Mammography density estimation with automated volumetic breast density measurement
Ko, Su Yeon; Kim, Eun Kyung; Kim, Min Jung; Moon, Hee Jung [Dept. of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul (Korea, Republic of)
2014-06-15
To compare automated volumetric breast density measurement (VBDM) with radiologists' evaluations based on the Breast Imaging Reporting and Data System (BI-RADS), and to identify the factors associated with technical failure of VBDM. In this study, 1129 women aged 19-82 years who underwent mammography from December 2011 to January 2012 were included. Breast density evaluations by radiologists based on BI-RADS and by VBDM (Volpara Version 1.5.1) were compared. The agreement in interpreting breast density between radiologists and VBDM was determined based on four density grades (D1, D2, D3, and D4) and a binary classification of fatty (D1-2) vs. dense (D3-4) breast using kappa statistics. The association between technical failure of VBDM and patient age, total breast volume, fibroglandular tissue volume, history of partial mastectomy, the frequency of mass > 3 cm, and breast density was analyzed. The agreement between breast density evaluations by radiologists and VBDM was fair (k value = 0.26) when the four density grades (D1/D2/D3/D4) were used and moderate (k value = 0.47) for the binary classification (D1-2/D3-4). Twenty-seven women (2.4%) showed failure of VBDM. Small total breast volume, history of partial mastectomy, and high breast density were significantly associated with technical failure of VBDM (p 0.001 to 0.015). There is fair or moderate agreement in breast density evaluation between radiologists and VBDM. Technical failure of VBDM may be related to small total breast volume, a history of partial mastectomy, and high breast density.
Automated volumetric breast density estimation: A comparison with visual assessment
Aim: To compare automated volumetric breast density (VBD) measurement with visual assessment according to Breast Imaging Reporting and Data System (BI-RADS), and to determine the factors influencing the agreement between them. Materials and methods: One hundred and ninety-three consecutive screening mammograms reported as negative were included in the study. Three radiologists assigned qualitative BI-RADS density categories to the mammograms. An automated volumetric breast-density method was used to measure VBD (% breast density) and density grade (VDG). Each case was classified into an agreement or disagreement group according to the comparison between visual assessment and VDG. The correlation between visual assessment and VDG was obtained. Various physical factors were compared between the two groups. Results: Agreement between visual assessment by the radiologists and VDG was good (ICC value = 0.757). VBD showed a highly significant positive correlation with visual assessment (Spearman's ρ = 0.754, p < 0.001). VBD and the x-ray tube target was significantly different between the agreement group and the disagreement groups (p = 0.02 and 0.04, respectively). Conclusion: Automated VBD is a reliable objective method to measure breast density. The agreement between VDG and visual assessment by radiologist might be influenced by physical factors
Purpose: Breast density has been found to be a potential indicator for breast cancer risk. The estimation of breast density can be seen as a segmentation problem on fibroglandular tissues from a breast magnetic resonance image. The classic moment preserving is a thresholding method, which can be applied to determine an appropriate threshold value for fibroglandular tissue segmentation. Methods: This study proposed an adaptive moment preserving method, which combines the classic moment preserving and a thresholding adjustment method. The breast MR images are firstly performed to extract the fibroglandular tissue from the breast tissue. The next step is to obtain the areas of the fibroglandular tissue and the whole breast tissue. Finally, breast density can be estimated for the given breast. Results: The Friedman test shows that the qualities of segmentation are insignificant with p < 0.000 and Friedman chi-squared = 1116.12. The Friedman test shows that there would be significant differences in the sum of the ranks of at least one segmentation method. Average ranks indicate that the performance of the four methods is ranked as adaptive moment preserving, fuzzy c-means, moment preserving, and Kapur's method in order. Among the four methods, adaptive moment preserving also achieves the minimum values of MAE and RMSE with 9.2 and 12. Conclusion: This study has verified that the proposed adaptive moment preserving can identify and segment the fibroglandular tissues from the 2D breast MR images and estimate the degrees of breast density.
Chen, Biao; Ruth, Chris; Jing, Zhenxue; Ren, Baorui; Smith, Andrew; Kshirsagar, Ashwini
2014-03-01
Breast density has been identified to be a risk factor of developing breast cancer and an indicator of lesion diagnostic obstruction due to masking effect. Volumetric density measurement evaluates fibro-glandular volume, breast volume, and breast volume density measures that have potential advantages over area density measurement in risk assessment. One class of volume density computing methods is based on the finding of the relative fibro-glandular tissue attenuation with regards to the reference fat tissue, and the estimation of the effective x-ray tissue attenuation differences between the fibro-glandular and fat tissue is key to volumetric breast density computing. We have modeled the effective attenuation difference as a function of actual x-ray skin entrance spectrum, breast thickness, fibro-glandular tissue thickness distribution, and detector efficiency. Compared to other approaches, our method has threefold advantages: (1) avoids the system calibration-based creation of effective attenuation differences which may introduce tedious calibrations for each imaging system and may not reflect the spectrum change and scatter induced overestimation or underestimation of breast density; (2) obtains the system specific separate and differential attenuation values of fibroglandular and fat for each mammographic image; and (3) further reduces the impact of breast thickness accuracy to volumetric breast density. A quantitative breast volume phantom with a set of equivalent fibro-glandular thicknesses has been used to evaluate the volume breast density measurement with the proposed method. The experimental results have shown that the method has significantly improved the accuracy of estimating breast density.
Effect of compression paddle tilt correction on volumetric breast density estimation
For the acquisition of a mammogram, a breast is compressed between a compression paddle and a support table. When compression is applied with a flexible compression paddle, the upper plate may be tilted, which results in variation in breast thickness from the chest wall to the breast margin. Paddle tilt has been recognized as a major problem in volumetric breast density estimation methods. In previous work, we developed a fully automatic method to correct the image for the effect of compression paddle tilt. In this study, we investigated in three experiments the effect of paddle tilt and its correction on volumetric breast density estimation. Results showed that paddle tilt considerably affected accuracy of volumetric breast density estimation, but that effect could be reduced by tilt correction. By applying tilt correction, a significant increase in correspondence between mammographic density estimates and measurements on MRI was established. We argue that in volumetric breast density estimation, tilt correction is both feasible and essential when mammographic images are acquired with a flexible compression paddle. (paper)
Robust estimation of mammographic breast density: a patient-based approach
Heese, Harald S.; Erhard, Klaus; Gooßen, Andre; Bulow, Thomas
2012-02-01
Breast density has become an established risk indicator for developing breast cancer. Current clinical practice reflects this by grading mammograms patient-wise as entirely fat, scattered fibroglandular, heterogeneously dense, or extremely dense based on visual perception. Existing (semi-) automated methods work on a per-image basis and mimic clinical practice by calculating an area fraction of fibroglandular tissue (mammographic percent density). We suggest a method that follows clinical practice more strictly by segmenting the fibroglandular tissue portion directly from the joint data of all four available mammographic views (cranio-caudal and medio-lateral oblique, left and right), and by subsequently calculating a consistently patient-based mammographic percent density estimate. In particular, each mammographic view is first processed separately to determine a region of interest (ROI) for segmentation into fibroglandular and adipose tissue. ROI determination includes breast outline detection via edge-based methods, peripheral tissue suppression via geometric breast height modeling, and - for medio-lateral oblique views only - pectoral muscle outline detection based on optimizing a three-parameter analytic curve with respect to local appearance. Intensity harmonization based on separately acquired calibration data is performed with respect to compression height and tube voltage to facilitate joint segmentation of available mammographic views. A Gaussian mixture model (GMM) on the joint histogram data with a posteriori calibration guided plausibility correction is finally employed for tissue separation. The proposed method was tested on patient data from 82 subjects. Results show excellent correlation (r = 0.86) to radiologist's grading with deviations ranging between -28%, (q = 0.025) and +16%, (q = 0.975).
Chen, Lin; Ray, Shonket; Keller, Brad M; Pertuz, Said; McDonald, Elizabeth S; Conant, Emily F; Kontos, Despina
2016-09-01
Purpose To investigate the impact of radiation dose on breast density estimation in digital mammography. Materials and Methods With institutional review board approval and Health Insurance Portability and Accountability Act compliance under waiver of consent, a cohort of women from the American College of Radiology Imaging Network Pennsylvania 4006 trial was retrospectively analyzed. All patients underwent breast screening with a combination of dose protocols, including standard full-field digital mammography, low-dose digital mammography, and digital breast tomosynthesis. A total of 5832 images from 486 women were analyzed with previously validated, fully automated software for quantitative estimation of density. Clinical Breast Imaging Reporting and Data System (BI-RADS) density assessment results were also available from the trial reports. The influence of image acquisition radiation dose on quantitative breast density estimation was investigated with analysis of variance and linear regression. Pairwise comparisons of density estimations at different dose levels were performed with Student t test. Agreement of estimation was evaluated with quartile-weighted Cohen kappa values and Bland-Altman limits of agreement. Results Radiation dose of image acquisition did not significantly affect quantitative density measurements (analysis of variance, P = .37 to P = .75), with percent density demonstrating a high overall correlation between protocols (r = 0.88-0.95; weighted κ = 0.83-0.90). However, differences in breast percent density (1.04% and 3.84%, P mammography are not substantially affected by variations in radiation dose; thus, the use of low-dose techniques for the purpose of density estimation may be feasible. (©) RSNA, 2016 Online supplemental material is available for this article. PMID:27002418
Breast Density and Your Breast Mammogram Report
Breast Density and Your Mammogram Report Regular mammograms are the best way to find breast cancer early. But if ... But in some women, there’s little change. Breast density is very common, and is not abnormal. How ...
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
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.
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
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
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, pbreast PD% could allow for a reproducible estimation of breast density, providing a rapid risk assessment tool for clinical practice.
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
Automatic breast density classification using neural network
According to studies, the risk of breast cancer directly associated with breast density. Many researches are done on automatic diagnosis of breast density using mammography. In the current study, artifacts of mammograms are removed by using image processing techniques and by using the method presented in this study, including the diagnosis of points of the pectoral muscle edges and estimating them using regression techniques, pectoral muscle is detected with high accuracy in mammography and breast tissue is fully automatically extracted. In order to classify mammography images into three categories: Fatty, Glandular, Dense, a feature based on difference of gray-levels of hard tissue and soft tissue in mammograms has been used addition to the statistical features and a neural network classifier with a hidden layer. Image database used in this research is the mini-MIAS database and the maximum accuracy of system in classifying images has been reported 97.66% with 8 hidden layers in neural network
Association between mammographic density and basal-like and luminal A breast cancer subtypes
Razzaghi, Hilda; Troester, Melissa A.; Gierach, Gretchen L.; Olshan, Andrew F.; Yankaskas, Bonnie C.; Millikan, Robert C.
2013-01-01
Introduction Mammographic density is a strong risk factor for breast cancer overall, but few studies have examined the association between mammographic density and specific subtypes of breast cancer, especially aggressive basal-like breast cancers. Because basal-like breast cancers are less frequently screen-detected, it is important to understand how mammographic density relates to risk of basal-like breast cancer. Methods We estimated associations between mammographic density and breast can...
Postmortem validation of breast density using dual-energy mammography
Molloi, Sabee, E-mail: symolloi@uci.edu; Ducote, Justin L.; Ding, Huanjun; Feig, Stephen A. [Department of Radiological Sciences, University of California, Irvine, California 92697 (United States)
2014-08-15
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 dual energy mammography system. Glandular and adipose equivalent phantoms of uniform thickness were used to calibrate a dual energy basis decomposition algorithm. Dual energy decomposition was applied after scatter correction to calculate breast density. Breast density was also estimated using radiologist reader assessment, standard histogram thresholding and a fuzzy C-mean algorithm. Chemical analysis was used as the reference standard to assess the accuracy of different techniques to measure breast composition. Results: Breast density measurements using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm, and dual energy were in good agreement with the measured fibroglandular volume fraction using chemical analysis. The standard error estimates using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean, and dual energy were 9.9%, 8.6%, 7.2%, and 4.7%, respectively. Conclusions: The results indicate that dual energy mammography can be used to accurately measure breast density. The variability in breast density estimation using dual energy mammography was lower than reader assessment rankings, standard histogram thresholding, and fuzzy C-mean algorithm. Improved quantification of breast density is expected to further enhance its utility as a risk factor for breast cancer.
Postmortem validation of breast density using dual-energy mammography
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 dual energy mammography system. Glandular and adipose equivalent phantoms of uniform thickness were used to calibrate a dual energy basis decomposition algorithm. Dual energy decomposition was applied after scatter correction to calculate breast density. Breast density was also estimated using radiologist reader assessment, standard histogram thresholding and a fuzzy C-mean algorithm. Chemical analysis was used as the reference standard to assess the accuracy of different techniques to measure breast composition. Results: Breast density measurements using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm, and dual energy were in good agreement with the measured fibroglandular volume fraction using chemical analysis. The standard error estimates using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean, and dual energy were 9.9%, 8.6%, 7.2%, and 4.7%, respectively. Conclusions: The results indicate that dual energy mammography can be used to accurately measure breast density. The variability in breast density estimation using dual energy mammography was lower than reader assessment rankings, standard histogram thresholding, and fuzzy C-mean algorithm. Improved quantification of breast density is expected to further enhance its utility as a risk factor for breast cancer
Breast density and mode of detection in relation to breast cancer specific survival: a cohort study
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
Breast density characterization using texton distributions.
Petroudi, Styliani; Brady, Michael
2011-01-01
Breast density has been shown to be one of the most significant risks for developing breast cancer, with women with dense breasts at four to six times higher risk. The Breast Imaging Reporting and Data System (BI-RADS) has a four class classification scheme that describes the different breast densities. However, there is great inter and intra observer variability among clinicians in reporting a mammogram's density class. This work presents a novel texture classification method and its application for the development of a completely automated breast density classification system. The new method represents the mammogram using textons, which can be thought of as the building blocks of texture under the operational definition of Leung and Malik as clustered filter responses. The new proposed method characterizes the mammographic appearance of the different density patterns by evaluating the texton spatial dependence matrix (TDSM) in the breast region's corresponding texton map. The TSDM is a texture model that captures both statistical and structural texture characteristics. The normalized TSDM matrices are evaluated for mammograms from the different density classes and corresponding texture models are established. Classification is achieved using a chi-square distance measure. The fully automated TSDM breast density classification method is quantitatively evaluated on mammograms from all density classes from the Oxford Mammogram Database. The incorporation of texton spatial dependencies allows for classification accuracy reaching over 82%. The breast density classification accuracy is better using texton TSDM compared to simple texton histograms. PMID:22255462
Quantitative assessment of breast density: comparison of different methods
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)
The Estimation of Conditional Densities
Xiaohong Chen; Oliver Linton; Robinson, Peter M.
2001-01-01
We discuss a number of issues in the smoothed nonparametric estimation of kernel conditional probability density functions for stationary processes. The kernel conditional density estimate is a ratio of joint and marginal density estimates. We point out the different implications of leading choices of bandwidths in numerator and denominator for the ability of the estimate to integrate to one and to have finite moments. Again bearing in mind different bandwidth possibilities, we discuss asympt...
A Review of Segmentation of Mammographic Images Based on Breast Density
Anusree Mohan
2015-03-01
Full Text Available Breast cancer is one of the leading causes of fatality in women. Mammogram is the effectual modality for early detection of breast cancer. Increased mammographic breast density is a moderate independent risk factor for breast cancer, Radiologists have estimated breast density using four broad categories (BI-RADS swearing on visual assessment of mammograms. The aim of this paper is to review approaches for segmentation of breast regions in mammograms according to breast density. Studies based on density have been undertaken because of the relationship between breast cancer and density. Breast cancer usually occurs in the fibroglandular area of breast tissue, which appears bright on mammograms and is described as breast density. Most of the studies are focused on the classification methods for glandular tissue detection. Others highlighted on the segmentation methods for fibroglandular tissue, while few researchers performed segmentation of the breast anatomical regions based on density. There have also been works on the segmentation of other specific parts of breast regions such as either detection of nipple position, skin-air interface or pectoral muscles. The problems on the evaluation performance of the segmentation results in relation to ground truth are also discussed in this paper.
Optimization of Barron density estimates
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 ostatní: 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
Mueller-Schimpfle, M P; Brandenbusch, V C; Degenhardt, F; Duda, V; Madjar, H; Mundinger, A; Rathmann, R; Hahn, M
2016-04-01
Mammographic breast density correlates with breast cancer risk and also with the number of false-negative calls. In the USA these facts lead to the "Breast Density and Mammography Reporting Act" of 2011. In the case of mammographically dense breasts, the Working Group on Breast Ultrasound in Germany recommends explaining the advantages of adjunct imaging to women, depending on the individual breast cancer risk. Due to the particular structure of German healthcare, quality-assured breast ultrasound would be the first choice. Possible overdiagnosis, costs, potentially increased emotional stress should be addressed. In high familial breast cancer risk, genetic counselling and an intensified early detection program should be performed. PMID:26882482
Variability of breast density assessment in short-term reimaging with digital mammography
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.
Variability of breast density assessment in short-term reimaging with digital mammography
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
Diffuse optical tomography with structured-light patterns to quantify breast density
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.
Quantification of breast density using dual-energy mammography with liquid phantom calibration
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)
Quantification of breast density using dual-energy mammography with liquid phantom calibration
Lam, Alfonso R.; Ding, Huanjun; Molloi, Sabee
2014-07-01
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.
Mammographic breast density as a biomarker of effects of isoflavones on the female breast
Atkinson, Charlotte; Bingham, Sheila A.
2001-01-01
Isoflavones possess both estrogenic and anti-estrogenic actions, and are hypothesized to protect against breast cancer. However, two intervention studies of markers of proliferation on breast tissue have raised concerns that soy isoflavones may have an estrogenic effect on breast tissue. Increased mammographic breast density is associated with an elevated risk of breast cancer, although the mechanism underlying this relationship has not been explained. Estrogens increase and anti-estrogens de...
Breast Cancer Risk Estimation Using Parenchymal Texture Analysis in Digital Breast Tomosynthesis
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 cm3 were segmented from the DBT images and corresponding 2.5 cm2 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 (p2 = 0.39) compared to DM (R2 = 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.
Vision 20/20: Mammographic breast density and its clinical applications
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
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.
Correlations between female breast density and biochemical markers
Kim, Ji-Hye; Lee, Hae-Kag; Cho, Jae-Hwan; Park, Hyong-Keun; Yang, Han-Jun
2015-01-01
[Purpose] The aim of this study was to identify biochemical markers related to breast density. The study was performed with 200 patients who received mammography and biochemical marker testing between March 1, 2014 to October 1, 2014. [Subjects and Methods] Following the American College of Radiology, Breast Imaging Reporting and Data System (ACR BI-RADS), breast parenchymal pattern density from mammography was categorized into four grades: grade 1, almost entirely fat; grade 2, fibroglandula...
Maskarinec, Gertraud; Morimoto, Yukiko; Laguana, Michelle B; Novotny, Rachel; Leon Guerrero, Rachael T
2016-01-01
Although high mammographic density is one of the strongest predictors of breast cancer risk, X-ray based mammography cannot be performed before the recommended screening age, especially not in adolescents and young women. Therefore, new techniques for breast density measurement are of interest. In this pilot study in Guam and Hawaii, we evaluated a radiation-free, bioimpedance device called Electrical Breast DensitometerTM (EBD; senoSENSE Medical Systems, Inc., Ontario, Canada) for measuring breast density in 95 women aged 31-82 years and 41 girls aged 8-18 years. Percent density (PD) was estimated in the women's most recent mammogram using a computer-assisted method. Correlation coefficients and linear regression were applied for statistical analysis. In adult women, mean EBD and PD values of the left and right breasts were 230±52 and 226±50 Ω and 23.7±15.1 and 24.2±15.2%, respectively. The EBD measurements were inversely correlated with PD (rSpearman=-0.52, passessment of breast cancer risk early in life and in populations without access to mammography. PMID:26838256
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
Breast density and outcome of mammography screening: a cohort study
Olsen, A H; Bihrmann, K; Jensen, M-B;
2009-01-01
The purpose of this study was to investigate the effect of breast density on breast cancer (BC) mortality in a mammography screening programme. The cohort included 48 052 women participating in mammography screening in Copenhagen, Denmark, where biennial screening is offered to women aged 50...
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......, childhood body mass index (BMI), and height with the risk of breast cancer....
Density Estimation Trees in High Energy Physics
Anderlini, Lucio
2015-01-01
Density Estimation Trees can play an important role in exploratory data analysis for multidimensional, multi-modal data models of large samples. I briefly discuss the algorithm, a self-optimization technique based on kernel density estimation, and some applications in High Energy Physics.
Bayesian multivariate mixed-scale density estimation
Canale, Antonio
2011-01-01
Although univariate continuous density estimation has received abundant attention in the Bayesian nonparametrics literature, there is essentially no theory on multivariate mixed scale density estimation. In this article, we consider a general framework to jointly model continuous, count and categorical variables under a nonparametric prior, which is induced through rounding latent variables having an unknown density with respect to Lesbesgue measure. For the proposed class of priors, we provide sufficient conditions for large support, strong consistency and rates of posterior contraction. These conditions, which primarily relate to the prior on the latent variable density and heaviness of the tails for the observed continuous variables, allow one to convert sufficient conditions obtained in the setting of multivariate continuous density estimation to the mixed scale case. We provide new results in the multivariate continuous density estimation case, showing the Kullback-Leibler property and strong consistency...
Brandt, Kathleen R; Scott, Christopher G; Ma, Lin; Mahmoudzadeh, Amir P; Jensen, Matthew R; Whaley, Dana H; Wu, Fang Fang; Malkov, Serghei; Hruska, Carrie B; Norman, Aaron D; Heine, John; Shepherd, John; Pankratz, V Shane; Kerlikowske, Karla; Vachon, Celine M
2016-06-01
Purpose To compare the classification of breast density with two automated methods, Volpara (version 1.5.0; Matakina Technology, Wellington, New Zealand) and Quantra (version 2.0; Hologic, Bedford, Mass), with clinical Breast Imaging Reporting and Data System (BI-RADS) density classifications and to examine associations of these measures with breast cancer risk. Materials and Methods In this study, 1911 patients with breast cancer and 4170 control subjects matched for age, race, examination date, and mammography machine were evaluated. Participants underwent mammography at Mayo Clinic or one of four sites within the San Francisco Mammography Registry between 2006 and 2012 and provided informed consent or a waiver for research, in compliance with HIPAA regulations and institutional review board approval. Digital mammograms were retrieved a mean of 2.1 years (range, 6 months to 6 years) before cancer diagnosis, with the corresponding clinical BI-RADS density classifications, and Volpara and Quantra density estimates were generated. Agreement was assessed with weighted κ statistics among control subjects. Breast cancer associations were evaluated with conditional logistic regression, adjusted for age and body mass index. Odds ratios, C statistics, and 95% confidence intervals (CIs) were estimated. Results Agreement between clinical BI-RADS density classifications and Volpara and Quantra BI-RADS estimates was moderate, with κ values of 0.57 (95% CI: 0.55, 0.59) and 0.46 (95% CI: 0.44, 0.47), respectively. Differences of up to 14% in dense tissue classification were found, with Volpara classifying 51% of women as having dense breasts, Quantra classifying 37%, and clinical BI-RADS assessment used to classify 43%. Clinical and automated measures showed similar breast cancer associations; odds ratios for extremely dense breasts versus scattered fibroglandular densities were 1.8 (95% CI: 1.5, 2.2), 1.9 (95% CI: 1.5, 2.5), and 2.3 (95% CI: 1.9, 2.8) for Volpara, Quantra
Nonparametric Estimation of Risk-Neutral Densities
Grith, Maria; Härdle, Wolfgang Karl; Schienle, Melanie
2010-01-01
This chapter deals with nonparametric estimation of the risk neutral density. We present three different approaches which do not require parametric functional assumptions on the underlying asset price dynamics nor on the distributional form of the risk neutral density. The first estimator is a kernel smoother of the second derivative of call prices, while the second procedure applies kernel type smoothing in the implied volatility domain. In the conceptually different third approach we assume...
ADN* Density log estimation Using Rockcell*
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
Characterization of breast density in women from Lima, Peru
Casado, Fanny L.; Manrique, Susan; Guerrero, Jorge; Pinto, Joseph; Ferrer, Jose; Castaneda, Benjamin
2015-03-01
Data from GLOBOCAN show that around 4,000 Peruvian women are diagnosed with breast cancer every year. From these new cases, the clinical presentation of 36% corresponded to advanced stages (III and IV). Therefore, there is an urgent need to strengthen current screening and early detection strategies. The American College of Radiology (ACR) breast density classification is a risk assessment and quality assurance tool in mammography to standardize and facilitate report to non-radiologists. In our sample of Peruvian women, we found that 45.3% of women have a breast density classified as ACR II, 32.3% as ACR I, 19.7% as ACR III and only 2.7% as ACR IV. Also, premenopausal women are more likely to have breast density types ACR III and IV than postmenopausal women. These results show certain similarity to other populations showing that most breast densities are classified as ACR I and II, but shows a unique distribution when taking into account all four ACR types. Our results are consistent with epidemiological evidence suggesting that the Peruvian population may have a different stratification of risk based on its particular genetic and/or ethnic background. The present work will aid to develop novel strategies for screening and early detection of breast malignancies.
ESTIMATES OF BIOMASS DENSITY FOR TROPICAL FORESTS
An accurate estimation of the biomass density in forests is a necessary step in understanding the global carbon cycle and production of other atmospheric trace gases from biomass burning. n this paper the authors summarize the various approaches that have developed for estimating...
Parametric density estimation by minimizing nonextensive entropy
Davide Ferrari
2008-01-01
In this paper, we consider parametric density estimation based on minimizing the Havrda-Charvat-Tsallis nonextensive entropy. The resulting estimator, called the Maximum Lq-Likelihood estimator (MLqE), is indexed by a single distortion parameter q, which controls the trade-off between bias and variance. The method has two notable special cases. If q tends to 1, the MLqE is the Maximum Likelihood Estimator (MLE). When q = 1=2, the MLqE is a minimum Hellinger distance type of estimator with the...
Hori,Keisuke
2013-08-01
Full Text Available A relatively large number of women in their 40s with high-density breasts, in which it can be difficult to detect lesions, are encountered in mammography cancer screenings in Japan. Here, we retrospectively investigated factors related to breast density. Two hundred women (40-49 years old were examined at the screening center in our hospital. Multivariate analysis showed that factors such as small abdominal circumference, high HDL cholesterol, and no history of childbirth were related to high breast density in women in their 40s undergoing mammography. Other non-mammographic screening methods should be considered in women with abdominal circumferences ＜76cm, HDL-C >53mg/dl, and no history of childbirth, as there is a strong possibility of these women having high-density breasts that can make lesion detection difficult.
Implementation of several mathematical algorithms to breast tissue density classification
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.
Classification of breast density in X-ray mammography
Remeš, Václav; Haindl, Michal
New York, NY,: IEEE, 2015, s. 1-5. ISBN 978-1-4673-8457-5. [2015 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM). Praha (CZ), 29.10.2015-30.10.2015] R&D Projects: GA ČR(CZ) GA14-10911S Institutional support: RVO:67985556 Keywords : Breast cancer * breast density * Mammography * MRF * ACR * BI-RADS Subject RIV: BD - Theory of Information http://library.utia.cas.cz/separaty/2016/RO/haindl-0454449.pdf
Estimation of mean glandular dose for mammography of augmented breasts
Beckett, J. R.; Kotre, C. J.
2000-11-01
The standard quantity used to relate breast surface exposure to radiation risk is the mean dose received by the radiation sensitive tissue contained within the female breast, the mean glandular dose (MGD). At present, little is known about the MGD received by women with breast implants as there is no technique available to facilitate its calculation. The present work has involved modification of the conventional method for MGD estimation to make it applicable to women with augmented breasts. The technique was used to calculate MGDs for a cohort of 80 women with breast implants, which were compared with similar data calculated for a total of 1258 non-augmented women. Little difference was found in median MGD at low compressed breast thickness. At high breast thickness, however, the MGDs received by women with augmented breasts were found to be considerably lower than those relating to their non-augmented counterparts.
Estimation of mean glandular dose for mammography of augmented breasts
The standard quantity used to relate breast surface exposure to radiation risk is the mean dose received by the radiation sensitive tissue contained within the female breast, the mean glandular dose (MGD). At present, little is known about the MGD received by women with breast implants as there is no technique available to facilitate its calculation. The present work has involved modification of the conventional method for MGD estimation to make it applicable to women with augmented breasts. The technique was used to calculate MGDs for a cohort of 80 women with breast implants, which were compared with similar data calculated for a total of 1258 non-augmented women. Little difference was found in median MGD at low compressed breast thickness. At high breast thickness, however, the MGDs received by women with augmented breasts were found to be considerably lower than those relating to their non-augmented counterparts. (author)
Local mammographic density as a predictor of breast cancer
Otsuka, Mayu; Harkness, Elaine F.; Chen, Xin; Moschidis, Emmanouil; Bydder, Megan; Gadde, Soujanya; Lim, Yit Y.; Maxwell, Anthony J.; Evans, Gareth D.; Howell, Anthony; Stavrinos, Paula; Wilson, Mary; Astley, Susan M.
2015-03-01
High overall mammographic density is associated with both an increased risk of developing breast cancer and the risk of cancer being masked. We compared local density at cancer sites in diagnostic images with corresponding previous screening mammograms (priors), and matched controls. VolparaTM density maps were obtained for 54 mammograms showing unilateral breast cancer and their priors which had been previously read as normal. These were each matched to 3 controls on age, menopausal status, hormone replacement therapy usage, body mass index and year of prior. Local percent density was computed in 15mm square regions at lesion sites and similar locations in the corresponding images. Conditional logistic regression was used to predict case-control status. In diagnostic and prior images, local density was increased at the lesion site compared with the opposite breast (medians 21.58%, 9.18%, pcancer than those in the lowest tertile (OR 42.09, 95% CI 5.37-329.94). Those in the highest tertile of VolparaTM gland volume were also more likely to develop cancer (OR 2.89, 95% CI 1.30-6.42). Local density is increased where cancer will develop compared with corresponding regions in the opposite breast and matched controls, and its measurement could enhance computer-aided mammography.
The Discrepancy Principle in Density Estimation
Mildenberger, Thoralf
2011-01-01
We investigate the discrepancy principle, a simple method for choosing smoothing parameters for nonparametric density estimation. The main idea is to maximally smooth subject to a constraint on the data. This technique was originally suggested for the regularization of (deterministic) inverse problems, and in this area, it is one of the most widely known methods for choosing a regularization parameter. However, it has only rarely been applied in statistics, although some authors have suggested its use. The most important examples originate in Statistical Learning Theory and the so-called Data Approximation approach. We unify and extend previous results on kernel density estimation with bandwidths chosen by the discrepancy principle and derive some analogous results for regular histograms. We also show that for certain densities with infinite peaks using the discrepancy principle leads inconsistent estimators. Furthermore, we compare the discrepancy principle to standard methods in a simulation study. The resu...
Multivariate density estimation theory, practice, and visualization
Scott, David W
1992-01-01
Written to convey an intuitive feel for both theory and practice, its main objective is to illustrate what a powerful tool density estimation can be when used not only with univariate and bivariate data but also in the higher dimensions of trivariate and quadrivariate information. Major concepts are presented in the context of a histogram in order to simplify the treatment of advanced estimators. Features 12 four-color plates, numerous graphic illustrations as well as a multitude of problems and solutions
Density Estimation in Infinite Dimensional Exponential Families
Sriperumbudur, Bharath; Fukumizu, Kenji; Kumar, Revant; Gretton, Arthur; Hyvärinen, Aapo
2013-01-01
In this paper, we consider an infinite dimensional exponential family, $\\mathcal{P}$ of probability densities, which are parametrized by functions in a reproducing kernel Hilbert space, $H$ and show it to be quite rich in the sense that a broad class of densities on $\\mathbb{R}^d$ can be approximated arbitrarily well in Kullback-Leibler (KL) divergence by elements in $\\mathcal{P}$. The main goal of the paper is to estimate an unknown density, $p_0$ through an element in $\\mathcal{P}$. Standar...
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
Particle Size Estimation Based on Edge Density
WANG Wei-xing
2005-01-01
Given image sequences of closely packed particles, the underlying aim is to estimate diameters without explicit segmentation. In a way, this is similar to the task of counting objects without directly counting them. Such calculations may, for example, be useful fast estimation of particle size in different application areas. The topic is that of estimating average size (=average diameter) of packed particles, from formulas involving edge density, and the edges from moment-based thresholding are used. An average shape factor is involved in the calculations, obtained for some frames from crude partial segmentation. Measurement results from about 80 frames have been analyzed.
Anisotropic Density Estimation in Global Illumination
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 and...
Uniform Convergence of Multivariate Spectral Density Estimates
Wu, Wei Biao; Zaffaroni, Paolo
2015-01-01
We consider uniform moment convergence of lag-window spectral density estimates for univariate and multivariate stationary processes. Optimal rates of convergence are obtained under mild and easily verifiable conditions. Our theory complements earlier results which primarily concern weak or in-probability convergence.
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
Automatic breast density segmentation: an integration of different approaches
Mammographic breast density has been found to be a strong risk factor for breast cancer. In most studies, it is assessed with a user-assisted threshold method, which is time consuming and subjective. In this study, we develop a breast density segmentation method that is fully automatic. The method is based on pixel classification in which different approaches known in the literature to segment breast density are integrated and extended. In addition, the method incorporates the knowledge of a trained observer, by using segmentations obtained by the user-assisted threshold method as training data. The method is trained and tested using 1300 digitized film mammographic images acquired with a variety of systems. Results show a high correspondence between the automated method and the user-assisted threshold method. Pearson's correlation coefficient between our method and the user-assisted method is R = 0.911 for percent density and R = 0.895 for dense area, which is substantially higher than the best correlation found in the literature (R = 0.70, R = 0.68). The area under the receiver operating characteristic curve obtained when discriminating between fatty and dense pixels is 0.987. A combination of segmentation strategies outperforms the application of single segmentation techniques.
DENSITY ESTIMATION FOR PROJECTED EXOPLANET QUANTITIES
Exoplanet searches using radial velocity (RV) and microlensing (ML) produce samples of 'projected' mass and orbital radius, respectively. We present a new method for estimating the probability density distribution (density) of the unprojected quantity from such samples. For a sample of n data values, the method involves solving n simultaneous linear equations to determine the weights of delta functions for the raw, unsmoothed density of the unprojected quantity that cause the associated cumulative distribution function (CDF) of the projected quantity to exactly reproduce the empirical CDF of the sample at the locations of the n data values. We smooth the raw density using nonparametric kernel density estimation with a normal kernel of bandwidth σ. We calibrate the dependence of σ on n by Monte Carlo experiments performed on samples drawn from a theoretical density, in which the integrated square error is minimized. We scale this calibration to the ranges of real RV samples using the Normal Reference Rule. The resolution and amplitude accuracy of the estimated density improve with n. For typical RV and ML samples, we expect the fractional noise at the PDF peak to be approximately 80 n-log2. For illustrations, we apply the new method to 67 RV values given a similar treatment by Jorissen et al. in 2001, and to the 308 RV values listed at exoplanets.org on 2010 October 20. In addition to analyzing observational results, our methods can be used to develop measurement requirements-particularly on the minimum sample size n-for future programs, such as the microlensing survey of Earth-like exoplanets recommended by the Astro 2010 committee.
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
Breast Cancer Patients with High Density Mammograms Do Not Have Increased Risk of Death
... density mammograms do not have increased risk of death High mammographic breast density, which is a marker ... does not seem to increase the risk of death among breast cancer patients, according to a study ...
Breast cancer research output, 1945-2008: a bibliometric and density-equalizing analysis
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 detailed breast model and its application for Chinese female breast dose estimation in mammography
The IAEA protocol for breast dosimetry is based on the calculations of Dance which used very simple models of the breast based on the western female data. In order to estimate the dose more precisely in the mammographic examination for the Chinese female, a 3D detailed breast model was constructed. This model contains the detailed structures in the breast, including skin, subcutaneous fat, Cooper's ligaments, and fibro-glandular region containing intra-glandular fat, gland, ductal system, and terminal ductal lobular units. And the size of this breast model was adjusted to represent the characteristic of Chinese female. The glandular tissue conversion coefficients were calculated using this detailed breast model with Monte Carlo code, Geant4. The comparison between the data obtained in this study and the data from Dance was carried out. The discrepancy may come from both the structure and size of the breast models
Sandberg, Maria EC; Li, Jingmei; Hall, Per; Hartman, Mikael; dos-Santos-Silva, Isabel; Humphreys, Keith; Czene, Kamila
2013-01-01
Introduction Mammographic density is a strong risk factor for breast cancer, but it is unknown whether density at first breast cancer diagnosis and changes during follow-up influences risk of non-simultaneous contralateral breast cancer (CBC). Methods We collected mammograms for CBC-patients (cases, N = 211) and unilateral breast cancer patients (controls, N = 211), individually matched on age and calendar period of first breast cancer diagnosis, type of adjuvant therapy and length of follow-...
Height, adiposity and body fat distribution and breast density in young women
Dorgan, Joanne F; Klifa, Catherine; John A. Shepherd; Egleston, Brian L.; Kwiterovich, Peter O.; Himes, John H.; Gabriel, Kelley; Van Horn, Linda; Snetselaar, Linda G; Stevens, Victor J.; Barton, Bruce A; Robson, Alan M.; Lasser, Norman L.; Deshmukh, Snehal; Hylton, Nola M.
2012-01-01
Abstract Introduction Breast density is one of the strongest risk factors for breast cancer, but determinants of breast density in young women remain largely unknown. Methods Associations of height, adiposity and body fat distribution with percentage dense breast volume (%DBV) and absolute dense breast volume (ADBV) were evaluated in a cross-sectional study of 174 healthy women, 25 to 29 years old. Adiposity and body fat distribution were measured by anthropometry and dual-energy...
Estimating the breast surface using UWB microwave monostatic backscatter measurements.
Winters, David W; Shea, Jacob D; Madsen, Ernest L; Frank, Gary R; Van Veen, Barry D; Hagness, Susan C
2008-01-01
This paper presents an algorithm for estimating the location of the breast surface from scattered ultrawideband (UWB) microwave signals recorded across an antenna array. Knowing the location of the breast surface can improve imaging performance if incorporated as a priori information into recently proposed microwave imaging algorithms. These techniques transmit low-power microwaves into the breast using an antenna array, which in turn measures the scattered microwave signals for the purpose of detecting anomalies or changes in the dielectric properties of breast tissue. Our proposed surface identification algorithm consists of three procedures, the first of which estimates M points on the breast surface given M channels of measured microwave backscatter data. The second procedure applies interpolation and extrapolation to these M points to generate N > M points that are approximately uniformly distributed over the breast surface, while the third procedure uses these N points to generate a 3-D estimated breast surface. Numerical as well as experimental tests indicate that the maximum absolute error in the estimated surface generated by the algorithm is on the order of several millimeters. An error analysis conducted for a basic microwave radar imaging algorithm (least-squares narrowband beamforming) indicates that this level of error is acceptable. A key advantage of the algorithm is that it uses the same measured signals that are used for UWB microwave imaging, thereby minimizing patient scan time and avoiding the need for additional hardware. PMID:18232368
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.
Conroy, Shannon M; Koga, Karin; Woolcott, Christy G.; Dahl, Timothy; Byrne, Celia; Nagata, Chisato; Ursin, Giske; Yaffe, Martin J.; Vachon, Celine M.; Maskarinec, Gertraud
2012-01-01
Alcohol consumption and mammographic density are established risk factors for breast cancer. This study examined whether the association of mammographic density with breast cancer varies by alcohol intake. Mammographic density was assessed in digitized images for 1,207 cases and 1,663 controls from three populations (Japan, Hawaii, California) using a computer-assisted method. Associations were estimated by logistic regression. When comparing ever to never drinking, mean density was similar a...
Mammographic density and breast cancer risk in White and African American Women
Razzaghi, Hilda; Troester, Melissa A.; Gierach, Gretchen L.; Olshan, Andrew F.; Yankaskas, Bonnie C.; Millikan, Robert C.
2012-01-01
Mammographic density is a strong risk factor for breast cancer, but limited data are available in African American (AA) women. We examined the association between mammographic density and breast cancer risk in AA and white women. Cases (n = 491) and controls (n = 528) were from the Carolina Breast Cancer Study (CBCS) who also had mammograms recorded in the Carolina Mammography Registry (CMR). Mammographic density was reported to CMR using Breast Imaging Reporting and Data System (BI-RADS) cat...
Mammographic Breast Density Patterns in Asymptomatic Mexican Women
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 (X2) 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
Ana Laura Calderón-Garcidueñas
2012-01-01
Full Text Available 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 ( 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
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 (χ2) 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
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 (χ2) 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. PMID:23346398
Multivariate density estimation theory, practice, and visualization
Scott, David W
2015-01-01
David W. Scott, PhD, is Noah Harding Professor in the Department of Statistics at Rice University. The author of over 100 published articles, papers, and book chapters, Dr. Scott is also Fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics. He is recipient of the ASA Founder's Award and the Army Wilks Award. His research interests include computational statistics, data visualization, and density estimation. Dr. Scott is also Coeditor of Wiley Interdisciplinary Reviews: Computational Statistics and previous Editor of the Journal of Computational and
Linton, Linda; Martin, Lisa J.; Li, Qing; Huszti, Ella; Minkin, Salomon; John, Esther M.; Rommens, Johanna; Paterson, Andrew D.; Boyd, Norman F
2013-01-01
Introduction Percent mammographic density (PMD) is a strong and highly heritable risk factor for breast cancer. Studies of the role of PMD in familial breast cancer may require controls, such as the sisters of cases, selected from the same 'risk set' as the cases. The use of sister controls would allow control for factors that have been shown to influence risk of breast cancer such as race/ethnicity, socioeconomic status and a family history of breast cancer, but may introduce 'overmatching' ...
Devolli-Disha, Emine; Manxhuka-Kërliu, Suzana; Ymeri, Halit; Kutllovci, Arben
2009-05-01
Breast cancer is the most common cancer and the second most common cause of death from cancer in women.The aim of this studywas to determine which is more accurate imaging test mammography or ultrasound for diagnosis of breast cancer based on the women's age and breast density. We examined 546 patients with breast symptoms, by clinical breast examination, mammography and ultrasound. A total of 546 breast lesions were examined by histopathology analyses. Histopathology results revealed the presence of 259 invasive cancers, and 287 benign lesions. Sensitivity varied significantly with age and breast density. In the 259 women who had both tests, ultrasound had a higher sensitivity than mammography in women younger than 45 years, whereas mammography had a higher sensitivity than ultrasound in women older than 60 years. The sensitivity according to age was 52,1% for mammography and 72,6% for ultrasound. The specificity according to age was 88, 5% for ultrasound and 73, 9% for mammography. Comparing the sensitivity of mammography and ultrasound according to the breast density indicates that mammographic sensitivity was 82,2% among women with predominantly fatty breast, but 23.7% in women with heterogeneous dense breasts, with the increase of fibro glandular density the level of sensitivity with mammography decreases, while ultrasonographic sensitivity was 71,1% among women with predominantly fatty breast and 57,0% for heterogeneous dense breasts. Our data indicate that sensitivity and specificity of ultrasound was statistically significantly greater than mammography in patients with breast symptoms for the detection of breast cancer and benign lesions particularly in dense breast and in young women. PMID:19485945
Colin, Catherine; Prince, Violaine
2009-01-01
High breast density (HBD) tends to be seen as a significant and independent risk factor for breast cancer. This article describes a methodological and quantitative study of the variables selected by the large DMIST study, i.e., age, hormonal status and breast density, in correlation with cancer occurrence frequency. The statistical analysis of cancer rates in every patient subgroup of a study involving more than 42,000 women in screening, shows that HBD, when isolated from other variables, do...
Unified treatment of the asymptotics of asymmetric kernel density estimators
Hoffmann, Till; Jones, Nick S.
2015-01-01
We extend balloon and sample-smoothing estimators, two types of variable-bandwidth kernel density estimators, by a shift parameter and derive their asymptotic properties. Our approach facilitates the unified study of a wide range of density estimators which are subsumed under these two general classes of kernel density estimators. We demonstrate our method by deriving the asymptotic bias, variance, and mean (integrated) squared error of density estimators with gamma, log-normal, Birnbaum-Saun...
Association between body mass index and breast density using digital mammography
Kim, Mi Young [Dept. of Diagnostic Radiology, Dankook University Hospital, Cheonan (Korea, Republic of); Kim, Hwa Sun [Dept. of Radiological Technology, Ansan University, Ansan (Korea, Republic of)
2014-12-15
It is well known that low body mass index and younger age are associated with high breast density. Mammographic dense breast has been reported both as a cause of false-negative findings on mammography and as an indicator of increased breast cancer risk. The purpose of this study was to evaluate the association between breast density and body mass index. Furthermore, we considered proper screening method of breast cancer in Korean women. The study was performed on 496 women who underwent health checkup in a university hospital. Age and body mass index were negatively associated with breast density respectively. In postmenopausal women, age and body mass index showed statistically significant association with breast density. Therefore, we should consider sensitive additional method for breast cancer screening especially in younger age and underweight women.
Taghipour, S.; Banjevic, D; Miller, A.B.; Montgomery, N; A K S Jardine; Harvey, B. J.
2013-01-01
Background: The aim of screening is to detect a cancer in the preclinical state. However, a false-positive or a false-negative test result is a real possibility. Methods: We describe invasive breast cancer progression in the Canadian National Breast Screening Study and construct progression models with and without covariates. The effect of risk factors on transition intensities and false-negative probability is investigated. We estimate the transition rates, the sojourn time and sensitivity o...
Keller, Brad M.; Chen, Jinbo; Conant, Emily F.; Kontos, Despina
2014-03-01
Accurate assessment of a woman's risk to develop specific subtypes of breast cancer is critical for appropriate utilization of chemopreventative measures, such as with tamoxifen in preventing estrogen-receptor positive breast cancer. In this context, we investigate quantitative measures of breast density and parenchymal texture, measures of glandular tissue content and tissue structure, as risk factors for estrogen-receptor positive (ER+) breast cancer. Mediolateral oblique (MLO) view digital mammograms of the contralateral breast from 106 women with unilateral invasive breast cancer were retrospectively analyzed. Breast density and parenchymal texture were analyzed via fully-automated software. Logistic regression with feature selection and was performed to predict ER+ versus ER- cancer status. A combined model considering all imaging measures extracted was compared to baseline models consisting of density-alone and texture-alone features. Area under the curve (AUC) of the receiver operating characteristic (ROC) and Delong's test were used to compare the models' discriminatory capacity for receptor status. The density-alone model had a discriminatory capacity of 0.62 AUC (p=0.05). The texture-alone model had a higher discriminatory capacity of 0.70 AUC (p=0.001), which was not significantly different compared to the density-alone model (p=0.37). In contrast the combined density-texture logistic regression model had a discriminatory capacity of 0.82 AUC (prisk for estrogen-receptor positive breast cancer and could be useful in triaging women into appropriate risk-reduction strategies.
Kyoo il Kim
2006-01-01
This paper studies the uniform convergence rate of the turncated SNP (semi-nonparametric) density estimator. Using the uniform convergence rate result we obtain, we propose a test statistic testing the equivalence of two unknown densities where two densities are estimated using the SNP estimator and supports of densities are possibly unbounded.
ESTIMATING MICROORGANISM DENSITIES IN AEROSOLS FROM SPRAY IRRIGATION OF WASTEWATER
This document summarizes current knowledge about estimating the density of microorganisms in the air near wastewater management facilities, with emphasis on spray irrigation sites. One technique for modeling microorganism density in air is provided and an aerosol density estimati...
A nonparametric copula density estimator incorporating information on bivariate marginals
Cheng, Yu-Hsiang; Huang, Tzee-Ming
2016-01-01
We propose a copula density estimator that can include information on bivariate marginals when the information is available. We use B-splines for copula density approximation and include information on bivariate marginals via a penalty term. Our estimator satisfies the constraints for a copula density. Under mild conditions, the proposed estimator is consistent.
Baek, Ji Eun [Department of Radiology, Seoul St. Mary' s Hospital, College of Medicine, The Catholic University of Korea (Korea, Republic of); Kim, Sung Hun, E-mail: rad-ksh@catholic.ac.kr [Department of Radiology, Seoul St. Mary' s Hospital, College of Medicine, The Catholic University of Korea (Korea, Republic of); Lee, Ah Won [Department of Hospital Pathology, Seoul St. Mary' s Hospital, College of Medicine, The Catholic University of Korea (Korea, Republic of)
2014-08-15
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.
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
Llobet Azpitarte, Rafael; Pollán, Marina; Antón Guirao, Joaquín; Miranda-García, Josefa; Casals el Busto, María; Martinez Gomez, Inmaculada; Ruiz Perales, Francisco; Pérez Gómez, Beatriz; Salas-Trejo, Dolores; Perez-Cortes, Juan-Carlos
2014-01-01
The task of breast density quantification is becoming increasingly relevant due to its association with breast cancer risk. In this work, a semi-automated and a fully automated tools to assess breast density from full-field digitized mammograms are presented. The first tool is based on a supervised interactive thresholding procedure for segmenting dense from fatty tissue and is used with a twofold goal: for assessing mammographic density(MD) in a more objective and accurate way than via visua...
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.
iDensity: an automatic Gabor filter-based algorithm for breast density assessment
Gamdonkar, Ziba; Tay, Kevin; Ryder, Will; Brennan, Patrick C.; Mello-Thoms, Claudia
2015-03-01
Abstract Although many semi-automated and automated algorithms for breast density assessment have been recently proposed, none of these have been widely accepted. In this study a novel automated algorithm, named iDensity, inspired by the human visual system is proposed for classifying mammograms into four breast density categories corresponding to the Breast Imaging Reporting and Data System (BI-RADS). For each BI-RADS category 80 cases were taken from the normal volumes of the Digital Database for Screening Mammography (DDSM). For each case only the left medio-lateral oblique was utilized. After image calibration using the provided tables of each scanner in the DDSM, the pectoral muscle and background were removed. Images were filtered by a median filter and down sampled. Images were then filtered by a filter bank consisting of Gabor filters in six orientations and 3 scales, as well as a Gaussian filter. Three gray level histogram-based features and three second order statistics features were extracted from each filtered image. Using the extracted features, mammograms were separated initially separated into two groups, low or high density, then in a second stage, the low density group was subdivided into BI-RADS I or II, and the high density group into BI-RADS III or IV. The algorithm achieved a sensitivity of 95% and specificity of 94% in the first stage, sensitivity of 89% and specificity of 95% when classifying BIRADS I and II cases, and a sensitivity of 88% and 91% specificity when classifying BI-RADS III and IV.
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.
Seiji Hashimoto; Syuji Yoshiki; Ryoko Saeki; Yasuhiro Mimura; Ryosuke Ando; Shutaro Nanba
2016-01-01
Traffic accident frequency has been decreasing in Japan in recent years. Nevertheless, many accidents still occur on residential roads. Area-wide traffic calming measures including Zone 30, which discourages traffic by setting a speed limit of 30 km/h in residential areas, have been implemented. However, no objective implementation method has been established. Development of a model for traffic accident density estimation explained by GIS data can enable the determination of dangerous areas o...
Kernel density estimates used in stochastic precipitation generator
Hnilica, J. (Jan); Puš, V.
2014-01-01
The kernel density estimates were tested to be suitable to describe the probability distribution of daily precipitation sums. For this purpose, a stochastic precipitation generator using the kernel density estimates was constructed and it was compared with the LARS-WG generator. The data from meteorological stations from the Cidlina river basin were used to evaluate the performances of the generators. It was found that the kernel density estimates capture the probability density better th...
Percent Mammographic Density and Dense Area as Risk Factors for Breast Cancer
Rauh, C.; Hack, C. C.; Häberle, L.; Hein, A.; Engel, A.; Schrauder, M. G.; Fasching, P. A.; Jud, S. M.; Ekici, A.B.; Loehberg, C. R.; Meier-Meitinger, M.; Ozan, S.; Schulz-Wendtland, R.; Uder, M.; A. Hartmann
2012-01-01
Purpose: Mammographic characteristics are known to be correlated to breast cancer risk. Percent mammographic density (PMD), as assessed by computer-assisted methods, is an established risk factor for breast cancer. Along with this assessment the absolute dense area (DA) of the breast is reported as well. Aim of this study was to assess the predictive value of DA concerning breast cancer risk in addition to other risk factors and in addition to PMD. Methods: We conducted a case control study w...
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...
Concrete density estimation by rebound hammer method
Ismail, Mohamad Pauzi bin, E-mail: pauzi@nm.gov.my; Masenwat, Noor Azreen bin; Sani, Suhairy bin; Mohd, Shukri [NDT Group, Nuclear Malaysia, Bangi, Kajang, Selangor (Malaysia); Jefri, Muhamad Hafizie Bin; Abdullah, Mahadzir Bin [Material Technology Program, Faculty of Applied Sciences, UiTM, Shah Alam, Selangor (Malaysia); Isa, Nasharuddin bin; Mahmud, Mohamad Haniza bin [Pusat Penyelidikan Mineral, Jabatan Mineral dan Geosains, Ipoh, Perak (Malaysia)
2016-01-22
Concrete is the most common and cheap material for radiation shielding. Compressive strength is the main parameter checked for determining concrete quality. However, for shielding purposes density is the parameter that needs to be considered. X- and -gamma radiations are effectively absorbed by a material with high atomic number and high density such as concrete. The high strength normally implies to higher density in concrete but this is not always true. This paper explains and discusses the correlation between rebound hammer testing and density for concrete containing hematite aggregates. A comparison is also made with normal concrete i.e. concrete containing crushed granite.
Concrete density estimation by rebound hammer method
Concrete is the most common and cheap material for radiation shielding. Compressive strength is the main parameter checked for determining concrete quality. However, for shielding purposes density is the parameter that needs to be considered. X- and -gamma radiations are effectively absorbed by a material with high atomic number and high density such as concrete. The high strength normally implies to higher density in concrete but this is not always true. This paper explains and discusses the correlation between rebound hammer testing and density for concrete containing hematite aggregates. A comparison is also made with normal concrete i.e. concrete containing crushed granite
Using ultrasound tomography to identify the distributions of density throughout the breast
Sak, Mark; Duric, Neb; Littrup, Peter; Sherman, Mark E.; Gierach, Gretchen L.
2016-04-01
Women with high breast density are at increased risk of developing breast cancer. Breast density has usually been defined using mammography as the ratio of fibroglandular tissue to total breast area. Ultrasound tomography (UST) is an emerging modality that can also be used to measure breast density. UST creates tomographic sound speed images of the patient's breast which is useful as sound speed is directly proportional to tissue density. Furthermore, the volumetric and quantitative information contained in the sound speed images can be used to describe the distribution of breast density. The work presented here measures the UST sound speed density distributions of 165 women with negative screening mammography. Frequency distributions of the sound speed voxel information were examined for each patient. In a preliminary analysis, the UST sound speed distributions were averaged across patients and grouped by various patient and density-related factors (e.g., age, body mass index, menopausal status, average mammographic breast density). It was found that differences in the distribution of density could be easily visualized for different patient groupings. Furthermore, findings suggest that the shape of the distributions may be used to identify participants with varying amounts of dense and non-dense tissue.
Diet across the Lifespan and the Association with Breast Density in Adulthood.
Lindgren, Jessica; Dorgan, Joanne; Savage-Williams, Jennifer; Coffman, Donna; Hartman, Terryl
2013-01-01
Studies have shown inconsistent results regarding the association between dietary factors across the lifespan and breast density and breast cancer in women. Breast density is a strong risk factor for breast cancer, and the mechanism through which it influences cancer risk remains unclear. Breast density has been shown to be modifiable, potentially through dietary modifications. The goal of this paper is to summarize the current studies on diet and diet-related factors across all ages, determine which dietary factors show the strongest association with breast density, the most critical age of exposure, and identify future directions. We identified 28 studies, many of which are cross-sectional, and found that the strongest associations are among vitamin D, calcium, dietary fat, and alcohol in premenopausal women. Longitudinal studies with repeated dietary measures as well as the examination of overall diet over time are needed to confirm these findings. PMID:23431461
Diet across the Lifespan and the Association with Breast Density in Adulthood
Jessica Lindgren
2013-01-01
Full Text Available Studies have shown inconsistent results regarding the association between dietary factors across the lifespan and breast density and breast cancer in women. Breast density is a strong risk factor for breast cancer, and the mechanism through which it influences cancer risk remains unclear. Breast density has been shown to be modifiable, potentially through dietary modifications. The goal of this paper is to summarize the current studies on diet and diet-related factors across all ages, determine which dietary factors show the strongest association with breast density, the most critical age of exposure, and identify future directions. We identified 28 studies, many of which are cross-sectional, and found that the strongest associations are among vitamin D, calcium, dietary fat, and alcohol in premenopausal women. Longitudinal studies with repeated dietary measures as well as the examination of overall diet over time are needed to confirm these findings.
The estimation of body density in rugby union football players.
Bell, W
1995-01-01
The general regression equation of Durnin and Womersley for estimating body density from skinfold thicknesses in young men, was examined by comparing the estimated density from this equation, with the measured density of a group of 45 rugby union players of similar age. Body density was measured by hydrostatic weighing with simultaneous measurement of residual volume. Additional measurements included stature, body mass and skinfold thicknesses at the biceps, triceps, subscapular and suprailia...
Breast density measurements using ultrasound tomography for patients undergoing tamoxifen treatment
Sak, Mark; Duric, Neb; Littrup, Peter; Li, Cuiping; Bey-Knight, Lisa; Sherman, Mark; Boyd, Norman; Gierach, Gretchen
2013-03-01
Women with high breast density have an increased risk of developing breast cancer. Women treated with the selective estrogen receptor modulator tamoxifen for estrogen receptor positive breast cancer experience a 50% reduction in risk of contralateral breast cancer and overall reduction of similar magnitude has been identified among high-risk women receiving the drug for prevention. Tamoxifen has been shown to reduce mammographic density, and in the IBIS-1 chemoprevention trial, risk reduction and decline in density were significantly associated. Ultrasound tomography (UST) is an imaging modality that can create tomographic sound speed images of the breast. These sound speed images are useful because breast density is proportional to sound speed. The aim of this work is to examine the relationship between USTmeasured breast density and the use of tamoxifen. So far, preliminary results for a small number of patients have been observed and are promising. Correlations between the UST-measured density and mammographic density are strong and positive, while relationships between UST density with some patient specific risk factors behave as expected. Initial results of UST examinations of tamoxifen treated patients show that approximately 45% of the patients have a decrease in density in the contralateral breast after only several months of treatment. The true effect of tamoxifen on UST-measured density cannot yet be fully determined until more data are collected. However, these promising results suggest that UST can be used to reliably assess quantitative changes in breast density over short intervals and therefore suggest that UST may enable rapid assessment of density changes associated with therapeutic and preventative interventions.
Statistical Analysis of Distance Estimators with Density Differences and Density Ratios
Takafumi Kanamori
2014-02-01
Full Text Available Estimating a discrepancy between two probability distributions from samples is an important task in statistics and machine learning. There are mainly two classes of discrepancy measures: distance measures based on the density difference, such as the Lp-distances, and divergence measures based on the density ratio, such as the Φ-divergences. The intersection of these two classes is the L1-distance measure, and thus, it can be estimated either based on the density difference or the density ratio. In this paper, we first show that the Bregman scores, which are widely employed for the estimation of probability densities in statistical data analysis, allows us to estimate the density difference and the density ratio directly without separately estimating each probability distribution. We then theoretically elucidate the robustness of these estimators and present numerical experiments.
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
Estimation of scattered radiation in digital breast tomosynthesis
Diaz, O.; Dance, D. R.; Young, K. C.; Elangovan, P.; Bakic, P. R.; Wells, K.
2014-08-01
Digital breast tomosynthesis (DBT) is a promising technique to overcome the tissue superposition limitations found in planar 2D x-ray mammography. However, as most DBT systems do not employ an anti-scatter grid, the levels of scattered radiation recorded within the image receptor are significantly higher than that observed in planar 2D x-ray mammography. Knowledge of this field is necessary as part of any correction scheme and for computer modelling and optimisation of this examination. Monte Carlo (MC) simulations are often used for this purpose, however they are computationally expensive and a more rapid method of calculation is desirable. This issue is addressed in this work by the development of a fast kernel-based methodology for scatter field estimation using a detailed realistic DBT geometry. Thickness-dependent scatter kernels, which were validated against the literature with a maximum discrepancy of 4% for an idealised geometry, have been calculated and a new physical parameter (air gap distance) was used to estimate more accurately the distribution of scattered radiation for a series of anthropomorphic breast phantom models. The proposed methodology considers, for the first time, the effects of scattered radiation from the compression paddle and breast support plate, which can represent more than 30% of the total scattered radiation recorded within the image receptor. The results show that the scatter field estimator can calculate scattered radiation images in an average of 80 min for projection angles up to 25° with equal to or less than a 10% error across most of the breast area when compared with direct MC simulations.
Estimation of scattered radiation in digital breast tomosynthesis
Digital breast tomosynthesis (DBT) is a promising technique to overcome the tissue superposition limitations found in planar 2D x-ray mammography. However, as most DBT systems do not employ an anti-scatter grid, the levels of scattered radiation recorded within the image receptor are significantly higher than that observed in planar 2D x-ray mammography. Knowledge of this field is necessary as part of any correction scheme and for computer modelling and optimisation of this examination. Monte Carlo (MC) simulations are often used for this purpose, however they are computationally expensive and a more rapid method of calculation is desirable. This issue is addressed in this work by the development of a fast kernel-based methodology for scatter field estimation using a detailed realistic DBT geometry. Thickness-dependent scatter kernels, which were validated against the literature with a maximum discrepancy of 4% for an idealised geometry, have been calculated and a new physical parameter (air gap distance) was used to estimate more accurately the distribution of scattered radiation for a series of anthropomorphic breast phantom models. The proposed methodology considers, for the first time, the effects of scattered radiation from the compression paddle and breast support plate, which can represent more than 30% of the total scattered radiation recorded within the image receptor. The results show that the scatter field estimator can calculate scattered radiation images in an average of 80 min for projection angles up to 25° with equal to or less than a 10% error across most of the breast area when compared with direct MC simulations. (paper)
Uniform consistency of generalized kernel estimators of quantile density
Cheng, C.
1995-01-01
Various smoothing methods for quantile density estimation are unified into a generalized kernel smoothing. Based on a stochastic upper bound of the derivatives sequence for a sequence of smoothed Brownian bridges, uniform in-probability consistency of generalized kernel quantile density estimators on any closed subinterval of the open unit interval is derived.
Nonparametric volatility density estimation for discrete time models.
A.J. van Es; P.J.C. Spreij; J.H. van Zanten
2005-01-01
Abstract: 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 i
Density estimation using the trapping web design: A geometric analysis
Link, W.A.; Barker, R.J.
1994-01-01
Population densities for small mammal and arthropod populations can be estimated using capture frequencies for a web of traps. A conceptually simple geometric analysis that avoid the need to estimate a point on a density function is proposed. This analysis incorporates data from the outermost rings of traps, explaining large capture frequencies in these rings rather than truncating them from the analysis.
Quantification of breast density with dual energy mammography: An experimental feasibility study
Purpose: Breast density, the percentage of glandular breast tissue, has been shown to be a strong indicator of breast cancer risk. A quantitative method to measure breast density with dual energy mammography was investigated using physical phantoms. Methods: The dual energy mammography system used a tungsten anode x-ray tube with a 50 μm rhodium beam filter for low energy images and a 300 μm copper beam filter for high energy images. Glandular and adipose equivalent phantoms of uniform thickness were used to calibrate a dual energy basis decomposition algorithm. Four different phantom studies were used to evaluate the technique. The first study consisted of phantoms with thicknesses of 2.5-8.5 cm in 0.5 cm steps with variable densities centered at a mean of 28%. The second study consisted of phantoms at a fixed thickness of 4.0 cm, which ranged in densities from 0% to 100% in increments of 12.5%. The third study consisted of 4.0 cm thick phantoms at densities of 25%, 50% and 75% each imaged at three areal sizes, approximately 62.5, 125, and 250 cm2, in order to assess the effect of breast size on density measurement. The fourth study consisted of step phantoms designed to more closely mimic the shape of a female breast with maximal thicknesses from 3.0 to 7.0 cm at a fixed density of 50%. All images were corrected for x-ray scatter. Results: The RMS errors in breast density measurements were 0.44% for the variable thickness phantoms, 0.64% for the variable density phantoms, 2.87% for the phantoms of different areal sizes, and 4.63% for step phantoms designed to closely resemble the shape of a breast. Conclusions: The results of the phantom studies indicate that dual energy mammography can be used to measure breast density with an RMS error of approximately 5%.
Contribution of breast density to the volume of the augmented breast: A preliminary study
Hill, Sean M; Huettner, Franziska; Murray, John; Elwood, Eric; Barrick, Rebecca; Jones, Glyn
2011-01-01
Breast augmentation is one of the most common procedures performed, and obtaining symmetry and the correct postoperative volume is of the utmost importance. Currently, three-dimensional analysis is used to calculate breast volume, shape, size, etc, which aids the surgeon in deciding on the correct implant size. This study used three-dimensional analysis on 38 breasts (21 women) to measure breast volume and compare it with the implant size, in addition to comparing the results with breast dens...
Current Source Density Estimation for Single Neurons
Dorottya Cserpán
2014-03-01
Full Text Available Recent developments of multielectrode technology made it possible to measure the extracellular potential generated in the neural tissue with spatial precision on the order of tens of micrometers and on submillisecond time scale. Combining such measurements with imaging of single neurons within the studied tissue opens up new experimental possibilities for estimating distribution of current sources along a dendritic tree. In this work we show that if we are able to relate part of the recording of extracellular potential to a specific cell of known morphology we can estimate the spatiotemporal distribution of transmembrane currents along it. We present here an extension of the kernel CSD method (Potworowski et al., 2012 applicable in such case. We test it on several model neurons of progressively complicated morphologies from ball-and-stick to realistic, up to analysis of simulated neuron activity embedded in a substantial working network (Traub et al, 2005. We discuss the caveats and possibilities of this new approach.
Continuous iterated density estimation evolutionary algorithms within the IDEA framework
Bosman, P.A.N.; Thierens, D.
2001-01-01
In this paper, we formalize the notion of performing optimization by iterated density estimation evolutionary algorithms as the IDEA framework. These algorithms build probabilistic models and estimate probability densities based upon a selection of available points. We show how these probabilistic models can be built and used for different probability density functions within the IDEA framework. We put the emphasis on techniques for vectors of continuous random variables and thereby introduce...
Angiogenesis in Paget's Disease of the Vulva and the Breast: Correlation with Microvessel Density
Crow, Julie C.; Perrett, Christopher W.; Patricia E. Ellis; Wong Te Fong, L. F.; MacLean, Allan B.
2012-01-01
Our understanding of the pathogenesis of Paget's disease of the vulva and the breast remains limited. Current evidence supports the fact that angiogenesis plays an important role in the pathogenesis of several diseases. Therefore, we sought to define its role, as correlated with microvessel density, in Paget's disease of the vulva and the breast. Microvessels were analysed using anti-von Willebrand factor antibody in 105 cases of Paget's disease of the vulva and the breast comprising 71 cases...
Crowd Density Estimation based on Improved Harris Algorithm & Optics Alg.
Cheng Xu
2014-05-01
Full Text Available In this paper, we propose a method to estimate crowd density using improved Harris and Optics Algorithm. First of all, the images are pre-processed, corner features of the crowd are detected by the improved Harris algorithm, and then the formed density point data will be analyzed. Then, we use the optics density clustering theory to analyze the corner characters of crowd density which based on the distribution of the feature points. At last, the crowd density is estimated by the machine learning algorithm. The experiments are using PETS2009 database and the self-shooting datasets. The proposed approach has been tested on a number of image sequences, and it has good performance.The results show that our approach is superior to other methods, compared to the original Harris algorithm. Our method improves the efficiency of estimation and has a significant impact on preventing the accidents of crowd area with high density.
Sample size re-estimation in a breast cancer trial
Hade, Erinn; Jarjoura, David; Wei, Lai
2016-01-01
Background During the recruitment phase of a randomized breast cancer trial, investigating the time to recurrence, we found evidence that the failure probabilities used at the design stage were too high. Since most of the methodological research involving sample size re-estimation has focused on normal or binary outcomes, we developed a method which preserves blinding to re-estimate sample size in our time to event trial. Purpose A mistakenly high estimate of the failure rate at the design stage may reduce the power unacceptably for a clinically important hazard ratio. We describe an ongoing trial and an application of a sample size re-estimation method that combines current trial data with prior trial data or assumes a parametric model to re-estimate failure probabilities in a blinded fashion. Methods Using our current blinded trial data and additional information from prior studies, we re-estimate the failure probabilities to be used in sample size re-calculation. We employ bootstrap resampling to quantify uncertainty in the re-estimated sample sizes. Results At the time of re-estimation data from 278 patients was available, averaging 1.2 years of follow up. Using either method, we estimated an increase of 0 for the hazard ratio proposed at the design stage. We show that our method of blinded sample size re-estimation preserves the Type I error rate. We show that when the initial guess of the failure probabilities are correct; the median increase in sample size is zero. Limitations Either some prior knowledge of an appropriate survival distribution shape or prior data is needed for re-estimation. Conclusions In trials when the accrual period is lengthy, blinded sample size re-estimation near the end of the planned accrual period should be considered. In our examples, when assumptions about failure probabilities and HRs are correct the methods usually do not increase sample size or otherwise increase it by very little. PMID:20392786
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
Age as a predictive factor of mammographic breast density in Jamaican women
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)
On The Density Estimation by Super-Parametric Method
Tsai, Yeong-Shyeong; Hsu, Ying-Lin; Shung, Mung-Chung
2007-01-01
The super-parametric density estimators and its related algorism were suggested by Y. -S. Tsai et al [7]. The number of parameters is unlimited in the super- parametric estimators and it is a general theory in sense of unifying or connecting nonparametric and parametric estimators. Before applying to numerical examples, we can not give any comment of the estimators. In this paper, we will focus on the implementation, the computer programming, of the algorism and strategies of choosing window ...
Estimators of integrals of powers of density derivatives
Rodney C Wolff; Peter Hall
2006-01-01
Simple kernel-type estimators of integrals of general powers of general derivatives of probability densities are proposed. They are based on two simple properties, and in many circumstances enjoy optimal convergence rate.
KERNEL SPATIAL DENSITY ESTIMATION IN INFINITE DIMENSION SPACE
Dabo-Niang, Sophie; Yao, Anne-Françoise
2011-01-01
In this paper, we propose a nonparametric estimation of the spatial density of a functional stationary random field. This later is with values in some infinite dimensional space and admitted a density with respect to some reference measure. The weak and strong consistencies of the estimator are shown and rates of convergence are given. Special attention is paid to the links between the probabilities of small balls in the concerned infinite dimensional space and the rates of convergence. The p...
Evaluating parasite densities and estimation of parameters in transmission systems
Heinzmann D.
2008-09-01
Full Text Available Mathematical modelling of parasite transmission systems can provide useful information about host parasite interactions and biology and parasite population dynamics. In addition good predictive models may assist in designing control programmes to reduce the burden of human and animal disease. Model building is only the first part of the process. These models then need to be confronted with data to obtain parameter estimates and the accuracy of these estimates has to be evaluated. Estimation of parasite densities is central to this. Parasite density estimates can include the proportion of hosts infected with parasites (prevalence or estimates of the parasite biomass within the host population (abundance or intensity estimates. Parasite density estimation is often complicated by highly aggregated distributions of parasites within the hosts. This causes additional challenges when calculating transmission parameters. Using Echinococcus spp. as a model organism, this manuscript gives a brief overview of the types of descriptors of parasite densities, how to estimate them and on the use of these estimates in a transmission model.
Darker, Iain T.; Chen, Yan; Gale, Alastair G.
2011-03-01
Higher breast density is associated with a greater chance of developing breast cancer. Additionally, it is well known that higher mammographic breast density is associated with increased difficulty in accurately identifying breast cancer. However, comparatively little is known of the reliability of breast density judgements. All UK breast screeners (primarily radiologists and technologists) annually participate in the PERFORMS self-assessment scheme where they make several judgements about series of challenging recent screening cases of known outcomes. As part of this process, for each case, they provide a radiological assessment of the likelihood of cancer on a confidence scale, alongside an assessment of case density using a three point scale. Analysis of the data from two years of the scheme found that the degree of agreement on case density was significantly greater than no agreement (p < .001). However, only a moderate degree of inter-rater reliability was exhibited (κ = .44) with significant differences between the occupational groups. The reasons for differences between the occupational groups and the relationship between agreement on density rating and case reading ability are explored.
Correlation of Asymmetric Breast Density Findings in Mammograms with Cytologic Findings
"M. Sirus
2005-08-01
Full Text Available Introduction & objectives: Mammography has had a major impact on the early detection of breast cancer; its ability to locate early stage cancer provides the physicians and patients with several treatment op-tions. Many studies have assessed the predictive value of specific mamaographic findings; however, few studies have covered asymmetric breast tissue, which has been reported to occur in 3% of all mammograms. Asymmetric breast tissue is usually benign and ap-pears secondary to variations in normal breast tissue, postoperative changes, or hormone replacement ther-apy. However, it may also indicate a developing mass or an underlying cancer. Patients & Methods: In this study, of the 2360 pa-tients referred to Seyedoshohada hospital for mam-mography during 1997-1998, 112 had asymmetric breast density in their mammographic reports from which 76 were selected. They later underwent FNA after clinical breast examination. Results: The incidence of asymmetric breast density finding in mamographies was reported to be 4.75% in this study. Half of the patients had clinical findings in their breast examination such as palpable breast mass. Normal cytologic findings were observed in 59.2% of the patients; 32.8% had non-proliferative lesions, 2.6% had hyperplasia without atypism, 1.3% had hy-perplasia with atypism, and in 1.3% carcinoma was detected. Discussion: Asymmetric breast tissue densities are frequently encountered at screening and diagnostic mammography. These findings are significant because they may indicate a neoplasm, especially if an associ-ated palpable mass is present. Once these lesions are detected at standard mammography, a thorough knowledge of the patient’s clinical history plus sup-plementary breast imaging with additional mammo-graphic views and ultrasound will allow more accu-rate interpretation of these potentially perplexing soft-tissue findings.
Quantification of Regional Breast Density in Four Quadrants Using 3D MRI—A Pilot Study
Peter T. Fwu
2015-08-01
Full Text Available PURPOSE: This study presented a three-dimensional magnetic resonance (MR–based method to separate a breast into four quadrants for quantitative measurements of the quadrant breast volume (BV and density. METHODS: Breast MR images from 58 healthy women were studied. The breast and the fibroglandular tissue were segmented by using a computer-based algorithm. A breast was divided into four quadrants using two perpendicular planes intersecting at the nipple or the nipple-centroid line. After the separation, the BV, the fibroglandular tissue volume, and the percent density (PD were calculated. The symmetry of the quadrant BV in the left and right breasts separated by using the nipple alone, or the nipple-centroid line, was compared. RESULTS: The quadrant separation made on the basis of the nipple-centroid line showed closer BVs in four quadrants than using the nipple alone. The correlation and agreement for the BV in corresponding quadrants of the left and the right breasts were improved after the nipple-centroid reorientation. Among the four quadrants, PD was the highest in the lower outer and the lowest in the upper outer (significant than the other three quadrants (P < .05. CONCLUSIONS: We presented a quantitative method to divide a breast into four quadrants. The reorientation based on the nipple-centroid line improved the left to right quadrant symmetry, and this may provide a better standardized method to measure quantitative quadrant density. The cancer occurrence rates are known to vary in different sites of a breast, and our method may provide a tool for investigating its association with the quantitative breast density.
Mean thermospheric density estimation derived from satellite constellations
Li, Alan; Close, Sigrid
2015-10-01
This paper defines a method to estimate the mean neutral density of the thermosphere given many satellites of the same form factor travelling in similar regions of space. A priori information to the estimation scheme include ranging measurements and a general knowledge of the onboard ADACS, although precise measurements are not required for the latter. The estimation procedure seeks to utilize order statistics to estimate the probability of the minimum drag coefficient achievable, and amalgamating all measurements across multiple time periods allows estimation of the probability density of the ballistic factor itself. The model does not depend on prior models of the atmosphere; instead we require estimation of the minimum achievable drag coefficient which is based upon physics models of simple shapes in free molecular flow. From the statistics of the minimum, error statistics on the estimated atmospheric density can be calculated. Barring measurement errors from the ranging procedure itself, it is shown that with a constellation of 10 satellites, we can achieve a standard deviation of roughly 4% on the estimated mean neutral density. As more satellites are added to the constellation, the result converges towards the lower limit of the achievable drag coefficient, and accuracy becomes limited by the quality of the ranging measurements and the probability of the accommodation coefficient. Comparisons are made to existing atmospheric models such as NRLMSISE-00 and JB2006.
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.
Spatially Adaptive Density Estimation by Localised Haar Projections
Gach, Florian; Spokoiny, Vladimir
2011-01-01
Given a random sample from some unknown density $f_0: \\mathbb R \\to [0, \\infty)$ we devise Haar wavelet estimators for $f_0$ with variable resolution levels constructed from localised test procedures (as in Lepski, Mammen, and Spokoiny (1997, Ann. Statist.)). We show that these estimators adapt to spatially heterogeneous smoothness of $f_0$, simultaneously for every point $x$ in a fixed interval, in sup-norm loss. The thresholding constants involved in the test procedures can be chosen in practice under the idealised assumption that the true density is locally constant in a neighborhood of the point $x$ of estimation, and an information theoretic justification of this practice is given.
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
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 (It1/It0 ratio) and in comparison to the surrounding tissue (It1/It1-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
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/106 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
Crowd Density and Counting Estimation Based on Image Textural Feature
Jianjie Yang
2014-10-01
Full Text Available This paper proposes an image textural analytical method for estimating the crowd density and counting. At first, the target detection is conducted to obtain the foreground image. This crowd image is used to calculate the gray level co-occurrence matrix (GLCM. Then, according to the characteristic values of the gray level co-occurrence matrix, i.e., energy, entropy, contrast, homogeneity, we use support vector machine (SVM to estimate crowd density. Simultaneously, the method of linear regression is used to estimate the crowd counting. The accuracy of evaluation is improved since we extract the target image textural traits to overcome the influence of background for estimation results. Finally, the experimental results show that the proposed approaches of crowd density and counting are feasible and effective
Angiogenesis in Paget's Disease of the Vulva and the Breast: Correlation with Microvessel Density
Patricia E. Ellis
2012-01-01
Full Text Available Our understanding of the pathogenesis of Paget's disease of the vulva and the breast remains limited. Current evidence supports the fact that angiogenesis plays an important role in the pathogenesis of several diseases. Therefore, we sought to define its role, as correlated with microvessel density, in Paget's disease of the vulva and the breast. Microvessels were analysed using anti-von Willebrand factor antibody in 105 cases of Paget's disease of the vulva and the breast comprising 71 cases of Paget's disease of the vulva, including 8 cases with invasive disease, and 34 cases of Paget's disease of the breast. The latter included 12 cases with DCIS, 5 cases with both DCIS and invasive carcinoma, and 6 with carcinoma alone. Eleven cases had no underlying tumour identified. Increased microvessel density was demonstrated in Paget's disease of the breast with DCIS and with carcinoma alone compared to Paget's disease of the breast alone, <0.08 and <0.013, respectively. There were no significant differences in microvessel density in the vulval cases. Neovascularisation is an important process in the development of Paget's disease of the breast. Other biological and molecular processes are more involved in the pathogenesis of Paget's disease of the vulva.
Lukui Yang; Long Li; Xiangyu Cui; Dalei Yang
2015-01-01
Objective The purpose of this study was to evaluate the distribution of vascular endothelial growth factor (VEGF) and CD105-microvessel density (MVD) in invasive breast carcinomas. We also aimed to analyze the relationship between VEGF and MVD expression with other standard prognostic parameters associated with invasive breast cancer, such as size, grade, stage of the cancer, metastases, and tumor recurrence. Methods Immunohistochemistry via the Ultra SensitiveTM S-P method was used to detect VEGF and MVD expression in 128 cases of invasive breast carcinoma. Specimens were evaluated for CD105 expres-sion. Positively stained microvessels were counted in dense vascular foci under 400× magnification. MVD in the peripheral area adjacent to the lesion and in the central area within the lesion in invasive breast carcinomas and benign leisions groups were also assessed. Fifty cases of benign breast disease tissue were selected as the control group. Results Results showed that 64.1% of invasive breast cancer samples were VEGF-positive, higher than in benign breast disease tissue (22.0%, P 0.05). MVD of the peripheral area adja-cent to the lesion was significantly higher than those central area within the lesion in both invasive breast cancer and benign breast disease groups (P 50 years) or the two tumor diameter groups (≤2 cm vs.>2 cm), P > 0.05. Conclusion Overexpression of VEGF and MVD may be important biological markers for invasion and lymph node and distant metastases of invasive breast cancer. Combined detection of the two tumor mark-ers could provide better prognostic monitoring for disease recurrence and metastasis, as wel as aid with clinical staging of breast tumors. Prediction of the risk for metastasis and recurrence, as wel as recurrence patterns based on VEGF and MVD post-surgery, could aid design of better fol ow-up regimens and appro-priate treatment strategies for breast cancer patients.
Atmospheric Density Corrections Estimated from Fitted Drag Coefficients
McLaughlin, C. A.; Lechtenberg, T. F.; Mance, S. R.; Mehta, P.
2010-12-01
Fitted drag coefficients estimated using GEODYN, the NASA Goddard Space Flight Center Precision Orbit Determination and Geodetic Parameter Estimation Program, are used to create density corrections. The drag coefficients were estimated for Stella, Starlette and GFZ using satellite laser ranging (SLR) measurements; and for GEOSAT Follow-On (GFO) using SLR, Doppler, and altimeter crossover measurements. The data analyzed covers years ranging from 2000 to 2004 for Stella and Starlette, 2000 to 2002 and 2005 for GFO, and 1995 to 1997 for GFZ. The drag coefficient was estimated every eight hours. The drag coefficients over the course of a year show a consistent variation about the theoretical and yearly average values that primarily represents a semi-annual/seasonal error in the atmospheric density models used. The atmospheric density models examined were NRLMSISE-00 and MSIS-86. The annual structure of the major variations was consistent among all the satellites for a given year and consistent among all the years examined. The fitted drag coefficients can be converted into density corrections every eight hours along the orbit of the satellites. In addition, drag coefficients estimated more frequently can provide a higher frequency of density correction.
Density estimation using KNN and a potential model
Lu, Yonggang; Qiao, Jiangang; Liao, Li; Yang, Wuyang
2013-10-01
Density-based clustering methods are usually more adaptive than other classical methods in that they can identify clusters of various shapes and can handle noisy data. A novel density estimation method is proposed using both the knearest neighbor (KNN) graph and a hypothetical potential field of the data points to capture the local and global data distribution information respectively. An initial density score computed using KNN is used as the mass of the data point in computing the potential values. Then the computed potential is used as the new density estimation, from which the final clustering result is derived. All the parameters used in the proposed method are determined from the input data automatically. The new clustering method is evaluated by comparing with K-means++, DBSCAN, and CSPV. The experimental results show that the proposed method can determine the number of clusters automatically while producing competitive clustering results compared to the other three methods.
Kernel density estimation of a multidimensional efficiency profile
Poluektov, Anton
2014-01-01
Kernel density estimation is a convenient way to estimate the probability density of a distribution given the sample of data points. However, it has certain drawbacks: proper description of the density using narrow kernels needs large data samples, whereas if the kernel width is large, boundaries and narrow structures tend to be smeared. Here, an approach to correct for such effects, is proposed that uses an approximate density to describe narrow structures and boundaries. The approach is shown to be well suited for the description of the efficiency shape over a multidimensional phase space in a typical particle physics analysis. An example is given for the five-dimensional phase space of the $\\Lambda_b^0\\to D^0p\\pi$ decay.
Comparison of bone mineral density in young patients with breast cancer and healthy women
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.
Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring
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...
State of the Art in Photon Density Estimation
Hachisuka, Toshiya; Jarosz, Wojciech; Bouchard, Guillaume;
2012-01-01
scattering. Since its introduction, photon-density estimation has been significantly extended in computer graphics with the introduction of: specialized techniques that intelligently modify the positions or bandwidths to reduce visual error using a small number of photons, approaches that eliminate error...... completely in the limit, and methods that use higher-order samples and queries to reduce error in participating media. This two-part course explains how to implement all these latest advances in photon-density estimation. It begins with a short introduction using classical photon mapping, but the remainder...
State of the Art in Photon-Density Estimation
Hachisuka, Toshiya; Jarosz, Wojciech; Georgiev, Iliyan;
2013-01-01
completely in the limit, and methods that use higher-order samples and queries to reduce error in participating media. This two-part course explains how to implement all these latest advances in photon-density estimation. It begins with a short introduction using classical photon mapping, but the remainder...... of the course provides new, hands-on explanations of the latest developments in this area by experts in each technique. Attendees gain concrete and practical understanding of the latest developments in photon-density-estimation techniques that have not been presented before in SIGGRAPH courses....
Comparison of bone mineral density in young patients with breast cancer and healthy women
Sousan Kolahi; Hamid Noshad; Jamal Eivazi Ziaei; Alireza Nikanfar; Parvin Shakori Partovi; Iraj Asvadi Kermani; Farid Panahi; Nassim Mahmoudzade
2014-01-01
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 brea...
The estimation of body density in rugby union football players.
Bell, W
1995-03-01
The general regression equation of Durnin and Womersley for estimating body density from skinfold thicknesses in young men, was examined by comparing the estimated density from this equation, with the measured density of a group of 45 rugby union players of similar age. Body density was measured by hydrostatic weighing with simultaneous measurement of residual volume. Additional measurements included stature, body mass and skinfold thicknesses at the biceps, triceps, subscapular and suprailiac sites. The estimated density was significantly different from the measured density (P 0.05), total errors ranging from 0.0067 to 0.0092 g ml-1. An exploratory assessment was also made of the effect of fatness and aerobic fitness on the prediction equations. The equations should be applied to players of similar age and playing ability, and for the purpose of identifying group characteristics. Application of the equations to individuals may give rise to errors of between -3.9% to +2.5% total body fat in two-thirds of cases. PMID:7788218
Forecasting Electricity Smart Meter Data Using Conditional Kernel Density Estimation
Arora, Siddharth; Taylor, James W.
2014-01-01
The recent advent of smart meters has led to large micro-level datasets. For the first time, the electricity consumption at individual sites is available on a near real-time basis. Efficient management of energy resources, electric utilities, and transmission grids, can be greatly facilitated by harnessing the potential of this data. The aim of this study is to generate probability density estimates for consumption recorded by individual smart meters. Such estimates can assist decision making...
Density Estimation in Several Populations With Uncertain Population Membership
Ma, Yanyuan
2011-09-01
We devise methods to estimate probability density functions of several populations using observations with uncertain population membership, meaning from which population an observation comes is unknown. The probability of an observation being sampled from any given population can be calculated. We develop general estimation procedures and bandwidth selection methods for our setting. We establish large-sample properties and study finite-sample performance using simulation studies. We illustrate our methods with data from a nutrition study.
Fu, Wanyi; Tian, Xiaoyu; Sturgeon, Gregory; Agasthya, Greeshma; Segars, William Paul; Goodsitt, Mitchell M.; Kazerooni, Ella A.; Samei, Ehsan
2016-04-01
In thoracic CT, organ-based tube current modulation (OTCM) reduces breast dose by lowering the tube current in the 120° anterior dose reduction zone of patients. However, in practice the breasts usually expand to an angle larger than the dose reduction zone. This work aims to simulate a breast positioning technique (BPT) to constrain the breast tissue to within the dose reduction zone for OTCM and to evaluate the corresponding potential reduction in breast dose. Thirteen female anthropomorphic computational phantoms were studied (age range: 27-65 y.o., weight range: 52-105.8 kg). Each phantom was modeled in the supine position with and without application of the BPT. Attenuation-based tube current (ATCM, reference mA) was generated by a ray-tracing program, taking into account the patient attenuation change in the longitudinal and angular plane (CAREDose4D, Siemens Healthcare). OTCM was generated by reducing the mA to 20% between +/- 60° anterior of the patient and increasing the mA in the remaining projections correspondingly (X-CARE, Siemens Healthcare) to maintain the mean tube current. Breast tissue dose was estimated using a validated Monte Carlo program for a commercial scanner (SOMATOM Definition Flash, Siemens Healthcare). Compared to standard tube current modulation, breast dose was significantly reduced using OTCM by 19.8+/-4.7%. With the BPT, breast dose was reduced by an additional 20.4+/-6.5% to 37.1+/-6.9%, using the same CTDIvol. BPT was more effective for phantoms simulating women with larger breasts with the average breast dose reduction of 30.2%, 39.2%, and 49.2% from OTCMBP to ATCM, using the same CTDIvol for phantoms with 0.5, 1.5, and 2.5 kg breasts, respectively. This study shows that a specially designed BPT improves the effectiveness of OTCM.
A new approach for estimating the density of liquids.
Sakagami, T; Fuchizaki, K; Ohara, K
2016-10-01
We propose a novel approach with which to estimate the density of liquids. The approach is based on the assumption that the systems would be structurally similar when viewed at around the length scale (inverse wavenumber) of the first peak of the structure factor, unless their thermodynamic states differ significantly. The assumption was implemented via a similarity transformation to the radial distribution function to extract the density from the structure factor of a reference state with a known density. The method was first tested using two model liquids, and could predict the densities within an error of several percent unless the state in question differed significantly from the reference state. The method was then applied to related real liquids, and satisfactory results were obtained for predicted densities. The possibility of applying the method to amorphous materials is discussed. PMID:27494268
An Infrastructureless Approach to Estimate Vehicular Density in Urban Environments
Juan-Carlos Cano
2013-02-01
Full Text Available In Vehicular Networks, communication success usually depends on the density of vehicles, since a higher density allows having shorter and more reliable wireless links. Thus, knowing the density of vehicles in a vehicular communications environment is important, as better opportunities for wireless communication can show up. However, vehicle density is highly variable in time and space. This paper deals with the importance of predicting the density of vehicles in vehicular environments to take decisions for enhancing the dissemination of warning messages between vehicles. We propose a novel mechanism to estimate the vehicular density in urban environments. Our mechanism uses as input parameters the number of beacons received per vehicle, and the topological characteristics of the environment where the vehicles are located. Simulation results indicate that, unlike previous proposals solely based on the number of beacons received, our approach is able to accurately estimate the vehicular density, and therefore it could support more efficient dissemination protocols for vehicular environments, as well as improve previously proposed schemes.
An infrastructureless approach to estimate vehicular density in urban environments.
Sanguesa, Julio A; Fogue, Manuel; Garrido, Piedad; Martinez, Francisco J; Cano, Juan-Carlos; Calafate, Carlos T; Manzoni, Pietro
2013-01-01
In Vehicular Networks, communication success usually depends on the density of vehicles, since a higher density allows having shorter and more reliable wireless links. Thus, knowing the density of vehicles in a vehicular communications environment is important, as better opportunities for wireless communication can show up. However, vehicle density is highly variable in time and space. This paper deals with the importance of predicting the density of vehicles in vehicular environments to take decisions for enhancing the dissemination of warning messages between vehicles. We propose a novel mechanism to estimate the vehicular density in urban environments. Our mechanism uses as input parameters the number of beacons received per vehicle, and the topological characteristics of the environment where the vehicles are located. Simulation results indicate that, unlike previous proposals solely based on the number of beacons received, our approach is able to accurately estimate the vehicular density, and therefore it could support more efficient dissemination protocols for vehicular environments, as well as improve previously proposed schemes. PMID:23435054
An Infrastructureless Approach to Estimate Vehicular Density in Urban Environments
Sanguesa, Julio A.; Fogue, Manuel; Garrido, Piedad; Martinez, Francisco J.; Cano, Juan-Carlos; Calafate, Carlos T.; Manzoni, Pietro
2013-01-01
In Vehicular Networks, communication success usually depends on the density of vehicles, since a higher density allows having shorter and more reliable wireless links. Thus, knowing the density of vehicles in a vehicular communications environment is important, as better opportunities for wireless communication can show up. However, vehicle density is highly variable in time and space. This paper deals with the importance of predicting the density of vehicles in vehicular environments to take decisions for enhancing the dissemination of warning messages between vehicles. We propose a novel mechanism to estimate the vehicular density in urban environments. Our mechanism uses as input parameters the number of beacons received per vehicle, and the topological characteristics of the environment where the vehicles are located. Simulation results indicate that, unlike previous proposals solely based on the number of beacons received, our approach is able to accurately estimate the vehicular density, and therefore it could support more efficient dissemination protocols for vehicular environments, as well as improve previously proposed schemes. PMID:23435054
A formalism is proposed for the estimation of mean glandular dose for breast tomosynthesis, which is a simple extension of the UK, European and IAEA protocols for dosimetry in conventional projection mammography. The formalism introduces t-factors for the calculation of breast dose from a single projection and T-factors for a complete exposure series. Monte Carlo calculations of t-factors have been made for an imaging geometry with full-field irradiation of the breast for a wide range of x-ray spectra, breast sizes and glandularities. The t-factors show little dependence on breast glandularity and tables are provided as a function of projection angle and breast thickness, which may be used for all x-ray spectra simulated. The T-factors for this geometry depend upon the choice of projection angles and weights per projection, but various example calculations gave values in the range 0.93-1.00. T-factors are also provided for the Sectra tomosynthesis system, which employs a scanned narrow-beam imaging geometry. In this quite different configuration, the factor (denoted TS) shows an important dependence on breast thickness, varying between 0.98 and 0.76 for 20 and 110 mm thick breasts, respectively. Additional data are given to extend the current tabulations of g-, c- and s-factors used for dosimetry of conventional 2D mammography.
Estimating neuronal connectivity from axonal and dendritic density fields
Jaap evan Pelt
2013-11-01
Full Text Available Neurons innervate space by extending axonal and dendritic arborizations. When axons and dendrites come in close proximity of each other, synapses between neurons can be formed. Neurons vary greatly in their morphologies and synaptic connections with other neurons. The size and shape of the arborizations determine the way neurons innervate space. A neuron may therefore be characterized by the spatial distribution of its axonal and dendritic 'mass'. A population mean 'mass' density field of a particular neuron type can be obtained by averaging over the individual variations in neuron geometries. Connectivity in terms of candidate synaptic contacts between neurons can be determined directly on the basis of their arborizations but also indirectly on the basis of their density fields. To decide when a candidate synapse can be formed, we previously developed a criterion defining that axonal and dendritic line pieces should cross in 3D and have an orthogonal distance less than a threshold value. In this paper, we developed new methodology for applying this criterion to density fields. We show that estimates of the number of contacts between neuron pairs calculated from their density fields are fully consistent with the number of contacts calculated from the actual arborizations. However, the estimation of the connection probability and the expected number of contacts per connection cannot be calculated directly from density fields, because density fields do not carry anymore the correlative structure in the spatial distribution of synaptic contacts. Alternatively, these two connectivity measures can be estimated from the expected number of contacts by using empirical mapping functions. The neurons used for the validation studies were generated by our neuron simulator NETMORPH. An example is given of the estimation of average connectivity and Euclidean pre- and postsynaptic distance distributions in a network of neurons represented by their population
To compare the conspicuity of different histopathological types of breast cancer according to breast density and mammographic imaging in patients with screen-detected breast cancers undergoing both full-field digital mammography (FFDM) and screen-film mammography (SFM) in the United Kingdom National Health Service Breast Screening Programme (NHSBSP). 185 patients underwent routine screening with SFM followed by further imaging using FFDM with consequent diagnosis of breast cancer. All SFM and soft-copy FFDM images were evaluated by two readers in an independent, retrospective review. The visualisation and conspicuity of the mammographic abnormality were recorded and graded using a four-level scale. Conspicuity of breast cancer was qualitatively evaluated. Breast density and conspicuity were correlated with histopathological diagnosis and inter-observer correlation was calculated. Mixed Model ANOVA demonstrated significant differences between FFDM and SFM (p < 0.001) and breast densities (p = 0.009): conspicuity of the mammographic abnormality (p < 0.001) and visualisation of the dominant mammographic feature (p < 0.001) were significantly greater with FFDM than SFM. This held true for both readers and for all histopathological tumour types with no significant differences between each tumour type. FFDM is significantly superior to SFM for conspicuity of screen-detected breast cancers for all histopathological types and breast densities. (orig.)
Pinker, Katja [Medical University Vienna, Department of Radiology, Divison of Molecular and Gender Imaging, Vienna (Austria); Medical University Vienna, Department of Radiology, MR Centre of Excellence, Vienna (Austria); Perry, Nicholas [St Bartholomew' s Hospital, Breast Unit, Barts and The London Cancer Centre, London (United Kingdom); The Princess Grace Hospital, The London Breast Institute, London (United Kingdom); Vinnicombe, S.; Shiel, S. [St Bartholomew' s Hospital, Breast Unit, Barts and The London Cancer Centre, London (United Kingdom); Weber, M. [Medical University Vienna, Department of Radiology, Vienna (Austria)
2011-01-15
To compare the conspicuity of different histopathological types of breast cancer according to breast density and mammographic imaging in patients with screen-detected breast cancers undergoing both full-field digital mammography (FFDM) and screen-film mammography (SFM) in the United Kingdom National Health Service Breast Screening Programme (NHSBSP). 185 patients underwent routine screening with SFM followed by further imaging using FFDM with consequent diagnosis of breast cancer. All SFM and soft-copy FFDM images were evaluated by two readers in an independent, retrospective review. The visualisation and conspicuity of the mammographic abnormality were recorded and graded using a four-level scale. Conspicuity of breast cancer was qualitatively evaluated. Breast density and conspicuity were correlated with histopathological diagnosis and inter-observer correlation was calculated. Mixed Model ANOVA demonstrated significant differences between FFDM and SFM (p < 0.001) and breast densities (p = 0.009): conspicuity of the mammographic abnormality (p < 0.001) and visualisation of the dominant mammographic feature (p < 0.001) were significantly greater with FFDM than SFM. This held true for both readers and for all histopathological tumour types with no significant differences between each tumour type. FFDM is significantly superior to SFM for conspicuity of screen-detected breast cancers for all histopathological types and breast densities. (orig.)
Face Value: Towards Robust Estimates of Snow Leopard Densities.
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.
Kernel density estimates used in stochastic precipitation generator
Hnilica, Jan; Puš, V.
Praha: Ústav pro hydrodynamiku, 2014 - (Brych, K.; Tesař, M.), s. 120-124 ISBN 978-80-02-02525-2. [Hydrologie malého povodí 2014. Praha (CZ), 22.04.2014-24.04.2014] Institutional support: RVO:67985874 Keywords : kernel density estimate * daily precipitation sum * stochastic generator Subject RIV: DA - Hydrology ; Limnology
Estimation of a multivariate stochastic volatility density by kernel deconvolution
B. van Es; P. Spreij
2011-01-01
We consider a continuous time stochastic volatility model. The model contains a stationary volatility process. We aim to estimate the multivariate density of the finite-dimensional distributions of this process. We assume that we observe the process at discrete equidistant instants of time. The dist
Corruption clubs: empirical evidence from kernel density estimates
Herzfeld, T.; Weiss, Ch.
2007-01-01
A common finding of many analytical models is the existence of multiple equilibria of corruption. Countries characterized by the same economic, social and cultural background do not necessarily experience the same levels of corruption. In this article, we use Kernel Density Estimation techniques to
Density estimation in tiger populations: combining information for strong inference
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.
Density estimates for phase transitions with a trace
Sire, Yannick
2010-01-01
We consider a functional obtained by adding a trace term to the Allen-Cahn phase segregation model and we prove some density estimates for the level sets of the interfaces. We treat in a unified way also the cases of possible degeneracy and singularity of the ellipticity of the model and the quasiminimal case.
Density Estimation Classifiers Uses Geometry of Neighbors Space
Jiřina, Marcel
IEEE, 2003 - (Rudas, I.; Szakál, A.) ISBN 963-7154-183. [ICCC 2003. IEEE International Conference on Computational Cybernetics . Siófok (HU), 29.08.2003-31.08.2003] R&D Projects: GA MŠk LN00B096 Keywords : density * estimation * neighbors space * classification Subject RIV: BA - General Mathematics
Nonparametric density estimators based on nonstationary absolutely regular random sequences
Michel Harel
1996-01-01
Full Text Available In this paper, the central limit theorems for the density estimator and for the integrated square error are proved for the case when the underlying sequence of random variables is nonstationary. Applications to Markov processes and ARMA processes are provided.
Regularized Regression and Density Estimation based on Optimal Transport
Burger, M.
2012-03-11
The aim of this paper is to investigate a novel nonparametric approach for estimating and smoothing density functions as well as probability densities from discrete samples based on a variational regularization method with the Wasserstein metric as a data fidelity. The approach allows a unified treatment of discrete and continuous probability measures and is hence attractive for various tasks. In particular, the variational model for special regularization functionals yields a natural method for estimating densities and for preserving edges in the case of total variation regularization. In order to compute solutions of the variational problems, a regularized optimal transport problem needs to be solved, for which we discuss several formulations and provide a detailed analysis. Moreover, we compute special self-similar solutions for standard regularization functionals and we discuss several computational approaches and results. © 2012 The Author(s).
Ionospheric electron density profile estimation using commercial AM broadcast signals
Yu, De; Ma, Hong; Cheng, Li; Li, Yang; Zhang, Yufeng; Chen, Wenjun
2015-08-01
A new method for estimating the bottom electron density profile by using commercial AM broadcast signals as non-cooperative signals is presented in this paper. Without requiring any dedicated transmitters, the required input data are the measured elevation angles of signals transmitted from the known locations of broadcast stations. The input data are inverted for the QPS model parameters depicting the electron density profile of the signal's reflection area by using a probabilistic inversion technique. This method has been validated on synthesized data and used with the real data provided by an HF direction-finding system situated near the city of Wuhan. The estimated parameters obtained by the proposed method have been compared with vertical ionosonde data and have been used to locate the Shijiazhuang broadcast station. The simulation and experimental results indicate that the proposed ionospheric sounding method is feasible for obtaining useful electron density profiles.
Density estimates for deep-sea gastropod assemblages
Rex, Michael A.; Etter, Ron J.; Nimeskern, Phillip W.
1990-04-01
Extensive boxcore sampling in the Atlantic Continental Slope and Rise study permitted the first precise measurement of gastropod density in the bathyal region of the deep sea. Gastropod density decreases significantly and exponentially with depth (250-3494 m), and density-depth regression lines do not differ significantly in either slope or elevatiob over horizontal scales of approximately 1000 km. The subclasses Prosobranchia and Opisthobranchia both show significant decreases in density with depth. Predatory taxa (neogastropods and opisthobranchs) exhibit significantly steeper declines in density with depth than do taxa dominated by deposit feeders (archaeogastropods and mesogastropods). Members of upper trophic levels may be more sensitive to the reduction in nutrient input with increased depth because of the energy loss between trophic levels in the food chain. A comparison of density estimates of gastropods from boxcore, grab and anchor-dredge samples taken in the same region revealed no significant differences in density-depth relationships among the sampling methods. A synthesis of data from 777 boxcore samples collected from the Atlantic, Caribbean and Pacific over a depth range of 250-7298 m indicates that the decline in gastropod density with depth is a global trend with only moderate inter-regional variation.
Evaluating lidar point densities for effective estimation of aboveground biomass
Wu, Zhuoting; Dye, Dennis G.; Stoker, Jason; Vogel, John M.; Velasco, Miguel G.; Middleton, Barry R.
2016-01-01
The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) was recently established to provide airborne lidar data coverage on a national scale. As part of a broader research effort of the USGS to develop an effective remote sensing-based methodology for the creation of an operational biomass Essential Climate Variable (Biomass ECV) data product, we evaluated the performance of airborne lidar data at various pulse densities against Landsat 8 satellite imagery in estimating above ground biomass for forests and woodlands in a study area in east-central Arizona, U.S. High point density airborne lidar data, were randomly sampled to produce five lidar datasets with reduced densities ranging from 0.5 to 8 point(s)/m2, corresponding to the point density range of 3DEP to provide national lidar coverage over time. Lidar-derived aboveground biomass estimate errors showed an overall decreasing trend as lidar point density increased from 0.5 to 8 points/m2. Landsat 8-based aboveground biomass estimates produced errors larger than the lowest lidar point density of 0.5 point/m2, and therefore Landsat 8 observations alone were ineffective relative to airborne lidar for generating a Biomass ECV product, at least for the forest and woodland vegetation types of the Southwestern U.S. While a national Biomass ECV product with optimal accuracy could potentially be achieved with 3DEP data at 8 points/m2, our results indicate that even lower density lidar data could be sufficient to provide a national Biomass ECV product with accuracies significantly higher than that from Landsat observations alone.
Estimation of Enceladus Plume Density Using Cassini Flight Data
Wang, Eric K.; Lee, Allan Y.
2011-01-01
The Cassini spacecraft was launched on October 15, 1997 by a Titan 4B launch vehicle. After an interplanetary cruise of almost seven years, it arrived at Saturn on June 30, 2004. In 2005, Cassini completed three flybys of Enceladus, a small, icy satellite of Saturn. Observations made during these flybys confirmed the existence of water vapor plumes in the south polar region of Enceladus. Five additional low-altitude flybys of Enceladus were successfully executed in 2008-9 to better characterize these watery plumes. During some of these Enceladus flybys, the spacecraft attitude was controlled by a set of three reaction wheels. When the disturbance torque imparted on the spacecraft was predicted to exceed the control authority of the reaction wheels, thrusters were used to control the spacecraft attitude. Using telemetry data of reaction wheel rates or thruster on-times collected from four low-altitude Enceladus flybys (in 2008-10), one can reconstruct the time histories of the Enceladus plume jet density. The 1 sigma uncertainty of the estimated density is 5.9-6.7% (depending on the density estimation methodology employed). These plume density estimates could be used to confirm measurements made by other onboard science instruments and to support the modeling of Enceladus plume jets.
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.
Open-cluster density profiles derived using a kernel estimator
Seleznev, Anton F
2016-01-01
Surface and spatial radial density profiles in open clusters are derived using a kernel estimator method. Formulae are obtained for the contribution of every star into the spatial density profile. The evaluation of spatial density profiles is tested against open-cluster models from N-body experiments with N = 500. Surface density profiles are derived for seven open clusters (NGC 1502, 1960, 2287, 2516, 2682, 6819 and 6939) using Two-Micron All-Sky Survey data and for different limiting magnitudes. The selection of an optimal kernel half-width is discussed. It is shown that open-cluster radius estimates hardly depend on the kernel half-width. Hints of stellar mass segregation and structural features indicating cluster non-stationarity in the regular force field are found. A comparison with other investigations shows that the data on open-cluster sizes are often underestimated. The existence of an extended corona around the open cluster NGC 6939 was confirmed. A combined function composed of the King density pr...
Can modeling improve estimation of desert tortoise population densities?
Nussear, K.E.; Tracy, C.R.
2007-01-01
The federally listed desert tortoise (Gopherus agassizii) is currently monitored using distance sampling to estimate population densities. Distance sampling, as with many other techniques for estimating population density, assumes that it is possible to quantify the proportion of animals available to be counted in any census. Because desert tortoises spend much of their life in burrows, and the proportion of tortoises in burrows at any time can be extremely variable, this assumption is difficult to meet. This proportion of animals available to be counted is used as a correction factor (g0) in distance sampling and has been estimated from daily censuses of small populations of tortoises (6-12 individuals). These censuses are costly and produce imprecise estimates of g0 due to small sample sizes. We used data on tortoise activity from a large (N = 150) experimental population to model activity as a function of the biophysical attributes of the environment, but these models did not improve the precision of estimates from the focal populations. Thus, to evaluate how much of the variance in tortoise activity is apparently not predictable, we assessed whether activity on any particular day can predict activity on subsequent days with essentially identical environmental conditions. Tortoise activity was only weakly correlated on consecutive days, indicating that behavior was not repeatable or consistent among days with similar physical environments. ?? 2007 by the Ecological Society of America.
Bayesian error estimation in density-functional theory
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...... for molecules and solids. Fluctuations within the ensemble can then be used to estimate errors relative to experiment on calculated quantities such as binding energies, bond lengths, and vibrational frequencies. It is demonstrated that the error bars on energy differences may vary by orders of...
Photo-z Estimation: An Example of Nonparametric Conditional Density Estimation under Selection Bias
Izbicki, Rafael; Freeman, Peter E
2016-01-01
Redshift is a key quantity for inferring cosmological model parameters. In photometric redshift estimation, cosmologists use the coarse data collected from the vast majority of galaxies to predict the redshift of individual galaxies. To properly quantify the uncertainty in the predictions, however, one needs to go beyond standard regression and instead estimate the full conditional density f(z|x) of a galaxy's redshift z given its photometric covariates x. The problem is further complicated by selection bias: usually only the rarest and brightest galaxies have known redshifts, and these galaxies have characteristics and measured covariates that do not necessarily match those of more numerous and dimmer galaxies of unknown redshift. Unfortunately, there is not much research on how to best estimate complex multivariate densities in such settings. Here we describe a general framework for properly constructing and assessing nonparametric conditional density estimators under selection bias, and for combining two o...
Preventive oncology is in need of a risk assessment technique that can identify individuals at high risk for breast cancer and has the ability to monitor the efficacy of a risk reducing intervention. Optical transillumination spectroscopy (OTS) gives information about breast tissue composition and tissue density. OTS is noninvasive and in contrast to mammography, uses nonionizing radiation. It is safe and can be used frequently on younger women, potentially permitting early risk detection and thus increasing the time available for risk reduction interventions to assert their influence. Before OTS can be used as a risk assessment and/or monitoring technique, its predictive ability needs to be demonstrated and maximized through the construction of various mathematical models relating OTS and breast tissue density, and hence, risk. To establish a correlation between OTS and mammographic density principal components analysis (PCA), using risk classification, is calculated. The PCA scores are presented in three-dimensional cluster plots and a plane of differentiation that separates the high and low tissue densities is used to calculate the predictive value. Stratification of PCA for measurement position on the breast in cranial-caudal projection is introduced. Analysis of PCA scores as a function of the volunteer's age and body mass index (BMI) is examined. A small but significant correlation between the component scores and age or BMI is noted but the correlation is dependent on the tissue density category examined. Correction of the component scores for age and BMI is not recommended, since a priori knowledge of a woman's breast tissue density is required. Stratification for the center and distal measurement positions provide a predictive value for OTS above 96%
Probability Density Estimation by Decomposition of Correlation Integral
Jiřina, Marcel; Jiřina jr., M.
- : ISRST, 2008 - (Prasad, B.; Sinha, P.; Ram, A.; Kerre, E.), s. 113-119 ISBN 978-1-60651-000-1. [AIPR 2008. International Conference on Artificial Intelligence and Pattern Recognition. Orlando (US), 07.07.2008-10.07.2008] Institutional research plan: CEZ:AV0Z10300504 Keywords : correlation integral * decomposition of correlation integral * probability density estimation * polynomial approximation * classifier Subject RIV: BA - General Mathematics
Probability Density Estimation by Decomposition of Correlation Integral
Jiřina, Marcel; Jiřina jr., M.
-: ISRST, 2008 - (Prasad, B.; Sinha, P.; Ram, A.; Kerre, E.), s. 113-119 ISBN 978-1-60651-000-1. [AIPR 2008. International Conference on Artificial Intelligence and Pattern Recognition. Orlando (US), 07.07.2008-10.07.2008] Institutional research plan: CEZ:AV0Z10300504 Keywords : correlation integral * decomposition of correlation integral * probability density estimation * polynomial approximation * classifier Subject RIV: BA - General Mathematics
Density Estimation of Comet 103P/Hartley 2
Bowling, T.; Richardson, J.; Melosh, J.; Thomas, P.
2011-10-01
Our analysis was constrained to the region of the neck that was directly imaged and well illuminated during the encounter. A homogeneous density and a rotation period of 18.34 hours are assumed. Only rotation about the principal axis was accounted for. The principal rotation period was likely shorter on timescales effective for surface modification [2]. Additionally, spin components about minor axes introduce a further degree of error. A global minimum is found for a bulk density ? = 220 kg m-3 (one sigma = 130-620 kg m-3) which corresponds to a comet mass of m = 1.84 x 1011 kg (one sigma = 1.51-5.18 x 1011 kg). This is lower than, but within error ranges of, previous comet density estimates (sec. 4.2 of [3]).
Estimating black bear density using DNA data from hair snares
Gardner, B.; Royle, J. Andrew; Wegan, M.T.; Rainbolt, R.E.; Curtis, P.D.
2010-01-01
DNA-based mark-recapture has become a methodological cornerstone of research focused on bear species. The objective of such studies is often to estimate population size; however, doing so is frequently complicated by movement of individual bears. Movement affects the probability of detection and the assumption of closure of the population required in most models. To mitigate the bias caused by movement of individuals, population size and density estimates are often adjusted using ad hoc methods, including buffering the minimum polygon of the trapping array. We used a hierarchical, spatial capturerecapture model that contains explicit components for the spatial-point process that governs the distribution of individuals and their exposure to (via movement), and detection by, traps. We modeled detection probability as a function of each individual's distance to the trap and an indicator variable for previous capture to account for possible behavioral responses. We applied our model to a 2006 hair-snare study of a black bear (Ursus americanus) population in northern New York, USA. Based on the microsatellite marker analysis of collected hair samples, 47 individuals were identified. We estimated mean density at 0.20 bears/km2. A positive estimate of the indicator variable suggests that bears are attracted to baited sites; therefore, including a trap-dependence covariate is important when using bait to attract individuals. Bayesian analysis of the model was implemented in WinBUGS, and we provide the model specification. The model can be applied to any spatially organized trapping array (hair snares, camera traps, mist nests, etc.) to estimate density and can also account for heterogeneity and covariate information at the trap or individual level. ?? The Wildlife Society.
Uncertainties in estimating heart doses from 2D-tangential breast cancer radiotherapy
Lorenzen, Ebbe L; Brink, Carsten; Taylor, Carolyn W; Darby, Sarah C; Ewertz, Marianne
2016-01-01
-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...... uncertainty of estimates based on individual CT-scans. For right-sided breast cancer patients, mean heart dose based on individual CT-scans was always <1Gy and maximum dose always <5Gy for all three regimens. CONCLUSIONS: The use of stored individual simulator films provides a method for estimating heart......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...
Sun, Wei; Jiang, Yi-Zhou; Liu, Yi-Rong; Ma, Ding; Shao, Zhi-Ming
2016-04-12
Luminal breast cancer constitutes a group of highly heterogeneous diseases with a sustained high risk of late recurrence. We aimed to develop comprehensive and practical nomograms to better estimate the long-term survival of luminal breast cancer.Patients with luminal breast cancer diagnosed between 1990 and 2006 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into the training (n = 87,867) and validation (n = 88,215) cohorts. The cumulative incidence function (CIF) and a competing-risks model were used to estimate the probability of breast cancer-specific survival (BCSS) and death from other causes. We integrated significant prognostic factors to build nomograms and subjected the nomograms to bootstrap internal validation and to external validation.We screened 176,082 luminal breast cancer cases. The 5- and 10-year probabilities of overall death were 0.089 and 0.202, respectively. The 5- and 10-year probabilities of breast cancer-specific mortality (BCSM) were 0.053 and 0.112, respectively. Nine independent prognostic factors for both OS and BCSS were integrated to construct the nomograms. The calibration curves for the probabilities of 5- and 10-year OS and BCSS showed excellent agreement between the nomogram prediction and actual observation. The C-indexes of the nomograms were high in both internal validation (0.732 for OS and 0.800 for BCSS) and external validation (0.731 for OS and 0.794 for BCSS).We established nomograms that accurately predict OS and BCSS for patients with luminal breast cancer. The nomograms can identify patients with higher risk of late overall mortality and BCSM, helping physicians in facilitating individualized treatment. PMID:26967253
The Mammographic Density of a Mass Is a Significant Predictor of Breast Cancer
Woods, Ryan W.; Sisney, Gale S.; Salkowski, Lonie R.; Shinki, Kazuhiko; Lin, Yunzhi
2011-01-01
Purpose: To determine whether the mammographic density of noncalcified solid breast masses is associated with malignancy and to measure the agreement between prospective and retrospective assessment. Materials and Methods: The institutional review board approved this study and waived informed consent. Three hundred forty-eight consecutive breast masses in 328 women who underwent image-guided or surgical biopsy between October 2005 and December 2007 were included. All 348 biopsy-proved masses were randomized and assigned to a radiologist who was blinded to biopsy results for retrospective assessment by using the Breast Imaging Reporting and Data System (retrospectively assessed data set). Clinical radiologists prospectively assessed the density of 180 of these masses (prospectively assessed data set). Pathologic result at biopsy was the reference standard. Benign masses were followed for at least 1 year by linking each patient to a cancer registry. Univariate analyses were performed on the retrospectively assessed data set. The association of mass density and malignancy was examined by creating a logistic model for the prospectively assessed data set. Agreement between prospective and retrospective assessments was calculated by using the κ statistic. Results: In the retrospectively assessed data set, 70.2% of high-density masses were malignant, and 22.3% of the isodense or low-density masses were malignant (P < .0001). In the prospective logistic model, high density (odds ratio, 6.6), irregular shape (odds ratio, 9.9), spiculated margin (odds ratio, 20.3), and age (β = 0.09, P < .0001) were significantly associated with the probability of malignancy. The κ value for prospective-retrospective agreement of mass density was 0.53. Conclusion: High mass density is significantly associated with malignancy in both retrospectively and prospectively assessed data sets, with moderate prospective-retrospective agreement. Radiologists should consider mass density as a
A Concept of Approximated Densities for Efficient Nonlinear Estimation
Virginie F. Ruiz
2002-10-01
Full Text Available This paper presents the theoretical development of a nonlinear adaptive filter based on a concept of filtering by approximated densities (FAD. The most common procedures for nonlinear estimation apply the extended Kalman filter. As opposed to conventional techniques, the proposed recursive algorithm does not require any linearisation. The prediction uses a maximum entropy principle subject to constraints. Thus, the densities created are of an exponential type and depend on a finite number of parameters. The filtering yields recursive equations involving these parameters. The update applies the Bayes theorem. Through simulation on a generic exponential model, the proposed nonlinear filter is implemented and the results prove to be superior to that of the extended Kalman filter and a class of nonlinear filters based on partitioning algorithms.
Image Representation Using EPANECHNIKOV Density Feature Points Estimator
Tranos Zuva
2013-03-01
Full Text Available In image retrieval most of the existing visual content based representation methods are usually applicationdependent or non robust, making them not suitable for generic applications. These representation methodsuse visual contents such as colour, texture, shape,size etc. Human image recognition is largely basedonshape, thus making it very appealing for image representation algorithms in computer vision.In this paper we propose a generic image representation algorithm using Epanechnikov Density FeaturePoints Estimator (EDFPE. It is invariant to rotation, scale and translation. The image density featurepoints within defined rectangular rings around thegravitational centre of the image are obtained in theform of a vector. The EDFPE is applied to the vector representation of the image. The Cosine AngleDistance (CAD algorithm is used to measure similarity of the images in the database. Quantitativeevaluation of the performance of the system and comparison with other algorithms was done
Image Representation Using EPANECHNIKOV Density Feature Points Estimator
Tranos Zuva
2013-02-01
Full Text Available In image retrieval most of the existing visual content based representation methods are usually application dependent or non robust, making them not suitable for generic applications. These representation methods use visual contents such as colour, texture, shape, size etc. Human image recognition is largely based on shape, thus making it very appealing for image repr esentation algorithms in computer vision. In this paper we propose a generic image representation algorithm using Epanechnikov Density Feature Points Estimator (EDFPE. It is invariant to rotation, scale and translation. The image density feature points within defined rectangular rings around the gravitational centre of the image are obtained in the form of a vector. The EDFPE is applied to the vecto r representation of the image. The Cosine Angle Distance (CAD algorithm is used to measure similarity of the images in the database. Quantitative evaluation of the performance of the system and comparison with other algorithms was done.
Some asymptotic results on density estimators by wavelet projections
Varron, Davit
2012-01-01
Let $(X_i)_{i\\geq 1}$ be an i.i.d. sample on $\\RRR^d$ having density $f$. Given a real function $\\phi$ on $\\RRR^d$ with finite variation and given an integer valued sequence $(j_n)$, let $\\fn$ denote the estimator of $f$ by wavelet projection based on $\\phi$ and with multiresolution level equal to $j_n$. We provide exact rates of almost sure convergence to 0 of the quantity $\\sup_{x\\in H}\\mid \\fn(x)-\\EEE(\\fn)(x)\\mid$, when $n2^{-dj_n}/\\log n \\rar \\infty$ and $H$ is a given hypercube of $\\RRR^...
Atkinson, Charlotte; Warren, Ruth M. L.; Sala, Evis; Dowsett, Mitch; Dunning, Alison M; Healey, Catherine S.; Runswick, Shirley; Day, Nicholas E; Bingham, Sheila
2004-01-01
Abstract Introduction Isoflavones are hypothesized to protect against breast cancer, but it is not clear whether they act as oestrogens or anti-oestrogens in breast tissue. Our aim was to determine the effects of taking a red clover-derived isoflavone supplement daily for 1 year on mammographic breast density. Effects on oestradiol, follicle-stimulating hormone (FSH), luteinizing hormone (LH), lymphocyte tyrosine kinase activity and menopausal symptoms were also assessed. Methods A total of 2...
To compare the pre-and postenhanced power Doppler sonographic (PDS) findings in malignant and benign breast masses and to correlate power Doppler area (PDA) and histologic microvessel density (MVD). Forty-seven women with breast masses (27 malignant and 20 benign) underwent PDS before and after an injection of microbubble contrast agent (Levovist) for five minutes. Using computer-assisted quantification, intratumoral % PDA was calculated. Microvessel density was counted on the digitized image of slides stained with anti CD31 moonclonal antibody. The statistical analysis was performed using t-test to determine the difference of % PDA between malignant and benign masses and between 0.05). MVD was well correlated with % PDA on post-enhancement PDS (r=0.69), and a stronger correlation was seen in malignant masses (r=0.71). Contrast-enhanced PDS is superior in demonstrating the difference of vascularity between malignant and benign breast masses. % PDA after contrast-enhancement exhibited a statistically significant correlation and benign breast masses. %PDA after contrast-enhancement exhibited a statistically significant correlation with histologic MVD.
Estimation of the Space Density of Low Surface Brightness Galaxies
Briggs, F H
1997-01-01
The space density of low surface brightness and tiny gas-rich dwarf galaxies are estimated for two recent catalogs: The Arecibo Survey of Northern Dwarf and Low Surface Brightness Galaxies (Schneider, Thuan, Magri & Wadiak 1990) and The Catalog of Low Surface Brightness Galaxy, List II (Schombert, Bothun, Schneider & McGaugh 1992). The goals are (1) to evaluate the additions to the completeness of the Fisher and Tully (1981) 10 Mpc Sample and (2) to estimate whether the density of galaxies contained in the new catalogs adds a significant amount of neutral gas mass to the the inventory of HI already identified in the nearby, present-epoch universe. Although tiny dwarf galaxies (M_HI < ~10^7 solar masses) may be the most abundant type of extragalactic stellar system in the nearby Universe, if the new catalogs are representative, the LSB and dwarf populations they contain make only a small addition (<10%) to the total HI content of the local Universe and probably constitute even smaller fractions o...
Acoustic Signal based Traffic Density State Estimation using SVM
Prashant Borkar
2013-06-01
Full Text Available Based on the information present in cumulative acoustic signal acquired from a roadside-installed single microphone, this paper considers the problem of vehicular traffic density state estimation. The occurrence and mixture weightings of traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc are determined by the prevalent traffic density conditions on the road segment. In this work, we extract the short-term spectral envelope features of the cumulative acoustic signals using MFCC (Mel-Frequency Cepstral Coefficients. Support Vector Machines (SVM is used as classifier is used to model the traffic density state as Low (40 Km/h and above, Medium (20-40 Km/h, and Heavy (0-20 Km/h. For the developing geographies where the traffic is non-lane driven and chaotic, other techniques (magnetic loop detectors are inapplicable. SVM classifier with different kernels are used to classify the acoustic signal segments spanning duration of 20–40 s, which results in average classification accuracy of 96.67% for Quadratic kernel function and 98.33% for polynomial kernel function, when entire frames are considered for classification.
Probability Density and CFAR Threshold Estimation for Hyperspectral Imaging
Clark, G A
2004-09-21
The work reported here shows the proof of principle (using a small data set) for a suite of algorithms designed to estimate the probability density function of hyperspectral background data and compute the appropriate Constant False Alarm Rate (CFAR) matched filter decision threshold for a chemical plume detector. Future work will provide a thorough demonstration of the algorithms and their performance with a large data set. The LASI (Large Aperture Search Initiative) Project involves instrumentation and image processing for hyperspectral images of chemical plumes in the atmosphere. The work reported here involves research and development on algorithms for reducing the false alarm rate in chemical plume detection and identification algorithms operating on hyperspectral image cubes. The chemical plume detection algorithms to date have used matched filters designed using generalized maximum likelihood ratio hypothesis testing algorithms [1, 2, 5, 6, 7, 12, 10, 11, 13]. One of the key challenges in hyperspectral imaging research is the high false alarm rate that often results from the plume detector [1, 2]. The overall goal of this work is to extend the classical matched filter detector to apply Constant False Alarm Rate (CFAR) methods to reduce the false alarm rate, or Probability of False Alarm P{sub FA} of the matched filter [4, 8, 9, 12]. A detector designer is interested in minimizing the probability of false alarm while simultaneously maximizing the probability of detection P{sub D}. This is summarized by the Receiver Operating Characteristic Curve (ROC) [10, 11], which is actually a family of curves depicting P{sub D} vs. P{sub FA}parameterized by varying levels of signal to noise (or clutter) ratio (SNR or SCR). Often, it is advantageous to be able to specify a desired P{sub FA} and develop a ROC curve (P{sub D} vs. decision threshold r{sub 0}) for that case. That is the purpose of this work. Specifically, this work develops a set of algorithms and MATLAB
Change-in-ratio density estimator for feral pigs is less biased than closed mark-recapture estimates
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.
Bilateral mammographic density asymmetry and breast cancer risk: A preliminary assessment
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
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.
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
Estimating tropical-forest density profiles from multibaseline interferometric SAR
Treuhaft, Robert; Chapman, Bruce; dos Santos, Joao Roberto; Dutra, Luciano; Goncalves, Fabio; da Costa Freitas, Corina; Mura, Jose Claudio; de Alencastro Graca, Paulo Mauricio
2006-01-01
Vertical profiles of forest density are potentially robust indicators of forest biomass, fire susceptibility and ecosystem function. Tropical forests, which are among the most dense and complicated targets for remote sensing, contain about 45% of the world's biomass. Remote sensing of tropical forest structure is therefore an important component to global biomass and carbon monitoring. This paper shows preliminary results of a multibasline interfereomtric SAR (InSAR) experiment over primary, secondary, and selectively logged forests at La Selva Biological Station in Costa Rica. The profile shown results from inverse Fourier transforming 8 of the 18 baselines acquired. A profile is shown compared to lidar and field measurements. Results are highly preliminary and for qualitative assessment only. Parameter estimation will eventually replace Fourier inversion as the means to producing profiles.
Some asymptotic results on density estimators by wavelet projections
Varron, Davit
2012-01-01
Let $(X_i)_{i\\geq 1}$ be an i.i.d. sample on $\\RRR^d$ having density $f$. Given a real function $\\phi$ on $\\RRR^d$ with finite variation and given an integer valued sequence $(j_n)$, let $\\fn$ denote the estimator of $f$ by wavelet projection based on $\\phi$ and with multiresolution level equal to $j_n$. We provide exact rates of almost sure convergence to 0 of the quantity $\\sup_{x\\in H}\\mid \\fn(x)-\\EEE(\\fn)(x)\\mid$, when $n2^{-dj_n}/\\log n \\rar \\infty$ and $H$ is a given hypercube of $\\RRR^d$. We then show that, if $n2^{-dj_n}/\\log n \\rar c$ for a constant $c>0$, then the quantity $\\sup_{x\\in H}\\mid \\fn(x)-f\\mid$ almost surely fails to converge to 0.
Estimating Foreign-Object-Debris Density from Photogrammetry Data
Long, Jason; Metzger, Philip; Lane, John
2013-01-01
Within the first few seconds after launch of STS-124, debris traveling vertically near the vehicle was captured on two 16-mm film cameras surrounding the launch pad. One particular piece of debris caught the attention of engineers investigating the release of the flame trench fire bricks. The question to be answered was if the debris was a fire brick, and if it represented the first bricks that were ejected from the flame trench wall, or was the object one of the pieces of debris normally ejected from the vehicle during launch. If it was typical launch debris, such as SRB throat plug foam, why was it traveling vertically and parallel to the vehicle during launch, instead of following its normal trajectory, flying horizontally toward the north perimeter fence? By utilizing the Runge-Kutta integration method for velocity and the Verlet integration method for position, a method that suppresses trajectory computational instabilities due to noisy position data was obtained. This combination of integration methods provides a means to extract the best estimate of drag force and drag coefficient under the non-ideal conditions of limited position data. This integration strategy leads immediately to the best possible estimate of object density, within the constraints of unknown particle shape. These types of calculations do not exist in readily available off-the-shelf simulation software, especially where photogrammetry data is needed as an input.
Blyschak, Kristina; Simick, Michelle K.; Jong, Roberta A.; Lilge, Lothar D.
2003-12-01
Preventive oncology is in need of a risk assessment technique that can identify individuals at high risk for breast cancer and that has the ability to monitor the efficacy of a risk reducing intervention. Optical transillumination spectroscopy (OTS) was shown to give information about breast tissue composition and tissue density. OTS is non-invasive, and in contrast to mammography, uses non-ionizing radiation. It is safe and can be used frequently on younger women, potentially permitting early risk detection and thus increasing the time available for risk reduction interventions to assert their influence. Before OTS can be used as a risk assessment and/or monitoring technique, its predictive ability needs to be demonstrated and maximized through the construction of mathematical models relating OTS and risk. To establish a correlation between OTS and mammographic density, Principle Components Analysis (PCA), using risk classification, is employed. PCA scores are presented in 3D cluster plots and a plane of differentiation that separates high and low tissue density is used to calculate the predictive value. Stratificaiton of PCA for measurement position on the breast in cranial-caudal projection is introduced. Analysis of PCA scores as a function of the volunteer's age and body mass index (BMI) is examined. A small but significant correlation between the component scores and age or BMI is noted but the correlation is dependnet on the tissue density category examined. Correction of the component scores for age and BMI is not recommended, since a priori knowledge of a women's tissue density is required. Stratification for the center and distal measurement positions provides a predictive value for OTS above 96%.
Estimates of transition densities and their derivatives for jump L\\'evy processes
Kaleta, Kamil; Sztonyk, Paweł
2013-01-01
We give upper and lower estimates of densities of convolution semigroups of probability measures under explicit assumptions on the corresponding Levy measure and the Levy--Khinchin exponent. We obtain also estimates of derivatives of densities.
Breast cancer size estimation with MRI in BRCA mutation carriers and other high risk patients
Mann, R.M., E-mail: r.mann@rad.umcn.nl [Radboud University Nijmegen Medical Centre, Department of Radiology, Nijmegen (Netherlands); Bult, P., E-mail: p.bult@path.umcn.nl [Radboud University Nijmegen Medical Centre, Department of Pathology, Nijmegen (Netherlands); Laarhoven, H.W.M. van, E-mail: h.vanlaarhoven@amc.uva.nl [Academic Medical Centre, University of Amsterdam, Department of Medical Oncology, Amsterdam (Netherlands); Radboud University Nijmegen Medical Centre, Department of Medical Oncology, Nijmegen (Netherlands); Span, P.N., E-mail: p.span@rther.umcn.nl [Radboud University Nijmegen Medical Centre, Department of Radiation Oncology, Nijmegen (Netherlands); Schlooz, M., E-mail: m.schlooz@chir.umcn.nl [Radboud University Nijmegen Medical Centre, Department of Surgery, Nijmegen (Netherlands); Veltman, J., E-mail: j.veltman@zgt.nl [Hospital group Twente (ZGT), Department of Radiology, Almelo (Netherlands); Hoogerbrugge, N., E-mail: n.hoogerbrugge@gen.umcn.nl [Radboud University Nijmegen Medical Centre, Department of Human Genetics, Nijmegen (Netherlands)
2013-09-15
Objective: To assess the value of breast MRI in size assessment of breast cancers in high risk patients, including those with a BRCA 1 or 2 mutation. Guidelines recommend invariably breast MRI screening for these patients and therapy is thus based on these findings. However, the accuracy of breast MRI for staging purposes is only tested in sporadic cancers. Methods: We assessed concordance of radiologic staging using MRI with histopathology in 49 tumors in 46 high risk patients (23 BRCA1, 12 BRCA2 and 11 Non-BRCA patients). The size of the total tumor area (TTA) was compared to pathology. In invasive carcinomas (n = 45) the size of the largest focus (LF) was also addressed. Results: Correlation of MRI measurements with pathology was 0.862 for TTA and 0.793 for LF. TTA was underestimated in 8(16%), overestimated in 5(10%), and correctly measured in 36(73%) cases. LF was underestimated in 4(9%), overestimated in 5(11%), and correctly measured in 36(80%) cases. Impact of BRCA 1 or 2 mutations on the quality of size estimation was not observed. Conclusions: Tumor size estimation using breast MRI in high risk patients is comparable to its performance in sporadic cancers. Therefore, breast MRI can safely be used for treatment planning.
Breast cancer size estimation with MRI in BRCA mutation carriers and other high risk patients
Objective: To assess the value of breast MRI in size assessment of breast cancers in high risk patients, including those with a BRCA 1 or 2 mutation. Guidelines recommend invariably breast MRI screening for these patients and therapy is thus based on these findings. However, the accuracy of breast MRI for staging purposes is only tested in sporadic cancers. Methods: We assessed concordance of radiologic staging using MRI with histopathology in 49 tumors in 46 high risk patients (23 BRCA1, 12 BRCA2 and 11 Non-BRCA patients). The size of the total tumor area (TTA) was compared to pathology. In invasive carcinomas (n = 45) the size of the largest focus (LF) was also addressed. Results: Correlation of MRI measurements with pathology was 0.862 for TTA and 0.793 for LF. TTA was underestimated in 8(16%), overestimated in 5(10%), and correctly measured in 36(73%) cases. LF was underestimated in 4(9%), overestimated in 5(11%), and correctly measured in 36(80%) cases. Impact of BRCA 1 or 2 mutations on the quality of size estimation was not observed. Conclusions: Tumor size estimation using breast MRI in high risk patients is comparable to its performance in sporadic cancers. Therefore, breast MRI can safely be used for treatment planning
This paper introduces a probability density estimator based on Green's function identities. A density model is constructed under the sole assumption that the probability density is differentiable. The method is implemented as a binary likelihood estimator for classification purposes, so issues such as mis-modeling and overtraining are also discussed. The identity behind the density estimator can be interpreted as a real-valued, non-scalar kernel method which is able to reconstruct differentiable density functions.
González, Francisco Javier
2011-01-01
In this work the metabolic heat generated by breast tumors was estimated indirectly and noninvasively from digital infrared images and numerically simulating a simplified breast model and a cancerous tumor, this parameter can be of clinical importance since it has been related to the doubling volume's time and malignancy for that particular tumor. The results indicate that digital infrared imaging has the potential to estimate in a non-invasive way the malignancy of a tumor by calculating its metabolic heat generation from bioheat thermal transfer models.
Medical image segmentation to estimate HER2 gene status in breast cancer
Palacios-Navarro, Guillermo; Acirón-Pomar, José Manuel; Vilchez-Sorribas, Enrique; Zambrano, Eddie Galarza
2016-02-01
This work deals with the estimation of HER2 Gene status in breast tumour images treated with in situ hybridization techniques (ISH). We propose a simple algorithm to obtain the amplification factor of HER2 gene. The obtained results are very close to those obtained by specialists in a manual way. The developed algorithm is based on colour image segmentation and has been included in a software application tool for breast tumour analysis. The developed tool focus on the estimation of the seriousness of tumours, facilitating the work of pathologists and contributing to a better diagnosis.
EuroMInd-D: A Density Estimate of Monthly Gross Domestic Product for the Euro Area
Proietti, Tommaso; Marczak, Martyna; Mazzi, Gianluigi
EuroMInd-D is a density estimate of monthly gross domestic product (GDP) constructed according to a bottom–up approach, pooling the density estimates of eleven GDP components, by output and expenditure type. The components density estimates are obtained from a medium-size dynamic factor model of a...
LIMIT THEOREMS FOR KERNEL DENSITY ESTIMATORS IN SPACES OF ARBITRARY NATURE
Orlov A. I.
2015-04-01
Full Text Available Some estimators of the probability density function in spaces of arbitrary nature are used for various tasks in statistics of non-numerical data. Systematic exposition of the theory of such estimators had a start in our work [2]. This article is a direct continuation of the article [2]. We will regularly use references to conditions and theorems of the article [2], in which we introduced several types of nonparametric estimators of the probability density. We studied more linear estimators. In this article we consider particular cases - kernel density estimates in spaces of arbitrary nature. When estimating the density of the one-dimensional random variable, kernel estimators become the Parzen-Rosenblatt estimators. Asymptotic behavior of kernel density estimators in the general case of an arbitrary nature spaces are devoted to Theorem 1 - 8. Under different conditions we prove the consistency and asymptotic normality of kernel density estimators. We have studied uniform convergence. We have introduced the concept of "preferred rate differences" and studied nuclear density estimators based on it. We have also introduced and studied natural affinity measures which are used in the analysis of the asymptotic behavior of kernel density estimators. We have found the asymptotic behavior of dispersions of kernel density estimators and considered the examples including kernel density estimators in finite-dimensional spaces and in the space of square-integrable functions
Theoretical and Experimental Estimations of Volumetric Inductive Phase Shift in Breast Cancer Tissue
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.
Late regional density changes of the lung after radiotherapy for breast cancer
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
Gabrielson, Marike; Chiesa, Flaminia; Paulsson, Janna; Strell, Carina; Behmer, Catharina; Rönnow, Katarina; Czene, Kamila; Östman, Arne; Hall, Per
2016-07-01
Following female sex and age, mammographic density is considered one of the strongest risk factors for breast cancer. Despite the association between mammographic density and breast cancer risk, little is known about the underlying histology and biological basis of breast density. To better understand the mechanisms behind mammographic density we assessed morphology, proliferation and hormone receptor status in relation to mammographic density in breast tissues from healthy women. Tissues were obtained from 2012-2013 by ultrasound-guided core needle biopsy from 160 women as part of the Karma (Karolinska mammography project for risk prediction for breast cancer) project. Mammograms were collected through routine mammography screening and mammographic density was calculated using STRATUS. The histological composition, epithelial and stromal proliferation status and hormone receptor status were assessed through immunohistochemical staining. Higher mammographic density was significantly associated with a greater proportion of stromal and epithelial tissue and a lower proportion of adipose tissue. Epithelial expression levels of Ki-67, oestrogen receptor (ER) and progesterone receptor (PR) were not associated with mammographic density. Epithelial Ki-67 was associated with a greater proportion of epithelial tissue, and epithelial PR was associated with a greater proportion of stromal and a lower proportion of adipose tissue. Epithelial ER was not associated with any tissues. In contrast, expression of ER in the stroma was significantly associated with a greater proportion of stroma, and negatively associated with the amount of adipose tissue. High mammographic density is associated with higher amount of stroma and epithelium and less amount of fat, but is not associated with a change in epithelial proliferation or receptor status. Increased expressions of both epithelial PR and stromal ER are associated with a greater proportion of stroma, suggesting hormonal involvement
Study of the effect of breast tissue density on detection of masses in mammograms.
García-Manso, A; García-Orellana, C J; González-Velasco, H M; Gallardo-Caballero, R; Macías-Macías, M
2013-01-01
One of the parameters that are usually stored for mammograms is the BI-RADS density, which gives an idea of the breast tissue composition. In this work, we study the effect of BI-RADS density in our ongoing project for developing an image-based CAD system to detect masses in mammograms. This system consists of two stages. First, a blind feature extraction is performed for regions of interest (ROIs), using Independent Component Analysis (ICA). Next, in the second stage, those features form the input vectors to a classifier, neural network, or SVM classifier. To train and test our system, the Digital Database for Screening Mammography (DDSM) was used. The results obtained show that the maximum variation in the performance of our system considering only prototypes obtained from mammograms with a concrete value of density (both for training and test) is about 7%, yielding the best values for density equal to 1, and the worst for density equal to 4, for both classifiers. Finally, with the overall results (i.e., using prototypes from mammograms with all the possible values of densities), we obtained a difference in performance that is only 2% lower than the maximum, also for both classifiers. PMID:23573165
Bayesian Approaches to Non-parametric Estimation of Densities on the Unit Interval
Song Li; Silvapulle, Mervyn J.; Param Silvapulle; Xibin Zhang
2012-01-01
This paper investigates nonparametric estimation of density on [0,1]. The kernel estimator of density on [0,1] has been found to be sensitive to both bandwidth and kernel. This paper proposes a unified Bayesian framework for choosing both the bandwidth and kernel function. In a simulation study, the Bayesian bandwidth estimator performed better than others, and kernel estimators were sensitive to the choice of the kernel and the shapes of the population densities on [0,1]. The simulation and ...
Glycemic index, glycemic load and mammographic breast density: the EPIC Florence longitudinal study.
Giovanna Masala
Full Text Available A few studies have evaluated the association between diet and mammographic breast density (MBD and results are inconsistent. MBD, a well-recognized risk factor for breast cancer, has been proposed as a marker of cumulative exposure to hormones and growth factors. Diets with a high glycemic index (GI or glycemic load (GL may increase breast cancer risk, via an effect on the insulin-like growth factor axis. We have investigated the association between carbohydrate intake, GI, GL and MBD in a prospective study. We identified a large series of women, in the frame of the EPIC-Florence cohort, with a mammogram taken five years after enrolment, when detailed information on dietary and lifestyle habits and anthropometric measurements had been collected. Mammograms have been retrieved (1,668, 83% and MBD assessed according to Wolfe's classification. We compared women with high MBD (P2+DY Wolfe's categories with those with low MBD (N1+P1 through logistic models adjusted for age, education, body mass index, menopause, number of children, breast feeding, physical activity, non-alcohol energy, fibers, saturated fat and alcohol. A direct association between GL and high MBD emerged in the highest quintile of intake in comparison with the lowest quintile (OR = 1.73, 95%CI 1.13-2.67, p for trend = 0.048 while no association with glycemic index was evident. These results were confirmed after exclusion of women reporting to be on a diet or affected with diabetes, and when Hormone Replacement Therapy at the date of mammographic examination used to assess MBD was considered. The effect was particularly evident among leaner women, although no interaction was found. A positive association was suggested for increasing simple sugar and total carbohydrates intakes limited to the highest quintiles. In this Italian population we observed an association between glycemic load, total and rapidly absorbed carbohydrates and high MBD. These novel results warrant further
张路平; 王鲁平; 李飚; 赵明
2015-01-01
In order to improve the performance of the probability hypothesis density (PHD) algorithm based particle filter (PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD.
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
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.)
Functional regression approximate Bayesian computation for Gaussian process density estimation
RODRIGUES G.S.; Nott, David J.; Sisson, S. A.
2014-01-01
We propose a novel Bayesian nonparametric method for hierarchical modelling on a set of related density functions, where grouped data in the form of samples from each density function are available. Borrowing strength across the groups is a major challenge in this context. To address this problem, we introduce a hierarchically structured prior, defined over a set of univariate density functions, using convenient transformations of Gaussian processes. Inference is performed through approximate...
Simulation study of diffuse photon density waves traveling through normal and abnormal breast tissue
Deng, Xiaoyuan; Xing, Da
1999-09-01
Diffuse Photon Density Waves (DPDW) is new concept and principle, which takes advantage of the difference of absorption and scattering coefficients of different tissues to study the law of photon density fluctuation during a modulated light traveling through the tissues, thus to explore the structure and function of tissues. With the combination of image reconstruction technology, we can directly `visualize' the tissues. As a non- invasive exam, it has highly extensive applied prospect in medical field and brain cognition study. In this paper, the behavior of DPDW traveling through simulated breast tissue is tentatively examined. Under various conditions, we observed the characteristics of DPDW. We observed the reasonability of the distribution of sources and detectors, the factors that influence the distortion of DPDW. Simulation results could be used as a good theoretical guideline for experiments.
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
Vedantham, Srinivasan; Shi, Linxi; Karellas, Andrew; O'Connell, Avice M.; Conover, David L.
2013-11-01
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
Pilot study of bone mineral density in breast cancer patients treated with adjuvant chemotherapy
Headley, J. A.; Theriault, R. L.; LeBlanc, A. D.; Vassilopoulou-Sellin, R.; Hortobagyi, G. N.
1998-01-01
The objective of this cross-sectional study was to determine lumbar spine bone mineral density (BMD) in breast cancer patients previously treated with adjuvant chemotherapy. Sixteen of 27 patients who received adjuvant chemotherapy became permanently amenorrheic as a result of chemotherapy. BMD was measured at the lumbar spine using dual energy X-ray absorptiometry (DEXA). Chemotherapy drugs and dosages along with a history of risk factors for reduced bone density including activity level, tobacco and/or alcohol use, metabolic bone disease, family history, and hormone exposure were identified. Results showed that women who became permanently amenorrheic as a result of chemotherapy had BMD 14% lower than women who maintained menses after chemotherapy. Chemotherapy-treated women who maintained ovarian function had normal BMD. This study suggests that women who have premature menopause as a result of chemotherapy for breast cancer are at increased risk of bone loss and may be at risk for early development of osteoporosis. Women who maintain menses do not appear to be at risk for accelerated trabecular bone loss.
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
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
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%.
Purpose: Biologically meaningful predictors for locoregional recurrence (LRR) in patients undergoing breast-conserving surgery (BCS) and radiotherapy (RT) are lacking. Tissue components, including extracellular matrix, could confer resistance to ionizing radiation. Fibroglandular and extracellular matrix components of breast tissue relative to adipose tissue can be quantified by the mammographic breast density (MBD), the proportion of dense area relative to the total breast area on mammography. We hypothesized that the MBD might be a predictor of LRR after BCS and RT for invasive breast cancer. Methods and Materials: We conducted a nested case-control study of 136 women with invasive breast cancer who had undergone BCS and RT and had had the MBD ascertained before, or at, diagnosis. Women with known recurrence were matched to women without recurrence by year of diagnosis. The median follow-up was 7.7 years. The percentage of MBD was measured using a computer-based threshold method. Results: Patients with a high MBD (≥75% density) vs. low (≤25%) were at increased risk of LRR (hazard ratio, 4.30; 95% confidence interval, 0.88-021.0; p = 0.071) but not distant recurrence. In addition, we found a complete inverse correlation between high MBD and obesity (body mass index, ≥30 kg/m2). In a multivariate Cox proportional hazards model, patients with MBD in the greatest quartile were at significantly greater risk of LRR (hazard ratio, 6.6; 95% confidence interval, 1.6-27.7; p = 0.01). Obesity without a high MBD also independently predicted for LRR (hazard ratio, 19.3; 95% confidence interval, 4.5-81.7; p < 0.001). Conclusion: The results of our study have shown that a high MBD and obesity are significant independent predictors of LRR after BCS and RT for invasive breast cancer. Additional studies are warranted to validate these findings
Breast density and polymorphisms in genes coding for CYP1A2 and COMT: the Multiethnic Cohort
Maskarinec Gertraud
2007-02-01
Full Text Available Abstract Background Mammographic density is a strong predictor of breast cancer risk and is increased by hormone replacement therapy (HRT. Some associations with genetic polymorphisms in enzymes involved in estrogen metabolism have been described. This cross-sectional analysis examined the relation between mammographic density and the CYP1A2*1F and COMT Val58 Met polymorphisms among 332 breast cancer cases and 254 controls in the Hawaii component of the Multiethnic Cohort. Methods Mammographic density, before diagnosis in cases, was quantified by using a validated computer-assisted method. Blood samples were genotyped by standard PCR/RFLP methods. Adjusted mean percent density was calculated by genotype using mixed models with the unstructured covariance option. Results A positive association between the C allele in the CYP1A2*1F gene and percent density, but not the dense area, was suggested (p = 0.11. The relation was limited to controls (p = 0.045, postmenopausal women not using HRT (p = 0.08, and normal weight subjects (p = 0.046. We did not observe any relation between the COMT Val58 Met polymorphism and breast density. Conclusion The lack of an association between the CYP1A2 genotype and the size of the dense areas suggests an effect on the non-dense, i.e., fatty breast tissue. The discrepancies among studies may be due to differential susceptibility; changes in enzyme activity as a result of the CYP1A2*1F polymorphism may influence breast tissue differently depending on hormonal status. Larger studies with the ability to look at interactions would be useful to elucidate the influence of genetic variation in CYP1A2 and COMT on the risk of developing breast cancer.
EuroMInd-D: A density estimate of monthly gross domestic product for the euro area
Proietti, Tommaso; Marczak, Martyna; Mazzi, Gianluigi
2015-01-01
EuroMInd-D is a density estimate of monthly gross domestic product (GDP) constructed according to a bottom–up approach, pooling the density estimates of eleven GDP components, by output and expenditure type. The components density estimates are obtained from a medium-size dynamic factor model of a set of coincident time series handling mixed frequencies of observation and ragged–edged data structures. They reflect both parameter and filtering uncertainty and are obtained by implementing a boo...
Hendra Gunawan
2014-06-01
Full Text Available http://dx.doi.org/10.17014/ijog.vol3no3.20084The precision of topographic density (Bouguer density estimation by the Nettleton approach is based on a minimum correlation of Bouguer gravity anomaly and topography. The other method, the Parasnis approach, is based on a minimum correlation of Bouguer gravity anomaly and Bouguer correction. The precision of Bouguer density estimates was investigated by both methods on simple 2D syntetic models and under an assumption free-air anomaly consisting of an effect of topography, an effect of intracrustal, and an isostatic compensation. Based on simulation results, Bouguer density estimates were then investigated for a gravity survey of 2005 on La Soufriere Volcano-Guadeloupe area (Antilles Islands. The Bouguer density based on the Parasnis approach is 2.71 g/cm3 for the whole area, except the edifice area where average topography density estimates are 2.21 g/cm3 where Bouguer density estimates from previous gravity survey of 1975 are 2.67 g/cm3. The Bouguer density in La Soufriere Volcano was uncertainly estimated to be 0.1 g/cm3. For the studied area, the density deduced from refraction seismic data is coherent with the recent Bouguer density estimates. New Bouguer anomaly map based on these Bouguer density values allows to a better geological intepretation.
Estimation of the cost of treatment by chemotherapy for early breast cancer in Morocco
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
Dorgan, Joanne F.; Liu, Lea; Klifa, Catherine; Hylton, Nola; Shepherd, John A.; Stanczyk, Frank Z.; Snetselaar, Linda G.; Van Horn, Linda; Stevens, Victor J.; Robson, Alan; Kwiterovich, Peter O.; Lasser, Norman L.; Himes, John H.; Gabriel, Kelley Pettee; Kriska, Andrea; Ruder, Elizabeth H.; Fang, Carolyn Y.; Barton, Bruce A.
2010-01-01
Background Adolescent diet is hypothesized to influence breast cancer risk. We evaluated the long-term effects of an intervention to lower fat intake among adolescent girls on biomarkers that are related to breast cancer risk in adults. Methods A follow-up study was conducted of 230 girls who participated in the Dietary Intervention Study in Children (DISC), in which healthy, prepubertal 8-10 year olds were randomly assigned to usual care or to a behavioral intervention that promoted a reduced fat diet. Participants were 25-29 years old at follow-up visits. All tests of statistical significance are two-sided. Results In analyses that did not take account of diet at the time of the follow-up visit, the only statistically significant treatment group difference was higher bone mineral content (BMC) in intervention group participants compared to usual care group participants; their mean BMCs were 2,444g and 2,377g, respectively. After adjustment for current diet, the intervention group also had statistically significantly higher bone mineral density and luteal phase serum estradiol concentrations. Serum progesterone concentrations and breast density did not differ by treatment group in unadjusted or adjusted analyses. Conclusion Results do not support the hypothesis that consumption of a lower fat diet during adolescence reduces breast cancer risk via effects on subsequent serum estradiol and progesterone levels, breast density or BMD. Impact Additional research is needed to clarify the association of adolescent diet with breast cancer risk and to determine if the results reported here are specific to the DISC intervention or more broadly applicable. PMID:20501774
Sandhu, Narinder; Schetter, Susann E; Liao, Jason; Hartman, Terryl J; Richie, John P; McGinley, John; Thompson, Henry J; Prokopczyk, Bogdan; DuBrock, Cynthia; Signori, Carina; Hamilton, Christopher; Calcagnotto, Ana; Trushin, Neil; Aliaga, Cesar; Demers, Laurence M; El-Bayoumy, Karam; Manni, Andrea
2016-04-01
Preclinical data indicate that omega-3 fatty acids (n-3FA) potentiate the chemopreventive effect of the antiestrogen (AE) tamoxifen against mammary carcinogenesis. The role of n-3FA in breast cancer prevention in humans is controversial. Preclinical and epidemiologic data suggest that n-3FA may be preferentially protective in obese subjects. To directly test the protective effect of n-3FA against breast cancer, we conducted a 2-year, open-label randomized clinical trial in 266 healthy postmenopausal women (50% normal weight, 30% overweight, 20% obese) with high breast density (BD; ≥25%) detected on their routine screening mammograms. Eligible women were randomized to one of the following five groups (i) no treatment, control; (ii) raloxifene 60 mg; (iii) raloxifene 30 mg; (iv) n-3FA lovaza 4 g; and (v) lovaza 4 g plus raloxifene 30 mg. The 2-year change in BD, a validated biomarker of breast cancer risk, was the primary endpoint of the study. In subset analysis, we tested the prespecified hypothesis that body mass index (BMI) influences the relationship between plasma n-3FA on BD. While none of the interventions affected BD in the intention-to-treat analysis, increase in plasma DHA was associated with a decrease in absolute breast density but only in participants with BMI >29. Our results suggest that obese women may preferentially experience breast cancer risk reduction from n-3FA administration.Cancer Prev Res; 9(4); 275-82. ©2015 AACR. PMID:26714774
Kirsten Knutson, MPH
2008-01-01
Full Text Available Introduction Populations eligible for public health programs are often narrowly defined and, therefore, difficult to describe quantitatively, particularly at the local level, because of lack of data. This information, however, is vital for program planning and evaluation. We demonstrate the application of a statistical method using multiple sources of data to generate county estimates of women eligible for free breast cancer screening and diagnostic services through California’s Cancer Detection Programs: Every Woman Counts.MethodsWe used the small-area estimation method to determine the proportion of eligible women by county and racial/ethnic group. To do so, we included individual and community data in a generalized, linear, mixed-effect model.ResultsOur method yielded widely varied estimated proportions of service-eligible women at the county level. In all counties, the estimated proportion of eligible women was higher for Hispanics than for whites, blacks, Asian/Pacific Islanders, or American Indian/Alaska Natives. Across counties, the estimated proportions of eligible Hispanic women varied more than did those of women of other races.ConclusionThe small-area estimation method is a powerful tool for approximating narrowly defined eligible or target populations that are not represented fully in any one data source. The variability and reliability of the estimates are measurable and meaningful. Public health programs can use this method to estimate the size of local populations eligible for, or in need of, preventive health services and interventions.
Estimation of current density distribution under electrodes for external defibrillation
Papazov Sava P
2002-12-01
Full Text Available Abstract Background Transthoracic defibrillation is the most common life-saving technique for the restoration of the heart rhythm of cardiac arrest victims. The procedure requires adequate application of large electrodes on the patient chest, to ensure low-resistance electrical contact. The current density distribution under the electrodes is non-uniform, leading to muscle contraction and pain, or risks of burning. The recent introduction of automatic external defibrillators and even wearable defibrillators, presents new demanding requirements for the structure of electrodes. Method and Results Using the pseudo-elliptic differential equation of Laplace type with appropriate boundary conditions and applying finite element method modeling, electrodes of various shapes and structure were studied. The non-uniformity of the current density distribution was shown to be moderately improved by adding a low resistivity layer between the metal and tissue and by a ring around the electrode perimeter. The inclusion of openings in long-term wearable electrodes additionally disturbs the current density profile. However, a number of small-size perforations may result in acceptable current density distribution. Conclusion The current density distribution non-uniformity of circular electrodes is about 30% less than that of square-shaped electrodes. The use of an interface layer of intermediate resistivity, comparable to that of the underlying tissues, and a high-resistivity perimeter ring, can further improve the distribution. The inclusion of skin aeration openings disturbs the current paths, but an appropriate selection of number and size provides a reasonable compromise.
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.
An evidence-based estimate of appropriate radiotherapy utilization rate for breast cancer
Purpose: Current estimates of the proportion of cancer patients who will require radiotherapy (RT) are based almost entirely on expert opinion. We sought to use an evidence-based approach to estimate the proportion of incident cases of breast cancer that will require RT at any point in the evolution of the illness. Methods and Materials: We undertook a systematic review of the literature to identify indications for RT for breast cancer and to ascertain the level of evidence that supported each indication. An epidemiologic approach was then used to estimate the incidence of each indication for RT in a typical North American population of breast cancer patients. The effect of sampling error on the estimated appropriate rate of RT was calculated mathematically, and the effect of systematic error was estimated by sensitivity analysis. Results: It was estimated that 66.4% ± 4.8% of breast cancer patients develop one or more indications for RT at some point in the course of the illness. The plausible range for this rate was 56.3%-72.4% on sensitivity analysis. Of all breast cancer patients, 57.3% ± 4.7% require RT in their initial treatment and 9.1% ± 1.0% do so later for recurrence or progression. The proportion of patients who ever require RT is stage dependent: 39.8% ± 1.1% in ductal carcinoma in situ; 68.6% ± 4.1% in Stage I invasive carcinoma; 81.5% ± 2.3% in Stage II; 95.3% ± 0.3% in Stage III; and 63.7% ± 0.3% in Stage IV. Conclusion: This method provides a rational starting point for the long-term planning of RT services and for the audit of access to RT at the population level. By completing such evaluations in the major cancer sites, it will be possible to estimate the appropriate RT treatment rate for the cancer population as a whole
Kim, Eun Kyung; Lee, Yong Hee; Son, Hong Ju; Oh, Ki Keun; Chung, Hyun Cheol; Lee, Kyong Sik [Yonsei University College of Medicine, Seoul (Korea, Republic of)
2000-12-15
To evaluate how breast cancer vascularity detected by power Doppler sonography correlates with microvessel density(MVD) and lymph node involvement. Power Doppler US was performed on 47 patients with pathologically diagnosed invasive ductal carcinoma. According to the presense or absence of vessels, power Doppler findings were categorized regarding total tumors and tumors less than 2 cm. MVD was assessed immunohistochemically using polyclonal antisera against factor VIII. Tumor vascularity was correlated with MVD and lymph node involvement. Among 47 infiltrating ductal carcinomas, 35 (74.5%) cases were vascular and 12 (25.5%) cases were avascular on power Doppler US. The mean MVD of the vascular and avascular carcinomas did not show any statistical difference. However, lymph mode involvement showed significant statistical difference between the two groups (51.4% versus 25.0%, p=0.036), even though in cancers less than 2 cm (40% versus 22.2%, p=0.047). Breast cancer vascularity detected by power Doppler sonography was not correlated with MVD but correlated with lymph node involvement. So we suggest that power Doppler sonography may be useful for predicting lymph node metastasis.
In Japan it is considered that mammography should be introduced with physical examination for the mass screening of breast cancer instead of physical examination alone, which is performed at present. Before the introduction of mammography, a mathematical simulation should be performed to show the reduction in breast cancer mortality by mass screening compared with an unscreened population. A mathematical model of cancer screening devised by the authors was used to estimate the number of deaths due to breast cancer (A) in the screened group and those (B) in the unscreened group within the same population. Then the relative risk (RR) and attributable risk (RD) were calculated as (A/B) and (B-A) respectively. Three methods of mass screening were compared: (1) physical examination (1-year interval), (2) mammography with physical examination (1-year interval), (3) mammography with physical examination (2-year interval). The calculated RR values were 0.85 for (1), 0.60 for (2) and 0.69 for (3). Assuming that the incidence of breast cancer was 100/105 person-years, the calculated RD values were 3.0, 8.1 and 6.2 persons/105 person-years for (1), (2) and (3), respectively. The 95% confidence interval of RR for three methods was over 1.0, and thus the reduction of breast cancer mortality was not statistically significant in the present population. In conclusion, mammography with physical examination may reduce breast cancer mortality in comparison with physical examination alone, but a larger number of women must be screened in order to obtain a significant RR value. (author)
Huerga, Iker; Gerrikagoitia, Jon Kepa
2010-01-01
Risk estimation of developing breast cancer poses as the first prevention method for early diagnosis. Furthermore, data integration from different departments involved in the process plays a key role. In order to guarantee patient safety, the whole process should be orchestrated and monitored automatically. Support for the solution will be a linked data cloud, composed by all the departments that take part in the process, combined with rule engines.
Estimation of Ga-67 radiation dose to a breast-fed infant
Full text: The quality assurance programs for the practice of nuclear medicine in Colombia include optimization of public exposure by the radioactive sources used in medical diagnosis and treatment. Precautionary measures notwithstanding, occasionally members of the public receive doses larger than expected; usually there is no direct data to evaluate dose. We briefly describe the process to estimate the radiation dose absorbed by a 5-month-old baby that was inadvertently breast-fed for 48 hours by his 22-year-old mother that was administered 296 MBq of radioactive Ga-67 citrate. After the intravenous administration, the Ga-67 is commonly observed in the intestine, and other organs, and in the breasts of lactating women, thus possibly leading to radiation exposure of the infant's lower intestine. We had 256x256 single photon emission computed tomography (SPECT) images, and whole-body 1024 x 256 planar images, taken 48h and 72 h after Ga-67 intake. The images were obtained with a Siemens E-cam gamma camera, a 20% energy window was placed at the 93, 184 and 296 keV gamma photopeaks. From these images we calculated the concentration of Ga-67 in mother's milk (using Medical Internal Radiation Dose MIRD methodology). Hence, the dose absorbed by the baby. The radioactivity in breast milk per liter was 4% of the injected activity. The dose absorbed by the infant's lower intestine was 11 mGy. The effective dose to the infant was estimated as 7 mSv, which is above the annual limit to the general public (1 mSv/year). Hence, an infant breast-fed by a mother undergoing Ga-67 treatment may easily receive a radiation dose in excess of the annual upper limit. From the viewpoint of radiation protection it is extremely important to convey to the mother the necessity of abiding by the standard medical recommendation of discontinuing breast feeding for at least 2 weeks after Ga-67 intake. (author)
Change in bone mineral density during adjuvant chemotherapy for early-stage breast cancer
Christensen, Carina Ørts; Cronin-Fenton, Deirdre; Frøslev, Trine;
2016-01-01
PURPOSE: Adjuvant chemotherapy has been associated with loss of bone mineral density (BMD) either as a direct effect or due to glucocorticoids used as supportive care medication. A prospective cohort study was conducted to evaluate changes in BMD from baseline to right after completion of...... chemotherapy, i.e., 4 months. METHODS: Dual-imaging X-ray absorptiometry (DXA) was performed at baseline and after completing anthracycline- and taxane-based chemotherapy to measure BMD in the spine, hip, and forearm in early-stage breast cancer patients. High-dose prednisolone was used at three weekly...... % CI -3.3; -0.1, p = 0.04) compared to never/former smokers. CONCLUSIONS: Adjuvant chemotherapy supplemented with prednisolone was not associated with loss of BMD. Postmenopausal women gained bone mass, whereas current smokers lost bone mass....
Offersen, Birgitte Vrou; Riisbro, Rikke; Knoop, Ann;
2007-01-01
Plasminogen Activator Inhibitor type-1 (PAI-1) is involved in tumor invasion and progression. High levels of PAI-1 are associated with poor prognosis in breast cancer, and PAI-1 has been shown to play a role in angiogenic processes. Since estimates of tumor angiogenesis may predict poor prognosis...... we studied the relationship between PAI-1 and estimates of angiogenesis in breast cancer. Tumor tissue specimens from 438 breast cancer patients were included. Median follow-up was 10.3 years. Protein levels of PAI-1 were measured using an ELISA. Angiogenesis scores were performed using a Chalkley.......009) were independent markers of death from breast cancer. This study confirms high PAI-1 or high Chalkley counts as markers of poor prognosis in breast cancer patients, and suggests that the prognostic impact of PAI-1 is independent of its supposed involvement in tumor angiogenesis. Udgivelsesdato: 2007...
Singh, Harpreet; Arvind; Dorai, Kavita
2016-09-01
Estimation of quantum states is an important step in any quantum information processing experiment. A naive reconstruction of the density matrix from experimental measurements can often give density matrices which are not positive, and hence not physically acceptable. How do we ensure that at all stages of reconstruction, we keep the density matrix positive? Recently a method has been suggested based on maximum likelihood estimation, wherein the density matrix is guaranteed to be positive definite. We experimentally implement this protocol on an NMR quantum information processor. We discuss several examples and compare with the standard method of state estimation.
Nielsen, Mads; Raundahl, Jakob; Pettersen, Paola; Loog, Marco; Karemore, Gopal Raghunath; Karsdal, Morten; Christiansen, Claus
2009-01-01
Objective: To investigate whether transdermal low dose estradiol treatment induces changes in mammographic density or heterogeneity compared to raloxifene. Secondarily, if these changes relate to changes in bone formation/resorption markers, and if these findings indicate elevation of breast cancer...
Use of health plan data to estimate cost and outcomes of a breast cancer population
Purpose/Objective: To compare insurance billing data with tumor registry data for estimating date of diagnosis and date of recurrence. To collect and estimate cost of treatment from billing data as a step towards performing cost-effective or cost-utility analysis. To correlate first year treatment cost first year with overall cost to enable the former to serve as a proxy for the latter for patients migrating out of insurance plans. Materials and Methods: Billing data for patients(pts.) diagnosed with breast cancer between 1990-1992 was obtained from Paramount Health Plans, a NCQA accredited health plan in Northwest Ohio. Tumor registry and hospital records were surveyed for the clinical data. Total cost of care received by pts., cost of care associated with treatment of breast cancer, and cost of care billed as breast cancer care was collected for each 12 month period from the date of diagnosis. Costs were measured from a payers, i.e. health plans, perspective. Net present value (NPV) costs discounted at a rate of 3% to the year of diagnosis are reported. Pts. were considered in the plan for the entire duration of the study if they were in the plan from the time of diagnosis to the end of the analysis, (12(96)). Students t-test was used to determined statistical differences between groups analyzed. Results: Paramount Health Plan was a small health plan with approximately 10,000-13,000 female enrolees during the study period. Breast cancer was diagnosed in 21 women during 1990-1992 with 18 pts. diagnosed while in the insurance plan and 3 diagnosed prior to entry into the plan. (12(18)) pts. were in the plan for the entire duration of the study. The mean deviation for the date of diagnosis as recorded from tumor registry data, compared to the first date that a diagnosis of breast cancer appears on the billing record is 18 days (range:0-158). Four pts. experienced a recurrence. A determination of a recurrence from insurance records was only possible in (1(4)) pts. who
Chalkley estimates of angiogenesis in early breast cancer--relevance to prognosis
Offersen, Birgitte V; Sørensen, Flemming Brandt; Yilmaz, Mette; Knoop, Ann; Overgaard, Jens
The aim of this study was to investigate whether Chalkley estimates of angiogenesis add new knowledge regarding prediction of prognosis in 455 consecutive early breast carcinomas, both node-positive (52%) and node-negative (48%). Median follow-up was 101 months. Intense vascularization indicated......, high malignancy grade, negative oestrogen receptor, and increasing Chalkley counts (both tertiles and continuous) were independent markers of disease-specific death. Thus, in a univariate analysis it was found that high Chalkley estimates of angiogenesis indicated a poor prognosis, but high Chalkley...
Carlos B. Kasper; Fábio D. Mazim; José B. G. Soares; Tadeu G. de Oliveira
2015-01-01
ABSTRACT Using camera traps and capture/recapture analyses we recorded the presence and abundance of cat species at Turvo State Park, in southern Brazil. Ocelot [Leopardus pardalis (Linnaeus, 1758)] population density was estimated for two areas of the park, with differing management profiles. Density estimates varied from 0.14 to 0.26 indiv. km2. Another five cat species were recorded at very low frequencies, precluding more accurate analyses. We estimate 24 to 45 ocelots occur in the reserv...
We aimed to determine whether background 18F-FDG uptake in positron emission mammography (PEM) was related to mammographic density or background parenchymal enhancement in breast MRI. Methods: We studied a total of 52 patients (mean age, 50.9 years, 26 premenopausal, 26 postmenopausal) with newly diagnosed breast cancer who underwent 18F-FDG PEM (positron emission mammography), conventional mammography and breast MRI. The background mean 18F-FDG uptake value on PEM was obtained by drawing a user-defined region of interest (ROI) in a normal area of the contralateral breast. We reviewed the mammography retrospectively for overall breast density of contralateral breast according to the four-point scale (grade 1–4) of the Breast Imaging Reporting and Data System (BI-RADS) classification. The background parenchymal enhancement of breast MRI was classified as minimal, mild, moderate, or marked. All imaging findings were interpreted by two readers in consensus without knowledge of image findings of other modalities. Results: Multiple linear regression analysis revealed a significant correlation between background 18F-FDG uptake on PEM and mammographic density after adjustment for age and menopausal status (P 18F-FDG uptake on PEM and background parenchymal enhancement on MRI. Conclusion: Background 18F-FDG uptake on PEM significantly increases as mammographic density increases. Background parenchymal enhancement in breast MRI was not an independent predictor of the background 18F-FDG uptake on PEM unlike mammographic density
Using mortality data to estimate radiation effects on breast cancer incidence
In this paper we combine Japanese data on radiation exposure and cancer mortality with U.S. data on cancer incidence and lethality to estimate the effects of ionizing radiation on cancer incidence. The analysis is based on the mathematical relationship between the mortality rate and the incidence and lethality rates, as well as on statistical models that relate Japanese incidence rates to U.S. incidence rates and radiation risk factors. Our approach assumes that the risk of death from causes other than the cancer does not depend on whether or not the cancer is present, and among individuals with the cancer, the risk of death attributable to the cancer is the same in Japan and the U.S. and is not affected by radiation exposure. In particular, we focus on the incidence of breast cancer in Japanese women and how this incidence is affected by radiation risk factors. The analysis uses Japanese exposure and mortality data from the Radiation Effects Research Foundation study of atomic bomb survivors and U.S. incidence and lethality data from the Surveillance, Epidemiology, and End Results Registry. Even without Japanese incidence data, we obtain reasonable estimates of the incidence of breast cancer in unexposed Japanese women and identify the radiation risk factors that affect this incidence. Our analysis demonstrates that the age at exposure is an important risk factor, but that the incidence of breast cancer is not affected by the city of residence (Nagasaki versus Hiroshima) or the time since exposure
Performances of Different Algorithms for Tracer Kinetics Parameters Estimation in Breast DCE-MRI
Roberta Fusco
2014-07-01
Full Text Available Objective of this study was to evaluate the performances of different algorithms for tracer kinetics parameters estimation in breast Dynamic Contrast Enhanced-MRI. We considered four algorithms: two non-iterative algorithms based on impulsive and linear approximation of the Arterial Input Function respectively; and two iterative algorithms widely used for non-linear regression (Levenberg-Marquardt, LM and VARiable PROjection, VARPRO. Per each value of the kinetic parameters within a physiological range, we simulated 100 noisy curves and estimated the parameters with all algorithms. Sampling time, total duration and noise level have been chosen as in a typical breast examination. We compared the performances with respect to the Cramer-Rao Lower Bound (CRLB. Moreover, in order to gain further insight we applied the algorithms to a real breast examination. Accuracy of all the methods depends on the specific value of the parameters. The methods are in general biased: however, VARPRO showed small bias in a region of the parameter space larger than the other methods; moreover, VARPRO approached CRLB and the number of iterations were smaller than LM. In the specific conditions analyzed, VARPRO showed better performances with respect to LM and to non-iterative algorithms