex steroid hormones and adipokines, elevated pro-inflammatory cytokines, and insulin resistance. Metformin is a widely used antidiabetic drug that has demonstrated favorable effects on metabolic disturbances and as such may lead to lower breast cancer risk in obese women. Further, the anti-proliferative effects of metformin suggest it may decrease breast density, an accepted biomarker of breast cancer risk. This is a Phase II randomized, double-blind, placebo-controlled trial of metformin in overweight/obese premenopausal women who have elements of metabolic syndrome. Eligible participants will be randomized to receive metformin 850 mg BID (n = 75) or placebo (n = 75) for 12 months. The primary endpoint is change in breast density, based on magnetic resonance imaging (MRI) acquired fat-water features. Secondary outcomes include changes in serum insulin levels, serum insulin-like growth factor (IGF)-1 to insulin-like growth factor binding protein (IGFBP)-3 ratio, serum IGF-2 levels, serum testosterone levels, serum leptin to adiponectin ratio, body weight, and waist circumference. Exploratory outcomes include changes in metabolomic profiles in plasma and nipple aspirate fluid. Changes in tissue architecture as well as cellular and molecular targets in breast tissue collected in a subgroup of participants will also be explored. The study will evaluate whether metformin can result in favorable changes in breast density, select proteins and hormones, products of body metabolism, and body weight and composition. The study should help determine the potential breast cancer preventive activity of metformin in a growing population at risk for multiple diseases. ClinicalTrials.gov Identifier: NCT02028221 . Registered on January 2, 2014. Grant #: 1R01CA172444-01A1 awarded on Sept 11, 2013.

In vivo visualization and ex vivo quantification of murine breast cancer cells in the mouse brain using MRI cell tracking and electron paramagnetic resonance.

PubMed

Danhier, Pierre; Magat, Julie; Levêque, Philippe; De Preter, Géraldine; Porporato, Paolo E; Bouzin, Caroline; Jordan, Bénédicte F; Demeur, Gladys; Haufroid, Vincent; Feron, Olivier; Sonveaux, Pierre; Gallez, Bernard

2015-03-01

Cell tracking could be useful to elucidate fundamental processes of cancer biology such as metastasis. The aim of this study was to visualize, using MRI, and to quantify, using electron paramagnetic resonance (EPR), the entrapment of murine breast cancer cells labeled with superparamagnetic iron oxide particles (SPIOs) in the mouse brain after intracardiac injection. For this purpose, luciferase-expressing murine 4 T1-luc breast cancer cells were labeled with fluorescent Molday ION Rhodamine B SPIOs. Following intracardiac injection, SPIO-labeled 4 T1-luc cells were imaged using multiple gradient-echo sequences. Ex vivo iron oxide quantification in the mouse brain was performed using EPR (9 GHz). The long-term fate of 4 T1-luc cells after injection was characterized using bioluminescence imaging (BLI), brain MRI and immunofluorescence. We observed hypointense spots due to SPIO-labeled cells in the mouse brain 4 h after injection on T2 *-weighted images. Histology studies showed that SPIO-labeled cancer cells were localized within blood vessels shortly after delivery. Ex vivo quantification of SPIOs showed that less than 1% of the injected cells were taken up by the mouse brain after injection. MRI experiments did not reveal the development of macrometastases in the mouse brain several days after injection, but immunofluorescence studies demonstrated that these cells found in the brain established micrometastases. Concerning the metastatic patterns of 4 T1-luc cells, an EPR biodistribution study demonstrated that SPIO-labeled 4 T1-luc cells were also entrapped in the lungs of mice after intracardiac injection. BLI performed 6 days after injection of 4 T1-luc cells showed that this cell line formed macrometastases in the lungs and in the bones. Conclusively, EPR and MRI were found to be complementary for cell tracking applications. MRI cell tracking at 11.7 T allowed sensitive detection of isolated SPIO-labeled cells in the mouse brain, whereas EPR allowed the assessment of the number of SPIO-labeled cells in organs shortly after injection. Copyright © 2015 John Wiley & Sons, Ltd.

Development, validation and clinical application of a method for the simultaneous quantification of lamivudine, emtricitabine and tenofovir in dried blood and dried breast milk spots using LC-MS/MS.

PubMed

Waitt, Catriona; Diliiy Penchala, Sujan; Olagunju, Adeniyi; Amara, Alieu; Else, Laura; Lamorde, Mohammed; Khoo, Saye

2017-08-15

To present the validation and clinical application of a LC-MS/MS method for the quantification of lamivudine (3TC), emtricitabine (FTC) and tenofovir (TFV) in dried blood spots (DBS) and dried breast milk spots (DBMS). DBS and DBMS were prepared from 50 and 30μL of drug-spiked whole blood and human breast milk, respectively. Following extraction with acetonitrile and water, chromatographic separation utilised a Synergi polar column with a gradient mobile phase program consisting of 0.1% formic acid in water and 0.1% formic acid in acetonitrile. Detection and quantification was performed using a TSQ Quantum Ultra triple quadrupole mass spectrometer. The analytical method was used to evaluate NRTI drug levels in HIV-positive nursing mothers-infant pairs. The assay was validated over the concentration range of 16.6-5000ng/mL for 3TC, FTC and TFV in DBS and DBMS except for TFV in DBMS where linearity was established from 4.2-1250ng/mL. Intra and inter-day precision (%CV) ranged from 3.5-8.7 and accuracy was within 15% for all analytes in both matrices. The mean recovery in DBS was >61% and in DBMS >43% for all three analytes. Matrix effect was insignificant. Median AUC 0-8 values in maternal DBS and DBMS, respectively, were 4683 (4165-6057) and 6050 (5217-6417)ngh/mL for 3TC, 3312 (2259-4312) and 4853 (4124-6691)ngh/mL for FTC and 1559 (930-1915) and 56 (45-80)ngh/mL for TFV. 3TC and FTC were quantifiable (>16.6ng/mL) in DBS from 2/6 and 1/6 infants respectively whereas TFV was undetectable in all infants. DBS and DBMS sampling for bioanalysis of 3TC, FTC and TFV is straightforward, robust, accurate and precise, and ideal for use in low-resource settings. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

GoIFISH: a system for the quantification of single cell heterogeneity from IFISH images.

PubMed

Trinh, Anne; Rye, Inga H; Almendro, Vanessa; Helland, Aslaug; Russnes, Hege G; Markowetz, Florian

2014-08-26

Molecular analysis has revealed extensive intra-tumor heterogeneity in human cancer samples, but cannot identify cell-to-cell variations within the tissue microenvironment. In contrast, in situ analysis can identify genetic aberrations in phenotypically defined cell subpopulations while preserving tissue-context specificity. GoIFISHGoIFISH is a widely applicable, user-friendly system tailored for the objective and semi-automated visualization, detection and quantification of genomic alterations and protein expression obtained from fluorescence in situ analysis. In a sample set of HER2-positive breast cancers GoIFISHGoIFISH is highly robust in visual analysis and its accuracy compares favorably to other leading image analysis methods. GoIFISHGoIFISH is freely available at www.sourceforge.net/projects/goifish/.

Quantification of prebiotics in commercial infant formulas.

PubMed

Sabater, Carlos; Prodanov, Marin; Olano, Agustín; Corzo, Nieves; Montilla, Antonia

2016-03-01

Since breastfeeding is not always possible, infant formulas (IFs) are supplemented with prebiotic oligosaccharides, such as galactooligosaccharides (GOS) and/or fructooligosaccharides (FOS) to exert similar effects to those of the breast milk. Nowadays, a great number of infant formulas enriched with prebiotics are disposal in the market, however there are scarce data about their composition. In this study, the combined use of two chromatographic methods (GC-FID and HPLC-RID) for the quantification of carbohydrates present in commercial infant formulas have been used. According to the results obtained by GC-FID for products containing prebiotics, the content of FOS, GOS and GOS/FOS was in the ranges of 1.6-5.0, 1.7-3.2, and 0.08-0.25/2.3-3.8g/100g of product, respectively. HPLC-RID analysis allowed quantification of maltodextrins with degree of polymerization (DP) up to 19. The methodology proposed here may be used for routine quality control of infant formula and other food ingredients containing prebiotics. Copyright © 2015 Elsevier Ltd. All rights reserved.

An UPLC-MS/MS method for separation and accurate quantification of tamoxifen and its metabolites isomers.

PubMed

Arellano, Cécile; Allal, Ben; Goubaa, Anwar; Roché, Henri; Chatelut, Etienne

2014-11-01

A selective and accurate analytical method is needed to quantify tamoxifen and its phase I metabolites in a prospective clinical protocol, for evaluation of pharmacokinetic parameters of tamoxifen and its metabolites in adjuvant treatment of breast cancer. The selectivity of the analytical method is a fundamental criteria to allow the quantification of the main active metabolites (Z)-isomers from (Z)'-isomers. An UPLC-MS/MS method was developed and validated for the quantification of (Z)-tamoxifen, (Z)-endoxifen, (E)-endoxifen, Z'-endoxifen, (Z)'-endoxifen, (Z)-4-hydroxytamoxifen, (Z)-4'-hydroxytamoxifen, N-desmethyl tamoxifen, and tamoxifen-N-oxide. The validation range was set between 0.5ng/mL and 125ng/mL for 4-hydroxytamoxifen and endoxifen isomers, and between 12.5ng/mL and 300ng/mL for tamoxifen, tamoxifen N-desmethyl and tamoxifen-N-oxide. The application to patient plasma samples was performed. Copyright © 2014 Elsevier B.V. All rights reserved.

Psychological impact of providing women with personalised 10-year breast cancer risk estimates.

PubMed

French, David P; Southworth, Jake; Howell, Anthony; Harvie, Michelle; Stavrinos, Paula; Watterson, Donna; Sampson, Sarah; Evans, D Gareth; Donnelly, Louise S

2018-05-08

The Predicting Risk of Cancer at Screening (PROCAS) study estimated 10-year breast cancer risk for 53,596 women attending NHS Breast Screening Programme. The present study, nested within the PROCAS study, aimed to assess the psychological impact of receiving breast cancer risk estimates, based on: (a) the Tyrer-Cuzick (T-C) algorithm including breast density or (b) T-C including breast density plus single-nucleotide polymorphisms (SNPs), versus (c) comparison women awaiting results. A sample of 2138 women from the PROCAS study was stratified by testing groups: T-C only, T-C(+SNPs) and comparison women; and by 10-year risk estimates received: 'moderate' (5-7.99%), 'average' (2-4.99%) or 'below average' (<1.99%) risk. Postal questionnaires were returned by 765 (36%) women. Overall state anxiety and cancer worry were low, and similar for women in T-C only and T-C(+SNPs) groups. Women in both T-C only and T-C(+SNPs) groups showed lower-state anxiety but slightly higher cancer worry than comparison women awaiting results. Risk information had no consistent effects on intentions to change behaviour. Most women were satisfied with information provided. There was considerable variation in understanding. No major harms of providing women with 10-year breast cancer risk estimates were detected. Research to establish the feasibility of risk-stratified breast screening is warranted.

Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples.

PubMed

Turkki, Riku; Linder, Nina; Kovanen, Panu E; Pellinen, Teijo; Lundin, Johan

2016-01-01

Immune cell infiltration in tumor is an emerging prognostic biomarker in breast cancer. The gold standard for quantification of immune cells in tissue sections is visual assessment through a microscope, which is subjective and semi-quantitative. In this study, we propose and evaluate an approach based on antibody-guided annotation and deep learning to quantify immune cell-rich areas in hematoxylin and eosin (H&E) stained samples. Consecutive sections of formalin-fixed parafin-embedded samples obtained from the primary tumor of twenty breast cancer patients were cut and stained with H&E and the pan-leukocyte CD45 antibody. The stained slides were digitally scanned, and a training set of immune cell-rich and cell-poor tissue regions was annotated in H&E whole-slide images using the CD45-expression as a guide. In analysis, the images were divided into small homogenous regions, superpixels, from which features were extracted using a pretrained convolutional neural network (CNN) and classified with a support of vector machine. The CNN approach was compared to texture-based classification and to visual assessments performed by two pathologists. In a set of 123,442 labeled superpixels, the CNN approach achieved an F-score of 0.94 (range: 0.92-0.94) in discrimination of immune cell-rich and cell-poor regions, as compared to an F-score of 0.88 (range: 0.87-0.89) obtained with the texture-based classification. When compared to visual assessment of 200 images, an agreement of 90% (κ = 0.79) to quantify immune infiltration with the CNN approach was achieved while the inter-observer agreement between pathologists was 90% (κ = 0.78). Our findings indicate that deep learning can be applied to quantify immune cell infiltration in breast cancer samples using a basic morphology staining only. A good discrimination of immune cell-rich areas was achieved, well in concordance with both leukocyte antigen expression and pathologists' visual assessment.

Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors.

PubMed

Cho, Gene Young; Moy, Linda; Kim, Sungheon G; Baete, Steven H; Moccaldi, Melanie; Babb, James S; Sodickson, Daniel K; Sigmund, Eric E

2016-08-01

To examine heterogeneous breast cancer through intravoxel incoherent motion (IVIM) histogram analysis. This HIPAA-compliant, IRB-approved retrospective study included 62 patients (age 48.44 ± 11.14 years, 50 malignant lesions and 12 benign) who underwent contrast-enhanced 3 T breast MRI and diffusion-weighted imaging. Apparent diffusion coefficient (ADC) and IVIM biomarkers of tissue diffusivity (Dt), perfusion fraction (fp), and pseudo-diffusivity (Dp) were calculated using voxel-based analysis for the whole lesion volume. Histogram analysis was performed to quantify tumour heterogeneity. Comparisons were made using Mann-Whitney tests between benign/malignant status, histological subtype, and molecular prognostic factor status while Spearman's rank correlation was used to characterize the association between imaging biomarkers and prognostic factor expression. The average values of the ADC and IVIM biomarkers, Dt and fp, showed significant differences between benign and malignant lesions. Additional significant differences were found in the histogram parameters among tumour subtypes and molecular prognostic factor status. IVIM histogram metrics, particularly fp and Dp, showed significant correlation with hormonal factor expression. Advanced diffusion imaging biomarkers show relationships with molecular prognostic factors and breast cancer malignancy. This analysis reveals novel diagnostic metrics that may explain some of the observed variability in treatment response among breast cancer patients. • Novel IVIM biomarkers characterize heterogeneous breast cancer. • Histogram analysis enables quantification of tumour heterogeneity. • IVIM biomarkers show relationships with breast cancer malignancy and molecular prognostic factors.

Strontium biokinetic model for the lactating woman and transfer to breast milk: application to Techa River studies.

PubMed

Shagina, N B; Tolstykh, E I; Fell, T P; Smith, T J; Harrison, J D; Degteva, M O

2015-09-01

This paper presents a biokinetic model for strontium metabolism in the lactating woman and transfer to breast milk for members of Techa River communities exposed as a result of discharges of liquid radioactive wastes from the Mayak plutonium production facility (Russia) in the early 1950s. This model was based on that developed for the International Commission for Radiological Protection with modifications to account for population specific features of breastfeeding and maternal bone mineral metabolism. The model is based on a biokinetic model for the adult female with allowances made for changes in mineral metabolism during periods of exclusive and partial breast-feeding. The model for females of all ages was developed earlier from extensive data on (90)Sr-body measurements for Techa Riverside residents. Measurements of (90)Sr concentrations in the maternal skeleton and breast milk obtained in the1960s during monitoring of global fallout in the Southern Urals region were used for evaluation of strontium transfer to breast and breast milk. The model was validated with independent data from studies of global fallout in Canada and measurements of (90)Sr body-burden in women living in the Techa River villages who were breastfeeding during maximum (90)Sr-dietary intakes. The model will be used in evaluations of the intake of strontium radioisotopes in breast milk by children born in Techa River villages during the radioactive releases and quantification of (90)Sr retention in the maternal skeleton.

The need for supplemental breast cancer screening modalities: a perspective of population-based breast cancer screening programs in Japan.

PubMed

Uematsu, Takayoshi

2017-01-01

This article discusses possible supplemental breast cancer screening modalities for younger women with dense breasts from a perspective of population-based breast cancer screening program in Japan. Supplemental breast cancer screening modalities have been proposed to increase the sensitivity and detection rates of early stage breast cancer in women with dense breasts; however, there are no global guidelines that recommend the use of supplemental breast cancer screening modalities in such women. Also, no criterion standard exists for breast density assessment. Based on the current situation of breast imaging in Japan, the possible supplemental breast cancer screening modalities are ultrasonography, digital breast tomosynthesis, and breast magnetic resonance imaging. An appropriate population-based breast cancer screening program based on the balance between cost and benefit should be a high priority. Further research based on evidence-based medicine is encouraged. It is very important that the ethnicity, workforce, workflow, and resources for breast cancer screening in each country should be considered when considering supplemental breast cancer screening modalities for women with dense breasts.

Quantifying Dynamic Deformity After Dual Plane Breast Augmentation.

PubMed

Cheffe, Marcelo Recondo; Valentini, Jorge Diego; Collares, Marcus Vinicius Martins; Piccinini, Pedro Salomão; da Silva, Jefferson Luis Braga

2018-06-01

Dynamic breast deformity (DBD) is characterized by visible distortion and deformity of the breast due to contraction of the pectoralis major muscle after submuscular breast augmentation; fortunately, in most cases, this is not a clinically significant complaint from patients. The purpose of this study is to present a simple method for objectively measuring DBD in patients submitted to dual plane breast augmentation (DPBA). We studied 32 women, between 18 and 50 years old, who underwent primary DPBA with at least 1 year of follow-up. Anthropometric landmarks of the breast were marked, creating linear segments. Standardized photographs were obtained both during no pectoralis contraction (NPC) and during maximum pectoralis muscle contraction (MPC); measurements of the linear segments were taken through ImageJ imaging software, and both groups were compared. We found statistically significant differences in all analyzed segments when comparing measurements of the breasts during NPC and MPC (p < 0.001). Our study proposes a novel, standardized method for measuring DBD after DPBA. This technique is reproducible, allowing for objective quantification of the deformity in any patient, which can be valuable for both patients and surgeons, as it allows for a more thorough discussion on DBD, both pre- and postoperatively, and may help both patients and surgeons to make more informed decisions regarding potential animation deformities after breast augmentation. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Downregulation of hPMC2 imparts chemotherapeutic sensitivity to alkylating agents in breast cancer cells.

PubMed

Krishnamurthy, Nirmala; Liu, Lili; Xiong, Xiahui; Zhang, Junran; Montano, Monica M

2015-01-01

Triple negative breast cancer cell lines have been reported to be resistant to the cyotoxic effects of temozolomide (TMZ). We have shown previously that a novel protein, human homolog of Xenopus gene which Prevents Mitotic Catastrophe (hPMC2) has a role in the repair of estrogen-induced abasic sites. Our present study provides evidence that downregulation of hPMC2 in MDA-MB-231 and MDA-MB-468 breast cancer cells treated with temozolomide (TMZ) decreases cell survival. This increased sensitivity to TMZ is associated with an increase in number of apurinic/apyrimidinic (AP) sites in the DNA. We also show that treatment with another alkylating agent, BCNU, results in an increase in AP sites and decrease in cell survival. Quantification of western blot analyses and immunofluorescence experiments reveal that treatment of hPMC2 downregulated cells with TMZ results in an increase in γ-H2AX levels, suggesting an increase in double strand DNA breaks. The enhancement of DNA double strand breaks in TMZ treated cells upon downregulation of hPCM2 is also revealed by the comet assay. Overall, we provide evidence that downregulation of hPMC2 in breast cancer cells increases cytotoxicity of alkylating agents, representing a novel mechanism of treatment for breast cancer. Our data thus has important clinical implications in the management of breast cancer and brings forth potentially new therapeutic strategies.

Measurement of pressure-displacement kinetics of hemoglobin in normal breast tissue with near-infrared spectral imaging.

PubMed

Jiang, Shudong; Pogue, Brian W; Laughney, Ashley M; Kogel, Christine A; Paulsen, Keith D

2009-04-01

Applying localized external displacement to the breast surface can change the interstitial fluid pressure such that regional transient microvascular changes occur in oxygenation and vascular volume. Imaging these dynamic responses over time, while different pressures are applied, could provide selective temporal contrast for cancer relative to the surrounding normal breast. In order to investigate this possibility in normal breast tissue, a near-infrared spectral tomography system was developed that can simultaneously acquire data at three wavelengths with a 15 s time resolution per scan. The system was tested first with heterogeneous blood phantoms. Changes in regional blood concentrations were found to be linearly related to recovered mean hemoglobin concentration (Hb(T)) values (R(2)=0.9). In a series of volunteer breast imaging exams, data from 17 asymptomatic subjects were acquired under increasing and decreasing breast compression. Calculations show that a 10 mm displacement applied to the breast results in surface pressures in the range of 0-55 kPa depending on breast density. The recovered human data indicate that Hb(T) was reduced under compression and the normalized change was significantly correlated to the applied pressure with a p value of 0.005. The maximum Hb(T) decreases in breast tissue were associated with body mass index (BMI), which is a surrogate indicator of breast density. No statistically valid correlations were found between the applied pressure and the changes in tissue oxygen saturation (S(t)O(2)) or water percentage (H(2)O) across the range of BMI values studied.

Measurement of pressure-displacement kinetics of hemoglobin in normal breast tissue with near-infrared spectral imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Shudong; Pogue, Brian W.; Laughney, Ashley M.

2009-04-01

Applying localized external displacement to the breast surface can change the interstitial fluid pressure such that regional transient microvascular changes occur in oxygenation and vascular volume. Imaging these dynamic responses over time, while different pressures are applied, could provide selective temporal contrast for cancer relative to the surrounding normal breast. In order to investigate this possibility in normal breast tissue, a near-infrared spectral tomography system was developed that can simultaneously acquire data at three wavelengths with a 15 s time resolution per scan. The system was tested first with heterogeneous blood phantoms. Changes in regional blood concentrations were found tomore » be linearly related to recovered mean hemoglobin concentration (HbT) values (R{sup 2}=0.9). In a series of volunteer breast imaging exams, data from 17 asymptomatic subjects were acquired under increasing and decreasing breast compression. Calculations show that a 10 mm displacement applied to the breast results in surface pressures in the range of 0-55 kPa depending on breast density. The recovered human data indicate that HbT was reduced under compression and the normalized change was significantly correlated to the applied pressure with a p value of 0.005. The maximum HbT decreases in breast tissue were associated with body mass index (BMI), which is a surrogate indicator of breast density. No statistically valid correlations were found between the applied pressure and the changes in tissue oxygen saturation (StO2) or water percentage (H2O) across the range of BMI values studied.« less

Effects of tamoxifen on bone mineral density and metabolism in postmenopausal women with early-stage breast cancer.

PubMed

Zidan, Jamal; Keidar, Zohar; Basher, Walid; Israel, Ora

2004-01-01

At the present time, tamoxifen is the most widely used anti-estrogen for adjuvant therapy and metastatic disease in postmenopausal women with breast cancer, a population at high risk for osteoporosis. This prospective study was designed to evaluate the effect of adjuvant tamoxifen on bone mineral density and all biochemical markers concomitantly in women with early-stage breast cancer in one study. Using dual-energy X-ray absorptiometry, prior to and 12 mo after tamoxifen treatment, bone mineral density in lumbar spine and femoral neck was measured in 44 women with T1-T2N0M0 estrogen-receptor-positive breast cancer receiving adjuvant treatment with tamoxifen 20 mg/d. Biomarkers that can affect bone mineral metabolism were measured before and after 3 and 12 mo of tamoxifen treatment. Bone mineral density was minimally increased in lumbar spine and femoral neck after 12 mo treatment with tamoxifen (p = 0.79 and 0.55, respectively). No differences were found in serum levels of calcium, phosphate, creatinine, ALAT, albumin, LDH, calcitonin, or estradiol. A significant decrease in osteocalcin levels was found after 3 and 12 mo (p < or = 0.01). TSH and PTH levels were increased (p < or = 0.05) after 3 mo, returning to baseline after 12 mo. In conclusion, tamoxifen has an estrogen-like effect on bone metabolism in postmenopausal women and is associated with preservation of bone mineral density in lumbar spine and femoral neck. Changes in serum concentration of biochemical markers may reflect decreased bone turnover or bone remodeling and add to the understanding of tamoxifen's effect on bone mineral density.

Tumor-infiltrating lymphocyte composition, organization and PD-1/ PD-L1 expression are linked in breast cancer

PubMed Central

Garaud, Soizic; de Wind, Alexandre; Van den Eynden, Gert; Boisson, Anais; Gu-Trantien, Chunyan; Naveaux, Céline; Lodewyckx, Jean-Nicolas; Duvillier, Hugues; Craciun, Ligia; Veys, Isabelle; Larsimont, Denis; Piccart-Gebhart, Martine; Stagg, John; Sotiriou, Christos

2017-01-01

ABSTRACT The clinical relevance of tumor-infiltrating lymphocytes (TIL) in breast cancer (BC) has been clearly established by their demonstrated correlation with long-term positive outcomes. Nevertheless, the relationship between protective immunity, observed in some patients, and critical features of the infiltrate remains unresolved. This study examined TIL density, composition and organization together with PD-1 and PD-L1 expression in freshly collected and paraffin-embedded tissues from 125 patients with invasive primary BC. Tumor and normal breast tissues were analyzed using both flow cytometry and immunohistochemistry. TIL density distribution is a continuum with 25% of tumors identified as TIL-negative at a TIL density equivalent to normal breast tissues. TIL-positive tumors (75%) were equally divided into TIL-intermediate and TIL-high. Tumors had higher mean frequencies of CD4+ T cells and CD19+ B cells and a lower mean frequency of CD8+ T cells compare with normal tissues, increasing the CD4+/CD8+ T-cell ratio. Tertiary lymphoid structures (TLS), principally located in the peri-tumoral stroma, were detected in 60% of tumors and correlated with higher TIL infiltration. PD-1 and PD-L1 expression were also associated with higher TIL densities and TLS. TIL density, TLS and PD-L1 expression were correlated with more aggressive tumor characteristics, including higher proliferation and hormone receptor negativity. Our findings reveal an important relationship between PD-1/PD-L1 expression, increased CD4+ T and B-cell infiltration, TIL density and TLS, suggesting that evaluating not only the extent but also the nature and location of the immune infiltrate should be considered when evaluating antitumor immunity and the potential for benefit from immunotherapies. PMID:28197375

A model of primary and scattered photon fluence for mammographic x-ray image quantification

NASA Astrophysics Data System (ADS)

Tromans, Christopher E.; Cocker, Mary R.; Brady, Michael, Sir

2012-10-01

We present an efficient method to calculate the primary and scattered x-ray photon fluence component of a mammographic image. This can be used for a range of clinically important purposes, including estimation of breast density, personalized image display, and quantitative mammogram analysis. The method is based on models of: the x-ray tube; the digital detector; and a novel ray tracer which models the diverging beam emanating from the focal spot. The tube model includes consideration of the anode heel effect, and empirical corrections for wear and manufacturing tolerances. The detector model is empirical, being based on a family of transfer functions that cover the range of beam qualities and compressed breast thicknesses which are encountered clinically. The scatter estimation utilizes optimal information sampling and interpolation (to yield a clinical usable computation time) of scatter calculated using fundamental physics relations. A scatter kernel arising around each primary ray is calculated, and these are summed by superposition to form the scatter image. Beam quality, spatial position in the field (in particular that arising at the air-boundary due to the depletion of scatter contribution from the surroundings), and the possible presence of a grid, are considered, as is tissue composition using an iterative refinement procedure. We present numerous validation results that use a purpose designed tissue equivalent step wedge phantom. The average differences between actual acquisitions and modelled pixel intensities observed across the adipose to fibroglandular attenuation range vary between 5% and 7%, depending on beam quality and, for a single beam quality are 2.09% and 3.36% respectively with and without a grid.

Mammographic density measured as changes in tissue structure caused by HRT

NASA Astrophysics Data System (ADS)

Raundahl, Jakob; Loog, Marco; Nielsen, Mads

2006-03-01

Numerous studies have investigated the relation between mammographic density and breast cancer risk. These studies indicate that women with high breast density have a four to six fold risk increase. An investigation of whether or not this relation is causal is important for, e.g., hormone replacement therapy (HRT), which has been shown to actually increase the density. No gold standard for automatic assessment of mammographic density exists. Manual methods such as Wolfe patterns and BI-RADS are helpful for communication of diagnostic sensitivity, but they are both time consuming and crude. They may be sufficient in certain cases and for single measurements, but for serial, temporal analysis it is necessary to be able to detect more subtle changes and, in addition, to be more reproducible. In this work an automated method for measuring the effect of HRT w.r.t. changes in biological density in the breast is presented. This measure is a novel measure, which provides structural information orthogonal to intensity-based methods. Hessian eigenvalues at different scales are used as features and a clustering of these is employed to divide a mammogram into four structurally different areas. Subsequently, based on the relative size of the areas, a density score is determined. In the experiments, two sets of mammograms of 50 patients from a double blind, placebo controlled HRT experiment were used. The change in density for the HRT group, measured with the new method, was significantly higher (p = 0.0002) than the change in the control group.

Experimental validation of absolute SPECT/CT quantification for response monitoring in breast cancer.

PubMed

Collarino, Angela; Pereira Arias-Bouda, Lenka M; Valdés Olmos, Renato A; van der Tol, Pieternel; Dibbets-Schneider, Petra; de Geus-Oei, Lioe-Fee; van Velden, Floris H P

2018-05-01

Recent developments in iterative image reconstruction enable absolute quantification of SPECT/CT studies by incorporating compensation for collimator-detector response, attenuation, and scatter as well as resolution recovery into the reconstruction process (Evolution; Q.Metrix package; GE Healthcare, Little Chalfont, UK). The aim of this experimental study is to assess its quantitative accuracy for potential clinical 99m Tc-sestamibi (MIBI)-related SPECT/CT application in neoadjuvant chemotherapy response studies in breast cancer. Two phantoms were filled with MIBI and acquired on a SPECT/CT gamma camera (Discovery 670 Pro; GE Healthcare), that is, a water cylinder and a NEMA body phantom containing six spheres that were filled with an activity concentration reflecting clinical MIBI uptake. Subsequently, volumes-of-interest (VOI) of each sphere were drawn (semi)automatically on SPECT using various isocontour methods or manually on CT. Finally, prone MIBI SPECT/CT scans were acquired 5 and 90 min p.i. in a locally advanced breast cancer patient. Activity concentration in the four largest spheres converged after nine iterations of evolution. Depending on the count statistics, the accuracy of the reconstructed activity concentration varied between -4.7 and -0.16% (VOI covering the entire phantom) and from 6.9% to 10% (8.8 cm ⌀ cylinder VOI placed in the center of the phantom). Recovery coefficients of SUV max were 1.89 ± 0.18, 1.76 ± 0.17, 2.00 ± 0.38, 1.89 ± 0.35, and 0.90 ± 0.26 for spheres with 37, 28, 22, 17, and 13 mm ⌀, respectively. Recovery coefficients of SUV mean were 1.07 ± 0.06, 1.03 ± 0.09, 1.17 ± 0.21, 1.10 ± 0.20, and 0.52 ± 0.14 (42% isocontour); 1.10 ± 0.07, 1.02 ± 0.09, 1.13 ± 0.19, 1.06 ± 0.19, and 0.51 ± 0.13 (36% isocontour with local background correction); and 0.96, 1.09, 1.03, 1.03, and 0.29 (CT). Patient study results were concordant with the phantom validation. Absolute SPECT/CT quantification of breast studies using MIBI seems feasible (<17% deviation) when a 42% isocontour is used for delineation for tumors of at least 17 mm diameter. However, with tumor shrinkage, response evaluation should be handled with caution, especially when using SUV max . © 2018 American Association of Physicists in Medicine.

Mammographic Breast Density in a Cohort of Medically Underserved Women

DTIC Science & Technology

2011-10-01

1.08 relative to ញ years) or birth order (2+ OR 0.99, 95% CI 0.67-1.46 relative to 1) and breast cancer. However, there was a significant...increase in breast cancer risk among women whose mothers smoked during pregnancy (OR 1.73, 95% CI 1.04-2.88). With the exception of birth order , our

Total Xenoestrogen Body Burden in Relation to Mammographic Density, a Marker of Breast Cancer Risk

DTIC Science & Technology

2009-10-01

Conference.1  Funding has been obtained from the Susan Komen Foundation for an ancillary study of sex hormones and breast density. The Komen...Foundation is providing funds to analyze sex hormone levels in the blood samples obtained in this study. The relation between sex hormone levels and...menopause Use of birth control pills or female hormones Hysterectomy (removal of the uterus) Removal of one or both ovaries Other: Please

Hispanic and Immigrant Paradoxes in U.S. Breast Cancer Mortality: Impact of Neighborhood Poverty and Hispanic Density.

PubMed

Pruitt, Sandi L; Tiro, Jasmin A; Xuan, Lei; Lee, Simon J Craddock

2016-12-14

To test the Hispanic and Immigrant Paradoxes-i.e., survival advantages despite a worse risk factor profile-and the modifying role of neighborhood context, we examined associations between patient ethnicity, birthplace, neighborhood Hispanic density and neighborhood poverty among 166,254 female breast cancer patients diagnosed 1995-2009 in Texas, U.S. Of all, 79.9% were non-Hispanic White, 15.8% Hispanic U.S.-born, and 4.2% Hispanic foreign-born. We imputed birthplace for the 60.7% of Hispanics missing birthplace data using multiple imputation. Shared frailty Cox proportional hazard models (patients nested within census tracts) adjusted for age, diagnosis year, stage, grade, histology, urban/rural residence, and local mammography capacity. Whites (vs. U.S.-born Hispanics) had increased all-cause and breast cancer mortality. Foreign-born (vs. U.S.-born) Hispanics had increased all-cause and breast cancer mortality. Living in higher Hispanic density neighborhoods was generally associated with increased mortality, although associations differed slightly in magnitude and significance by ethnicity, birthplace, and neighborhood poverty. We found no evidence of an Immigrant Paradox and some evidence of a Hispanic Paradox where protective effects were limited to U.S.-born Hispanics. Contrary to prior studies, foreign birthplace and residence in higher Hispanic density neighborhoods were associated with increased mortality. More research on intersections between ethnicity, birthplace and neighborhood context are needed.

Correlates of mammographic density in B-mode ultrasound and real time elastography.

PubMed

Jud, Sebastian Michael; Häberle, Lothar; Fasching, Peter A; Heusinger, Katharina; Hack, Carolin; Faschingbauer, Florian; Uder, Michael; Wittenberg, Thomas; Wagner, Florian; Meier-Meitinger, Martina; Schulz-Wendtland, Rüdiger; Beckmann, Matthias W; Adamietz, Boris R

2012-07-01

The aim of our study involved the assessment of B-mode imaging and elastography with regard to their ability to predict mammographic density (MD) without X-rays. Women, who underwent routine mammography, were prospectively examined with additional B-mode ultrasound and elastography. MD was assessed quantitatively with a computer-assisted method (Madena). The B-mode and elastography images were assessed by histograms with equally sized gray-level intervals. Regression models were built and cross validated to examine the ability to predict MD. The results of this study showed that B-mode imaging and elastography were able to predict MD. B-mode seemed to give a more accurate prediction. R for B-mode image and elastography were 0.67 and 0.44, respectively. Areas in the B-mode images that correlated with mammographic dense areas were either dark gray or of intermediate gray levels. Concerning elastography only the gray levels that represent extremely stiff tissue correlated positively with MD. In conclusion, ultrasound seems to be able to predict MD. Easy and cheap utilization of regular breast ultrasound machines encourages the use of ultrasound in larger case-control studies to validate this method as a breast cancer risk predictor. Furthermore, the application of ultrasound for breast tissue characterization could enable comprehensive research concerning breast cancer risk and breast density in young and pregnant women.

Comparative Multifractal Analysis of Dynamic Infrared Thermograms and X-Ray Mammograms Enlightens Changes in the Environment of Malignant Tumors.

PubMed

Gerasimova-Chechkina, Evgeniya; Toner, Brian; Marin, Zach; Audit, Benjamin; Roux, Stephane G; Argoul, Francoise; Khalil, Andre; Gileva, Olga; Naimark, Oleg; Arneodo, Alain

2016-01-01

There is growing evidence that the microenvironment surrounding a tumor plays a special role in cancer development and cancer therapeutic resistance. Tumors arise from the dysregulation and alteration of both the malignant cells and their environment. By providing tumor-repressing signals, the microenvironment can impose and sustain normal tissue architecture. Once tissue homeostasis is lost, the altered microenvironment can create a niche favoring the tumorigenic transformation process. A major challenge in early breast cancer diagnosis is thus to show that these physiological and architectural alterations can be detected with currently used screening techniques. In a recent study, we used a 1D wavelet-based multi-scale method to analyze breast skin temperature temporal fluctuations collected with an IR thermography camera in patients with breast cancer. This study reveals that the multifractal complexity of temperature fluctuations superimposed on cardiogenic and vasomotor perfusion oscillations observed in healthy breasts is lost in malignant tumor foci in cancerous breasts. Here we use a 2D wavelet-based multifractal method to analyze the spatial fluctuations of breast density in the X-ray mammograms of the same panel of patients. As compared to the long-range correlations and anti-correlations in roughness fluctuations, respectively observed in dense and fatty breast areas, some significant change in the nature of breast density fluctuations with some clear loss of correlations is detected in the neighborhood of malignant tumors. This attests to some architectural disorganization that may deeply affect heat transfer and related thermomechanics in breast tissues, corroborating the change to homogeneous monofractal temperature fluctuations recorded in cancerous breasts with the IR camera. These results open new perspectives in computer-aided methods to assist in early breast cancer diagnosis.

Comparative Multifractal Analysis of Dynamic Infrared Thermograms and X-Ray Mammograms Enlightens Changes in the Environment of Malignant Tumors

PubMed Central

Gerasimova-Chechkina, Evgeniya; Toner, Brian; Marin, Zach; Audit, Benjamin; Roux, Stephane G.; Argoul, Francoise; Khalil, Andre; Gileva, Olga; Naimark, Oleg; Arneodo, Alain

2016-01-01

There is growing evidence that the microenvironment surrounding a tumor plays a special role in cancer development and cancer therapeutic resistance. Tumors arise from the dysregulation and alteration of both the malignant cells and their environment. By providing tumor-repressing signals, the microenvironment can impose and sustain normal tissue architecture. Once tissue homeostasis is lost, the altered microenvironment can create a niche favoring the tumorigenic transformation process. A major challenge in early breast cancer diagnosis is thus to show that these physiological and architectural alterations can be detected with currently used screening techniques. In a recent study, we used a 1D wavelet-based multi-scale method to analyze breast skin temperature temporal fluctuations collected with an IR thermography camera in patients with breast cancer. This study reveals that the multifractal complexity of temperature fluctuations superimposed on cardiogenic and vasomotor perfusion oscillations observed in healthy breasts is lost in malignant tumor foci in cancerous breasts. Here we use a 2D wavelet-based multifractal method to analyze the spatial fluctuations of breast density in the X-ray mammograms of the same panel of patients. As compared to the long-range correlations and anti-correlations in roughness fluctuations, respectively observed in dense and fatty breast areas, some significant change in the nature of breast density fluctuations with some clear loss of correlations is detected in the neighborhood of malignant tumors. This attests to some architectural disorganization that may deeply affect heat transfer and related thermomechanics in breast tissues, corroborating the change to homogeneous monofractal temperature fluctuations recorded in cancerous breasts with the IR camera. These results open new perspectives in computer-aided methods to assist in early breast cancer diagnosis. PMID:27555823

Positive predictive values by mammographic density and screening mode in the Norwegian Breast Cancer Screening Program.

PubMed

Moshina, Nataliia; Ursin, Giske; Roman, Marta; Sebuødegård, Sofie; Hofvind, Solveig

2016-01-01

To investigate the probability of breast cancer among women recalled due to abnormal findings on the screening mammograms (PPV-1) and among women who underwent an invasive procedure (PPV-2) by mammographic density (MD), screening mode and age. We used information about 28,826 recall examinations from 26,951 subsequently screened women in the Norwegian Breast Cancer Screening Program, 1996-2010. The radiologists who performed the recall examinations subjectively classified MD on the mammograms into three categories: fatty (<30% fibroglandular tissue); medium dense (30-70%) and dense (>70%). Screening mode was defined as screen-film mammography (SFM) and full-field digital mammography (FFDM). We examined trends of PPVs by MD, screening mode and age. We used logistic regression to estimate odds ratio (OR) of screen-detected breast cancer associated with MD among women recalled, adjusting for screening mode and age. PPV-1 and PPV-2 decreased by increasing MD, regardless of screening mode (p for trend <0.05 for both PPVs). PPV-1 and PPV-2 were statistically significantly higher for FFDM compared with SFM for women with fatty breasts. Among women recalled, the adjusted OR of breast cancer decreased with increasing MD. Compared with women with fatty breasts, the OR was 0.90 (95% CI: 0.84-0.96) for those with medium dense breasts and 0.85 (95% CI: 0.76-0.95) for those with dense breasts. PPVs decreased by increasing MD. Fewer women needed to be recalled or undergo an invasive procedure to detect one breast cancer among those with fatty versus dense breasts in the screening program in Norway, 1996-2010. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Sex steroid metabolism polymorphisms and mammographic density in pre- and early perimenopausal women

PubMed Central

Crandall, Carolyn J; Sehl, Mary E; Crawford, Sybil L; Gold, Ellen B; Habel, Laurel A; Butler, Lesley M; Sowers, MaryFran R; Greendale, Gail A; Sinsheimer, Janet S

2009-01-01

Introduction We examined the association between mammographic density and single-nucleotide polymorphisms (SNPs) in genes encoding CYP1A1, CYP1B1, aromatase, 17β-HSD, ESR1, and ESR2 in pre- and early perimenopausal white, African-American, Chinese, and Japanese women. Methods The Study of Women's Health Across the Nation is a longitudinal community-based cohort study. We analyzed data from 451 pre- and early perimenopausal participants of the ancillary SWAN Mammographic Density study for whom we had complete information regarding mammographic density, genotypes, and covariates. With multivariate linear regression, we examined the relation between percentage mammographic breast density (outcome) and each SNP (primary predictor), adjusting for age, race/ethnicity, parity, cigarette smoking, and body mass index (BMI). Results After multivariate adjustment, the CYP1B1 rs162555 CC genotype was associated with a 9.4% higher mammographic density than the TC/TT genotype (P = 0.04). The CYP19A1 rs936306 TT genotype was associated with 6.2% lower mammographic density than the TC/CC genotype (P = 0.02). The positive association between CYP1A1 rs2606345 and mammographic density was significantly stronger among participants with BMI greater than 30 kg/m2 than among those with BMI less than 25 kg/m2 (Pinteraction = 0.05). Among white participants, the ESR1 rs2234693 CC genotype was associated with a 7.0% higher mammographic density than the CT/TT genotype (P = 0.01). Conclusions SNPs in certain genes encoding sex steroid metabolism enzymes and ESRs were associated with mammographic density. Because the encoded enzymes and ESR1 are expressed in breast tissue, these SNPs may influence breast cancer risk by altering mammographic density. PMID:19630952

Applying a new mammographic imaging marker to predict breast cancer risk

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Danala, Gopichandh; Hollingsworth, Alan B.; Stoug, Rebecca G.; Pearce, Melanie; Liu, Hong; Zheng, Bin

2018-02-01

Identifying and developing new mammographic imaging markers to assist prediction of breast cancer risk has been attracting extensive research interest recently. Although mammographic density is considered an important breast cancer risk, its discriminatory power is lower for predicting short-term breast cancer risk, which is a prerequisite to establish a more effective personalized breast cancer screening paradigm. In this study, we presented a new interactive computer-aided detection (CAD) scheme to generate a new quantitative mammographic imaging marker based on the bilateral mammographic tissue density asymmetry to predict risk of cancer detection in the next subsequent mammography screening. An image database involving 1,397 women was retrospectively assembled and tested. Each woman had two digital mammography screenings namely, the "current" and "prior" screenings with a time interval from 365 to 600 days. All "prior" images were originally interpreted negative. In "current" screenings, these cases were divided into 3 groups, which include 402 positive, 643 negative, and 352 biopsy-proved benign cases, respectively. There is no significant difference of BIRADS based mammographic density ratings between 3 case groups (p < 0.6). When applying the CAD-generated imaging marker or risk model to classify between 402 positive and 643 negative cases using "prior" negative mammograms, the area under a ROC curve is 0.70+/-0.02 and the adjusted odds ratios show an increasing trend from 1.0 to 8.13 to predict the risk of cancer detection in the "current" screening. Study demonstrated that this new imaging marker had potential to yield significantly higher discriminatory power to predict short-term breast cancer risk.

Introduction of an automated user-independent quantitative volumetric magnetic resonance imaging breast density measurement system using the Dixon sequence: comparison with mammographic breast density assessment.

PubMed

Wengert, Georg Johannes; Helbich, Thomas H; Vogl, Wolf-Dieter; Baltzer, Pascal; Langs, Georg; Weber, Michael; Bogner, Wolfgang; Gruber, Stephan; Trattnig, Siegfried; Pinker, Katja

2015-02-01

The purposes of this study were to introduce and assess an automated user-independent quantitative volumetric (AUQV) breast density (BD) measurement system on the basis of magnetic resonance imaging (MRI) using the Dixon technique as well as to compare it with qualitative and quantitative mammographic (MG) BD measurements. Forty-three women with normal mammogram results (Breast Imaging Reporting and Data System 1) were included in this institutional review board-approved prospective study. All participants were subjected to BD assessment with MRI using the following sequence with the Dixon technique (echo time/echo time, 6 milliseconds/2.45 milliseconds/2.67 milliseconds; 1-mm isotropic; 3 minutes 38 seconds). To test the reproducibility, a second MRI after patient repositioning was performed. The AUQV magnetic resonance (MR) BD measurement system automatically calculated percentage (%) BD. The qualitative BD assessment was performed using the American College of Radiology Breast Imaging Reporting and Data System BD categories. Quantitative BD was estimated semiautomatically using the thresholding technique Cumulus4. Appropriate statistical tests were used to assess the agreement between the AUQV MR measurements and to compare them with qualitative and quantitative MG BD estimations. The AUQV MR BD measurements were successfully performed in all 43 women. There was a nearly perfect agreement of AUQV MR BD measurements between the 2 MR examinations for % BD (P < 0.001; intraclass correlation coefficient, 0.998) with no significant differences (P = 0.384). The AUQV MR BD measurements were significantly lower than quantitative and qualitative MG BD assessment (P < 0.001). The AUQV MR BD measurement system allows a fully automated, user-independent, robust, reproducible, as well as radiation- and compression-free volumetric quantitative BD assessment through different levels of BD. The AUQV MR BD measurements were significantly lower than the currently used qualitative and quantitative MG-based approaches, implying that the current assessment might overestimate breast density with MG.

Single cell genomic quantification by non-fluorescence nonlinear microscopy

NASA Astrophysics Data System (ADS)

Kota, Divya; Liu, Jing

2017-02-01

Human epidermal growth receptor 2 (Her2) is a gene which plays a major role in breast cancer development. The quantification of Her2 expression in single cells is limited by several drawbacks in existing fluorescence-based single molecule techniques, such as low signal-to-noise ratio (SNR), strong autofluorescence and background signals from biological components. For rigorous genomic quantification, a robust method of orthogonal detection is highly desirable and we demonstrated it by two non-fluorescent imaging techniques -transient absorption microscopy (TAM) and second harmonic generation (SHG). In TAM, gold nanoparticles (AuNPs) are chosen as an orthogonal probes for detection of single molecules which gives background-free quantifications of single mRNA transcript. In SHG, emission from barium titanium oxide (BTO) nanoprobes was demonstrated which allows stable signal beyond the autofluorescence window. Her2 mRNA was specifically labeled with nanoprobes which are conjugated with antibodies or oligonucleotides and quantified at single copy sensitivity in the cancer cells and tissues. Furthermore, a non-fluorescent super-resolution concept, named as second harmonic super-resolution microscopy (SHaSM), was proposed to quantify individual Her2 transcripts in cancer cells beyond the diffraction limit. These non-fluorescent imaging modalities will provide new dimensions in biomarker quantification at single molecule sensitivity in turbid biological samples, offering a strong cross-platform strategy for clinical monitoring at single cell resolution.

Quantification of ligand density and stoichiometry on the surface of liposomes using single-molecule fluorescence imaging.

PubMed

Belfiore, Lisa; Spenkelink, Lisanne M; Ranson, Marie; van Oijen, Antoine M; Vine, Kara L

2018-05-28

Despite the longstanding existence of liposome technology in drug delivery applications, there have been no ligand-directed liposome formulations approved for clinical use to date. This lack of translation is due to several factors, one of which is the absence of molecular tools for the robust quantification of ligand density on the surface of liposomes. We report here for the first time the quantification of proteins attached to the surface of small unilamellar liposomes using single-molecule fluorescence imaging. Liposomes were surface-functionalized with fluorescently labeled human proteins previously validated to target the cancer cell surface biomarkers plasminogen activator inhibitor-2 (PAI-2) and trastuzumab (TZ, Herceptin®). These protein-conjugated liposomes were visualized using a custom-built wide-field fluorescence microscope with single-molecule sensitivity. By counting the photobleaching steps of the fluorescently labeled proteins, we calculated the number of attached proteins per liposome, which was 11 ± 4 proteins for single-ligand liposomes. Imaging of dual-ligand liposomes revealed stoichiometries of the two attached proteins in accordance with the molar ratios of protein added during preparation. Preparation of PAI-2/TZ dual-ligand liposomes via two different methods revealed that the post-insertion method generated liposomes with a more equal representation of the two differently sized proteins, demonstrating the ability of this preparation method to enable better control of liposome protein densities. We conclude that the single-molecule imaging method presented here is an accurate and reliable quantification tool for determining ligand density and stoichiometry on the surface of liposomes. This method has the potential to allow for comprehensive characterization of novel ligand-directed liposomes that should facilitate the translation of these nanotherapies through to the clinic. Copyright © 2018 Elsevier B.V. All rights reserved.

Clinical photoacoustic computed tomography of the human breast in vivo within a single breath hold

NASA Astrophysics Data System (ADS)

Lin, Li; Hu, Peng; Shi, Junhui; Appleton, Catherine M.; Maslov, Konstantin; Wang, Lihong V.

2018-03-01

We have developed a single-breath-hold photoacoustic computed tomography (SBH-PACT) system to detect tumors and reveal detailed angiographic information about human breasts. SBH-PACT provides high spatial and temporal resolutions with a deep in vivo penetration depth of over 4 cm. A volumetric breast image can be acquired by scanning the breast within a single breath hold ( 15 sec). We imaged a healthy female volunteer and seven breast cancer patients. SBH-PACT clearly identified all tumors by revealing higher blood vessel densities and lower compliance associated with the tumors

Quantification of Pulmonary Fibrosis in a Bleomycin Mouse Model Using Automated Histological Image Analysis.

PubMed

Gilhodes, Jean-Claude; Julé, Yvon; Kreuz, Sebastian; Stierstorfer, Birgit; Stiller, Detlef; Wollin, Lutz

2017-01-01

Current literature on pulmonary fibrosis induced in animal models highlights the need of an accurate, reliable and reproducible histological quantitative analysis. One of the major limits of histological scoring concerns the fact that it is observer-dependent and consequently subject to variability, which may preclude comparative studies between different laboratories. To achieve a reliable and observer-independent quantification of lung fibrosis we developed an automated software histological image analysis performed from digital image of entire lung sections. This automated analysis was compared to standard evaluation methods with regard to its validation as an end-point measure of fibrosis. Lung fibrosis was induced in mice by intratracheal administration of bleomycin (BLM) at 0.25, 0.5, 0.75 and 1 mg/kg. A detailed characterization of BLM-induced fibrosis was performed 14 days after BLM administration using lung function testing, micro-computed tomography and Ashcroft scoring analysis. Quantification of fibrosis by automated analysis was assessed based on pulmonary tissue density measured from thousands of micro-tiles processed from digital images of entire lung sections. Prior to analysis, large bronchi and vessels were manually excluded from the original images. Measurement of fibrosis has been expressed by two indexes: the mean pulmonary tissue density and the high pulmonary tissue density frequency. We showed that tissue density indexes gave access to a very accurate and reliable quantification of morphological changes induced by BLM even for the lowest concentration used (0.25 mg/kg). A reconstructed 2D-image of the entire lung section at high resolution (3.6 μm/pixel) has been performed from tissue density values allowing the visualization of their distribution throughout fibrotic and non-fibrotic regions. A significant correlation (p<0.0001) was found between automated analysis and the above standard evaluation methods. This correlation establishes automated analysis as a novel end-point measure of BLM-induced lung fibrosis in mice, which will be very valuable for future preclinical drug explorations.

Quantification of Pulmonary Fibrosis in a Bleomycin Mouse Model Using Automated Histological Image Analysis

PubMed Central

Gilhodes, Jean-Claude; Kreuz, Sebastian; Stierstorfer, Birgit; Stiller, Detlef; Wollin, Lutz

2017-01-01

Current literature on pulmonary fibrosis induced in animal models highlights the need of an accurate, reliable and reproducible histological quantitative analysis. One of the major limits of histological scoring concerns the fact that it is observer-dependent and consequently subject to variability, which may preclude comparative studies between different laboratories. To achieve a reliable and observer-independent quantification of lung fibrosis we developed an automated software histological image analysis performed from digital image of entire lung sections. This automated analysis was compared to standard evaluation methods with regard to its validation as an end-point measure of fibrosis. Lung fibrosis was induced in mice by intratracheal administration of bleomycin (BLM) at 0.25, 0.5, 0.75 and 1 mg/kg. A detailed characterization of BLM-induced fibrosis was performed 14 days after BLM administration using lung function testing, micro-computed tomography and Ashcroft scoring analysis. Quantification of fibrosis by automated analysis was assessed based on pulmonary tissue density measured from thousands of micro-tiles processed from digital images of entire lung sections. Prior to analysis, large bronchi and vessels were manually excluded from the original images. Measurement of fibrosis has been expressed by two indexes: the mean pulmonary tissue density and the high pulmonary tissue density frequency. We showed that tissue density indexes gave access to a very accurate and reliable quantification of morphological changes induced by BLM even for the lowest concentration used (0.25 mg/kg). A reconstructed 2D-image of the entire lung section at high resolution (3.6 μm/pixel) has been performed from tissue density values allowing the visualization of their distribution throughout fibrotic and non-fibrotic regions. A significant correlation (p<0.0001) was found between automated analysis and the above standard evaluation methods. This correlation establishes automated analysis as a novel end-point measure of BLM-induced lung fibrosis in mice, which will be very valuable for future preclinical drug explorations. PMID:28107543

Selective magnetic resonance imaging (MRI) in invasive lobular breast cancer based on mammographic density: does it lead to an appropriate change in surgical treatment?

PubMed

Bansal, Gaurav J; Santosh, Divya; Davies, Eleri L

2016-01-01

The purpose of this study was to evaluate whether high mammographic density can be used as one of the selection criteria for MRI in invasive lobular breast cancer (ILC). In our institute, high breast density has been used as one of the indications for performing MRI scan in patients with ILC. We divided the patients in two groups, one with MRI performed pre-operatively and other without MRI. We compared their surgical procedures and analyzed whether surgical plan was altered after MRI. In case of alteration of plan, we analyzed whether the change was adequate by comparing post-operative histological findings. Between 2011 and 2015, there were a total of 1601 breast cancers with 97 lobular cancers, out of which 36 had pre-operative MRI and 61 had no MRI scan. 12 (33.3%) had mastectomy following MRI, out of which 9 (25%) had change in surgical plan from conservation to mastectomy following MRI. There were no unnecessary mastectomies in the MRI group. However, utilization of MRI in this cohort of patients did not reduce reoperation rate (19.3%). Lobular carcinoma in situ (LCIS) was identified in 60% of reoperations on post-surgical histology. Patients in the "No MRI" group had higher mastectomy rate 26 (42.6%), which was again appropriate. High mammographic density is a useful risk stratification criterion for selective MRI in ILC within a multidisciplinary team meeting setting. Provided additional lesions identified on MRI are confirmed with biopsy, pre-operative MRI does not cause unnecessary mastectomies. Used in this selective manner, reoperation rates were not eliminated, albeit reduced when compared to literature. High mammographic breast density can be used as one of the selection criteria for pre-operative MRI in ILC without an increase in inappropriate mastectomies with potential time and cost savings. In this cohort, re-excisions were not reduced markedly with pre-operative MRI.

Mammographic density defined by higher than conventional brightness thresholds better predicts breast cancer risk

PubMed Central

Nguyen, Tuong L; Aung, Ye K; Evans, Christopher F; Dite, Gillian S; Stone, Jennifer; MacInnis, Robert J; Dowty, James G; Bickerstaffe, Adrian; Aujard, Kelly; Rommens, Johanna M; Song, Yun-Mi; Sung, Joohon; Jenkins, Mark A; Southey, Melissa C; Giles, Graham G; Apicella, Carmel; Hopper, John L

2017-01-01

Abstract Background: Mammographic density defined by the conventional pixel brightness threshold, and adjusted for age and body mass index (BMI), is a well-established risk factor for breast cancer. We asked if higher thresholds better separate women with and without breast cancer. Methods: We studied Australian women, 354 with breast cancer over-sampled for early-onset and family history, and 944 unaffected controls frequency-matched for age at mammogram. We measured mammographic dense area and percent density using the CUMULUS software at the conventional threshold, which we call Cumulus, and at two increasingly higher thresholds, which we call Altocumulus and Cirrocumulus, respectively. All measures were Box–Cox transformed and adjusted for age and BMI. We estimated the odds per adjusted standard deviation (OPERA) using logistic regression and the area under the receiver operating characteristic curve (AUC). Results: Altocumulus and Cirrocumulus were correlated with Cumulus (r ∼ 0.8 and 0.6, respectively). For dense area, the OPERA was 1.62, 1.74 and 1.73 for Cumulus, Altocumulus and Cirrocumulus, respectively (all P < 0.001). After adjusting for Altocumulus and Cirrocumulus, Cumulus was not significant (P > 0.6). The OPERAs for percent density were less but gave similar findings. The mean of the standardized adjusted Altocumulus and Cirrocumulus dense area measures was the best predictor; OPERA = 1.87 [95% confidence interval (CI): 1.64–2.14] and AUC = 0.68 (0.65–0.71). Conclusions: The areas of higher mammographically dense regions are associated with almost 30% stronger breast cancer risk gradient, explain the risk association of the conventional measure and might be more aetiologically important. This has substantial implications for clinical translation and molecular, genetic and epidemiological research. PMID:28338721

Mammographic density defined by higher than conventional brightness thresholds better predicts breast cancer risk.

PubMed

Nguyen, Tuong L; Aung, Ye K; Evans, Christopher F; Dite, Gillian S; Stone, Jennifer; MacInnis, Robert J; Dowty, James G; Bickerstaffe, Adrian; Aujard, Kelly; Rommens, Johanna M; Song, Yun-Mi; Sung, Joohon; Jenkins, Mark A; Southey, Melissa C; Giles, Graham G; Apicella, Carmel; Hopper, John L

2017-04-01

Mammographic density defined by the conventional pixel brightness threshold, and adjusted for age and body mass index (BMI), is a well-established risk factor for breast cancer. We asked if higher thresholds better separate women with and without breast cancer. We studied Australian women, 354 with breast cancer over-sampled for early-onset and family history, and 944 unaffected controls frequency-matched for age at mammogram. We measured mammographic dense area and percent density using the CUMULUS software at the conventional threshold, which we call Cumulus , and at two increasingly higher thresholds, which we call Altocumulus and Cirrocumulus , respectively. All measures were Box-Cox transformed and adjusted for age and BMI. We estimated the odds per adjusted standard deviation (OPERA) using logistic regression and the area under the receiver operating characteristic curve (AUC). Altocumulus and Cirrocumulus were correlated with Cumulus (r ∼ 0.8 and 0.6 , respectively) . For dense area, the OPERA was 1.62, 1.74 and 1.73 for Cumulus, Altocumulus and Cirrocumulus , respectively (all P  < 0.001). After adjusting for Altocumulus and Cirrocumulus , Cumulus was not significant ( P  > 0.6). The OPERAs for percent density were less but gave similar findings. The mean of the standardized adjusted Altocumulus and Cirrocumulus dense area measures was the best predictor; OPERA = 1.87 [95% confidence interval (CI): 1.64-2.14] and AUC = 0.68 (0.65-0.71). The areas of higher mammographically dense regions are associated with almost 30% stronger breast cancer risk gradient, explain the risk association of the conventional measure and might be more aetiologically important. This has substantial implications for clinical translation and molecular, genetic and epidemiological research. © The Author 2016. Published by Oxford University Press on behalf of the International Epidemiological Association

Radiographic absorptiometry method in measurement of localized alveolar bone density changes.

PubMed

Kuhl, E D; Nummikoski, P V

2000-03-01

The objective of this study was to measure the accuracy and precision of a radiographic absorptiometry method by using an occlusal density reference wedge in quantification of localized alveolar bone density changes. Twenty-two volunteer subjects had baseline and follow-up radiographs taken of mandibular premolar-molar regions with an occlusal density reference wedge in both films and added bone chips in the baseline films. The absolute bone equivalent densities were calculated in the areas that contained bone chips from the baseline and follow-up radiographs. The differences in densities described the masses of the added bone chips that were then compared with the true masses by using regression analysis. The correlation between the estimated and true bone-chip masses ranged from R = 0.82 to 0.94, depending on the background bone density. There was an average 22% overestimation of the mass of the bone chips when they were in low-density background, and up to 69% overestimation when in high-density background. The precision error of the method, which was calculated from duplicate bone density measurements of non-changing areas in both films, was 4.5%. The accuracy of the intraoral radiographic absorptiometry method is low when used for absolute quantification of bone density. However, the precision of the method is good and the correlation is linear, indicating that the method can be used for serial assessment of bone density changes at individual sites.

Screening for Breast Cancer: U.S. Preventive Services Task Force Recommendation Statement.

PubMed

Siu, Albert L

2016-02-16

Update of the 2009 U.S. Preventive Services Task Force (USPSTF) recommendation on screening for breast cancer. The USPSTF reviewed the evidence on the following: effectiveness of breast cancer screening in reducing breast cancer-specific and all-cause mortality, as well as the incidence of advanced breast cancer and treatment-related morbidity; harms of breast cancer screening; test performance characteristics of digital breast tomosynthesis as a primary screening strategy; and adjunctive screening in women with increased breast density. In addition, the USPSTF reviewed comparative decision models on optimal starting and stopping ages and intervals for screening mammography; how breast density, breast cancer risk, and comorbidity level affect the balance of benefit and harms of screening mammography; and the number of radiation-induced breast cancer cases and deaths associated with different screening mammography strategies over the course of a woman's lifetime. This recommendation applies to asymptomatic women aged 40 years or older who do not have preexisting breast cancer or a previously diagnosed high-risk breast lesion and who are not at high risk for breast cancer because of a known underlying genetic mutation (such as a BRCA1 or BRCA2 gene mutation or other familial breast cancer syndrome) or a history of chest radiation at a young age. The USPSTF recommends biennial screening mammography for women aged 50 to 74 years. (B recommendation) The decision to start screening mammography in women prior to age 50 years should be an individual one. Women who place a higher value on the potential benefit than the potential harms may choose to begin biennial screening between the ages of 40 and 49 years. (C recommendation) The USPSTF concludes that the current evidence is insufficient to assess the balance of benefits and harms of screening mammography in women aged 75 years or older. (I statement) The USPSTF concludes that the current evidence is insufficient to assess the benefits and harms of digital breast tomosynthesis (DBT) as a primary screening method for breast cancer. (I statement) The USPSTF concludes that the current evidence is insufficient to assess the balance of benefits and harms of adjunctive screening for breast cancer using breast ultrasonography, magnetic resonance imaging (MRI), DBT, or other methods in women identified to have dense breasts on an otherwise negative screening mammogram. (I statement).

Comparison Between Digital and Synthetic 2D Mammograms in Breast Density Interpretation.

PubMed

Alshafeiy, Taghreed I; Wadih, Antoine; Nicholson, Brandi T; Rochman, Carrie M; Peppard, Heather R; Patrie, James T; Harvey, Jennifer A

2017-07-01

The purpose of this study was to compare assessments of breast density on synthetic 2D images as compared with digital 2D mammograms. This retrospective study included consecutive women undergoing screening with digital 2D mammography and tomosynthesis during May 2015 with a negative or benign outcome. In separate reading sessions, three radiologists with 5-25 years of clinical experience and 1 year of experience with synthetic 2D mammography read digital 2D and synthetic 2D images and assigned breast density categories according to the 5th edition of BI-RADS. Inter- and intrareader agreement was assessed for each BI-RADS density assessment and combined dense and nondense categories using percent agreement and Cohen kappa coefficient for consensus and all reads. A total of 309 patients met study inclusion criteria. Agreement between consensus BI-RADS density categories assigned for digital and synthetic 2D mammography was 80.3% (95% CI, 75.4-84.5%) with κ = 0.73 (95% CI, 0.66-0.79). For combined dense and nondense categories, agreement reached 91.9% (95% CI, 88.2-94.7%). For consensus readings, similar numbers of patients were shifted between nondense and dense categories (11 and 14, respectively) with the synthetic 2D compared with digital 2D mammography. Interreader differences were apparent; assignment to dense categories was greater with digital 2D mammography for reader 1 (odds ratio [OR], 1.26; p = 0.002), the same for reader 2 (OR, 0.91; p = 0.262), and greater with synthetic 2D mammography for reader 3 (OR, 0.86; p = 0.033). Overall, synthetic 2D mammography is comparable with digital 2D mammography in assessment of breast density, though there is some variability by reader. Practices can readily adopt synthetic 2D mammography without concern that it will affect density assessment and subsequent recommendations for supplemental screening.

Bioanalytical methods for determination of tamoxifen and its phase I metabolites: a review.

PubMed

Teunissen, S F; Rosing, H; Schinkel, A H; Schellens, J H M; Beijnen, J H

2010-12-17

The selective estrogen receptor modulator tamoxifen is used in the treatment of early and advanced breast cancer and in selected cases for breast cancer prevention in high-risk subjects. The cytochrome P450 enzyme system and flavin-containing monooxygenase are responsible for the extensive metabolism of tamoxifen into several phase I metabolites that vary in toxicity and potencies towards estrogen receptor (ER) alpha and ER beta. An extensive overview of publications on the determination of tamoxifen and its phase I metabolites in biological samples is presented. In these publications techniques were used such as capillary electrophoresis, liquid, gas and thin layer chromatography coupled with various detection techniques (mass spectrometry, ultraviolet or fluorescence detection, liquid scintillation counting and nuclear magnetic resonance spectroscopy). A trend is seen towards the use of liquid chromatography coupled to mass spectrometry (LC-MS). State-of-the-art LC-MS equipment allowed for identification of unknown metabolites and quantification of known metabolites reaching lower limit of quantification levels in the sub pg mL(-1) range. Although tamoxifen is also metabolized into phase II metabolites, the number of publications reporting on phase II metabolism of tamoxifen is scarce. Therefore the focus of this review is on phase I metabolites of tamoxifen. We conclude that in the past decades tamoxifen metabolism has been studied extensively and numerous metabolites have been identified. Assays have been developed for both the identification and quantification of tamoxifen and its metabolites in an array of biological samples. This review can be used as a resource for method transfer and development of analytical methods used to support pharmacokinetic and pharmacodynamic studies of tamoxifen and its phase I metabolites. Copyright © 2010 Elsevier B.V. All rights reserved.

Serum osteoprotegerin levels and mammographic density among high-risk women.

PubMed

Moran, Olivia; Zaman, Tasnim; Eisen, Andrea; Demsky, Rochelle; Blackmore, Kristina; Knight, Julia A; Elser, Christine; Ginsburg, Ophira; Zbuk, Kevin; Yaffe, Martin; Narod, Steven A; Salmena, Leonardo; Kotsopoulos, Joanne

2018-06-01

Mammographic density is a risk factor for breast cancer but the mechanism behind this association is unclear. The receptor activator of nuclear factor κB (RANK)/RANK ligand (RANKL) pathway has been implicated in the development of breast cancer. Given the role of RANK signaling in mammary epithelial cell proliferation, we hypothesized this pathway may also be associated with mammographic density. Osteoprotegerin (OPG), a decoy receptor for RANKL, is known to inhibit RANK signaling. Thus, it is of interest to evaluate whether OPG levels modify breast cancer risk through mammographic density. We quantified serum OPG levels in 57 premenopausal and 43 postmenopausal women using an enzyme-linked immunosorbent assay (ELISA). Cumulus was used to measure percent density, dense area, and non-dense area for each mammographic image. Subjects were classified into high versus low OPG levels based on the median serum OPG level in the entire cohort (115.1 pg/mL). Multivariate models were used to assess the relationship between serum OPG levels and the measures of mammographic density. Serum OPG levels were not associated with mammographic density among premenopausal women (P ≥ 0.42). Among postmenopausal women, those with low serum OPG levels had higher mean percent mammographic density (20.9% vs. 13.7%; P = 0.04) and mean dense area (23.4 cm 2 vs. 15.2 cm 2 ; P = 0.02) compared to those with high serum OPG levels after covariate adjustment. These findings suggest that low OPG levels may be associated with high mammographic density, particularly in postmenopausal women. Targeting RANK signaling may represent a plausible, non-surgical prevention option for high-risk women with high mammographic density, especially those with low circulating OPG levels.

Identification and prognostic value of anterior gradient protein 2 expression in breast cancer based on tissue microarray.

PubMed

Guo, Jilong; Gong, Guohua; Zhang, Bin

2017-07-01

Breast cancer has attracted substantial attention as one of the major cancers causing death in women. It is crucial to find potential biomarkers of prognostic value in breast cancer. In this study, the expression pattern of anterior gradient protein 2 in breast cancer was identified based on the main molecular subgroups. Through analysis of 69 samples from the Gene Expression Omnibus database, we found that anterior gradient protein 2 expression was significantly higher in non-triple-negative breast cancer tissues compared with normal tissues and triple-negative breast cancer tissues (p < 0.05). The data from a total of 622 patients from The Cancer Genome Atlas were analysed. The data from The Cancer Genome Atlas and results from quantitative reverse transcription polymerase chain reaction also verified the anterior gradient protein 2 expression pattern. Furthermore, we performed immunohistochemical analysis. The quantification results revealed that anterior gradient protein 2 is highly expressed in non-triple-negative breast cancer (grade 3 excluded) and grade 1 + 2 (triple-negative breast cancer excluded) tumours compared with normal tissues. Anterior gradient protein 2 was significantly highly expressed in non-triple-negative breast cancer (grade 3 excluded) and non-triple-negative breast cancer tissues compared with triple-negative breast cancer tissues (p < 0.01). In addition, anterior gradient protein 2 was significantly highly expressed in grade 1 + 2 (triple-negative breast cancer excluded) and grade 1 + 2 tissues compared with grade 3 tissues (p < 0.05). Analysis by Fisher's exact test revealed that anterior gradient protein 2 expression was significantly associated with histologic type, histological grade, oestrogen status and progesterone status. Univariate analysis of clinicopathological variables showed that anterior gradient protein 2 expression, tumour size and lymph node status were significantly correlated with overall survival in patients with grade 1 and 2 tumours. Cox multivariate analysis revealed anterior gradient protein 2 as a putative independent indicator of unfavourable outcomes (p = 0.031). All these data clearly showed that anterior gradient protein 2 is highly expressed in breast cancer and can be regarded as a putative biomarker for breast cancer prognosis.

Accuracy of Liver Fat Quantification With Advanced CT, MRI, and Ultrasound Techniques: Prospective Comparison With MR Spectroscopy.

PubMed

Kramer, Harald; Pickhardt, Perry J; Kliewer, Mark A; Hernando, Diego; Chen, Guang-Hong; Zagzebski, James A; Reeder, Scott B

2017-01-01

The purpose of this study was to prospectively evaluate the accuracy of proton-density fat-fraction, single- and dual-energy CT (SECT and DECT), gray-scale ultrasound (US), and US shear-wave elastography (US-SWE) in the quantification of hepatic steatosis with MR spectroscopy (MRS) as the reference standard. Fifty adults who did not have symptoms (23 men, 27 women; mean age, 57 ± 5 years; body mass index, 27 ± 5) underwent liver imaging with un-enhanced SECT, DECT, gray-scale US, US-SWE, proton-density fat-fraction MRI, and MRS for this prospective trial. MRS voxels for the reference standard were colocalized with all other modalities under investigation. For SECT (120 kVp), attenuation values were recorded. For rapid-switching DECT (80/140 kVp), monochromatic images (70-140 keV) and fat density-derived material decomposition images were reconstructed. For proton-density fat fraction MRI, a quantitative chemical shift-encoded method was used. For US, echogenicity was evaluated on a qualitative 0-3 scale. Quantitative US shear-wave velocities were also recorded. Data were analyzed by linear regression for each technique compared with MRS. There was excellent correlation between MRS and both proton-density fat-fraction MRI (r 2 = 0.992; slope, 0.974; intercept, -0.943) and SECT (r 2 = 0.856; slope, -0.559; intercept, 35.418). DECT fat attenuation had moderate correlation with MRS measurements (r 2 = 0.423; slope, 0.034; intercept, 8.459). There was good correlation between qualitative US echogenicity and MRS measurements with a weighted kappa value of 0.82. US-SWE velocity did not have reliable correlation with MRS measurements (r 2 = 0.004; slope, 0.069; intercept, 6.168). Quantitative MRI proton-density fat fraction and SECT fat attenuation have excellent linear correlation with MRS measurements and can serve as accurate noninvasive biomarkers for quantifying steatosis. Material decomposition with DECT does not improve the accuracy of fat quantification over conventional SECT attenuation. US-SWE has poor accuracy for liver fat quantification.

Localized-atlas-based segmentation of breast MRI in a decision-making framework.

PubMed

Fooladivanda, Aida; Shokouhi, Shahriar B; Ahmadinejad, Nasrin

2017-03-01

Breast-region segmentation is an important step for density estimation and Computer-Aided Diagnosis (CAD) systems in Magnetic Resonance Imaging (MRI). Detection of breast-chest wall boundary is often a difficult task due to similarity between gray-level values of fibroglandular tissue and pectoral muscle. This paper proposes a robust breast-region segmentation method which is applicable for both complex cases with fibroglandular tissue connected to the pectoral muscle, and simple cases with high contrast boundaries. We present a decision-making framework based on geometric features and support vector machine (SVM) to classify breasts in two main groups, complex and simple. For complex cases, breast segmentation is done using a combination of intensity-based and atlas-based techniques; however, only intensity-based operation is employed for simple cases. A novel atlas-based method, that is called localized-atlas, accomplishes the processes of atlas construction and registration based on the region of interest (ROI). Atlas-based segmentation is performed by relying on the chest wall template. Our approach is validated using a dataset of 210 cases. Based on similarity between automatic and manual segmentation results, the proposed method achieves Dice similarity coefficient, Jaccard coefficient, total overlap, false negative, and false positive values of 96.3, 92.9, 97.4, 2.61 and 4.77%, respectively. The localization error of the breast-chest wall boundary is 1.97 mm, in terms of averaged deviation distance. The achieved results prove that the suggested framework performs the breast segmentation with negligible errors and efficient computational time for different breasts from the viewpoints of size, shape, and density pattern.

Development of array piezoelectric fingers towards in vivo breast tumor detection

NASA Astrophysics Data System (ADS)

Xu, Xin; Chung, Youngsoo; Brooks, Ari D.; Shih, Wei-Heng; Shih, Wan Y.

2016-12-01

We have investigated the development of a handheld 4 × 1 piezoelectric finger (PEF) array breast tumor detector system towards in vivo patient testing, particularly, on how the duration of the DC applied voltage, the depression depth of the handheld unit, and breast density affect the PEF detection sensitivity on 40 patients. The tests were blinded and carried out in four phases: with DC voltage durations 5, 3, 2, to 0.8 s corresponding to scanning a quadrant, a half, a whole breast, and both breasts within 30 min, respectively. The results showed that PEF detection sensitivity was unaffected by shortening the applied voltage duration from 5 to 0.8 s nor was it affected by increasing the depression depth from 2 to 6 mm. Over the 40 patients, PEF detected 46 of the 48 lesions (46/48)—with the smallest lesion detected being 5 mm in size. Of 28 patients (some have more than one lesion) with mammography records, PEF detected 31/33 of all lesions (94%) and 14/15 of malignant lesions (93%), while mammography detected 30/33 of all lesions (91%) and 12/15 of malignant lesions (80%), indicating that PEF could detect malignant lesions not detectable by mammography without significantly increasing false positives. PEF's detection sensitivity is also shown to be independent of breast density, suggesting that PEF could be a potential tool for detecting breast cancer in young women and women with dense breasts.

Clinicopathologic and prognostic implications of progranulin in breast carcinoma.

PubMed

Li, Li-qin; Huang, Hui-lian; Ping, Jin-liang; Wang, Xiao-hong; Zhong, Jing; Dai, Li-cheng

2011-07-05

Progranulin is a newly discovered 88-kDa glycoprotein originally purified from the highly tumorigenic mouse teratoma-derived cell line PC. Its expression is closely correlated with the development and metastasis of several cancers. However, no immunohistochemical evidence currently exists to correlate progranulin expression with clinicopathologic features in breast carcinoma biopsies, and the role of progranulin as a new marker of metastatic risk and prognosis in breast cancer has not yet been studied. The aim of this study was to investigate the clinicopathologic and prognostic implications of progranulin expression in breast carcinoma and its correlation with tumor angiogenesis. Progranulin expression was determined immunohistochemically in 183 surgical specimens from patients with breast cancer and 20 tissue samples from breast fibroadenomas. The tumor angiogenesis-related biomarker, vascular endothelial growth factor was assayed and microvessel density was assessed by counting vascular endothelial cells in tumor tissues labeled with endoglin antibody. The relationship between progranulin expression and the clinicopathologic data were analyzed. Progranulin proteins were overexpressed in breast cancer. The level of progranulin expression was significantly correlated with tumor size (P = 0.004), lymph node metastasis (P < 0.001) and TNM staging (P < 0.001). High progranulin expression was associated with higher tumor angiogenesis, reflected by increased vascular endothelial growth factor expression (P < 0.001) and higher microvessel density (P = 0.002). Progranulin may be a valuable marker for assessing the metastasis and prognosis of breast cancer, and could provide the basis for new combination regimens with antiangiogenic activity.

Association of Catechol-O-methyltransferase polymorphism Val158Met and mammographic density: A meta-analysis.

PubMed

Kallionpää, Roope A; Uusitalo, Elina; Peltonen, Juha

2017-08-15

The Val158Met polymorphism in catechol-O-methyltransferase (COMT) enzyme reduces the methylation of catechol estrogens, which may affect mammographic density. High mammographic density is a known risk factor of breast cancer. Our aim was to perform meta-analysis of the effect of COMT Val158Met polymorphism on mammographic density. Original studies reporting data on mammographic density, stratified by the presence of COMT Val158Met polymorphism, were identified and combined using genetic models Met/Val vs. Val/Val, Met/Met vs. Val/Val, Val/Met+Met/Met vs. Val/Val (dominant model) and Met/Met vs. Val/Met+Val/Val (recessive model). Subgroup analyses by breast cancer status, menopausal status and use of hormone replacement therapy (HRT) were also performed. Eight studies were included in the meta-analysis. The overall effect in percent mammographic density was -1.41 (CI -2.86 to 0.05; P=0.06) in the recessive model. Exclusion of breast cancer patients increased the effect size to -1.93 (CI -3.49 to -0.37; P=0.02). The results suggested opposite effect of COMT Val158Met for postmenopausal users of HRT versus premenopausal women or postmenopausal non-users of HRT. COMT Val158Met polymorphism may be associated with mammographic density at least in healthy women. Menopausal status and HRT should be taken into account in future studies to avoid masking of the underlying effects. Copyright © 2017 Elsevier B.V. All rights reserved.

Lymphedema following treatment for breast cancer: a new approach to an old problem.

PubMed

O'Toole, Jean; Jammallo, Lauren S; Skolny, Melissa N; Miller, Cynthia L; Elliott, Krista; Specht, Michelle C; Taghian, Alphonse G

2013-11-01

Lymphedema following treatment for breast cancer can be an irreversible condition with a profound negative impact on quality of life. The lack of consensus regarding standard definitions of clinically significant lymphedema and optimal methods of measurement and quantification are unresolved problems. Inconsistencies persist regarding the appropriate timing of intervention and what forms of treatment should be the standard of care. There are reports that early detection and intervention can prevent progression, however,the Level 1 evidence to support this hypothesis has yet to be generated. To assess these controversies, we propose the implementation of a screening program to detect early lymphedema in conjunction with a randomized, prospective trial designed to generate Level 1 evidence regarding the efficacy of early intervention and appropriate treatment strategies. Collaboration among institutions that manage breast cancer patients is essential to establish a standardized approach to lymphedema and to establish guidelines for best practice. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Improved algorithm for computerized detection and quantification of pulmonary emphysema at high-resolution computed tomography (HRCT)

NASA Astrophysics Data System (ADS)

Tylen, Ulf; Friman, Ola; Borga, Magnus; Angelhed, Jan-Erik

2001-05-01

Emphysema is characterized by destruction of lung tissue with development of small or large holes within the lung. These areas will have Hounsfield values (HU) approaching -1000. It is possible to detect and quantificate such areas using simple density mask technique. The edge enhancement reconstruction algorithm, gravity and motion of the heart and vessels during scanning causes artefacts, however. The purpose of our work was to construct an algorithm that detects such image artefacts and corrects them. The first step is to apply inverse filtering to the image removing much of the effect of the edge enhancement reconstruction algorithm. The next step implies computation of the antero-posterior density gradient caused by gravity and correction for that. Motion artefacts are in a third step corrected for by use of normalized averaging, thresholding and region growing. Twenty healthy volunteers were investigated, 10 with slight emphysema and 10 without. Using simple density mask technique it was not possible to separate persons with disease from those without. Our algorithm improved separation of the two groups considerably. Our algorithm needs further refinement, but may form a basis for further development of methods for computerized diagnosis and quantification of emphysema by HRCT.

Application of texture analysis method for mammogram density classification

NASA Astrophysics Data System (ADS)

Nithya, R.; Santhi, B.

2017-07-01

Mammographic density is considered a major risk factor for developing breast cancer. This paper proposes an automated approach to classify breast tissue types in digital mammogram. The main objective of the proposed Computer-Aided Diagnosis (CAD) system is to investigate various feature extraction methods and classifiers to improve the diagnostic accuracy in mammogram density classification. Texture analysis methods are used to extract the features from the mammogram. Texture features are extracted by using histogram, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Difference Matrix (GLDM), Local Binary Pattern (LBP), Entropy, Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT), Gabor transform and trace transform. These extracted features are selected using Analysis of Variance (ANOVA). The features selected by ANOVA are fed into the classifiers to characterize the mammogram into two-class (fatty/dense) and three-class (fatty/glandular/dense) breast density classification. This work has been carried out by using the mini-Mammographic Image Analysis Society (MIAS) database. Five classifiers are employed namely, Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). Experimental results show that ANN provides better performance than LDA, NB, KNN and SVM classifiers. The proposed methodology has achieved 97.5% accuracy for three-class and 99.37% for two-class density classification.

Charge Density Quantification of Polyelectrolyte Polysaccharides by Conductometric Titration: An Analytical Chemistry Experiment

ERIC Educational Resources Information Center

Farris, Stefano; Mora, Luigi; Capretti, Giorgio; Piergiovanni, Luciano

2012-01-01

An easy analytical method for determination of the charge density of polyelectrolytes, including polysaccharides and other biopolymers, is presented. The basic principles of conductometric titration, which is used in the pulp and paper industry as well as in colloid and interface science, were adapted to quantify the charge densities of a…

Background parenchymal uptake on molecular breast imaging as a breast cancer risk factor: a case-control study.

PubMed

Hruska, Carrie B; Scott, Christopher G; Conners, Amy Lynn; Whaley, Dana H; Rhodes, Deborah J; Carter, Rickey E; O'Connor, Michael K; Hunt, Katie N; Brandt, Kathleen R; Vachon, Celine M

2016-04-26

Molecular breast imaging (MBI) is a functional test used for supplemental screening of women with mammographically dense breasts. Additionally, MBI depicts variable levels of background parenchymal uptake (BPU) within nonmalignant, dense fibroglandular tissue. We investigated whether BPU is a risk factor for breast cancer. We conducted a retrospective case-control study of 3027 eligible women who had undergone MBI between February 2004 and February 2014. Sixty-two incident breast cancer cases were identified. A total of 179 controls were matched on age, menopausal status, and MBI year. Two radiologists blinded to case status independently assessed BPU as one of four categories: photopenic, minimal to mild, moderate, or marked. Conditional logistic regression analysis was performed to estimate the associations (OR) of BPU categories (moderate or marked vs. minimal to mild or photopenic) and breast cancer risk, adjusted for other risk factors. The median age was 60.2 years (range 38-86 years) for cases vs. 60.2 years (range 38-88 years) for controls (p = 0.88). Women with moderate or marked BPU had a 3.4-fold (95 % CI 1.6-7.3) and 4.8-fold (95 % CI 2.1-10.8) increased risk of breast cancer, respectively, compared with women with photopenic or minimal to mild BPU, for two radiologists. The results were similar after adjustment for BI-RADS density (OR 3.3 [95 % CI 1.6-7.2] and OR 4.6 [95 % CI 2.1-10.5]) or postmenopausal hormone use (OR 3.6 [95 % CI 1.7-7.7] and OR 5.0 [95 % CI 2.2-11.4]). The association of BPU with breast cancer remained in analyses limited to postmenopausal women only (OR 3.8 [95 % CI 1.5-9.3] and OR 4.1 [95 % CI 1.6-10.2]) and invasive breast cancer cases only (OR 3.6 [95 % CI 1.5-8.8] and OR 4.4 [95 % CI 1.7-11.1]). Variable BPU was observed among women with similar mammographic density; the distribution of BPU categories differed across density categories (p < 0.0001). This study provides the first evidence for BPU as a risk factor for breast cancer. Among women with dense breasts, who comprise >40 % of the screening population, BPU may serve as a functional imaging biomarker to identify the subset at greatest risk.

Measuring of Mycobacterium tuberculosis growth. A correlation of the optical measurements with colony forming units

PubMed Central

Peñuelas-Urquides, Katia; Villarreal-Treviño, Licet; Silva-Ramírez, Beatriz; Rivadeneyra-Espinoza, Liliana; Said-Fernández, Salvador; de León, Mario Bermúdez

2013-01-01

The quantification of colony forming units (cfu), turbidity, and optical density at 600 nm (OD600) measurements were used to evaluate Mycobacterium tuberculosis growth. Turbidity and OD600 measurements displayed similar growth curves, while cfu quantification showed a continuous growth curve. We determined the cfu equivalents to McFarland and OD600 units. PMID:24159318

Measuring of Mycobacterium tuberculosis growth. A correlation of the optical measurements with colony forming units.

PubMed

Peñuelas-Urquides, Katia; Villarreal-Treviño, Licet; Silva-Ramírez, Beatriz; Rivadeneyra-Espinoza, Liliana; Said-Fernández, Salvador; de León, Mario Bermúdez

2013-01-01

The quantification of colony forming units (cfu), turbidity, and optical density at 600 nm (OD600) measurements were used to evaluate Mycobacterium tuberculosis growth. Turbidity and OD600 measurements displayed similar growth curves, while cfu quantification showed a continuous growth curve. We determined the cfu equivalents to McFarland and OD600 units.

Combination of two-dimensional shear wave elastography with ultrasound breast imaging reporting and data system in the diagnosis of breast lesions: a new method to increase the diagnostic performance.

PubMed

Li, Dan-Dan; Xu, Hui-Xiong; Guo, Le-Hang; Bo, Xiao-Wan; Li, Xiao-Long; Wu, Rong; Xu, Jun-Mei; Zhang, Yi-Feng; Zhang, Kun

2016-09-01

To evaluate the diagnostic performance of a new method of combined two-dimensional shear wave elastography (i.e. virtual touch imaging quantification, VTIQ) and ultrasound (US) Breast Imaging Reporting and Data System (BI-RADS) in the differential diagnosis of breast lesions. From September 2014 to December 2014, 276 patients with 296 pathologically proven breast lesions were enrolled in this study. The conventional US images were interpreted by two independent readers. The diagnosis performances of BI-RADS and combined BI-RADS and VTIQ were evaluated, including the area under the receiver operating characteristic curve (AUROC), sensitivity and specificity. Observer consistency was also evaluated. Pathologically, 212 breast lesions were benign and 84 were malignant. Compared with BI-RADS alone, the AUROCs and specificities of the combined method for both readers increased significantly (AUROC: 0.862 vs. 0.693 in reader 1, 0.861 vs. 0.730 in reader 2; specificity: 91.5 % vs. 38.7 % in reader 1, 94.8 % vs. 47.2 % in reader 2; all P < .05). The Kappa value between the two readers for BI-RADS assessment was 0.614, and 0.796 for the combined method. The combined VTIQ and BI-RADS had a better diagnostic performance in the diagnosis of breast lesions in comparison with BI-RADS alone. • Combination of conventional ultrasound and elastography distinguishes breast cancers more effectively. • Combination of conventional ultrasound and elastography increases observer consistency. • BI-RADS weights more than the 2D-SWE with an increase in malignancy probability.

Recommendations for standardized pathological characterization of residual disease for neoadjuvant clinical trials of breast cancer by the BIG-NABCG collaboration

PubMed Central

Bossuyt, V.; Provenzano, E.; Symmans, W. F.; Boughey, J. C.; Coles, C.; Curigliano, G.; Dixon, J. M.; Esserman, L. J.; Fastner, G.; Kuehn, T.; Peintinger, F.; von Minckwitz, G.; White, J.; Yang, W.; Badve, S.; Denkert, C.; MacGrogan, G.; Penault-Llorca, F.; Viale, G.; Cameron, D.; Earl, Helena; Alba, Emilio; Lluch, Ana; Albanell, Joan; Amos, Keith; Biernat, Wojciech; Bonnefoi, Hervé; Buzdar, Aman; Cane, Paul; Pinder, Sarah; Carson, Lesley; Dickson-Witmer, Diana; Gong, Gyungyub; Green, Jimmy; Hsu, Chih-Yi; Tseng, Ling-Ming; Kroep, Judith; Leitch, A. Marilyn; Sarode, Venetia; Mamounas, Eleftherios; Marcom, Paul Kelly; Nuciforo, Paolo; Paik, Soonmyung; Peg, Vicente; Peston, David; Pierga, Jean-Yves; Quintela-Fandino, Miguel; Salgado, Roberto; Sikov, William; Thomas, Jeremy; Unzeitig, Gary; Wesseling, Jelle

2015-01-01

Neoadjuvant systemic therapy (NAST) provides the unique opportunity to assess response to treatment after months rather than years of follow-up. However, significant variability exists in methods of pathologic assessment of response to NAST, and thus its interpretation for subsequent clinical decisions. Our international multidisciplinary working group was convened by the Breast International Group-North American Breast Cancer Group (BIG-NABCG) collaboration and tasked to recommend practical methods for standardized evaluation of the post-NAST surgical breast cancer specimen for clinical trials that promote accurate and reliable designation of pathologic complete response (pCR) and meaningful characterization of residual disease. Recommendations include multidisciplinary communication; clinical marking of the tumor site (clips); and radiologic, photographic, or pictorial imaging of the sliced specimen, to map the tissue sections and reconcile macroscopic and microscopic findings. The information required to define pCR (ypT0/is ypN0 or ypT0 yp N0), residual ypT and ypN stage using the current AJCC/UICC system, and the Residual Cancer Burden system were recommended for quantification of residual disease in clinical trials. PMID:26019189

Tight-frame based iterative image reconstruction for spectral breast CT

PubMed Central

Zhao, Bo; Gao, Hao; Ding, Huanjun; Molloi, Sabee

2013-01-01

Purpose: To investigate tight-frame based iterative reconstruction (TFIR) technique for spectral breast computed tomography (CT) using fewer projections while achieving greater image quality. Methods: The experimental data were acquired with a fan-beam breast CT system based on a cadmium zinc telluride photon-counting detector. The images were reconstructed with a varying number of projections using the TFIR and filtered backprojection (FBP) techniques. The image quality between these two techniques was evaluated. The image's spatial resolution was evaluated using a high-resolution phantom, and the contrast to noise ratio (CNR) was evaluated using a postmortem breast sample. The postmortem breast samples were decomposed into water, lipid, and protein contents based on images reconstructed from TFIR with 204 projections and FBP with 614 projections. The volumetric fractions of water, lipid, and protein from the image-based measurements in both TFIR and FBP were compared to the chemical analysis. Results: The spatial resolution and CNR were comparable for the images reconstructed by TFIR with 204 projections and FBP with 614 projections. Both reconstruction techniques provided accurate quantification of water, lipid, and protein composition of the breast tissue when compared with data from the reference standard chemical analysis. Conclusions: Accurate breast tissue decomposition can be done with three fold fewer projection images by the TFIR technique without any reduction in image spatial resolution and CNR. This can result in a two-third reduction of the patient dose in a multislit and multislice spiral CT system in addition to the reduced scanning time in this system. PMID:23464320

Between-race differences in the effects of breast density information and information about new imaging technology on breast-health decision-making.

PubMed

Manning, Mark; Purrington, Kristen; Penner, Louis; Duric, Neb; Albrecht, Terrance L

2016-06-01

Some US states have mandated that women be informed when they have dense breasts; however, little is known about how general knowledge about breast density (BD) affects related health decision-making. We examined the effects of BD information and imaging technology information on 138 African-American (AA) and European-American (EA) women's intentions to discuss breast cancer screening with their physicians. Women were randomly assigned to receive BD information and/or imaging technology information via 2 by 2 factorial design, and completed planned behavior measures (e.g., attitudes, intentions) related to BC screening. Attitudes mediated the effects of BD information, and the mediation was stronger for AA women compared to EA women. Effects were more robust for BD information compared to imaging technology information. Results of moderator analyses revealed suppressor effects of injunctive norms that were moderated by imaging technology information. Information about BD favorably influences women's intentions to engage in relevant breast health behaviors. Stronger attitude mediated-effects for AA women suggest greater scrutiny of BD information. Since BD information may influence women's intentions to discuss BC screening, strategies to effectively present BD information to AA women should be investigated given the likelihood of their increased scrutiny of BD information. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

ApoA-I mimetic administration, but not increased apoA-I-containing HDL, inhibits tumour growth in a mouse model of inherited breast cancer.

PubMed

Cedó, Lídia; García-León, Annabel; Baila-Rueda, Lucía; Santos, David; Grijalva, Victor; Martínez-Cignoni, Melanie Raquel; Carbó, José M; Metso, Jari; López-Vilaró, Laura; Zorzano, Antonio; Valledor, Annabel F; Cenarro, Ana; Jauhiainen, Matti; Lerma, Enrique; Fogelman, Alan M; Reddy, Srinivasa T; Escolà-Gil, Joan Carles; Blanco-Vaca, Francisco

2016-11-03

Low levels of high-density lipoprotein cholesterol (HDLc) have been associated with breast cancer risk, but several epidemiologic studies have reported contradictory results with regard to the relationship between apolipoprotein (apo) A-I and breast cancer. We aimed to determine the effects of human apoA-I overexpression and administration of specific apoA-I mimetic peptide (D-4F) on tumour progression by using mammary tumour virus-polyoma middle T-antigen transgenic (PyMT) mice as a model of inherited breast cancer. Expression of human apoA-I in the mice did not affect tumour onset and growth in PyMT transgenic mice, despite an increase in the HDLc level. In contrast, D-4F treatment significantly increased tumour latency and inhibited the development of tumours. The effects of D-4F on tumour development were independent of 27-hydroxycholesterol. However, D-4F treatment reduced the plasma oxidized low-density lipoprotein (oxLDL) levels in mice and prevented oxLDL-mediated proliferative response in human breast adenocarcinoma MCF-7 cells. In conclusion, our study shows that D-4F, but not apoA-I-containing HDL, hinders tumour growth in mice with inherited breast cancer in association with a higher protection against LDL oxidative modification.

Sunlight Exposure and Breast Density: A Population-Based Study

PubMed Central

Wu, Sheng-Hui; So, Edwin; Lam, Tsz-ping; Woo, Jean; Yuen, PY; Qin, Ling; Ku, Susanna

2013-01-01

Purpose This study aims to assess the association of sunlight exposure with breast cancer risk, measured by the breast density assessed from Tabár's mammographic pattern in Chinese women. Methods A total of 676 premenopausal women were recruited to participate in this study, in which 650 completed a validated sunlight exposure questionnaire via telephone. The mammograms were classified according to Tabár's classification for parenchyma, and patterns IV & V and I, II & III indicated respectively high and low risk mammographic patterns for breast cancer. The odds ratios (OR) and 95% confidence intervals (CIs) for sun exposure-related variables were estimated using unconditional logistic regression with adjustment for potential confounders. Results Among 646 participants, women with high breast cancer risk (Tabár's patterns IV &V) had less hours spent in the sun than those with low risk (I, II & III) at any age stage. A higher level of sunlight exposure was associated with a significantly lower risk having high risk Tabár's pattern. Women aged 40 to 44 years who were in the highest tertile of lifetime total hours spent in the sun had a multi-adjusted OR of 0.41 (95% CI, 0.18-0.92; p for trend=0.03) compared with those in the lowest tertile (>2.19 hr/day vs. <1.32 hr/day). For hours spent in the sun across the ages of 6 to 12 years, the comparable OR was 0.37 (95% CI, 0.15-0.91; p for trend=0.03). Conclusion These findings suggest that higher sunlight exposure is related to a lower risk of having high risk breast density pattern in premenopausal women. Our results also suggest the most relevant period of exposure is during earlier life. PMID:23843849

Cost-effectiveness of annual versus biennial screening mammography for women with high mammographic breast density.

PubMed

Pataky, Reka; Ismail, Zahra; Coldman, Andrew J; Elwood, Mark; Gelmon, Karen; Hedden, Lindsay; Hislop, Greg; Kan, Lisa; McCoy, Bonnie; Olivotto, Ivo A; Peacock, Stuart

2014-12-01

The sensitivity of screening mammography is much lower among women who have dense breast tissue, compared with women who have largely fatty breasts, and they are also at much higher risk of developing the disease. Increasing mammography screening frequency from biennially to annually has been suggested as a policy option to address the elevated risk in this population. The purpose of this study was to assess the cost-effectiveness of annual versus biennial screening mammography among women aged 50-79 with dense breast tissue. A Markov model was constructed based on screening, diagnostic, and treatment pathways for the population-based screening and cancer care programme in British Columbia, Canada. Model probabilities and screening costs were calculated from screening programme data. Costs for breast cancer treatment were calculated from treatment data, and utility values were obtained from the literature. Incremental cost-effectiveness was expressed as cost per quality adjusted life year (QALY), and probabilistic sensitivity analysis was conducted. Compared with biennial screening, annual screening generated an additional 0.0014 QALYs (95% CI: -0.0480-0.0359) at a cost of $819 ($ = Canadian dollars) per patient (95% CI: 506-1185), resulting in an incremental cost effectiveness ratio of $565,912/QALY. Annual screening had a 37.5% probability of being cost-effective at a willingness-to-pay threshold of $100,000/QALY. There is considerable uncertainty about the incremental cost-effectiveness of annual mammography. Further research on the comparative effectiveness of screening strategies for women with high mammographic breast density is warranted, particularly as digital mammography and density measurement become more widespread, before cost-effectiveness can be reevaluated. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Influences of race and breast density on related cognitive and emotion outcomes before mandated breast density notification.

PubMed

Manning, Mark; Albrecht, Terrance L; Yilmaz-Saab, Zeynep; Shultz, Julie; Purrington, Kristen

2016-11-01

Many states have adopted laws mandating breast density (BD) notification for applicable women; however, very little is known about what women knew or felt about BD and related breast cancer (BC) risk before implementation of BD notification laws. We examined between-race differences in the extent to which having dense breasts was associated with women's related BD cognition and emotion, and with health care providers' communication about BD. We received surveys between May and October of 2015 assessing health care provider (HCP) communication about BD, BD-related knowledge, BD-related anxiety and BC worry from 182 African American (AA) and 113 European American (EA) women in the state of Michigan for whom we had radiologists' assessments of BD. Whereas having dense breasts was not associated with any BD-related cognition or emotion, there were robust effects of race as follows: EA women were more likely to have been told about BD by a HCP, more likely to know their BD status, had greater knowledge of BD and of BC risk, and had greater perceptions of BC risk and worry; AA women had greater BD-related anxieties. EA women's greater knowledge of their own BD status was directly related to the increased likelihood of HCP communication about BD. However, HCP communication about BD attenuated anxiety for AA women only. We present the only data of which we are aware that examines between-race differences in the associations between actual BD, HCP communication and BD related cognition and emotion before the implementation of BD notification laws. Our findings suggest that the BD notification laws could yield positive benefits for disparities in BD-related knowledge and anxiety when the notifications are followed by discussions with health care providers. Copyright © 2016 Elsevier Ltd. All rights reserved.

Aspirin use is associated with lower mammographic density in a large screening cohort.

PubMed

Wood, Marie E; Sprague, Brian L; Oustimov, Andrew; Synnstvedt, Marie B; Cuke, Melissa; Conant, Emily F; Kontos, Despina

2017-04-01

Observational and biologic studies suggest that aspirin is a promising prevention therapy for breast cancer. However, clinical trials to date have not corroborated this evidence, potentially due to study design. We evaluated the effect of aspirin on mammographic density (MD), an established modifiable risk factor for breast cancer. Electronic medical records from the University of Pennsylvania were evaluated for women who underwent screening mammography, saw their primary care provider, and had a confirmed list of medications during 2012-2013. Logistic regression was performed to test for associations between clinically recorded MD and aspirin use, after adjusting for age, body mass index (BMI), and ethnicity. We identified 26,000 eligible women. Mean age was 57.3, mean BMI was 28.9 kg/m 2 , 41% were African American, and 19.7% reported current aspirin use. Aspirin users were significantly older and had higher BMI. There was an independent, inverse association between aspirin use and MD (P trend  < 0.001). Women with extremely dense breasts were less likely to be aspirin users than women with scattered fibroglandular density (OR 0.73; 95% CI 0.57-0.93). This association was stronger for younger women (P = 0.0002) and for African Americans (P = 0.011). The likelihood of having dense breasts decreased with aspirin dose (P trend  = 0.007), suggesting a dose response. We demonstrate an independent association between aspirin use and lower MD in a large, diverse screening cohort. This association was stronger for younger and African American women: two groups at greater risk for ER- breast cancer. These results contribute to the importance of investigating aspirin for breast cancer prevention.

Breast ultrasound tomography with two parallel transducer arrays

NASA Astrophysics Data System (ADS)

Huang, Lianjie; Shin, Junseob; Chen, Ting; Lin, Youzuo; Gao, Kai; Intrator, Miranda; Hanson, Kenneth

2016-03-01

Breast ultrasound tomography is an emerging imaging modality to reconstruct the sound speed, density, and ultrasound attenuation of the breast in addition to ultrasound reflection/beamforming images for breast cancer detection and characterization. We recently designed and manufactured a new synthetic-aperture breast ultrasound tomography prototype with two parallel transducer arrays consisting of a total of 768 transducer elements. The transducer arrays are translated vertically to scan the breast in a warm water tank from the chest wall/axillary region to the nipple region to acquire ultrasound transmission and reflection data for whole-breast ultrasound tomography imaging. The distance of these two ultrasound transducer arrays is adjustable for scanning breasts with different sizes. We use our breast ultrasound tomography prototype to acquire phantom and in vivo patient ultrasound data to study its feasibility for breast imaging. We apply our recently developed ultrasound imaging and tomography algorithms to ultrasound data acquired using our breast ultrasound tomography system. Our in vivo patient imaging results demonstrate that our breast ultrasound tomography can detect breast lesions shown on clinical ultrasound and mammographic images.

IGF-I and mammographic density in four geographic locations: a pooled analysis.

PubMed

Maskarinec, Gertraud; Takata, Yumie; Chen, Zhao; Gram, Inger Torhild; Nagata, Chisato; Pagano, Ian; Hayashi, Kentaro; Arendell, Leslie; Skeie, Guri; Rinaldi, Sabina; Kaaks, Rudolph

2007-10-15

Insulin-like growth factor (IGF-I) and prolactin have been found to be associated with breast cancer risk and with mammographic density. In a pooled analysis from 4 geographic locations, we investigated the association of percent mammographic density with serum levels of IGF-I, IGFBP-3 and prolactin. The pooled data set included 1,327 pre- and postmenopausal women: Caucasians from Norway, Arizona and Hawaii, Japanese from Hawaii and Japan, Latina from Arizona, and Native Hawaiians from Hawaii. Serum samples were assayed for IGF-I, IGFBP-3 and prolactin levels using ELISA assays. Mammographic density was quantified using a computer-assisted density method. After stratification by menopausal status, multiple regression models estimated the relation between serum analytes and breast density. All serum analytes except prolactin among postmenopausal women differed significantly by location/ethnicity group. Among premenopausal subjects, IGF-I levels and the molar ratio were highest in Hawaii, intermediate in Japan and lowest in Arizona. For IGFBP-3, the order was reversed. Among postmenopausal subjects, Norwegian women had the highest IGF-I levels and women in Arizona had the lowest while women in Japan and Hawaii had intermediate levels. We observed no significant relation between percent density and IGF-I or prolactin levels among pre-and postmenopausal women. The significant differences in IGF-I levels by location but not ethnicity suggest that environmental factors influence IGF-I levels, whereas percent breast density varies more according to ethnic background than by location. Based on this analysis, the influence of circulating levels of IGF-I, IGFBP-3, and prolactin on percent density appears to be very small. (c) 2007 Wiley-Liss, Inc.

Studies on Antiretroviral Drug Concentrations in Breast Milk: Validation of a Liquid Chromatography–Tandem Mass Spectrometric Method for the Determination of 7 Anti-Human Immunodeficiency Virus Medications

PubMed Central

Rezk, Naser L.; White, Nicole; Bridges, Arlene S.; Abdel-Megeed, Mohamed F.; Mohamed, Tarek M.; Moselhy, Said S.; Kashuba, Angela D. M.

2010-01-01

Studying the pharmacokinetics of antiretroviral drugs in breast milk has important implications for the health of both the mother and the infant, particularly in resource-poor countries. Breast milk is a highly complex biological matrix, yet it is necessary to develop and validate methods in this matrix, which simultaneously measure multiple analytes, as women may be taking any number of drug combinations to combat human immunodeficiency virus infection. Here, we report a novel extraction method coupled to high-performance liquid chromatography and tandem mass spectrometry for the accurate, precise, and specific measurement of 7 antiretroviral drugs currently prescribed to infected mothers. Using 200 µL of human breast milk, simultaneous quantification of lamivudine (3TC), stavudine (d4T), zidovudine (ZDV), nevirapine (NVP), nelfinavir (NFV), ritonavir, and lopinavir was validated over the range of 10–10,000 ng/mL. Intraday accuracy and precision for all analytes were 99.3% and 5.0 %, respectively. Interday accuracy and precision were 99.4 % and 7.8%, respectively. Cross-assay validation with UV detection was performed using clinical breast milk samples, and the results of the 2 assays were in good agreement (P = 0.0001, r = 0.97). Breast milk to plasma concentration ratios for the different antiretroviral drugs were determined as follows: 3TC = 2.96, d4T = 1.73, ZDV = 1.17, NVP = 0.82, and NFV = 0.21. PMID:18758393

Vascular endothelial growth factor and platelet-derived growth factor are potential angiogenic and metastatic factors in human breast cancer.

PubMed

Anan, K; Morisaki, T; Katano, M; Ikubo, A; Kitsuki, H; Uchiyama, A; Kuroki, S; Tanaka, M; Torisu, M

1996-03-01

Angiogenesis is a prerequisite for tumor growth and metastasis. Tumor angiogenesis may be mediated by several angiogenic factors such as vascular endothelial growth factor (VEGF), platelet-derived growth factor (PDGF), transforming growth factor-alpha, and basic fibroblast growth factor. Differential mRNA expressions of VEGF, PDGF (A chain), transforming growth factor-alpha and basic fibroblast growth factor in 32 primary invasive breast tumors were examined by reverse transcriptase-polymerase chain reaction. We analyzed relationships between mRNA expressions of these angiogenic factors and the degree of angiogenesis, tumor size, and metastasis. Quantification of angiogenesis was achieved by the immunohistochemical staining of endothelial cells with antibody to CD31. VEGF and PDGF-A mRNAs were expressed more frequently in breast tumors than in nontumor breast tissues, whereas no difference was found in expression frequency of either transforming growth factor-alpha or basic fibroblast growth factor mRNA. Vascular counts in tumors correlated with each expression frequency of VEGF and PDGF-A mRNA. PDGF-A mRNA was expressed more frequently in tumors with lymph node metastasis than in those without metastasis. Expression of VEGF and PDGF mRNAs detected by reverse transcriptase-polymerase chain reaction in breast tumors correlates with tumor-related characteristics of angiogenesis and metastatic potential. Analysis of these mRNAs by reverse transcriptase-polymerase chain reaction may be useful for assessing the biologic behavior of a breast tumor before surgical treatment.

Conservation status of the buff-breasted sandpiper: Historic and contemporary distribution and abundance in south America

USGS Publications Warehouse

Lanctot, Richard B.; Blanco, D.E.; Dias, Rafael A.; Isacch, Juan P.; Gill, Verena A.; Almeida, Juliana B.; Delhey, Kaspar; Petracci, Pablo F.; Bencke, Glayson A.; Balbueno, Rodrigo A.

2002-01-01

We present historic and contemporary information on the distribution and abundance of Buff-breasted Sandpipers (Tryngites subruficollis) in South America. Historic information was collated from the literature, area ornithologists, and museums, whereas contemporary data were derived from surveys conducted throughout the main wintering range in Argentina, Uruguay, and Brazil during the austral summers of 1999 and 2001. Variable circular plot sampling was used to estimate population densities. During 1999, the highest concentration of Buff-breasted Sandpipers in Argentina was in southern Bahía Samborombón (General Lavalle District) and areas north of Mar Chiquita coastal lagoon. During 2001, the highest concentrations in Brazil were at Ilha da Torotama and Lagoa do Peixe National Park. During 1999 and 2001, the highest concentrations of Buff-breasted Sandpipers in Uruguay were found along three lagoons (Laguna de Rocha, Laguna de Castillos, and Laguna Garzón) bordering the Atlantic Ocean. Population densities (birds/ha) of Buff-breasted Sandpipers were 0.11 (95% C.I. = 0.04–0.31) in Argentina, 1.62 (0.67–3.93) in Brazil, and 1.08 (0.37–3.18) in Uruguay. High turnover rates at survey sites, due to the formation of large, mobile flocks, contributed to moderately large confidence intervals around our population density estimates. Nevertheless, compared with historic accounts of Buff-breasted Sandpipers, our survey data indicate the population size of this species has declined substantially since the late 1800s and contemporary information suggests the species has continued to decline during the past three decades. Buff-breasted Sandpipers were found almost exclusively in pasturelands and appear to depend heavily upon intensive grazing by livestock, which maintain suitable short grass conditions. We discuss the need for protection of critical areas and proper range management to ensure appropriate habitat remains available for the species, and provide suggestions for future research needs.

Mammographic Breast Density in a Cohort of Medically Underserved Women

DTIC Science & Technology

2014-10-01

chronic diseases, adult weight history, diet , and health literacy. A trained radiologic technician completed full- field digital screening mammograms on... Mediterranean population. Int J Cancer 118:1782-1789 12. El-Bastawissi AY, White E, Mandelson MT, Taplin S (2001) Variation in mammographic breast

Detection of masses in mammogram images using CNN, geostatistic functions and SVM.

PubMed

Sampaio, Wener Borges; Diniz, Edgar Moraes; Silva, Aristófanes Corrêa; de Paiva, Anselmo Cardoso; Gattass, Marcelo

2011-08-01

Breast cancer occurs with high frequency among the world's population and its effects impact the patients' perception of their own sexuality and their very personal image. This work presents a computational methodology that helps specialists detect breast masses in mammogram images. The first stage of the methodology aims to improve the mammogram image. This stage consists in removing objects outside the breast, reducing noise and highlighting the internal structures of the breast. Next, cellular neural networks are used to segment the regions that might contain masses. These regions have their shapes analyzed through shape descriptors (eccentricity, circularity, density, circular disproportion and circular density) and their textures analyzed through geostatistic functions (Ripley's K function and Moran's and Geary's indexes). Support vector machines are used to classify the candidate regions as masses or non-masses, with sensitivity of 80%, rates of 0.84 false positives per image and 0.2 false negatives per image, and an area under the ROC curve of 0.87. Copyright © 2011 Elsevier Ltd. All rights reserved.

A Review on Automatic Mammographic Density and Parenchymal Segmentation

PubMed Central

He, Wenda; Juette, Arne; Denton, Erika R. E.; Oliver, Arnau

2015-01-01

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models. PMID:26171249

Flow Cytometric Quantification of Peripheral Blood Cell β-Adrenergic Receptor Density and Urinary Endothelial Cell-Derived Microparticles in Pulmonary Arterial Hypertension.

PubMed

Rose, Jonathan A; Wanner, Nicholas; Cheong, Hoi I; Queisser, Kimberly; Barrett, Patrick; Park, Margaret; Hite, Corrine; Naga Prasad, Sathyamangla V; Erzurum, Serpil; Asosingh, Kewal

2016-01-01

Pulmonary arterial hypertension (PAH) is a heterogeneous disease characterized by severe angiogenic remodeling of the pulmonary artery wall and right ventricular hypertrophy. Thus, there is an increasing need for novel biomarkers to dissect disease heterogeneity, and predict treatment response. Although β-adrenergic receptor (βAR) dysfunction is well documented in left heart disease while endothelial cell-derived microparticles (Ec-MPs) are established biomarkers of angiogenic remodeling, methods for easy large clinical cohort analysis of these biomarkers are currently absent. Here we describe flow cytometric methods for quantification of βAR density on circulating white blood cells (WBC) and Ec-MPs in urine samples that can be used as potential biomarkers of right heart failure in PAH. Biotinylated β-blocker alprenolol was synthesized and validated as a βAR specific probe that was combined with immunophenotyping to quantify βAR density in circulating WBC subsets. Ec-MPs obtained from urine samples were stained for annexin-V and CD144, and analyzed by a micro flow cytometer. Flow cytometric detection of alprenolol showed that βAR density was decreased in most WBC subsets in PAH samples compared to healthy controls. Ec-MPs in urine was increased in PAH compared to controls. Furthermore, there was a direct correlation between Ec-MPs and Tricuspid annular plane systolic excursion (TAPSE) in PAH patients. Therefore, flow cytometric quantification of peripheral blood cell βAR density and urinary Ec-MPs may be useful as potential biomarkers of right ventricular function in PAH.

Flow Cytometric Quantification of Peripheral Blood Cell β-Adrenergic Receptor Density and Urinary Endothelial Cell-Derived Microparticles in Pulmonary Arterial Hypertension

PubMed Central

Rose, Jonathan A.; Wanner, Nicholas; Cheong, Hoi I.; Queisser, Kimberly; Barrett, Patrick; Park, Margaret; Hite, Corrine; Naga Prasad, Sathyamangla V.; Erzurum, Serpil; Asosingh, Kewal

2016-01-01

Pulmonary arterial hypertension (PAH) is a heterogeneous disease characterized by severe angiogenic remodeling of the pulmonary artery wall and right ventricular hypertrophy. Thus, there is an increasing need for novel biomarkers to dissect disease heterogeneity, and predict treatment response. Although β-adrenergic receptor (βAR) dysfunction is well documented in left heart disease while endothelial cell-derived microparticles (Ec-MPs) are established biomarkers of angiogenic remodeling, methods for easy large clinical cohort analysis of these biomarkers are currently absent. Here we describe flow cytometric methods for quantification of βAR density on circulating white blood cells (WBC) and Ec-MPs in urine samples that can be used as potential biomarkers of right heart failure in PAH. Biotinylated β-blocker alprenolol was synthesized and validated as a βAR specific probe that was combined with immunophenotyping to quantify βAR density in circulating WBC subsets. Ec-MPs obtained from urine samples were stained for annexin-V and CD144, and analyzed by a micro flow cytometer. Flow cytometric detection of alprenolol showed that βAR density was decreased in most WBC subsets in PAH samples compared to healthy controls. Ec-MPs in urine was increased in PAH compared to controls. Furthermore, there was a direct correlation between Ec-MPs and Tricuspid annular plane systolic excursion (TAPSE) in PAH patients. Therefore, flow cytometric quantification of peripheral blood cell βAR density and urinary Ec-MPs may be useful as potential biomarkers of right ventricular function in PAH. PMID:27270458

Mammographic features and subsequent risk of breast cancer: a comparison of qualitative and quantitative evaluations in the Guernsey prospective studies.

PubMed

Torres-Mejía, Gabriela; De Stavola, Bianca; Allen, Diane S; Pérez-Gavilán, Juan J; Ferreira, Jorge M; Fentiman, Ian S; Dos Santos Silva, Isabel

2005-05-01

Mammographic features are known to be associated with breast cancer but the magnitude of the effect differs markedly from study to study. Methods to assess mammographic features range from subjective qualitative classifications to computer-automated quantitative measures. We used data from the UK Guernsey prospective studies to examine the relative value of these methods in predicting breast cancer risk. In all, 3,211 women ages > or =35 years who had a mammogram taken in 1986 to 1989 were followed-up to the end of October 2003, with 111 developing breast cancer during this period. Mammograms were classified using the subjective qualitative Wolfe classification and several quantitative mammographic features measured using computer-based techniques. Breast cancer risk was positively associated with high-grade Wolfe classification, percent breast density and area of dense tissue, and negatively associated with area of lucent tissue, fractal dimension, and lacunarity. Inclusion of the quantitative measures in the same model identified area of dense tissue and lacunarity as the best predictors of breast cancer, with risk increasing by 59% [95% confidence interval (95% CI), 29-94%] per SD increase in total area of dense tissue but declining by 39% (95% CI, 53-22%) per SD increase in lacunarity, after adjusting for each other and for other confounders. Comparison of models that included both the qualitative Wolfe classification and these two quantitative measures to models that included either the qualitative or the two quantitative variables showed that they all made significant contributions to prediction of breast cancer risk. These findings indicate that breast cancer risk is affected not only by the amount of mammographic density but also by the degree of heterogeneity of the parenchymal pattern and, presumably, by other features captured by the Wolfe classification.