Imaging Breast Density: Established and Emerging Modalities

Directory of Open Access Journals (Sweden)

Jeon-Hor Chen

2015-12-01

Full Text Available 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.

Quantitative assessment of breast density: comparison of different methods

International Nuclear Information System (INIS)

Qin Naishan; Guo Li; Dang Yi; Song Luxin; Wang Xiaoying

2011-01-01

Objective: To Compare different methods of quantitative breast density measurement. Methods: The study included sixty patients who underwent both mammography and breast MRI. The breast density was computed automatically on digital mammograms with R2 workstation, Two experienced radiologists read the mammograms and assessed the breast density with Wolfe and ACR classification respectively. Fuzzy C-means clustering algorithm (FCM) was used to assess breast density on MRI. Each assessment method was repeated after 2 weeks. Spearman and Pearson correlations of inter- and intrareader and intermodality were computed for density estimates. Results: Inter- and intrareader correlation of Wolfe classification were 0.74 and 0.65, and they were 0.74 and 0.82 for ACR classification respectively. Correlation between Wolfe and ACR classification was 0.77. High interreader correlation of 0.98 and intrareader correlation of 0.96 was observed with MR FCM measurement. And the correlation between digital mammograms and MRI was high in the assessment of breast density (r=0.81, P<0.01). Conclusion: High correlation of breast density estimates on digital mammograms and MRI FCM suggested the former could be used as a simple and accurate method. (authors)

Volumetric breast density estimation from full-field digital mammograms.

Science.gov (United States)

van Engeland, Saskia; Snoeren, Peter R; Huisman, Henkjan; Boetes, Carla; Karssemeijer, Nico

2006-03-01

A method is presented for estimation of dense breast tissue volume from mammograms obtained with full-field digital mammography (FFDM). The thickness of dense tissue mapping to a pixel is determined by using a physical model of image acquisition. This model is based on the assumption that the breast is composed of two types of tissue, fat and parenchyma. Effective linear attenuation coefficients of these tissues are derived from empirical data as a function of tube voltage (kVp), anode material, filtration, and compressed breast thickness. By employing these, tissue composition at a given pixel is computed after performing breast thickness compensation, using a reference value for fatty tissue determined by the maximum pixel value in the breast tissue projection. Validation has been performed using 22 FFDM cases acquired with a GE Senographe 2000D by comparing the volume estimates with volumes obtained by semi-automatic segmentation of breast magnetic resonance imaging (MRI) data. The correlation between MRI and mammography volumes was 0.94 on a per image basis and 0.97 on a per patient basis. Using the dense tissue volumes from MRI data as the gold standard, the average relative error of the volume estimates was 13.6%.

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

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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.

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

Science.gov (United States)

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.

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.

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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.

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

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Khodr, Zeina G.; Pfeiffer, Ruth M.; Gierach, Gretchen L., E-mail: GierachG@mail.nih.gov [Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive MSC 9774, Bethesda, Maryland 20892 (United States); Sak, Mark A.; Bey-Knight, Lisa [Karmanos Cancer Institute, Wayne State University, 4100 John R, Detroit, Michigan 48201 (United States); Duric, Nebojsa; Littrup, Peter [Karmanos Cancer Institute, Wayne State University, 4100 John R, Detroit, Michigan 48201 and Delphinus Medical Technologies, 46701 Commerce Center Drive, Plymouth, Michigan 48170 (United States); Ali, Haythem; Vallieres, Patricia [Henry Ford Health System, 2799 W Grand Boulevard, Detroit, Michigan 48202 (United States); Sherman, Mark E. [Division of Cancer Prevention, National Cancer Institute, Department of Health and Human Services, 9609 Medical Center Drive MSC 9774, Bethesda, Maryland 20892 (United States)

2015-10-15

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.

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

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Keller, Brad M.; Nathan, Diane L.; Wang Yan; Zheng Yuanjie; Gee, James C.; Conant, Emily F.; Kontos, Despina [Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States)

2012-08-15

Purpose: 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. Methods: 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

Volumetric breast density estimation from full-field digital mammograms.

NARCIS (Netherlands)

Engeland, S. van; Snoeren, P.R.; Huisman, H.J.; Boetes, C.; Karssemeijer, N.

2006-01-01

A method is presented for estimation of dense breast tissue volume from mammograms obtained with full-field digital mammography (FFDM). The thickness of dense tissue mapping to a pixel is determined by using a physical model of image acquisition. This model is based on the assumption that the breast

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

International Nuclear Information System (INIS)

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

2012-01-01

Purpose: 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. Methods: 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

Local breast density assessment using reacquired mammographic images.

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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 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.

Variability of breast density assessment in short-term reimaging with digital mammography

International Nuclear Information System (INIS)

Kim, Won Hwa; Moon, Woo Kyung; Kim, Sun Mi; Yi, Ann; Chang, Jung Min; Koo, Hye Ryoung; Lee, Su Hyun; Cho, Nariya

2013-01-01

Objective: To evaluate the variability of breast density assessments in short-term reimaging with digital mammography. Materials and methods: In 186 women, short term (mean interval, 27.6 days) serial digital mammograms including CC and MLO views were obtained without any treatment. Mammographic density assessments were performed by three blinded radiologists for Breast Imaging Report and Data System (BI-RADS, grades 1–4) and visual percentage density (PD) estimation, and by one radiologist for computer-aided PD estimation. The variability of assessments was analyzed according to the age, breast density, and mammography types by multivariate logistic regression. Results: In BI-RADS assessments, 29% (161 of 558) of breast density categories were assessed differently after short-term reimaging and the mean absolute difference in PD for CC and MLO view was 7.6% and 8.1% for visual assessments, and 7.4% and 6.4% for computer-aided assessments, respectively. Among all computer-aided assessments, 29% (54 of 186) of CC view and 22% (41 of 186) of MLO view assessments had discrepancy over 10% in PD. Younger age (<50), greater breast density (grades 3 and 4), and different mammography types were significantly associated with the variability. Conclusion: Considerable variability in breast density assessments occurred in short-term reimaging with digital mammography, particularly in women with younger age and greater breast density and when examined using different types of mammography

Variability of breast density assessment in short-term reimaging with digital mammography

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Kim, Won Hwa [Department of Radiology, Seoul National University Hospital, Seoul (Korea, Republic of); Moon, Woo Kyung, E-mail: moonwk@snu.ac.kr [Department of Radiology, Seoul National University Hospital, Seoul (Korea, Republic of); Kim, Sun Mi [Department of Radiology, Seoul National University Bundang Hospital, Gyeonggi-do (Korea, Republic of); Yi, Ann [Department of Radiology, Seoul Metropolitan Government Seoul National University, Boramae Medical Center, Seoul (Korea, Republic of); Chang, Jung Min; Koo, Hye Ryoung; Lee, Su Hyun; Cho, Nariya [Department of Radiology, Seoul National University Hospital, Seoul (Korea, Republic of)

2013-10-01

Objective: To evaluate the variability of breast density assessments in short-term reimaging with digital mammography. Materials and methods: In 186 women, short term (mean interval, 27.6 days) serial digital mammograms including CC and MLO views were obtained without any treatment. Mammographic density assessments were performed by three blinded radiologists for Breast Imaging Report and Data System (BI-RADS, grades 1–4) and visual percentage density (PD) estimation, and by one radiologist for computer-aided PD estimation. The variability of assessments was analyzed according to the age, breast density, and mammography types by multivariate logistic regression. Results: In BI-RADS assessments, 29% (161 of 558) of breast density categories were assessed differently after short-term reimaging and the mean absolute difference in PD for CC and MLO view was 7.6% and 8.1% for visual assessments, and 7.4% and 6.4% for computer-aided assessments, respectively. Among all computer-aided assessments, 29% (54 of 186) of CC view and 22% (41 of 186) of MLO view assessments had discrepancy over 10% in PD. Younger age (<50), greater breast density (grades 3 and 4), and different mammography types were significantly associated with the variability. Conclusion: Considerable variability in breast density assessments occurred in short-term reimaging with digital mammography, particularly in women with younger age and greater breast density and when examined using different types of mammography.

Breast density quantification with cone-beam CT: a post-mortem study

International Nuclear Information System (INIS)

Johnson, Travis; Ding, Huanjun; Le, Huy Q; Ducote, Justin L; Molloi, Sabee

2013-01-01

Forty post-mortem breasts were imaged with a flat-panel based cone-beam x-ray CT system at 50 kVp. The feasibility of breast density quantification has been investigated using standard histogram thresholding and an automatic segmentation method based on the fuzzy c-means algorithm (FCM). The breasts were chemically decomposed into water, lipid, and protein immediately after image acquisition was completed. The per cent fibroglandular volume (%FGV) from chemical analysis was used as the gold standard for breast density comparison. Both image-based segmentation techniques showed good precision in breast density quantification with high linear coefficients between the right and left breast of each pair. When comparing with the gold standard using %FGV from chemical analysis, Pearson's r-values were estimated to be 0.983 and 0.968 for the FCM clustering and the histogram thresholding techniques, respectively. The standard error of the estimate was also reduced from 3.92% to 2.45% by applying the automatic clustering technique. The results of the postmortem study suggested that breast tissue can be characterized in terms of water, lipid and protein contents with high accuracy by using chemical analysis, which offers a gold standard for breast density studies comparing different techniques. In the investigated image segmentation techniques, the FCM algorithm had high precision and accuracy in breast density quantification. In comparison to conventional histogram thresholding, it was more efficient and reduced inter-observer variation. (paper)

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

Science.gov (United States)

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.

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

International Nuclear Information System (INIS)

Ng, Kwan-Hoong; Lau, Susie

2015-01-01

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 believe 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

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

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Ng, Kwan-Hoong, E-mail: ngkh@ummc.edu.my; Lau, Susie [Department of Biomedical Imaging and University of Malaya Research Imaging Centre, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur (Malaysia)

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 believe 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.

Mammographic Breast Density and Breast Cancer Molecular Subtypes: The Kenyan-African Aspect

Directory of Open Access Journals (Sweden)

Asim Jamal Shaikh

2018-01-01

Full Text Available Introduction. Data examining mammographic breast density (MBD among patients in Sub-Saharan Africa are sparse. We evaluated how MBD relates to breast cancer characteristics in Kenyan women undergoing diagnostic mammography. Methods. This cross-sectional study included women with pathologically confirmed breast cancers (n=123. Pretreatment mammograms of the unaffected breast were assessed to estimate absolute dense area (cm2, nondense area (cm2, and percent density (PD. Relationships between density measurements and clinical characteristics were evaluated using analysis of covariance. Results. Median PD and dense area were 24.9% and 85.3 cm2. Higher PD and dense area were observed in younger women (P<0.01. Higher dense and nondense areas were observed in obese women (P-trend < 0.01. Estrogen receptor (ER positive patients (73% had higher PD and dense area than ER-negative patients (P≤0.02. Triple negative breast cancer (TNBC patients (17% had lower PD and dense area (P≤0.01 compared with non-TNBCs. No associations were observed between MBD and tumor size and grade. Conclusions. Our findings show discordant relationships between MBD and molecular tumor subtypes to those previously observed in Western populations. The relatively low breast density observed at diagnosis may have important implications for cancer prevention initiatives in Kenya. Subsequent larger studies are needed to confirm these findings.

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

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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.

Breast density and mode of detection in relation to breast cancer specific survival: a cohort study

International Nuclear Information System (INIS)

Olsson, Åsa; Sartor, Hanna; Borgquist, Signe; Zackrisson, Sophia; Manjer, Jonas

2014-01-01

The aim of this study was to examine breast density in relation to breast cancer specific survival and to assess if this potential association was modified by mode of detection. An additional aim was to study whether the established association between mode of detection and survival is modified by breast density. The study included 619 cases from a prospective cohort, The Malmö Diet and Cancer Study. Breast density estimated qualitatively, was analyzed in relation to breast cancer death, in non-symptomatic and symptomatic women, using Cox regression calculating hazard ratios (HR) with 95% confidence intervals. Adjustments were made in several steps for; diagnostic age, tumour size, axillary lymph node involvement, grade, hormone receptor status, body mass index (baseline), diagnostic period, use of hormone replacement therapy at diagnosis and mode of detection. Detection mode in relation to survival was analyzed stratified for breast density. Differences in HR following different adjustments were analyzed by Freedmans%. After adjustment for age and other prognostic factors, women with dense, as compared to fatty breasts, had an increased risk of breast cancer death, HR 2.56:1.07-6.11, with a statistically significant trend over density categories, p = 0.04. In the stratified analysis, the effect was less pronounced in non-symptomatic women, HR 2.04:0.49-8.49 as compared to symptomatic, HR 3.40:1.06-10.90. In the unadjusted model, symptomatic women had a higher risk of breast cancer death, regardless of breast density. Analyzed by Freedmans%, age, tumour size, lymph nodes, grade, diagnostic period, ER and PgR explained 55.5% of the observed differences in mortality between non-symptomatic and symptomatic cases. Additional adjustment for breast density caused only a minor change. High breast density at diagnosis may be associated with decreased breast cancer survival. This association appears to be stronger in women with symptomatic cancers but breast density could

Mammographic Breast Density in Malaysian Women with Breast Cancer

International Nuclear Information System (INIS)

Noriah Jamal; Humairah Samad Cheung

2016-01-01

The objective of this study was to examine the mammographic breast density of women with breast cancer detected on voluntary mammographic screening at two selected screening centers in Malaysia. This was a retrospective study of Full-Field Digital Mammography (FFDM) images of 150 Malaysian women with biopsy-proven breast cancer. The study population comprised 73 Malays (37.7 %), 59 Chinese (39.3 %) and 18 Indians (12.0 %). The Tabar breast density Patterns (I - V) were used to evaluate mammographic breast density. Data were analyzed using descriptive statistics. The results were compared with findings from a similar study on a group of 668 women who did not have breast cancer. The results showed that 44.7 % of the study population had dense breasts (Patterns IV and V), 14.7 % had predominantly fatty breasts (Patterns II and III) while 40.7 % had Pattern I. The proportion of study population with dense breasts decreased with age. In conclusion, the proportion of women with dense breasts decreased with age. Majority of the women with cancer (44.7 %) had dense breasts of Tabar Patterns IV and V, which has been associated with increased risk of breast cancer detected by voluntary mammographic screening. The results support the notion that increased breast density is a risk factor of breast cancer. (author)

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

International Nuclear Information System (INIS)

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

2017-01-01

Objectives: 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. Methods: 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. Results: 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. Conclusion: 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. Advances in knowledge: We are the first group to have produced deformable phantoms that are recognized as breasts by Volpara software. - Highlights: • Several phantoms of different configurations were created. • Three methods to assess phantom density were implemented. • All phantoms were identified as breasts by the Volpara software. • Reducing phantom thickness caused a change in phantom density.

Association between breast cancer, breast density, and body adiposity evaluated by MRI

International Nuclear Information System (INIS)

Zhu, Wenlian; Huang, Peng; Macura, Katarzyna J.; Artemov, Dmitri

2016-01-01

Despite the lack of reliable methods with which to measure breast density from 2D mammograms, numerous studies have demonstrated a positive association between breast cancer and breast density. The goal of this study was to study the association between breast cancer and body adiposity, as well as breast density quantitatively assessed from 3D MRI breast images. Breast density was calculated from 3D T1-weighted MRI images. The thickness of the upper abdominal adipose layer was used as a surrogate marker for body adiposity. We evaluated the correlation between breast density, age, body adiposity, and breast cancer. Breast density was calculated for 410 patients with unilateral invasive breast cancer, 73 patients with ductal carcinoma in situ (DCIS), and 361 controls without breast cancer. Breast density was inversely related to age and the thickness of the upper abdominal adipose layer. Breast cancer was only positively associated with body adiposity and age. Age and body adiposity are predictive of breast density. Breast cancer was not associated with breast density; however, it was associated with the thickness of the upper abdominal adipose layer, a surrogate marker for body adiposity. Our results based on a limited number of patients warrant further investigations. (orig.)

Should breast density influence patient selection for breast-conserving surgery?

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Kapoor, Nimmi S; Eaton, Anne; King, Tari A; Patil, Sujata; Stempel, Michelle; Morris, Elizabeth; Brogi, Edi; Morrow, Monica

2013-02-01

In a previous study of the relationship between breast density and primary tumor features, we observed a higher mastectomy rate in patients with extremely dense breasts. Here we examine possible reasons for this finding. Data were obtained from a prospectively maintained database of 1,056 invasive breast cancer patients from January 2005 to June 2007. Mammographic density was assigned by Breast Imaging-Reporting and Data System (BI-RADS) classification. Initial and final surgical procedures, and patient and tumor variables were recorded. Breast-conserving surgery (BCS) was attempted in 758 patients (72 %), 385 (51 %) of whom had preoperative magnetic resonance imaging (MRI). Initial BCS was less common among patients with the highest (BI-RADS 4) breast density compared to patients with less-dense breasts (52 vs. 74 %; p mastectomy compared to patients with less-dense breasts. After initial BCS, 387 patients (51 %) had positive shaved margins, 96 (25 %) of whom converted to mastectomy. MRI did not correlate with the rate of positive margins overall or among those with dense breasts. Adjusting for clinical and pathologic variables, density did not predict margin status or conversion to mastectomy. In a multivariate model, age, histologic grade, extensive intraductal component, and multicentricity/multifocality were independently associated with conversion to mastectomy. Density alone seems to influence the decision to proceed with initial mastectomy. When BCS was attempted, breast density was not associated with positive margins or conversion to mastectomy. A benefit of MRI in decreasing positive margins was not observed. These data do not support the use of breast density as a selection criterion for BCS.

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

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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.

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

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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.

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

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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.

Endocrine determinants of breast density and breast cancer

NARCIS (Netherlands)

Verheus, M.

2007-01-01

Worldwide, breast cancer is the most common malignancy among females. The total breast area on a mammogram can be dived in a radiologicaly dense area (glandular and stromal tissue) and a non-dense area (mainly fat tissue). Women with a high proportion of dense breast tissue (percent breast density)

Quantification of breast density using dual-energy mammography with liquid phantom calibration

International Nuclear Information System (INIS)

Lam, Alfonso R; Ding, Huanjun; Molloi, Sabee

2014-01-01

Breast density is a widely recognized potential risk factor for breast cancer. However, accurate quantification of breast density is a challenging task in mammography. The current use of plastic breast-equivalent phantoms for calibration provides limited accuracy in dual-energy mammography due to the chemical composition of the phantom. We implemented a breast-equivalent liquid phantom for dual-energy calibration in order to improve the accuracy of breast density measurement. To design these phantoms, three liquid compounds were chosen: water, isopropyl alcohol, and glycerol. Chemical compositions of glandular and adipose tissues, obtained from NIST database, were used as reference materials. Dual-energy signal of the liquid phantom at different breast densities (0% to 100%) and thicknesses (1 to 8 cm) were simulated. Glandular and adipose tissue thicknesses were estimated from a higher order polynomial of the signals. Our results indicated that the linear attenuation coefficients of the breast-equivalent liquid phantoms match those of the target material. Comparison between measured and known breast density data shows a linear correlation with a slope close to 1 and a non-zero intercept of 7%, while plastic phantoms showed a slope of 0.6 and a non-zero intercept of 8%. Breast density results derived from the liquid calibration phantoms showed higher accuracy than those derived from the plastic phantoms for different breast thicknesses and various tube voltages. We performed experimental phantom studies using liquid phantoms and then compared the computed breast density with those obtained using a bovine tissue model. The experimental data and the known values were in good correlation with a slope close to 1 (∼1.1). In conclusion, our results indicate that liquid phantoms are a reliable alternative for calibration in dual-energy mammography and better reproduce the chemical properties of the target material. (paper)

Cumulative sum quality control for calibrated breast density measurements

International Nuclear Information System (INIS)

Heine, John J.; Cao Ke; Beam, Craig

2009-01-01

Purpose: Breast density is a significant breast cancer risk factor. Although various methods are used to estimate breast density, there is no standard measurement for this important factor. The authors are developing a breast density standardization method for use in full field digital mammography (FFDM). The approach calibrates for interpatient acquisition technique differences. The calibration produces a normalized breast density pixel value scale. The method relies on first generating a baseline (BL) calibration dataset, which required extensive phantom imaging. Standardizing prospective mammograms with calibration data generated in the past could introduce unanticipated error in the standardized output if the calibration dataset is no longer valid. Methods: Sample points from the BL calibration dataset were imaged approximately biweekly over an extended timeframe. These serial samples were used to evaluate the BL dataset reproducibility and quantify the serial calibration accuracy. The cumulative sum (Cusum) quality control method was used to evaluate the serial sampling. Results: There is considerable drift in the serial sample points from the BL calibration dataset that is x-ray beam dependent. Systematic deviation from the BL dataset caused significant calibration errors. This system drift was not captured with routine system quality control measures. Cusum analysis indicated that the drift is a sign of system wear and eventual x-ray tube failure. Conclusions: The BL calibration dataset must be monitored and periodically updated, when necessary, to account for sustained system variations to maintain the calibration accuracy.

Cumulative sum quality control for calibrated breast density measurements

Energy Technology Data Exchange (ETDEWEB)

Heine, John J.; Cao Ke; Beam, Craig [Cancer Prevention and Control Division, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, Florida 33612 (United States); Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, 1603 W. Taylor St., Chicago, Illinois 60612 (United States)

2009-12-15

Purpose: Breast density is a significant breast cancer risk factor. Although various methods are used to estimate breast density, there is no standard measurement for this important factor. The authors are developing a breast density standardization method for use in full field digital mammography (FFDM). The approach calibrates for interpatient acquisition technique differences. The calibration produces a normalized breast density pixel value scale. The method relies on first generating a baseline (BL) calibration dataset, which required extensive phantom imaging. Standardizing prospective mammograms with calibration data generated in the past could introduce unanticipated error in the standardized output if the calibration dataset is no longer valid. Methods: Sample points from the BL calibration dataset were imaged approximately biweekly over an extended timeframe. These serial samples were used to evaluate the BL dataset reproducibility and quantify the serial calibration accuracy. The cumulative sum (Cusum) quality control method was used to evaluate the serial sampling. Results: There is considerable drift in the serial sample points from the BL calibration dataset that is x-ray beam dependent. Systematic deviation from the BL dataset caused significant calibration errors. This system drift was not captured with routine system quality control measures. Cusum analysis indicated that the drift is a sign of system wear and eventual x-ray tube failure. Conclusions: The BL calibration dataset must be monitored and periodically updated, when necessary, to account for sustained system variations to maintain the calibration accuracy.

Area and volumetric density estimation in processed full-field digital mammograms for risk assessment of breast cancer.

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Abbas Cheddad

Full Text Available INTRODUCTION: Mammographic density, the white radiolucent part of a mammogram, is a marker of breast cancer risk and mammographic sensitivity. There are several means of measuring mammographic density, among which are area-based and volumetric-based approaches. Current volumetric methods use only unprocessed, raw mammograms, which is a problematic restriction since such raw mammograms are normally not stored. We describe fully automated methods for measuring both area and volumetric mammographic density from processed images. METHODS: The data set used in this study comprises raw and processed images of the same view from 1462 women. We developed two algorithms for processed images, an automated area-based approach (CASAM-Area and a volumetric-based approach (CASAM-Vol. The latter method was based on training a random forest prediction model with image statistical features as predictors, against a volumetric measure, Volpara, for corresponding raw images. We contrast the three methods, CASAM-Area, CASAM-Vol and Volpara directly and in terms of association with breast cancer risk and a known genetic variant for mammographic density and breast cancer, rs10995190 in the gene ZNF365. Associations with breast cancer risk were evaluated using images from 47 breast cancer cases and 1011 control subjects. The genetic association analysis was based on 1011 control subjects. RESULTS: All three measures of mammographic density were associated with breast cancer risk and rs10995190 (p0.10 for risk, p>0.03 for rs10995190. CONCLUSIONS: Our results show that it is possible to obtain reliable automated measures of volumetric and area mammographic density from processed digital images. Area and volumetric measures of density on processed digital images performed similar in terms of risk and genetic association.

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

Science.gov (United States)

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.

Breast density in multiethnic women presenting for screening mammography.

Science.gov (United States)

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 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.

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

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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 breasts and fourth

Quantitative assessment of breast density from mammograms

International Nuclear Information System (INIS)

Jamal, N.; Ng, K.H.

2004-01-01

Full text: It is known that breast density is increasingly used as a risk factor for breast cancer. This study was undertaken to develop and validate a semi-automated computer technique for the quantitative assessment of breast density from digitised mammograms. A computer technique had been developed using MATLAB (Version 6.1) based GUI applications. This semi-automated image analysis tool consists of gradient correction, segmentation of breast region from background, segmentation of fibroglandular and adipose region within the breast area and calculation of breast density. The density is defined as the percentage of fibroglandular tissue area divided by the total breast area in the mammogram. This technique was clinically validated with 122 normal mammograms; these were subjectively evaluated and classified according to the five parenchyma patterns of the Tabar's scheme (Class I- V) by a consultant radiologist. There was a statistical significant correlation between the computer technique and subjective classification (r 2 = 0.84, p<0.05). 71.3% of subjective classification was correctly classified using the computer technique. We had developed a computer technique for the quantitative assessment of breast density and validated its accuracy for computerized classification based on Tabar's scheme. This quantitative tool is useful for the evaluation of a large dataset of mammograms to predict breast cancer risk based on density. Furthermore it has the potential to provide an early marker for success or failure in chemoprevention studies such as hormonal replacement therapy. Copyright (2004) Australasian College of Physical Scientists and Engineers in Medicine

Breast density quantification using magnetic resonance imaging (MRI) with bias field correction: a postmortem study.

Science.gov (United States)

Ding, Huanjun; Johnson, Travis; Lin, Muqing; Le, Huy Q; Ducote, Justin L; Su, Min-Ying; Molloi, Sabee

2013-12-01

Quantification of breast density based on three-dimensional breast MRI may provide useful information for the early detection of breast cancer. However, the field inhomogeneity can severely challenge the computerized image segmentation process. In this work, the effect of the bias field in breast density quantification has been investigated with a postmortem study. T1-weighted images of 20 pairs of postmortem breasts were acquired on a 1.5 T breast MRI scanner. Two computer-assisted algorithms were used to quantify the volumetric breast density. First, standard fuzzy c-means (FCM) clustering was used on raw images with the bias field present. Then, the coherent local intensity clustering (CLIC) method estimated and corrected the bias field during the iterative tissue segmentation process. Finally, FCM clustering was performed on the bias-field-corrected images produced by CLIC method. The left-right correlation for breasts in the same pair was studied for both segmentation algorithms to evaluate the precision of the tissue classification. Finally, the breast densities measured with the three methods were compared to the gold standard tissue compositions obtained from chemical analysis. The linear correlation coefficient, Pearson's r, was used to evaluate the two image segmentation algorithms and the effect of bias field. The CLIC method successfully corrected the intensity inhomogeneity induced by the bias field. In left-right comparisons, the CLIC method significantly improved the slope and the correlation coefficient of the linear fitting for the glandular volume estimation. The left-right breast density correlation was also increased from 0.93 to 0.98. When compared with the percent fibroglandular volume (%FGV) from chemical analysis, results after bias field correction from both the CLIC the FCM algorithms showed improved linear correlation. As a result, the Pearson's r increased from 0.86 to 0.92 with the bias field correction. The investigated CLIC method

Breast density quantification using magnetic resonance imaging (MRI) with bias field correction: A postmortem study

International Nuclear Information System (INIS)

Ding, Huanjun; Johnson, Travis; Lin, Muqing; Le, Huy Q.; Ducote, Justin L.; Su, Min-Ying; Molloi, Sabee

2013-01-01

Purpose: Quantification of breast density based on three-dimensional breast MRI may provide useful information for the early detection of breast cancer. However, the field inhomogeneity can severely challenge the computerized image segmentation process. In this work, the effect of the bias field in breast density quantification has been investigated with a postmortem study. Methods: T1-weighted images of 20 pairs of postmortem breasts were acquired on a 1.5 T breast MRI scanner. Two computer-assisted algorithms were used to quantify the volumetric breast density. First, standard fuzzy c-means (FCM) clustering was used on raw images with the bias field present. Then, the coherent local intensity clustering (CLIC) method estimated and corrected the bias field during the iterative tissue segmentation process. Finally, FCM clustering was performed on the bias-field-corrected images produced by CLIC method. The left–right correlation for breasts in the same pair was studied for both segmentation algorithms to evaluate the precision of the tissue classification. Finally, the breast densities measured with the three methods were compared to the gold standard tissue compositions obtained from chemical analysis. The linear correlation coefficient, Pearson'sr, was used to evaluate the two image segmentation algorithms and the effect of bias field. Results: The CLIC method successfully corrected the intensity inhomogeneity induced by the bias field. In left–right comparisons, the CLIC method significantly improved the slope and the correlation coefficient of the linear fitting for the glandular volume estimation. The left–right breast density correlation was also increased from 0.93 to 0.98. When compared with the percent fibroglandular volume (%FGV) from chemical analysis, results after bias field correction from both the CLIC the FCM algorithms showed improved linear correlation. As a result, the Pearson'sr increased from 0.86 to 0.92 with the bias field correction

Approximation of the breast height diameter distribution of two-cohort stands by mixture models III Kernel density estimators vs mixture models

Science.gov (United States)

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 new look at breast density and breast cancer risk

NARCIS (Netherlands)

Haars, G.

2008-01-01

Breast density, as visible on mammograms, comprises connective and epithelial tissue and can be seen to represent the glandular target tissue for breast cancer, whereas the non-dense tissue mainly comprises fat. High percentages of density are established to be one of the strongest risk factors of

Psychological impact of providing women with personalised 10-year breast cancer risk estimates.

Science.gov (United States)

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.

Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method

Energy Technology Data Exchange (ETDEWEB)

Wu, Shandong; Weinstein, Susan P.; Conant, Emily F.; Kontos, Despina, E-mail: despina.kontos@uphs.upenn.edu [Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States)

2013-12-15

Purpose: Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Studies suggest that the relative amount of fibroglandular (i.e., dense) tissue in the breast as quantified in MR images can be predictive of the risk for developing breast cancer, especially for high-risk women. Automated segmentation of the fibroglandular tissue and volumetric density estimation in breast MRI could therefore be useful for breast cancer risk assessment. Methods: In this work the authors develop and validate a fully automated segmentation algorithm, namely, an atlas-aided fuzzy C-means (FCM-Atlas) method, to estimate the volumetric amount of fibroglandular tissue in breast MRI. The FCM-Atlas is a 2D segmentation method working on a slice-by-slice basis. FCM clustering is first applied to the intensity space of each 2D MR slice to produce an initial voxelwise likelihood map of fibroglandular tissue. Then a prior learned fibroglandular tissue likelihood atlas is incorporated to refine the initial FCM likelihood map to achieve enhanced segmentation, from which the absolute volume of the fibroglandular tissue (|FGT|) and the relative amount (i.e., percentage) of the |FGT| relative to the whole breast volume (FGT%) are computed. The authors' method is evaluated by a representative dataset of 60 3D bilateral breast MRI scans (120 breasts) that span the full breast density range of the American College of Radiology Breast Imaging Reporting and Data System. The automated segmentation is compared to manual segmentation obtained by two experienced breast imaging radiologists. Segmentation performance is assessed by linear regression, Pearson's correlation coefficients, Student's pairedt-test, and Dice's similarity coefficients (DSC). Results: The inter-reader correlation is 0.97 for FGT% and 0.95 for |FGT|. When compared to the average of the two readers’ manual segmentation, the proposed FCM-Atlas method achieves a

Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method

International Nuclear Information System (INIS)

Wu, Shandong; Weinstein, Susan P.; Conant, Emily F.; Kontos, Despina

2013-01-01

Purpose: Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Studies suggest that the relative amount of fibroglandular (i.e., dense) tissue in the breast as quantified in MR images can be predictive of the risk for developing breast cancer, especially for high-risk women. Automated segmentation of the fibroglandular tissue and volumetric density estimation in breast MRI could therefore be useful for breast cancer risk assessment. Methods: In this work the authors develop and validate a fully automated segmentation algorithm, namely, an atlas-aided fuzzy C-means (FCM-Atlas) method, to estimate the volumetric amount of fibroglandular tissue in breast MRI. The FCM-Atlas is a 2D segmentation method working on a slice-by-slice basis. FCM clustering is first applied to the intensity space of each 2D MR slice to produce an initial voxelwise likelihood map of fibroglandular tissue. Then a prior learned fibroglandular tissue likelihood atlas is incorporated to refine the initial FCM likelihood map to achieve enhanced segmentation, from which the absolute volume of the fibroglandular tissue (|FGT|) and the relative amount (i.e., percentage) of the |FGT| relative to the whole breast volume (FGT%) are computed. The authors' method is evaluated by a representative dataset of 60 3D bilateral breast MRI scans (120 breasts) that span the full breast density range of the American College of Radiology Breast Imaging Reporting and Data System. The automated segmentation is compared to manual segmentation obtained by two experienced breast imaging radiologists. Segmentation performance is assessed by linear regression, Pearson's correlation coefficients, Student's pairedt-test, and Dice's similarity coefficients (DSC). Results: The inter-reader correlation is 0.97 for FGT% and 0.95 for |FGT|. When compared to the average of the two readers’ manual segmentation, the proposed FCM-Atlas method achieves a correlation ofr = 0

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

Science.gov (United States)

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.

The association of breast density with breast cancer mortality in African American and white women screened in community practice.

Science.gov (United States)

Zhang, Shengfan; Ivy, Julie S; Diehl, Kathleen M; Yankaskas, Bonnie C

2013-01-01

The effect of breast density on survival outcomes for American women who participate in screening remains unknown. We studied the role of breast density on both breast cancer and other cause of mortality in screened women. Data for women with breast cancer, identified from the community-based Carolina Mammography Registry, were linked with the North Carolina cancer registry and NC death tapes for this study. Cause-specific Cox proportional hazards models were developed to analyze the effect of several covariates on breast cancer mortality-namely, age, race (African American/White), cancer stage at diagnosis (in situ, local, regional, and distant), and breast density (BI-RADS( ® ) 1-4). Two stratified Cox models were considered controlling for (1) age and race, and (2) age and cancer stage, respectively, to further study the effect of density. The cumulative incidence function with confidence interval approximation was used to quantify mortality probabilities over time. For this study, 22,597 screened women were identified as having breast cancer. The non-stratified and stratified Cox models showed no significant statistical difference in mortality between dense tissue and fatty tissue, while controlling for other covariate effects (p value = 0.1242, 0.0717, and 0.0619 for the non-stratified, race-stratified, and cancer stage-stratified models, respectively). The cumulative mortality probability estimates showed that women with dense breast tissues did not have significantly different breast cancer mortality than women with fatty breast tissue, regardless of age (e.g., 10-year confidence interval of mortality probabilities for whites aged 60-69 white: 0.056-0.090 vs. 0.054-0.083). Aging, African American race, and advanced cancer stage were found to be significant risk factors for breast cancer mortality (hazard ratio >1.0). After controlling for cancer incidence, there was not a significant association between mammographic breast density and mortality, adjusting

Relationship between breast sound speed and mammographic percent density

Science.gov (United States)

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.

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

NARCIS (Netherlands)

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

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

NARCIS (Netherlands)

Waal, D. van der; Heeten, GJ. den; Pijnappel, R.M.; Schuur, K.H.; Timmers, J.M.; Verbeek, A.L.; Broeders, M.J.

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

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

NARCIS (Netherlands)

van der Waal, Danielle; den Heeten, Gerard J.; Pijnappel, Ruud M.; Schuur, Klaas H.; Timmers, Johanna M. H.; Verbeek, Andre 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

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

OpenAIRE

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.

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 suppo...

Breast Density and Breast Cancer Incidence in the Lebanese Population: Results from a Retrospective Multicenter Study

Directory of Open Access Journals (Sweden)

Christine Salem

2017-01-01

Full Text Available Purpose. To study the distribution of breast mammogram density in Lebanese women and correlate it with breast cancer (BC incidence. Methods. Data from 1,049 women who had screening or diagnostic mammography were retrospectively reviewed. Age, menopausal status, contraceptives or hormonal replacement therapy (HRT, parity, breastfeeding, history of BC, breast mammogram density, and final BI-RADS assessment were collected. Breast density was analyzed in each age category and compared according to factors that could influence breast density and BC incidence. Results. 120 (11.4% patients had BC personal history with radiation and/or chemotherapy; 66 patients were postmenopausal under HRT. Mean age was 52.58±11.90 years. 76.4% of the patients (30–39 years had dense breasts. Parity, age, and menopausal status were correlated to breast density whereas breastfeeding and personal/family history of BC and HRT were not. In multivariate analysis, it was shown that the risk of breast cancer significantly increases 3.3% with age (P=0.005, 2.5 times in case of menopause (P=0.004, and 1.4 times when breast density increases (P=0.014. Conclusion. Breast density distribution in Lebanon is similar to the western society. Similarly to other studies, it was shown that high breast density was statistically related to breast cancer, especially in older and menopausal women.

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

Science.gov (United States)

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 (association was seen for any type of physical activity among women with 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.

Postmortem validation of breast density using dual-energy mammography

OpenAIRE

Molloi, Sabee; Ducote, Justin L.; Ding, Huanjun; Feig, Stephen A.

2014-01-01

Purpose: Mammographic density has been shown to be an indicator of breast cancer risk and also reduces the sensitivity of screening mammography. Currently, there is no accepted standard for measuring breast density. Dual energy mammography has been proposed as a technique for accurate measurement of breast density. The purpose of this study is to validate its accuracy in postmortem breasts and compare it with other existing techniques. Methods: Forty postmortem breasts were imaged using a dua...

Changes in mammographic density and breast cancer risk

NARCIS (Netherlands)

Lokate, A.J.M.

2012-01-01

Breast cancer is the most frequently occurring cancer among women worldwide. One of the most important risk factors for breast cancer is high mammographic density. Mammographic density represents the amount of fibroglandular tissue relative to the fat tissue in the breast. Women with >75% of their

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

Science.gov (United States)

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.

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

Energy Technology Data Exchange (ETDEWEB)

Wienbeck, Susanne; Uhlig, Johannes; Fintel, Eva von; Stahnke, Vera; Lotz, Joachim [University Medical Center Goettingen, Institute for Diagnostic and Interventional Radiology, Goettingen (Germany); Luftner-Nagel, Susanne; Fischer, Uwe [Diagnostic Breast Center Goettingen, Goettingen (Germany); Zapf, Antonia [University Medical Center Goettingen, Department of Medical Statistics, Goettingen (Germany); Surov, Alexey [University of Leipzig, Department of Diagnostic and Interventional Radiology, Leipzig (Germany)

2017-12-15

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. (orig.)

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

Science.gov (United States)

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.

Mammographic breast density patterns among a group of women in ...

African Journals Online (AJOL)

Introduction: Mammographic breast density is a measure of parenchymal breast patterns on film and in part a marker of cumulative exposure to oestrogen. The risk of breast cancer for women with increased density is up to six fold more than in women with less dense tissues. The pattern of mammographic breast density ...

Volumetric mammographic density: heritability and association with breast cancer susceptibility loci.

Science.gov (United States)

Brand, Judith S; Humphreys, Keith; Thompson, Deborah J; Li, Jingmei; Eriksson, Mikael; Hall, Per; Czene, Kamila

2014-12-01

Mammographic density is a strong heritable trait, but data on its genetic component are limited to area-based and qualitative measures. We studied the heritability of volumetric mammographic density ascertained by a fully-automated method and the association with breast cancer susceptibility loci. Heritability of volumetric mammographic density was estimated with a variance component model in a sib-pair sample (N pairs = 955) of a Swedish screening based cohort. Associations with 82 established breast cancer loci were assessed in an independent sample of the same cohort (N = 4025 unrelated women) using linear models, adjusting for age, body mass index, and menopausal status. All tests were two-sided, except for heritability analyses where one-sided tests were used. After multivariable adjustment, heritability estimates (standard error) for percent dense volume, absolute dense volume, and absolute nondense volume were 0.63 (0.06) and 0.43 (0.06) and 0.61 (0.06), respectively (all P associated with rs10995190 (ZNF365; P = 9.0 × 10(-6) and 8.9 × 10(-7), respectively) and rs9485372 (TAB2; P = 1.8 × 10(-5) and 1.8 × 10(-3), respectively). We also observed associations of rs9383938 (ESR1) and rs2046210 (ESR1) with the absolute dense volume (P = 2.6 × 10(-4) and 4.6 × 10(-4), respectively), and rs6001930 (MLK1) and rs17356907 (NTN4) with the absolute nondense volume (P = 6.7 × 10(-6) and 8.4 × 10(-5), respectively). Our results support the high heritability of mammographic density, though estimates are weaker for absolute than percent dense volume. We also demonstrate that the shared genetic component with breast cancer is not restricted to dense tissues only. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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

Science.gov (United States)

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

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

Science.gov (United States)

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%, pdensity group (pdensity breasts. • Overall sensitivity for non-contrast CBBCT ranged between 88%-91%. • Sensitivity was higher for CBBCT than mammography in both density types (pdensity types (pdensity types (p<0.001).

Breast fat volume measurement using wide-bore 3 T MRI: comparison of traditional mammographic density evaluation with MRI density measurements using automatic segmentation.

Science.gov (United States)

Petridou, E; Kibiro, M; Gladwell, C; Malcolm, P; Toms, A; Juette, A; Borga, M; Dahlqvist Leinhard, O; Romu, T; Kasmai, B; Denton, E

2017-07-01

To compare magnetic resonance imaging (MRI)-derived breast density measurements using automatic segmentation algorithms with radiologist estimations using the Breast Imaging Reporting and Data Systems (BI-RADS) density classification. Forty women undergoing mammography and dynamic breast MRI as part of their clinical management were recruited. Fat-water separated MRI images derived from a two-point Dixon technique, phase-sensitive reconstruction, and atlas-based segmentation were obtained before and after intravenous contrast medium administration. Breast density was assessed using software from Advanced MR Analytics (AMRA), Linköping, Sweden, with results compared to the widely used four-quartile quantitative BI-RADS scale. The proportion of glandular tissue in the breast on MRI was derived from the AMRA sequence. The mean unenhanced breast density was 0.31±0.22 (mean±SD; left) and 0.29±0.21 (right). Mean breast density on post-contrast images was 0.32±0.19 (left) and 0.32±0.2 (right). There was "almost perfect" correlation between pre- and post-contrast breast density quantification: Spearman's correlation rho=0.98 (95% confidence intervals [CI]: 0.97-0.99; left) and rho=0.99 (95% CI: 0.98-0.99; right). The 95% limits of agreement were -0.11-0.08 (left) and -0.08-0.03 (right). Interobserver reliability for BI-RADS was "substantial": weighted Kappa k=0.8 (95% CI: 0.74-0.87). The Spearman correlation coefficient between BI-RADS and MRI breast density was rho=0.73 (95% CI: 0.60-0.82; left) and rho=0.75 (95% CI: 0.63-0.83; right) which was also "substantial". The AMRA sequence provides a fully automated, reproducible, objective assessment of fibroglandular breast tissue proportion that correlates well with mammographic assessment of breast density with the added advantage of avoidance of ionising radiation. Copyright © 2017 The Royal College of Radiologists. All rights reserved.

Annual Screening Mammogram and its Relation to Breast Density

Directory of Open Access Journals (Sweden)

Sabek EAS

2017-11-01

Full Text Available Background: Current national screening programs totally depend on mammographic evaluation. After increased incidence of breast cancer in women under the age of 35, mammography sensitivity in now a question. Several factors added to decrease sensitivity of mammography, such as increased density in older age groups and increased aggressiveness of tumour biology. All these factors will change the reliability of the screening program. The study is a retrospective study conducted at Ain Shams University. Method: 138 patients diagnosed with cancer breast underwent both mammography and sonography to determine percentage of patient with more than one focus, age and density distribution breast cancer in the affected patient and accuracy of both mammography and US. Results: By studying this population, we found that around 61,44% have areas of density ranging from dense breast, heterogenous density or scattered density. These areas of density render the mammography a less sensitive tool as its sensitivity fall to 34.09%, while that of US was 77.27%. Conclusion: As breast cancer is prevalent in younger population, also with increased density in older population who are relatively insensitive to mammography, we recommended the use of Automated Breast Ultrasound (ABUS in the national screening program.

Association between mammogram density and background parenchymal enhancement of breast MRI

Science.gov (United States)

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.

Mammographic density and histopathologic characteristics of screen-detected tumors in the Norwegian Breast Cancer Screening Program

International Nuclear Information System (INIS)

Moshina, Nataliia; Ursin, Giske; Hoff, Solveig Roth; Akslen, Lars A; Roman, Marta; Sebuødegård, Sofie; Hofvind, Solveig

2015-01-01

High mammographic density might mask breast tumors, resulting in delayed diagnosis or missed cancers. To investigate the association between mammographic density and histopathologic tumor characteristics (histologic type, size, grade, and lymph node status) among women screened in the Norwegian Breast Cancer Screening Program. Information about 1760 screen-detected ductal carcinoma in situ (DCIS) and 7366 invasive breast cancers diagnosed among women aged 50–69 years, 1996–2010, was analyzed. The screening mammograms were classified subjectively according to the amount of fibroglandular tissue into fatty, medium dense, and dense by breast radiologists. Chi-square test was used to compare the distribution of tumor characteristics by mammographic density. Odds ratio (OR) of tumor characteristics by density was estimated by means of logistic regression, adjusting for screening mode (screen-film and full-field digital mammography), and age. Mean and median tumor size of invasive breast cancers was 13.8 and 12 mm, respectively, for women with fatty breasts, and 16.2 and 14 mm for those with dense breasts. Lymph node positive tumors were identified among 20.6% of women with fatty breasts compared with 27.2% of those with dense breasts (P < 0.001). The proportion of DCIS was significantly lower for women with fatty (15.8%) compared with dense breasts (22.0%). Women with dense breasts had an increased risk of large (OR, 1.44; 95% CI, 1.18–1.73) and lymph node positive tumors (OR, 1.26; 95% CI, 1.05–1.51) compared with women with fatty and medium dense breasts. High mammographic density was positively associated with tumor size and lymph node positive tumors

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

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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.

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

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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 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.

Reader performance in visual assessment of breast density using visual analogue scales: Are some readers more predictive of breast cancer?

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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.

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

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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.

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

Science.gov (United States)

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.

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

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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.

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

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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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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 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.

Breast cancer research output, 1945-2008: a bibliometric and density-equalizing analysis

LENUS (Irish Health Repository)

Glynn, Ronan W

2010-12-22

Abstract Introduction Breast cancer is the most common form of cancer among women, with an estimated 194,280 new cases diagnosed in the United States in 2009 alone. The primary aim of this work was to provide an in-depth evaluation of research yield in breast cancer from 1945 to 2008, using large-scale data analysis, the employment of bibliometric indicators of production and quality, and density-equalizing mapping. Methods Data were retrieved from the Web of Science (WOS) Science Citation Expanded database; this was searched using the Boolean operator, \\'OR\\

A deep learning method for classifying mammographic breast density categories.

Science.gov (United States)

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

Breast Cancer Risk Estimation Using Parenchymal Texture Analysis in Digital Breast Tomosynthesis

International Nuclear Information System (INIS)

Ikejimba, Lynda C.; Kontos, Despina; Maidment, Andrew D. A.

2010-01-01

Mammographic parenchymal texture has been shown to correlate with genetic markers of developing breast cancer. Digital breast tomosynthesis (DBT) is a novel x-ray imaging technique in which tomographic images of the breast are reconstructed from multiple source projections acquired at different angles of the x-ray tube. Compared to digital mammography (DM), DBT eliminates breast tissue overlap, offering superior parenchymal tissue visualization. We hypothesize that texture analysis in DBT could potentially provide a better assessment of parenchymal texture and ultimately result in more accurate assessment of breast cancer risk. As a first step towards validating this hypothesis, we investigated the association between DBT parenchymal texture and breast percent density (PD), a known breast cancer risk factor, and compared it to DM. Bilateral DBT and DM images from 71 women participating in a breast cancer screening trial were analyzed. Filtered-backprojection was used to reconstruct DBT tomographic planes in 1 mm increments with 0.22 mm in-plane resolution. Corresponding DM images were acquired at 0.1 mm pixel resolution. Retroareolar regions of interest (ROIs) equivalent to 2.5 cm 3 were segmented from the DBT images and corresponding 2.5 cm 2 ROIs were segmented from the DM images. Breast PD was mammographically estimated using the Cumulus scale. Overall, DBT texture features demonstrated a stronger correlation than DM to PD. The Pearson correlation coefficients for DBT were r = 0.40 (p 2 = 0.39) compared to DM (R 2 = 0.33). We attribute these observations to the superior parenchymal tissue visualization in DBT. Our study is the first to perform DBT texture analysis in a screening population of women, showing that DBT could potentially provide better breast cancer risk assessment in the future.

Agreement of mammographic measures of volumetric breast density to MRI.

Directory of Open Access Journals (Sweden)

Jeff Wang

Full Text Available Clinical scores of mammographic breast density are highly subjective. Automated technologies for mammography exist to quantify breast density objectively, but the technique that most accurately measures the quantity of breast fibroglandular tissue is not known.To compare the agreement of three automated mammographic techniques for measuring volumetric breast density with a quantitative volumetric MRI-based technique in a screening population.Women were selected from the UCSF Medical Center screening population that had received both a screening MRI and digital mammogram within one year of each other, had Breast Imaging Reporting and Data System (BI-RADS assessments of normal or benign finding, and no history of breast cancer or surgery. Agreement was assessed of three mammographic techniques (Single-energy X-ray Absorptiometry [SXA], Quantra, and Volpara with MRI for percent fibroglandular tissue volume, absolute fibroglandular tissue volume, and total breast volume.Among 99 women, the automated mammographic density techniques were correlated with MRI measures with R(2 values ranging from 0.40 (log fibroglandular volume to 0.91 (total breast volume. Substantial agreement measured by kappa statistic was found between all percent fibroglandular tissue measures (0.72 to 0.63, but only moderate agreement for log fibroglandular volumes. The kappa statistics for all percent density measures were highest in the comparisons of the SXA and MRI results. The largest error source between MRI and the mammography techniques was found to be differences in measures of total breast volume.Automated volumetric fibroglandular tissue measures from screening digital mammograms were in substantial agreement with MRI and if associated with breast cancer could be used in clinical practice to enhance risk assessment and prevention.

Agreement of mammographic measures of volumetric breast density to MRI.

Science.gov (United States)

Wang, Jeff; Azziz, Ania; Fan, Bo; Malkov, Serghei; Klifa, Catherine; Newitt, David; Yitta, Silaja; Hylton, Nola; Kerlikowske, Karla; Shepherd, John A

2013-01-01

Clinical scores of mammographic breast density are highly subjective. Automated technologies for mammography exist to quantify breast density objectively, but the technique that most accurately measures the quantity of breast fibroglandular tissue is not known. To compare the agreement of three automated mammographic techniques for measuring volumetric breast density with a quantitative volumetric MRI-based technique in a screening population. Women were selected from the UCSF Medical Center screening population that had received both a screening MRI and digital mammogram within one year of each other, had Breast Imaging Reporting and Data System (BI-RADS) assessments of normal or benign finding, and no history of breast cancer or surgery. Agreement was assessed of three mammographic techniques (Single-energy X-ray Absorptiometry [SXA], Quantra, and Volpara) with MRI for percent fibroglandular tissue volume, absolute fibroglandular tissue volume, and total breast volume. Among 99 women, the automated mammographic density techniques were correlated with MRI measures with R(2) values ranging from 0.40 (log fibroglandular volume) to 0.91 (total breast volume). Substantial agreement measured by kappa statistic was found between all percent fibroglandular tissue measures (0.72 to 0.63), but only moderate agreement for log fibroglandular volumes. The kappa statistics for all percent density measures were highest in the comparisons of the SXA and MRI results. The largest error source between MRI and the mammography techniques was found to be differences in measures of total breast volume. Automated volumetric fibroglandular tissue measures from screening digital mammograms were in substantial agreement with MRI and if associated with breast cancer could be used in clinical practice to enhance risk assessment and prevention.

Breast density does not impact the ability of Videssa® Breast to detect breast cancer in women under age 50.

Directory of Open Access Journals (Sweden)

David E Reese

Full Text Available Breast density is associated with reduced imaging resolution in the detection of breast cancer. A biochemical approach that is not affected by density would provide an important tool to healthcare professionals who are managing women with dense breasts and suspicious imaging findings. Videssa® Breast is a combinatorial proteomic biomarker assay (CPBA, comprised of Serum Protein Biomarkers (SPB and Tumor Associated Autoantibodies (TAAb integrated with patient-specific clinical data to produce a diagnostic score that reliably detects breast cancer (BC as an adjunctive tool to imaging. The performance of Videssa® Breast was evaluated in the dense (a and b and non-dense (c and d groups in a population of n = 545 women under age 50. The sensitivity and specificity in the dense breast group were calculated to be 88.9% and 81.2%, respectively, and 92.3% and 86.6%, respectively, for the non-dense group. No significant differences were observed in the sensitivity (p = 1.0 or specificity (p = 0.18 between these groups. The NPV was 99.3% and 99.1% in non-dense and dense groups, respectively. Unlike imaging, Videssa® Breast does not appear to be impacted by breast density; it can effectively detect breast cancer in women with dense and non-dense breasts alike. Thus, Videssa® Breast provides a powerful tool for healthcare providers when women with dense breasts present with challenging imaging findings. In addition, Videssa® Breast provides assurance to women with dense breasts that they do not have breast cancer, reducing further anxiety in this higher risk patient population.

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

Science.gov (United States)

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 Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Implementation of several mathematical algorithms to breast tissue density classification

International Nuclear Information System (INIS)

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

2014-01-01

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. - Highlights: • Breast density classification can be obtained by suitable mathematical algorithms. • Mathematical processing help radiologists to obtain the BI-RADS classification. • The entropy and joint entropy show high performance for density classification

Background enhancement in breast MR: Correlation with breast density in mammography and background echotexture in ultrasound

International Nuclear Information System (INIS)

Ko, Eun Sook; Lee, Byung Hee; Choi, Hye Young; Kim, Rock Bum; Noh, Woo-Chul

2011-01-01

Objective: This study aimed to determine whether background enhancement on MR was related to mammographic breast density or ultrasonographic background echotexture in premenopausal and postmenopausal women. Materials and methods: We studied 142 patients (79 premenopausal, 63 postmenopausal) who underwent mammography, ultrasonography, and breast MR. We reviewed the mammography for overall breast density of the contralateral normal breast according to the four-point scale of the BI-RADS classification. Ultrasound findings were classified as homogeneous or heterogeneous background echotexture according to the BI-RADS lexicon. We rated background enhancement on a contralateral breast MR into four categories based on subtraction images: absent, mild, moderate, and marked. All imaging findings were interpreted independently by two readers without knowledge of menstrual status, imaging findings of other modalities. Results: There were significant differences between the premenopausal and postmenopausal group in distribution of mammographic breast density, ultrasonographic background echotexture, and degree of background enhancement. Regarding the relationship between mammographic density and background enhancement, there was no significant correlation. There was significant relationship between ultrasonographic background echotexture and background enhancement in both premenopausal and postmenopausal groups. Conclusion: There is a significant correlation between ultrasonographic background echotexture and background enhancement in MR regardless of menopausal status. Interpreting breast MR, or scheduling for breast MR of women showing heterogeneous background echotexture needs more caution.

Breast density pattern characterization by histogram features and texture descriptors

OpenAIRE

Carneiro,Pedro Cunha; Franco,Marcelo Lemos Nunes; Thomaz,Ricardo de Lima; Patrocinio,Ana Claudia

2017-01-01

Abstract Introduction Breast cancer is the first leading cause of death for women in Brazil as well as in most countries in the world. Due to the relation between the breast density and the risk of breast cancer, in medical practice, the breast density classification is merely visual and dependent on professional experience, making this task very subjective. The purpose of this paper is to investigate image features based on histograms and Haralick texture descriptors so as to separate mammo...

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

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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

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

Science.gov (United States)

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

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

Science.gov (United States)

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 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 (pdensity was evident (r s =0.622, p=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.

Breast Tissue Composition and Immunophenotype and Its Relationship with Mammographic Density in Women at High Risk of Breast Cancer.

Directory of Open Access Journals (Sweden)

Jia-Min B Pang

Full Text Available To investigate the cellular and immunophenotypic basis of mammographic density in women at high risk of breast cancer.Mammograms and targeted breast biopsies were accrued from 24 women at high risk of breast cancer. Mammographic density was classified into Wolfe categories and ranked by increasing density. The histological composition and immunophenotypic profile were quantified from digitized haematoxylin and eosin-stained and immunohistochemically-stained (ERα, ERβ, PgR, HER2, Ki-67, and CD31 slides and correlated to mammographic density.Increasing mammographic density was significantly correlated with increased fibrous stroma proportion (rs (22 = 0.5226, p = 0.0088 and significantly inversely associated with adipose tissue proportion (rs (22 = -0.5409, p = 0.0064. Contrary to previous reports, stromal expression of ERα was common (19/20 cases, 95%. There was significantly higher stromal PgR expression in mammographically-dense breasts (p=0.026.The proportion of stroma and fat underlies mammographic density in women at high risk of breast cancer. Increased expression of PgR in the stroma of mammographically dense breasts and frequent and unexpected presence of stromal ERα expression raises the possibility that hormone receptor expression in breast stroma may have a role in mediating the effects of exogenous hormonal therapy on mammographic density.

Age as a predictive factor of mammographic breast density in Jamaican women

Energy Technology Data Exchange (ETDEWEB)

Soares, Deanne; Reid, Marvin; James, Michael

2002-06-01

AIM: We sought to determine the relationship between age, and other clinical characteristics such as parity, oestrogen use, dietary factors and menstrual history on breast density in Jamaican women. METHODS AND MATERIALS: A retrospective study was done of 891 patients who attended the breast imaging unit. The clinical characteristics were extracted from the patient records. Mammograms were assessed independently by two radiologists who were blinded to the patient clinical characteristics. Breast densities were assigned using the American College of Radiology (ACR) classification. RESULTS: The concordance between the ACR classification of breast density between the two independent radiologists was 92% with k = 0.76 (SE = 0.02, P < 0.001). Women with low breast density were heavier (81.3 {+-} 15.5 kg vs 68.4 {+-} 14.3 kg,P < 0.0001, mean {+-} standard deviation (SD)) and more obese (body mass index (BMI), 30.3 {+-} 5.8 kg m{sup -2} vs 26.0 {+-} 5.2 kg m{sup -2}, P < 0.0001). Mammographic breast density decreased with age. The age adjusted odds ratios (ORs) for predictors significantly related to high breast density were parity, OR = 0.79 (95%CI:0.71, 0.88), weight, OR = 0.92 (95% CI:0.91, 0.95), BMI, OR = 0.83 (95% CI:0.78, 0.89), menopause, OR = 0.51 (95% CI:0.36, 0.74) and a history of previous breast surgery, OR 1.6 (95% CI:1.1, 2.3). CONCLUSION: The rate decline of breast density with age in our population was influenced by parity and body composition. Soares, D. et al. (2002)

Age as a predictive factor of mammographic breast density in Jamaican women

International Nuclear Information System (INIS)

Soares, Deanne; Reid, Marvin; James, Michael

2002-01-01

AIM: We sought to determine the relationship between age, and other clinical characteristics such as parity, oestrogen use, dietary factors and menstrual history on breast density in Jamaican women. METHODS AND MATERIALS: A retrospective study was done of 891 patients who attended the breast imaging unit. The clinical characteristics were extracted from the patient records. Mammograms were assessed independently by two radiologists who were blinded to the patient clinical characteristics. Breast densities were assigned using the American College of Radiology (ACR) classification. RESULTS: The concordance between the ACR classification of breast density between the two independent radiologists was 92% with k = 0.76 (SE = 0.02, P -2 vs 26.0 ± 5.2 kg m -2 , P < 0.0001). Mammographic breast density decreased with age. The age adjusted odds ratios (ORs) for predictors significantly related to high breast density were parity, OR = 0.79 (95%CI:0.71, 0.88), weight, OR = 0.92 (95% CI:0.91, 0.95), BMI, OR = 0.83 (95% CI:0.78, 0.89), menopause, OR = 0.51 (95% CI:0.36, 0.74) and a history of previous breast surgery, OR 1.6 (95% CI:1.1, 2.3). CONCLUSION: The rate decline of breast density with age in our population was influenced by parity and body composition. Soares, D. et al. (2002)

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

Science.gov (United States)

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 density changes and treatment response. 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018. © 2018 International Society for Magnetic Resonance in Medicine.

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

Science.gov (United States)

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.

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

International Nuclear Information System (INIS)

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

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. 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. 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. 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

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

Science.gov (United States)

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 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.

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

Directory of Open Access Journals (Sweden)

Park B

2017-12-01

Full Text Available Boyoung Park,1,2 Hye Mi Cho,2 Eun Hye Lee,3 Seunghoon Song,2 Mina Suh,2 Kui Son Choi,1,2 Bong Joo Kang,4 Kyungran Ko,5 Ann Yi,6 Hae Kyoung Jung,7 Joo Hee Cha,8 Jae Kwan Jun,1,2 1National Cancer Center Graduate School of Cancer Science and Policy, Goyang, Republic of Korea; 2National Cancer Control Institute, National Cancer Center, Goyang, Republic of Korea; 3Department of Radiology, Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Republic of Korea; 4Department of Radiology, Seoul St Mary’s Hospital, Catholic University of Korea College of Medicine, Seoul, Republic of Korea; 5Center for Breast Cancer, National Cancer Center Hospital, National Cancer Center, Goyang, Republic of Korea; 6Department of Radiology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea; 7Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea; 8Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea 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

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

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Timmermans, Lore; Bacher, Klaus; Thierens, Hubert [Ghent University, Department of Basic Medical Sciences, QCC-Gent, Ghent (Belgium); Bleyen, Luc; Herck, Koen van [Ghent University, Centrum voor Preventie en Vroegtijdige Opsporing van Kanker, Ghent (Belgium); Lemmens, Kim; Ongeval, Chantal van; Steen, Andre van [University Hospitals Leuven, Department of Radiology, Leuven (Belgium); Martens, Patrick [Centrum voor Kankeropsporing, Bruges (Belgium); Brabander, Isabel de [Belgian Cancer Registry, Brussels (Belgium); Goossens, Mathieu [UZ Brussel, Dienst Kankerpreventie, Brussels (Belgium)

2017-09-15

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. (orig.)

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

International Nuclear Information System (INIS)

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

2017-01-01

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. (orig.)

Influence of mammographic density on the diagnostic accuracy of tumor size assessment and association with breast cancer tumor characteristics

International Nuclear Information System (INIS)

Fasching, Peter A.; Heusinger, Katharina; Loehberg, Christian R.; Wenkel, Evelyn; Lux, Michael P.; Schrauder, Michael; Koscheck, Thomas; Bautz, Werner; Schulz-Wendtland, Ruediger; Beckmann, Matthias W.; Bani, Mayada R.

2006-01-01

Purpose: The accuracy of breast cancer staging involves the estimation of the tumor size for the initial decision-making in the treatment. We investigated the accuracy of tumor size estimation and the association between tumor characteristics and breast density (BD). Materials and methods: A total of 434 women with a primary diagnosis of breast cancer were included in this prospective study at a specialist breast unit. Estimated tumor characteristics included tumor size, nodal status, estrogen/progesterone receptor status, Ki-67, HER2/neu, vascular invasion. Radiomorphological data included tumor size as assessed by mammography, breast ultrasonography, and clinical examination, and American College of Radiology (ACR) categories for BD. Results: BD did not have a significant impact on the assessment of tumor size using breast ultrasound (deviation from ACR categories 1-4: 0.55-0.68 cm; P = 0.331). The deviation in mammography was significantly different dependent on BD (0.42-0.9 cm; P 2 cm). Conclusion: Breast ultrasonography is more accurate than mammography for assessing tumor size in breasts with a higher BD. The difference in tumor size assessment needs to be taken into consideration in the design of clinical trials and treatment decisions

Breast tissue classification in digital breast tomosynthesis images using texture features: a feasibility study

Science.gov (United States)

Kontos, Despina; Berger, Rachelle; Bakic, Predrag R.; Maidment, Andrew D. A.

2009-02-01

Mammographic breast density is a known breast cancer risk factor. Studies have shown the potential to automate breast density estimation by using computerized texture-based segmentation of the dense tissue in mammograms. Digital breast tomosynthesis (DBT) is a tomographic x-ray breast imaging modality that could allow volumetric breast density estimation. We evaluated the feasibility of distinguishing between dense and fatty breast regions in DBT using computer-extracted texture features. Our long-term hypothesis is that DBT texture analysis can be used to develop 3D dense tissue segmentation algorithms for estimating volumetric breast density. DBT images from 40 women were analyzed. The dense tissue area was delineated within each central source projection (CSP) image using a thresholding technique (Cumulus, Univ. Toronto). Two (2.5cm)2 ROIs were manually selected: one within the dense tissue region and another within the fatty region. Corresponding (2.5cm)3 ROIs were placed within the reconstructed DBT images. Texture features, previously used for mammographic dense tissue segmentation, were computed. Receiver operating characteristic (ROC) curve analysis was performed to evaluate feature classification performance. Different texture features appeared to perform best in the 3D reconstructed DBT compared to the 2D CSP images. Fractal dimension was superior in DBT (AUC=0.90), while contrast was best in CSP images (AUC=0.92). We attribute these differences to the effects of tissue superimposition in CSP and the volumetric visualization of the breast tissue in DBT. Our results suggest that novel approaches, different than those conventionally used in projection mammography, need to be investigated in order to develop DBT dense tissue segmentation algorithms for estimating volumetric breast density.

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

Science.gov (United States)

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 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.

Quantitative assessment of breast density from digitized mammograms into Tabar's patterns

International Nuclear Information System (INIS)

Jamal, N; Ng, K-H; Looi, L-M; McLean, D; Zulfiqar, A; Tan, S-P; Liew, W-F; Shantini, A; Ranganathan, S

2006-01-01

We describe a semi-automated technique for the quantitative assessment of breast density from digitized mammograms in comparison with patterns suggested by Tabar. It was developed using the MATLAB-based graphical user interface applications. It is based on an interactive thresholding method, after a short automated method that shows the fibroglandular tissue area, breast area and breast density each time new thresholds are placed on the image. The breast density is taken as a percentage of the fibroglandular tissue to the breast tissue areas. It was tested in four different ways, namely by examining: (i) correlation of the quantitative assessment results with subjective classification, (ii) classification performance using the quantitative assessment technique, (iii) interobserver agreement and (iv) intraobserver agreement. The results of the quantitative assessment correlated well (r 2 = 0.92) with the subjective Tabar patterns classified by the radiologist (correctly classified 83% of digitized mammograms). The average kappa coefficient for the agreement between the readers was 0.63. This indicated moderate agreement between the three observers in classifying breast density using the quantitative assessment technique. The kappa coefficient of 0.75 for intraobserver agreement reflected good agreement between two sets of readings. The technique may be useful as a supplement to the radiologist's assessment in classifying mammograms into Tabar's pattern associated with breast cancer risk

Breast density pattern characterization by histogram features and texture descriptors

Directory of Open Access Journals (Sweden)

Pedro Cunha Carneiro

2017-04-01

Full Text Available Abstract Introduction Breast cancer is the first leading cause of death for women in Brazil as well as in most countries in the world. Due to the relation between the breast density and the risk of breast cancer, in medical practice, the breast density classification is merely visual and dependent on professional experience, making this task very subjective. The purpose of this paper is to investigate image features based on histograms and Haralick texture descriptors so as to separate mammographic images into categories of breast density using an Artificial Neural Network. Methods We used 307 mammographic images from the INbreast digital database, extracting histogram features and texture descriptors of all mammograms and selecting them with the K-means technique. Then, these groups of selected features were used as inputs of an Artificial Neural Network to classify the images automatically into the four categories reported by radiologists. Results An average accuracy of 92.9% was obtained in a few tests using only some of the Haralick texture descriptors. Also, the accuracy rate increased to 98.95% when texture descriptors were mixed with some features based on a histogram. Conclusion Texture descriptors have proven to be better than gray levels features at differentiating the breast densities in mammographic images. From this paper, it was possible to automate the feature selection and the classification with acceptable error rates since the extraction of the features is suitable to the characteristics of the images involving the problem.

A study on microvascular density in breast carcinoma

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D Pyakurel

2014-04-01

Full Text Available Background: Breast Cancer is the most frequent neoplasm causing death in women between 35-55 years of age. Of the Prognostic indicators existing for breast cancer, axillary lymph node status has been regarded as the most important one. Twenty to thirty percent of all lymph node negative patients will still develop a recurrence of the disease within 10 years of initial treatment. Therefore, a new prognostic marker that could identify patients at high risk of tumor recurrence more accurately than existing indicators would be of great value, one potential indicator is tumor-induced angiogenesis. Materials and Methods: This is a six months prospective (January 2010-June 2010 and 1 year retrospective (Jan2009-Dec2009 study which included thirty five breast cancer cases visiting the Surgical OPD. Angiogenesis was estimated by determining micro vessel counting after immune staining the paraffin embedded tissue sections using anti-CD34 antibody. Results: Age of the patients ranged from 25 to 80 years with a mean age of 45.48 years. Most of the cases were infiltrating ductal carcinoma comprising of 33 cases (94.28%. Three cases (9.10% showed vascular invasion by the tumor. Majority showed (63.64% vessel count of less than 200 per 10 high power fields. Conclusion: Micro vascular density positively correlated with size of the tumor, lymph nodes involved by the tumor and Nottingham prognostic index. In the future, Antibodies specific to proliferating endothelium, together with the development of automated image analysis, may improve the accuracy and value of measuring angiogenesis-induced microvessel density. DOI: http://dx.doi.org/10.3126/jpn.v4i7.10315 Journal of Pathology of Nepal (2014 Vol. 4, 570-575  

Effective radiation attenuation calibration for breast density: compression thickness influences and correction

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Thomas Jerry A

2010-11-01

Full Text Available Abstract Background Calibrating mammograms to produce a standardized breast density measurement for breast cancer risk analysis requires an accurate spatial measure of the compressed breast thickness. Thickness inaccuracies due to the nominal system readout value and compression paddle orientation induce unacceptable errors in the calibration. Method A thickness correction was developed and evaluated using a fully specified two-component surrogate breast model. A previously developed calibration approach based on effective radiation attenuation coefficient measurements was used in the analysis. Water and oil were used to construct phantoms to replicate the deformable properties of the breast. Phantoms consisting of measured proportions of water and oil were used to estimate calibration errors without correction, evaluate the thickness correction, and investigate the reproducibility of the various calibration representations under compression thickness variations. Results The average thickness uncertainty due to compression paddle warp was characterized to within 0.5 mm. The relative calibration error was reduced to 7% from 48-68% with the correction. The normalized effective radiation attenuation coefficient (planar representation was reproducible under intra-sample compression thickness variations compared with calibrated volume measures. Conclusion Incorporating this thickness correction into the rigid breast tissue equivalent calibration method should improve the calibration accuracy of mammograms for risk assessments using the reproducible planar calibration measure.

First pregnancy characteristics, postmenopausal breast density, and salivary sex hormone levels in a population at high risk for breast cancer

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Mary Mockus

2015-06-01

Conclusions and general significance: While reproductive characteristics, in particular parity, generally demonstrated independent associations with postmenopausal breast density and E, P and DHEA levels, T levels showed concordant inverse associations with age-at-first birth and breast density. These findings suggest that reproductive effects and later life salivary sex steroid hormone levels may have independent effects on later life breast density and cancer risk.

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

NARCIS (Netherlands)

J.G.H. van Nes; L.V. Beex (Louk); C.M. Seynaeve (Caroline); H. Putter (Hein); A. Sramek (Alexandr); S. Lardenoije (Susanne); M.D.-D.E. Carpentier (Marjolijn Duijm-D.E.); I. Van Rongen (Inge); J.W.R. Nortier (Johan W. R.); H.M. Zonderland (Harmine); C.J.H. van de Velde (Cornelis)

2015-01-01

textabstractBackground. 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

Mammographic Breast Density in a Cohort of Medically Underserved Women

Science.gov (United States)

2015-12-01

was a training year and during Years 2 through 4 a case-control study of obesity , insulin resistance and mammographic breast density was conducted. A...factors including health literacy, and to collect anthropometric measurements and fasting blood, 3) to assay blood for select hormones and growth...factors, 4) to perform statistical analyses to determine the associations between obesity and insulin resistance and mammographic breast density, and 5

Birth weight, childhood body mass index, and height in relation to mammographic density and breast cancer

DEFF Research Database (Denmark)

Andersen, Zorana Jovanovic; Baker, Jennifer Lyn; Bihrmann, Kristine

2014-01-01

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, child...

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

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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

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

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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

Correlation between enhancement characteristics of MR mammography and capillary density of breast lesions

International Nuclear Information System (INIS)

Poellinger, Alexander; El-Ghannam, Sahra; Diekmann, Susanne; Fischer, Thomas; Kristiansen, Glen; Fritzsche, Florian; Fallenberg, Eva; Morawietz, Lars; Diekmann, Felix

2014-01-01

Highlights: • We correlate capillary density of breast lesions with MRM. • Capillary density correlates with tumor enhancement for all lesions. • However no such correlation exists for the malignant or benign groups separately. • Mean vessel number of lymphatic vessels do not correlate with tumor enhancement.These results might be of help in the workup of MR-guided breast biopsies. • These results might be of help in the workup of MR-guided breast biopsies. - Abstract: Objective: To correlate capillary density of breast lesions using the markers D2-40, CD31, and CD34 with early and late enhancement of magnetic resonance mammography (MRM). Materials and methods: The local ethics committee approved this study, and informed consent was available from all patients. The study included 64 women with 66 histologically proven breast lesions (41 malignant, 25 benign). MR-enhancement 1 min after contrast medium administration was determined in the tumor (I t1 /I t0 ratio) and in comparison to the surrounding tissue (I t1 /I t1-fat ratio). Capillary density was quantified based on immunohistological staining with D2-40, CD31, and CD34 in breast tumors and surrounding breast tissue. Mean capillary densities were correlated with contrast enhancement in the tumor and surrounding breast tissue. The Kruskal–Wallis test was used to test whether lesions with different MR enhancement patterns differed in terms of capillary density. Results: For CD34, there was statistically significant correlation between capillary density and tumor enhancement (r = 0.329, p = 0.012), however not for the malignant or benign groups separately. Mean vessel number identified by staining with D2-40 and CD31 did not correlate significantly with tumor enhancement (D2-40: r = −0.188, p = 0.130; CD31: r = 0.095, p = 0.448). There were no statistically significant differences in capillary density between breast lesions with delayed enhancement or a plateau and lesions showing washout (Kruskal�

Correlation between enhancement characteristics of MR mammography and capillary density of breast lesions

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Poellinger, Alexander, E-mail: alexander.poellinger@charite.de [Charité, Universitätsmedizin Berlin, Department of Radiology, Augustenburger Platz 1, 13353 Berlin (Germany); El-Ghannam, Sahra; Diekmann, Susanne; Fischer, Thomas [Charité, Universitätsmedizin Berlin, Department of Radiology, Augustenburger Platz 1, 13353 Berlin (Germany); Kristiansen, Glen [Universitätsklinikum Bonn, Department of Pathology, Sigmund-Freud-Str. 25, D-53127 Bonn (Germany); Fritzsche, Florian [Institut für Histologie und Zytologie, Bahnhofplatz 11, Postfach, 9101 Herisau (Switzerland); Fallenberg, Eva [Charité, Universitätsmedizin Berlin, Department of Radiology, Augustenburger Platz 1, 13353 Berlin (Germany); Morawietz, Lars [Diagnostik Ernst von Bergmann GmbH, Charlottenstr. 72, 14467 Potsdam (Germany); Diekmann, Felix [Charité, Universitätsmedizin Berlin, Department of Radiology, Augustenburger Platz 1, 13353 Berlin (Germany)

2014-12-15

Highlights: • We correlate capillary density of breast lesions with MRM. • Capillary density correlates with tumor enhancement for all lesions. • However no such correlation exists for the malignant or benign groups separately. • Mean vessel number of lymphatic vessels do not correlate with tumor enhancement.These results might be of help in the workup of MR-guided breast biopsies. • These results might be of help in the workup of MR-guided breast biopsies. - Abstract: Objective: To correlate capillary density of breast lesions using the markers D2-40, CD31, and CD34 with early and late enhancement of magnetic resonance mammography (MRM). Materials and methods: The local ethics committee approved this study, and informed consent was available from all patients. The study included 64 women with 66 histologically proven breast lesions (41 malignant, 25 benign). MR-enhancement 1 min after contrast medium administration was determined in the tumor (I{sub t1}/I{sub t0} ratio) and in comparison to the surrounding tissue (I{sub t1}/I{sub t1-fat} ratio). Capillary density was quantified based on immunohistological staining with D2-40, CD31, and CD34 in breast tumors and surrounding breast tissue. Mean capillary densities were correlated with contrast enhancement in the tumor and surrounding breast tissue. The Kruskal–Wallis test was used to test whether lesions with different MR enhancement patterns differed in terms of capillary density. Results: For CD34, there was statistically significant correlation between capillary density and tumor enhancement (r = 0.329, p = 0.012), however not for the malignant or benign groups separately. Mean vessel number identified by staining with D2-40 and CD31 did not correlate significantly with tumor enhancement (D2-40: r = −0.188, p = 0.130; CD31: r = 0.095, p = 0.448). There were no statistically significant differences in capillary density between breast lesions with delayed enhancement or a plateau and lesions showing

Comparison of Breast Density Between Synthesized Versus Standard Digital Mammography.

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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.

Implementation of several mathematical algorithms to breast tissue density classification

Science.gov (United States)

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.

The relationship between anatomic noise and volumetric breast density for digital mammography

International Nuclear Information System (INIS)

Mainprize, James G.; Tyson, Albert H.; Yaffe, Martin J.

2012-01-01

Purpose: The appearance of parenchymal/stromal patterns in mammography have been characterized as having a Wiener power spectrum with an inverse power-law shape described by the exponential parameter, β. The amount of fibroglandular tissue, which can be quantified in terms of volumetric breast density (VBD), influences the texture and appearance of the patterns formed in a mammogram. Here, a large study is performed to investigate the variations in β in a clinical population and to indicate the relationship between β and breast density. Methods: From a set of 2686 cranio-caudal normal screening mammograms, the parameter β was extracted from log-log fits to the Wiener spectrum over the range 0.15–1 mm −1 . The Wiener spectrum was calculated from regions of interest in the compression paddle contact region of the breast. An in-house computer program, Cumulus V, was used to extract the volumetric breast density and identify the compression paddle contact regions of the breast. The Wiener spectra were calculated with and without modulation transfer function (MTF) correction to determine the impact of VBD on the intrinsic anatomic noise. Results: The mean volumetric breast density was 25.5% (±12.6%) over all images. The mean β following a MTF correction which decreased the β slightly (≈−0.08) was found to be 2.87. Varying the maximum of the spatial frequency range of the fits from 0.7 to 1.0, 1.25 or 1.5 mm −1 showing small decreases in the result, although the effect of the quantum noise power component on reducing β was clearly observed at 1.5 mm −1 . Conclusions: The texture parameter, β, was found to increase with VBD at low volumetric breast densities with an apparent leveling off at higher densities. The relationship between β and VBD measured here can be used to create probabilistic models for computer simulations of detectability. As breast density is a known risk predictor for breast cancer, the correlation between β and VBD suggests that

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

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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 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

A fully-automated software pipeline for integrating breast density and parenchymal texture analysis for digital mammograms: parameter optimization in a case-control breast cancer risk assessment study

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Zheng, Yuanjie; Wang, Yan; Keller, Brad M.; Conant, Emily; Gee, James C.; Kontos, Despina

2013-02-01

Estimating a woman's risk of breast cancer is becoming increasingly important in clinical practice. Mammographic density, estimated as the percent of dense (PD) tissue area within the breast, has been shown to be a strong risk factor. Studies also support a relationship between mammographic texture and breast cancer risk. We have developed a fullyautomated software pipeline for computerized analysis of digital mammography parenchymal patterns by quantitatively measuring both breast density and texture properties. Our pipeline combines advanced computer algorithms of pattern recognition, computer vision, and machine learning and offers a standardized tool for breast cancer risk assessment studies. Different from many existing methods performing parenchymal texture analysis within specific breast subregions, our pipeline extracts texture descriptors for points on a spatial regular lattice and from a surrounding window of each lattice point, to characterize the local mammographic appearance throughout the whole breast. To demonstrate the utility of our pipeline, and optimize its parameters, we perform a case-control study by retrospectively analyzing a total of 472 digital mammography studies. Specifically, we investigate the window size, which is a lattice related parameter, and compare the performance of texture features to that of breast PD in classifying case-control status. Our results suggest that different window sizes may be optimal for raw (12.7mm2) versus vendor post-processed images (6.3mm2). We also show that the combination of PD and texture features outperforms PD alone. The improvement is significant (p=0.03) when raw images and window size of 12.7mm2 are used, having an ROC AUC of 0.66. The combination of PD and our texture features computed from post-processed images with a window size of 6.3 mm2 achieves an ROC AUC of 0.75.

Evidence that breast tissue stiffness is associated with risk of breast cancer.

Science.gov (United States)

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.

The association between breast tissue optical content and mammographic density in pre- and post-menopausal women.

Directory of Open Access Journals (Sweden)

Kristina M Blackmore

Full Text Available Mammographic density (MD, associated with higher water and lower fat content in the breast, is strongly related to breast cancer risk. Optical attenuation spectroscopy (OS is a non-imaging method of evaluating breast tissue composition by red and near-infrared light transmitted through the breast that, unlike mammography, does not involve radiation. OS provides information on wavelength dependent light scattering of tissue and on absorption by water, lipid, oxy-, deoxy-hemoglobin. We propose that OS could be an alternative marker of breast cancer risk and that OS breast tissue measures will be associated with MD. In the present analysis, we developed an algorithm to estimate breast tissue composition and light scattering parameters using a spectrally constrained global fitting procedure employing a diffuse light transport model. OS measurements were obtained from 202 pre- and post-menopausal women with normal mammograms. Percent density (PD and dense area (DA were measured using Cumulus. The association between OS tissue composition and PD and DA was analyzed using linear regression adjusted for body mass index. Among pre-menopausal women, lipid content was significantly inversely associated with square root transformed PD (β = -0.05, p = 0.0002 and DA (β = -0.05, p = 0.019; water content was significantly positively associated with PD (β = 0.06, p = 0.008. Tissue oxygen saturation was marginally inversely associated with PD (β = -0.03, p = 0.057 but significantly inversely associated with DA (β = -0.10, p = 0.002. Among post-menopausal women lipid and water content were significantly associated (negatively and positively, respectively with PD (β lipid = -0.08, β water = 0.14, both p<0.0001 and DA (β lipid = -0.10, p<0.0001; β water = 0.11, p = 0.001. The association between OS breast content and PD and DA is consistent with more proliferation in dense tissue of younger women, greater lipid content in low density tissue and higher water

Graphs to estimate an individualized risk of breast cancer.

Science.gov (United States)

Benichou, J; Gail, M H; Mulvihill, J J

1996-01-01

Clinicians who counsel women about their risk for developing breast cancer need a rapid method to estimate individualized risk (absolute risk), as well as the confidence limits around that point. The Breast Cancer Detection Demonstration Project (BCDDP) model (sometimes called the Gail model) assumes no genetic model and simultaneously incorporates five risk factors, but involves cumbersome calculations and interpolations. This report provides graphs to estimate the absolute risk of breast cancer from the BCDDP model. The BCDDP recruited 280,000 women from 1973 to 1980 who were monitored for 5 years. From this cohort, 2,852 white women developed breast cancer and 3,146 controls were selected, all with complete risk-factor information. The BCDDP model, previously developed from these data, was used to prepare graphs that relate a specific summary relative-risk estimate to the absolute risk of developing breast cancer over intervals of 10, 20, and 30 years. Once a summary relative risk is calculated, the appropriate graph is chosen that shows the 10-, 20-, or 30-year absolute risk of developing breast cancer. A separate graph gives the 95% confidence limits around the point estimate of absolute risk. Once a clinician rules out a single gene trait that predisposes to breast cancer and elicits information on age and four risk factors, the tables and figures permit an estimation of a women's absolute risk of developing breast cancer in the next three decades. These results are intended to be applied to women who undergo regular screening. They should be used only in a formal counseling program to maximize a woman's understanding of the estimates and the proper use of them.

Comparison of density estimators. [Estimation of probability density functions

Energy Technology Data Exchange (ETDEWEB)

Kao, S.; Monahan, J.F.

1977-09-01

Recent work in the field of probability density estimation has included the introduction of some new methods, such as the polynomial and spline methods and the nearest neighbor method, and the study of asymptotic properties in depth. This earlier work is summarized here. In addition, the computational complexity of the various algorithms is analyzed, as are some simulations. The object is to compare the performance of the various methods in small samples and their sensitivity to change in their parameters, and to attempt to discover at what point a sample is so small that density estimation can no longer be worthwhile. (RWR)

Visually assessed breast density, breast cancer risk and the importance of the craniocaudal view.

NARCIS (Netherlands)

Duffy, S.W.; Nagtegaal, I.D.; Astley, S.M.; Gillan, M.G.; McGee, M.A.; Boggis, C.R.; Wilson, M.; Beetles, U.M.; Griffiths, M.A.; Jain, A.K.; Johnson, J.; Roberts, R.; Deans, H.; Duncan, K.A.; Iyengar, G.; Griffiths, P.M.; Warwick, J.; Cuzick, J.; Gilbert, F.J.

2008-01-01

INTRODUCTION: Mammographic density is known to be a strong risk factor for breast cancer. A particularly strong association with risk has been observed when density is measured using interactive threshold software. This, however, is a labour-intensive process for large-scale studies. METHODS: Our

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

Science.gov (United States)

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. 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.

Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning.

Science.gov (United States)

Li, Songfeng; Wei, Jun; Chan, Heang-Ping; Helvie, Mark A; Roubidoux, Marilyn A; Lu, Yao; Zhou, Chuan; Hadjiiski, Lubomir M; Samala, Ravi K

2018-01-09

Breast density is one of the most significant factors that is associated with cancer risk. In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DMs). The input 'for processing' DMs was first log-transformed, enhanced by a multi-resolution preprocessing scheme, and subsampled to a pixel size of 800 µm  ×  800 µm from 100 µm  ×  100 µm. A deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD) by using a domain adaptation resampling method. The PD was estimated as the ratio of the dense area to the breast area based on the PMD. The DCNN approach was compared to a feature-based statistical learning approach. Gray level, texture and morphological features were extracted and a least absolute shrinkage and selection operator was used to combine the features into a feature-based PMD. With approval of the Institutional Review Board, we retrospectively collected a training set of 478 DMs and an independent test set of 183 DMs from patient files in our institution. Two experienced mammography quality standards act radiologists interactively segmented PD as the reference standard. Ten-fold cross-validation was used for model selection and evaluation with the training set. With cross-validation, DCNN obtained a Dice's coefficient (DC) of 0.79  ±  0.13 and Pearson's correlation (r) of 0.97, whereas feature-based learning obtained DC  =  0.72  ±  0.18 and r  =  0.85. For the independent test set, DCNN achieved DC  =  0.76  ±  0.09 and r  =  0.94, while feature-based learning achieved DC  =  0.62  ±  0.21 and r  =  0.75. Our DCNN approach was significantly better and more robust than the feature-based learning approach for automated PD estimation on DMs, demonstrating its potential use for automated density reporting as well as

Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning

Science.gov (United States)

Li, Songfeng; Wei, Jun; Chan, Heang-Ping; Helvie, Mark A.; Roubidoux, Marilyn A.; Lu, Yao; Zhou, Chuan; Hadjiiski, Lubomir M.; Samala, Ravi K.

2018-01-01

Breast density is one of the most significant factors that is associated with cancer risk. In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DMs). The input ‘for processing’ DMs was first log-transformed, enhanced by a multi-resolution preprocessing scheme, and subsampled to a pixel size of 800 µm  ×  800 µm from 100 µm  ×  100 µm. A deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD) by using a domain adaptation resampling method. The PD was estimated as the ratio of the dense area to the breast area based on the PMD. The DCNN approach was compared to a feature-based statistical learning approach. Gray level, texture and morphological features were extracted and a least absolute shrinkage and selection operator was used to combine the features into a feature-based PMD. With approval of the Institutional Review Board, we retrospectively collected a training set of 478 DMs and an independent test set of 183 DMs from patient files in our institution. Two experienced mammography quality standards act radiologists interactively segmented PD as the reference standard. Ten-fold cross-validation was used for model selection and evaluation with the training set. With cross-validation, DCNN obtained a Dice’s coefficient (DC) of 0.79  ±  0.13 and Pearson’s correlation (r) of 0.97, whereas feature-based learning obtained DC  =  0.72  ±  0.18 and r  =  0.85. For the independent test set, DCNN achieved DC  =  0.76  ±  0.09 and r  =  0.94, while feature-based learning achieved DC  =  0.62  ±  0.21 and r  =  0.75. Our DCNN approach was significantly better and more robust than the feature-based learning approach for automated PD estimation on DMs, demonstrating its potential use for automated density reporting as

Monte Carlo simulation for the estimation of the glandular breast dose for a digital breast tomosynthesis system

International Nuclear Information System (INIS)

Rodrigues, Leonardo; Braz, Delson; Goncalves Magalhaes, Luis Alexandre

2015-01-01

Digital breast tomosynthesis (DBT) is a screening and diagnostic modality that acquires images of the breast at multiple angles during a short scan. The Selenia Dimensions (Hologic, Bedford, Mass) DBT system can perform both full-field digital mammography and DBT. The system acquires 15 projections over a 15 deg. angular range (from -7.5 deg. to +7.5 deg.). An important factor in determining the optimal imaging technique for breast tomosynthesis is the radiation dose. In breast imaging, the radiation dose of concern is that deposited in the glandular tissue of the breast because this is the tissue that has a risk of developing cancer. The concept of the normalised mean glandular dose (DgN) has been introduced as the metric for the dose in breast imaging. The DgN is difficult to measure. The Monte Carlo techniques offer an alternative method for a realistic estimation of the radiation dose. The purpose of this work was to use the Monte Carlo code MCNPX technique to generate monoenergetic glandular dose data for estimating the breast tissue dose in tomosynthesis for arbitrary spectra as well as to observe the deposited radiation dose by projection on the glandular portion of the breast in a Selenia Dimensions DBT system. A Monte Carlo simulation of the system was developed to compute the DgN in a craniocaudal view. Monoenergetic X-ray beams from 10 to 49 keV in 1-keV increments were used. The simulation utilised the assumption of a homogeneous breast composition and three compositions (0 % glandular, 50 % glandular and 100 % glandular). The glandular and adipose tissue compositions were specified according ICRU Report 44. A skin layer of 4 mm was assumed to encapsulate the breast on all surfaces. The breast size was varied using the chest wall-to-nipple distance (CND) and compressed breast thickness (t). In this work, the authors assumed a CND of 5 cm and the thicknesses ranged from 2 to 8 cm, in steps of 2 cm. The fractional energy absorption increases (up to 44

Quantitative assessment of breast density from digitized mammograms into Tabar's patterns

Energy Technology Data Exchange (ETDEWEB)

Jamal, N [Medical Technology Division, Malaysian Institute for Nuclear Technology Research (MINT) 43000 Kajang (Malaysia); Ng, K-H [Department of Radiology, University of Malaya, 50603 Kuala Lumpur (Malaysia); Looi, L-M [Department of Pathology, University of Malaya, 50603 Kuala Lumpur (Malaysia); McLean, D [Medical Physics Department, Westmead Hospital, Sydney, NSW 2145 (Australia); Zulfiqar, A [Department of Radiology, Hospital Universiti Kebangsaan Malaysia, 56000 Malaysia, Kuala Lumpur, Malaysia (Malaysia); Tan, S-P [Department of Radiology, Hospital Universiti Kebangsaan Malaysia, 56000 Malaysia, Kuala Lumpur, Malaysia (Malaysia); Liew, W-F [Department of Radiology, Hospital Universiti Kebangsaan Malaysia, 56000 Malaysia, Kuala Lumpur, Malaysia (Malaysia); Shantini, A [Department of Radiology, Kuala Lumpur Hospital, 50586 Kuala Lumpur (Malaysia); Ranganathan, S [Department of Radiology, University of Malaya, 50603 Kuala Lumpur (Malaysia)

2006-11-21

We describe a semi-automated technique for the quantitative assessment of breast density from digitized mammograms in comparison with patterns suggested by Tabar. It was developed using the MATLAB-based graphical user interface applications. It is based on an interactive thresholding method, after a short automated method that shows the fibroglandular tissue area, breast area and breast density each time new thresholds are placed on the image. The breast density is taken as a percentage of the fibroglandular tissue to the breast tissue areas. It was tested in four different ways, namely by examining: (i) correlation of the quantitative assessment results with subjective classification, (ii) classification performance using the quantitative assessment technique, (iii) interobserver agreement and (iv) intraobserver agreement. The results of the quantitative assessment correlated well (r{sup 2} = 0.92) with the subjective Tabar patterns classified by the radiologist (correctly classified 83% of digitized mammograms). The average kappa coefficient for the agreement between the readers was 0.63. This indicated moderate agreement between the three observers in classifying breast density using the quantitative assessment technique. The kappa coefficient of 0.75 for intraobserver agreement reflected good agreement between two sets of readings. The technique may be useful as a supplement to the radiologist's assessment in classifying mammograms into Tabar's pattern associated with breast cancer risk.

3D MRI for Quantitative Analysis of Quadrant Percent Breast Density: Correlation with Quadrant Location of Breast Cancer.

Science.gov (United States)

Chen, Jeon-Hor; Liao, Fuyi; Zhang, Yang; Li, Yifan; Chang, Chia-Ju; Chou, Chen-Pin; Yang, Tsung-Lung; Su, Min-Ying

2017-07-01

Breast cancer occurs more frequently in the upper outer (UO) quadrant, but whether this higher cancer incidence is related to the greater amount of dense tissue is not known. Magnetic resonance imaging acquires three-dimensional volumetric images and is the most suitable among all breast imaging modalities for regional quantification of density. This study applied a magnetic resonance imaging-based method to measure quadrant percent density (QPD), and evaluated its association with the quadrant location of the developed breast cancer. A total of 126 cases with pathologically confirmed breast cancer were reviewed. Only women who had unilateral breast cancer located in a clear quadrant were selected for analysis. A total of 84 women, including 47 Asian women and 37 western women, were included. An established computer-aided method was used to segment the diseased breast and the contralateral normal breast, and to separate the dense and fatty tissues. Then, a breast was further separated into four quadrants using the nipple and the centroid as anatomic landmarks. The tumor was segmented using a computer-aided method to determine its quadrant location. The distribution of cancer quadrant location, the quadrant with the highest QPD, and the proportion of cancers occurring in the highest QPD were analyzed. The highest incidence of cancer occurred in the UO quadrant (36 out of 84, 42.9%). The highest QPD was also noted most frequently in the UO quadrant (31 out of 84, 36.9%). When correlating the highest QPD with the quadrant location of breast cancer, only 17 women out of 84 (20.2%) had breast cancer occurring in the quadrant with the highest QPD. The results showed that the development of breast cancer in a specific quadrant could not be explained by the density in that quadrant, and further studies are needed to find the biological reasons accounting for the higher breast cancer incidence in the UO quadrant. Copyright © 2017 The Association of University Radiologists

Mammographic breast density patterns in asymptomatic mexican women.

Science.gov (United States)

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.

Mammographic Breast Density Patterns in Asymptomatic Mexican Women

International Nuclear Information System (INIS)

Garciduenas, A.L.C.; Amador, N.; Mondragon, M.S.; Hernaan, L.; Cerda-Flores, R.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 (X 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

Mammographic Breast Density Patterns in Asymptomatic Mexican Women

International Nuclear Information System (INIS)

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

Background parenchymal enhancement in breast MRIs of breast cancer patients: Impact on tumor size estimation

International Nuclear Information System (INIS)

Baek, Ji Eun; Kim, Sung Hun; Lee, Ah Won

2014-01-01

Objective: To evaluate whether the degree of background parenchymal enhancement affects the accuracy of tumor size estimation based on breast MRI. Methods: Three hundred and twenty-two patients who had known breast cancer and underwent breast MRIs were recruited in our study. The total number of breast cancer cases was 339. All images were assessed retrospectively for the level of background parenchymal enhancement based on the BI-RADS criteria. Maximal lesion diameters were measured on the MRIs, and tumor types (mass vs. non-mass) were assessed. Tumor size differences between the MRI-based estimates and estimates based on pathological examinations were analyzed. The relationship between accuracy and tumor types and clinicopathologic features were also evaluated. Results: The cases included minimal (47.5%), mild (28.9%), moderate (12.4%) and marked background parenchymal enhancement (11.2%). The tumors of patients with minimal or mild background parenchymal enhancement were more accurately estimated than those of patients with moderate or marked enhancement (72.1% vs. 56.8%; p = 0.003). The tumors of women with mass type lesions were significantly more accurately estimated than those of the women with non-mass type lesions (81.6% vs. 28.6%; p < 0.001). The tumor of women negative for HER2 was more accurately estimated than those of women positive for HER2 (72.2% vs. 51.6%; p = 0.047). Conclusion: Moderate and marked background parenchymal enhancement is related to the inaccurate estimation of tumor size based on MRI. Non-mass type breast cancer and HER2-positive breast cancer are other factors that may cause inaccurate assessment of tumor size

AUTOMATED ANALYSIS OF CELL DENSITY IN BREAST CANCER AS AN ADDITIONAL METHOD OF INCREASING OBJECTIVITY AND ACCURACY OF BREAST CANCER PROGNOSIS

Directory of Open Access Journals (Sweden)

R. M. Paltuev

2017-01-01

Full Text Available Introduction. In the last ten years, it became obvious that on the molecular level breast cancer is a group of heterogenous tumors. The current objective of routine clinical practice of treatment prescription includes accurate disease prognosis for every individual patient and conviction that the risk of breast cancer recurrence after adjuvant hormone therapy without adjuvant chemotherapy doesn’t increase.The study objective is to evaluate how clinical use of risk associated with cell density can in practice improve prognosis of recurrence risk in patients with breast cancer after standard clinical and pathomorphological examinations.Materials and methods. The article analyzes therapy results using data from the cumulative cancer registry of breast cancer diagnosis and treatment of the N.N. Petrov National Medical Research Oncology Center in 2000–2009. The database includes information on diagnosis, treatment, and survival of 5106 patients with breast cancer. Archived material (from 2000 to 2009 from paraffin blocks of the “targeted group” for methods of molecular and genetic profiling was poured into recipient blocks, stained with corresponding antibodies such as widely used ER, PR, HER2/neu, Ki-67 markers as well as poorly studied markers: cell density, р53, CK5/6, CK14, CD4/CD8, p63, EGFR, FOXP3, AR, FOX1.Results. The study of 1118 patients with stage T1–2N0M0 breast cancer has shown that analysis of risk associated with cell density allows to predict disease outcome. Correlation between the marker and the grade of histological malignancy is more rare than for Ki-67 determined in this patient group. As a result, determination of cell density is an additional method to increase objectivity and accuracy of breast cancer prognosis.Conclusions. Automated cell density analysis for breast cancer is almost fully operator-independent which increases accuracy and objectivity of the results. Cell density in breast cancer lower than 3000

Breast cancer research output, 1945-2008: a bibliometric and density-equalizing analysis.

Science.gov (United States)

Glynn, Ronan W; Scutaru, Cristian; Kerin, Michael J; Sweeney, Karl J

2010-01-01

Breast cancer is the most common form of cancer among women, with an estimated 194,280 new cases diagnosed in the United States in 2009 alone. The primary aim of this work was to provide an in-depth evaluation of research yield in breast cancer from 1945 to 2008, using large-scale data analysis, the employment of bibliometric indicators of production and quality, and density-equalizing mapping. Data were retrieved from the Web of Science (WOS) Science Citation Expanded database; this was searched using the Boolean operator, 'OR', with different terms related to breast cancer, including "breast cancer", "mammary ductal carcinoma" and "breast tumour". Data were then extracted from each file, transferred to Excel charts and visualised as diagrams. Mapping was performed as described by Groneberg-Kloft et al. in 2008. A total of 180,126 breast cancer-associated items were produced over the study period; these had been cited 4,136,224 times. The United States returned the greatest level of output (n = 77,101), followed by the UK (n = 18,357) and Germany (n = 12,529). International cooperation peaked in 2008, with 3,127 entries produced as a result; relationships between the United States and other countries formed the basis for the 10 most common forms of bilateral cooperation. Publications from nations with high levels of international cooperation were associated with greater average citation rates. A total of 4,096 journals published at least one item on breast cancer, although the top 50 most prolific titles together accounted for over 43% (77,517/180,126) of the total output. Breast cancer-associated research output continues to increase annually. In an era when bibliometric indicators are increasingly being employed in performance assessment, these findings should provide useful information for those tasked with improving that performance.

Mammographic Density Reduction as a Prognostic Marker for Postmenopausal Breast Cancer: Results Using a Joint Longitudinal-Survival Modeling Approach.

Science.gov (United States)

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.

Effective radiation attenuation calibration for breast density: compression thickness influences and correction

OpenAIRE

Thomas Jerry A; Cao Ke; Heine John J

2010-01-01

Abstract Background Calibrating mammograms to produce a standardized breast density measurement for breast cancer risk analysis requires an accurate spatial measure of the compressed breast thickness. Thickness inaccuracies due to the nominal system readout value and compression paddle orientation induce unacceptable errors in the calibration. Method A thickness correction was developed and evaluated using a fully specified two-component surrogate breast model. A previously developed calibrat...

Small-mammal density estimation: A field comparison of grid-based vs. web-based density estimators

Science.gov (United States)

Parmenter, R.R.; Yates, Terry L.; Anderson, D.R.; Burnham, K.P.; Dunnum, J.L.; Franklin, A.B.; Friggens, M.T.; Lubow, B.C.; Miller, M.; Olson, G.S.; Parmenter, Cheryl A.; Pollard, J.; Rexstad, E.; Shenk, T.M.; Stanley, T.R.; White, Gary C.

2003-01-01

Statistical models for estimating absolute densities of field populations of animals have been widely used over the last century in both scientific studies and wildlife management programs. To date, two general classes of density estimation models have been developed: models that use data sets from capture–recapture or removal sampling techniques (often derived from trapping grids) from which separate estimates of population size (NÌ‚) and effective sampling area (AÌ‚) are used to calculate density (DÌ‚ = NÌ‚/AÌ‚); and models applicable to sampling regimes using distance-sampling theory (typically transect lines or trapping webs) to estimate detection functions and densities directly from the distance data. However, few studies have evaluated these respective models for accuracy, precision, and bias on known field populations, and no studies have been conducted that compare the two approaches under controlled field conditions. In this study, we evaluated both classes of density estimators on known densities of enclosed rodent populations. Test data sets (n = 11) were developed using nine rodent species from capture–recapture live-trapping on both trapping grids and trapping webs in four replicate 4.2-ha enclosures on the Sevilleta National Wildlife Refuge in central New Mexico, USA. Additional “saturation” trapping efforts resulted in an enumeration of the rodent populations in each enclosure, allowing the computation of true densities. Density estimates (DÌ‚) were calculated using program CAPTURE for the grid data sets and program DISTANCE for the web data sets, and these results were compared to the known true densities (D) to evaluate each model's relative mean square error, accuracy, precision, and bias. In addition, we evaluated a variety of approaches to each data set's analysis by having a group of independent expert analysts calculate their best density estimates without a priori knowledge of the true densities; this �

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

Science.gov (United States)

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.

Parameter estimation in stochastic mammogram model by heuristic optimization techniques.

NARCIS (Netherlands)

Selvan, S.E.; Xavier, C.C.; Karssemeijer, N.; Sequeira, J.; Cherian, R.A.; Dhala, B.Y.

2006-01-01

The appearance of disproportionately large amounts of high-density breast parenchyma in mammograms has been found to be a strong indicator of the risk of developing breast cancer. Hence, the breast density model is popular for risk estimation or for monitoring breast density change in prevention or

Mammographic Density Phenotypes and Risk of Breast Cancer: A Meta-analysis

Science.gov (United States)

Graff, Rebecca E.; Ursin, Giske; dos Santos Silva, Isabel; McCormack, Valerie; Baglietto, Laura; Vachon, Celine; Bakker, Marije F.; Giles, Graham G.; Chia, Kee Seng; Czene, Kamila; Eriksson, Louise; Hall, Per; Hartman, Mikael; Warren, Ruth M. L.; Hislop, Greg; Chiarelli, Anna M.; Hopper, John L.; Krishnan, Kavitha; Li, Jingmei; Li, Qing; Pagano, Ian; Rosner, Bernard A.; Wong, Chia Siong; Scott, Christopher; Stone, Jennifer; Maskarinec, Gertraud; Boyd, Norman F.; van Gils, Carla H.

2014-01-01

Background Fibroglandular breast tissue appears dense on mammogram, whereas fat appears nondense. It is unclear whether absolute or percentage dense area more strongly predicts breast cancer risk and whether absolute nondense area is independently associated with risk. Methods We conducted a meta-analysis of 13 case–control studies providing results from logistic regressions for associations between one standard deviation (SD) increments in mammographic density phenotypes and breast cancer risk. We used random-effects models to calculate pooled odds ratios and 95% confidence intervals (CIs). All tests were two-sided with P less than .05 considered to be statistically significant. Results Among premenopausal women (n = 1776 case patients; n = 2834 control subjects), summary odds ratios were 1.37 (95% CI = 1.29 to 1.47) for absolute dense area, 0.78 (95% CI = 0.71 to 0.86) for absolute nondense area, and 1.52 (95% CI = 1.39 to 1.66) for percentage dense area when pooling estimates adjusted for age, body mass index, and parity. Corresponding odds ratios among postmenopausal women (n = 6643 case patients; n = 11187 control subjects) were 1.38 (95% CI = 1.31 to 1.44), 0.79 (95% CI = 0.73 to 0.85), and 1.53 (95% CI = 1.44 to 1.64). After additional adjustment for absolute dense area, associations between absolute nondense area and breast cancer became attenuated or null in several studies and summary odds ratios became 0.82 (95% CI = 0.71 to 0.94; P heterogeneity = .02) for premenopausal and 0.85 (95% CI = 0.75 to 0.96; P heterogeneity women. Conclusions The results suggest that percentage dense area is a stronger breast cancer risk factor than absolute dense area. Absolute nondense area was inversely associated with breast cancer risk, but it is unclear whether the association is independent of absolute dense area. PMID:24816206

Conservative surgery and radiotherapy for stage I/II breast cancer using lung density correction: 10-year and 15-year results

International Nuclear Information System (INIS)

Pierce, Lori J.; Griffith, Kent A.; Hayman, James A.; Douglas, Kathye R.; Lichter, Allen S.

2005-01-01

Purpose: Radiotherapy (RT) planning for breast cancer using lung density correction improves dose homogeneity. Its use obviates the need for a medial wedge, thus reducing scatter to the opposite breast. Although lung density correction is used at many centers in planning for early-stage breast cancer, long-term results of local control and survival have not been reported. Since 1984, we have used lung density correction for dose calculations at the University of Michigan. We now present our 10-year and 15-year results. Methods and Materials: The records of 867 patients with Stage I/II breast cancer treated with breast-conserving surgery and RT with or without systemic therapy were reviewed. Tangential fields delivering 45-50 Gy to the whole breast calculated using lung density correction were used. A boost was added in 96.8% of patients for a total median dose of 61.8 Gy. Results: With a median follow-up of 6.6 years (range, 0.2-18.9 years), 5-, 10-, and 15-year actuarial rates of in-breast tumor recurrence as only first failure were 2.2%, 3.6%, and 5.4%, respectively. With surgical salvage, the 15-year cumulative rate of local control was 99.7%. Factors that significantly predicted for increased rate of local recurrence in multivariate analysis were age ≤ 35 years, hazard ratio 4.8 (95% confidence interval [CI], 1.6-13.9) p = 0.004; negative progesterone receptor status, hazard ratio 6.8 (95% CI, 2.3-20.3) p = < 0.001; negative estrogen receptor status, hazard ratio 4.0 (95% CI, 1.5-11.1) p = 0.007; and lack of adjuvant tamoxifen therapy, hazard ratio 7.7 (95% CI, 1.7-33.3) p = 0.008. Relapse-free survival rates at 5, 10, and 15 years were 84.6%, 70.8%, and 55.9%, respectively; breast cancer-specific survival rates were 94.4%, 90.5%, and 86.9%, respectively; and corresponding estimates for overall survival were 89.7%, 75.7%, and 61.3%. Conclusions: Use of lung density correction was associated with high rates of local control, relapse-free survival, breast

Dietary Energy Density and Postmenopausal Breast Cancer Incidence in the Cancer Prevention Study II Nutrition Cohort.

Science.gov (United States)

Hartman, Terryl J; Gapstur, Susan M; Gaudet, Mia M; Shah, Roma; Flanders, W Dana; Wang, Ying; McCullough, Marjorie L

2016-10-01

Dietary energy density (ED) is a measure of diet quality that estimates the amount of energy per unit of food (kilocalories per gram) consumed. Low-ED diets are generally high in fiber and fruits and vegetables and low in fat. Dietary ED has been positively associated with body mass index (BMI) and other risk factors for postmenopausal breast cancer. We evaluated the associations of total dietary ED and energy-dense (high-ED) foods with postmenopausal breast cancer incidence. Analyses included 56,795 postmenopausal women from the Cancer Prevention Study II Nutrition Cohort with no previous history of breast or other cancers and who provided information on diet, lifestyle, and medical history in 1999. Multivariable-adjusted breast cancer incidence rate ratios (RRs and 95% CIs) were estimated for quintiles of total dietary ED and for the consumption of high-ED foods in Cox proportional hazards regression models. During a median follow-up of 11.7 y, 2509 invasive breast cancer cases were identified, including 1857 estrogen receptor-positive and 277 estrogen receptor-negative tumors. Median dietary ED was 1.5 kcal/g (IQR: 1.3-1.7 kcal/g). After adjusting for age, race, education, reproductive characteristics, and family history, high compared with low dietary ED was associated with a statistically significantly higher risk of breast cancer (RR for fifth quintile compared with first quintile: 1.20; 95% CI: 1.05, 1.36; P-trend = 0.03). The association between the amount of high-ED foods consumed and breast cancer risk was not statistically significant. We observed no differences by estrogen receptor status or effect modification by BMI, age, or physical activity. These results suggest a modest positive association between total dietary ED and risk of postmenopausal breast cancer. © 2016 American Society for Nutrition.

Positive predictive values by mammographic density and screening mode in the Norwegian Breast Cancer Screening Program.

Science.gov (United States)

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 (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 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.

Background parenchymal enhancement on breast MRI and mammographic breast density: correlation with tumour characteristics

International Nuclear Information System (INIS)

Kim, M.Y.; Choi, N.; Yang, J.-H.; Yoo, Y.B.; Park, K.S.

2015-01-01

Aim: To investigate the relationship between mammographic breast density (MGD) and background parenchymal enhancement (BPE) at breast MRI and histopathological features of invasive breast cancers. Materials and methods: A total of 178 women with unilateral invasive breast cancer who preoperatively underwent mammography and breast MRI were included in the study. Two radiologists rated MGD and BPE according to BI-RADS criteria in consensus. The relationship between MGD and BPE was investigated, and compared with histopathological features of invasive breast cancers according to the level of MGD and BPE. Results: At MRI, there is no significant difference in the distribution of MGD and BPE of the contralateral breast in women with invasive breast cancer according to menopausal status (p=0.226, 0.384). Women with high MGD (>50% glandular) were more likely to have oestrogen-receptor (ER)-positive breast cancer (p=0.045) and progesterone receptor (PR)-positive breast cancer (p=0.020). With regard to BPE, PR positivity correlated with moderate or marked BPE with borderline significance (p=0.054). Multivariate logistic regression analyses revealed that women with high MGD were less likely to have triple-negative (i.e., a cancer that is ER negative, PR negative, and human epidermal growth factor receptor type 2 [HER2] negative) breast cancer compared with ER (+)/HER2 (−) cancer (OR=0.231, 95% CI: 0.070, 0.760; p=0.016). No association between the histological tumour characteristics and BPE was observed. Conclusion: In women with invasive breast cancer, high MGD is associated with ER positivity of the invasive breast cancer. However, at MRI, BPE of the contralateral breast seems to be independent of tumour characteristics. -- Highlights: •There is no difference in distribution of MGD and BPE of contralateral breast on MRI. •High MGD is associated with ER positivity of the invasive breast cancer. •BPE of the contralateral breast on MRI is independent of tumor

Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring

DEFF Research Database (Denmark)

Kallenberg, Michiel Gijsbertus J.; Petersen, Peter Kersten; Nielsen, Mads

2016-01-01

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...

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

Science.gov (United States)

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 density data, including percentage breast density, were significantly different among density categories (p density assessment had good correlation with visual assessment for both editions of BI-RADS.

Estimation of breast doses and breast cancer risk associated with repeated fluoroscopic chest examinations of women with tuberculosis

International Nuclear Information System (INIS)

Boice, J.D. Jr.; Rosenstein, M.; Trout, E.D.

1978-01-01

A methodology is presented to estimate cumulative breast dose and breast cancer risk for women exposed to repeated fluoroscopic chest examinations during air collapse therapy for pulmonary tuberculosis. Medical record abstraction, physician interview, patient contact, machine exposure measurements, and absorbed dose computations were combined to estimate average breast doses for 1047 Massachusetts women who were treated between 1930 and 1954. The methodology presented considers breast size and composition, patient orientation, x-ray field size and location, beam quality, type of examination, machine exposure rate, and exposure time during fluoroscopic examinations. The best estimate for the risk of radiation-induced cancer for the women living longer than 10 years after initial fluoroscopic exposure is 6.2 excess breast cancers per million woman-year-rad with 90% confidence limits of 2.8 and 10.7 cancers/10 6 WY-rad. When breast cancer risk is considered as a function of absorbed dose in the breast, instead of as a function of the number of fluoroscopic examinations, a linear dose--response relationship over the range of estimated doses is consistent with the data. However, because of the uncertainty due to small-sample variability and because of the wide range of assumptions regarding certain fluoroscopy conditions, other dose--response relationships are compatible with the data

Awareness of breast density and its impact on breast cancer detection and risk.

Science.gov (United States)

Rhodes, Deborah J; Radecki Breitkopf, Carmen; Ziegenfuss, Jeanette Y; Jenkins, Sarah M; Vachon, Celine M

2015-04-01

Legislation mandating disclosure of breast density (BD) information has passed in 21 states; however, actual awareness of BD and knowledge of its impact on breast cancer detection and risk are unknown. We conducted a national cross-sectional survey administered in English and Spanish using a probability-based sample of screening-age women, with oversampling of Connecticut, the only state with BD legislation in effect for > 1 year before the survey. Of 2,311 women surveyed, 65% responded. Overall, 58% of women had heard of BD, 49% knew that BD affects breast cancer detection, and 53% knew that BD affects cancer risk. After multivariable adjustment, increased BD awareness was associated with white non-Hispanic race/ethnicity (Hispanic v white non-Hispanic: odds ratio [OR], 0.23; P awareness and knowledge exist by race/ethnicity, education, and income. BD legislation seems to be effective in increasing knowledge of BD impact on breast cancer detection. These findings support continued and targeted efforts to improve BD awareness and knowledge among women eligible for screening mammography. © 2015 by American Society of Clinical Oncology.

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

Science.gov (United States)

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.

Breast Density Among the Three Major Ethnic Groups of Women in Malaysia from a Full-Filed Digital Mammography System

International Nuclear Information System (INIS)

Noriah Jamal; Cheung, H.S.

2014-01-01

We carried out a prospective study of breast density Full-Field Digital mammograms performed on 668 Malaysian women from three ethnic groups at a voluntary screening mammographic center in Kuala Lumpur, Malaysia. Of these women, 259 were Malays (38.8 %), 298 were Chinese (44.6 %) and 111 were Indians (16.6 %). The Tabar breast density classification (I-V) was used to evaluate breast density. A Stepwise multiple regression analysis was used to test for significant difference in breast density for the three ethnic groups. Data was also analyzed using descriptive statistics. We found that Pattern I was seen in 49.4 % Malay, 47.3 % Chinese and 40.5 % Indian women. Predominantly fatty breasts (Pattern II and III) were seen on mammograms of 36.3 % Malay, 11.1 % Chinese and 25.2 % Indian women. Dense type IV and V patterns were seen in 20.8 % Malay, 29.5 % Chinese and 42 % Indian women. There was no statistically significant difference (p>0.01) in breast density in the three ethnic groups. The breast density reduces with increasing age in all three ethnic groups. (Author)

Ant-inspired density estimation via random walks.

Science.gov (United States)

Musco, Cameron; Su, Hsin-Hao; Lynch, Nancy A

2017-10-03

Many ant species use distributed population density estimation in applications ranging from quorum sensing, to task allocation, to appraisal of enemy colony strength. It has been shown that ants estimate local population density by tracking encounter rates: The higher the density, the more often the ants bump into each other. We study distributed density estimation from a theoretical perspective. We prove that a group of anonymous agents randomly walking on a grid are able to estimate their density within a small multiplicative error in few steps by measuring their rates of encounter with other agents. Despite dependencies inherent in the fact that nearby agents may collide repeatedly (and, worse, cannot recognize when this happens), our bound nearly matches what would be required to estimate density by independently sampling grid locations. From a biological perspective, our work helps shed light on how ants and other social insects can obtain relatively accurate density estimates via encounter rates. From a technical perspective, our analysis provides tools for understanding complex dependencies in the collision probabilities of multiple random walks. We bound the strength of these dependencies using local mixing properties of the underlying graph. Our results extend beyond the grid to more general graphs, and we discuss applications to size estimation for social networks, density estimation for robot swarms, and random walk-based sampling for sensor networks.

Residential traffic noise and mammographic breast density in the Diet, Cancer, and Health cohort

DEFF Research Database (Denmark)

Roswall, Nina; Andersen, Zorana Jovanovic; von Euler-Chelpin, My

2018-01-01

OBJECTIVES: Traffic is the most important source of community noise, and it has been proposed to be associated with a range of disease outcomes, including breast cancer. As mammographic breast density (MD) is one of the strongest risk factors for developing breast cancer, the present study...

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

Science.gov (United States)

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

The impact of using weight estimated from mammographic images vs. self-reported weight on breast cancer risk calculation

Science.gov (United States)

Nair, Kalyani P.; Harkness, Elaine F.; Gadde, Soujanye; Lim, Yit Y.; Maxwell, Anthony J.; Moschidis, Emmanouil; Foden, Philip; Cuzick, Jack; Brentnall, Adam; Evans, D. Gareth; Howell, Anthony; Astley, Susan M.

2017-03-01

Personalised breast screening requires assessment of individual risk of breast cancer, of which one contributory factor is weight. Self-reported weight has been used for this purpose, but may be unreliable. We explore the use of volume of fat in the breast, measured from digital mammograms. Volumetric breast density measurements were used to determine the volume of fat in the breasts of 40,431 women taking part in the Predicting Risk Of Cancer At Screening (PROCAS) study. Tyrer-Cuzick risk using self-reported weight was calculated for each woman. Weight was also estimated from the relationship between self-reported weight and breast fat volume in the cohort, and used to re-calculate Tyrer-Cuzick risk. Women were assigned to risk categories according to 10 year risk (below average =8%) and the original and re-calculated Tyrer-Cuzick risks were compared. Of the 716 women diagnosed with breast cancer during the study, 15 (2.1%) moved into a lower risk category, and 37 (5.2%) moved into a higher category when using weight estimated from breast fat volume. Of the 39,715 women without a cancer diagnosis, 1009 (2.5%) moved into a lower risk category, and 1721 (4.3%) into a higher risk category. The majority of changes were between below average and average risk categories (38.5% of those with a cancer diagnosis, and 34.6% of those without). No individual moved more than one risk group. Automated breast fat measures may provide a suitable alternative to self-reported weight for risk assessment in personalized screening.

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

Science.gov (United States)

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

Collagen Matrix Density Drives the Metabolic Shift in Breast Cancer Cells

Directory of Open Access Journals (Sweden)

Brett A. Morris

2016-11-01

Full Text Available 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.

Comparison of bone mineral density in young patients with breast cancer and healthy women

Directory of Open Access Journals (Sweden)

Sousan Kolahi

2014-05-01

Full Text Available BACKGROUND: Almost 1 in 8 women will have breast cancer during their lifetime. Several risk factors were identified; however, 70% of females with breast cancer have no risk factors. Many risk factors are associated with sex steroid hormones. Some studies have been focused on identification of the indices of cumulative exposures to estrogen during the patients’ life. One of these indicators is bone mineral density (BMD. Our aim was the comparison of BMD in young patients with and without breast cancer, and finding a relationship between breast cancer and bone density. METHODS: In this case-control study, 120 people were enrolled; 40 patients with breast cancer and 80 normal healthy persons as control group. Measurement of BMD was performed in both groups and compared. RESULTS: Both groups were matched in age, weight, age at menarche, age at first marriage and first pregnancy, number of pregnancies over 32 weeks and lactation period, and taking supplemental calcium and vitamin D. However, there was a significant difference between the two groups in terms of estrogen intake, family history of breast cancer, and history of breast masses (P = 0.03, P = 0.03, P ≤ 0.01, respectively. A significant difference was found between BMD, bone mineral content (BMC, and t-scores of lumbar spine of the two groups; they were higher in the control group (P = 0.08, P ≤ 0.01, P = 0.06, respectively. CONCLUSIONS: This study shows that bone mineral density of young patients with breast cancer is not higher than normal similar age females; thus, BMD is not directly a risk factor for breast cancer.

ADN* Density log estimation Using Rockcell*

International Nuclear Information System (INIS)

Okuku, C.; Iloghalu, Emeka. M.; Omotayo, O.

2003-01-01

This work is intended to inform on the possibilities of estimating good density data in zones associated with sliding in a reservoir with ADN* tool with or without ADOS in string in cases where repeat sections were not done, possibly due to hole stability or directional concerns. This procedure has been equally used to obtain a better density data in corkscrew holes. Density data (ROBB) was recomputed using neural network in RockCell* to estimate the density over zones of interest. RockCell* is a Schlumberger software that has neural network functionally which can be used to estimate missing logs using the combination of the responses of other log curves and intervals that are not affected by sliding. In this work, an interval was selected and within this interval twelve litho zones were defined using the unsupervised neural network. From this a training set was selected based on intervals of very good log responses outside the sliding zones. This training set was used to train and run the neural network for a specific lithostratigraphic interval. The results matched the known good density curve. Then after this, an estimation of the density curve was done using the supervised neural network. The output from this estimation matched very closely in the good portions of the log, thus providing some density measurements in the sliding zone. This methodology provides a scientific solution to missing data during the process of Formation evaluation

Uncertainties in estimating heart doses from 2D-tangential breast cancer radiotherapy

DEFF Research Database (Denmark)

Laugaard Lorenzen, Ebbe; Brink, Carsten; Taylor, Carolyn W.

2016-01-01

BACKGROUND AND PURPOSE: We evaluated the accuracy of three methods of estimating radiation dose to the heart from two-dimensional tangential radiotherapy for breast cancer, as used in Denmark during 1982-2002. MATERIAL AND METHODS: Three tangential radiotherapy regimens were reconstructed using CT......-based planning scans for 40 patients with left-sided and 10 with right-sided breast cancer. Setup errors and organ motion were simulated using estimated uncertainties. For left-sided patients, mean heart dose was related to maximum heart distance in the medial field. RESULTS: For left-sided breast cancer, mean...... to the uncertainty of estimates based on individual CT-scans. For right-sided breast cancer patients, mean heart dose based on individual CT-scans was always

Histogram Estimators of Bivariate Densities

National Research Council Canada - National Science Library

Husemann, Joyce A

1986-01-01

One-dimensional fixed-interval histogram estimators of univariate probability density functions are less efficient than the analogous variable-interval estimators which are constructed from intervals...

Estimating the Risks of Breast Cancer Radiotherapy

DEFF Research Database (Denmark)

Taylor, Carolyn; Correa, Candace; Duane, Frances K

2017-01-01

Purpose Radiotherapy reduces the absolute risk of breast cancer mortality by a few percentage points in suitable women but can cause a second cancer or heart disease decades later. We estimated the absolute long-term risks of modern breast cancer radiotherapy. Methods First, a systematic literature...... review was performed of lung and heart doses in breast cancer regimens published during 2010 to 2015. Second, individual patient data meta-analyses of 40,781 women randomly assigned to breast cancer radiotherapy versus no radiotherapy in 75 trials yielded rate ratios (RRs) for second primary cancers...... and cause-specific mortality and excess RRs (ERRs) per Gy for incident lung cancer and cardiac mortality. Smoking status was unavailable. Third, the lung or heart ERRs per Gy in the trials and the 2010 to 2015 doses were combined and applied to current smoker and nonsmoker lung cancer and cardiac mortality...

A New Model for the Estimation of Breast Cancer Risk

National Research Council Canada - National Science Library

Giger, Maryellen Lissak

2001-01-01

... for use in estimating risk of breast cancer. The specific aims include 1. Creating a database of mammograms, along with tabulated clinical information of women at low risk and high risk for breast cancer; 2...

The inter-observer variability of breast density scoring between mammography technologists and breast radiologists and its effect on the rate of adjuvant ultrasound.

Science.gov (United States)

Mazor, Roei D; Savir, Avital; Gheorghiu, David; Weinstein, Yuliana; Abadi-Korek, Ifat; Shabshin, Nogah

2016-05-01

This study assesses the inter-observer variability of mammographic breast density scoring (BDS) between technologists and radiologists and evaluates the effect of technologist patient referral on the load of adjuvant ultrasounds. In this IRB approved study, a retrospective analysis of 503 prospectively acquired, random mammograms was performed between January and March 2014. Each mammogram was evaluated for BDS independently and blindly by both the performing technologist and the interpreting radiologist. Statistical calculation of the Spearman correlation coefficient and weighted kappa were obtained to evaluate the inter-observer variability between technologists and radiologists and to examine whether it relates to the technologist's seniority or women's age. The effect on the load of adjuvant ultrasounds was evaluated. 10 mammography technologists and 7 breast radiologists participated in this study. BDS agreement levels between technologists and radiologists were in the fair to moderate range (kappa values: 0.3-0.45, Spearman coefficient values: 0.59-0.65). The technologists markedly over-graded the density compared to the radiologists in all the subsets evaluated. Comparison between low and high-density groups demonstrated a similar trend of over-grading by technologists, who graded 51% of the women as having dense breasts (scores 3-4) compared to 27% of the women graded as such by the radiologists. This trend of over grading breast density by technologists was unrelated to the women's age or to the technologists' seniority. Mammography technologists over-grade breast density. Technologists' referral to an adjuvant ultrasound leads to redundant ultrasound studies, unnecessary breast biopsies, costs and increased patient anxiety. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Variable Kernel Density Estimation

OpenAIRE

Terrell, George R.; Scott, David W.

1992-01-01

We investigate some of the possibilities for improvement of univariate and multivariate kernel density estimates by varying the window over the domain of estimation, pointwise and globally. Two general approaches are to vary the window width by the point of estimation and by point of the sample observation. The first possibility is shown to be of little efficacy in one variable. In particular, nearest-neighbor estimators in all versions perform poorly in one and two dimensions, but begin to b...

Should breast MRI be performed with adjustment for the phase in patients’ menstrual cycle? Correlation between mammographic density, age, and background enhancement on breast MRI without adjusting for the phase in patients’ menstrual cycle

International Nuclear Information System (INIS)

Uematsu, Takayoshi; Kasami, Masako; Watanabe, Junichiro

2012-01-01

Purpose: The purpose of this study was to assess the correlation between mammographic density, age, and background enhancement on breast MRI without adjusting for the phase in patients’ menstrual cycle. Material and methods: The background enhancement of bilateral breast MRI and the breast density of mammography in 146 consecutive women without adjusting for the phase in patients’ menstrual cycle were reviewed. The breast density was classified into four categories according to the American College of Radiology the Breast Imaging Reporting and Data System lexicon. The background enhancement was classified into four categories: minimal, mild, moderate, and marked. The correlations of mammographic breast density as well as age with background enhancement on breast MRI were examined. Results: There was a significant correlation between mammographic breast density and background enhancement (p = 0.011). All nine cases with almost completely fat mammographic breast density showed minimal (78%) or mild (12%) background enhancement on breast MRI. There was a significant inverse correlation between age and background enhancement (p < 0.0001). Younger patients with dense breasts were more likely to demonstrate moderate/marked background enhancement. Conclusion: When no adjusting for the phase in patients’ menstrual cycle, a significant correlation was observed between background enhancement and mammographic density. A significant inverse correlation was also observed between age and background enhancement.

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

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Zheng, Bin, E-mail: zhengb@upmc.edu [Department of Radiology, University of Pittsburgh, 3362 Fifth Ave, Pittsburgh, PA 15213 (United States); Sumkin, Jules H., E-mail: jsumkin@mail.magee.edu [Department of Radiology, Magee Womens Hospital, 300 Halket Street, Pittsburgh, PA 15213 (United States); Zuley, Margarita L., E-mail: zuleyml@upmc.edu [Department of Radiology, Magee Womens Hospital, 300 Halket Street, Pittsburgh, PA 15213 (United States); Wang, Xingwei, E-mail: wangx6@upmc.edu [Department of Radiology, University of Pittsburgh, 3362 Fifth Ave, Pittsburgh, PA 15213 (United States); Klym, Amy H., E-mail: klymah@upmc.edu [Department of Radiology, University of Pittsburgh, 3362 Fifth Ave, Pittsburgh, PA 15213 (United States); Gur, David, E-mail: gurd@upmc.edu [Department of Radiology, University of Pittsburgh, 3362 Fifth Ave, Pittsburgh, PA 15213 (United States)

2012-11-15

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

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

International Nuclear Information System (INIS)

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

2012-01-01

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

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

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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 (quantitative BE than postmenopausal women. No association was found between BE and breast density.

Breast density as indicator for the use of mammography or MRI to screen women with familial risk for breast cancer (FaMRIsc: a multicentre randomized controlled trial

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Saadatmand Sepideh

2012-10-01

Full Text Available Abstract Background To reduce mortality, women with a family history of breast cancer often start mammography screening at a younger age than the general population. Breast density is high in over 50% of women younger than 50 years. With high breast density, breast cancer incidence increases, but sensitivity of mammography decreases. Therefore, mammography might not be the optimal method for breast cancer screening in young women. Adding MRI increases sensitivity, but also the risk of false-positive results. The limitation of all previous MRI screening studies is that they do not contain a comparison group; all participants received both MRI and mammography. Therefore, we cannot empirically assess in which stage tumours would have been detected by either test. The aim of the Familial MRI Screening Study (FaMRIsc is to compare the efficacy of MRI screening to mammography for women with a familial risk. Furthermore, we will assess the influence of breast density. Methods/Design This Dutch multicentre, randomized controlled trial, with balanced randomisation (1:1 has a parallel grouped design. Women with a cumulative lifetime risk for breast cancer due to their family history of ≥20%, aged 30–55 years are eligible. Identified BRCA1/2 mutation carriers or women with 50% risk of carrying a mutation are excluded. Group 1 receives yearly mammography and clinical breast examination (n = 1000, and group 2 yearly MRI and clinical breast examination, and mammography biennially (n = 1000. Primary endpoints are the number and stage of the detected breast cancers in each arm. Secondary endpoints are the number of false-positive results in both screening arms. Furthermore, sensitivity and positive predictive value of both screening strategies will be assessed. Cost-effectiveness of both strategies will be assessed. Analyses will also be performed with mammographic density as stratification factor. Discussion Personalized breast cancer screening

Radiomic modeling of BI-RADS density categories

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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.

Comparative evaluation of average glandular dose and breast cancer detection between single-view digital breast tomosynthesis (DBT) plus single-view digital mammography (DM) and two-view DM: correlation with breast thickness and density

International Nuclear Information System (INIS)

Shin, Sung Ui; Chang, Jung Min; Bae, Min Sun; Lee, Su Hyun; Cho, Nariya; Seo, Mirinae; Kim, Won Hwa; Moon, Woo Kyung

2015-01-01

To compare the average glandular dose (AGD) and diagnostic performance of mediolateral oblique (MLO) digital breast tomosynthesis (DBT) plus cranio-caudal (CC) digital mammography (DM) with two-view DM, and to evaluate the correlation of AGD with breast thickness and density. MLO and CC DM and DBT images of both breasts were obtained in 149 subjects. AGDs of DBT and DM per exposure were recorded, and their correlation with breast thickness and density were evaluated. Paired data of MLO DBT plus CC DM and two-view DM were reviewed for presence of malignancy in a jack-knife alternative free-response ROC (JAFROC) method. The AGDs of both DBT and DM, and differences in AGD between DBT and DM (ΔAGD), were correlated with breast thickness and density. The average JAFROC figure of merit (FOM) was significantly higher on the combined technique than two-view DM (P = 0.005). In dense breasts, the FOM and sensitivity of the combined technique was higher than that of two-view DM (P = 0.003) with small ΔAGD. MLO DBT plus CC DM provided higher diagnostic performance than two-view DM in dense breasts with a small increase in AGD. (orig.)

Comparative evaluation of average glandular dose and breast cancer detection between single-view digital breast tomosynthesis (DBT) plus single-view digital mammography (DM) and two-view DM: correlation with breast thickness and density

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Shin, Sung Ui; Chang, Jung Min; Bae, Min Sun; Lee, Su Hyun; Cho, Nariya; Seo, Mirinae; Kim, Won Hwa; Moon, Woo Kyung [Seoul National University Hospital, Department of Radiology, Seoul (Korea, Republic of)

2015-01-15

To compare the average glandular dose (AGD) and diagnostic performance of mediolateral oblique (MLO) digital breast tomosynthesis (DBT) plus cranio-caudal (CC) digital mammography (DM) with two-view DM, and to evaluate the correlation of AGD with breast thickness and density. MLO and CC DM and DBT images of both breasts were obtained in 149 subjects. AGDs of DBT and DM per exposure were recorded, and their correlation with breast thickness and density were evaluated. Paired data of MLO DBT plus CC DM and two-view DM were reviewed for presence of malignancy in a jack-knife alternative free-response ROC (JAFROC) method. The AGDs of both DBT and DM, and differences in AGD between DBT and DM (ΔAGD), were correlated with breast thickness and density. The average JAFROC figure of merit (FOM) was significantly higher on the combined technique than two-view DM (P = 0.005). In dense breasts, the FOM and sensitivity of the combined technique was higher than that of two-view DM (P = 0.003) with small ΔAGD. MLO DBT plus CC DM provided higher diagnostic performance than two-view DM in dense breasts with a small increase in AGD. (orig.)

Bone Mass Density and Risk of Breast Cancer and Survival in Older Women

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Ganry, O.; Baudoin, C.; Fardellone, P.; Peng, J.; Raverdy, N.

2004-01-01

Study objective: Older women with high bone mineral density (BMD) have an increased risk of breast cancer but it is not well known whether this association is associated with the stage of the tumor. The objective of the study is to determine if older women with high BMD are likely to develop a more aggressive form of breast cancer, as defined by mortality. Patients: We prospectively studied 1504 women who were 75 years of age or older at the entry in the study (range, 75-90 years), between 1992 and 1994. BMD was measured by dual-photon X-ray absorptiometry at three skeletal sites (trochanter, Ward's triangle, femoral neck). The women were followed for a mean of 7 years for the occurrence of breast cancer. Cox proportional-hazards models were used to obtain estimates of the relative risk of breast cancer and relative risk of death according to the BMD. Main results: Forty-five incident breast cancer cases were identified. In multivariate analyses of the risk of breast cancer for women in the highest tertile of BMD was greater than for women in the lowest tertile. Indeed, the women with a trochanter BMD in the highest tertile were at 2.3-fold increased risk compared with women in the lowest tertile. The women with highest tertile BMD measured at the Ward's triangle and at the femoral neck were respectively at 2.2-and 3.3-fold increased risk compared with women at the lowest risk. The 7-year survival rates were markedly less favorable for women in the second and third tertile of the three skeletal sites compared with the lowest tertile. The risk of death was greater for women in the highest tertile of BMD than for women in the lowest tertile at every skeletal site. Conclusion: Elderly women with high BMD have an increased risk of breast cancer, especially advanced cancer, compared with women with low BMD

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

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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.

Breast tissue stiffness estimation for surgical guidance using gravity-induced excitation.

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Griesenauer, Rebekah H; Weis, Jared A; Arlinghaus, Lori R; Meszoely, Ingrid M; Miga, Michael I

2017-06-21

Tissue stiffness interrogation is fundamental in breast cancer diagnosis and treatment. Furthermore, biomechanical models for predicting breast deformations have been created for several breast cancer applications. Within these applications, constitutive mechanical properties must be defined and the accuracy of this estimation directly impacts the overall performance of the model. In this study, we present an image-derived computational framework to obtain quantitative, patient specific stiffness properties for application in image-guided breast cancer surgery and interventions. The method uses two MR acquisitions of the breast in different supine gravity-loaded configurations to fit mechanical properties to a biomechanical breast model. A reproducibility assessment of the method was performed in a test-retest study using healthy volunteers and was further characterized in simulation. In five human data sets, the within subject coefficient of variation ranged from 10.7% to 27% and the intraclass correlation coefficient ranged from 0.91-0.944 for assessment of fibroglandular and adipose tissue stiffness. In simulation, fibroglandular content and deformation magnitude were shown to have significant effects on the shape and convexity of the objective function defined by image similarity. These observations provide an important step forward in characterizing the use of nonrigid image registration methodologies in conjunction with biomechanical models to estimate tissue stiffness. In addition, the results suggest that stiffness estimation methods using gravity-induced excitation can reliably and feasibly be implemented in breast cancer surgery/intervention workflows.

Breast tissue stiffness estimation for surgical guidance using gravity-induced excitation

Science.gov (United States)

Griesenauer, Rebekah H.; Weis, Jared A.; Arlinghaus, Lori R.; Meszoely, Ingrid M.; Miga, Michael I.

2017-06-01

Tissue stiffness interrogation is fundamental in breast cancer diagnosis and treatment. Furthermore, biomechanical models for predicting breast deformations have been created for several breast cancer applications. Within these applications, constitutive mechanical properties must be defined and the accuracy of this estimation directly impacts the overall performance of the model. In this study, we present an image-derived computational framework to obtain quantitative, patient specific stiffness properties for application in image-guided breast cancer surgery and interventions. The method uses two MR acquisitions of the breast in different supine gravity-loaded configurations to fit mechanical properties to a biomechanical breast model. A reproducibility assessment of the method was performed in a test-retest study using healthy volunteers and was further characterized in simulation. In five human data sets, the within subject coefficient of variation ranged from 10.7% to 27% and the intraclass correlation coefficient ranged from 0.91-0.944 for assessment of fibroglandular and adipose tissue stiffness. In simulation, fibroglandular content and deformation magnitude were shown to have significant effects on the shape and convexity of the objective function defined by image similarity. These observations provide an important step forward in characterizing the use of nonrigid image registration methodologies in conjunction with biomechanical models to estimate tissue stiffness. In addition, the results suggest that stiffness estimation methods using gravity-induced excitation can reliably and feasibly be implemented in breast cancer surgery/intervention workflows.

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

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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.

High throughput nonparametric probability density estimation.

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Farmer, Jenny; Jacobs, Donald

2018-01-01

In high throughput applications, such as those found in bioinformatics and finance, it is important to determine accurate probability distribution functions despite only minimal information about data characteristics, and without using human subjectivity. Such an automated process for univariate data is implemented to achieve this goal by merging the maximum entropy method with single order statistics and maximum likelihood. The only required properties of the random variables are that they are continuous and that they are, or can be approximated as, independent and identically distributed. A quasi-log-likelihood function based on single order statistics for sampled uniform random data is used to empirically construct a sample size invariant universal scoring function. Then a probability density estimate is determined by iteratively improving trial cumulative distribution functions, where better estimates are quantified by the scoring function that identifies atypical fluctuations. This criterion resists under and over fitting data as an alternative to employing the Bayesian or Akaike information criterion. Multiple estimates for the probability density reflect uncertainties due to statistical fluctuations in random samples. Scaled quantile residual plots are also introduced as an effective diagnostic to visualize the quality of the estimated probability densities. Benchmark tests show that estimates for the probability density function (PDF) converge to the true PDF as sample size increases on particularly difficult test probability densities that include cases with discontinuities, multi-resolution scales, heavy tails, and singularities. These results indicate the method has general applicability for high throughput statistical inference.

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

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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.

Toward accurate and precise estimates of lion density.

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Elliot, Nicholas B; Gopalaswamy, Arjun M

2017-08-01

Reliable estimates of animal density are fundamental to understanding ecological processes and population dynamics. Furthermore, their accuracy is vital to conservation because wildlife authorities rely on estimates to make decisions. However, it is notoriously difficult to accurately estimate density for wide-ranging carnivores that occur at low densities. In recent years, significant progress has been made in density estimation of Asian carnivores, but the methods have not been widely adapted to African carnivores, such as lions (Panthera leo). Although abundance indices for lions may produce poor inferences, they continue to be used to estimate density and inform management and policy. We used sighting data from a 3-month survey and adapted a Bayesian spatially explicit capture-recapture (SECR) model to estimate spatial lion density in the Maasai Mara National Reserve and surrounding conservancies in Kenya. Our unstructured spatial capture-recapture sampling design incorporated search effort to explicitly estimate detection probability and density on a fine spatial scale, making our approach robust in the context of varying detection probabilities. Overall posterior mean lion density was estimated to be 17.08 (posterior SD 1.310) lions >1 year old/100 km 2 , and the sex ratio was estimated at 2.2 females to 1 male. Our modeling framework and narrow posterior SD demonstrate that SECR methods can produce statistically rigorous and precise estimates of population parameters, and we argue that they should be favored over less reliable abundance indices. Furthermore, our approach is flexible enough to incorporate different data types, which enables robust population estimates over relatively short survey periods in a variety of systems. Trend analyses are essential to guide conservation decisions but are frequently based on surveys of differing reliability. We therefore call for a unified framework to assess lion numbers in key populations to improve management and

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

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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 density categories was k w  = 0.5 (95% CI = 0.47-0.53; P density increased with increasing density category of the subjective classifications. The agreement between BI-RADS and volumetric breast density categories was moderate.

Validation of a method for measuring the volumetric breast density from digital mammograms

International Nuclear Information System (INIS)

Alonzo-Proulx, O; Shen, S Z; Yaffe, M J; Packard, N; Boone, J M; Al-Mayah, A; Brock, K K

2010-01-01

The purpose of this study was to evaluate the performance of an algorithm used to measure the volumetric breast density (VBD) from digital mammograms. The algorithm is based on the calibration of the detector signal versus the thickness and composition of breast-equivalent phantoms. The baseline error in the density from the algorithm was found to be 1.25 ± 2.3% VBD units (PVBD) when tested against a set of calibration phantoms, of thicknesses 3-8 cm, with compositions equivalent to fibroglandular content (breast density) between 0% and 100% and under x-ray beams between 26 kVp and 32 kVp with a Rh/Rh anode/filter. The algorithm was also tested against images from a dedicated breast computed tomography (CT) scanner acquired on 26 volunteers. The CT images were segmented into regions representing adipose, fibroglandular and skin tissues, and then deformed using a finite-element algorithm to simulate the effects of compression in mammography. The mean volume, VBD and thickness of the compressed breast for these deformed images were respectively 558 cm 3 , 23.6% and 62 mm. The displaced CT images were then used to generate simulated digital mammograms, considering the effects of the polychromatic x-ray spectrum, the primary and scattered energy transmitted through the breast, the anti-scatter grid and the detector efficiency. The simulated mammograms were analyzed with the VBD algorithm and compared with the deformed CT volumes. With the Rh/Rh anode filter, the root mean square difference between the VBD from CT and from the algorithm was 2.6 PVBD, and a linear regression between the two gave a slope of 0.992 with an intercept of -1.4 PVBD and a correlation with R 2 = 0.963. The results with the Mo/Mo and Mo/Rh anode/filter were similar.

Validation of a method for measuring the volumetric breast density from digital mammograms

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Alonzo-Proulx, O; Shen, S Z; Yaffe, M J [Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario M4N 3M5 (Canada); Packard, N; Boone, J M [UC Davis Medical Center, University of California-Davis, Sacramento, CA 95817 (United States); Al-Mayah, A; Brock, K K, E-mail: oliviera@sri.utoronto.c [University Health Network, University of Toronto, Toronto, Ontario M5G 2M9 (Canada)

2010-06-07

The purpose of this study was to evaluate the performance of an algorithm used to measure the volumetric breast density (VBD) from digital mammograms. The algorithm is based on the calibration of the detector signal versus the thickness and composition of breast-equivalent phantoms. The baseline error in the density from the algorithm was found to be 1.25 {+-} 2.3% VBD units (PVBD) when tested against a set of calibration phantoms, of thicknesses 3-8 cm, with compositions equivalent to fibroglandular content (breast density) between 0% and 100% and under x-ray beams between 26 kVp and 32 kVp with a Rh/Rh anode/filter. The algorithm was also tested against images from a dedicated breast computed tomography (CT) scanner acquired on 26 volunteers. The CT images were segmented into regions representing adipose, fibroglandular and skin tissues, and then deformed using a finite-element algorithm to simulate the effects of compression in mammography. The mean volume, VBD and thickness of the compressed breast for these deformed images were respectively 558 cm{sup 3}, 23.6% and 62 mm. The displaced CT images were then used to generate simulated digital mammograms, considering the effects of the polychromatic x-ray spectrum, the primary and scattered energy transmitted through the breast, the anti-scatter grid and the detector efficiency. The simulated mammograms were analyzed with the VBD algorithm and compared with the deformed CT volumes. With the Rh/Rh anode filter, the root mean square difference between the VBD from CT and from the algorithm was 2.6 PVBD, and a linear regression between the two gave a slope of 0.992 with an intercept of -1.4 PVBD and a correlation with R{sup 2} = 0.963. The results with the Mo/Mo and Mo/Rh anode/filter were similar.

Mammographic density and risk of breast cancer by tumor characteristics: a case-control study.

Science.gov (United States)

Krishnan, Kavitha; Baglietto, Laura; Stone, Jennifer; McLean, Catriona; Southey, Melissa C; English, Dallas R; Giles, Graham G; Hopper, John L

2017-12-16

In a previous paper, we had assumed that the risk of screen-detected breast cancer mostly reflects inherent risk, and the risk of whether a breast cancer is interval versus screen-detected mostly reflects risk of masking. We found that inherent risk was predicted by body mass index (BMI) and dense area (DA) or percent dense area (PDA), but not by non-dense area (NDA). Masking, however, was best predicted by PDA but not BMI. In this study, we aimed to investigate if these associations vary by tumor characteristics and mode of detection. We conducted a case-control study nested within the Melbourne Collaborative Cohort Study of 244 screen-detected cases matched to 700 controls and 148 interval cases matched to 446 controls. DA, NDA and PDA were measured using the Cumulus software. Tumor characteristics included size, grade, lymph node involvement, and ER, PR, and HER2 status. Conditional and unconditional logistic regression were applied as appropriate to estimate the Odds per Adjusted Standard Deviation (OPERA) adjusted for age and BMI, allowing the association with BMI to be a function of age at diagnosis. For screen-detected cancer, both DA and PDA were associated to an increased risk of tumors of large size (OPERA ~ 1.6) and positive lymph node involvement (OPERA ~ 1.8); no association was observed for BMI and NDA. For risk of interval versus screen-detected breast cancer, the association with risk for any of the three mammographic measures did not vary by tumor characteristics; an association was observed for BMI for positive lymph nodes (OPERA ~ 0.6). No associations were observed for tumor grade and ER, PR and HER2 status of tumor. Both DA and PDA were predictors of inherent risk of larger breast tumors and positive nodal status, whereas for each of the three mammographic density measures the association with risk of masking did not vary by tumor characteristics. This might raise the hypothesis that the risk of breast tumours with poorer prognosis

Computer-aided-detection marker value and breast density in the detection of invasive lobular carcinoma

International Nuclear Information System (INIS)

Destounis, Stamatia; Hanson, Sarah; Roehrig, Jimmy

2007-01-01

Invasive Lobular Carcinoma (ILC) is frequently a mammographic and diagnostic dilemma; thus any additional information that CAD (Computer-Aided Detection) systems can give radiologists may be helpful. Our study was to evaluate the role of CAD numeric values as indicators of malignancy and the effect of breast density in the diagnosis of ILC. Eighty consecutive biopsy-proven ILC cases with CAD (ImageChecker registered , Hologic vertical stroke R2, Santa Clara, CA, versions 2.3, 3.1, 3.2, 5.0, 5.2) diagnosed between June 2002 and December 2004 were retrospectively reviewed. Data included: BIRADS registered breast density, whether CAD marked the cancer at diagnosis year or years prior, and lesion type. Study mammograms underwent additional CAD scans (Image Checker registered V5.3, V8.0, V8.1) to obtain a numeric value associated with each marker, low values represent increasingly suspicious features. CAD correctly marked 65% (52/80) of ILC cases, detection was found to decrease with increased breast density. Numeric values of CAD marks at sites of carcinoma showed median score of 171 (range 0 - 1121). The CAD marker may potentially be used as an additional indicator of suspicious lesion features in all breast densities and higher likelihood that an area on the mammogram requires further investigation. (orig.)

Fertility drug use and mammographic breast density in a mammography screening cohort of premenopausal women

OpenAIRE

Sprague, Brian L.; Trentham-Dietz, Amy; Terry, Mary Beth; Nichols, Hazel B.; Bersch, Andy J.; Buist, Diana S. M.

2008-01-01

The widespread use of ovulation-inducing drugs to enhance fertility has raised concerns regarding potential effects on breast cancer risk, as ovarian stimulation is associated with increases in estrogen and progesterone levels. We investigated the short-term relation between fertility drug use and mammographic breast density, a strong marker of breast cancer risk, among participants in the Group Health Breast Cancer Screening Program. Data linkage with Group Health’s automated pharmacy record...

Interaction of mammographic breast density with menopausal status and postmenopausal hormone use in relation to the risk of aggressive breast cancer subtypes.

Science.gov (United States)

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

Automated Volumetric Mammographic Breast Density Measurements May Underestimate Percent Breast Density for High-density Breasts

NARCIS (Netherlands)

Rahbar, K.; Gubern Merida, A.; Patrie, J.T.; Harvey, J.A.

2017-01-01

RATIONALE AND OBJECTIVES: The purpose of this study was to evaluate discrepancy in breast composition measurements obtained from mammograms using two commercially available software methods for systematic trends in overestimation or underestimation compared to magnetic resonance-derived

Comparison Between Digital and Synthetic 2D Mammograms in Breast Density Interpretation.

Science.gov (United States)

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.

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

Science.gov (United States)

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 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.

A study of breast composition using radiographic techniques

International Nuclear Information System (INIS)

Noriah Jamal

2005-01-01

Accurate knowledge of breast composition is fundamental to the diagnosis and treatment of breast diseases. The goal of the research described in this thesis is to develop a system of experimental methods and theoretical models for studying breast composition using radiographic techniques. This research is divided into three parts. In part one, a technique based on the decomposition of breast components was developed and validated under laboratory conditions through the measurement of mastectomy specimen composition. The technique used double-exposure dual energy subtraction microradiography in conjunction with computed radiography in a five steps sequence, namely; a specimen was exposed at low and high energies alternately, followed by image registration, image subtraction and subsequent analysis of the subtracted image. The technique was then applied to quantify composition of mastectomy specimens. In part two, a quantitative assessment technique of breast density developed using MATLAB based image processing of digital mammograms. A comparison of breast density, classified by computer and subjective evaluation based on Tabar's patterns, was carried out. Inter-observer variations were analysed. In part three, a technique to estimate breast glandularity using phantom attenuation was developed and was used to estimate breast density for a large population sample. The difference in breast glandularity between three ethnic groups was tested for significance using the nonparametric Kruskal-Wallis test. This part also included an assessment of mammographic mean glandular dose using phantom-based and patient-based methods. Results from part 1 showed that in the double-exposure dual energy microradiography, the resulting fibroglandular tissue area was in 90.3 % agreement with the corresponding manually delineated areas by radiologists. In part 2 the results of computerised assessment of breast density from digitised mammograms correlated well (r 2 =0.92) with the

Computer-aided-detection marker value and breast density in the detection of invasive lobular carcinoma

Energy Technology Data Exchange (ETDEWEB)

Destounis, Stamatia; Hanson, Sarah [The Elizabeth Wende Breast Clinic, Rochester, NY (United States); Roehrig, Jimmy [R2/Hologic, Inc., Santa Clara, CA (United States)

2007-08-15

Invasive Lobular Carcinoma (ILC) is frequently a mammographic and diagnostic dilemma; thus any additional information that CAD (Computer-Aided Detection) systems can give radiologists may be helpful. Our study was to evaluate the role of CAD numeric values as indicators of malignancy and the effect of breast density in the diagnosis of ILC. Eighty consecutive biopsy-proven ILC cases with CAD (ImageChecker {sup registered}, Hologic vertical stroke R2, Santa Clara, CA, versions 2.3, 3.1, 3.2, 5.0, 5.2) diagnosed between June 2002 and December 2004 were retrospectively reviewed. Data included: BIRADS {sup registered} breast density, whether CAD marked the cancer at diagnosis year or years prior, and lesion type. Study mammograms underwent additional CAD scans (Image Checker {sup registered} V5.3, V8.0, V8.1) to obtain a numeric value associated with each marker, low values represent increasingly suspicious features. CAD correctly marked 65% (52/80) of ILC cases, detection was found to decrease with increased breast density. Numeric values of CAD marks at sites of carcinoma showed median score of 171 (range 0 - 1121). The CAD marker may potentially be used as an additional indicator of suspicious lesion features in all breast densities and higher likelihood that an area on the mammogram requires further investigation. (orig.)

Change-in-ratio density estimator for feral pigs is less biased than closed mark-recapture estimates

Science.gov (United States)

Hanson, L.B.; Grand, J.B.; Mitchell, M.S.; Jolley, D.B.; Sparklin, B.D.; Ditchkoff, S.S.

2008-01-01

Closed-population capture-mark-recapture (CMR) methods can produce biased density estimates for species with low or heterogeneous detection probabilities. In an attempt to address such biases, we developed a density-estimation method based on the change in ratio (CIR) of survival between two populations where survival, calculated using an open-population CMR model, is known to differ. We used our method to estimate density for a feral pig (Sus scrofa) population on Fort Benning, Georgia, USA. To assess its validity, we compared it to an estimate of the minimum density of pigs known to be alive and two estimates based on closed-population CMR models. Comparison of the density estimates revealed that the CIR estimator produced a density estimate with low precision that was reasonable with respect to minimum known density. By contrast, density point estimates using the closed-population CMR models were less than the minimum known density, consistent with biases created by low and heterogeneous capture probabilities for species like feral pigs that may occur in low density or are difficult to capture. Our CIR density estimator may be useful for tracking broad-scale, long-term changes in species, such as large cats, for which closed CMR models are unlikely to work. ?? CSIRO 2008.

Optimization of Barron density estimates

Czech Academy of Sciences Publication Activity Database

Vajda, Igor; van der Meulen, E. C.

2001-01-01

Roč. 47, č. 5 (2001), s. 1867-1883 ISSN 0018-9448 R&D Projects: GA ČR GA102/99/1137 Grant - others:Copernicus(XE) 579 Institutional research plan: AV0Z1075907 Keywords : Barron estimator * chi-square criterion * density estimation Subject RIV: BD - Theory of Information Impact factor: 2.077, year: 2001

Volumetric breast density measurement: sensitivity analysis of a relative physics approach.

Science.gov (United States)

Lau, Susie; Ng, Kwan Hoong; Abdul Aziz, Yang Faridah

2016-10-01

To investigate the sensitivity and robustness of a volumetric breast density (VBD) measurement system to errors in the imaging physics parameters including compressed breast thickness (CBT), tube voltage (kVp), filter thickness, tube current-exposure time product (mAs), detector gain, detector offset and image noise. 3317 raw digital mammograms were processed with Volpara(®) (Matakina Technology Ltd, Wellington, New Zealand) to obtain fibroglandular tissue volume (FGV), breast volume (BV) and VBD. Errors in parameters including CBT, kVp, filter thickness and mAs were simulated by varying them in the Digital Imaging and Communications in Medicine (DICOM) tags of the images up to ±10% of the original values. Errors in detector gain and offset were simulated by varying them in the Volpara configuration file up to ±10% from their default values. For image noise, Gaussian noise was generated and introduced into the original images. Errors in filter thickness, mAs, detector gain and offset had limited effects on FGV, BV and VBD. Significant effects in VBD were observed when CBT, kVp, detector offset and image noise were varied (p applications such as tracking density change over time, it remains to be seen how accurate the measures need to be.

Breast density in women with premature ovarian failure or postmenopausal women using hormone therapy: analytical cross-sectional study

Directory of Open Access Journals (Sweden)

Patrícia Magda Soares

Full Text Available CONTEXT AND OBJECTIVE: Studies on postmenopausal women have reported increased risk of breast cancer relating to the type and duration of hormone therapy (HT used. Women with premature ovarian failure (POF represent a challenge, since they require prolonged HT. Little is known about the impact of prolonged HT use on these women's breasts. This study aimed to evaluate the effects of one type of HT on the breast density of women with POF, compared with postmenopausal women. DESIGN AND SETTING: Cross-sectional study at the Department of Obstetrics and Gynecology, Universidade Estadual de Campinas (Unicamp. METHODS: 31 women with POF and 31 postmenopausal women, all using HT consisting of conjugated equine estrogen combined with medroxyprogesterone acetate, and matched according to HT duration, were studied. Mammography was performed on all subjects and was analyzed by means of digitization or Wolfe's classification, stratified into two categories: non-dense (N1 and P1 patterns and dense (P2 and Dy. RESULTS: No significant difference in breast density was found between the two groups through digitization or Wolfe's classification. From digitization, the mean breast density was 24.1% ± 14.6 and 18.1% ± 17.2 in the POF and postmenopausal groups, respectively (P = 0.15. Wolfe's classification identified dense breasts in 51.6% and 29.0%, respectively (P = 0.171. CONCLUSION: There was no difference in breast density between the women with POF and postmenopausal women, who had used HT for the same length of time. These results may help towards compliance with HT use among women with POF.

Optimal Bandwidth Selection for Kernel Density Functionals Estimation

Directory of Open Access Journals (Sweden)

Su Chen

2015-01-01

Full Text Available The choice of bandwidth is crucial to the kernel density estimation (KDE and kernel based regression. Various bandwidth selection methods for KDE and local least square regression have been developed in the past decade. It has been known that scale and location parameters are proportional to density functionals ∫γ(xf2(xdx with appropriate choice of γ(x and furthermore equality of scale and location tests can be transformed to comparisons of the density functionals among populations. ∫γ(xf2(xdx can be estimated nonparametrically via kernel density functionals estimation (KDFE. However, the optimal bandwidth selection for KDFE of ∫γ(xf2(xdx has not been examined. We propose a method to select the optimal bandwidth for the KDFE. The idea underlying this method is to search for the optimal bandwidth by minimizing the mean square error (MSE of the KDFE. Two main practical bandwidth selection techniques for the KDFE of ∫γ(xf2(xdx are provided: Normal scale bandwidth selection (namely, “Rule of Thumb” and direct plug-in bandwidth selection. Simulation studies display that our proposed bandwidth selection methods are superior to existing density estimation bandwidth selection methods in estimating density functionals.

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

Science.gov (United States)

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.

Kernel bandwidth estimation for non-parametric density estimation: a comparative study

CSIR Research Space (South Africa)

Van der Walt, CM

2013-12-01

Full Text Available We investigate the performance of conventional bandwidth estimators for non-parametric kernel density estimation on a number of representative pattern-recognition tasks, to gain a better understanding of the behaviour of these estimators in high...

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

Science.gov (United States)

Domingo, Laia; Salas, Dolores; Zubizarreta, Raquel; Baré, Marisa; Sarriugarte, Garbiñe; Barata, Teresa; Ibáñez, Josefa; Blanch, Jordi; Puig-Vives, Montserrat; Fernández, Ana; Castells, Xavier; Sala, Maria

2014-01-10

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. 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: 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. 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 (breasts (28.7%, 21.4%, 11.3% and 14.3%, respectively; cancers, extreme breast density being strongly associated with occult tumors (OR�

A comprehensive tool for measuring mammographic density changes over time.

Science.gov (United States)

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 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.

Deep convolutional neural network for mammographic density segmentation

Science.gov (United States)

Wei, Jun; Li, Songfeng; Chan, Heang-Ping; Helvie, Mark A.; Roubidoux, Marilyn A.; Lu, Yao; Zhou, Chuan; Hadjiiski, Lubomir; Samala, Ravi K.

2018-02-01

Breast density is one of the most significant factors for cancer risk. In this study, we proposed a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammography (DM). The deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD). PD was calculated as the ratio of the dense area to the breast area based on the probability of each pixel belonging to dense region or fatty region at a decision threshold of 0.5. The DCNN estimate was compared to a feature-based statistical learning approach, in which gray level, texture and morphological features were extracted from each ROI and the least absolute shrinkage and selection operator (LASSO) was used to select and combine the useful features to generate the PMD. The reference PD of each image was provided by two experienced MQSA radiologists. With IRB approval, we retrospectively collected 347 DMs from patient files at our institution. The 10-fold cross-validation results showed a strong correlation r=0.96 between the DCNN estimation and interactive segmentation by radiologists while that of the feature-based statistical learning approach vs radiologists' segmentation had a correlation r=0.78. The difference between the segmentation by DCNN and by radiologists was significantly smaller than that between the feature-based learning approach and radiologists (p approach has the potential to replace radiologists' interactive thresholding in PD estimation on DMs.

Variable kernel density estimation in high-dimensional feature spaces

CSIR Research Space (South Africa)

Van der Walt, Christiaan M

2017-02-01

Full Text Available Estimating the joint probability density function of a dataset is a central task in many machine learning applications. In this work we address the fundamental problem of kernel bandwidth estimation for variable kernel density estimation in high...

Conservative surgery and radiotherapy for early-stage breast cancer using a lung density correction: the University of Michigan experience

International Nuclear Information System (INIS)

Pierce, Lori J.; Strawderman, Myla H.; Douglas, Kathye R.; Lichter, Allen S.

1997-01-01

Purpose: Although an abundance of reports detail the successful use of definitive radiotherapy of the breast in the treatment in Stage I or II breast cancer, little data have been published concerning the use of lung density correction and its effect upon long-term outcome. As it has been the practice at the University of Michigan to routinely use lung density correction in the dose calculations to the breast, we retrospectively analyzed our results for local control, relapse-free, and overall survival. Methods and Materials: Clinical records were reviewed of 429 women with Stage I or II breast cancer treated with lumpectomy, axillary dissection, and breast irradiation with or without systemic chemo/hormonal therapy. Tangential radiotherapy fields delivering 45 to 50 Gy were used to treat the entire breast. A boost was delivered in 95% of cases for a total tumor bed dose of 60 to 66 Gy. All treatment plans were calculated using a lung density correction. Results: With a median follow up of 4.4 years, the 5-year actuarial rate of local control with local failure as the only site of first failure was 96% (95% CI 94-98%). Univariate analysis for local failure as only first failure found the following factors to statistically predict for increased risk of breast recurrence: young age (≤35 years old), premenopausal status, tumor size >2 cm, positive family history, and positive microscopic margins. Multivariate analysis revealed young age and margin status to be the only factors remaining significant for local failure. The 5-year actuarial relapse-free survival was 85% (95% CI 81-89%); overall survival at 5 years was 90% (95% CI 87-94%). Conclusions: Lung density correction results in rates of local control, disease-free, and overall survival at 5 years that compare favorably with series using noncorrected unit density calculations. While we will continue to update our results with increasing follow-up, our 5-year data indicate that the use of lung-density correction

Nonparametric volatility density estimation for discrete time models

NARCIS (Netherlands)

Es, van Bert; Spreij, P.J.C.; Zanten, van J.H.

2005-01-01

We consider discrete time models for asset prices with a stationary volatility process. We aim at estimating the multivariate density of this process at a set of consecutive time instants. A Fourier-type deconvolution kernel density estimator based on the logarithm of the squared process is proposed

Information geometry of density matrices and state estimation

International Nuclear Information System (INIS)

Brody, Dorje C

2011-01-01

Given a pure state vector |x) and a density matrix ρ-hat, the function p(x|ρ-hat)= defines a probability density on the space of pure states parameterised by density matrices. The associated Fisher-Rao information measure is used to define a unitary invariant Riemannian metric on the space of density matrices. An alternative derivation of the metric, based on square-root density matrices and trace norms, is provided. This is applied to the problem of quantum-state estimation. In the simplest case of unitary parameter estimation, new higher-order corrections to the uncertainty relations, applicable to general mixed states, are derived. (fast track communication)

Theoretical and Experimental Estimations of Volumetric Inductive Phase Shift in Breast Cancer Tissue

Science.gov (United States)

González, C. A.; Lozano, L. M.; Uscanga, M. C.; Silva, J. G.; Polo, S. M.

2013-04-01

Impedance measurements based on magnetic induction for breast cancer detection has been proposed in some studies. This study evaluates theoretical and experimentally the use of a non-invasive technique based on magnetic induction for detection of patho-physiological conditions in breast cancer tissue associated to its volumetric electrical conductivity changes through inductive phase shift measurements. An induction coils-breast 3D pixel model was designed and tested. The model involves two circular coils coaxially centered and a human breast volume centrally placed with respect to the coils. A time-harmonic numerical simulation study addressed the effects of frequency-dependent electrical properties of tumoral tissue on the volumetric inductive phase shift of the breast model measured with the circular coils as inductor and sensor elements. Experimentally; five female volunteer patients with infiltrating ductal carcinoma previously diagnosed by the radiology and oncology departments of the Specialty Clinic for Women of the Mexican Army were measured by an experimental inductive spectrometer and the use of an ergonomic inductor-sensor coil designed to estimate the volumetric inductive phase shift in human breast tissue. Theoretical and experimental inductive phase shift estimations were developed at four frequencies: 0.01, 0.1, 1 and 10 MHz. The theoretical estimations were qualitatively in agreement with the experimental findings. Important increments in volumetric inductive phase shift measurements were evident at 0.01MHz in theoretical and experimental observations. The results suggest that the tested technique has the potential to detect pathological conditions in breast tissue associated to cancer by non-invasive monitoring. Further complementary studies are warranted to confirm the observations.

Global Population Density Grid Time Series Estimates

Data.gov (United States)

National Aeronautics and Space Administration — Global Population Density Grid Time Series Estimates provide a back-cast time series of population density grids based on the year 2000 population grid from SEDAC's...

Density estimates of monarch butterflies overwintering in central Mexico

Directory of Open Access Journals (Sweden)

Wayne E. Thogmartin

2017-04-01

Full Text Available Given the rapid population decline and recent petition for listing of the monarch butterfly (Danaus plexippus L. under the Endangered Species Act, an accurate estimate of the Eastern, migratory population size is needed. Because of difficulty in counting individual monarchs, the number of hectares occupied by monarchs in the overwintering area is commonly used as a proxy for population size, which is then multiplied by the density of individuals per hectare to estimate population size. There is, however, considerable variation in published estimates of overwintering density, ranging from 6.9–60.9 million ha−1. We develop a probability distribution for overwinter density of monarch butterflies from six published density estimates. The mean density among the mixture of the six published estimates was ∼27.9 million butterflies ha−1 (95% CI [2.4–80.7] million ha−1; the mixture distribution is approximately log-normal, and as such is better represented by the median (21.1 million butterflies ha−1. Based upon assumptions regarding the number of milkweed needed to support monarchs, the amount of milkweed (Asclepias spp. lost (0.86 billion stems in the northern US plus the amount of milkweed remaining (1.34 billion stems, we estimate >1.8 billion stems is needed to return monarchs to an average population size of 6 ha. Considerable uncertainty exists in this required amount of milkweed because of the considerable uncertainty occurring in overwinter density estimates. Nevertheless, the estimate is on the same order as other published estimates. The studies included in our synthesis differ substantially by year, location, method, and measures of precision. A better understanding of the factors influencing overwintering density across space and time would be valuable for increasing the precision of conservation recommendations.

Density estimates of monarch butterflies overwintering in central Mexico

Science.gov (United States)

Thogmartin, Wayne E.; Diffendorfer, James E.; Lopez-Hoffman, Laura; Oberhauser, Karen; Pleasants, John M.; Semmens, Brice X.; Semmens, Darius J.; Taylor, Orley R.; Wiederholt, Ruscena

2017-01-01

Given the rapid population decline and recent petition for listing of the monarch butterfly (Danaus plexippus L.) under the Endangered Species Act, an accurate estimate of the Eastern, migratory population size is needed. Because of difficulty in counting individual monarchs, the number of hectares occupied by monarchs in the overwintering area is commonly used as a proxy for population size, which is then multiplied by the density of individuals per hectare to estimate population size. There is, however, considerable variation in published estimates of overwintering density, ranging from 6.9–60.9 million ha−1. We develop a probability distribution for overwinter density of monarch butterflies from six published density estimates. The mean density among the mixture of the six published estimates was ∼27.9 million butterflies ha−1 (95% CI [2.4–80.7] million ha−1); the mixture distribution is approximately log-normal, and as such is better represented by the median (21.1 million butterflies ha−1). Based upon assumptions regarding the number of milkweed needed to support monarchs, the amount of milkweed (Asclepias spp.) lost (0.86 billion stems) in the northern US plus the amount of milkweed remaining (1.34 billion stems), we estimate >1.8 billion stems is needed to return monarchs to an average population size of 6 ha. Considerable uncertainty exists in this required amount of milkweed because of the considerable uncertainty occurring in overwinter density estimates. Nevertheless, the estimate is on the same order as other published estimates. The studies included in our synthesis differ substantially by year, location, method, and measures of precision. A better understanding of the factors influencing overwintering density across space and time would be valuable for increasing the precision of conservation recommendations.

Impact of errors in recorded compressed breast thickness measurements on volumetric density classification using volpara v1.5.0 software

OpenAIRE

Waade, G; Highnam, R; Hauge, I; McEntee, M; Hofvind, S; Denton, E; Kelly, J; Sarwar, J; Hogg, P

2016-01-01

Purpose: Mammographic density has been demonstrated to predict breast cancer risk. It has been proposed that it could be used for stratifying screening pathways and recommending additional imaging. Volumetric density tools use the recorded compressed breast thickness (CBT) of the breast measured at the x-ray unit in their calculation, however the accuracy of the recorded thickness can vary. The aim of this study was to investigate whether inaccuracies in recorded CBT impact upon volumetric de...

Estimation of Trace Elements (Iodine and Iron Content in Breast Milk

Directory of Open Access Journals (Sweden)

N.A. Belykh

2013-08-01

Full Text Available The estimation of iodine and iron content in breast milk (n = 88 has been carried out. The study shows that the concentration of iodine and iron in breast milk of the women did not correspond to the physiological needs of an infant. It has been demonstrated that the use of iron-containing vitamin-mineral supplements during lactation has no significant effect on the level of iron in breast milk. The iodine content in breast milk depends on the iodine subsidies. It is shown that the level of micronutrients in breast milk is not affected by the presence of gestational maternal anemia and goiter, due date and place of residence of the family. It is concluded that a statistically significant impact on the level of iodine in the breast milk of combined iodine prophylaxis (acceptance by the mother during lactation drugs potassium iodide (200 mg/day due to the use of iodized salt.

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

Science.gov (United States)

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: 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 (breasts (28.7%, 21.4%, 11.3% and 14.3%, respectively; screening-detected cancers, extreme breast density

On Improving Convergence Rates for Nonnegative Kernel Density Estimators

OpenAIRE

Terrell, George R.; Scott, David W.

1980-01-01

To improve the rate of decrease of integrated mean square error for nonparametric kernel density estimators beyond $0(n^{-\\frac{4}{5}}),$ we must relax the constraint that the density estimate be a bonafide density function, that is, be nonnegative and integrate to one. All current methods for kernel (and orthogonal series) estimators relax the nonnegativity constraint. In this paper we show how to achieve similar improvement by relaxing the integral constraint only. This is important in appl...

Inter-observer agreement according to three methods of evaluating mammographic density and parenchymal pattern in a case control study

DEFF Research Database (Denmark)

Winkel, Rikke Rass; von Euler-Chelpin, My Catarina; Nielsen, Mads

2015-01-01

, Tabár's PIV and PV and the upper two quartiles (within density range) of PMD. The relative risk of breast cancer was estimated using logistic regression to calculate odds ratios (ORs) adjusted for age, which were compared between the two readers. RESULTS: Substantial inter-observer agreement was seen......, respectively. Inter-reader variability showed different impact on the relative risk of breast cancer estimated by the two readers on a multiple-category scale, however, not on a high/low-risk scale. Tabár's pattern IV demonstrated the highest ORs of all density patterns investigated. CONCLUSIONS: Our study......BACKGROUND: Mammographic breast density and parenchymal patterns are well-established risk factors for breast cancer. We aimed to report inter-observer agreement on three different subjective ways of assessing mammographic density and parenchymal pattern, and secondarily to examine what potential...

Personalized estimates of radiation dose from dedicated breast CT in a diagnostic population and comparison with diagnostic mammography

International Nuclear Information System (INIS)

Vedantham, Srinivasan; Shi, Linxi; Karellas, Andrew; O'Connell, Avice M; Conover, David L

2013-01-01

This study retrospectively analyzed the mean glandular dose (MGD) to 133 breasts from 132 subjects, all women, who participated in a clinical trial evaluating dedicated breast CT in a diagnostic population. The clinical trial was conducted in adherence to a protocol approved by institutional review boards and the study participants provided written informed consent. Individual estimates of MGD to each breast from dedicated breast CT was obtained by combining x-ray beam characteristics with estimates of breast dimensions and fibroglandular fraction from volumetric breast CT images, and using normalized glandular dose coefficients. For each study participant and for the breast corresponding to that imaged with breast CT, an estimate of the MGD from diagnostic mammography (including supplemental views) was obtained from the DICOM image headers for comparison. This estimate uses normalized glandular dose coefficients corresponding to a breast with 50% fibroglandular weight fraction. The median fibroglandular weight fraction for the study cohort determined from volumetric breast CT images was 15%. Hence, the MGD from diagnostic mammography was corrected to be representative of the study cohort. Individualized estimates of MGD from breast CT ranged from 5.7 to 27.8 mGy. Corresponding to the breasts imaged with breast CT, the MGD from diagnostic mammography ranged from 2.6 to 31.6 mGy. The mean (± inter-breast SD) and the median MGD (mGy) from dedicated breast CT exam were 13.9 ± 4.6 and 12.6, respectively. For the corresponding breasts, the mean (± inter-breast SD) and the median MGD (mGy) from diagnostic mammography were 12.4 ± 6.3 and 11.1, respectively. Statistical analysis indicated that at the 0.05 level, the distributions of MGD from dedicated breast CT and diagnostic mammography were significantly different (Wilcoxon signed ranks test, p = 0.007). While the interquartile range and the range (maximum–minimum) of MGD from dedicated breast CT was lower than

Breast composition: Measurement and clinical use

International Nuclear Information System (INIS)

Ekpo, E.U.; Hogg, P.; Highnam, R.; McEntee, M.F.

2015-01-01

Breast density is a measure of the extent of radiodense fibroglandular tissue in the breast. The risk of developing breast cancer and the risk of missing cancer at screening rise with higher breast density. In this paper, the historical background to breast density measurement is outlined and current evidence based practice is explained. The relevance of breast density knowledge to mammographic practice and image interpretation is considered in the light of clinical assessment and notification of mammographic breast density (MBD). The current work also discusses risk stratification for decision-making regarding screening frequency and better modalities for earlier detection of breast cancer in the dense breast. Automated volumetric approaches are explained while ultrasound, digital breast tomosynthesis, molecular breast imaging, and magnetic resonance imaging are introduced as valuable adjuncts to digital mammography for imaging the dense breast. The work concludes on the important note that screened women should be notified of their breast density, and such notification should be accompanied with clear and adequate information about breast density and cancer risk, strategies associated with lower MBD, as well as best screening intervals and pathways for women with dense breasts. Adoption of these strategies may be crucial to early detection and treatment of cancer and improving survival from the disease. - Highlights: • Breast density is associated with breast cancer risk and interval breast cancer. • Breast density can be measured manually or automatically. • Radiographic exposure factors impact on breast density measurements. • Dense breast often require imaging with three-dimensional modalities. • National breast density data could enable breast cancer risk stratification.

Mammographic density changes following discontinuation of tamoxifen in premenopausal women with oestrogen receptor-positive breast cancer.

Science.gov (United States)

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 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.

Estimating the incidence of breast cancer in Africa: a systematic review and meta-analysis

Science.gov (United States)

Adeloye, Davies; Sowunmi, Olaperi Y.; Jacobs, Wura; David, Rotimi A; Adeosun, Adeyemi A; Amuta, Ann O.; Misra, Sanjay; Gadanya, Muktar; Auta, Asa; Harhay, Michael O; Chan, Kit Yee

2018-01-01

Background Breast cancer is estimated to be the most common cancer worldwide. We sought to assemble publicly available data from Africa to provide estimates of the incidence of breast cancer on the continent. Methods A systematic search of Medline, EMBASE, Global Health and African Journals Online (AJOL) was conducted. We included population- or hospital-based registry studies on breast cancer conducted in Africa, and providing estimates of the crude incidence of breast cancer among women. A random effects meta-analysis was employed to determine the pooled incidence of breast cancer across studies. Results The literature search returned 4648 records, with 41 studies conducted across 54 study sites in 22 African countries selected. We observed important variations in reported cancer incidence between population- and hospital-based cancer registries. The overall pooled crude incidence of breast cancer from population-based registries was 24.5 per 100 000 person years (95% confidence interval (CI) 20.1-28.9). The incidence in North Africa was higher at 29.3 per 100 000 (95% CI 20.0-38.7) than Sub-Saharan Africa (SSA) at 22.4 per 100 000 (95% CI 17.2-28.0). In hospital-based registries, the overall pooled crude incidence rate was estimated at 23.6 per 100 000 (95% CI 18.5-28.7). SSA and Northern Africa had relatively comparable rates at 24.0 per 100 000 (95% CI 17.5-30.4) and 23.2 per 100 000 (95% CI 6.6-39.7), respectively. Across both registries, incidence rates increased considerably between 2000 and 2015. Conclusions The available evidence suggests a growing incidence of breast cancer in Africa. The representativeness of these estimates is uncertain due to the paucity of data in several countries and calendar years, as well as inconsistency in data collation and quality across existing cancer registries. PMID:29740502

Retrospective methods to estimate radiation dose at the site of breast cancer development after Hodgkin lymphoma radiotherapy

Directory of Open Access Journals (Sweden)

Nicola S. Russell

2017-12-01

Full Text Available Background: An increased risk of breast cancer following radiotherapy for Hodgkin lymphoma (HL has now been robustly established. In order to estimate the dose–response relationship more accurately, and to aid clinical decision making, a retrospective estimation of the radiation dose delivered to the site of the subsequent breast cancer is required. Methods: For 174 Dutch and 170 UK female patients with breast cancer following HL treatment, the 3-dimensional position of the breast cancer in the affected breast was determined and transferred onto a CT-based anthropomorphic phantom. Using a radiotherapy treatment planning system the dose distribution on the CT-based phantom was calculated for the 46 different radiation treatment field set-ups used in the study population. The estimated dose at the centre of the breast cancer, and a margin to reflect dose uncertainty were determined on the basis of the location of the tumour and the isodose lines from the treatment planning. We assessed inter-observer variation and for 47 patients we compared the results with a previously applied dosimetry method. Results: The estimated median point dose at the centre of the breast cancer location was 29.75 Gy (IQR 5.8–37.2, or about 75% of the prescribed radiotherapy dose. The median dose uncertainty range was 5.97 Gy. We observed an excellent inter-observer variation (ICC 0.89 (95% CI: 0.74–0.95. The absolute agreement intra-class correlation coefficient (ICC for inter-method variation was 0.59 (95% CI: 0.37–0.75, indicating (nearly good agreement. There were no systematic differences in the dose estimates between observers or methods. Conclusion: Estimates of the dose at the point of a subsequent breast cancer show good correlation between methods, but the retrospective nature of the estimates means that there is always some uncertainty to be accounted for. Keywords: Retrospective dosimetry, Hodgkin lymphoma, Breast carcinogenesis

Study of electron densities of normal and neoplastic human breast tissues by Compton scattering using synchrotron radiation

Energy Technology Data Exchange (ETDEWEB)

Antoniassi, M.; Conceicao, A.L.C. [Departamento de Fisica-Faculdade de Filosofia Ciencias e Letras de Ribeirao Preto-Universidade de Sao Paulo, Ribeirao Preto, Sao Paulo (Brazil); Poletti, M.E., E-mail: poletti@ffclrp.usp.br [Departamento de Fisica-Faculdade de Filosofia Ciencias e Letras de Ribeirao Preto-Universidade de Sao Paulo, Ribeirao Preto, Sao Paulo (Brazil)

2012-07-15

Electron densities of 33 samples of normal (adipose and fibroglangular) and neoplastic (benign and malignant) human breast tissues were determined through Compton scattering data using a monochromatic synchrotron radiation source and an energy dispersive detector. The area of Compton peaks was used to determine the electron densities of the samples. Adipose tissue exhibits the lowest values of electron density whereas malignant tissue the highest. The relationship with their histology was discussed. Comparison with previous results showed differences smaller than 4%. - Highlights: Black-Right-Pointing-Pointer Electron density of normal and neoplastic breast tissues was measured using Compton scattering. Black-Right-Pointing-Pointer Monochromatic synchrotron radiation was used to obtain the Compton scattering data. Black-Right-Pointing-Pointer The area of Compton peaks was used to determine the electron densities of samples. Black-Right-Pointing-Pointer Adipose tissue shows the lowest electron density values whereas the malignant tissue the highest. Black-Right-Pointing-Pointer Comparison with previous results showed differences smaller than 4%.

Study of electron densities of normal and neoplastic human breast tissues by Compton scattering using synchrotron radiation

International Nuclear Information System (INIS)

Antoniassi, M.; Conceição, A.L.C.; Poletti, M.E.

2012-01-01

Electron densities of 33 samples of normal (adipose and fibroglangular) and neoplastic (benign and malignant) human breast tissues were determined through Compton scattering data using a monochromatic synchrotron radiation source and an energy dispersive detector. The area of Compton peaks was used to determine the electron densities of the samples. Adipose tissue exhibits the lowest values of electron density whereas malignant tissue the highest. The relationship with their histology was discussed. Comparison with previous results showed differences smaller than 4%. - Highlights: ► Electron density of normal and neoplastic breast tissues was measured using Compton scattering. ► Monochromatic synchrotron radiation was used to obtain the Compton scattering data. ► The area of Compton peaks was used to determine the electron densities of samples. ► Adipose tissue shows the lowest electron density values whereas the malignant tissue the highest. ► Comparison with previous results showed differences smaller than 4%.

KDE-Track: An Efficient Dynamic Density Estimator for Data Streams

KAUST Repository

Qahtan, Abdulhakim Ali Ali; Wang, Suojin; Zhang, Xiangliang

2016-01-01

Recent developments in sensors, global positioning system devices and smart phones have increased the availability of spatiotemporal data streams. Developing models for mining such streams is challenged by the huge amount of data that cannot be stored in the memory, the high arrival speed and the dynamic changes in the data distribution. Density estimation is an important technique in stream mining for a wide variety of applications. The construction of kernel density estimators is well studied and documented. However, existing techniques are either expensive or inaccurate and unable to capture the changes in the data distribution. In this paper, we present a method called KDE-Track to estimate the density of spatiotemporal data streams. KDE-Track can efficiently estimate the density function with linear time complexity using interpolation on a kernel model, which is incrementally updated upon the arrival of new samples from the stream. We also propose an accurate and efficient method for selecting the bandwidth value for the kernel density estimator, which increases its accuracy significantly. Both theoretical analysis and experimental validation show that KDE-Track outperforms a set of baseline methods on the estimation accuracy and computing time of complex density structures in data streams.

KDE-Track: An Efficient Dynamic Density Estimator for Data Streams

KAUST Repository

Qahtan, Abdulhakim Ali Ali

2016-11-08

Recent developments in sensors, global positioning system devices and smart phones have increased the availability of spatiotemporal data streams. Developing models for mining such streams is challenged by the huge amount of data that cannot be stored in the memory, the high arrival speed and the dynamic changes in the data distribution. Density estimation is an important technique in stream mining for a wide variety of applications. The construction of kernel density estimators is well studied and documented. However, existing techniques are either expensive or inaccurate and unable to capture the changes in the data distribution. In this paper, we present a method called KDE-Track to estimate the density of spatiotemporal data streams. KDE-Track can efficiently estimate the density function with linear time complexity using interpolation on a kernel model, which is incrementally updated upon the arrival of new samples from the stream. We also propose an accurate and efficient method for selecting the bandwidth value for the kernel density estimator, which increases its accuracy significantly. Both theoretical analysis and experimental validation show that KDE-Track outperforms a set of baseline methods on the estimation accuracy and computing time of complex density structures in data streams.

A simple method to estimate the episode and programme sensitivity of breast cancer screening programmes.

Science.gov (United States)

Zorzi, Manuel; Guzzinati, Stefano; Puliti, Donella; Paci, Eugenio

2010-01-01

The estimation of breast cancer screening sensitivity is a major aim in the quality assessment of screening programmes. The proportional incidence method for the estimation of the sensitivity of breast cancer screening programmes is rarely used to estimate the underlying incidence rates. We present a method to estimate episode and programme sensitivity of screening programmes, based solely on cancers detected within screening cycles (excluding breast cancer cases at prevalent screening round) and on the number of incident cases in the total target population (steady state). The assumptions, strengths and limitations of the method are discussed. An example of calculation of episode and programme sensitivities is given, on the basis of the data from the IMPACT study, a large observational study of breast cancer screening programmes in Italy. The programme sensitivity from the fifth year of screening onwards ranged between 41% and 48% of the total number of cases in the target population. At steady state episode sensitivity was 0.70, with a trend across age groups, with lowest values in women aged 50-54 years (0.52) and highest in those 65-69 (0.77). The method is a very serviceable tool for estimating sensitivity in service screening programmes, and the results are comparable with those of other methods of estimation.

Age- and Tumor Subtype-Specific Breast Cancer Risk Estimates for CHEK2*1100delC Carriers

DEFF Research Database (Denmark)

Schmidt, Marjanka K; Hogervorst, Frans; van Hien, Richard R

2016-01-01

PURPOSE: CHEK2*1100delC is a well-established breast cancer risk variant that is most prevalent in European populations; however, there are limited data on risk of breast cancer by age and tumor subtype, which limits its usefulness in breast cancer risk prediction. We aimed to generate tumor...... subtype- and age-specific risk estimates by using data from the Breast Cancer Association Consortium, including 44,777 patients with breast cancer and 42,997 controls from 33 studies genotyped for CHEK2*1100delC. PATIENTS AND METHODS: CHEK2*1100delC genotyping was mostly done by a custom Taqman assay....... Breast cancer odds ratios (ORs) for CHEK2*1100delC carriers versus noncarriers were estimated by using logistic regression and adjusted for study (categorical) and age. Main analyses included patients with invasive breast cancer from population- and hospital-based studies. RESULTS: Proportions...

Nonparametric Collective Spectral Density Estimation and Clustering

KAUST Repository

Maadooliat, Mehdi; Sun, Ying; Chen, Tianbo

2017-01-01

In this paper, we develop a method for the simultaneous estimation of spectral density functions (SDFs) for a collection of stationary time series that share some common features. Due to the similarities among the SDFs, the log-SDF can be represented using a common set of basis functions. The basis shared by the collection of the log-SDFs is estimated as a low-dimensional manifold of a large space spanned by a pre-specified rich basis. A collective estimation approach pools information and borrows strength across the SDFs to achieve better estimation efficiency. Also, each estimated spectral density has a concise representation using the coefficients of the basis expansion, and these coefficients can be used for visualization, clustering, and classification purposes. The Whittle pseudo-maximum likelihood approach is used to fit the model and an alternating blockwise Newton-type algorithm is developed for the computation. A web-based shiny App found at

Nonparametric Collective Spectral Density Estimation and Clustering

KAUST Repository

Maadooliat, Mehdi

2017-04-12

In this paper, we develop a method for the simultaneous estimation of spectral density functions (SDFs) for a collection of stationary time series that share some common features. Due to the similarities among the SDFs, the log-SDF can be represented using a common set of basis functions. The basis shared by the collection of the log-SDFs is estimated as a low-dimensional manifold of a large space spanned by a pre-specified rich basis. A collective estimation approach pools information and borrows strength across the SDFs to achieve better estimation efficiency. Also, each estimated spectral density has a concise representation using the coefficients of the basis expansion, and these coefficients can be used for visualization, clustering, and classification purposes. The Whittle pseudo-maximum likelihood approach is used to fit the model and an alternating blockwise Newton-type algorithm is developed for the computation. A web-based shiny App found at

A Balanced Approach to Adaptive Probability Density Estimation

Directory of Open Access Journals (Sweden)

Julio A. Kovacs

2017-04-01

Full Text Available Our development of a Fast (Mutual Information Matching (FIM of molecular dynamics time series data led us to the general problem of how to accurately estimate the probability density function of a random variable, especially in cases of very uneven samples. Here, we propose a novel Balanced Adaptive Density Estimation (BADE method that effectively optimizes the amount of smoothing at each point. To do this, BADE relies on an efficient nearest-neighbor search which results in good scaling for large data sizes. Our tests on simulated data show that BADE exhibits equal or better accuracy than existing methods, and visual tests on univariate and bivariate experimental data show that the results are also aesthetically pleasing. This is due in part to the use of a visual criterion for setting the smoothing level of the density estimate. Our results suggest that BADE offers an attractive new take on the fundamental density estimation problem in statistics. We have applied it on molecular dynamics simulations of membrane pore formation. We also expect BADE to be generally useful for low-dimensional applications in other statistical application domains such as bioinformatics, signal processing and econometrics.

Late regional density changes of the lung after radiotherapy for breast cancer

International Nuclear Information System (INIS)

Vagane, Randi; Danielsen, Turi; Fossa, Sophie Dorothea; Lokkevik, Erik; Olsen, Dag Rune

2009-01-01

Background and purpose: To investigate density changes in lung tissue, 3-4 years after postoperative adjuvant radiotherapy for breast cancer, based on dose dependence and regional differences. Material and methods: Sixty-one breast cancer patients, who had received computed tomography (CT) based postoperative radiotherapy, were included. CT scans were performed 35-51 months after start of radiotherapy. Dose information and CT scans from before and after radiotherapy were geometrically aligned in order to analyse changes in air-filled fraction (derived from CT density) as a function of dose for different regions of the lung. Results: Dose-dependent reduction of the air-filled fraction was shown to vary between the different regions of the lung. For lung tissue receiving about 50 Gy, the largest reduction in air-filled fraction was found in the cranial part of the lung. An increased air-filled fraction was observed for lung tissue irradiated to doses below 20 Gy, indicating compensatory response. Conclusions: The treatment-induced change in whole-lung density is a weighted response, involving the different regions, the irradiated volumes, and dose levels to these volumes. Simplistic models may therefore not be appropriate for describing the whole-lung dose-volume-response relationship following inhomogeneous irradiation

The prognostic relevance of estimates of proliferative activity in early breast cancer

DEFF Research Database (Denmark)

Offersen, B V; Sørensen, Flemming Brandt; Knoop, A

2003-01-01

clinicopathological parameters at diagnosis in early breast cancer patients.MATERIALS AND METHODS: Tumour specimens from 365 consecutively treated breast cancer patients were immunostained for MIB-1 and evaluated under the microscope using systematic random sampling accomplished by the CAST-grid system.......RESULTS: The systematic random sampling technique resulted in MIB-1 estimates with very high interobserver and intraobserver reproducibilities (P

Analysis and comparison of breast density according to age on mammogram between Korean and Western women

International Nuclear Information System (INIS)

Kim, Seung Hyung; Kim, Mi Hye; Oh, Ki Keun

2000-01-01

To compare changes in breast parenchymal density among diverse age groups in asymptomatic Korean women with those of Western women, and to evaluate the effect of different patterns of breast parenchymal density on the sensitivity of screening mammography in Korean women. We analyzed the distribution of breast parenchymal density among diverse age groups in 823 asymptomatic Korean women aged 30-64 who underwent screening mammography between January and December 1998. On the basis of ACR BI-RADS breast composition, four density patterns were designated: patterns 1 and 2 related to fatty mammograms, and patterns 3 and 4 to dense mammograms. We compared the results with those for western women. In Korean women, the frequency of dense mammogram was 88.1% (30-34 years old), 91.1% (35-39), 78.3% (40-44), 61.1% (45-49), 30.1% (50-54), 21.1% (55-59), and 7.0% (60-64). Korean women in their 40s thus showed a higher frequency of dense mammograms, but this frequency decreased abruptly between the ages of 40 and 54. In Western women, however, there was little difference between 40 and 54-year-olds: the figures were 47.2% (40-44 years), 44.8% (45-49), and 44.4% (50-54). Because the frequency of their dense mammograms shows little change between Western women in their forties and in their fifties, it is clear that between these two age groups, mammographic sensitivity is only slightly different. Because the frequency of dense mammograms is much greater among Korean women in their forties than among Western women of the same age, and among korean women this frequency decreases abruptly, it appears, however, that the mammographic sensitivity of korean women is less among those in their forties than among those in their fifties. It is therefore thought that mammography combined with ultrasonography may increase screening sensitivity among Korean women under 50, who have a relatively higher incidence of breast cancer in the younger age groups than do Western women. (author)

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

Science.gov (United States)

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

The Application of Extended Cox Proportional Hazard Method for Estimating Survival Time of Breast Cancer

Science.gov (United States)

Husain, Hartina; Astuti Thamrin, Sri; Tahir, Sulaiha; Mukhlisin, Ahmad; Mirna Apriani, M.

2018-03-01

Breast cancer is one type of cancer that is the leading cause of death worldwide. This study aims to model the factors that affect the survival time and rate of cure of breast cancer patients. The extended cox model, which is a modification of the proportional hazard cox model in which the proportional hazard assumptions are not met, is used in this study. The maximum likelihood estimation approach is used to estimate the parameters of the model. This method is then applied to medical record data of breast cancer patient in 2011-2016, which is taken from Hasanuddin University Education Hospital. The results obtained indicate that the factors that affect the survival time of breast cancer patients are malignancy and leukocyte levels.

Update on breast cancer risk prediction and prevention.

Science.gov (United States)

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.

Association between mammographic density and pregnancies relative to age and BMI: a breast cancer case-only analysis.

Science.gov (United States)

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.

Lymphatic Vessel Density as Prognostic Factor in Breast Carcinoma: Relation to Clinico pathologic Parameters

International Nuclear Information System (INIS)

El-Gendi, S.; Abdel-Hadi, M.

2009-01-01

Angiogenesis and lymphangiogenesis are essential for breast cancer growth and progression. This study aimed at investigating lymphatic micro vessel density (LVD) and microvessel density (MVD) as prognostic markers in breast carcinoma. Forty breast carcinomas were immuno stained for D2-40, CD31 and VEGF. Median lymphatic and blood micro vessel densities, as well as VEGF expression, were related to each other and to clinico pathologic parameters including lymph node (Ln) status. The efficacy of haematoxylin and eosin (H and E) in detecting lymphatic vessel invasion (LVI) compared to D2-40 immunostaining was also investigated. D2-40 stained normal lymphatic endothelium and myoepithelial cells, but with different staining patterns. D2-40 LVD related significantly to CD31 counts (r=0.470; p=0.002), and LN metastasis (Mann-Whitney U=101.500; p=0.043); however, it did not relate to age, tumor grade, tumor size or LVI. D2-40 identified LVI in 3 more cases (7.5%) than those detected by H and E. VEGF was expressed in 85% of cases, and was significantly related to CD31 and D2-40 counts (p=0.033 and 0.007, respectively). In conclusion, D2-40 LVD showed a significant association with LN metastasis, and can be considered to segregate patients with positive from those with negative LNs. D2-40 enhances the detection of LVI relative to H and E staining reflecting a potential for lymphatic metastatic spread and possible poor prognosis

Systematic feasibility analysis of a quantitative elasticity estimation for breast anatomy using supine/prone patient postures.

Science.gov (United States)

Hasse, Katelyn; Neylon, John; Sheng, Ke; Santhanam, Anand P

2016-03-01

Breast elastography is a critical tool for improving the targeted radiotherapy treatment of breast tumors. Current breast radiotherapy imaging protocols only involve prone and supine CT scans. There is a lack of knowledge on the quantitative accuracy with which breast elasticity can be systematically measured using only prone and supine CT datasets. The purpose of this paper is to describe a quantitative elasticity estimation technique for breast anatomy using only these supine/prone patient postures. Using biomechanical, high-resolution breast geometry obtained from CT scans, a systematic assessment was performed in order to determine the feasibility of this methodology for clinically relevant elasticity distributions. A model-guided inverse analysis approach is presented in this paper. A graphics processing unit (GPU)-based linear elastic biomechanical model was employed as a forward model for the inverse analysis with the breast geometry in a prone position. The elasticity estimation was performed using a gradient-based iterative optimization scheme and a fast-simulated annealing (FSA) algorithm. Numerical studies were conducted to systematically analyze the feasibility of elasticity estimation. For simulating gravity-induced breast deformation, the breast geometry was anchored at its base, resembling the chest-wall/breast tissue interface. Ground-truth elasticity distributions were assigned to the model, representing tumor presence within breast tissue. Model geometry resolution was varied to estimate its influence on convergence of the system. A priori information was approximated and utilized to record the effect on time and accuracy of convergence. The role of the FSA process was also recorded. A novel error metric that combined elasticity and displacement error was used to quantify the systematic feasibility study. For the authors' purposes, convergence was set to be obtained when each voxel of tissue was within 1 mm of ground-truth deformation. The authors

Digital versus screen-film mammography: impact of mammographic density and hormone therapy on breast cancer detection.

Science.gov (United States)

Chiarelli, Anna M; Prummel, Maegan V; Muradali, Derek; Shumak, Rene S; Majpruz, Vicky; Brown, Patrick; Jiang, Hedy; Done, Susan J; Yaffe, Martin J

2015-11-01

Most studies that have examined the effects of mammographic density and hormone therapy use on breast cancer detection have included screen-film mammography. This study further examines this association in post-menopausal women screened by digital mammography. Approved by the University of Toronto Research Ethics Board, this study identified 688,418 women of age 50-74 years screened with digital or screen-film mammography from 2008 to 2009 within the Ontario Breast Screening Program. Of 2993 eligible women with invasive breast cancer, 2450 were contacted and 1421 participated (847 screen-film mammography, 574 digital direct radiography). Mammographic density was measured by study radiologists using the standard BI-RADS classification system and by a computer-assisted method. Information on hormone therapy use was collected by a telephone-administered questionnaire. Logistic regression and two-tailed tests for significance evaluated associations between factors and detection method by mammography type. Women with >75 % radiologist-measured mammographic density compared to those with diagnosed with an interval than screen-detected cancer, with the difference being greater for those screened with screen-film (OR = 6.40, 95 % CI 2.30-17.85) than digital mammography (OR = 2.41, 95 % CI 0.67-8.58) and aged 50-64 years screened with screen-film mammography (OR = 10.86, 95 % CI 2.96-39.57). Recent former hormone therapy users were also at an increased risk of having an interval cancer with the association being significant for women screened with digital mammography (OR = 2.08, 95 % CI 1.17-3.71). Breast screening using digital mammography lowers the risk of having an interval cancer for post-menopausal women aged 50-64 with greater mammographic density.

Improved Variable Window Kernel Estimates of Probability Densities

OpenAIRE

Hall, Peter; Hu, Tien Chung; Marron, J. S.

1995-01-01

Variable window width kernel density estimators, with the width varying proportionally to the square root of the density, have been thought to have superior asymptotic properties. The rate of convergence has been claimed to be as good as those typical for higher-order kernels, which makes the variable width estimators more attractive because no adjustment is needed to handle the negativity usually entailed by the latter. However, in a recent paper, Terrell and Scott show that these results ca...

Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening

DEFF Research Database (Denmark)

Winkel, Rikke Rass; von Euler-Chelpin, My Catarina; Nielsen, Mads

2016-01-01

and jointly with density can improve the ability to identify screening women at increased risk of breast cancer. METHODS: The study included 121 cases and 259 age- and time matched controls based on a cohort of 14,736 women with negative screening mammograms from a population-based screening programme...... in Denmark in 2007 (followed until 31 December 2010). Mammograms were assessed using the Breast Imaging-Reporting and Data System (BI-RADS) density classification, Tabár's classification on parenchymal patterns and a fully automated texture quantification technique. The individual and combined association...

Curve fitting of the corporate recovery rates: the comparison of Beta distribution estimation and kernel density estimation.

Science.gov (United States)

Chen, Rongda; Wang, Ze

2013-01-01

Recovery rate is essential to the estimation of the portfolio's loss and economic capital. Neglecting the randomness of the distribution of recovery rate may underestimate the risk. The study introduces two kinds of models of distribution, Beta distribution estimation and kernel density distribution estimation, to simulate the distribution of recovery rates of corporate loans and bonds. As is known, models based on Beta distribution are common in daily usage, such as CreditMetrics by J.P. Morgan, Portfolio Manager by KMV and Losscalc by Moody's. However, it has a fatal defect that it can't fit the bimodal or multimodal distributions such as recovery rates of corporate loans and bonds as Moody's new data show. In order to overcome this flaw, the kernel density estimation is introduced and we compare the simulation results by histogram, Beta distribution estimation and kernel density estimation to reach the conclusion that the Gaussian kernel density distribution really better imitates the distribution of the bimodal or multimodal data samples of corporate loans and bonds. Finally, a Chi-square test of the Gaussian kernel density estimation proves that it can fit the curve of recovery rates of loans and bonds. So using the kernel density distribution to precisely delineate the bimodal recovery rates of bonds is optimal in credit risk management.

Gradient-based stochastic estimation of the density matrix

Science.gov (United States)

Wang, Zhentao; Chern, Gia-Wei; Batista, Cristian D.; Barros, Kipton

2018-03-01

Fast estimation of the single-particle density matrix is key to many applications in quantum chemistry and condensed matter physics. The best numerical methods leverage the fact that the density matrix elements f(H)ij decay rapidly with distance rij between orbitals. This decay is usually exponential. However, for the special case of metals at zero temperature, algebraic decay of the density matrix appears and poses a significant numerical challenge. We introduce a gradient-based probing method to estimate all local density matrix elements at a computational cost that scales linearly with system size. For zero-temperature metals, the stochastic error scales like S-(d+2)/2d, where d is the dimension and S is a prefactor to the computational cost. The convergence becomes exponential if the system is at finite temperature or is insulating.

Early detection of breast cancer: benefits and risks of supplemental breast ultrasound in asymptomatic women with mammographically dense breast tissue. A systematic review

International Nuclear Information System (INIS)

Nothacker, Monika; Duda, Volker; Hahn, Markus; Warm, Mathias; Degenhardt, Friedrich; Madjar, Helmut; Weinbrenner, Susanne; Albert, Ute-Susann

2009-01-01

Mammographic screening alone will miss a certain fraction of malignancies, as evidenced by retrospective reviews of mammograms following a subsequent screening. Mammographic breast density is a marker for increased breast cancer risk and is associated with a higher risk of interval breast cancer, i.e. cancer detected between screening tests. The purpose of this review is to estimate risks and benefits of supplemental breast ultrasound in women with negative mammographic screening with dense breast tissue. A systematic search and review of studies involving mammography and breast ultrasound for screening of breast cancer was conducted. The search was performed for the period 1/2000-8/2008 within the data source of PubMed, DARE, and Cochrane databases. Inclusion and exclusion criteria were determined prospectively, and the Oxford evidence classification system for diagnostic studies was used for evidence level. The parameters biopsy rate, positive predictive value (PPV) for biopsy, cancer yield for breast ultrasound alone, and carcinoma detection rate by breast density were extracted or constructed. The systematic search identified no randomized controlled trials or systematic reviews, six cohort studies of intermediate level of evidence (3b) were found. Only two of the studies included adequate follow-up of subjects with negative or benign findings. Supplemental breast ultrasound after negative mammographic screening permitted diagnosis of primarily invasive carcinomas in 0.32% of women in breast density type categories 2-4 of the American College of Radiology (ACR); mean tumor size for those identified was 9.9 mm, 90% with negative lymph node status. Most detected cancers occurred in mammographically dense breast ACR types 3 and 4. Biopsy rates were in the range 2.3%-4.7%, with PPV of 8.4-13.7% for those biopsied due to positive ultrasound, or about one third of the PPV of biopsies due to mammography. Limitations: The study populations included wide age ranges, and

Density estimation by maximum quantum entropy

International Nuclear Information System (INIS)

Silver, R.N.; Wallstrom, T.; Martz, H.F.

1993-01-01

A new Bayesian method for non-parametric density estimation is proposed, based on a mathematical analogy to quantum statistical physics. The mathematical procedure is related to maximum entropy methods for inverse problems and image reconstruction. The information divergence enforces global smoothing toward default models, convexity, positivity, extensivity and normalization. The novel feature is the replacement of classical entropy by quantum entropy, so that local smoothing is enforced by constraints on differential operators. The linear response of the estimate is proportional to the covariance. The hyperparameters are estimated by type-II maximum likelihood (evidence). The method is demonstrated on textbook data sets

Nonparametric methods for volatility density estimation

NARCIS (Netherlands)

Es, van Bert; Spreij, P.J.C.; Zanten, van J.H.

2009-01-01

Stochastic volatility modelling of financial processes has become increasingly popular. The proposed models usually contain a stationary volatility process. We will motivate and review several nonparametric methods for estimation of the density of the volatility process. Both models based on

Cancer risks in BRCA2 families: estimates for sites other than breast and ovary

NARCIS (Netherlands)

van Asperen, C. J.; Brohet, R. M.; Meijers-Heijboer, E. J.; Hoogerbrugge, N.; Verhoef, S.; Vasen, H. F. A.; Ausems, M. G. E. M.; Menko, F. H.; Gomez Garcia, E. B.; Klijn, J. G. M.; Hogervorst, F. B. L.; van Houwelingen, J. C.; van't Veer, L. J.; Rookus, M. A.; van Leeuwen, F. E.

2005-01-01

In BRCA2 mutation carriers, increased risks have been reported for several cancer sites besides breast and ovary. As most of the families included in earlier reports were selected on the basis of multiple breast/ovarian cancer cases, it is possible that risk estimates may differ in mutation carriers

Quantification of gravity-induced skin strain across the breast surface.

Science.gov (United States)

Sanchez, Amy; Mills, Chris; Haake, Steve; Norris, Michelle; Scurr, Joanna

2017-12-01

Quantification of the magnitude of skin strain in different regions of the breast may help to estimate possible gravity-induced damage whilst also being able to inform the selection of incision locations during breast surgery. The aim of this study was to quantify static skin strain over the breast surface and to estimate the risk of skin damage caused by gravitational loading. Fourteen participants had 21 markers applied to their torso and left breast. The non-gravity breast position was estimated as the mid-point of the breast positions in water and soybean oil (higher and lower density than breast respectively). The static gravity-loaded breast position was also measured. Skin strain was calculated as the percentage extension between adjacent breast markers in the gravity and non-gravity loaded conditions. Gravity induced breast deformation caused peak strains ranging from 14 to 75% across participants, with potentially damaging skin strain (>60%) in one participant and skin strains above 30% (skin resistance zone) in a further four participants. These peak strain values all occurred in the longitudinal direction in the upper region of the breast skin. In the latitudinal direction, smaller-breasted participants experienced greater strain on the outer (lateral) breast regions and less strain on the inner (medial) breast regions, a trend which was reversed in the larger breasted participants (above size 34D). To reduce tension on surgical incisions it is suggested that preference should be given to medial latitudinal locations for smaller breasted women and lateral latitudinal locations for larger breasted women. Copyright © 2017 Elsevier Ltd. All rights reserved.

Probability Density Estimation Using Neural Networks in Monte Carlo Calculations

International Nuclear Information System (INIS)

Shim, Hyung Jin; Cho, Jin Young; Song, Jae Seung; Kim, Chang Hyo

2008-01-01

The Monte Carlo neutronics analysis requires the capability for a tally distribution estimation like an axial power distribution or a flux gradient in a fuel rod, etc. This problem can be regarded as a probability density function estimation from an observation set. We apply the neural network based density estimation method to an observation and sampling weight set produced by the Monte Carlo calculations. The neural network method is compared with the histogram and the functional expansion tally method for estimating a non-smooth density, a fission source distribution, and an absorption rate's gradient in a burnable absorber rod. The application results shows that the neural network method can approximate a tally distribution quite well. (authors)

Breast Tomosynthesis

Science.gov (United States)

... in distinguishing non-cancerous breast conditions from breast cancers. Breast implants may also impede accurate mammogram readings because both ... view as much as possible without rupturing the implant. top of ... discuss breast cancer screening options with their doctors: Breast Density and ...

Automatic estimation of elasticity parameters in breast tissue

Science.gov (United States)

Skerl, Katrin; Cochran, Sandy; Evans, Andrew

2014-03-01

Shear wave elastography (SWE), a novel ultrasound imaging technique, can provide unique information about cancerous tissue. To estimate elasticity parameters, a region of interest (ROI) is manually positioned over the stiffest part of the shear wave image (SWI). The aim of this work is to estimate the elasticity parameters i.e. mean elasticity, maximal elasticity and standard deviation, fully automatically. Ultrasonic SWI of a breast elastography phantom and breast tissue in vivo were acquired using the Aixplorer system (SuperSonic Imagine, Aix-en-Provence, France). First, the SWI within the ultrasonic B-mode image was detected using MATLAB then the elasticity values were extracted. The ROI was automatically positioned over the stiffest part of the SWI and the elasticity parameters were calculated. Finally all values were saved in a spreadsheet which also contains the patient's study ID. This spreadsheet is easily available for physicians and clinical staff for further evaluation and so increase efficiency. Therewith the efficiency is increased. This algorithm simplifies the handling, especially for the performance and evaluation of clinical trials. The SWE processing method allows physicians easy access to the elasticity parameters of the examinations from their own and other institutions. This reduces clinical time and effort and simplifies evaluation of data in clinical trials. Furthermore, reproducibility will be improved.

Curve fitting of the corporate recovery rates: the comparison of Beta distribution estimation and kernel density estimation.

Directory of Open Access Journals (Sweden)

Rongda Chen

Full Text Available Recovery rate is essential to the estimation of the portfolio's loss and economic capital. Neglecting the randomness of the distribution of recovery rate may underestimate the risk. The study introduces two kinds of models of distribution, Beta distribution estimation and kernel density distribution estimation, to simulate the distribution of recovery rates of corporate loans and bonds. As is known, models based on Beta distribution are common in daily usage, such as CreditMetrics by J.P. Morgan, Portfolio Manager by KMV and Losscalc by Moody's. However, it has a fatal defect that it can't fit the bimodal or multimodal distributions such as recovery rates of corporate loans and bonds as Moody's new data show. In order to overcome this flaw, the kernel density estimation is introduced and we compare the simulation results by histogram, Beta distribution estimation and kernel density estimation to reach the conclusion that the Gaussian kernel density distribution really better imitates the distribution of the bimodal or multimodal data samples of corporate loans and bonds. Finally, a Chi-square test of the Gaussian kernel density estimation proves that it can fit the curve of recovery rates of loans and bonds. So using the kernel density distribution to precisely delineate the bimodal recovery rates of bonds is optimal in credit risk management.

Curve Fitting of the Corporate Recovery Rates: The Comparison of Beta Distribution Estimation and Kernel Density Estimation

Science.gov (United States)

Chen, Rongda; Wang, Ze

2013-01-01

Recovery rate is essential to the estimation of the portfolio’s loss and economic capital. Neglecting the randomness of the distribution of recovery rate may underestimate the risk. The study introduces two kinds of models of distribution, Beta distribution estimation and kernel density distribution estimation, to simulate the distribution of recovery rates of corporate loans and bonds. As is known, models based on Beta distribution are common in daily usage, such as CreditMetrics by J.P. Morgan, Portfolio Manager by KMV and Losscalc by Moody’s. However, it has a fatal defect that it can’t fit the bimodal or multimodal distributions such as recovery rates of corporate loans and bonds as Moody’s new data show. In order to overcome this flaw, the kernel density estimation is introduced and we compare the simulation results by histogram, Beta distribution estimation and kernel density estimation to reach the conclusion that the Gaussian kernel density distribution really better imitates the distribution of the bimodal or multimodal data samples of corporate loans and bonds. Finally, a Chi-square test of the Gaussian kernel density estimation proves that it can fit the curve of recovery rates of loans and bonds. So using the kernel density distribution to precisely delineate the bimodal recovery rates of bonds is optimal in credit risk management. PMID:23874558

Accounting for individualized competing mortality risks in estimating postmenopausal breast cancer risk

Science.gov (United States)

Schonberg, Mara A.; Li, Vicky W.; Eliassen, A. Heather; Davis, Roger B.; LaCroix, Andrea Z.; McCarthy, Ellen P.; Rosner, Bernard A.; Chlebowski, Rowan T.; Hankinson, Susan E.; Marcantonio, Edward R.; Ngo, Long H.

2016-01-01

Purpose Accurate risk assessment is necessary for decision-making around breast cancer prevention. We aimed to develop a breast cancer prediction model for postmenopausal women that would take into account their individualized competing risk of non-breast cancer death. Methods We included 73,066 women who completed the 2004 Nurses’ Health Study (NHS) questionnaire (all ≥57 years) and followed participants until May 2014. We considered 17 breast cancer risk factors (health behaviors, demographics, family history, reproductive factors), 7 risk factors for non-breast cancer death (comorbidities, functional dependency), and mammography use. We used competing risk regression to identify factors independently associated with breast cancer. We validated the final model by examining calibration (expected-to-observed ratio of breast cancer incidence, E/O) and discrimination (c-statistic) using 74,887 subjects from the Women’s Health Initiative Extension Study (WHI-ES; all were ≥55 years and followed for 5 years). Results Within 5 years, 1.8% of NHS participants were diagnosed with breast cancer (vs. 2.0% in WHI-ES, p=0.02) and 6.6% experienced non-breast cancer death (vs. 5.2% in WHI-ES, prisk factors, 5 comorbidities, functional dependency, and mammography use. The model’s c-statistic was 0.61 (95% CI [0.60–0.63]) in NHS and 0.57 (0.55–0.58) in WHI-ES. On average our model under predicted breast cancer in WHI-ES (E/O 0.92 [0.88–0.97]). Conclusions We developed a novel prediction model that factors in postmenopausal women’s individualized competing risks of non-breast cancer death when estimating breast cancer risk. PMID:27770283

Impact of errors in recorded compressed breast thickness measurements on volumetric density classification using volpara v1.5.0 software.

Science.gov (United States)

Waade, Gunvor Gipling; Highnam, Ralph; Hauge, Ingrid H R; McEntee, Mark F; Hofvind, Solveig; Denton, Erika; Kelly, Judith; Sarwar, Jasmine J; Hogg, Peter

2016-06-01

Mammographic density has been demonstrated to predict breast cancer risk. It has been proposed that it could be used for stratifying screening pathways and recommending additional imaging. Volumetric density tools use the recorded compressed breast thickness (CBT) of the breast measured at the x-ray unit in their calculation; however, the accuracy of the recorded thickness can vary. The aim of this study was to investigate whether inaccuracies in recorded CBT impact upon volumetric density classification and to examine whether the current quality control (QC) standard is sufficient for assessing mammographic density. Raw data from 52 digital screening mammograms were included in the study. For each image, the clinically recorded CBT was artificially increased and decreased in increments of 1 mm to simulate measurement error, until ±15% from the recorded CBT was reached. New images were created for each 1 mm step in thickness resulting in a total of 974 images which then had volpara density grade (VDG) and volumetric density percentage assigned. A change in VDG was observed in 38.5% (n = 20) of mammograms when applying ±15% error to the recorded CBT and 11.5% (n = 6) was within the QC standard prescribed error of ±5 mm. The current QC standard of ±5 mm error in recorded CBT creates the potential for error in mammographic density measurement. This may lead to inaccurate classification of mammographic density. The current QC standard for assessing mammographic density should be reconsidered.

Estimated risks and optimistic self-perception of breast cancer risk in Korean women.

Science.gov (United States)

Chung, ChaeWeon; Lee, Suk Jeong

2013-11-01

To determine women's perceived personal and comparative risks of breast cancer, and to examine the relationships with risk factors. Despite the increasing incidence of breast cancer in younger women and the availability of screening, women's health behaviors have not advanced accordingly. A cross-sectional survey design utilized a convenience sample of 222 women in their 30s and 40s recruited from community settings in Seoul. Self-administered questionnaire data were analyzed by descriptive statistics, the chi-squared test, and ANOVA. Risk perception levels differed significantly by breast cancer risk factors. Half of the women were optimistic about their breast cancer risk, while perceived personal risk did not reflect women's own risk factors and comparative risk differed only by the practice of clinical breast exam. Women's knowledge and awareness of their breast cancer risk factors need to be improved for appropriate risk perception and health behaviors, and accurate risk estimation could be utilized to educate them in clinical settings. © 2013.

Estimation of the cost of treatment by chemotherapy for early breast cancer in Morocco

Directory of Open Access Journals (Sweden)

Boutayeb Saber

2010-09-01

Full Text Available Abstract Background Breast cancer is the first cancer in women both in incidence and mortality. The treatment of breast cancer benefited from the progress of chemotherapy and targeted therapies, but there was a parallel increase in treatment costs. Despite a relatively high incidence of many sites of cancer, so far, there is no national register for this disease in Morocco. The main goal of this paper is to estimate the total cost of chemotherapy in the early stages of breast cancer due to its frequency and the chances of patients being cured. This study provides health decision-makers with a first estimate of costs and the opportunity to achieve the optimal use of available data to estimate the needs of antimitotics and trastuzumab in Morocco. Method We start by evaluating the individual cost according to the therapeutic sub-groups, namely: 1. Patients needing chemotherapy with only anthracycline-based therapy. 2. Patients needing chemotherapy with both anthracycline and taxane but without trastuzumab. 3. Patients needing trastuzumab in addition to chemotherapy. For each sub-group, the protocol of treatment is described, and the individual costs per unit, and for the whole cycle, are evaluated. Then we estimate the number of women suffering from breast cancer on the basis of two data bases available in Morocco. Finally, we calculate the total annual cost of treatment of breast cancer in Morocco. Results The total cost of breast cancer in Morocco is given in Moroccan dirhams (MAD, the US dollar at the current exchange rate (MAD 10 = USD 1.30 and in international dollars or purchasing power parity (MAD 10 = PPP 1.95. The cost of a therapy with trastuzumab is 8.4 times the cost of a sequential chemotherapy combining anthracycline and taxane, and nearly 60 times the cost of chemotherapy based on anthracycline alone. Globally, between USD 13.3 million and USD 28.6 million need to be devoted every year by the Moroccan health authorities to treat

Dual-Layer Density Estimation for Multiple Object Instance Detection

Directory of Open Access Journals (Sweden)

Qiang Zhang

2016-01-01

Full Text Available This paper introduces a dual-layer density estimation-based architecture for multiple object instance detection in robot inventory management applications. The approach consists of raw scale-invariant feature transform (SIFT feature matching and key point projection. The dominant scale ratio and a reference clustering threshold are estimated using the first layer of the density estimation. A cascade of filters is applied after feature template reconstruction and refined feature matching to eliminate false matches. Before the second layer of density estimation, the adaptive threshold is finalized by multiplying an empirical coefficient for the reference value. The coefficient is identified experimentally. Adaptive threshold-based grid voting is applied to find all candidate object instances. Error detection is eliminated using final geometric verification in accordance with Random Sample Consensus (RANSAC. The detection results of the proposed approach are evaluated on a self-built dataset collected in a supermarket. The results demonstrate that the approach provides high robustness and low latency for inventory management application.

Anisotropic Density Estimation in Global Illumination

DEFF Research Database (Denmark)

Schjøth, Lars

2009-01-01

Density estimation employed in multi-pass global illumination algorithms gives cause to a trade-off problem between bias and noise. The problem is seen most evident as blurring of strong illumination features. This thesis addresses the problem, presenting four methods that reduce both noise...

Glandularity estimation in Japanese women by using a breast model made from mammographic findings of European women

International Nuclear Information System (INIS)

Kawaguchi, Ai; Matsunaga, Yuta; Chida, Koichi; Asada, Yasuki; Suzuki, Shoichi

2016-01-01

This study aimed to estimate breast glandularity in Japanese women using patient exposure conditions and tissue-equivalent materials by a conventional method. Typical glandularities in Japanese women were compared with those in European women to verify the validity of the average glandular dose estimation manual based on the EUREF protocol. Glandularity was estimated from the data of 600 patients and the model breast of the tissue-equivalent materials which had various amounts of glandular contents and thicknesses. The model breasts were measured to examine the relationships between the thickness of the glandular contents and tube loading by using an automatic exposure control system. Then, equations were established to determine glandularity from patient data. The mean glandularity in the highest compressed breast thickness (CBT) group of 36–45 mm was 72%. The mean CBT of the 184 (31%) patients with glandularities exceeding 100% was 31 mm. Glandularities in patients with CBT of 30–70 mm in the present study were higher compared to those in European women by approximately 10–20%. The results suggest that the model breast of European women might not be a suitable reference standard for more than 30% of Japanese women, who have breasts with lower CBT. (author)

Estimating the Need for Radiotherapy for Patients With Prostate, Breast, and Lung Cancers: Verification of Model Estimates of Need With Radiotherapy Utilization Data From British Columbia

International Nuclear Information System (INIS)

Tyldesley, Scott; Delaney, Geoff; Foroudi, Farshad; Barbera, Lisa; Kerba, Marc; Mackillop, William

2011-01-01

Purpose: Estimates of the need for radiotherapy (RT) using different methods (criterion based benchmarking [CBB] and the Canadian [C-EBEST] and Australian [A-EBEST] epidemiologically based estimates) exist for various cancer sites. We compared these model estimates to actual RT rates for lung, breast, and prostate cancers in British Columbia (BC). Methods and Materials: All cases of lung, breast, and prostate cancers in BC from 1997 to 2004 and all patients receiving RT within 1 year (RT 1Y ) and within 5 years (RT 5Y ) of diagnosis were identified. The RT 1Y and RT 5Y proportions in health regions with a cancer center for the most recent year were then calculated. RT rates were compared with CBB and EBEST estimates of RT needs. Variation was assessed by time and region. Results: The RT 1Y in regions with a cancer center for lung, breast, and prostate cancers were 51%, 58%, and 33% compared with 45%, 57%, and 32% for C-EBEST and 41%, 61%, and 37% for CBB models. The RT 5Y rates in regions with a cancer center for lung, breast, and prostate cancers were 59%, 61%, and 40% compared with 61%, 66%, and 61% for C-EBEST and 75%, 83%, and 60% for A-EBEST models. The RT 1Y rates increased for breast and prostate cancers. Conclusions: C-EBEST and CBB model estimates are closer to the actual RT rates than the A-EBEST estimates. Application of these model estimates by health care decision makers should be undertaken with an understanding of the methods used and the assumptions on which they were based.

Quantitative background parenchymal uptake on molecular breast imaging and breast cancer risk: a case-control study.

Science.gov (United States)

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 Quantitative BPU was strongly correlated with subjective BPU categories (Spearman's r = 0.59 to 0.69, p 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

Estimated risk for secondary cancer in the contra-lateral breast following radiation therapy of breast cancer

International Nuclear Information System (INIS)

Johansen, Safora; Danielsen, Turi; Olsen, Dag Rune

2008-01-01

Purpose. To facilitate a discussion about the impact of dose heterogeneity on the risk for secondary contralateral breast (CB) cancer predicted with linear and non linear models associated with primary breast irradiation. Methods and materials. Dose volume statistics of the CB calculated for eight patients using a collapsed cone algorithm were used to predict the excess relative risk (ERR) for cancer induction in CB. Both linear and non-linear models were employed. A sensitivity analysis demonstrating the impact of different parameter values on calculated ERR for the eight patients was also included in this study. Results. A proportionality assumption was established to make the calculations with a linear and non-linear model comparable. ERR of secondary cancer predicted by the linear model varied considerably between the patients, while the predicted ERR for the same patients using the non-linear model showed very small variation. The predicted ERRs by the two models were indistinguishable for small doses, i.e. below ∼3 Gy. The sensitivity analysis showed that the quadratic component of the radiation-induction pre-malignant cell term is negligible for lower dose level. The ERR is highly sensitive to the value of agr1 and agr2. Conclusions. Optimization of breast cancer radiation therapy, where also the risk for radiation induced secondary malignancies in the contralateral breast is taken into account, requires robust and valid risk assessment. The linear dose-risk model does not account for the complexity in the mechanisms underlying the development of secondary malignancies following exposure to radiation; this is particularly important when estimating risk associated with highly heterogeneous dose distributions as is the case in the contralateral breast of women receiving breast cancer irradiation

Cost-effectiveness of annual versus biennial screening mammography for women with high mammographic breast density.

Science.gov (United States)

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.

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

Science.gov (United States)

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

Density meter algorithm and system for estimating sampling/mixing uncertainty

International Nuclear Information System (INIS)

Shine, E.P.

1986-01-01

The Laboratories Department at the Savannah River Plant (SRP) has installed a six-place density meter with an automatic sampling device. This paper describes the statistical software developed to analyze the density of uranyl nitrate solutions using this automated system. The purpose of this software is twofold: to estimate the sampling/mixing and measurement uncertainties in the process and to provide a measurement control program for the density meter. Non-uniformities in density are analyzed both analytically and graphically. The mean density and its limit of error are estimated. Quality control standards are analyzed concurrently with process samples and used to control the density meter measurement error. The analyses are corrected for concentration due to evaporation of samples waiting to be analyzed. The results of this program have been successful in identifying sampling/mixing problems and controlling the quality of analyses

Density meter algorithm and system for estimating sampling/mixing uncertainty

International Nuclear Information System (INIS)

Shine, E.P.

1986-01-01

The Laboratories Department at the Savannah River Plant (SRP) has installed a six-place density meter with an automatic sampling device. This paper describes the statisical software developed to analyze the density of uranyl nitrate solutions using this automated system. The purpose of this software is twofold: to estimate the sampling/mixing and measurement uncertainties in the process and to provide a measurement control program for the density meter. Non-uniformities in density are analyzed both analytically and graphically. The mean density and its limit of error are estimated. Quality control standards are analyzed concurrently with process samples and used to control the density meter measurement error. The analyses are corrected for concentration due to evaporation of samples waiting to be analyzed. The results of this program have been successful in identifying sampling/mixing problems and controlling the quality of analyses

Technical Note: Cortical thickness and density estimation from clinical CT using a prior thickness-density relationship

Energy Technology Data Exchange (ETDEWEB)

Humbert, Ludovic, E-mail: ludohumberto@gmail.com [Galgo Medical, Barcelona 08036 (Spain); Hazrati Marangalou, Javad; Rietbergen, Bert van [Orthopaedic Biomechanics, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB (Netherlands); Río Barquero, Luis Miguel del [CETIR Centre Medic, Barcelona 08029 (Spain); Lenthe, G. Harry van [Biomechanics Section, KU Leuven–University of Leuven, Leuven 3001 (Belgium)

2016-04-15

Purpose: Cortical thickness and density are critical components in determining the strength of bony structures. Computed tomography (CT) is one possible modality for analyzing the cortex in 3D. In this paper, a model-based approach for measuring the cortical bone thickness and density from clinical CT images is proposed. Methods: Density variations across the cortex were modeled as a function of the cortical thickness and density, location of the cortex, density of surrounding tissues, and imaging blur. High resolution micro-CT data of cadaver proximal femurs were analyzed to determine a relationship between cortical thickness and density. This thickness-density relationship was used as prior information to be incorporated in the model to obtain accurate measurements of cortical thickness and density from clinical CT volumes. The method was validated using micro-CT scans of 23 cadaver proximal femurs. Simulated clinical CT images with different voxel sizes were generated from the micro-CT data. Cortical thickness and density were estimated from the simulated images using the proposed method and compared with measurements obtained using the micro-CT images to evaluate the effect of voxel size on the accuracy of the method. Then, 19 of the 23 specimens were imaged using a clinical CT scanner. Cortical thickness and density were estimated from the clinical CT images using the proposed method and compared with the micro-CT measurements. Finally, a case-control study including 20 patients with osteoporosis and 20 age-matched controls with normal bone density was performed to evaluate the proposed method in a clinical context. Results: Cortical thickness (density) estimation errors were 0.07 ± 0.19 mm (−18 ± 92 mg/cm{sup 3}) using the simulated clinical CT volumes with the smallest voxel size (0.33 × 0.33 × 0.5 mm{sup 3}), and 0.10 ± 0.24 mm (−10 ± 115 mg/cm{sup 3}) using the volumes with the largest voxel size (1.0 × 1.0 × 3.0 mm{sup 3}). A trend for the

Technical Note: Cortical thickness and density estimation from clinical CT using a prior thickness-density relationship

International Nuclear Information System (INIS)

Humbert, Ludovic; Hazrati Marangalou, Javad; Rietbergen, Bert van; Río Barquero, Luis Miguel del; Lenthe, G. Harry van

2016-01-01

Purpose: Cortical thickness and density are critical components in determining the strength of bony structures. Computed tomography (CT) is one possible modality for analyzing the cortex in 3D. In this paper, a model-based approach for measuring the cortical bone thickness and density from clinical CT images is proposed. Methods: Density variations across the cortex were modeled as a function of the cortical thickness and density, location of the cortex, density of surrounding tissues, and imaging blur. High resolution micro-CT data of cadaver proximal femurs were analyzed to determine a relationship between cortical thickness and density. This thickness-density relationship was used as prior information to be incorporated in the model to obtain accurate measurements of cortical thickness and density from clinical CT volumes. The method was validated using micro-CT scans of 23 cadaver proximal femurs. Simulated clinical CT images with different voxel sizes were generated from the micro-CT data. Cortical thickness and density were estimated from the simulated images using the proposed method and compared with measurements obtained using the micro-CT images to evaluate the effect of voxel size on the accuracy of the method. Then, 19 of the 23 specimens were imaged using a clinical CT scanner. Cortical thickness and density were estimated from the clinical CT images using the proposed method and compared with the micro-CT measurements. Finally, a case-control study including 20 patients with osteoporosis and 20 age-matched controls with normal bone density was performed to evaluate the proposed method in a clinical context. Results: Cortical thickness (density) estimation errors were 0.07 ± 0.19 mm (−18 ± 92 mg/cm"3) using the simulated clinical CT volumes with the smallest voxel size (0.33 × 0.33 × 0.5 mm"3), and 0.10 ± 0.24 mm (−10 ± 115 mg/cm"3) using the volumes with the largest voxel size (1.0 × 1.0 × 3.0 mm"3). A trend for the cortical thickness and

Simplified large African carnivore density estimators from track indices

Directory of Open Access Journals (Sweden)

Christiaan W. Winterbach

2016-12-01

Full Text Available Background The range, population size and trend of large carnivores are important parameters to assess their status globally and to plan conservation strategies. One can use linear models to assess population size and trends of large carnivores from track-based surveys on suitable substrates. The conventional approach of a linear model with intercept may not intercept at zero, but may fit the data better than linear model through the origin. We assess whether a linear regression through the origin is more appropriate than a linear regression with intercept to model large African carnivore densities and track indices. Methods We did simple linear regression with intercept analysis and simple linear regression through the origin and used the confidence interval for ß in the linear model y = αx + ß, Standard Error of Estimate, Mean Squares Residual and Akaike Information Criteria to evaluate the models. Results The Lion on Clay and Low Density on Sand models with intercept were not significant (P > 0.05. The other four models with intercept and the six models thorough origin were all significant (P < 0.05. The models using linear regression with intercept all included zero in the confidence interval for ß and the null hypothesis that ß = 0 could not be rejected. All models showed that the linear model through the origin provided a better fit than the linear model with intercept, as indicated by the Standard Error of Estimate and Mean Square Residuals. Akaike Information Criteria showed that linear models through the origin were better and that none of the linear models with intercept had substantial support. Discussion Our results showed that linear regression through the origin is justified over the more typical linear regression with intercept for all models we tested. A general model can be used to estimate large carnivore densities from track densities across species and study areas. The formula observed track density = 3.26�

Breast cancer screening: Evidence of the effect of adjunct ultrasound screening in women with unilateral mammography-negative dense breasts

Directory of Open Access Journals (Sweden)

Atoosa Adibi

2015-01-01

Full Text Available Background: Patients with the previous history of breast cancer are in risk of contralateral breast cancer. On the other hand, increased breast density is a risk factor for breast cancer and the sensitivity of detecting nonpalpable cancers in screening mammography in radiographically dense breasts is low. The use of ultrasonography in dense breast remains a controversial topic. The purpose of this study was to assess the usefulness of routine ultrasonography in follow-up of women with the previous history of breast cancer and negative mammography but dense breasts. Materials and Methods: In a cross-sectional study, a total of 267 individuals with unilateral postmastectomy mammogram screened and 153 subjects assigned to study. There were 28 subjects with American College of Radiology (ACR breast density 2 and 125 with ACR breast density 3-4, which there was no new finding in their mammogram in comparison to previous studies. We assumed subjects with ACR breast density 3-4 as mammographic Breast Imaging Reporting and Data System (BI-RADS category 0 for malignancy. Standard two-view mammogram was performed for all participants, and breast ultrasound (US examinations were performed by an expert radiologist in radial and anti-radial planes. The data were analyzed using SPSS version 20.0 (SPSS Inc., Chicago, Illinois, USA. Results: The results showed that in subjects with ACR breast density 3-4, when there was no new density in two consecutive mammograms in comparison to previous studies, US also showed no possibility for malignancy (BI-RADS 1-2. And also in subjects with ACR breast density 2, when the mammographic results were BI-RADS 1-2, the US results was the same. Conclusion: Our data indicate that for the detection of breast cancer, sensitivity of US was not greater than mammography in patients with postmastectomy unilateral dense breast if there is not any new density.

Efficient estimation of dynamic density functions with an application to outlier detection

KAUST Repository

Qahtan, Abdulhakim Ali Ali; Zhang, Xiangliang; Wang, Suojin

2012-01-01

In this paper, we propose a new method to estimate the dynamic density over data streams, named KDE-Track as it is based on a conventional and widely used Kernel Density Estimation (KDE) method. KDE-Track can efficiently estimate the density with linear complexity by using interpolation on a kernel model, which is incrementally updated upon the arrival of streaming data. Both theoretical analysis and experimental validation show that KDE-Track outperforms traditional KDE and a baseline method Cluster-Kernels on estimation accuracy of the complex density structures in data streams, computing time and memory usage. KDE-Track is also demonstrated on timely catching the dynamic density of synthetic and real-world data. In addition, KDE-Track is used to accurately detect outliers in sensor data and compared with two existing methods developed for detecting outliers and cleaning sensor data. © 2012 ACM.

Hispanic and Immigrant Paradoxes in U.S. Breast Cancer Mortality: Impact of Neighborhood Poverty and Hispanic Density

Directory of Open Access Journals (Sweden)

Sandi L. Pruitt

2016-12-01

Full Text Available 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.

Novel Application of Density Estimation Techniques in Muon Ionization Cooling Experiment

Energy Technology Data Exchange (ETDEWEB)

Mohayai, Tanaz Angelina [IIT, Chicago; Snopok, Pavel [IIT, Chicago; Neuffer, David [Fermilab; Rogers, Chris [Rutherford

2017-10-12

The international Muon Ionization Cooling Experiment (MICE) aims to demonstrate muon beam ionization cooling for the first time and constitutes a key part of the R&D towards a future neutrino factory or muon collider. Beam cooling reduces the size of the phase space volume occupied by the beam. Non-parametric density estimation techniques allow very precise calculation of the muon beam phase-space density and its increase as a result of cooling. These density estimation techniques are investigated in this paper and applied in order to estimate the reduction in muon beam size in MICE under various conditions.

Bayesian error estimation in density-functional theory

DEFF Research Database (Denmark)

Mortensen, Jens Jørgen; Kaasbjerg, Kristen; Frederiksen, Søren Lund

2005-01-01

We present a practical scheme for performing error estimates for density-functional theory calculations. The approach, which is based on ideas from Bayesian statistics, involves creating an ensemble of exchange-correlation functionals by comparing with an experimental database of binding energies...

Quantitative histopathological variables in in situ and invasive ductal and lobular carcinomas of the breast

DEFF Research Database (Denmark)

Ladekarl, M; Sørensen, Flemming Brandt

1993-01-01

This study was carried out to compare quantitative histopathological estimates obtained in normal breast epithelium (N = 15), lobular carcinoma in situ (N = 29), ductal carcinoma in situ (N = 24), invasive lobular carcinoma (N = 39), and invasive ductal carcinoma (N = 71) of the female breast....... Using unbiased stereology, the three-dimensional mean nuclear size, v v(nuc), was estimated in routine histological sections, along with morphometric point-counting based estimates of the mean nuclear profile area, aH(nuc), and estimates of the nuclear density index, NI, the mitotic index, MI......) with those obtained in tumors of pure lobular carcinoma in situ (N = 7), only the difference in mean NI reached statistical significance (2p = 0.001). Several significant differences were found between means of quantitative histopathological estimates obtained in normal breast epithelium, pure in situ...

Quantitative histopathological variables in in situ and invasive ductal and lobular carcinomas of the breast

DEFF Research Database (Denmark)

Ladekarl, M; Sørensen, Flemming Brandt

1993-01-01

This study was carried out to compare quantitative histopathological estimates obtained in normal breast epithelium (N = 15), lobular carcinoma in situ (N = 29), ductal carcinoma in situ (N = 24), invasive lobular carcinoma (N = 39), and invasive ductal carcinoma (N = 71) of the female breast....... Using unbiased stereology, the three-dimensional mean nuclear size, v v(nuc), was estimated in routine histological sections, along with morphometric point-counting based estimates of the mean nuclear profile area, aH(nuc), and estimates of the nuclear density index, NI, the mitotic index, MI...... obtained in tumors of pure lobular carcinoma in situ (N = 7), only the difference in mean NI reached statistical significance (2p = 0.001). Several significant differences were found between means of quantitative histopathological estimates obtained in normal breast epithelium, pure in situ lesions...

A hierarchical model for estimating density in camera-trap studies

Science.gov (United States)

Royle, J. Andrew; Nichols, James D.; Karanth, K.Ullas; Gopalaswamy, Arjun M.

2009-01-01

Estimating animal density using capture–recapture data from arrays of detection devices such as camera traps has been problematic due to the movement of individuals and heterogeneity in capture probability among them induced by differential exposure to trapping.We develop a spatial capture–recapture model for estimating density from camera-trapping data which contains explicit models for the spatial point process governing the distribution of individuals and their exposure to and detection by traps.We adopt a Bayesian approach to analysis of the hierarchical model using the technique of data augmentation.The model is applied to photographic capture–recapture data on tigers Panthera tigris in Nagarahole reserve, India. Using this model, we estimate the density of tigers to be 14·3 animals per 100 km2 during 2004.Synthesis and applications. Our modelling framework largely overcomes several weaknesses in conventional approaches to the estimation of animal density from trap arrays. It effectively deals with key problems such as individual heterogeneity in capture probabilities, movement of traps, presence of potential ‘holes’ in the array and ad hoc estimation of sample area. The formulation, thus, greatly enhances flexibility in the conduct of field surveys as well as in the analysis of data, from studies that may involve physical, photographic or DNA-based ‘captures’ of individual animals.

Estimation and display of beam density profiles

Energy Technology Data Exchange (ETDEWEB)

Dasgupta, S; Mukhopadhyay, T; Roy, A; Mallik, C

1989-03-15

A setup in which wire-scanner-type beam-profile monitor data are collected on-line in a nuclear data-acquisition system has been used and a simple algorithm for estimation and display of the current density distribution in a particle beam is described.

Epidemiologic Studies of Isoflavones & Mammographic Density

Science.gov (United States)

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

Epidemiologic Studies of Isoflavones & Mammographic Density

Directory of Open Access Journals (Sweden)

Jeffrey A. Tice

2010-01-01

Full Text Available 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.

Face Value: Towards Robust Estimates of Snow Leopard Densities.

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Justine S Alexander

Full Text Available When densities of large carnivores fall below certain thresholds, dramatic ecological effects can follow, leading to oversimplified ecosystems. Understanding the population status of such species remains a major challenge as they occur in low densities and their ranges are wide. This paper describes the use of non-invasive data collection techniques combined with recent spatial capture-recapture methods to estimate the density of snow leopards Panthera uncia. It also investigates the influence of environmental and human activity indicators on their spatial distribution. A total of 60 camera traps were systematically set up during a three-month period over a 480 km2 study area in Qilianshan National Nature Reserve, Gansu Province, China. We recorded 76 separate snow leopard captures over 2,906 trap-days, representing an average capture success of 2.62 captures/100 trap-days. We identified a total number of 20 unique individuals from photographs and estimated snow leopard density at 3.31 (SE = 1.01 individuals per 100 km2. Results of our simulation exercise indicate that our estimates from the Spatial Capture Recapture models were not optimal to respect to bias and precision (RMSEs for density parameters less or equal to 0.87. Our results underline the critical challenge in achieving sufficient sample sizes of snow leopard captures and recaptures. Possible performance improvements are discussed, principally by optimising effective camera capture and photographic data quality.

Phantom measurements and computed estimates of breast dose with radiotherapy for Hodgkin's lymphoma: dose reduction with the use of the involved field

International Nuclear Information System (INIS)

Wirth, A.; Kron, T.; Sorell, G.; Cramb, J.; Wittwer, H.; Sullivan, K.

2008-01-01

Full text: The risk of breast cancer following radiotherapy for Hodgkin's lymphoma appears to be dose related. In this study we compared breast dose in an anthropomorphic phantom for conventional 'mantle'; upper mediastinal/bilateral neck (minimantle) and unilateral neck fields, and evaluated the accuracy of computer planned dose estimates for out-of-field doses. For each field, computer-planned breast dose (CPD) estimates were compared with thermolu-minescence dosimetry measurements in five locations within 'breast tissue'. CPD were also compared with ion chamber measurements in a slab phantom. Measured dose and CPD were within 20% of each other up to approximately 10 cm from the field edge. Beyond 10 cm, the CPD underestimated dose by a factor of 2 or more. The minimantle reduced the breast dose by a factor of approximately 10 compared with the mantle treatment. Treating the neck field lowered the breast dose by a further 50% or more. Modern involved-field radiotherapy for lymphoma substantially reduces breast dose compared with mantle fields. Computer dosimetery underestimated dose at larger distances from the field. This needs to be considered if computer dosimetery is used to estimate breast dose and, by extrapolation, breast cancer risk.

Estimation of Wheat Plant Density at Early Stages Using High Resolution Imagery

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Shouyang Liu

2017-05-01

Full Text Available Crop density is a key agronomical trait used to manage wheat crops and estimate yield. Visual counting of plants in the field is currently the most common method used. However, it is tedious and time consuming. The main objective of this work is to develop a machine vision based method to automate the density survey of wheat at early stages. RGB images taken with a high resolution RGB camera are classified to identify the green pixels corresponding to the plants. Crop rows are extracted and the connected components (objects are identified. A neural network is then trained to estimate the number of plants in the objects using the object features. The method was evaluated over three experiments showing contrasted conditions with sowing densities ranging from 100 to 600 seeds⋅m-2. Results demonstrate that the density is accurately estimated with an average relative error of 12%. The pipeline developed here provides an efficient and accurate estimate of wheat plant density at early stages.

Density estimation in tiger populations: combining information for strong inference

Science.gov (United States)

Gopalaswamy, Arjun M.; Royle, J. Andrew; Delampady, Mohan; Nichols, James D.; Karanth, K. Ullas; Macdonald, David W.

2012-01-01

A productive way forward in studies of animal populations is to efficiently make use of all the information available, either as raw data or as published sources, on critical parameters of interest. In this study, we demonstrate two approaches to the use of multiple sources of information on a parameter of fundamental interest to ecologists: animal density. The first approach produces estimates simultaneously from two different sources of data. The second approach was developed for situations in which initial data collection and analysis are followed up by subsequent data collection and prior knowledge is updated with new data using a stepwise process. Both approaches are used to estimate density of a rare and elusive predator, the tiger, by combining photographic and fecal DNA spatial capture–recapture data. The model, which combined information, provided the most precise estimate of density (8.5 ± 1.95 tigers/100 km2 [posterior mean ± SD]) relative to a model that utilized only one data source (photographic, 12.02 ± 3.02 tigers/100 km2 and fecal DNA, 6.65 ± 2.37 tigers/100 km2). Our study demonstrates that, by accounting for multiple sources of available information, estimates of animal density can be significantly improved.

Preoperative TRAM free flap volume estimation for breast reconstruction in lean patients.

Science.gov (United States)

Minn, Kyung Won; Hong, Ki Yong; Lee, Sang Woo

2010-04-01

To obtain pleasing symmetry in breast reconstruction with transverse rectus abdominis myocutaneous (TRAM) free flap, a large amount of abdominal flap is elevated and remnant tissue is trimmed in most cases. However, elevation of abundant abdominal flap can cause excessive tension in donor site closure and increase the possibility of hypertrophic scarring especially in lean patients. The TRAM flap was divided into 4 zones in routine manner; the depth and dimension of the 4 zones were obtained using ultrasound and AutoCAD (Autodesk Inc., San Rafael, CA), respectively. The acquired numbers were then multiplied to obtain an estimate of volume of each zone and the each zone volume was added. To confirm the relation between the estimated volume and the actual volume, authors compared intraoperative actual TRAM flap volumes with preoperative estimated volumes in 30 consecutive TRAM free flap breast reconstructions. The estimated volumes and the actual elevated volumes of flap were found to be correlated by regression analysis (r = 0.9258, P Autodesk Inc.) allow the authors to attain the precise volume desired for elevation. This method provides advantages in terms of minimal flap trimming, easier closure of donor sites, reduced scar widening and symmetry, especially in lean patients.

Quantitative ultrasound characterization of locally advanced breast cancer by estimation of its scatterer properties

Energy Technology Data Exchange (ETDEWEB)

Tadayyon, Hadi [Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5 (Canada); Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, Ontario M5G 2M9 (Canada); Sadeghi-Naini, Ali; Czarnota, Gregory, E-mail: Gregory.Czarnota@sunnybrook.ca [Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5 (Canada); Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, Ontario M5G 2M9 (Canada); Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5 (Canada); Department of Radiation Oncology, Faculty of Medicine, University of Toronto, Toronto, Ontario M5T 1P5 (Canada); Wirtzfeld, Lauren [Department of Physics, Ryerson University, Toronto, Ontario M5B 2K3 (Canada); Wright, Frances C. [Division of Surgical Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5 (Canada)

2014-01-15

Purpose: Tumor grading is an important part of breast cancer diagnosis and currently requires biopsy as its standard. Here, the authors investigate quantitative ultrasound parameters in locally advanced breast cancers that can potentially separate tumors from normal breast tissue and differentiate tumor grades. Methods: Ultrasound images and radiofrequency data from 42 locally advanced breast cancer patients were acquired and analyzed. Parameters related to the linear regression of the power spectrum—midband fit, slope, and 0-MHz-intercept—were determined from breast tumors and normal breast tissues. Mean scatterer spacing was estimated from the spectral autocorrelation, and the effective scatterer diameter and effective acoustic concentration were estimated from the Gaussian form factor. Parametric maps of each quantitative ultrasound parameter were constructed from the gated radiofrequency segments in tumor and normal tissue regions of interest. In addition to the mean values of the parametric maps, higher order statistical features, computed from gray-level co-occurrence matrices were also determined and used for characterization. Finally, linear and quadratic discriminant analyses were performed using combinations of quantitative ultrasound parameters to classify breast tissues. Results: Quantitative ultrasound parameters were found to be statistically different between tumor and normal tissue (p < 0.05). The combination of effective acoustic concentration and mean scatterer spacing could separate tumor from normal tissue with 82% accuracy, while the addition of effective scatterer diameter to the combination did not provide significant improvement (83% accuracy). Furthermore, the two advanced parameters, including effective scatterer diameter and mean scatterer spacing, were found to be statistically differentiating among grade I, II, and III tumors (p = 0.014 for scatterer spacing, p = 0.035 for effective scatterer diameter). The separation of the tumor