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Sample records for breast density estimation

  1. Mammography density estimation with automated volumetic breast density measurement

    Energy Technology Data Exchange (ETDEWEB)

    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.

  2. Breast density estimation from high spectral and spatial resolution MRI

    Science.gov (United States)

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

    2016-01-01

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

  3. Computerized image analysis: estimation of breast density on mammograms.

    Science.gov (United States)

    Zhou, C; Chan, H P; Petrick, N; Helvie, M A; Goodsitt, M M; Sahiner, B; Hadjiiski, L M

    2001-06-01

    An automated image analysis tool is being developed for the estimation of mammographic breast density. This tool may be useful for risk estimation or for monitoring breast density change in prevention or intervention programs. In this preliminary study, a data set of 4-view mammograms from 65 patients was used to evaluate our approach. Breast density analysis was performed on the digitized mammograms in three stages. First, the breast region was segmented from the surrounding background by an automated breast boundary-tracking algorithm. Second, an adaptive dynamic range compression technique was applied to the breast image to reduce the range of the gray level distribution in the low frequency background and to enhance the differences in the characteristic features of the gray level histogram for breasts of different densities. Third, rule-based classification was used to classify the breast images into four classes according to the characteristic features of their gray level histogram. For each image, a gray level threshold was automatically determined to segment the dense tissue from the breast region. The area of segmented dense tissue as a percentage of the breast area was then estimated. To evaluate the performance of the algorithm, the computer segmentation results were compared to manual segmentation with interactive thresholding by five radiologists. A "true" percent dense area for each mammogram was obtained by averaging the manually segmented areas of the radiologists. We found that the histograms of 6% (8 CC and 8 MLO views) of the breast regions were misclassified by the computer, resulting in poor segmentation of the dense region. For the images with correct classification, the correlation between the computer-estimated percent dense area and the "truth" was 0.94 and 0.91, respectively, for CC and MLO views, with a mean bias of less than 2%. The mean biases of the five radiologists' visual estimates for the same images ranged from 0.1% to 11%. The

  4. Optimization of volumetric breast density estimation in digital mammograms

    Science.gov (United States)

    Holland, Katharina; Gubern-Mérida, Albert; Mann, Ritse M.; Karssemeijer, Nico

    2017-05-01

    Fibroglandular tissue volume and percent density can be estimated in unprocessed mammograms using a physics-based method, which relies on an internal reference value representing the projection of fat only. However, pixels representing fat only may not be present in dense breasts, causing an underestimation of density measurements. In this work, we investigate alternative approaches for obtaining a tissue reference value to improve density estimations, particularly in dense breasts. Two of three investigated reference values (F1, F2) are percentiles of the pixel value distribution in the breast interior (the contact area of breast and compression paddle). F1 is determined in a small breast interior, which minimizes the risk that peripheral pixels are included in the measurement at the cost of increasing the chance that no proper reference can be found. F2 is obtained using a larger breast interior. The new approach which is developed for very dense breasts does not require the presence of a fatty tissue region. As reference region we select the densest region in the mammogram and assume that this represents a projection of entirely dense tissue embedded between the subcutaneous fatty tissue layers. By measuring the thickness of the fat layers a reference (F3) can be computed. To obtain accurate breast density estimates irrespective of breast composition we investigated a combination of the results of the three reference values. We collected 202 pairs of MRI’s and digital mammograms from 119 women. We compared the percent dense volume estimates based on both modalities and calculated Pearson’s correlation coefficients. With the references F1-F3 we found respectively a correlation of \\text{R}=0.80 , \\text{R}=0.89 and \\text{R}=0.74 . Best results were obtained with the combination of the density estimations (\\text{R}=0.90 ). Results show that better volumetric density estimates can be obtained with the hybrid method, in particular for dense breasts, when

  5. Automated mammographic breast density estimation using a fully convolutional network.

    Science.gov (United States)

    Lee, Juhun; Nishikawa, Robert M

    2018-01-24

    The purpose of this study was to develop a fully automated algorithm for mammographic breast density estimation using deep learning. Our algorithm used a fully convolutional network, which is a deep learning framework for image segmentation, to segment both the breast and the dense fibroglandular areas on mammographic images. Using the segmented breast and dense areas, our algorithm computed the breast percent density (PD), which is the faction of dense area in a breast. Our dataset included full-field digital screening mammograms of 604 women, which included 1208 mediolateral oblique (MLO) and 1208 craniocaudal (CC) views. We allocated 455, 58, and 91 of 604 women and their exams into training, testing, and validation datasets, respectively. We established ground truth for the breast and the dense fibroglandular areas via manual segmentation and segmentation using a simple thresholding based on BI-RADS density assessments by radiologists, respectively. Using the mammograms and ground truth, we fine-tuned a pretrained deep learning network to train the network to segment both the breast and the fibroglandular areas. Using the validation dataset, we evaluated the performance of the proposed algorithm against radiologists' BI-RADS density assessments. Specifically, we conducted a correlation analysis between a BI-RADS density assessment of a given breast and its corresponding PD estimate by the proposed algorithm. In addition, we evaluated our algorithm in terms of its ability to classify the BI-RADS density using PD estimates, and its ability to provide consistent PD estimates for the left and the right breast and the MLO and CC views of the same women. To show the effectiveness of our algorithm, we compared the performance of our algorithm against a state of the art algorithm, laboratory for individualized breast radiodensity assessment (LIBRA). The PD estimated by our algorithm correlated well with BI-RADS density ratings by radiologists. Pearson's rho values of

  6. Quantitative volumetric breast density estimation using phase contrast mammography

    Science.gov (United States)

    Wang, Zhentian; Hauser, Nik; Kubik-Huch, Rahel A.; D'Isidoro, Fabio; Stampanoni, Marco

    2015-05-01

    Phase contrast mammography using a grating interferometer is an emerging technology for breast imaging. It provides complementary information to the conventional absorption-based methods. Additional diagnostic values could be further obtained by retrieving quantitative information from the three physical signals (absorption, differential phase and small-angle scattering) yielded simultaneously. We report a non-parametric quantitative volumetric breast density estimation method by exploiting the ratio (dubbed the R value) of the absorption signal to the small-angle scattering signal. The R value is used to determine breast composition and the volumetric breast density (VBD) of the whole breast is obtained analytically by deducing the relationship between the R value and the pixel-wise breast density. The proposed method is tested by a phantom study and a group of 27 mastectomy samples. In the clinical evaluation, the estimated VBD values from both cranio-caudal (CC) and anterior-posterior (AP) views are compared with the ACR scores given by radiologists to the pre-surgical mammograms. The results show that the estimated VBD results using the proposed method are consistent with the pre-surgical ACR scores, indicating the effectiveness of this method in breast density estimation. A positive correlation is found between the estimated VBD and the diagnostic ACR score for both the CC view (p=0.033 ) and AP view (p=0.001 ). A linear regression between the results of the CC view and AP view showed a correlation coefficient γ = 0.77, which indicates the robustness of the proposed method and the quantitative character of the additional information obtained with our approach.

  7. Estimation of Volumetric Breast Density from Digital Mammograms

    Science.gov (United States)

    Alonzo-Proulx, Olivier

    Mammographic breast density (MBD) is a strong risk factor for developing breast cancer. MBD is typically estimated by manually selecting the area occupied by the dense tissue on a mammogram. There is interest in measuring the volume of dense tissue, or volumetric breast density (VBD), as it could potentially be a stronger risk factor. This dissertation presents and validates an algorithm to measure the VBD from digital mammograms. The algorithm is based on an empirical calibration of the mammography system, supplemented by physical modeling of x-ray imaging that includes the effects of beam polychromaticity, scattered radation, anti-scatter grid and detector glare. It also includes a method to estimate the compressed breast thickness as a function of the compression force, and a method to estimate the thickness of the breast outside of the compressed region. The algorithm was tested on 26 simulated mammograms obtained from computed tomography images, themselves deformed to mimic the effects of compression. This allowed the determination of the baseline accuracy of the algorithm. The algorithm was also used on 55 087 clinical digital mammograms, which allowed for the determination of the general characteristics of VBD and breast volume, as well as their variation as a function of age and time. The algorithm was also validated against a set of 80 magnetic resonance images, and compared against the area method on 2688 images. A preliminary study comparing association of breast cancer risk with VBD and MBD was also performed, indicating that VBD is a stronger risk factor. The algorithm was found to be accurate, generating quantitative density measurements rapidly and automatically. It can be extended to any digital mammography system, provided that the compression thickness of the breast can be determined accurately.

  8. Accuracy of Breast Density Estimation from Mammographic Images

    OpenAIRE

    Geeraert, N.; Klaus, R.; Bloch, Isabelle; Muller, S.; Bosmans, H

    2013-01-01

    International audience; Breast density has been defined as an important risk factor for the development of breast cancer but the mechanisms of the impact on breast cancer development remain unsolved. One of the main discussions is the definition of breast density. Traditionally breast density is derived by dividing the area of the fibroglandular tissue in the image by the area of the total breast. From a physics point of view the ratio of volumes is a much more representative measure of the d...

  9. Automatic breast tissue density estimation scheme in digital mammography images

    Science.gov (United States)

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

    2017-03-01

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

  10. Volumetric breast density estimation from full-field digital mammograms.

    NARCIS (Netherlands)

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

    2006-01-01

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

  11. Breast Segmentation and Density Estimation in Breast MRI: A Fully Automatic Framework

    NARCIS (Netherlands)

    Gubern Merida, A.; Kallenberg, M.G.; Mann, R.M.; Marti, R.; Karssemeijer, N.

    2015-01-01

    Breast density measurement is an important aspect in breast cancer diagnosis as dense tissue has been related to the risk of breast cancer development. The purpose of this study is to develop a method to automatically compute breast density in breast MRI. The framework is a combination of image

  12. 3D density estimation in digital breast tomosynthesis: application to needle path planning for breast biopsy

    Science.gov (United States)

    Vancamberg, Laurence; Geeraert, Nausikaa; Iordache, Razvan; Palma, Giovanni; Klausz, Rémy; Muller, Serge

    2011-03-01

    Needle insertion planning for digital breast tomosynthesis (DBT) guided biopsy has the potential to improve patient comfort and intervention safety. However, a relevant planning should take into account breast tissue deformation and lesion displacement during the procedure. Deformable models, like finite elements, use the elastic characteristics of the breast to evaluate the deformation of tissue during needle insertion. This paper presents a novel approach to locally estimate the Young's modulus of the breast tissue directly from the DBT data. The method consists in computing the fibroglandular percentage in each of the acquired DBT projection images, then reconstructing the density volume. Finally, this density information is used to compute the mechanical parameters for each finite element of the deformable mesh, obtaining a heterogeneous DBT based breast model. Preliminary experiments were performed to evaluate the relevance of this method for needle path planning in DBT guided biopsy. The results show that the heterogeneous DBT based breast model improves needle insertion simulation accuracy in 71% of the cases, compared to a homogeneous model or a binary fat/fibroglandular tissue model.

  13. Volumetric breast density estimation from full-field digital mammograms: a validation study.

    Directory of Open Access Journals (Sweden)

    Albert Gubern-Mérida

    Full Text Available OBJECTIVES: To objectively evaluate automatic volumetric breast density assessment in Full-Field Digital Mammograms (FFDM using measurements obtained from breast Magnetic Resonance Imaging (MRI. MATERIAL AND METHODS: A commercially available method for volumetric breast density estimation on FFDM is evaluated by comparing volume estimates obtained from 186 FFDM exams including mediolateral oblique (MLO and cranial-caudal (CC views to objective reference standard measurements obtained from MRI. RESULTS: Volumetric measurements obtained from FFDM show high correlation with MRI data. Pearson's correlation coefficients of 0.93, 0.97 and 0.85 were obtained for volumetric breast density, breast volume and fibroglandular tissue volume, respectively. CONCLUSIONS: Accurate volumetric breast density assessment is feasible in Full-Field Digital Mammograms and has potential to be used in objective breast cancer risk models and personalized screening.

  14. Analysis of percent density estimates from digital breast tomosynthesis projection images

    Science.gov (United States)

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

    2007-03-01

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

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

    Science.gov (United States)

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

    2011-03-01

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

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

    Science.gov (United States)

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

    2017-03-01

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

  17. Mammographic density estimation: one-to-one comparison of digital mammography and digital breast tomosynthesis using fully automated software

    Energy Technology Data Exchange (ETDEWEB)

    Tagliafico, Alberto [University of Genoa, Institute of Anatomy, Department of Experimental Medicine, Genoa (Italy); Tagliafico, Giulio [University of Genoa, Dipartimento di Chimica e Chimica Industriale (DCCI), Genoa (Italy); Astengo, Davide [University of Genoa, Radiology Department, Genoa, Genova (Italy); Cavagnetto, Francesca; Rosasco, Raffaella [IRCCS Azienda Ospedaliera Universitaria San Martino, Department of Medical Physics, Ist Istituto Nazionale per la Ricerca sul Cancro, Genoa, Genova (Italy); Rescinito, Giuseppe; Monetti, Francesco; Calabrese, Massimo [IRCCS Azienda Ospedaliera Universitaria San Martino, Department of Diagnostic Senology, Ist Istituto Nazionale per la Ricerca sul Cancro, Genoa (Italy)

    2012-06-15

    To compare breast density on digital mammography and digital breast tomosynthesis using fully automated software. Following institutional approval and written informed consent from all participating women, both digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) were obtained. Breast percentage density was calculated with software on DBT and FFDM. Fifty consecutive patients (mean age, 51 years; range, 35-83 years) underwent both FFDM and DBT. Using a method based on the integral curve, breast density showed higher results on FFDM (68.1 {+-} 12.1 for FFDM and 51.9 {+-} 6.5 for DBT). FFDM overestimated breast density in 16.2% (P < 0.0001). Using a method based on maximum entropy thresholding, breast density showed higher results on FFDM (68.1 {+-} 12.1 for FFDM and 51.9 {+-} 6.5 for DBT). FFDM overestimated breast density in 11.4% (P < 0.0001). There was a good correlation among BI-RADS categories on a four-grade scale and the density evaluated with DBT and FFDM (r = 0.54, P < 0.01 and r = 0.44, P < 0.01). Breast density appeared to be significantly underestimated on digital breast tomosynthesis. Breast density is considered to be an independent risk factor for cancer Density can be assessed on full-field digital mammography and digital breast tomosynthesis Objective automated estimation of breast density eliminates subjectivity Automated estimation is more accurate than BI-RADS quantitative evaluation Breast density may be significantly underestimated on digital breast tomosynthesis. (orig.)

  18. Robust estimation of mammographic breast density: a patient-based approach

    Science.gov (United States)

    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).

  19. Automated Method for Analysis of Mammographic Breast Density - A Technique for Breast Cancer Risk Estimation

    Science.gov (United States)

    2006-07-01

    tion. The data set comprised the four- view craniocaudal (CC) and medio - lateral oblique (MLO) mammograms obtained in 65 patients who were ran- domly... Mexico . Radiology 1998;209:511–518. 4. Boyd NF, Byng RA, Jong EK, et al. Quantita- tive classification of mammographic densi- ties and breast cancer risk

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

    Science.gov (United States)

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

    2017-04-01

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

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

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

    2015-10-15

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

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

    Science.gov (United States)

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

    2013-05-01

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

  3. Imaging Breast Density: Established and Emerging Modalities

    Directory of Open Access Journals (Sweden)

    Jeon-Hor Chen

    2015-12-01

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

  4. Effect of compression paddle tilt correction on volumetric breast density estimation

    NARCIS (Netherlands)

    Kallenberg, M.G.J.; Gils, C.H. van; Lokate, M.; Heeten, G.J. den; Karssemeijer, N.

    2012-01-01

    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

  5. Breast MRI segmentation for density estimation: Do different methods give the same results and how much do differences matter?

    Science.gov (United States)

    Doran, Simon J; Hipwell, John H; Denholm, Rachel; Eiben, Björn; Busana, Marta; Hawkes, David J; Leach, Martin O; Silva, Isabel Dos Santos

    2017-09-01

    To compare two methods of automatic breast segmentation with each other and with manual segmentation in a large subject cohort. To discuss the factors involved in selecting the most appropriate algorithm for automatic segmentation and, in particular, to investigate the appropriateness of overlap measures (e.g., Dice and Jaccard coefficients) as the primary determinant in algorithm selection. Two methods of breast segmentation were applied to the task of calculating MRI breast density in 200 subjects drawn from the Avon Longitudinal Study of Parents and Children, a large cohort study with an MRI component. A semiautomated, bias-corrected, fuzzy C-means (BC-FCM) method was combined with morphological operations to segment the overall breast volume from in-phase Dixon images. The method makes use of novel, problem-specific insights. The resulting segmentation mask was then applied to the corresponding Dixon water and fat images, which were combined to give Dixon MRI density values. Contemporaneously acquired T1 - and T2 -weighted image datasets were analyzed using a novel and fully automated algorithm involving image filtering, landmark identification, and explicit location of the pectoral muscle boundary. Within the region found, fat-water discrimination was performed using an Expectation Maximization-Markov Random Field technique, yielding a second independent estimate of MRI density. Images are presented for two individual women, demonstrating how the difficulty of the problem is highly subject-specific. Dice and Jaccard coefficients comparing the semiautomated BC-FCM method, operating on Dixon source data, with expert manual segmentation are presented. The corresponding results for the method based on T1 - and T2 -weighted data are slightly lower in the individual cases shown, but scatter plots and interclass correlations for the cohort as a whole show that both methods do an excellent job in segmenting and classifying breast tissue. Epidemiological results

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

    Energy Technology Data Exchange (ETDEWEB)

    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

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

    Science.gov (United States)

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

    2012-03-01

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

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

    Science.gov (United States)

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

    2012-08-01

    The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screen-film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., "FOR PROCESSING") and vendor postprocessed (i.e., "FOR PRESENTATION"), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis. This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely fibroglandular, which are then aggregated into a

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

    Directory of Open Access Journals (Sweden)

    Abbas Cheddad

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

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

    Science.gov (United States)

    Rafal Podlaski; Francis A. Roesch

    2014-01-01

    Two-component mixtures of either the Weibull distribution or the gamma distribution and the kernel density estimator were used for describing the diameter at breast height (dbh) empirical distributions of two-cohort stands. The data consisted of study plots from the Å wietokrzyski National Park (central Poland) and areas close to and including the North Carolina section...

  11. Impact of lung density on the lung dose estimation for radiotherapy of breast cancer

    Directory of Open Access Journals (Sweden)

    Emma Hedin

    2017-07-01

    Conclusions: Dosimetric constrains for lung based on V20Gy required minimum changes due to implementation of the new algorithm regardless of breathing technique or type of treatment. Evaluation criteria utilizing V10Gy or V40Gy needed reconsideration, especially for treatments under DIBH involving low lung density.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2013-12-15

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

  13. Contingent kernel density estimation.

    Directory of Open Access Journals (Sweden)

    Scott Fortmann-Roe

    Full Text Available Kernel density estimation is a widely used method for estimating a distribution based on a sample of points drawn from that distribution. Generally, in practice some form of error contaminates the sample of observed points. Such error can be the result of imprecise measurements or observation bias. Often this error is negligible and may be disregarded in analysis. In cases where the error is non-negligible, estimation methods should be adjusted to reduce resulting bias. Several modifications of kernel density estimation have been developed to address specific forms of errors. One form of error that has not yet been addressed is the case where observations are nominally placed at the centers of areas from which the points are assumed to have been drawn, where these areas are of varying sizes. In this scenario, the bias arises because the size of the error can vary among points and some subset of points can be known to have smaller error than another subset or the form of the error may change among points. This paper proposes a "contingent kernel density estimation" technique to address this form of error. This new technique adjusts the standard kernel on a point-by-point basis in an adaptive response to changing structure and magnitude of error. In this paper, equations for our contingent kernel technique are derived, the technique is validated using numerical simulations, and an example using the geographic locations of social networking users is worked to demonstrate the utility of the method.

  14. Mammography: interobserver variability in breast density assessment

    NARCIS (Netherlands)

    Ooms, E. A.; Zonderland, H. M.; Eijkemans, M. J. C.; Kriege, M.; Mahdavian Delavary, B.; Burger, C. W.; Ansink, A. C.

    2007-01-01

    Our objective was to determine the interobserver variability of breast density assessment according to the Breast Imaging Reporting and Data System (BI-RADS) and to examine potential associations between breast density and risk factors for breast cancer. Four experienced breast radiologists received

  15. Airborne Crowd Density Estimation

    Science.gov (United States)

    Meynberg, O.; Kuschk, G.

    2013-10-01

    This paper proposes a new method for estimating human crowd densities from aerial imagery. Applications benefiting from an accurate crowd monitoring system are mainly found in the security sector. Normally crowd density estimation is done through in-situ camera systems mounted on high locations although this is not appropriate in case of very large crowds with thousands of people. Using airborne camera systems in these scenarios is a new research topic. Our method uses a preliminary filtering of the whole image space by suitable and fast interest point detection resulting in a number of image regions, possibly containing human crowds. Validation of these candidates is done by transforming the corresponding image patches into a low-dimensional and discriminative feature space and classifying the results using a support vector machine (SVM). The feature space is spanned by texture features computed by applying a Gabor filter bank with varying scale and orientation to the image patches. For evaluation, we use 5 different image datasets acquired by the 3K+ aerial camera system of the German Aerospace Center during real mass events like concerts or football games. To evaluate the robustness and generality of our method, these datasets are taken from different flight heights between 800 m and 1500 m above ground (keeping a fixed focal length) and varying daylight and shadow conditions. The results of our crowd density estimation are evaluated against a reference data set obtained by manually labeling tens of thousands individual persons in the corresponding datasets and show that our method is able to estimate human crowd densities in challenging realistic scenarios.

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

    Science.gov (United States)

    Rahbar, Kareem; Gubern-Merida, Albert; Patrie, James T; Harvey, Jennifer A

    2017-12-01

    The purpose of this study was to evaluate discrepancy in breast composition measurements obtained from mammograms using two commercially available software methods for systematic trends in overestimation or underestimation compared to magnetic resonance-derived measurements. An institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study was performed to calculate percent breast density (PBD) by quantifying fibroglandular volume and total breast volume derived from magnetic resonance imaging (MRI) segmentation and mammograms using two commercially available software programs (Volpara and Quantra). Consecutive screening MRI exams from a 6-month period with negative or benign findings were used. The most recent mammogram within 9 months was used to derive mean density values from "for processing" images at the per breast level. Bland-Altman statistical analyses were performed to determine the mean discrepancy and the limits of agreement. A total of 110 women with 220 breasts met the study criteria. Overall, PBD was not different between MRI (mean 10%, range 1%-41%) and Volpara (mean 10%, range 3%-29%); a small but significant difference was present in the discrepancy between MRI and Quantra (4.0%, 95% CI: 2.9 to 5.0, P breast densities, with Volpara slightly underestimating and Quantra slightly overestimating PBD compared to MRI. The mean discrepancy for both Volpara and Quantra for total breast volume was not significantly different from MRI (p = 0.89, 0.35, respectively). Volpara tended to underestimate, whereas Quantra tended to overestimate fibroglandular volume, with the highest discrepancy at higher breast volumes. Both Volpara and Quantra tend to underestimate PBD, which is most pronounced at higher densities. PBD can be accurately measured using automated volumetric software programs, but values should not be used interchangeably between vendors. Copyright © 2017. Published by Elsevier Inc.

  17. Imaging Breast Density: Established and Emerging Modalities1

    Science.gov (United States)

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

    2015-01-01

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

  18. Mammographic breast density refines Tyrer-Cuzick estimates of breast cancer risk in high-risk women: findings from the placebo arm of the International Breast Cancer Intervention Study I.

    Science.gov (United States)

    Warwick, Jane; Birke, Hanna; Stone, Jennifer; Warren, Ruth M L; Pinney, Elizabeth; Brentnall, Adam R; Duffy, Stephen W; Howell, Anthony; Cuzick, Jack

    2014-10-08

    Mammographic density is well-established as a risk factor for breast cancer, however, adjustment for age and body mass index (BMI) is vital to its clinical interpretation when assessing individual risk. In this paper we develop a model to adjust mammographic density for age and BMI and show how this adjusted mammographic density measure might be used with existing risk prediction models to identify high-risk women more precisely. We explored the association between age, BMI, visually assessed percent dense area and breast cancer risk in a nested case-control study of women from the placebo arm of the International Breast Cancer Intervention Study I (72 cases, 486 controls). Linear regression was used to adjust mammographic density for age and BMI. This adjusted measure was evaluated in a multivariable logistic regression model that included the Tyrer-Cuzick (TC) risk score, which is based on classical breast cancer risk factors. Percent dense area adjusted for age and BMI (the density residual) was a stronger measure of breast cancer risk than unadjusted percent dense area (odds ratio per standard deviation 1.55 versus 1.38; area under the curve (AUC) 0.62 versus 0.59). Furthermore, in this population at increased risk of breast cancer, the density residual added information beyond that obtained from the TC model alone, with the AUC for the model containing both TC risk and density residual being 0.62 compared to 0.51 for the model containing TC risk alone (P =0.002). In women at high risk of breast cancer, adjusting percent mammographic density for age and BMI provides additional predictive information to the TC risk score, which already incorporates BMI, age, family history and other classic breast cancer risk factors. Furthermore, simple selection criteria can be developed using mammographic density, age and BMI to identify women at increased risk in a clinical setting. http://www.controlled-trials.com/ISRCTN91879928 (Registered: 1 June 2006).

  19. Postmortem validation of breast density using dual-energy mammography

    Energy Technology Data Exchange (ETDEWEB)

    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.

  20. Endocrine determinants of breast density and breast cancer

    NARCIS (Netherlands)

    Verheus, M.

    2007-01-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

  2. Breast-density measurement using photon-counting spectral mammography.

    Science.gov (United States)

    Johansson, Henrik; von Tiedemann, Miriam; Erhard, Klaus; Heese, Harald; Ding, Huanjun; Molloi, Sabee; Fredenberg, Erik

    2017-07-01

    To evaluate a method for measuring breast density using photon-counting spectral mammography. Breast density is an indicator of breast cancer risk and diagnostic accuracy in mammography, and can be used as input to personalized screening, treatment monitoring and dose estimation. The measurement method employs the spectral difference in x-ray attenuation between adipose and fibro-glandular tissue, and does not rely on any a priori information. The method was evaluated using phantom measurements on tissue-equivalent material (slabs and breast-shaped phantoms) and using clinical data from a screening population (n=1329). A state-of-the-art nonspectral method for breast-density assessment was used for benchmarking. The precision of the spectral method was estimated to be 1.5-1.8 percentage points (pp) breast density. Expected correlations were observed in the screening population for thickness versus breast density, dense volume, breast volume, and compression height. Densities ranged between 4.5% and 99.6%, and exhibited a skewed distribution with a mode of 12.5%, a median of 18.3%, and a mean of 23.7%. The precision of the nonspectral method was estimated to be 2.7-2.8 pp. The major uncertainty of the nonspectral method originated from the thickness estimate, and in particular thin/dense breasts posed problems compared to the spectral method. The spectral method yielded reasonable results in a screening population with a precision approximately two times that of the nonspectral method, which may improve or enable applications of breast-density measurement on an individual basis such as treatment monitoring and personalized screening. © 2017 American Association of Physicists in Medicine.

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

    Science.gov (United States)

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

    2017-07-01

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

  4. Effects of breast density and compression on normal breast tissue hemodynamics through breast tomosynthesis guided near-infrared spectral tomography

    OpenAIRE

    Michaelsen, Kelly E.; Krishnaswamy, Venkataramanan; Shi, Linxi; Vedantham, Srinivasan; Karellas, Andrew; Pogue, Brian W.; Paulsen, Keith D.; Poplack, Steven P.

    2016-01-01

    Optically derived tissue properties across a range of breast densities and the effects of breast compression on estimates of hemoglobin, oxygen metabolism, and water and lipid concentrations were obtained from a coregistered imaging system that integrates near-infrared spectral tomography (NIRST) with digital breast tomosynthesis (DBT). Image data were analyzed from 27 women who underwent four IRB approved NIRST/DBT exams that included fully and mildly compressed breast acquisitions in two pr...

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

    Science.gov (United States)

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

    2017-06-01

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

  6. Association between air pollution and mammographic breast density in the Breast Cancer Surveilance Consortium.

    Science.gov (United States)

    Yaghjyan, Lusine; Arao, Robert; Brokamp, Cole; O'Meara, Ellen S; Sprague, Brian L; Ghita, Gabriela; Ryan, Patrick

    2017-04-06

    Mammographic breast density is a well-established strong risk factor for breast cancer. The environmental contributors to geographic variation in breast density in urban and rural areas are poorly understood. We examined the association between breast density and exposure to ambient air pollutants (particulate matter Breast Cancer Surveillance Consortium (2001-2009). We included women aged ≥40 years with known residential zip codes before the index mammogram (n = 279,967). Breast density was assessed using the American College of Radiology's Breast Imaging-Reporting and Data System (BI-RADS) four-category breast density classification. PM2.5 and O3 estimates for grids across the USA (2001-2008) were obtained from the US Environmental Protection Agency Hierarchical Bayesian Model (HBM). For the majority of women (94%), these estimates were available for the year preceding the mammogram date. Association between exposure to air pollutants and density was estimated using polytomous logistic regression, adjusting for potential confounders. Women with extremely dense breasts had higher mean PM2.5 and lower O3 exposures than women with fatty breasts (8.97 vs. 8.66 ug/m3 and 33.70 vs. 35.82 parts per billion (ppb), respectively). In regression analysis, women with heterogeneously dense vs. scattered fibroglandular breasts were more likely to have higher exposure to PM2.5 (fourth vs. first quartile odds ratio (OR) = 1.19, 95% confidence interval (CI) 1.16 - 1.23). Women with extremely dense vs. scattered fibroglandular breasts were less likely to have higher levels of ozone exposure (fourth vs. first quartile OR = 0.80, 95% CI 0.73-0.87). Exposure to PM2.5 and O3 may in part explain geographical variation in mammographic density. Further studies are warranted to determine the causal nature of these associations.

  7. Risk-based Breast Cancer Screening: Implications of Breast Density.

    Science.gov (United States)

    Lee, Christoph I; Chen, Linda E; Elmore, Joann G

    2017-07-01

    The approach to breast cancer screening has changed over time from a general approach to a more personalized, risk-based approach. Women with dense breasts, one of the most prevalent risk factors, are now being informed that they are at increased risk of developing breast cancer and should consider supplemental screening beyond mammography. This article reviews the current evidence regarding the impact of breast density relative to other known risk factors, the evidence regarding supplemental screening for women with dense breasts, supplemental screening options, and recommendations for physicians having shared decision-making discussions with women who have dense breasts. Copyright © 2017 Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2015-06-01

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

  10. A new look at breast density and breast cancer risk

    NARCIS (Netherlands)

    Haars, G.

    2008-01-01

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

  11. Impact of type of full-field digital image on mammographic density assessment and breast cancer risk estimation: a case-control study.

    Science.gov (United States)

    Busana, Marta Cecilia; Eng, Amanda; Denholm, Rachel; Dowsett, Mitch; Vinnicombe, Sarah; Allen, Steve; Dos-Santos-Silva, Isabel

    2016-09-26

    Full-field digital mammography, which is gradually being introduced in most clinical and screening settings, produces two types of images: raw and processed. However, the extent to which mammographic density measurements, and their ability to predict breast cancer risk, vary according to type of image is not fully known. We compared the performance of the semi-automated Cumulus method on digital raw, "analogue-like" raw and processed images, and the performance of a recently developed method - Laboratory for Breast Radiodensity Assessment (LIBRA) - on digital raw and processed images, in a case-control study (414 patients (cases) and 684 controls) by evaluating the extent to which their measurements were associated with breast cancer risk factors, and by comparing their ability to predict breast cancer risk. Valid Cumulus and LIBRA measurements were obtained from all available images, but the resulting distributions differed according to the method and type of image used. Both Cumulus and LIBRA percent density were inversely associated with age, body mass index (BMI), parity and postmenopausal status, regardless of type of image used. Cumulus percent density was strongly associated with breast cancer risk, but with the magnitude of the association slightly stronger for processed (risk increase per one SD increase in percent density (95 % CI): 1.55 (1.29, 1.85)) and "analogue-like" raw (1.52 (1.28, 1.80)) than for raw (1.35 (1.14, 1.60)) images. LIBRA percent density produced weaker associations with risk, albeit stronger for processed (1.32 (1.08, 1.61)) than raw images (1.17 (0.99, 1.37)). The percent density values yielded by the various density assessment/type of image combinations had similar ability to discriminate between patients and controls (area under the receiving operating curve values for percent density, age, BMI, parity and menopausal status combined ranged from 0.61 and 0.64). The findings showed that Cumulus can be used to measure density on all

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

    Science.gov (United States)

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

    2015-10-01

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

  13. Independent Association of Lobular Involution and Mammographic Breast Density With Breast Cancer Risk

    Science.gov (United States)

    Vachon, Celine M.; Pankratz, V. Shane; Vierkant, Robert A.; Anderson, Stephanie S.; Brandt, Kathleen R.; Visscher, Daniel W.; Reynolds, Carol; Frost, Marlene H.; Hartmann, Lynn C.

    2010-01-01

    Background Lobular involution, or age-related atrophy of breast lobules, is inversely associated with breast cancer risk, and mammographic breast density (MBD) is positively associated with breast cancer risk. Methods To evaluate whether lobular involution and MBD are independently associated with breast cancer risk in women with benign breast disease, we performed a nested cohort study among women (n = 2666) with benign breast disease diagnosed at Mayo Clinic between January 1, 1985, and December 31, 1991 and a mammogram available within 6 months of the diagnosis. Women were followed up for an average of 13.3 years to document any breast cancer incidence. Lobular involution was categorized as none, partial, or complete; parenchymal pattern was classified using the Wolfe classification as N1 (nondense), P1, P2 (ductal prominence occupying 25% of the breast, respectively), or DY (extremely dense). Hazard ratios (HRs) and 95% confidence intervals (CIs) to assess associations of lobular involution and MBD with breast cancer risk were estimated using adjusted Cox proportional hazards model. All tests of statistical significance were two-sided. Results After adjustment for MBD, having no or partial lobular involution was associated with a higher risk of breast cancer than having complete involution (none: HR of breast cancer incidence = 2.62, 95% CI = 1.39 to 4.94; partial: HR of breast cancer incidence = 1.61, 95% CI = 1.03 to 2.53; Ptrend = .002). Similarly, after adjustment for involution, having dense breasts was associated with higher risk of breast cancer than having nondense breasts (for DY: HR of breast cancer incidence = 1.67, 95% CI = 1.03 to 2.73; for P2: HR of breast cancer incidence = 1.96, 95% CI = 1.20 to 3.21; for P1: HR of breast cancer incidence = 1.23, 95% CI = 0.67 to 2.26; Ptrend = .02). Having a combination of no involution and dense breasts was associated with higher risk of breast cancer than having complete involution and nondense breasts (HR of

  14. Local breast density assessment using reacquired mammographic images.

    Science.gov (United States)

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

    2017-08-01

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

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

    Science.gov (United States)

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

    2016-07-01

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

  16. Density estimation in wildlife surveys

    Science.gov (United States)

    Bart, Jonathan; Droege, Sam; Geissler, Paul E.; Peterjohn, Bruce G.; Ralph, C. John

    2004-01-01

    Several authors have recently discussed the problems with using index methods to estimate trends in population size. Some have expressed the view that index methods should virtually never be used. Others have responded by defending index methods and questioning whether better alternatives exist. We suggest that index methods are often a cost-effective component of valid wildlife monitoring but that double-sampling or another procedure that corrects for bias or establishes bounds on bias is essential. The common assertion that index methods require constant detection rates for trend estimation is mathematically incorrect; the requirement is no long-term trend in detection "ratios" (index result/parameter of interest), a requirement that is probably approximately met by many well-designed index surveys. We urge that more attention be given to defining bird density rigorously and in ways useful to managers. Once this is done, 4 sources of bias in density estimates may be distinguished: coverage, closure, surplus birds, and detection rates. Distance, double-observer, and removal methods do not reduce bias due to coverage, closure, or surplus birds. These methods may yield unbiased estimates of the number of birds present at the time of the survey, but only if their required assumptions are met, which we doubt occurs very often in practice. Double-sampling, in contrast, produces unbiased density estimates if the plots are randomly selected and estimates on the intensive surveys are unbiased. More work is needed, however, to determine the feasibility of double-sampling in different populations and habitats. We believe the tension that has developed over appropriate survey methods can best be resolved through increased appreciation of the mathematical aspects of indices, especially the effects of bias, and through studies in which candidate methods are evaluated against known numbers determined through intensive surveys.

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

    African Journals Online (AJOL)

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

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

    Science.gov (United States)

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

    2009-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Vedantham, S; Shi, L; Karellas, A [University of Massachusetts Medical School, Worcester, MA (United States); O’Connell, A [University of Rochester Medical Center, Rochester, NY (United States)

    2015-06-15

    high. Considering the variability in VBD estimates from commercial software, the breast density information communicated to the patient should be based on radiologist’s interpretation. This work was supported in part by NIH R21 CA176470 and R21 CA134128. The contents are solely the responsibility of the authors and do not reflect the official views of the NIH or NCI.

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

    Directory of Open Access Journals (Sweden)

    Daniëlle van der Waal

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

  1. Mammographic density and breast cancer risk: a mediation analysis.

    Science.gov (United States)

    Rice, Megan S; Bertrand, Kimberly A; VanderWeele, Tyler J; Rosner, Bernard A; Liao, Xiaomei; Adami, Hans-Olov; Tamimi, Rulla M

    2016-09-21

    High mammographic density (MD) is a strong risk factor for breast cancer. However, it is unclear whether high MD is an intermediate phenotype or whether breast cancer risk factors influence breast cancer risk and MD independently. Our study population included 1290 invasive breast cancer cases and 3422 controls from the Nurses' Health Studies. We estimated the percent of the total association between the risk factor and breast cancer that was mediated by MD. In both pre- and postmenopausal women, the association between history of biopsy-confirmed benign breast disease and risk was partially mediated by percent MD (percent mediated (PM) = 17 %, p age 18) and breast cancer risk were substantially mediated by percent MD (PM = 73 %, p = 0.05 and PM = 82 %, p = 0.04, respectively). In postmenopausal women, the proportion of the associations of childhood somatotype and adolescent somatotype that were mediated by percent MD were lower (PM = 26 %, p = 0.01 for both measures). Hormone therapy use at mammogram was significantly mediated by percent MD in postmenopausal women (PM = 22 %, p risk factors, such as age at menarche or family history of breast cancer, were not mediated by percent MD. Percent MD partially mediated some of the associations between risk factors and breast cancer, though the magnitude varied by risk factor and menopausal status. These findings suggest that high MD may be an intermediate in some biological pathways for breast cancer development.

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

    Science.gov (United States)

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

    2017-04-01

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

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

    NARCIS (Netherlands)

    Waal, D. van der; Ripping, T.M.; Verbeek, A.L.M.; Broeders, M.J.

    2017-01-01

    Breast cancer screening is known to reduce breast cancer mortality. A high breast density may affect this reduction. We assessed the effect of screening on breast cancer mortality in women with dense and fatty breasts separately. Analyses were performed within the Nijmegen (Dutch) screening

  4. Changes in mammographic density and breast cancer risk

    NARCIS (Netherlands)

    Lokate, A.J.M.

    2012-01-01

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

  5. Global Population Density Grid Time Series Estimates

    Data.gov (United States)

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

  6. Fish population density estimates - 1969 Fredericton District

    National Research Council Canada - National Science Library

    Hyatt, R.A

    1969-01-01

    Discussions with biologists concerning electroseining methods for fish population density estimates led to the conduct of a series of comparisons between the Peterson Index type of estimate (mark and recapture...

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

  8. Density estimation from local structure

    CSIR Research Space (South Africa)

    Van der Walt, Christiaan M

    2009-11-01

    Full Text Available Mixture Model (GMM) density function of the data and the log-likelihood scores are compared to the scores of a GMM trained with the expectation maximization (EM) algorithm on 5 real-world classification datasets (from the UCI collection). They show...

  9. Mammographic breast density: impact on breast cancer risk and implications for screening.

    Science.gov (United States)

    Freer, Phoebe E

    2015-01-01

    Mammographic breast density is rapidly becoming a hot topic in both the medical literature and the lay press. In the United States, recent legislative changes in 19 states now require radiologists to notify patients regarding breast density as well as the possible need for supplemental screening. Federal legislation regarding breast density notification has been introduced, and its passage is likely on the horizon. An understanding of the context, scientific evidence, and controversies surrounding the topic of breast density as a risk factor for breast cancer is critical for radiologists. The current state of evidence is presented regarding supplemental screening for women with dense breasts, including the use of digital breast tomosynthesis, whole-breast ultrasonography, and gadolinium-enhanced magnetic resonance imaging. A review of current practice guidelines and additional sources of information will improve radiologists' understanding of the relevant subject of breast density and enable them to respond appropriately to questions from patients, clinicians, and the media. (©)RSNA, 2015.

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

    Science.gov (United States)

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

    2017-08-22

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

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

    Science.gov (United States)

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

    2013-01-01

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

  12. Breast density in multiethnic women presenting for screening mammography.

    Science.gov (United States)

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

    2017-10-24

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

  13. Determinants of volumetric breast density in Chilean premenopausal women.

    Science.gov (United States)

    Pereira, Ana; Garmendia, Maria Luisa; Uauy, Ricardo; Neira, Paulina; Lopez-Arana, Sandra; Malkov, Serghei; Shepherd, John

    2017-04-01

    High mammographic breast density (BD) is a strong risk factor of breast cancer; however, little is known in women under 40 years of age. Recently, dual-energy X-ray Absorptiometry (DXA) has been developed as a low-dose method to measure BD in young populations. Thus, our aims were to describe BD in relation to risk factors in Chilean women under 40 years old and to explore the equivalence of DXA to mammography for the measurement of BD. We selected 192 premenopausal Chilean female participants of the DERCAM study for whom we have anthropometric, sociodemographic, and gyneco-obstetric data. The subjects received both digital mammograms (Hologic) and breast DXA scans (GE iDXA). Mammographic BD was estimated using a fully automated commercial method (VOLPARA®) and BI-RADS. Breast DXA scans were performed using a standardized protocol and the % fibroglandular volume (%FGV) was estimated considering a two-compartment model of adipose and fibroglandular tissue. The mean age was 37 years (SD = 6.5) and 31.6% of the subjects were obese. The median %FGV and absolute FGV (AFGV) measured by DXA were 9% and 198.1 cm3 and for VOLPARA®, 8.6% and 58.0 cm3, respectively. The precision for %FGV after reposition was 2.8%. The correlation coefficients for %FGV, AFGV, and breast volume between DXA and mammography were over 0.7. Age and body mass index (BMI) were inversely associated with %FGV, and BMI was positively related to AFGV as estimated with DXA or mammography. We did not observe an association with gyneco-obstetric characteristics, education, and %FGV and AFGV; smoking was only associated with AFGV as measured by VOLPARA®. DXA is an alternative method to measure volumetric BD; thus, it could be used to continuously monitor BD in adult women in follow-up studies or to assess BD in young women.

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

    OpenAIRE

    Salem, Christine; Atallah, David; Safi, Joelle; Chahine, Georges; Haddad, Antoine; El Kassis, Nadine; Maalouly, Laura-Maria; Moubarak, Malak; Dib, Mary; Ghossain, Michel

    2017-01-01

    Purpose To study the distribution of breast mammogram density in Lebanese women and correlate it with breast cancer (BC) incidence. Methods Data from 1,049 women who had screening or diagnostic mammography were retrospectively reviewed. Age, menopausal status, contraceptives or hormonal replacement therapy (HRT), parity, breastfeeding, history of BC, breast mammogram density, and final BI-RADS assessment were collected. Breast density was analyzed in each age category and compared according t...

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

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

    2014-07-21

    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.

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

    Science.gov (United States)

    Ng, Kwan-Hoong; Lau, Susie

    2015-12-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

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

    Directory of Open Access Journals (Sweden)

    Christine Salem

    2017-01-01

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

  20. Relationship between breast sound speed and mammographic percent density

    Science.gov (United States)

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

    2011-03-01

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

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

    Science.gov (United States)

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

    2017-09-20

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

  2. Graph Bundling by Kernel Density Estimation

    NARCIS (Netherlands)

    Hurter, C.; Ersoy, O.; Telea, A.

    We present a fast and simple method to compute bundled layouts of general graphs. For this, we first transform a given graph drawing into a density map using kernel density estimation. Next, we apply an image sharpening technique which progressively merges local height maxima by moving the convolved

  3. Breast density and outcome of mammography screening: a cohort study

    DEFF Research Database (Denmark)

    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......-69 years. We collected information for the years 1991-2001 on screening outcome, incident BCs (screen-, interval-, and later detected), and BC deaths. Breast density was dichotomised into fatty (F) and mixed/dense (M/D) breasts. Screening sensitivity was measured as the odds ratio of interval versus screen...

  4. Estimating the Risks of Breast Cancer Radiotherapy

    DEFF Research Database (Denmark)

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

    2017-01-01

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

  5. Mammographic breast density and breast cancer risk: interactions of percent density, absolute dense, and non-dense areas with breast cancer risk factors.

    Science.gov (United States)

    Yaghjyan, Lusine; Colditz, Graham A; Rosner, Bernard; Tamimi, Rulla M

    2015-02-01

    We investigated if associations of breast density and breast cancer differ according to the level of other known breast cancer risk factors, including body mass index (BMI), age at menarche, parity, age at first child's birth, age at menopause, alcohol consumption, a family history of breast cancer, a history of benign breast disease, and physical activity. This study included 1,044 postmenopausal incident breast cancer cases diagnosed within the Nurses' Health Study cohort and 1,794 matched controls. Percent breast density, absolute dense, and non-dense areas were measured from digitized film images with computerized techniques. Information on breast cancer risk factors was obtained prospectively from biennial questionnaires. Percent breast density was more strongly associated with breast cancer risk in current postmenopausal hormone users (≥50 vs. 10 %: OR 5.34, 95 % CI 3.36-8.49) as compared to women with past (OR 2.69, 95 % CI 1.32-5.49) or no hormone history (OR 2.57, 95 % CI 1.18-5.60, p-interaction = 0.03). Non-dense area was inversely associated with breast cancer risk in parous women, but not in women without children (p-interaction = 0.03). Associations of density with breast cancer risk did not differ by the levels of BMI, age at menarche, parity, age at first child's birth, age at menopause, alcohol consumption, a family history of breast cancer, a history of benign breast disease, and physical activity. Women with dense breasts, who currently use menopausal hormone therapy are at a particularly high risk of breast cancer. Most breast cancer risk factors do not modify the association between mammographic breast density and breast cancer risk.

  6. Nonparametric Collective Spectral Density Estimation and Clustering

    KAUST Repository

    Maadooliat, Mehdi

    2017-04-12

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

  7. Quantum statistical inference for density estimation

    Energy Technology Data Exchange (ETDEWEB)

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

    1993-11-01

    A new penalized likelihood method for non-parametric density estimation is proposed, which is based on a mathematical analogy to quantum statistical physics. The mathematical procedure for density estimation is related to maximum entropy methods for inverse problems; the penalty function is a convex information divergence enforcing global smoothing toward default models, positivity, extensivity and normalization. The novel feature is the replacement of classical entropy by quantum entropy, so that local smoothing may be enforced by constraints on the expectation values of differential operators. Although the hyperparameters, covariance, and linear response to perturbations can be estimated by a variety of statistical methods, we develop the Bayesian interpretation. The linear response of the MAP estimate is proportional to the covariance. The hyperparameters are estimated by type-II maximum likelihood. The method is demonstrated on standard data sets.

  8. Same task, same observers, different values: the problem with visual assessment of breast density

    Science.gov (United States)

    Sergeant, Jamie C.; Walshaw, Lani; Wilson, Mary; Seed, Sita; Barr, Nicky; Beetles, Ursula; Boggis, Caroline; Bundred, Sara; Gadde, Soujanya; Lim, Yit; Whiteside, Sigrid; Evans, D. Gareth; Howell, Anthony; Astley, Susan M.

    2013-03-01

    The proportion of radio-opaque fibroglandular tissue in a mammographic image of the breast is a strong and modifiable risk factor for breast cancer. Subjective, area-based estimates made by expert observers provide a simple and efficient way of measuring breast density within a screening programme, but the degree of variability may render the reliable identification of women at increased risk impossible. This study examines the repeatability of visual assessment of percent breast density by expert observers. Five consultant radiologists and two breast physicians, all with at least two years' experience in mammographic density assessment, were presented with 100 digital mammogram cases for which they had estimated density at least 12 months previously. Estimates of percent density were made for each mammographic view and recorded on a printed visual analogue scale. The level of agreement between the two sets of estimates was assessed graphically using Bland-Altman plots. All but one observer had a mean difference of less than 6 percentage points, while the largest mean difference was 14.66 percentage points. The narrowest 95% limits of agreement for the differences were -11.15 to 17.35 and the widest were -13.95 to 40.43. Coefficients of repeatability ranged from 14.40 to 38.60. Although visual assessment of breast density has been shown to be strongly associated with cancer risk, the lack of agreement shown here between repeat assessments of the same images by the same observers questions the reliability of using visual assessment to identify women at high risk or to detect moderate changes in breast density over time.

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

    Science.gov (United States)

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

    2010-04-01

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

  10. Breast density changes associated with postmenopausal hormone replacement therapy

    DEFF Research Database (Denmark)

    Nielsen, Mads; Pettersen, Paola; Alexandersen, P

    2010-01-01

    .01) but not in the placebo group. Conclusions: HT for 2 years in postmenopausal women significantly increased radiologist-assessed breast density compared with placebo, in addition to significant changes in estrogen levels, markers of bone metabolism, and bone mineral density. Computer-automated techniques may be comparable......-based approaches (Breast Imaging Reporting and Data System score and interactive threshold) and one computer-based technique (heterogeneity examination of radiographs). Correlations of temporal changes in breast density with changes in serum estradiol levels, biochemical markers of bone metabolism, and bone...... mineral density at the spine and femur were also assessed. Results: Breast density assessed by the radiologist-based approaches increased significantly from baseline in the HT group (P

  11. The relationship between breast density and bone mineral density in never users of postmenopausal hormone therapy.

    Science.gov (United States)

    Seckin, Berna; Pekcan, Meryem Kuru; Inal, Hasan Ali; Gulerman, Cavidan

    2017-06-01

    Estrogen is known to affect both mammographic breast density and bone mineral density (BMD), but there are inconsistent results about the association of these density measurements in postmenopausal women. Furthermore, there are scarce data on the relationship between breast density and BMD in never users of postmenopausal hormone therapy. In this study, we examined the relationship between mammographic breast density and BMD in postmenopausal women who were never hormone replacement therapy users. A total of 293 postmenopausal women were enrolled in this cross-sectional study. Mammograms and BMD measurements for screening purposes were obtained. Assessment of mammographic breast density was performed by using breast imaging reporting and data system classification. The BMD was measured using dual-energy X-ray absorptiometry of the lumbar spine and femoral neck. Grade 1 breast density was observed in 64 women (21.8 %), grade 2 in 113 women (38.6 %) and grades 3 and 4 in 116 (39.6 %) women. Breast density decreased with increasing age and body mass index (BMI). Meanwhile, no significant differences were detected in BMD measures of the hip (p = 0.14) and lumbar spine (p = 0.29) among the breast density categories. After adjusting for age and BMI, the differences in the mean BMD at the hip and lumbar spine across the breast density categories remained insignificant (p = 0.26 and 0.11, respectively). There is no evidence of a relationship between mammographic breast density and BMD in postmenopausal women who had never used hormone replacement therapy.

  12. Breast Density as a Risk Factor for Breast Cancer amongst a Cohort ...

    African Journals Online (AJOL)

    Background: The incidence of breast cancer in Uganda has nearly tripled in three decades, the reasons not wholly understood. Country specific evidence is needed to better understand associated risk factors. Breast density is a strong predictor of breast cancer risk in Caucasian populations. The aim of this study was to ...

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

  15. Genetic Plymorphisms, Estrogens, and Breast Density

    National Research Council Canada - National Science Library

    Maskarinec, Gertraud

    2005-01-01

    This study investigated the association between genetic polymorphisms in hormone producing and metabolizing enzymes and several markers of breast cancer risk among women of different ethnic background...

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

    Energy Technology Data Exchange (ETDEWEB)

    Ding, Huanjun; Johnson, Travis; Lin, Muqing; Le, Huy Q.; Ducote, Justin L.; Su, Min-Ying; Molloi, Sabee, E-mail: symolloi@uci.edu [Department of Radiological Sciences, University of California, Irvine, California 92697 (United States)

    2013-12-15

    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

  17. Annual Screening Mammogram and its Relation to Breast Density

    Directory of Open Access Journals (Sweden)

    Sabek EAS

    2017-11-01

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

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

    Science.gov (United States)

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

    2018-01-01

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

  19. X-ray phase-shifts-based method of volumetric breast density measurement

    OpenAIRE

    Wu, Xizeng; Yan, Aimin; Liu, Hong

    2012-01-01

    Purpose: The high breast density is one of the biggest risk factors for breast cancer. Identifying patient having persistent high breast density is important for breast cancer screening and prevention. In this work the authors propose for the first time an x-ray phase-shifts-based method of breast density measurement.

  20. A study on microvascular density in breast carcinoma

    Directory of Open Access Journals (Sweden)

    D Pyakurel

    2014-04-01

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

  1. Anisotropic Density Estimation in Global Illumination

    DEFF Research Database (Denmark)

    Schjøth, Lars

    2009-01-01

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

  2. Breast Density Analysis with Automated Whole-Breast Ultrasound: Comparison with 3-D Magnetic Resonance Imaging.

    Science.gov (United States)

    Chen, Jeon-Hor; Lee, Yan-Wei; Chan, Si-Wa; Yeh, Dah-Cherng; Chang, Ruey-Feng

    2016-05-01

    In this study, a semi-automatic breast segmentation method was proposed on the basis of the rib shadow to extract breast regions from 3-D automated whole-breast ultrasound (ABUS) images. The density results were correlated with breast density values acquired with 3-D magnetic resonance imaging (MRI). MRI images of 46 breasts were collected from 23 women without a history of breast disease. Each subject also underwent ABUS. We used Otsu's thresholding method on ABUS images to obtain local rib shadow information, which was combined with the global rib shadow information (extracted from all slice projections) and integrated with the anatomy's breast tissue structure to determine the chest wall line. The fuzzy C-means classifier was used to extract the fibroglandular tissues from the acquired images. Whole-breast volume (WBV) and breast percentage density (BPD) were calculated in both modalities. Linear regression was used to compute the correlation of density results between the two modalities. The consistency of density measurement was also analyzed on the basis of intra- and inter-operator variation. There was a high correlation of density results between MRI and ABUS (R(2) = 0.798 for WBV, R(2) = 0.825 for PBD). The mean WBV from ABUS images was slightly smaller than the mean WBV from MR images (MRI: 342.24 ± 128.08 cm(3), ABUS: 325.47 ± 136.16 cm(3), p images was smaller than the BPD from ABUS images (MRI: 24.71 ± 15.16%, ABUS: 28.90 ± 17.73%, p breast density measurement variation between the two modalities. Our results revealed a high correlation in WBV and BPD between MRI and ABUS. Our study suggests that ABUS provides breast density information useful in the assessment of breast health. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

  3. Estimating animal population density using passive acoustics.

    Science.gov (United States)

    Marques, Tiago A; Thomas, Len; Martin, Stephen W; Mellinger, David K; Ward, Jessica A; Moretti, David J; Harris, Danielle; Tyack, Peter L

    2013-05-01

    Reliable estimation of the size or density of wild animal populations is very important for effective wildlife management, conservation and ecology. Currently, the most widely used methods for obtaining such estimates involve either sighting animals from transect lines or some form of capture-recapture on marked or uniquely identifiable individuals. However, many species are difficult to sight, and cannot be easily marked or recaptured. Some of these species produce readily identifiable sounds, providing an opportunity to use passive acoustic data to estimate animal density. In addition, even for species for which other visually based methods are feasible, passive acoustic methods offer the potential for greater detection ranges in some environments (e.g. underwater or in dense forest), and hence potentially better precision. Automated data collection means that surveys can take place at times and in places where it would be too expensive or dangerous to send human observers. Here, we present an overview of animal density estimation using passive acoustic data, a relatively new and fast-developing field. We review the types of data and methodological approaches currently available to researchers and we provide a framework for acoustics-based density estimation, illustrated with examples from real-world case studies. We mention moving sensor platforms (e.g. towed acoustics), but then focus on methods involving sensors at fixed locations, particularly hydrophones to survey marine mammals, as acoustic-based density estimation research to date has been concentrated in this area. Primary among these are methods based on distance sampling and spatially explicit capture-recapture. The methods are also applicable to other aquatic and terrestrial sound-producing taxa. We conclude that, despite being in its infancy, density estimation based on passive acoustic data likely will become an important method for surveying a number of diverse taxa, such as sea mammals, fish, birds

  4. Density estimation by maximum quantum entropy

    Energy Technology Data Exchange (ETDEWEB)

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

    1993-11-01

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

  5. Density Estimation for Projected Exoplanet Quantities

    Science.gov (United States)

    Brown, Robert A.

    2011-05-01

    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 -log 2. 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.

  6. Agreement of mammographic measures of volumetric breast density to MRI.

    Directory of Open Access Journals (Sweden)

    Jeff Wang

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

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

    Science.gov (United States)

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

    2015-04-01

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

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

    Science.gov (United States)

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

    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 Densitometer™ (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, pgirls, the mean EBD values in the left and right breast were 148±40 and 155±54 Ω; EBD values decreased from Tanner stages 1 to 4 (204±14, 154±79, 136±43, and 119±16 Ω for stages 1–4, respectively) but were higher at Tanner stage 5 (165±30 Ω). With further development, this bioimpedance method may allow for investigations of breast development among adolescent, as well as assessment of breast cancer risk early in life and in populations without access to mammography. PMID:26838256

  9. A deep learning method for classifying mammographic breast density categories.

    Science.gov (United States)

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

    2018-01-01

    Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS categories, i.e., "scattered density" and "heterogeneously dense". The aim of this work was to investigate a deep learning-based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI-RADS category in current clinical workflow. In this study, we constructed a convolutional neural network (CNN)-based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography screening from 2005 to 2016 at our institution. The truths of the density categories were based on standard clinical assessment made by board-certified breast imaging radiologists. Effects of direct training from scratch solely using digital mammogram images and transfer learning of a pretrained model on a large nonmedical imaging dataset were evaluated for the specific task of breast density classification. In order to measure the classification performance, the CNN classifier was also tested on a refined version of the mammogram image dataset by removing some potentially inaccurately labeled images. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to measure the accuracy of the classifier. The AUC was 0.9421 when the CNN-model was trained from scratch on our own mammogram images, and the accuracy increased gradually along with an increased size of training samples

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

    Science.gov (United States)

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

    2012-02-01

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

  11. Estimating snowpack density from Albedo measurement

    Science.gov (United States)

    James L. Smith; Howard G. Halverson

    1979-01-01

    Snow is a major source of water in Western United States. Data on snow depth and average snowpack density are used in mathematical models to predict water supply. In California, about 75 percent of the snow survey sites above 2750-meter elevation now used to collect data are in statutory wilderness areas. There is need for a method of estimating the water content of a...

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

    Science.gov (United States)

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

    2017-03-01

    Breast density is a strong risk factor for breast cancer and has potential use in breast cancer risk prediction, with subjective methods of density assessment providing a strong relationship with the development of breast cancer. This study aims to assess intra- and inter-observer variability in visual density assessment recorded on Visual Analogue Scales (VAS) among trained readers, and examine whether reader age, gender and experience are associated with assessed density. Eleven readers estimated the breast density of 120 mammograms on two occasions 3 years apart using VAS. Intra- and inter-observer agreement was assessed with Intraclass Correlation Coefficient (ICC) and variation between readers visualised on Bland-Altman plots. The mean scores of all mammograms per reader were used to analyse the effect of reader attributes on assessed density. Excellent intra-observer agreement (ICC>0.80) was found in the majority of the readers. All but one reader had a mean difference of gender, or with reading time. Overall, the readers were consistent in their scores, although some large variations were observed. Reader evaluation and targeted training may alleviate this problem.

  13. Improving breast cancer detection using ultrasonography in asymptomatic women with non-fatty breast density.

    Science.gov (United States)

    Korpraphong, Pornpim; Limsuwarn, Panida; Tangcharoensathien, Woranuj; Ansusingha, Tamnit; Thephamongkhol, Kullathorn; Chuthapisith, Suebwong

    2014-10-01

    Mammography (MX) is a reliable modality for detection of breast cancer in asymptomatic women. Use of additional whole breast ultrasonography (US) for breast cancer screening is widely recognized, in particular in women with dense breast parenchyma. To determine the subgroup of women, according to breast density and age, who receive most benefit from US following MX for detection of breast cancer in an asymptomatic condition. The study was conducted in asymptomatic women who had non-fatty breast parenchyma using MX and US during January 2006 and December 2007. Mammographic breast density was classified as recommended by ACR BI-RADS lexicon. Non-fatty breast referred to D2, D3, and D4. US was performed by the same radiologists who interpreted MX with a handheld machine during the same visit. Data on demographics, cancer detection rate (CDR), and incremental cancer detection rate (ICDR) were analyzed using 95% confident interval (CI). Of 14,483 breast cancer screenings in women who had non-fatty breast density, 115 cancers were documented. The mean age of cancer patients was 49.6 years. Of 115 cancers, 105 were evidenced on images (31 with MX alone, 19 with US alone, and 55 with both MX and US). Overall CDR was 7.9 per 1000 examination (95% CI, 6.5-9.5). CDR for MX only (MX-CDR) was 6.5 per 1000 examinations (95% CI, 5.2-7.9). Additional US could significantly improve CDR (P women for detection of breast cancer is a promising diagnostic procedure. A significant benefit was documented, in particular, in women aged 40-59 years old, and in women with D4 breast density. © The Foundation Acta Radiologica 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

  14. Breast density pattern characterization by histogram features and texture descriptors

    Directory of Open Access Journals (Sweden)

    Pedro Cunha Carneiro

    2017-04-01

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

  15. Characterization of breast density in women from Lima, Peru

    Science.gov (United States)

    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.

  16. Bird population density estimated from acoustic signals

    Science.gov (United States)

    Dawson, D.K.; Efford, M.G.

    2009-01-01

    Many animal species are detected primarily by sound. Although songs, calls and other sounds are often used for population assessment, as in bird point counts and hydrophone surveys of cetaceans, there are few rigorous methods for estimating population density from acoustic data. 2. The problem has several parts - distinguishing individuals, adjusting for individuals that are missed, and adjusting for the area sampled. Spatially explicit capture-recapture (SECR) is a statistical methodology that addresses jointly the second and third parts of the problem. We have extended SECR to use uncalibrated information from acoustic signals on the distance to each source. 3. We applied this extension of SECR to data from an acoustic survey of ovenbird Seiurus aurocapilla density in an eastern US deciduous forest with multiple four-microphone arrays. We modelled average power from spectrograms of ovenbird songs measured within a window of 0??7 s duration and frequencies between 4200 and 5200 Hz. 4. The resulting estimates of the density of singing males (0??19 ha -1 SE 0??03 ha-1) were consistent with estimates of the adult male population density from mist-netting (0??36 ha-1 SE 0??12 ha-1). The fitted model predicts sound attenuation of 0??11 dB m-1 (SE 0??01 dB m-1) in excess of losses from spherical spreading. 5.Synthesis and applications. Our method for estimating animal population density from acoustic signals fills a gap in the census methods available for visually cryptic but vocal taxa, including many species of bird and cetacean. The necessary equipment is simple and readily available; as few as two microphones may provide adequate estimates, given spatial replication. The method requires that individuals detected at the same place are acoustically distinguishable and all individuals vocalize during the recording interval, or that the per capita rate of vocalization is known. We believe these requirements can be met, with suitable field methods, for a significant

  17. The relationship of obesity, mammographic breast density, and magnetic resonance imaging in patients with breast cancer.

    Science.gov (United States)

    Gillman, Jennifer; Chun, Jennifer; Schwartz, Shira; Schnabel, Freya; Moy, Linda

    The purpose was to evaluate the relationship between body mass index (BMI), mammographic breast density, magnetic resonance (MR) background parenchymal enhancement (BPE), and MR fibroglandular tissue (FGT) in women with breast cancer. Our institutional database was queried for patients with preoperative mammography and breast MR imaging. There were 573 women eligible for analysis. Elevated BMI was associated with advanced stage of disease (P=.01), lower mammographic density (Pbreast density and FGT. Higher BMI was also associated with advanced stage disease and nonpalpable tumors on clinical exam. Copyright © 2016 Elsevier Inc. All rights reserved.

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

    DEFF Research Database (Denmark)

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

    2014-01-01

    High breast density, a strong predictor of breast cancer may be determined early in life. Childhood anthropometric factors have been related to breast cancer and breast density, but rarely simultaneously. We examined whether mammographic density (MD) mediates an association of birth weight...

  19. Mammographic density, MRI background parenchymal enhancement and breast cancer risk

    Science.gov (United States)

    Pike, M. C.; Pearce, C. L.

    2013-01-01

    Mammographic density (MD), representing connective and epithelial tissue (fibroglandular tissue, FGT) is a major risk factor for breast cancer. In an analysis of an autopsy series (Bartow SA, Pathak DR, Mettler FA. Radiographic microcalcification and parenchymal patterns as indicators of histologic “high-risk” benign breast disease. Cancer 1990; 66: 1721–1725, Bartow SA, Pathak DR, Mettler FA et al. Breast mammographic pattern: a concatenation of confounding and breast cancer risk factors. Am J Epidemiol 1995; 142: 813–819), MD was found to be strongly correlated with the collagen and epithelial content of the breast (Li T, Sun L, Miller N et al. The association of measured breast tissue characteristics with MD and other risk factors for breast cancer. Cancer Epidemiol Biomarkers Prev 2005; 14: 343–349), and another report showed that breast epithelium was highly concentrated in the areas of collagen concentration (Hawes D, Downey S, Pearce CL et al. Dense breast stromal tissue shows greatly increased concentration of breast epithelium but no increase in its proliferative activity. Breast Cancer Res 2006; 8: R24). Collagen comprises the overwhelming majority of the FGT, occupying an area on the slides obtained from the autopsy series some 15 times the area of glandular tissue. The relationship of MD with breast cancer risk appears likely to be due to a major extent to increasing epithelial cell numbers with increasing MD. FGT is also seen in breast magnetic resonance imaging (breast MRI) and, as expected, it has been shown that this measure of FGT (MRI-FGT) is highly correlated with MD. A contrast-enhanced breast MRI shows that normal FGT ‘enhances’ (background parenchymal enhancement, BPE) after contrast agent is administered(Morris EA. Diagnostic breast MR imaging: current status and future directions. Radiol Clin North Am 2007; 45: 863–880, vii., Kuhl C. The current status of breast MR imaging. Part I. Choice of technique, image interpretation

  20. Mammographic density, MRI background parenchymal enhancement and breast cancer risk.

    Science.gov (United States)

    Pike, M C; Pearce, C L

    2013-11-01

    Mammographic density (MD), representing connective and epithelial tissue (fibroglandular tissue, FGT) is a major risk factor for breast cancer. In an analysis of an autopsy series (Bartow SA, Pathak DR, Mettler FA. Radiographic microcalcification and parenchymal patterns as indicators of histologic "high-risk" benign breast disease. Cancer 1990; 66: 1721-1725, Bartow SA, Pathak DR, Mettler FA et al. Breast mammographic pattern: a concatenation of confounding and breast cancer risk factors. Am J Epidemiol 1995; 142: 813-819), MD was found to be strongly correlated with the collagen and epithelial content of the breast (Li T, Sun L, Miller N et al. The association of measured breast tissue characteristics with MD and other risk factors for breast cancer. Cancer Epidemiol Biomarkers Prev 2005; 14: 343-349), and another report showed that breast epithelium was highly concentrated in the areas of collagen concentration (Hawes D, Downey S, Pearce CL et al. Dense breast stromal tissue shows greatly increased concentration of breast epithelium but no increase in its proliferative activity. Breast Cancer Res 2006; 8: R24). Collagen comprises the overwhelming majority of the FGT, occupying an area on the slides obtained from the autopsy series some 15 times the area of glandular tissue. The relationship of MD with breast cancer risk appears likely to be due to a major extent to increasing epithelial cell numbers with increasing MD. FGT is also seen in breast magnetic resonance imaging (breast MRI) and, as expected, it has been shown that this measure of FGT (MRI-FGT) is highly correlated with MD. A contrast-enhanced breast MRI shows that normal FGT 'enhances' (background parenchymal enhancement, BPE) after contrast agent is administered(Morris EA. Diagnostic breast MR imaging: current status and future directions. Radiol Clin North Am 2007; 45: 863-880, vii., Kuhl C. The current status of breast MR imaging. Part I. Choice of technique, image interpretation, diagnostic

  1. Mammographic Breast Density in a Cohort of Medically Underserved Women

    Science.gov (United States)

    2014-10-01

    use as categorized in Table 1. Variables were considered confounders if their addition to the model changed the unadjusted mean percent breast density...postmenopausal women which met our criteria for model inclusion. Adjustment for WHR and percent body fat did not meet our criteria for confounding. We...markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev 15:1159- 1169 9. Lam PB, Vacek PM, Geller BM, Muss HB (2000) The

  2. Implementation of several mathematical algorithms to breast tissue density classification

    Science.gov (United States)

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

    2014-02-01

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

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

    Science.gov (United States)

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

    2010-10-01

    Mammographic parenchymal texture has been shown to correlate with genetic markers of developing breast cancer. Digital breast tomosynthesis (DBT) is a novel x-ray imaging technique in which tomographic images of the breast are reconstructed from multiple source projections acquired at different angles of the x-ray tube. Compared to digital mammography (DM), DBT eliminates breast tissue overlap, offering superior parenchymal tissue visualization. We hypothesize that texture analysis in DBT could potentially provide a better assessment of parenchymal texture and ultimately result in more accurate assessment of breast cancer risk. As a first step towards validating this hypothesis, we investigated the association between DBT parenchymal texture and breast percent density (PD), a known breast cancer risk factor, and compared it to DM. Bilateral DBT and DM images from 71 women participating in a breast cancer screening trial were analyzed. Filtered-backprojection was used to reconstruct DBT tomographic planes in 1 mm increments with 0.22 mm in-plane resolution. Corresponding DM images were acquired at 0.1 mm pixel resolution. Retroareolar regions of interest (ROIs) equivalent to 2.5 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 (p<0.001) for contrast and r = -0.52 (p<0.001) for homogeneity; the corresponding DM correlations were r = 0.26 (p = 0.002) and r = -0.33 (p<0.001). Multiple linear regression of the texture features versus breast PD also demonstrated significantly stronger associations in DBT (R2 = 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

  4. Multivariate density estimation theory, practice, and visualization

    CERN Document Server

    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

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

    Science.gov (United States)

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

    2015-04-02

    High physical activity has been shown to decrease the risk of breast cancer, potentially by a mechanism that also reduces mammographic density. We tested the hypothesis that the risk of developing breast cancer in the next 10 years according to the Tyrer-Cuzick prediction model influences the association between physical activity and mammographic density. We conducted a population-based cross-sectional study of 38,913 Swedish women aged 40-74 years. Physical activity was assessed using the validated web-questionnaire Active-Q and mammographic density was measured by the fully automated volumetric Volpara method. The 10-year risk of breast cancer was estimated using the Tyrer-Cuzick (TC) prediction model. Linear regression analyses were performed to assess the association between physical activity and volumetric mammographic density and the potential interaction with the TC breast cancer risk. Overall, high physical activity was associated with lower absolute dense volume. As compared to women with the lowest total activity level (association was seen for any type of physical activity among women with association between total activity and absolute dense volume was modified by the TC breast cancer risk (P interaction = 0.05). As anticipated, high physical activity was also associated with lower non-dense volume. No consistent association was found between physical activity and percent dense volume. Our results suggest that physical activity may decrease breast cancer risk through reducing mammographic density, and that the physical activity needed to reduce mammographic density may depend on background risk of breast cancer.

  6. Local mammographic density as a predictor of breast cancer

    Science.gov (United States)

    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.

  7. Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation.

    Science.gov (United States)

    Yamane, Ikko; Sasaki, Hiroaki; Sugiyama, Masashi

    2016-07-01

    Log-density gradient estimation is a fundamental statistical problem and possesses various practical applications such as clustering and measuring nongaussianity. A naive two-step approach of first estimating the density and then taking its log gradient is unreliable because an accurate density estimate does not necessarily lead to an accurate log-density gradient estimate. To cope with this problem, a method to directly estimate the log-density gradient without density estimation has been explored and demonstrated to work much better than the two-step method. The objective of this letter is to improve the performance of this direct method in multidimensional cases. Our idea is to regard the problem of log-density gradient estimation in each dimension as a task and apply regularized multitask learning to the direct log-density gradient estimator. We experimentally demonstrate the usefulness of the proposed multitask method in log-density gradient estimation and mode-seeking clustering.

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

    Science.gov (United States)

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

    2017-07-01

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

  9. Density Estimations in Laboratory Debris Flow Experiments

    Science.gov (United States)

    Queiroz de Oliveira, Gustavo; Kulisch, Helmut; Malcherek, Andreas; Fischer, Jan-Thomas; Pudasaini, Shiva P.

    2016-04-01

    Bulk density and its variation is an important physical quantity to estimate the solid-liquid fractions in two-phase debris flows. Here we present mass and flow depth measurements for experiments performed in a large-scale laboratory set up. Once the mixture is released and it moves down the inclined channel, measurements allow us to determine the bulk density evolution throughout the debris flow. Flow depths are determined by ultrasonic pulse reflection, and the mass is measured with a total normal force sensor. The data were obtained at 50 Hz. The initial two phase material was composed of 350 kg debris with water content of 40%. A very fine pebble with mean particle diameter of 3 mm, particle density of 2760 kg/m³ and bulk density of 1400 kg/m³ in dry condition was chosen as the solid material. Measurements reveal that the debris bulk density remains high from the head to the middle of the debris body whereas it drops substantially at the tail. This indicates lower water content at the tail, compared to the head and the middle portion of the debris body. This means that the solid and fluid fractions are varying strongly in a non-linear manner along the flow path, and from the head to the tail of the debris mass. Importantly, this spatial-temporal density variation plays a crucial role in determining the impact forces associated with the dynamics of the flow. Our setup allows for investigating different two phase material compositions, including large fluid fractions, with high resolutions. The considered experimental set up may enable us to transfer the observed phenomena to natural large-scale events. Furthermore, the measurement data allows evaluating results of numerical two-phase mass flow simulations. These experiments are parts of the project avaflow.org that intends to develop a GIS-based open source computational tool to describe wide spectrum of rapid geophysical mass flows, including avalanches and real two-phase debris flows down complex natural

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

    Science.gov (United States)

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

    2018-02-01

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

  11. The influence of mammogram acquisition on the mammographic density and breast cancer association in the Mayo Mammography Health Study cohort.

    Science.gov (United States)

    Olson, Janet E; Sellers, Thomas A; Scott, Christopher G; Schueler, Beth A; Brandt, Kathleen R; Serie, Daniel J; Jensen, Matthew R; Wu, Fang-Fang; Morton, Marilyn J; Heine, John J; Couch, Fergus J; Pankratz, V Shane; Vachon, Celine M

    2012-11-15

    Mammographic density is a strong risk factor for breast cancer. Image acquisition technique varies across mammograms to limit radiation and produce a clinically useful image. We examined whether acquisition technique parameters at the time of mammography were associated with mammographic density and whether the acquisition parameters confounded the density and breast cancer association. We examined this question within the Mayo Mammography Health Study (MMHS) cohort, comprised of 19,924 women (51.2% of eligible) seen in the Mayo Clinic mammography screening practice from 2003 to 2006. A case-cohort design, comprising 318 incident breast cancers diagnosed through December 2009 and a random subcohort of 2,259, was used to examine potential confounding of mammogram acquisition technique parameters (x-ray tube voltage peak (kVp), milliampere-seconds (mAs), thickness and compression force) on the density and breast cancer association. The Breast Imaging Reporting and Data System four-category tissue composition measure (BI-RADS) and percent density (PD) (Cumulus program) were estimated from screen-film mammograms at time of enrollment. Spearman correlation coefficients (r) and means (standard deviations) were used to examine the relationship of density measures with acquisition parameters. Hazard ratios (HR) and C-statistics were estimated using Cox proportional hazards regression, adjusting for age, menopausal status, body mass index and postmenopausal hormones. A change in the HR of at least 15% indicated confounding. Adjusted PD and BI-RADS density were associated with breast cancer (p-trends associations with breast cancer and their inclusion did not improve discriminatory accuracy. Results were similar for associations of dense and non-dense area with breast cancer. We confirmed a strong association between mammographic density and breast cancer risk that was not confounded by mammogram acquisition technique.

  12. Breast density classification to reduce false positives in CADe systems.

    Science.gov (United States)

    Vállez, Noelia; Bueno, Gloria; Déniz, Oscar; Dorado, Julián; Seoane, José Antonio; Pazos, Alejandro; Pastor, Carlos

    2014-02-01

    This paper describes a novel weighted voting tree classification scheme for breast density classification. Breast parenchymal density is an important risk factor in breast cancer. Moreover, it is known that mammogram interpretation is more difficult when dense tissue is involved. Therefore, automated breast density classification may aid in breast lesion detection and analysis. Several classification methods have been compared and a novel hierarchical classification procedure of combined classifiers with linear discriminant analysis (LDA) is proposed as the best solution to classify the mammograms into the four BIRADS tissue classes. The classification scheme is based on 298 texture features. Statistical analysis to test the normality and homoscedasticity of the data was carried out for feature selection. Thus, only features that are influenced by the tissue type were considered. The novel classification techniques have been incorporated into a CADe system to drive the detection algorithms and tested with 1459 images. The results obtained on the 322 screen-film mammograms (SFM) of the mini-MIAS dataset show that 99.75% of samples were correctly classified. On the 1137 full-field digital mammograms (FFDM) dataset results show 91.58% agreement. The results of the lesion detection algorithms were obtained from modules integrated within the CADe system developed by the authors and show that using breast tissue classification prior to lesion detection leads to an improvement of the detection results. The tools enhance the detectability of lesions and they are able to distinguish their local attenuation without local tissue density constraints. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

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

    Science.gov (United States)

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

    2017-10-01

    Adjuvant chemotherapy and anti-estrogenic therapy can result in decreased volume of the contralateral breast, following mastectomy for the treatment of breast cancer. However, no data on the effect of adjuvant therapy on contralateral breast volume have previously been reported. We aimed to evaluate the extent to which adjuvant therapy and differences in breast density contribute to decreased breast volume. We conducted a prospective cohort study, selecting 40 nonconsecutive patients who underwent immediate breast reconstruction with mastectomy and expander insertion followed by expander replacement. We measured the contralateral breast volume before each procedure. The extent of the change was analyzed with respect to adjuvant therapy and breast density measured by preoperative mammography. The greatest decrease in breast volume was 135.1 cm3. The decrease in breast volume was significantly larger in the adjuvant therapy (+) group, particularly in patients with high breast density, than in the adjuvant therapy (-) group. Significant differences between the chemotherapy (+), tamoxifen (+) group and the chemotherapy (-), tamoxifen (+) group were not found. Breast density scores had a range of 2.0-3.3 (mean: 2.8). In breast reconstruction, particularly when performed in one stage, preoperative mammography findings are valuable to plastic surgeons, and possible decreases in the contralateral breast volume due to adjuvant therapy, particularly in patients with high breast density, should be considered carefully. Copyright © 2017 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

  14. On Density Estimation from Censored Data by Penalized Likelihood Methods.

    Science.gov (United States)

    1985-06-01

    estimation , * 0 density estimation , censored data, Kaplan-Meier estimator , hazard rate, spline function , reproducing kernel Hilbert space. LJ Research...density function , cumulative distribution L. -’ function , and hazard function are proposed in the random censorship setting. The estimators are derived...in the independent random censorship model is to estimate the distribution function nonparametrically . The maximum

  15. Development and application of traffic accident density estimation models using kernel density estimation

    Directory of Open Access Journals (Sweden)

    Seiji Hashimoto

    2016-06-01

    Full Text Available 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 objectively and easily, indicating where area-wide traffic calming can be implemented preferentially. This study examined the relations between traffic accidents and city characteristics, such as population, road factors, and spatial factors. A model was developed to estimate traffic accident density. Kernel density estimation (KDE techniques were used to assess the relations efficiently. Besides, 16 models were developed by combining accident locations, accident types, and data types. By using them, the applicability of traffic accident density estimation models was examined. Results obtained using Spearman rank correlation show high coefficients between the predicted number and the actual number. The model can indicate the relative accident risk in cities. Results of this study can be used for objective determination of areas where area-wide traffic calming can be implemented preferentially, even if sufficient traffic accident data are not available.

  16. Genetic Plymorphisms, Estrogens, and Breast Density

    Science.gov (United States)

    2005-01-01

    and ovaries, did not ethnic backgrounds that applied to themselves and to their parents. use oral contraceptives or any hormone preparations at the...criteria for the 2 intervention studies, only 7 premeno- (p = 0.08). For premenopausal women, the difference in density pausal women were taking oral ...Kansas City, KS, USA ANTONIO LLOMBART-BOSCH Director, Departamento de Patologia Facultad de Medicina y Odontologia Universidad de Valencia Valencia

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

    LENUS (Irish Health Repository)

    Glynn, Ronan W

    2010-12-22

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

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

    Science.gov (United States)

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

    2018-02-01

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

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

    Science.gov (United States)

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

    2017-04-01

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

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

    Science.gov (United States)

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

    2017-06-01

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

  1. Breast density measurements with ultrasound tomography: a comparison with film and digital mammography.

    Science.gov (United States)

    Duric, Neb; Boyd, Norman; Littrup, Peter; Sak, Mark; Myc, Lukasz; Li, Cuiping; West, Erik; Minkin, Sal; Martin, Lisa; Yaffe, Martin; Schmidt, Steven; Faiz, Muhammad; Shen, Jason; Melnichouk, Olga; Li, Qing; Albrecht, Teri

    2013-01-01

    To investigate the use of the whole-breast sound speed measurement as a marker of breast density (BD), a known risk factor for breast cancer. As part of an ongoing study of breast cancer detection, 249 patients were scanned with a clinical prototype that operates on the principles of ultrasound tomography. Typically, 40-100 sound speed tomograms were reconstructed from the scan data, corresponding to the entire volume of the breast of each patient. The data were used to estimate the volume averaged sound speed (VASS) of the breast for each patient. The corresponding mammograms were used to calculate mammographic percent density (MPD) using CUMULUS software. Film mammograms were available for 164 patients while 85 digital mammograms were available for the remaining patients. Standard statistical techniques were used to determine associations of breast sound speed with a variety of mammographic measures such as percent density, area of dense tissue, and area of nondense tissue. Furthermore, associations of breast sound speed with continuous variables such as age and weight and dichotomous variables such as parity and menopausal status were also assessed. VASS was found to be significantly associated with MPD. The Spearman correlation coefficient (r(s)) between VASS and MPD was found to be 0.77 and 0.71 for film and digital mammography, respectively. VASS was positively correlated with dense areas by mammography, both digital (r(s) = 0.46) and film (r(s) = 0.56). VASS was negatively associated with nondense area by mammography, both digital (r(s) = -0.58) and film (r(s) = -0.63). BD by all methods was less in postmenopausal than in premenopausal women. The MPD was lower in the postmenopausal group (by 6.6%, p film group). The VASS was also lower in the postmenopausal group (by 15 m∕s, p film group). The association of MPD with age was characterized with r(s) = -0.06 (p film mammography. For weight, the MPD associations were characterized by r(s) = -0.53 (p film

  2. Parametrically guided nonparametric density and hazard estimation with censored data

    OpenAIRE

    Talamakrouni, Majda; Van Keilegom, Ingrid; El Ghouch, Anouar

    2016-01-01

    The parametrically guided kernel smoother is a promising nonparametric estimation approach that aims to reduce the bias of the classical kernel density estimator without increasing its variance. Theoretically, the estimator is unbiased if a correct parametric guide is used, which can never be achieved by the classical kernel estimator even with an optimal bandwidth. The estimator is generalized to the censored data case and used for density and hazard function estimation. The asymptotic prope...

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

    Science.gov (United States)

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

    2016-10-01

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

  4. The relationship of mammographic density and age: implications for breast cancer screening.

    Science.gov (United States)

    Checka, Cristina M; Chun, Jennifer E; Schnabel, Freya R; Lee, Jiyon; Toth, Hildegard

    2012-03-01

    Breast density is increasingly recognized as an independent risk factor for the development of breast cancer, because it has been shown to be associated with a four- to sixfold increase in a woman's risk of malignant breast disease. Increased breast density as identified on mammography is also known to decrease the diagnostic sensitivity of the examination, which is of great concern to women at increased risk for breast cancer. Dense tissue has generally been associated with younger age and premenopausal status, with the assumption that breast density gradually decreases after menopause. However, the actual proportion of older women with dense breasts is unknown. The purpose of this study was to examine the relationship between age and breast density, particularly focusing on postmenopausal women. All screening mammograms completed at the New York University Langone Medical Center in 2008 were retrospectively reviewed. Analysis of variance and descriptive analyses were used to evaluate the relationship between patient age and breast density. A total of 7007 screening mammograms were performed. The median age of our cohort was 57 years. Within each subgroup categorized by decade of age, there was a normal distribution among the categories of breast density. There was a significant inverse relationship between age and breast density (p breasts. This percentage decreased to 57% of women in their 50s. However, 44% of women in their 60s and 36% of women in their 70s had dense breasts as characterized on their screening mammograms. In general, we found an inverse relationship between patient age and mammographic breast density. However, there were outliers at the extremes of age. A meaningful proportion of young women had predominantly fatty breasts and a subset of older women had extremely dense breasts. Increased density renders mammography a less sensitive tool for early detection. Breast density should be considered when evaluating the potential benefit of extended

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

    Science.gov (United States)

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

    2017-10-01

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

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

    Directory of Open Access Journals (Sweden)

    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.

  7. Concrete density estimation by rebound hammer method

    Energy Technology Data Exchange (ETDEWEB)

    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.

  8. Mammographic Breast Density Patterns in Asymptomatic Mexican Women

    Science.gov (United States)

    Calderón-Garcidueñas, Ana Laura; Sanabria-Mondragón, Mónica; Hernández-Beltrán, Lourdes; López-Amador, Noé; Cerda-Flores, Ricardo M.

    2012-01-01

    Breast density (BD) is a risk factor for breast cancer. Aims. To describe BD patterns in asymptomatic Mexican women and the pathological mammographic findings. Methods and Material. Prospective, descriptive, and comparative study. Women answered a questionnaire and their mammograms were analyzed according to BI-RADS. Univariate (χ 2) and conditional logistic regression analyses were performed. Results. In 300 women studied the BD patterns were fat 56.7% (170), fibroglandular 29% (87), heterogeneously dense 5.7% (17), and dense pattern 8.6% (26). Prevalence of fat pattern was significantly different in women under 50 years (37.6%, 44/117) and older than 50 (68.8%, 126/183). Patterns of high breast density (BD) (dense + heterogeneously dense) were observed in 25.6% (30/117) of women ≤50 years and 7.1% (13/183) of women >50. Asymmetry in BD was observed in 22% (66/300). Compression cone ruled out underlying disease in 56 cases. In the remaining 10, biopsy revealed one fibroadenoma, one complex cyst, and 6 invasive and 2 intraductal carcinomas. 2.6% (8/300) of patients had non-palpable carcinomas. Benign lesions were observed in 63.3% (190/300) of cases, vascular calcification in 150 cases (78.9%), and fat necrosis in 38 cases (20%). Conclusions. Mexican women have a low percentage of high-density patterns. PMID:23346398

  9. Mammographic Breast Density Patterns in Asymptomatic Mexican Women

    Directory of Open Access Journals (Sweden)

    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.

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

    Directory of Open Access Journals (Sweden)

    Thomas Jerry A

    2010-11-01

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

  11. Efficient estimation of analytic density under random censorship

    NARCIS (Netherlands)

    Belitser, E.

    1996-01-01

    The nonparametric minimax estimation of an analytic density at a given point, under random censorship, is considered. Although the problem of estimating density is known to be irregular in a certain sense, we make some connections relating this problem to the problem of estimating smooth

  12. A Conditional Density Estimation Partition Model Using Logistic Gaussian Processes

    OpenAIRE

    Payne, Richard D.; Guha, Nilabja; Ding, Yu; Mallick, Bani K.

    2017-01-01

    Conditional density estimation (density regression) estimates the distribution of a response variable y conditional on covariates x. Utilizing a partition model framework, a conditional density estimation method is proposed using logistic Gaussian processes. The partition is created using a Voronoi tessellation and is learned from the data using a reversible jump Markov chain Monte Carlo algorithm. The Markov chain Monte Carlo algorithm is made possible through a Laplace approximation on the ...

  13. RELATIONSHIP OF MAMMOGRAPHIC DENSITY AND GENE EXPRESSION: ANALYSIS OF NORMAL BREAST TISSUE SURROUNDING BREAST CANCER

    Science.gov (United States)

    Sandhu, Rupninder; Williams, Tyisha; Midkiff, Bentley R.; Lissowska, Jolanta; Wesolowska, Ewa; Boyd, Norman F.; Johnson, Nicole B.; Figueroa, Jonine D.; Sherman, Mark E.; Troester, Melissa A.

    2014-01-01

    Purpose Previous studies of breast tissue gene expression have demonstrated that the extratumoral microenvironment has substantial variability across individuals, some of which can be attributed to epidemiologic factors. To evaluate how mammographic density (MD) and breast tissue composition relate to extratumoral microenvironment gene expression, we used data on 121 breast cancer patients from the population-based Polish Women's Breast Cancer Study. Design Breast cancer cases were classified based on a previously reported, biologically-defined extratumoral gene expression signature with two subtypes: an Active subtype, which is associated with high expression of genes related to fibrosis and wound response, and an Inactive subtype, which has high expression of cellular adhesion genes. MD of the contralateral breast was assessed using pre-treatment mammograms and a quantitative, reliable computer-assisted thresholding method. Breast tissue composition was evaluated based on digital image analysis of tissue sections. Results The Inactive extratumoral subtype was associated with significantly higher percentage mammographic density (PD) and dense area (DA) in univariate analysis (PD: p=0.001; DA: p=0.049) and in multivariable analyses adjusted for age and body mass index (PD: p=0.004; DA: p=0.049). Inactive/higher MD tissue was characterized by a significantly higher percentage of stroma and a significantly lower percentage of adipose tissue, with no significant change in epithelial content. Analysis of published gene expression signatures suggested that Inactive/higher MD tissue expressed increased estrogen response and decreased TGF-β signaling. Conclusions By linking novel molecular phenotypes with MD, our results indicate that MD reflects broad transcriptional changes, including changes in both epithelia- and stroma-derived signaling. PMID:23918601

  14. Mass eigendetection and the benefits of introducing breast density information

    Energy Technology Data Exchange (ETDEWEB)

    Freixenet, J.; Oliver, A.; Llado, X.; Marti, R.; Marti, J. [Inst. of Informatics and Applications, Univ. of Girona, Catalonia (Spain)

    2007-06-15

    The purpose of this paper is to present a novel algorithm for mass detection in a mammographic computer-aided diagnosis system. Four key points provide the novelty of our approach: (1) the use of eingenanalysis for describing variation in mass shape and size: (2) a Bayesian formulation providing a mathematical sound framework, flexible enough to include additional information; (3) the use of two dimensional PCA for false positive reduction; and (4) the incorporation of breast density information, an internal feature of the breasts closely related to the performance of most mass detection algorithms and which, in contrast, has not been considered in existing approaches. The robustness and the database independence of our approach are shown by the fact that different databases are used for training and testing procedures. (orig.)

  15. Evaluation of the association between quantitative mammographic density and breast cancer occurred in different quadrants.

    Science.gov (United States)

    Chan, Siwa; Chen, Jeon-Hor; Li, Shunshan; Chang, Rita; Yeh, Darh-Cherng; Chang, Ruey-Feng; Yeh, Lee-Ren; Kwong, Jessica; Su, Min-Ying

    2017-04-17

    To investigate the relationship between mammographic density measured in four quadrants of a breast with the location of the occurred cancer. One hundred and ten women diagnosed with unilateral breast cancer that could be determined in one specific breast quadrant were retrospectively studied. Women with previous cancer/breast surgery were excluded. The craniocaudal (CC) and mediolateral oblique (MLO) mammography of the contralateral normal breast were used to separate a breast into 4 quadrants: Upper-Outer (UO), Upper-Inner (UI), Lower-Outer (LO), and Lower-Inner (LI). The breast area (BA), dense area (DA), and percent density (PD) in each quadrant were measured by using the fuzzy-C-means segmentation. The BA, DA, and PD were compared between patients who had cancer occurring in different quadrants. The upper-outer quadrant had the highest BA (37 ± 15 cm2) and DA (7.1 ± 2.9 cm2), with PD = 20.0 ± 5.8%. The order of BA and DA in the 4 separated quadrants were: UO > UI > LO > LI, and almost all pair-wise comparisons showed significant differences. For tumor location, 67 women (60.9%) had tumor in UO, 16 (14.5%) in UI, 7 (6.4%) in LO, and 20 (18.2%) in LI quadrant, respectively. The estimated odds and the 95% confidence limits of tumor development in the UO, UI, LO and LI quadrants were 1.56 (1.06, 2.29), 0.17 (0.10, 0.29), 0.07 (0.03, 0.15), and 0.22 (0.14, 0.36), respectively. In these 4 groups of women, the order of quadrant BA and DA were all the same (UO > UI > LO > LI), and there was no significant difference in BA, DA or PD among them (all p > 0.05). Breast cancer was most likely to occur in the UO quadrant, which was also the quadrant with highest BA and DA; but for women with tumors in other quadrants, the density in that quadrant was not the highest. Therefore, there was no direct association between quadrant density and tumor occurrence.

  16. The effect of age and density of the breast on the sensitivity of breast cancer diagnostic by mammography and ultasonography.

    Science.gov (United States)

    Saarenmaa, I; Salminen, T; Geiger, U; Heikkinen, P; Hyvärinen, S; Isola, J; Kataja, V; Kokko, M L; Kokko, R; Kumpulainen, E; Kärkkäinen, A; Pakkanen, J; Peltonen, P; Piironen, A; Salo, A; Talviala, M L; Haka, M

    2001-05-01

    We studied which, age of the patient or density of the breast accounts for the sensitivity of mammography and ultrasonography (US). Furthermore we studied whether the overall impression on the density of the breast or the density in tumour area accounts for the sensitivity of mammography and ultrasonography. The material consisted of 572 consecutive histologically and 5 cytologically verified breast cancer cases. Mammography and US examinations were performed immediately before breast cancer operations and information on the findings were received from the original patient files and classified as malignant or benign. The density of breast parenchyma to fatty, mixed or dense in total breast and separately in tumour area was defined by a radiologist group from the original mammograms by comparing to model mammograms. The sensitivity (Se) of mammography and US was compared in 3 age groups (26-49, 50-59 and 60-92) and in the different density classes. Sensitivity of mammography increased by age (density-adjusted OR = 0.2, 95%, CI 0.1-0.5) in age group 26-49 compared to age group 60-92) and with fattiness of the breast (age-adjusted OR= 0.4, 95%, CI 0.1-1.0 for dense breast parenchyma in tumour area compared to fatty breast). Sensitivity of US was inversely related to age (density-adjusted OR = 2.3, 95%, CI 1.0-5.2 in age group 26-49 compared to age group 60-92) and directly related with fattiness of breast (age-adjusted OR = 0.5, 95%, CI 0.2-0.9 by dense breast parenchyma in tumour area compared to fatty breast). Density in the tumour area compared to total breast density was related only mariginally better sensitivity both of mammography (0.4 vs. 0.6) and of US (0.5 vs. 0.6). Sensitivity of both mammography and sensitivity of US are independently related both to the age of the patient and to the density of the breast. The effect of age is inverse and that of density parallel between mammography and US on sensitivity. The effect of overall breast density was close to

  17. Ant-inspired density estimation via random walks.

    Science.gov (United States)

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

    2017-10-03

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

  18. Field dynamics inference via spectral density estimation

    Science.gov (United States)

    Frank, Philipp; Steininger, Theo; Enßlin, Torsten A.

    2017-11-01

    Stochastic differential equations are of utmost importance in various scientific and industrial areas. They are the natural description of dynamical processes whose precise equations of motion are either not known or too expensive to solve, e.g., when modeling Brownian motion. In some cases, the equations governing the dynamics of a physical system on macroscopic scales occur to be unknown since they typically cannot be deduced from general principles. In this work, we describe how the underlying laws of a stochastic process can be approximated by the spectral density of the corresponding process. Furthermore, we show how the density can be inferred from possibly very noisy and incomplete measurements of the dynamical field. Generally, inverse problems like these can be tackled with the help of Information Field Theory. For now, we restrict to linear and autonomous processes. To demonstrate its applicability, we employ our reconstruction algorithm on a time-series and spatiotemporal processes.

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

    Energy Technology Data Exchange (ETDEWEB)

    Fasching, Peter A. [Department of Gynecology and Obstetrics, Erlangen University Hospital, Universitaetsstrasse 21-23, D-91054 Erlangen (Germany)]. E-mail: peter.fasching@gyn.med.uni-erlangen.de; Heusinger, Katharina [Department of Gynecology and Obstetrics, Erlangen University Hospital, Universitaetsstrasse 21-23, D-91054 Erlangen (Germany); Loehberg, Christian R. [Department of Gynecology and Obstetrics, Erlangen University Hospital, Universitaetsstrasse 21-23, D-91054 Erlangen (Germany); Wenkel, Evelyn [Institute of Diagnostic Radiology, Erlangen University Hospital, Erlangen (Germany); Lux, Michael P. [Department of Gynecology and Obstetrics, Erlangen University Hospital, Universitaetsstrasse 21-23, D-91054 Erlangen (Germany); Schrauder, Michael [Department of Gynecology and Obstetrics, Erlangen University Hospital, Universitaetsstrasse 21-23, D-91054 Erlangen (Germany); Koscheck, Thomas [Institute of Pathology, Friedrich Alexander University, Erlangen-Nuremberg (Germany); Bautz, Werner [Institute of Diagnostic Radiology, Erlangen University Hospital, Erlangen (Germany); Schulz-Wendtland, Ruediger [Institute of Diagnostic Radiology, Erlangen University Hospital, Erlangen (Germany); Beckmann, Matthias W. [Department of Gynecology and Obstetrics, Erlangen University Hospital, Universitaetsstrasse 21-23, D-91054 Erlangen (Germany); Bani, Mayada R. [Department of Gynecology and Obstetrics, Erlangen University Hospital, Universitaetsstrasse 21-23, D-91054 Erlangen (Germany)

    2006-12-15

    Purpose: The accuracy of breast cancer staging involves the estimation of the tumor size for the initial decision-making in the treatment. We investigated the accuracy of tumor size estimation and the association between tumor characteristics and breast density (BD). Materials and methods: A total of 434 women with a primary diagnosis of breast cancer were included in this prospective study at a specialist breast unit. Estimated tumor characteristics included tumor size, nodal status, estrogen/progesterone receptor status, Ki-67, HER2/neu, vascular invasion. Radiomorphological data included tumor size as assessed by mammography, breast ultrasonography, and clinical examination, and American College of Radiology (ACR) categories for BD. Results: BD did not have a significant impact on the assessment of tumor size using breast ultrasound (deviation from ACR categories 1-4: 0.55-0.68 cm; P = 0.331). The deviation in mammography was significantly different dependent on BD (0.42-0.9 cm; P < 0.001). The clinical examination was not affected by BD. Age and tumor size were the only parameters associated with a denser breast in the multivariate analysis. Older women were less likely to have dense breasts (odds ratio 0.157 for women aged {>=}70 years), and patients with larger tumors were less likely to have dense breasts (adjusted OR 0.36 for tumors > 2 cm). Conclusion: Breast ultrasonography is more accurate than mammography for assessing tumor size in breasts with a higher BD. The difference in tumor size assessment needs to be taken into consideration in the design of clinical trials and treatment decisions.

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

    CSIR Research Space (South Africa)

    Van der Walt, CM

    2013-12-01

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

  1. Fusion of Hard and Soft Information in Nonparametric Density Estimation

    Science.gov (United States)

    2015-06-10

    Statistica Sinica, 22:681–701, 2012. [47] M. Meyer and D. Habtzghib. Nonparametric estimation of density and hazard rate functions with shape restrictions...Laxenburg, Austria, 1990. [39] V. K. Klonias. Consistency of two nonparametric maximum penalized likelihood estimators of the probability density function ...Journal of Nonparametric Statistics, 23(2):455–470, 2011. 37 [48] J. Kumar Pal, M. Woodroofe, and M. Meyer. Estimating a polya frequency function . In R

  2. Nonparametric Probability Density Estimation by Discrete Maximum Penalized- Likelihood Criteria

    OpenAIRE

    SCOTT, D. W.; Tapia, R. A.; Thompson, J. R.

    1980-01-01

    A nonparametric probability density estimator is proposed that is optimal with respect to a discretized form of a continuous penalized-likelihood criterion functional. Approximation results relating the discrete estimator to the estimate obtained by solving the corresponding infinite-dimensional problem are presented. The discrete estimator is shown to be consistent. The numerical implementation of this discrete estimator is outlined and examples displayed. A simulation study compares the int...

  3. Toward accurate and precise estimates of lion density.

    Science.gov (United States)

    Elliot, Nicholas B; Gopalaswamy, Arjun M

    2017-08-01

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

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

    Science.gov (United States)

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

    2015-05-15

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

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

    Science.gov (United States)

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

    2018-02-14

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

  6. Mammographic Breast Density in Chinese Women: Spatial Distribution and Autocorrelation Patterns.

    Science.gov (United States)

    Lai, Christopher W K; Law, Helen K W

    2015-01-01

    Mammographic breast density (MBD) is a strong risk factor for breast cancer. The spatial distribution of MBD in the breast is variable and dependent on physiological, genetic, environmental and pathological factors. This pilot study aims to define the spatial distribution and autocorrelation patterns of MBD in Chinese women aged 40-60. By analyzing their digital mammographic images using a public domain Java image processing program for segmentation and quantification of MBD, we found their left and right breasts were symmetric to each other in regard to their breast size (Total Breast Area), the amount of BMD (overall PD) and Moran's I values. Their MBD was also spatially autocorrelated together in the anterior part of the breast in those with a smaller breast size, while those with a larger breast size tend to have their MBD clustered near the posterior part of the breast. Finally, we observed that the autocorrelation pattern of MBD was dispersed after a 3-year observation period.

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

    Directory of Open Access Journals (Sweden)

    Jia-Min B Pang

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

  8. Background parenchymal enhancement in breast MRIs of breast cancer patients: impact on tumor size estimation.

    Science.gov (United States)

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

    2014-08-01

    To evaluate whether the degree of background parenchymal enhancement affects the accuracy of tumor size estimation based on breast MRI. 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. 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%; penhancement 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. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

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

    Science.gov (United States)

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

    2017-11-15

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

  10. Volumetric breast density from full-field digital mammograms and its association with breast cancer risk factors: a comparison with a threshold method.

    NARCIS (Netherlands)

    Lokate, M.; Kallenberg, M.G.J.; Karssemeijer, N.; Bosch, M.H.J. van den; Peeters, P.H.M.; Gils, C.H. van

    2010-01-01

    INTRODUCTION: Breast density, a strong breast cancer risk factor, is usually measured on the projected breast area from film screen mammograms. This is far from ideal, as breast thickness and technical characteristics are not taken into account. We investigated whether volumetric density

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-09-15

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

  12. Statistical Evaluation of a Fully Automated Mammographic Breast Density Algorithm

    Directory of Open Access Journals (Sweden)

    Mohamed Abdolell

    2013-01-01

    estimated density in 5% increments for 138 “For Presentation” single MLO views; the median of the radiologists’ estimates was used as the reference standard. Agreement amongst radiologists was excellent, ICC = 0.884, 95% CI (0.854, 0.910. Similarly, the agreement between the algorithm and the reference standard was excellent, ICC = 0.862, falling within the 95% CI of the radiologists’ estimates. The Bland-Altman plot showed that the reference standard was slightly positively biased (+1.86% compared to the algorithm-generated densities. A scatter plot showed that the algorithm moderately overestimated low densities and underestimated high densities. A box plot showed that 95% of the algorithm-generated assessments fell within one BI-RADS category of the reference standard. This study demonstrates the effective use of several statistical techniques that collectively produce a comprehensive evaluation of the algorithm and its potential to provide mammographic density measures that can be used to inform clinical practice.

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

    Science.gov (United States)

    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.

  14. Automated skin lesion segmentation with kernel density estimation

    Science.gov (United States)

    Pardo, A.; Real, E.; Fernandez-Barreras, G.; Madruga, F. J.; López-Higuera, J. M.; Conde, O. M.

    2017-07-01

    Skin lesion segmentation is a complex step for dermoscopy pathological diagnosis. Kernel density estimation is proposed as a segmentation technique based on the statistic distribution of color intensities in the lesion and non-lesion regions.

  15. Nonparametric density estimation of continuous part of a mixed measure

    Directory of Open Access Journals (Sweden)

    Rachid Sabre

    2007-10-01

    Full Text Available We consider a pair of random variables(X,Y whose probability measure is the sum of an absolutely continuous measure, a discrete measure and a finite number of absolutely continuous measures on several lines(1. An asymptotically unbiased and consistent estimate, at all points, of the density of the continuous part is given as well as its rate of convergence. We also estimate the amplitude of the discrete measure and the densities on several lines.

  16. Comparison of breast density measurements made using ultrasound tomography and mammography

    Science.gov (United States)

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

    2015-03-01

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

  17. Estimating corresponding locations in ipsilateral breast tomosynthesis views

    Science.gov (United States)

    van Schie, Guido; Tanner, Christine; Karssemeijer, Nico

    2011-03-01

    To improve cancer detection in mammography, breast exams usually consist of two views per breast. To combine information from both views, radiologists and multiview computer-aided detection (CAD) systems need to match corresponding regions in the two views. In digital breast tomosynthesis (DBT), finding corresponding regions in ipsilateral volumes may be a difficult and time-consuming task for radiologists, because many slices have to be inspected individually. In this study we developed a method to quickly estimate corresponding locations in ipsilateral tomosynthesis views by applying a mathematical transformation. First a compressed breast model is matched to the tomosynthesis view containing a point of interest. Then we decompress, rotate and compress again to estimate the location of the corresponding point in the ipsilateral view. In this study we use a simple elastically deformable sphere model to obtain an analytical solution for the transformation in a given DBT case. The model is matched to the volume by using automatic segmentation of the pectoral muscle, breast tissue and nipple. For validation we annotated 181 landmarks in both views and applied our method to each location. Results show a median 3D distance between the actual location and estimated location of 1.5 cm; a good starting point for a feature based local search method to link lesions for a multiview CAD system. Half of the estimated locations were at most 1 slice away from the actual location, making our method useful as a tool in mammographic workstations to interactively find corresponding locations in ipsilateral tomosynthesis views.

  18. Density estimators in particle hydrodynamics - DTFE versus regular SPH

    NARCIS (Netherlands)

    Pelupessy, FI; Schaap, WE; van de Weygaert, R

    We present the results of a study comparing density maps reconstructed by the Delaunay Tessellation Field Estimator (DTFE) and by regular SPH kernel-based techniques. The density maps are constructed from the outcome of an SPH particle hydrodynamics simulation of a multiphase interstellar medium.

  19. Density estimates of monarch butterflies overwintering in central Mexico

    Directory of Open Access Journals (Sweden)

    Wayne E. Thogmartin

    2017-04-01

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

  20. Density estimates of monarch butterflies overwintering in central Mexico

    Science.gov (United States)

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

    2017-01-01

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

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

    Directory of Open Access Journals (Sweden)

    David E Reese

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

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

    Science.gov (United States)

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

    2016-03-01

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

  3. An Extreme Learning Machine Approach to Density Estimation Problems.

    Science.gov (United States)

    Cervellera, Cristiano; Maccio, Danilo

    2017-10-01

    In this paper, we discuss how the extreme learning machine (ELM) framework can be effectively employed in the unsupervised context of multivariate density estimation. In particular, two algorithms are introduced, one for the estimation of the cumulative distribution function underlying the observed data, and one for the estimation of the probability density function. The algorithms rely on the concept of F -discrepancy, which is closely related to the Kolmogorov-Smirnov criterion for goodness of fit. Both methods retain the key feature of the ELM of providing the solution through random assignment of the hidden feature map and a very light computational burden. A theoretical analysis is provided, discussing convergence under proper hypotheses on the chosen activation functions. Simulation tests show how ELMs can be successfully employed in the density estimation framework, as a possible alternative to other standard methods.

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

    Science.gov (United States)

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

    2017-08-01

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

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

    Science.gov (United States)

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

    2016-04-01

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

  6. Bone mineral density in healthy female adolescents according to age, bone age and pubertal breast stage.

    Science.gov (United States)

    Moretto de Oliveria, Maria Regina; Cristiane da Silva, Carla; Kurokawa, Cilmery Suemi; Teixeira Fortes, Cristina Maria; Campos Capela, Renata; Santos Teixeira, Altamir; Carlos Dalmas, José; Lederer Goldberg, Tamara Beres

    2011-01-01

    This study was designed to evaluate bone mineral density (BMD) in healthy female Brazilian adolescents in five groups looking at chronological age, bone age, and pubertal breast stage, and determining BMD behavior for each classification. Seventy-two healthy female adolescents aged between 10 to 20 incomplete years were divided into five groups and evaluated for calcium intake, weight, height, body mass index (BMI), pubertal breast stage, bone age, and BMD. Bone mass was measured by bone densitometry (DXA) in lumbar spine and proximal femur regions, and the total body. BMI was estimated by Quetelet index. Breast development was assessed by Tanner's criteria and skeletal maturity by bone age. BMD comparison according to chronologic and bone age, and breast development were analyzed by Anova, with Scheffe's test used to find significant differences between groups at P≤0.05. BMD (g·cm(-2)) increased in all studied regions as age advanced, indicating differences from the ages of 13 to 14 years. This group differed to the 10 and 11 to 12 years old groups for lumbar spine BMD (0.865±0.127 vs 0.672±0.082 and 0.689±0.083, respectively) and in girls at pubertal development stage B3, lumbar spine BMD differed from B5 (0.709±0.073 vs 0.936±0.130) and whole body BMD differed from B4 and B5 (0.867±0.056 vs 0.977±0.086 and 1.040±0.080, respectively). Bone mineralization increased in the B3 breast maturity group, and the critical years for bone mass acquisition were between 13 and 14 years of age for all sites evaluated by densitometry.

  7. Fully automated breast density assessment from low-dose chest CT

    Science.gov (United States)

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

    2017-03-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

  9. Baseline mammographic breast density and the risk of invasive breast cancer in postmenopausal women participating in the NSABP Study of Tamoxifen and Raloxifene (STAR)

    Science.gov (United States)

    Cecchini, Reena S.; Costantino, Joseph P.; Cauley, Jane A.; Cronin, Walter M.; Wickerham, D. Lawrence; Bandos, Hanna; Weissfeld, Joel L.; Wolmark, Norman

    2012-01-01

    Mammographic breast density is an established risk factor for breast cancer. However, results are inconclusive regarding its use in risk prediction models. The current study evaluated 13,409 postmenopausal participants in the NSABP Study of Tamoxifen and Raloxifene. A measure of breast density as reported on the entry mammogram report was extracted and categorized according to The American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) classifications. An increased risk of invasive breast cancer was associated with higher mammographic breast density (Pbreast density and Gail score. Breast density was also significant when added to an abbreviated model tailored for estrogen receptor-positive breast cancers (P=0.02). In this study, high BI-RADS breast density was significantly associated with increased breast cancer risk when considered in conjunction with Gail score but provided only slight improvement to the Gail score for predicting the incidence of invasive breast cancer. The BI-RADS breast composition classification system is a quick and readily available method for assessing breast density for risk prediction evaluations; however, its addition to the Gail model does not appear to provide substantial predictability improvements in this population of postmenopausal healthy women at increased risk for breast cancer. PMID:23060039

  10. State of the Art in Photon Density Estimation

    DEFF Research Database (Denmark)

    Hachisuka, Toshiya; Jarosz, Wojciech; Bouchard, Guillaume

    2012-01-01

    Photon-density estimation techniques are a popular choice for simulating light transport in scenes with complicated geometry and materials. This class of algorithms can be used to accurately simulate inter-reflections, caustics, color bleeding, scattering in participating media, and subsurface...... 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...

  11. State of the Art in Photon-Density Estimation

    DEFF Research Database (Denmark)

    Hachisuka, Toshiya; Jarosz, Wojciech; Georgiev, Iliyan

    2013-01-01

    Photon-density estimation techniques are a popular choice for simulating light transport in scenes with complicated geometry and materials. This class of algorithms can be used to accurately simulate inter-reflections, caustics, color bleeding, scattering in participating media, and subsurface...... 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...

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

    Science.gov (United States)

    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 (pmeasures may have the potential to identify women specifically at risk for estrogen-receptor positive breast cancer and could be useful in triaging women into appropriate risk-reduction strategies.

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

    Science.gov (United States)

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

    2017-08-14

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

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

    Science.gov (United States)

    Andersson, Therese M-L; Crowther, Michael J; Czene, Kamila; Hall, Per; Humphreys, Keith

    2017-11-01

    Previous studies have linked reductions in mammographic density after a breast cancer diagnosis to an improved prognosis. These studies focused on short-term change, using a 2-stage process, treating estimated change as a fixed covariate in a survival model. We propose the use of a joint longitudinal-survival model. This enables us to model long-term trends in density while accounting for dropout as well as for measurement error. We studied the change in mammographic density after a breast cancer diagnosis and its association with prognosis (measured by cause-specific mortality), overall and with respect to hormone replacement therapy and tamoxifen treatment. We included 1,740 women aged 50-74 years, diagnosed with breast cancer in Sweden during 1993-1995, with follow-up until 2008. They had a total of 6,317 mammographic density measures available from the first 5 years of follow-up, including baseline measures. We found that the impact of the withdrawal of hormone replacement therapy on density reduction was larger than that of tamoxifen treatment. Unlike previous studies, we found that there was an association between density reduction and survival, both for tamoxifen-treated women and women who were not treated with tamoxifen. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.

  15. Density Estimation in Several Populations With Uncertain Population Membership

    KAUST Repository

    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.

  16. Higher alcohol intake may modify the association between mammographic density and breast cancer: an analysis of three case-control studies.

    Science.gov (United States)

    Conroy, Shannon M; Koga, Karin; Woolcott, Christy G; Dahl, Timothy; Byrne, Celia; Nagata, Chisato; Ursin, Giske; Yaffe, Martin J; Vachon, Celine M; Maskarinec, Gertraud

    2012-10-01

    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 1207 cases and 1663 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 and consumption was not associated with breast cancer risk. However, within the Hawaii/Japan subset, women consuming >1 drink/day had a non-significantly elevated relative risk compared to never drinkers. Also in the Hawaii/Japan population, alcohol intake only modified the association between mammographic density and breast cancer in women consuming >1 drink/day (p(interaction)=0.05) with significant risk estimates of 3.65 and 6.58 for the 2nd and 3rd density tertiles as compared to 1.57 and 1.61 for never drinkers in Hawaii/Japan. Although these findings suggest a stronger association between mammographic density and breast cancer risk for alcohol consumers, the small number of cases requires caution in interpreting the results. Copyright © 2012 Elsevier Ltd. All rights reserved.

  17. Bone mineral density in postmenopausal breast cancer survivors.

    Science.gov (United States)

    Twiss, J J; Waltman, N; Ott, C D; Gross, G J; Lindsey, A M; Moore, T E

    2001-06-01

    The overall purpose of this longitudinal 18-month study was to test the feasibility and effectiveness of a multicomponent intervention for prevention and treatment of osteoporosis. The purpose of this article is to describe the baseline bone mineral density (BMD) findings for 30 postmenopausal women and to compare these BMD findings to time since menopause, body mass index, and tamoxifen use. Baseline data of BMD findings for 30 postmenopausal women, who have had a variety of treatments including surgery, adjuvant chemotherapy and or tamoxifen, and are enrolled in the 18-month longitudinal study. A demographic questionnaire and a three day dietary record were used to collect baseline data. Eighty percent of the women with breast cancer history had abnormal BMDs at baseline (t-scores below -1.00 SD). Thinner women showed a greater risk for accelerated trabecular bone loss at the spine and hip. These findings suggest the need for early BMD assessments and for aggressive health promotion intervention strategies that include a multifaceted protocol of drug therapy for bone remodeling, 1500 mg of daily calcium, 400 IU vitamin D and a strength weight training program that is implemented immediately following chemotherapy treatment and menopause in this high risk population of women.

  18. Mammographic Density Change With Estrogen and Progestin Therapy and Breast Cancer Risk.

    Science.gov (United States)

    Byrne, Celia; Ursin, Giske; Martin, Christopher F; Peck, Jennifer D; Cole, Elodia B; Zeng, Donglin; Kim, Eunhee; Yaffe, Martin D; Boyd, Norman F; Heiss, Gerardo; McTiernan, Anne; Chlebowski, Rowan T; Lane, Dorothy S; Manson, JoAnn E; Wactawski-Wende, Jean; Pisano, Etta D

    2017-09-01

    Estrogen plus progestin therapy increases both mammographic density and breast cancer incidence. Whether mammographic density change associated with estrogen plus progestin initiation predicts breast cancer risk is unknown. We conducted an ancillary nested case-control study within the Women's Health Initiative trial that randomly assigned postmenopausal women to daily conjugated equine estrogen 0.625 mg plus medroxyprogesterone acetate 2.5 mg or placebo. Mammographic density was assessed from mammograms taken prior to and one year after random assignment for 174 women who later developed breast cancer (cases) and 733 healthy women (controls). Logistic regression analyses included adjustment for confounders and baseline mammographic density when appropriate. Among women in the estrogen plus progestin arm (97 cases/378 controls), each 1% positive change in percent mammographic density increased breast cancer risk 3% (odds ratio [OR] = 1.03, 95% confidence interval [CI] = 1.01 to 1.06). For women in the highest quintile of mammographic density change (>19.3% increase), breast cancer risk increased 3.6-fold (95% CI = 1.52 to 8.56). The effect of estrogen plus progestin use on breast cancer risk (OR = 1.28, 95% CI = 0.90 to 1.82) was eliminated in this study, after adjusting for change in mammographic density (OR = 1.00, 95% CI = 0.66 to 1.51). We found the one-year change in mammographic density after estrogen plus progestin initiation predicted subsequent increase in breast cancer risk. All of the increased risk from estrogen plus progestin use was mediated through mammographic density change. Doctors should evaluate changes in mammographic density with women who initiate estrogen plus progestin therapy and discuss the breast cancer risk implications.

  19. Face Value: Towards Robust Estimates of Snow Leopard Densities.

    Science.gov (United States)

    Alexander, Justine S; Gopalaswamy, Arjun M; Shi, Kun; Riordan, Philip

    2015-01-01

    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.

  20. Face Value: Towards Robust Estimates of Snow Leopard Densities.

    Directory of Open Access Journals (Sweden)

    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.

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

    Science.gov (United States)

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

    2017-03-01

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

  2. Estimating forest canopy bulk density using six indirect methods

    Science.gov (United States)

    Robert E. Keane; Elizabeth D. Reinhardt; Joe Scott; Kathy Gray; James Reardon

    2005-01-01

    Canopy bulk density (CBD) is an important crown characteristic needed to predict crown fire spread, yet it is difficult to measure in the field. Presented here is a comprehensive research effort to evaluate six indirect sampling techniques for estimating CBD. As reference data, detailed crown fuel biomass measurements were taken on each tree within fixed-area plots...

  3. A Balanced Approach to Adaptive Probability Density Estimation

    Directory of Open Access Journals (Sweden)

    Julio A. Kovacs

    2017-04-01

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

  4. Estimating Soil Bulk Density and Total Nitrogen from Catchment ...

    African Journals Online (AJOL)

    Estimating Soil Bulk Density and Total Nitrogen from Catchment Attributes in Northern Ethiopia. ... The model coefficients of both analyses for the dependent variables showed higher for organic carbon (OC) as compared to the other variables even though higher values were found from GLM. This study thus confirmed that ...

  5. Estimation of larval density of Liriomyza sativae Blanchard (Diptera ...

    African Journals Online (AJOL)

    This study was conducted to develop sequential sampling plans to estimate larval density of Liriomyza sativae Blanchard (Diptera: Agromyzidae) at three precision levels in cucumber greenhouse. The within- greenhouse spatial patterns of larvae were aggregated. The slopes and intercepts of both Iwao's patchiness ...

  6. Density estimation in tiger populations: combining information for strong inference

    Science.gov (United States)

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

    2012-01-01

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

  7. Estimating Soil Bulk Density and Total Nitrogen from Catchment ...

    African Journals Online (AJOL)

    Bheema

    This is based on the fact that. BD and TN are .... reference the image and as training samples for supervised classification of the Landsat image 30 m x .... 2 change for soil bulk density estimation in the Mai-Negus catchment, northern Ethiopia. Parameter. Coefficient. Standard error. Significance level. R2 change. Constant.

  8. Corruption clubs: empirical evidence from kernel density estimates

    NARCIS (Netherlands)

    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

  9. Regularized Regression and Density Estimation based on Optimal Transport

    KAUST Repository

    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).

  10. Simplified large African carnivore density estimators from track indices

    Directory of Open Access Journals (Sweden)

    Christiaan W. Winterbach

    2016-12-01

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

  11. Variable kernel density estimation in high-dimensional feature spaces

    CSIR Research Space (South Africa)

    Van der Walt, Christiaan M

    2017-02-01

    Full Text Available with the KDE is non-parametric, since no parametric distribution is imposed on the estimate; instead the estimated distribution is defined by the sum of the kernel functions centred on the data points. KDEs thus require the selection of two design parameters... has become feasible – understanding and modelling high- dimensional data has thus become a crucial activity, espe- cially in the field of machine learning. Since non-parametric density estimators are data-driven and do not require or impose a pre...

  12. What Do Women Know About Breast Density? Results From a Population Survey of Virginia Women.

    Science.gov (United States)

    Guterbock, Thomas M; Cohn, Wendy F; Rexrode, Deborah L; Eggleston, Casey M; Dean-McKinney, Melissa; Novicoff, Wendy M; Yaffe, Martin J; Knaus, William A; Harvey, Jennifer A

    2017-01-01

    Breast density reduces the sensitivity of mammography and is a moderate independent risk factor for breast cancer. Virginia is one of 24 states that currently require notification of patients when they have dense breasts. However, little is known about what women in the general population know about breast density. This survey study assessed knowledge about breast density and about its impact on mammography and breast cancer risk. A random sample of 1024 Virginia women between ages 35 and 70 years without breast cancer, reached by landline and cell phone, who completed a 24-minute interview. Bivariate and multivariate analysis was performed. Thirty-six percent of respondents had been informed about their breast density. These women were more likely to be familiar with the term "breast density." Seventy-five percent of respondents reported being either somewhat or very familiar with risk factors for breast cancer, but <1% spontaneously listed breast density as a risk factor. About half of women who had a mammogram in the last year were aware of their breast density. Overall, only one in five women were aware that density reduces the sensitivity of mammography and only one in eight were aware that density increases breast cancer risk. Very few respondents (5.3%) were able to correctly answer three density knowledge questions. Lower-education, African-American, and Jewish women were less knowledgeable about breast density. Although women are becoming aware of the term "breast density," they may not understand its relationship to cancer detection on mammography and, especially, its relationship to breast cancer risk. Copyright © 2016 American College of Radiology. All rights reserved.

  13. Genetic sampling for estimating density of common species.

    Science.gov (United States)

    Cheng, Ellen; Hodges, Karen E; Sollmann, Rahel; Mills, L Scott

    2017-08-01

    Understanding population dynamics requires reliable estimates of population density, yet this basic information is often surprisingly difficult to obtain. With rare or difficult-to-capture species, genetic surveys from noninvasive collection of hair or scat has proved cost-efficient for estimating densities. Here, we explored whether noninvasive genetic sampling (NGS) also offers promise for sampling a relatively common species, the snowshoe hare (Lepus americanus Erxleben, 1777), in comparison with traditional live trapping. We optimized a protocol for single-session NGS sampling of hares. We compared spatial capture-recapture population estimates from live trapping to estimates derived from NGS, and assessed NGS costs. NGS provided population estimates similar to those derived from live trapping, but a higher density of sampling plots was required for NGS. The optimal NGS protocol for our study entailed deploying 160 sampling plots for 4 days and genotyping one pellet per plot. NGS laboratory costs ranged from approximately $670 to $3000 USD per field site. While live trapping does not incur laboratory costs, its field costs can be considerably higher than for NGS, especially when study sites are difficult to access. We conclude that NGS can work for common species, but that it will require field and laboratory pilot testing to develop cost-effective sampling protocols.

  14. Automatically assessed volumetric breast density and breast cancer risk : The era of digital screening mammography

    NARCIS (Netherlands)

    Wanders, J.O.P .

    2017-01-01

    Breast cancer is the most frequently diagnosed cancer among females worldwide. As the burden of breast cancer is high, many countries have introduced a breast cancer screening program with the aim to find and treat breast cancers in an early stage. In the Netherlands, women between the ages of 50

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

    Science.gov (United States)

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

    2013-06-01

    To perform semiautomated quantitative analysis of the background enhancement (BE) in a cohort of patients with newly diagnosed breast cancer and to correlate it with mammographic breast density and menstrual cycle. Informed consent was waived after the research ethics board approved this study. Results of 177 consecutive preoperative breast magnetic resonance (MR) examinations performed from February to December 2009 were reviewed; 147 female patients (median age, 48 years; range, 26-86 years) were included. Ordinal values of BE and breast density were described by two independent readers by using the Breast Imaging Reporting and Data System lexicon. The BE coefficient (BEC) was calculated thus: (SI2 · 100/SI1) - 100, where SI is signal intensity, SI2 is the SI enhancement measured in the largest anteroposterior dimension in the axial plane 1 minute after the contrast agent injection, and SI1is the SI before contrast agent injection. BEC was used for the quantitative analysis of BE. Menstrual cycle status was based on the last menstrual period. The Wilcoxon rank-sum or Kruskal-Wallis test was used to compare quantitative assessment groups. Cohen weighted κ was used to evaluate agreement. Of 147 patients, 68 (46%) were premenopausal and 79 (54%) were postmenopausal. The quantitative BEC was associated with the menstrual status (BEC in premenopausal women, 31.48 ± 20.68 [standard deviation]; BEC in postmenopausal women, 25.65 ± 16.74; P = .02). The percentage of overall BE was higher when the MR imaging was performed in women in the inadequate phase of the cycle (breast density groups. Premenopausal women with breast cancer, and specifically women in the inadequate phase of the cycle, presented with higher quantitative BE than postmenopausal women. No association was found between BE and breast density.

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

    Directory of Open Access Journals (Sweden)

    Kristina M Blackmore

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

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

    Science.gov (United States)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2017-04-01

    Obesity and breast density are both associated with an increased risk of breast cancer and are potentially modifiable. Weight loss surgery (WLS) causes a significant reduction in the amount of body fat and a decrease in breast cancer risk. The effect of WLS on breast density and its components has not been documented. Here, we analyze the impact of WLS on volumetric breast density (VBD) and on each of its components (fibroglandular volume and breast volume) by using three-dimensional methods. Fibroglandular volume, breast volume, and their ratio, the VBD, were calculated from mammograms before and after WLS by using Volpara™ automated software. For the 80 women included, average body mass index decreased from 46.0 ± 7.22 to 33.7 ± 7.06 kg/m2. Mammograms were performed on average 11.6 ± 9.4 months before and 10.1 ± 7 months after WLS. There was a significant reduction in average breast volume (39.4 % decrease) and average fibroglandular volume (15.5 % decrease), and thus, the average VBD increased from 5.15 to 7.87 % (p Breast volume and fibroglandular volume decreased, and VBD increased following WLS, with the most significant change observed in postmenopausal women and non-diabetics. Further studies are warranted to determine how physical and biological alterations in breast density components after WLS may impact breast cancer risk.

  19. Fast Estimation of Approximate Matrix Ranks Using Spectral Densities.

    Science.gov (United States)

    Ubaru, Shashanka; Saad, Yousef; Seghouane, Abd-Krim

    2017-05-01

    Many machine learning and data-related applications require the knowledge of approximate ranks of large data matrices at hand. This letter presents two computationally inexpensive techniques to estimate the approximate ranks of such matrices. These techniques exploit approximate spectral densities, popular in physics, which are probability density distributions that measure the likelihood of finding eigenvalues of the matrix at a given point on the real line. Integrating the spectral density over an interval gives the eigenvalue count of the matrix in that interval. Therefore, the rank can be approximated by integrating the spectral density over a carefully selected interval. Two different approaches are discussed to estimate the approximate rank, one based on Chebyshev polynomials and the other based on the Lanczos algorithm. In order to obtain the appropriate interval, it is necessary to locate a gap between the eigenvalues that correspond to noise and the relevant eigenvalues that contribute to the matrix rank. A method for locating this gap and selecting the interval of integration is proposed based on the plot of the spectral density. Numerical experiments illustrate the performance of these techniques on matrices from typical applications.

  20. The Effect of Lidar Point Density on LAI Estimation

    Science.gov (United States)

    Cawse-Nicholson, K.; van Aardt, J. A.; Romanczyk, P.; Kelbe, D.; Bandyopadhyay, M.; Yao, W.; Krause, K.; Kampe, T. U.

    2013-12-01

    Leaf Area Index (LAI) is an important measure of forest health, biomass and carbon exchange, and is most commonly defined as the ratio of the leaf area to ground area. LAI is understood over large spatial scales and describes leaf properties over an entire forest, thus airborne imagery is ideal for capturing such data. Spectral metrics such as the normalized difference vegetation index (NDVI) have been used in the past for LAI estimation, but these metrics may saturate for high LAI values. Light detection and ranging (lidar) is an active remote sensing technology that emits light (most often at the wavelength 1064nm) and uses the return time to calculate the distance to intercepted objects. This yields information on three-dimensional structure and shape, which has been shown in recent studies to yield more accurate LAI estimates than NDVI. However, although lidar is a promising alternative for LAI estimation, minimum acquisition parameters (e.g. point density) required for accurate LAI retrieval are not yet well known. The objective of this study was to determine the minimum number of points per square meter that are required to describe the LAI measurements taken in-field. As part of a larger data collect, discrete lidar data were acquired by Kucera International Inc. over the Hemlock-Canadice State Forest, NY, USA in September 2012. The Leica ALS60 obtained point density of 12 points per square meter and effective ground sampling distance (GSD) of 0.15m. Up to three returns with intensities were recorded per pulse. As part of the same experiment, an AccuPAR LP-80 was used to collect LAI estimates at 25 sites on the ground. Sites were spaced approximately 80m apart and nine measurements were made in a grid pattern within a 20 x 20m site. Dominant species include Hemlock, Beech, Sugar Maple and Oak. This study has the benefit of very high-density data, which will enable a detailed map of intra-forest LAI. Understanding LAI at fine scales may be particularly useful

  1. Early life growth, socioeconomic status, and mammographic breast density in an urban US birth cohort.

    Science.gov (United States)

    Akinyemiju, Tomi F; Tehranifar, Parisa; Flom, Julie D; Liao, Yuyan; Wei, Ying; Terry, Mary Beth

    2016-08-01

    Rapid infant and childhood growth has been associated with chronic disease later in life, including breast cancer. Early life socioeconomic status (SES) influences childhood growth, but few studies have prospective measures from birth to consider the effects of early life growth and SES on breast cancer risk. We used prospectively measured early life SES and growth (percentile weight change in height and weight between each pair of consecutive time points at birth, 4 months, 1 and 7 years). We performed linear regression models to obtain standardized estimates of the association between 1 standard deviation increase in early life SES and growth and adult mammographic density (MD), a strong risk factor for breast cancer, in a diverse birth cohort (n = 151; 37% white, 38% black, 25% Puerto Rican; average age at mammogram = 42.4). In models adjusted for race/ethnicity, prenatal factors, birthweight, infant and childhood growth, and adult body mass index, percentile weight change from 1 year to 7 years was inversely associated with percent MD (standardized coefficient (Stdβ) = -0.28, 95% CI: -0.55 to -0.01), and higher early life SES was positively associated with percent MD (Stdβ = 0.24, 95% CI: 0.04-0.43). Similar associations were observed for dense area, but those estimates were not statistically significant. These results suggest opposite and independent effects of early life SES and growth on MD. Copyright © 2016 Elsevier Inc. All rights reserved.

  2. Health professionals' agreement on density judgements and successful abnormality identification within the UK Breast Screening Programme

    Science.gov (United States)

    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.

  3. Building a Better Model: A Personalized Breast Cancer Risk Model Incorporating Breast Density to Stratify Risk and Improve Application of Resources

    Science.gov (United States)

    2015-12-01

    Gifu, Japan , June 29-July 2, 2014 2. “What do Women Know About Breast Density?” 24 Gutterbock TM, Rexrode DL, Eggleston C, Cohn WF, Novicoff W...Breast Imaging (IWDM) Gifu, Japan , June 29-July 2, 2014 25 2. “What do Women Know About Breast Density?” Gutterbock TM, Rexrode DL, Eggleston C...measurement of breast density. Scope: Assemble a cohort of women with known breast cancer risk factors and digital mammogram files for women diagnosed with

  4. 3D MRI for Quantitative Analysis of Quadrant Percent Breast Density: Correlation with Quadrant Location of Breast Cancer.

    Science.gov (United States)

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

    2017-07-01

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

  5. Thermal distribution analysis of three-dimensional tumor-embedded breast models with different breast density compositions.

    Science.gov (United States)

    Wahab, Asnida Abd; Salim, Maheza Irna Mohamad; Ahamat, Mohamad Asmidzam; Manaf, Noraida Abd; Yunus, Jasmy; Lai, Khin Wee

    2016-09-01

    Breast cancer is the most common cancer among women globally, and the number of young women diagnosed with this disease is gradually increasing over the years. Mammography is the current gold-standard technique although it is known to be less sensitive in detecting tumors in woman with dense breast tissue. Detecting an early-stage tumor in young women is very crucial for better survival chance and treatment. The thermography technique has the capability to provide an additional functional information on physiological changes to mammography by describing thermal and vascular properties of the tissues. Studies on breast thermography have been carried out to improve the accuracy level of the thermography technique in various perspectives. However, the limitation of gathering women affected by cancer in different age groups had necessitated this comprehensive study which is aimed to investigate the effect of different density levels on the surface temperature distribution profile of the breast models. These models, namely extremely dense (ED), heterogeneously dense (HD), scattered fibroglandular (SF), and predominantly fatty (PF), with embedded tumors were developed using the finite element method. A conventional Pennes' bioheat model was used to perform the numerical simulation on different case studies, and the results obtained were then compared using a hypothesis statistical analysis method to the reference breast model developed previously. The results obtained show that ED, SF, and PF breast models had significant mean differences in surface temperature profile with a p value <0.025, while HD breast model data pair agreed with the null hypothesis formulated due to the comparable tissue composition percentage to the reference model. The findings suggested that various breast density levels should be considered as a contributing factor to the surface thermal distribution profile alteration in both breast cancer detection and analysis when using the thermography

  6. Bayesian error estimation in density-functional theory

    DEFF Research Database (Denmark)

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

    2005-01-01

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

  7. Non-intrusive Load Disaggregation Based on Kernel Density Estimation

    Science.gov (United States)

    Sen, Wang; Dongsheng, Yang; Chuchen, Guo; Shengxian, Du

    2017-05-01

    Aiming at the problem of high cost and difficult implementation of high frequency non-intrusive load decomposition method, this paper proposes a new method based on kernel density estimation(KDE) for low frequency NILM (Non-intrusive load monitoring). The method establishes power reference model of electricity load in different working conditions and appliance’s possible combinations first, then probability distribution is calculated as appliances features by kernel density estimation. After that, target power data is divided by step changes, whose distributions will be compared with reference models, and the most similar reference model will be chosen as the decomposed consequence. The proposed approach was tested with data from the GREEND public data set, it showed better performance in terms of energy disaggregation accuracy compared with many traditional NILM approaches. Our results show good performance which can achieve more than 93% accuracy in simulation.

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

    Science.gov (United States)

    2011-01-01

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

  9. Quantification of Regional Breast Density in Four Quadrants Using 3D MRI—A Pilot Study

    Directory of Open Access Journals (Sweden)

    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.

  10. Age-related changes in mammographic density and breast cancer risk.

    Science.gov (United States)

    Lokate, Mariëtte; Stellato, Rebecca K; Veldhuis, Wouter B; Peeters, Petra H M; van Gils, Carla H

    2013-07-01

    High mammographic density is a strong breast cancer risk factor. Density normally declines with aging. We investigated whether the level of decline in mammographic density is related to breast cancer risk using a nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC)-Prospect cohort. This cohort was recruited among participants of a population-based breast cancer screening program in the Netherlands between 1993 and 1997. We examined whether age-related changes in mammographic density were different for 533 cases and 1,367 controls who were 49-69 years of age at the time of recruitment into the cohort. We used mixed models with linear and quadratic terms for age and interaction terms between age terms and case status. The percent mammographic density at the first available mammogram was higher for cases than for controls (25.2% vs. 22.5%) (P = 0.003). The average decline in density over 10 years was 11% in both cases and controls (P = 0.56). When studying changes among 4 categories of density, we saw some indication that large changes may influence breast cancer risk. Although no difference was seen in the average decline, we cannot exclude that large changes may influence breast cancer risk.

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

    Science.gov (United States)

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

    2017-03-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    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

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

    Science.gov (United States)

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

    2014-03-01

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

  14. Feasibility study of a breast density measurement within a direct photon-counting mammography scanner system.

    Science.gov (United States)

    Machida, Youichi; Tozaki, Mitsuhiro; Yoshida, Tamiko; Saita, Ai; Yakabe, Mari; Nii, Kanae

    2014-09-01

    To evaluate the clinical feasibility of breast density measurements by a new application within a direct photon-counting mammography scanner system. A retrospective study of consecutive women who underwent mammography using a direct photon-counting mammography scanner system (MicroDose mammography SI; Philips Digital Mammography Sweden AB) was performed at the authors' institution between September and December 2013. Quantitative volumetric glandularity measurements were performed automatically for each acquired mammographic image using an application (Breast Density Measurement; Philips Digital Mammography Sweden AB). The quantitative volumetric glandularity of each breast was defined as the average values for the mediolateral oblique (MLO) and craniocaudal (CC) mammogram views. Of the 44 women who underwent bilateral mammogram acquisitions, the breast density measurements were performed successfully in 40 patients (90.9%). A very good to excellent correlation in the quantitative breast density measurements acquired from the MLO and CC images was obtained in the 40 evaluable patients (R = 0.99). The calculated volumetric glandularity using this new application should correspond well with the true volumetric density of each breast.

  15. Simplified large African carnivore density estimators from track indices.

    Science.gov (United States)

    Winterbach, Christiaan W; Ferreira, Sam M; Funston, Paul J; Somers, Michael J

    2016-01-01

    The range, population size and trend of large carnivores are important parameters to assess their status globally and to plan conservation strategies. One can use linear models to assess population size and trends of large carnivores from track-based surveys on suitable substrates. The conventional approach of a linear model with intercept may not intercept at zero, but may fit the data better than linear model through the origin. We assess whether a linear regression through the origin is more appropriate than a linear regression with intercept to model large African carnivore densities and track indices. We did simple linear regression with intercept analysis and simple linear regression through the origin and used the confidence interval for ß in the linear model y = αx + ß, Standard Error of Estimate, Mean Squares Residual and Akaike Information Criteria to evaluate the models. The Lion on Clay and Low Density on Sand models with intercept were not significant (P > 0.05). The other four models with intercept and the six models thorough origin were all significant (P African carnivores using track counts on sandy substrates in areas where carnivore densities are 0.27 carnivores/100 km2 or higher. To improve the current models, we need independent data to validate the models and data to test for non-linear relationship between track indices and true density at low densities.

  16. Parameter estimates for invasive breast cancer progression in the Canadian National Breast Screening Study.

    Science.gov (United States)

    Taghipour, S; Banjevic, D; Miller, A B; Montgomery, N; Jardine, A K S; Harvey, B J

    2013-02-19

    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. 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 of diagnostic tests for women aged 40-49 and 50-59. Although younger women have a slower transition rate from healthy state to preclinical, their screen-detected tumour becomes evident sooner. Women aged 50-59 have a higher mortality rate compared with younger women. The mean sojourn times for women aged 40-49 and 50-59 are 2.5 years (95% CI: 1.7, 3.8) and 3.0 years (95% CI: 2.1, 4.3), respectively. Sensitivity of diagnostic procedures for older women is estimated to be 0.75 (95% CI: 0.55, 0.88), while women aged 40-49 have a lower sensitivity (0.61, 95% CI: 0.42, 0.77). Age is the only factor that affects the false-negative probability. For women aged 40-49, 'age at entry', 'history of breast disease' and 'families with breast cancer' are found to be significant for some of the transition rates. For the age-group 50-59, 'age at entry', 'history of breast disease', 'menstruation length' and 'number of live births' are found to affect the transition rates. Modelling and estimating the parameters of cancer progression are essential steps towards evaluating the effectiveness of screening policies. The parameters include the transition rates, the preclinical sojourn time, the sensitivity, and the effect of different risk factors on cancer progression.

  17. Mammographic density, parity and age at first birth, and risk of breast cancer: an analysis of four case-control studies.

    Science.gov (United States)

    Woolcott, Christy G; Koga, Karin; Conroy, Shannon M; Byrne, Celia; Nagata, Chisato; Ursin, Giske; Vachon, Celine M; Yaffe, Martin J; Pagano, Ian; Maskarinec, Gertraud

    2012-04-01

    Mammographic density is strongly and consistently associated with breast cancer risk. To determine if this association was modified by reproductive factors (parity and age at first birth), data were combined from four case-control studies conducted in the United States and Japan. To overcome the issue of variation in mammographic density assessment among the studies, a single observer re-read all the mammograms using one type of interactive thresholding software. Logistic regression was used to estimate odds ratios (OR) while adjusting for other known breast cancer risk factors. Included were 1,699 breast cancer cases and 2,422 controls, 74% of whom were postmenopausal. A positive association between mammographic density and breast cancer risk was evident in every group defined by parity and age at first birth (OR per doubling of percent mammographic density ranged between 1.20 and 1.39). Nonetheless, the association appeared to be stronger among nulliparous than parous women (OR per doubling of percent mammographic density = 1.39 vs. 1.24; P interaction = 0.054). However, when examined by study location, the effect modification by parity was apparent only in women from Hawaii and when examined by menopausal status, it was apparent in postmenopausal, but not premenopausal, women. Effect modification by parity was not significant in subgroups defined by body mass index or ethnicity. Adjusting for mammographic density did not attenuate the OR for the association between parity and breast cancer risk by more than 16.4%, suggesting that mammographic density explains only a small proportion of the reduction in breast cancer risk associated with parity. In conclusion, this study did not support the hypothesis that parity modifies the breast cancer risk attributed to mammographic density. Even though an effect modification was found in Hawaiian women, no such thing was found in women from the other three locations.

  18. Mammographic breast density: Predictive value for pathological response to neoadjuvant chemotherapy in breast cancer patients.

    Science.gov (United States)

    Elsamany, S; Alzahrani, A; Abozeed, W N; Rasmy, A; Farooq, M U; Elbiomy, M A; Rawah, E; Alsaleh, K; Abdel-Aziz, N M

    2015-10-01

    This study aims to evaluate the relation between mammographic breast density (BD) and pathological response to neoadjuvant chemotherapy. In this retrospective study, 241 breast cancer patients who received neoadjuvant chemotherapy were included. BD was assessed in mammograms already performed at diagnosis. Pathological complete response (pCR) and pathological stage were correlated with BD, tumour phenotype and other clinico-pathological factors. Patients with low BD had better pCR compared to those with high density (30.5% vs 19.5% respectively, OR = 1.8, 95% CI = 0.98-3.3, p = 0.056) which was more pronounced after adjustment with body mass index (BMI) (OR = 2.4, 95% CI = 1.2-4.8, p = 0.011). HER2-positive disease (32.5% vs. 18.4%, OR = 2.2, 95% = 1.2-4.0, p = 0.01), lower BMI (OR = 1.1, 95% CI = 1.03-1.15, p = 0.004) and lower clinical stage (p = 0.002) were significant predictors of pCR in univariate analysis. In multivariate analysis, low BD (OR = 2.7, 95% CI = 1.3-5.5, p = 0.006) and lower BMI (OR = 1.1, 95% CI = 1.03-1.17, p = 0.003) were independent predictors of better pCR, while early clinical stage (I, II) was of borderline significance (OR = 2.6, 95% CI = 0.99-6.7, p = 0.052). High BD (OR = 1.8, 95% CI = 1.1-3.2, p = 0.03), advanced clinical stage (III) (OR = 1.5, 95% CI = 1.03-2.1, p = 0.03) and higher BMI (OR = 1.06, 95% CI = 1.02-1.11, p = 0.006) were significant predictors of advanced pathological stage. Low mammographic BD, low BMI and early clinical stage were associated with improved pCR rate and lower pathological stage after neoadjuvant chemotherapy. BD had more pronounced association with response to chemotherapy after adjustment with BMI. Copyright © 2015 Elsevier Ltd. All rights reserved.

  19. Angiogenesis in Paget's Disease of the Vulva and the Breast: Correlation with Microvessel Density

    Directory of Open Access Journals (Sweden)

    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.

  20. Ambit determination method in estimating rice plant population density

    Directory of Open Access Journals (Sweden)

    Abu Bakar, B.,

    2017-11-01

    Full Text Available Rice plant population density is a key indicator in determining the crop setting and fertilizer application rate. It is therefore essential that the population density is monitored to ensure that a correct crop management decision is taken. The conventional method of determining plant population is by manually counting the total number of rice plant tillers in a 25 cm x 25 cm square frame. Sampling is done by randomly choosing several different locations within a plot to perform tiller counting. This sampling method is time consuming, labour intensive and costly. An alternative fast estimating method was developed to overcome this issue. The method relies on measuring the outer circumference or ambit of the contained rice plants in a 25 cm x 25 cm square frame to determine the number of tillers within that square frame. Data samples of rice variety MR219 were collected from rice plots in the Muda granary area, Sungai Limau Dalam, Kedah. The data were taken at 50 days and 70 days after seeding (DAS. A total of 100 data samples were collected for each sampling day. A good correlation was obtained for the variety of 50 DAS and 70 DAS. The model was then verified by taking 100 samples with the latching strap for 50 DAS and 70 DAS. As a result, this technique can be used as a fast, economical and practical alternative to manual tiller counting. The technique can potentially be used in the development of an electronic sensing system to estimate paddy plant population density.

  1. Combining Ratio Estimation for Low Density Parity Check (LDPC) Coding

    Science.gov (United States)

    Mahmoud, Saad; Hi, Jianjun

    2012-01-01

    The Low Density Parity Check (LDPC) Code decoding algorithm make use of a scaled receive signal derived from maximizing the log-likelihood ratio of the received signal. The scaling factor (often called the combining ratio) in an AWGN channel is a ratio between signal amplitude and noise variance. Accurately estimating this ratio has shown as much as 0.6 dB decoding performance gain. This presentation briefly describes three methods for estimating the combining ratio: a Pilot-Guided estimation method, a Blind estimation method, and a Simulation-Based Look-Up table. The Pilot Guided Estimation method has shown that the maximum likelihood estimates of signal amplitude is the mean inner product of the received sequence and the known sequence, the attached synchronization marker (ASM) , and signal variance is the difference of the mean of the squared received sequence and the square of the signal amplitude. This method has the advantage of simplicity at the expense of latency since several frames worth of ASMs. The Blind estimation method s maximum likelihood estimator is the average of the product of the received signal with the hyperbolic tangent of the product combining ratio and the received signal. The root of this equation can be determined by an iterative binary search between 0 and 1 after normalizing the received sequence. This method has the benefit of requiring one frame of data to estimate the combining ratio which is good for faster changing channels compared to the previous method, however it is computationally expensive. The final method uses a look-up table based on prior simulated results to determine signal amplitude and noise variance. In this method the received mean signal strength is controlled to a constant soft decision value. The magnitude of the deviation is averaged over a predetermined number of samples. This value is referenced in a look up table to determine the combining ratio that prior simulation associated with the average magnitude of

  2. A projection and density estimation method for knowledge discovery.

    Directory of Open Access Journals (Sweden)

    Adam Stanski

    Full Text Available A key ingredient to modern data analysis is probability density estimation. However, it is well known that the curse of dimensionality prevents a proper estimation of densities in high dimensions. The problem is typically circumvented by using a fixed set of assumptions about the data, e.g., by assuming partial independence of features, data on a manifold or a customized kernel. These fixed assumptions limit the applicability of a method. In this paper we propose a framework that uses a flexible set of assumptions instead. It allows to tailor a model to various problems by means of 1d-decompositions. The approach achieves a fast runtime and is not limited by the curse of dimensionality as all estimations are performed in 1d-space. The wide range of applications is demonstrated at two very different real world examples. The first is a data mining software that allows the fully automatic discovery of patterns. The software is publicly available for evaluation. As a second example an image segmentation method is realized. It achieves state of the art performance on a benchmark dataset although it uses only a fraction of the training data and very simple features.

  3. A projection and density estimation method for knowledge discovery.

    Science.gov (United States)

    Stanski, Adam; Hellwich, Olaf

    2012-01-01

    A key ingredient to modern data analysis is probability density estimation. However, it is well known that the curse of dimensionality prevents a proper estimation of densities in high dimensions. The problem is typically circumvented by using a fixed set of assumptions about the data, e.g., by assuming partial independence of features, data on a manifold or a customized kernel. These fixed assumptions limit the applicability of a method. In this paper we propose a framework that uses a flexible set of assumptions instead. It allows to tailor a model to various problems by means of 1d-decompositions. The approach achieves a fast runtime and is not limited by the curse of dimensionality as all estimations are performed in 1d-space. The wide range of applications is demonstrated at two very different real world examples. The first is a data mining software that allows the fully automatic discovery of patterns. The software is publicly available for evaluation. As a second example an image segmentation method is realized. It achieves state of the art performance on a benchmark dataset although it uses only a fraction of the training data and very simple features.

  4. A Concept of Approximated Densities for Efficient Nonlinear Estimation

    Directory of Open Access Journals (Sweden)

    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.

  5. A comparison of selected parametric and imputation methods for estimating snag density and snag quality attributes

    Science.gov (United States)

    Eskelson, Bianca N.I.; Hagar, Joan; Temesgen, Hailemariam

    2012-01-01

    Snags (standing dead trees) are an essential structural component of forests. Because wildlife use of snags depends on size and decay stage, snag density estimation without any information about snag quality attributes is of little value for wildlife management decision makers. Little work has been done to develop models that allow multivariate estimation of snag density by snag quality class. Using climate, topography, Landsat TM data, stand age and forest type collected for 2356 forested Forest Inventory and Analysis plots in western Washington and western Oregon, we evaluated two multivariate techniques for their abilities to estimate density of snags by three decay classes. The density of live trees and snags in three decay classes (D1: recently dead, little decay; D2: decay, without top, some branches and bark missing; D3: extensive decay, missing bark and most branches) with diameter at breast height (DBH) ≥ 12.7 cm was estimated using a nonparametric random forest nearest neighbor imputation technique (RF) and a parametric two-stage model (QPORD), for which the number of trees per hectare was estimated with a Quasipoisson model in the first stage and the probability of belonging to a tree status class (live, D1, D2, D3) was estimated with an ordinal regression model in the second stage. The presence of large snags with DBH ≥ 50 cm was predicted using a logistic regression and RF imputation. Because of the more homogenous conditions on private forest lands, snag density by decay class was predicted with higher accuracies on private forest lands than on public lands, while presence of large snags was more accurately predicted on public lands, owing to the higher prevalence of large snags on public lands. RF outperformed the QPORD model in terms of percent accurate predictions, while QPORD provided smaller root mean square errors in predicting snag density by decay class. The logistic regression model achieved more accurate presence/absence classification

  6. Simple estimation of snow density in an Alpine region

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    A. Pistocchi

    2016-06-01

    New hydrological insights: The simple equation presents similar uncertainty compared to the more complex empirical models. It appears robust for regions with snow of Alpine and Maritime characteristics, and can be easily recalibrated as more data become available. The proposed model estimates snowpack density as an initial value of 200 kg/m3 at the beginning of the snow cover season (November 1st, to be increased by 1 kg/m3 for each elapsed day. The model residuals standard deviation is about 13%, which is comparable to the within-site spatial variability.

  7. An Improved Convolutional Neural Network on Crowd Density Estimation

    Directory of Open Access Journals (Sweden)

    Pan Shao-Yun

    2016-01-01

    Full Text Available In this paper, a new method is proposed for crowd density estimation. An improved convolutional neural network is combined with traditional texture feature. The data calculated by the convolutional layer can be treated as a new kind of features.So more useful information of images can be extracted by different features.In the meantime, the size of image has little effect on the result of convolutional neural network. Experimental results indicate that our scheme has adequate performance to allow for its use in real world applications.

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

    DEFF Research Database (Denmark)

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

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Sousan Kolahi

    2014-05-01

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

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

    Science.gov (United States)

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

    2017-03-01

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

  11. Mammographic breast density and risk of breast cancer in women with atypical hyperplasia: an observational cohort study from the Mayo Clinic Benign Breast Disease (BBD) cohort.

    Science.gov (United States)

    Vierkant, Robert A; Degnim, Amy C; Radisky, Derek C; Visscher, Daniel W; Heinzen, Ethan P; Frank, Ryan D; Winham, Stacey J; Frost, Marlene H; Scott, Christopher G; Jensen, Matthew R; Ghosh, Karthik; Manduca, Armando; Brandt, Kathleen R; Whaley, Dana H; Hartmann, Lynn C; Vachon, Celine M

    2017-01-31

    Atypical hyperplasia (AH) and mammographic breast density (MBD) are established risk factors for breast cancer (BC), but their joint contributions are not well understood. We examine associations of MBD and BC by histologic impression, including AH, in a subcohort of women from the Mayo Clinic Benign Breast Disease Cohort. Women with a diagnosis of BBD and mammogram between 1985 and 2001 were eligible. Histologic impression was assessed via pathology review and coded as non-proliferative disease (NP), proliferative disease without atypia (PDWA) and AH. MBD was assessed clinically using parenchymal pattern (PP) or BI-RADS criteria and categorized as low, moderate or high. Percent density (PD) was also available for a subset of women. BC and clinical information were obtained by questionnaires, medical records and the Mayo Clinic Tumor Registry. Women were followed from date of benign biopsy to BC, death or last contact. Standardized incidence ratios (SIRs) compared the observed number of BCs to expected counts. Cox regression estimated multivariate-adjusted MBD hazard ratios. Of the 6271 women included in the study, 1132 (18.0%) had low MBD, 2921 (46.6%) had moderate MBD, and 2218 (35.4%) had high MBD. A total of 3532 women (56.3%) had NP, 2269 (36.2%) had PDWA and 470 (7.5%) had AH. Over a median follow-up of 14.3 years, 528 BCs were observed. The association of MBD and BC risk differed by histologic impression (p-interaction = 0.03), such that there was a strong MBD and BC association among NP (p women were not significant within subsets defined by type of MBD measure (PP vs. BI-RADS), age at biopsy, number of foci of AH, type of AH (lobular vs. ductal) and body mass index, and after adjustment for potential confounding variables. Women with atypia who also had high PD (>50%) demonstrated marginal evidence of increased BC risk (SIR 4.98), but results were not statistically significant. We found no evidence of an association between MBD and subsequent BC

  12. Breast Density in Mammography and Magnetic Resonance Imaging in High Risk Women and Women with Breast Cancer

    Science.gov (United States)

    Albert, Marissa; Schnabel, Freya; Chun, Jennifer; Schwartz, Shira; Lee, Jiyon; Leite, Ana Paula Klautau; Moy, Linda

    2015-01-01

    Structured Abstract Purpose To evaluate the relationship between mammographic breast density (MBD), background parenchymal enhancement (BPE), and fibroglandular tissue (FGT) in women with breast cancer (BC) and at high risk for developing BC. Methods Our institutional database was queried for patients who underwent mammography and MRI. Results 403 (85%) had BC and 72 (15%) were at high risk. MBD (p=0.0005), BPE (p<0.0001), and FGT (p=0.02) were all higher in high risk women compared to the BC group. Conclusions Higher levels of MBD, BPE and FGT are seen in women at higher risk for developing BC when compared to women with BC. PMID:26351036

  13. Automatic estimation of elasticity parameters in breast tissue

    Science.gov (United States)

    Skerl, Katrin; Cochran, Sandy; Evans, Andrew

    2014-03-01

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

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

    Science.gov (United States)

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

    2014-03-26

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Mainprize, James G.; Tyson, Albert H.; Yaffe, Martin J. [Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5 (Canada); Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5 (Canada) and Department of Medical Biophysics, University of Toronto, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5 (Canada)

    2012-08-15

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

  16. Validation of results from knowledge discovery: mass density as a predictor of breast cancer.

    Science.gov (United States)

    Woods, Ryan W; Oliphant, Louis; Shinki, Kazuhiko; Page, David; Shavlik, Jude; Burnside, Elizabeth

    2010-10-01

    The purpose of our study is to identify and quantify the association between high breast mass density and breast malignancy using inductive logic programming (ILP) and conditional probabilities, and validate this association in an independent dataset. We ran our ILP algorithm on 62,219 mammographic abnormalities. We set the Aleph ILP system to generate 10,000 rules per malignant finding with a recall >5% and precision >25%. Aleph reported the best rule for each malignant finding. A total of 80 unique rules were learned. A radiologist reviewed all rules and identified potentially interesting rules. High breast mass density appeared in 24% of the learned rules. We confirmed each interesting rule by calculating the probability of malignancy given each mammographic descriptor. High mass density was the fifth highest ranked predictor. To validate the association between mass density and malignancy in an independent dataset, we collected data from 180 consecutive breast biopsies performed between 2005 and 2007. We created a logistic model with benign or malignant outcome as the dependent variable while controlling for potentially confounding factors. We calculated odds ratios based on dichomotized variables. In our logistic regression model, the independent predictors high breast mass density (OR 6.6, CI 2.5-17.6), irregular mass shape (OR 10.0, CI 3.4-29.5), spiculated mass margin (OR 20.4, CI 1.9-222.8), and subject age (β = 0.09, p < 0.0001) significantly predicted malignancy. Both ILP and conditional probabilities show that high breast mass density is an important adjunct predictor of malignancy, and this association is confirmed in an independent data set of prospectively collected mammographic findings.

  17. An Adaptive Background Subtraction Method Based on Kernel Density Estimation

    Directory of Open Access Journals (Sweden)

    Mignon Park

    2012-09-01

    Full Text Available In this paper, a pixel-based background modeling method, which uses nonparametric kernel density estimation, is proposed. To reduce the burden of image storage, we modify the original KDE method by using the first frame to initialize it and update it subsequently at every frame by controlling the learning rate according to the situations. We apply an adaptive threshold method based on image changes to effectively subtract the dynamic backgrounds. The devised scheme allows the proposed method to automatically adapt to various environments and effectively extract the foreground. The method presented here exhibits good performance and is suitable for dynamic background environments. The algorithm is tested on various video sequences and compared with other state-of-the-art background subtraction methods so as to verify its performance.

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

    Science.gov (United States)

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

    2013-01-01

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

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

    Science.gov (United States)

    2014-10-01

    receptor size. In this retrospective HIPAA compliant study, automated breast density measurements for tiled images are compared with full MLO and CC views...tiled MLO views had very good correlation with full views (r = 0.88), while correlation between tiled and full CC views was poor (r = 0.31). VBD for...all women requiring tiled CC views was low (᝺%). In conclusion, VBD measured from a tiled MLO view is a reasonable substitute for a full MLO

  20. Cardiac dose estimates from Danish and Swedish breast cancer radiotherapy during 1977-2001

    DEFF Research Database (Denmark)

    Taylor, Carolyn W; Brønnum, Dorthe; Darby, Sarah C

    2011-01-01

    To estimate target and cardiac doses from breast cancer radiotherapy in Denmark and in the Stockholm and Umeå areas of Sweden during 1977-2001.......To estimate target and cardiac doses from breast cancer radiotherapy in Denmark and in the Stockholm and Umeå areas of Sweden during 1977-2001....

  1. Estimating Foreign-Object-Debris Density from Photogrammetry Data

    Science.gov (United States)

    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.

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

    Science.gov (United States)

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

    2017-03-01

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

  3. Comparison of two methods of estimation of low density lipoprotein cholesterol, the direct versus friedewald estimation

    OpenAIRE

    Sahu, Suchanda; Chawla, Rajinder; Uppal, Bharti

    2005-01-01

    Current recommendations of the Adult Treatment Panel and Adolescents Treatment Panel of National Cholesterol Education Program make the low-density lipoprotein cholesterol (LDL-C) levels in serum the basis of classification and management of hypercholesterolemia. A number of direct homogenous assays based on surfactant/solubility principles have evolved in the recent past. This has made LDL-C estimation less cumbersome than the earlier used methods. Here we compared one of the direct homogeno...

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

    Science.gov (United States)

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

    2013-11-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2007-08-15

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

  6. Low dose transdermal estradiol induces breast density and heterogeneity changes comparable to those of raloxifene

    DEFF Research Database (Denmark)

    Nielsen, Mads; Raundahl, Jakob; Pettersen, Paola

    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...

  7. Estimation of health state utilities in breast cancer

    Directory of Open Access Journals (Sweden)

    Kim SH

    2017-03-01

    Full Text Available Seon-Ha Kim,1 Min-Woo Jo,2 Minsu Ock,2 Hyeon-Jeong Lee,2 Jong-Won Lee3,4 1Department of Nursing, College of Nursing, Dankook University, Cheonan, 2Department of Preventive Medicine, University of Ulsan College of Medicine, Seoul, 3Department of Breast and Endocrine Surgery, Asan Medical Center, Seoul, 4Department of Surgery, University of Ulsan College of Medicine, Seoul, South Korea Purpose: The aim of this study is to determine the utility of breast cancer health states using the standard gamble (SG and visual analog scale (VAS methods in the Korean general population.Materials and methods: Eight hypothetical breast cancer health states were developed based on patient education material and previous publications. Data from 509 individuals from the Korean general population were used to evaluate breast cancer health states using the VAS and the SG methods, which were obtained via computer-assisted personal interviews. Mean utility values were calculated for each human papillomavirus (HPV-related health state.Results: The rank of health states was identical between two valuation methods. SG values were higher than VAS values in all health states. The utility values derived from SG were 0.801 (noninvasive breast cancer with mastectomy and followed by reconstruction, 0.790 (noninvasive breast cancer with mastectomy only, 0.779 (noninvasive breast cancer with breast-conserving surgery and radiation therapy, 0.731 (invasive breast cancer with surgery, radiation therapy, and/or chemotherapy, 0.610 (locally advanced breast cancer with radical mastectomy with radiation therapy, 0.587 (inoperable locally advanced breast cancer, 0.496 (loco-regional recurrent breast cancer, and 0.352 (metastatic breast cancer.Conclusion: Our findings might be useful for economic evaluation of breast cancer screening and interventions in general populations. Keywords: breast neoplasm, Korea, quality-adjusted life years, quality of life

  8. Mammographic density and risk of breast cancer by age and tumor characteristics.

    Science.gov (United States)

    Bertrand, Kimberly A; Tamimi, Rulla M; Scott, Christopher G; Jensen, Matthew R; Pankratz, V; Visscher, Daniel; Norman, Aaron; Couch, Fergus; Shepherd, John; Fan, Bo; Chen, Yunn-Yi; Ma, Lin; Beck, Andrew H; Cummings, Steven R; Kerlikowske, Karla; Vachon, Celine M

    2013-11-04

    Understanding whether mammographic density (MD) is associated with all breast tumor subtypes and whether the strength of association varies by age is important for utilizing MD in risk models. Data were pooled from six studies including 3414 women with breast cancer and 7199 without who underwent screening mammography. Percent MD was assessed from digitized film-screen mammograms using a computer-assisted threshold technique. We used polytomous logistic regression to calculate breast cancer odds according to tumor type, histopathological characteristics, and receptor (estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor (HER2)) status by age (ages, with a two-fold increased risk for high (>51%) versus average density (11-25%). Women ages ages 55-64 and ≥ 65 years (P(age-interaction) = 0.02). Among all ages, MD had a stronger association with large (>2.1 cm) versus small tumors and positive versus negative lymph node status (P's ages breast cancer than ER-positive tumors compared to women ages 55-64 and ≥ 65 years (P(age-interaction) = 0.04). MD was positively associated with both HER2-negative and HER2-positive tumors within each age group. MD is strongly associated with all breast cancer subtypes, but particularly tumors of large size and positive lymph nodes across all ages, and ER-negative status among women ages <55 years, suggesting high MD may play an important role in tumor aggressiveness, especially in younger women.

  9. Collagen Matrix Density Drives the Metabolic Shift in Breast Cancer Cells

    Directory of Open Access Journals (Sweden)

    Brett A. Morris

    2016-11-01

    Full Text Available Increased breast density attributed to collagen I deposition is associated with a 4–6 fold increased risk of developing breast cancer. Here, we assessed cellular metabolic reprogramming of mammary carcinoma cells in response to increased collagen matrix density using an in vitro 3D model. Our initial observations demonstrated changes in functional metabolism in both normal mammary epithelial cells and mammary carcinoma cells in response to changes in matrix density. Further, mammary carcinoma cells grown in high density collagen matrices displayed decreased oxygen consumption and glucose metabolism via the tricarboxylic acid (TCA cycle compared to cells cultured in low density matrices. Despite decreased glucose entry into the TCA cycle, levels of glucose uptake, cell viability, and ROS were not different between high and low density matrices. Interestingly, under high density conditions the contribution of glutamine as a fuel source to drive the TCA cycle was significantly enhanced. These alterations in functional metabolism mirrored significant changes in the expression of metabolic genes involved in glycolysis, oxidative phosphorylation, and the serine synthesis pathway. This study highlights the broad importance of the collagen microenvironment to cellular expression profiles, and shows that changes in density of the collagen microenvironment can modulate metabolic shifts of cancer cells.

  10. Learning multisensory integration and coordinate transformation via density estimation.

    Directory of Open Access Journals (Sweden)

    Joseph G Makin

    2013-04-01

    Full Text Available Sensory processing in the brain includes three key operations: multisensory integration-the task of combining cues into a single estimate of a common underlying stimulus; coordinate transformations-the change of reference frame for a stimulus (e.g., retinotopic to body-centered effected through knowledge about an intervening variable (e.g., gaze position; and the incorporation of prior information. Statistically optimal sensory processing requires that each of these operations maintains the correct posterior distribution over the stimulus. Elements of this optimality have been demonstrated in many behavioral contexts in humans and other animals, suggesting that the neural computations are indeed optimal. That the relationships between sensory modalities are complex and plastic further suggests that these computations are learned-but how? We provide a principled answer, by treating the acquisition of these mappings as a case of density estimation, a well-studied problem in machine learning and statistics, in which the distribution of observed data is modeled in terms of a set of fixed parameters and a set of latent variables. In our case, the observed data are unisensory-population activities, the fixed parameters are synaptic connections, and the latent variables are multisensory-population activities. In particular, we train a restricted Boltzmann machine with the biologically plausible contrastive-divergence rule to learn a range of neural computations not previously demonstrated under a single approach: optimal integration; encoding of priors; hierarchical integration of cues; learning when not to integrate; and coordinate transformation. The model makes testable predictions about the nature of multisensory representations.

  11. Basic physics and doubts about relationship between mammographically determined tissue density and breast cancer risk.

    Science.gov (United States)

    Kopans, Daniel B

    2008-02-01

    Numerous studies have suggested a link between breast tissue patterns, as defined with mammography, and risk for breast cancer. There may be a relationship, but the author believes all of these studies have methodological flaws. It is impossible, with the parameters used in these studies, to accurately measure the percentage of tissues by volume when two-dimensional x-ray mammographic images are used. Without exposure values, half-value layer information, and knowledge of the compressed thickness of the breast, an accurate volume of tissue cannot be calculated. The great variability in positioning the breast for a mammogram is also an uncontrollable factor in measuring tissue density. Computerized segmentation algorithms can accurately assess the percentage of the x-ray image that is "dense," but this does not accurately measure the true volume of tissue. Since the percentage of dense tissue is ultimately measured in relation to the complete volume of the breast, defining the true boundaries of the breast is also a problem. Studies that purport to show small percentage differences between groups are likely inaccurate. Future investigations need to use three-dimensional information. (c) RSNA, 2008.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2010-06-07

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

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

    Science.gov (United States)

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

    2010-06-01

    The purpose of this study was to evaluate the performance of an algorithm used to measure the volumetric breast density (VBD) from digital mammograms. The algorithm is based on the calibration of the detector signal versus the thickness and composition of breast-equivalent phantoms. The baseline error in the density from the algorithm was found to be 1.25 ± 2.3% VBD units (PVBD) when tested against a set of calibration phantoms, of thicknesses 3-8 cm, with compositions equivalent to fibroglandular content (breast density) between 0% and 100% and under x-ray beams between 26 kVp and 32 kVp with a Rh/Rh anode/filter. The algorithm was also tested against images from a dedicated breast computed tomography (CT) scanner acquired on 26 volunteers. The CT images were segmented into regions representing adipose, fibroglandular and skin tissues, and then deformed using a finite-element algorithm to simulate the effects of compression in mammography. The mean volume, VBD and thickness of the compressed breast for these deformed images were respectively 558 cm3, 23.6% and 62 mm. The displaced CT images were then used to generate simulated digital mammograms, considering the effects of the polychromatic x-ray spectrum, the primary and scattered energy transmitted through the breast, the anti-scatter grid and the detector efficiency. The simulated mammograms were analyzed with the VBD algorithm and compared with the deformed CT volumes. With the Rh/Rh anode filter, the root mean square difference between the VBD from CT and from the algorithm was 2.6 PVBD, and a linear regression between the two gave a slope of 0.992 with an intercept of -1.4 PVBD and a correlation with R2 = 0.963. The results with the Mo/Mo and Mo/Rh anode/filter were similar.

  14. Optical imaging correlates with magnetic resonance imaging breast density and reveals composition changes during neoadjuvant chemotherapy

    Science.gov (United States)

    2013-01-01

    Introduction In addition to being a risk factor for breast cancer, breast density has been hypothesized to be a surrogate biomarker for predicting response to endocrine-based chemotherapies. The purpose of this study was to evaluate whether a noninvasive bedside scanner based on diffuse optical spectroscopic imaging (DOSI) provides quantitative metrics to measure and track changes in breast tissue composition and density. To access a broad range of densities in a limited patient population, we performed optical measurements on the contralateral normal breast of patients before and during neoadjuvant chemotherapy (NAC). In this work, DOSI parameters, including tissue hemoglobin, water, and lipid concentrations, were obtained and correlated with magnetic resonance imaging (MRI)-measured fibroglandular tissue density. We evaluated how DOSI could be used to assess breast density while gaining new insight into the impact of chemotherapy on breast tissue. Methods This was a retrospective study of 28 volunteers undergoing NAC treatment for breast cancer. Both 3.0-T MRI and broadband DOSI (650 to 1,000 nm) were obtained from the contralateral normal breast before and during NAC. Longitudinal DOSI measurements were used to calculate breast tissue concentrations of oxygenated and deoxygenated hemoglobin, water, and lipid. These values were compared with MRI-measured fibroglandular density before and during therapy. Results Water (r = 0.843; P < 0.001), deoxyhemoglobin (r = 0.785; P = 0.003), and lipid (r = -0.707; P = 0.010) concentration measured with DOSI correlated strongly with MRI-measured density before therapy. Mean DOSI parameters differed significantly between pre- and postmenopausal subjects at baseline (water, P < 0.001; deoxyhemoglobin, P = 0.024; lipid, P = 0.006). During NAC treatment measured at about 90 days, significant reductions were observed in oxyhemoglobin for pre- (-20.0%; 95% confidence interval (CI), -32.7 to -7.4) and postmenopausal subjects (-20

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

    Science.gov (United States)

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

    2018-01-01

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

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

    Science.gov (United States)

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

    2017-12-06

    Breast density is one of the most significant factors that is associated with cancer risk. In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DM). The input "for processing" DM was first log-transformed, enhanced by a multi-resolution preprocessing scheme, and subsampled to a pixel size of 800µm×800µm from 100µm×100µm. A deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD) by using a domain adaptation resampling method. The PD was estimated as the ratio of the dense area to the breast area based on the PMD. The DCNN approach was compared to a feature-based statistical learning approach. Gray level, texture and morphological features were extracted and least absolute shrinkage and selection operator (LASSO) was used to combine the features into a feature-based PMD. With IRB approval, we retrospectively collected a training set of 478 DMs and an independent test set of 183 DMs from patient files in our institution. Two experienced MQSA radiologists interactively segmented PD as the reference standard. Ten-fold cross-validation was used for model selection and evaluation with the training set. With cross-validation, DCNN obtained a Dice's coefficient (DC) of 0.79±0.13 and Pearson's correlation (r) of 0.97 whereas feature-based learning obtained DC=0.72±0.18 and r=0.85. For the independent test set, DCNN achieved DC=0.76±0.09 and r=0.94 while feature-based learning achieved DC=0.62±0.21 and r=0.75. Our DCNN approach was significantly better and more robust than the feature-based learning approach for automated PD estimation on DMs, demonstrating its potential use for automated density reporting as well as for model-based risk prediction. © 2017 Institute of Physics and Engineering in Medicine.

  17. A novel ultrasound methodology for estimating spine mineral density.

    Science.gov (United States)

    Conversano, Francesco; Franchini, Roberto; Greco, Antonio; Soloperto, Giulia; Chiriacò, Fernanda; Casciaro, Ernesto; Aventaggiato, Matteo; Renna, Maria Daniela; Pisani, Paola; Di Paola, Marco; Grimaldi, Antonella; Quarta, Laura; Quarta, Eugenio; Muratore, Maurizio; Laugier, Pascal; Casciaro, Sergio

    2015-01-01

    We investigated the possible clinical feasibility and accuracy of an innovative ultrasound (US) method for diagnosis of osteoporosis of the spine. A total of 342 female patients (aged 51-60 y) underwent spinal dual X-ray absorptiometry and abdominal echographic scanning of the lumbar spine. Recruited patients were subdivided into a reference database used for US spectral model construction and a study population for repeatability and accuracy evaluation. US images and radiofrequency signals were analyzed via a new fully automatic algorithm that performed a series of spectral and statistical analyses, providing a novel diagnostic parameter called the osteoporosis score (O.S.). If dual X-ray absorptiometry is assumed to be the gold standard reference, the accuracy of O.S.-based diagnoses was 91.1%, with k = 0.859 (p < 0.0001). Significant correlations were also found between O.S.-estimated bone mineral densities and corresponding dual X-ray absorptiometry values, with r(2) values up to 0.73 and a root mean square error of 6.3%-9.3%. The results obtained suggest that the proposed method has the potential for future routine application in US-based diagnosis of osteoporosis. Copyright © 2015 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

  18. Relationship of mast cell density with lymphangiogenesis and prognostic parameters in breast carcinoma.

    Science.gov (United States)

    Keser, Sevinc H; Kandemir, Nilufer O; Ece, Dilek; Gecmen, Gonca G; Gul, Aylin E; Barisik, Nagehan O; Sensu, Sibel; Buyukuysal, Cagatay; Barut, Figen

    2017-04-01

    In many cancers, mast cell density (MCD) in the tumor microenvironment is associated with tumor progression and, to a greater extent, angiogenesis. Our study was designed to investigate the correlation between MCD, tumor lymphangiogenesis, and several well-established prognostic parameters in breast cancer. One hundred and four cases of invasive breast carcinoma diagnosed in our clinic between 2007 and 2011 were included. Mast cells and lymphatic vessels were stained with toluidine blue and D2-40, respectively, and their densities were calculated in various areas of tumors and lymph nodes. The variables of MCD and lymphatic vessel density (LVD) were compared using prognostic parameters as well as with each other. As tumor size and volume increased, MCD increased comparably in metastatic lymph nodes; intratumoral and peritumoral LVD also increased. Lymphovascular invasion, lymphatic invasion, perineural invasion, and estrogen receptor positivity were positively related to intratumoral MCD. The relationship between peritumoral MCD and nontumoral breast tissue MCD was statistically significant. Stage was correlated with MCD in metastatic lymph nodes. Metastatic lymph node MCD and intratumoral MCD were also significantly related. Stage, lymphatic invasion, perineural invasion, lymphovascular invasion, and metastatic lymph node MCD were all correlated with intratumoral and/or peritumoral LVD. As nuclear grade increased, intratumoral LVD became higher. In breast carcinoma, MCD, depending on its location, was related to several prognostic parameters. Notably, mast cells may have at least some effect on lymphangiogenesis, which appears to be a predictor of tumor progression. Copyright © 2017. Published by Elsevier Taiwan.

  19. Relationship of mast cell density with lymphangiogenesis and prognostic parameters in breast carcinoma

    Directory of Open Access Journals (Sweden)

    Sevinc H. Keser

    2017-04-01

    Full Text Available In many cancers, mast cell density (MCD in the tumor microenvironment is associated with tumor progression and, to a greater extent, angiogenesis. Our study was designed to investigate the correlation between MCD, tumor lymphangiogenesis, and several well-established prognostic parameters in breast cancer. One hundred and four cases of invasive breast carcinoma diagnosed in our clinic between 2007 and 2011 were included. Mast cells and lymphatic vessels were stained with toluidine blue and D2-40, respectively, and their densities were calculated in various areas of tumors and lymph nodes. The variables of MCD and lymphatic vessel density (LVD were compared using prognostic parameters as well as with each other. As tumor size and volume increased, MCD increased comparably in metastatic lymph nodes; intratumoral and peritumoral LVD also increased. Lymphovascular invasion, lymphatic invasion, perineural invasion, and estrogen receptor positivity were positively related to intratumoral MCD. The relationship between peritumoral MCD and nontumoral breast tissue MCD was statistically significant. Stage was correlated with MCD in metastatic lymph nodes. Metastatic lymph node MCD and intratumoral MCD were also significantly related. Stage, lymphatic invasion, perineural invasion, lymphovascular invasion, and metastatic lymph node MCD were all correlated with intratumoral and/or peritumoral LVD. As nuclear grade increased, intratumoral LVD became higher. In breast carcinoma, MCD, depending on its location, was related to several prognostic parameters. Notably, mast cells may have at least some effect on lymphangiogenesis, which appears to be a predictor of tumor progression.

  20. Estimation of mean glandular dose for breast tomosynthesis: factors for use with the UK, European and IAEA breast dosimetry protocols

    Energy Technology Data Exchange (ETDEWEB)

    Dance, D R; Young, K C [NCCPM, Medical Physics Department, Royal Surrey County Hospital, Guildford GU2 7XX (United Kingdom); Van Engen, R E, E-mail: daviddance@nhs.net [National Training and Expert Centre for Breast Cancer Screening (LRCB), Radboud University Nijmegen Medical Centre, PO Box 6873, 6532 SZ Nijmegen (Netherlands)

    2011-01-21

    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 T{sub S}) 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.

  1. Estimation of Breast Tumor Conductivity using Parabolic Phase Fitting

    OpenAIRE

    Katscher, U.; Djamshidi, K.; Voigt, T; Ivancevic, M.; Abe, H.; Newstead, G.; Keupp, J.

    2012-01-01

    According to ex vivo studies, breast tumors exhibit a significantlyaltered electric conductivity. This feature opens the chance to increase the specificity of breast tumor characterization with MRI. Theelectric conductivity can be measured in vivo using “Electric Properties Tomography (EPT). EPT has shown its potential in phantom, volunteer, and initial clinical studies. However, the complex frayed structure of fat and ductile tissue in the breast hampers the straight-forward application of E...

  2. Effects of age, breast density and volume on breast cancer diagnosis: a retrospective comparison of sensitivity of mammography and ultrasonography in China's rural areas.

    Science.gov (United States)

    Wang, Feng-Liang; Chen, Fei; Yin, Hong; Xu, Nan; Wu, Xiao-Xiang; Ma, Jing-Jing; Gao, Shen; Tang, Jin-Hai; Lu, Cheng

    2013-01-01

    Mammography has been confirmed as the only effective mode to improve the prognosis of patients with breast cancer in Western developed countries, but might not be a good choice in other areas of the world. One of the major challenges in China is to determine an optimal imaging modality for breast cancer screening. This study was designed to clarify the sensitivity of ultrasonography compared with that of mammography in rural China. We retrospectively studied the sensitivity of mammography and ultrasonography based on 306 breast cancer patients detected by the program of "screening for cervical cancer and breast cancer" performed in Chinese rural areas between January 2009 and December 2011, and analyzed the effects of age, breast density and volume on the sensitivity. Stratified analysis showed that the sensitivity of breast ultrasonography was significantly higher than that of mammography in premenopausal patients (81.4% vs. 61.1%, p=0.02), in women ≤ 55 years of age (82.2% vs. 63.4%, pbreast density group (American College of Radiology [ACR] levels 3-4) (85.9% vs. 60.6%, pbreast volume group (≤ 400 ml) (87.1% vs. 66.7%, pAge had a significant effect on sensitivity of mammography (breast density and volume-adjusted odds ratio, 6.39; 95% confidence interval, 2.8-14.4 in age group > 55 compared to age group ≤ 45), but not that of ultrasonography. Neither breast density nor volume had significant effect on sensitivity of mammography or ultrasonography. Ultrasonography is more sensitive than mammography in detecting breast cancer in women under 55 year-old Chinese, especially in those with high-density and relatively small breasts.

  3. Comparing methods to estimate Reineke’s maximum size-density relationship species boundary line slope

    Science.gov (United States)

    Curtis L. VanderSchaaf; Harold E. Burkhart

    2010-01-01

    Maximum size-density relationships (MSDR) provide natural resource managers useful information about the relationship between tree density and average tree size. Obtaining a valid estimate of how maximum tree density changes as average tree size changes is necessary to accurately describe these relationships. This paper examines three methods to estimate the slope of...

  4. Maximum likelihood estimation for predicting the probability of obtaining variable shortleaf pine regeneration densities

    Science.gov (United States)

    Thomas B. Lynch; Jean Nkouka; Michael M. Huebschmann; James M. Guldin

    2003-01-01

    A logistic equation is the basis for a model that predicts the probability of obtaining regeneration at specified densities. The density of regeneration (trees/ha) for which an estimate of probability is desired can be specified by means of independent variables in the model. When estimating parameters, the dependent variable is set to 1 if the regeneration density (...

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

    NARCIS (Netherlands)

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

    2005-01-01

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

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

    NARCIS (Netherlands)

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

    2005-01-01

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

  7. Quantification of masking risk in screening mammography with volumetric breast density maps.

    Science.gov (United States)

    Holland, Katharina; van Gils, Carla H; Mann, Ritse M; Karssemeijer, Nico

    2017-04-01

    Fibroglandular tissue may mask breast cancers, thereby reducing the sensitivity of mammography. Here, we investigate methods for identification of women at high risk of a masked tumor, who could benefit from additional imaging. The last negative screening mammograms of 111 women with interval cancer (IC) within 12 months after the examination and 1110 selected normal screening exams from women without cancer were used. From the mammograms, volumetric breast density maps were computed, which provide the dense tissue thickness for each pixel location. With these maps, three measurements were derived: (1) percent dense volume (PDV), (2) percent area where dense tissue thickness exceeds 1 cm (PDA), and (3) dense tissue masking model (DTMM). Breast density was scored by a breast radiologist using BI-RADS. Women with heterogeneously and extremely dense breasts were considered at high masking risk. For each masking measure, mammograms were divided into a high- and low-risk category such that the same proportion of the controls is at high masking risk as with BI-RADS. Of the women with IC, 66.1, 71.9, 69.2, and 63.0% were categorized to be at high masking risk with PDV, PDA, DTMM, and BI-RADS, respectively, against 38.5% of the controls. The proportion of IC at high masking risk is statistically significantly different between BI-RADS and PDA (p-value 0.022). Differences between BI-RADS and PDV, or BI-RADS and DTMM, are not statistically significant. Measures based on density maps, and in particular PDA, are promising tools to identify women at high risk for a masked cancer.

  8. Association between computed tissue density asymmetry in bilateral mammograms and near-term breast cancer risk.

    Science.gov (United States)

    Zheng, Bin; Tan, Maxine; Ramalingam, Pandiyarajan; Gur, David

    2014-01-01

    This study investigated association between bilateral mammographic density asymmetry and near-term breast cancer risk. A data base of digital mammograms acquired from 690 women was retrospectively collected. All images were originally interpreted as negative by radiologists. During the next subsequent screening examinations (between 12 and 36 months later), 230 women were diagnosed positive for cancer, 230 were recalled for additional diagnostic workups and proved to be benign, and 230 remained negative (not recalled). We applied a computerized scheme to compute the differences of five image features between the left and right mammograms, and trained an artificial neural network (ANN) to compute a bilateral mammographic density asymmetry score. Odds ratios (ORs) were used to assess associations between the ANN-generated scores and risk of women having detectable cancers during the next screening examinations. A logistic regression method was applied to test for trend as a function of the increase in ANN-generated scores. The results were also compared with ORs computed using other existing cancer risk factors. The ORs showed an increasing risk trend with the increase in ANN-generated scores (from 1.00 to 9.07 between positive and negative case groups). The regression analysis also showed a significant increase trend in slope (p breast density, or family history of breast cancer. This study demonstrated that the computed bilateral mammographic density asymmetry had potential to be used as a new risk factor to improve discriminatory power in predicting near-term risk of women developing breast cancer. © 2014 Wiley Periodicals, Inc.

  9. Estimates of high absolute densities and emergence rates of demersal zooplankton from the Agatti Atoll, laccadives

    Digital Repository Service at National Institute of Oceanography (India)

    Madhupratap, M.; Achuthankutty, C.T.; Nair, S.R.S.

    Direct sampling of the sandy substratus of the Agatti Lagoon with a corer showed the presence of vary high densities of epibenthic forms. On average, densities were about 25 times higher than previously estimated with emergence traps. About 80...

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

    Science.gov (United States)

    Alshafeiy, Taghreed I; Wadih, Antoine; Nicholson, Brandi T; Rochman, Carrie M; Peppard, Heather R; Patrie, James T; Harvey, Jennifer A

    2017-07-01

    The purpose of this study was to compare assessments of breast density on synthetic 2D images as compared with digital 2D mammograms. This retrospective study included consecutive women undergoing screening with digital 2D mammography and tomosynthesis during May 2015 with a negative or benign outcome. In separate reading sessions, three radiologists with 5-25 years of clinical experience and 1 year of experience with synthetic 2D mammography read digital 2D and synthetic 2D images and assigned breast density categories according to the 5th edition of BI-RADS. Inter- and intrareader agreement was assessed for each BI-RADS density assessment and combined dense and nondense categories using percent agreement and Cohen kappa coefficient for consensus and all reads. A total of 309 patients met study inclusion criteria. Agreement between consensus BI-RADS density categories assigned for digital and synthetic 2D mammography was 80.3% (95% CI, 75.4-84.5%) with κ = 0.73 (95% CI, 0.66-0.79). For combined dense and nondense categories, agreement reached 91.9% (95% CI, 88.2-94.7%). For consensus readings, similar numbers of patients were shifted between nondense and dense categories (11 and 14, respectively) with the synthetic 2D compared with digital 2D mammography. Interreader differences were apparent; assignment to dense categories was greater with digital 2D mammography for reader 1 (odds ratio [OR], 1.26; p = 0.002), the same for reader 2 (OR, 0.91; p = 0.262), and greater with synthetic 2D mammography for reader 3 (OR, 0.86; p = 0.033). Overall, synthetic 2D mammography is comparable with digital 2D mammography in assessment of breast density, though there is some variability by reader. Practices can readily adopt synthetic 2D mammography without concern that it will affect density assessment and subsequent recommendations for supplemental screening.

  11. Density estimation for grouped data with application to line transect sampling

    OpenAIRE

    Jang, Woncheol; Loh, Ji Meng

    2010-01-01

    Line transect sampling is a method used to estimate wildlife populations, with the resulting data often grouped in intervals. Estimating the density from grouped data can be challenging. In this paper we propose a kernel density estimator of wildlife population density for such grouped data. Our method uses a combined cross-validation and smoothed bootstrap approach to select the optimal bandwidth for grouped data. Our simulation study shows that with the smoothing parameter selected with thi...

  12. Effect of Broadband Nature of Marine Mammal Echolocation Clicks on Click-Based Population Density Estimates

    Science.gov (United States)

    2015-09-30

    Clicks on Click-Based Population Density Estimates Michael Ainslie PO Box 96864 2509 JG The Hague, The Netherlands phone: +31 888 669099...simplifying assumptions in the modeling process. Our main purposes are to quantify potential biases in the population density estimate for selected species...the passive sonar equation can be used to obtain unbiased population density estimation, or for which more elaborate and time-consuming modeling of

  13. EuroMInd-D: A Density Estimate of Monthly Gross Domestic Product for the Euro Area

    DEFF Research Database (Denmark)

    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 ...... and evaluated with different tests based on the probability integral transform and by applying scoring rules....

  14. Estimating probability density functions and entropies of chua's circuit using b-spline functions

    OpenAIRE

    Savacı, Ferit Acar; Güngör, Mesut

    2012-01-01

    n this paper, first the probability density functions (PDFs) of the states of Chua's circuit have been estimated using B-spline functions and then the state entropies of Chua's circuit with respect to the bifurcation parameter have been obtained. The results of the proposed B-spline density estimator have been compared with the results obtained from the Parzen density estimator. © 2012 World Scientific Publishing Company.

  15. Estimation of Bouguer Density Precision: Development of Method for Analysis of La Soufriere Volcano Gravity Data

    Directory of Open Access Journals (Sweden)

    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.    

  16. Moving Point Density Estimation Algorithm Based on a Generated Bayesian Prior

    Directory of Open Access Journals (Sweden)

    Akinori Asahara

    2015-04-01

    Full Text Available To improve decision making, real-time population density must be known. However, calculating the point density of a huge dataset in real time is impractical in terms of processing time. Accordingly, a fast algorithm for estimating the distribution of the density of moving points is proposed. The algorithm, which is based on variational Bayesian estimation, takes a parametric approach to speed up the estimation process. Although the parametric approach has a drawback, that is the processes to be carried out on the server are very slow, the proposed algorithm overcomes the drawback by using the result of an estimation of an adjacent past density distribution.

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

    Science.gov (United States)

    Pataky, Reka; Ismail, Zahra; Coldman, Andrew J; Elwood, Mark; Gelmon, Karen; Hedden, Lindsay; Hislop, Greg; Kan, Lisa; McCoy, Bonnie; Olivotto, Ivo A; Peacock, Stuart

    2014-12-01

    The sensitivity of screening mammography is much lower among women who have dense breast tissue, compared with women who have largely fatty breasts, and they are also at much higher risk of developing the disease. Increasing mammography screening frequency from biennially to annually has been suggested as a policy option to address the elevated risk in this population. The purpose of this study was to assess the cost-effectiveness of annual versus biennial screening mammography among women aged 50-79 with dense breast tissue. A Markov model was constructed based on screening, diagnostic, and treatment pathways for the population-based screening and cancer care programme in British Columbia, Canada. Model probabilities and screening costs were calculated from screening programme data. Costs for breast cancer treatment were calculated from treatment data, and utility values were obtained from the literature. Incremental cost-effectiveness was expressed as cost per quality adjusted life year (QALY), and probabilistic sensitivity analysis was conducted. Compared with biennial screening, annual screening generated an additional 0.0014 QALYs (95% CI: -0.0480-0.0359) at a cost of $819 ($ = Canadian dollars) per patient (95% CI: 506-1185), resulting in an incremental cost effectiveness ratio of $565,912/QALY. Annual screening had a 37.5% probability of being cost-effective at a willingness-to-pay threshold of $100,000/QALY. There is considerable uncertainty about the incremental cost-effectiveness of annual mammography. Further research on the comparative effectiveness of screening strategies for women with high mammographic breast density is warranted, particularly as digital mammography and density measurement become more widespread, before cost-effectiveness can be reevaluated. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

  18. Nestin-positive microvessel density is an independent prognostic factor in breast cancer.

    Science.gov (United States)

    Nowak, Aleksandra; Grzegrzolka, Jedrzej; Paprocka, Maria; Piotrowska, Aleksandra; Rys, Janusz; Matkowski, Rafal; Dziegiel, Piotr

    2017-08-01

    The process of angiogenesis based on new vessel formation within the tumour area plays a significant role in the progression of breast cancer. Nestin is an intermediate filament protein and participates in the cytoskeleton organization. Nestin expression in the endothelium of blood vessels is mainly limited to newly forming vessels, thus being a more specific marker of angiogenesis than the commonly used vascular antigens. The aim of this study was to determine the prognostic value of nestin-positive microvessel density (Nes+MVD) in breast cancer patients and to confirm that nestin expression is related to newly forming tumour vessels. In this study, 137 cases of ductal breast carcinoma and 19 cases of non-malignant breast tissue lesions (NBTLs) were examined. Immunohistochemical reactions were performed on paraffin sections using antibodies against nestin, CD34 and CD31 antigens. For each marker, the microvessel density (MVD) was determined. Nestin expression was also examined in human endothelial cell lines (HUVEC-SVT, HMEC-1 and HEPC-CB.1) representing a different level of endothelial cell maturity. HUVEC-SVT and HMEC-1 cells represent the endothelium of mature vessels, whereas HEPC-CB.1 cells represent the early endothelial progenitor cells (EPCs). We have demonstrated that high Nes+MVD may be associated with a more aggressive course of the disease and a poorer prognosis. We have also found a higher Nes+MVD in the cases with lymph node metastases, with higher histological grade, with advanced-stage disease and with the triple-negative (TN) breast cancer. In addition, nestin expression in vessels was associated with a shorter overall survival (OS) and earlier relapse, and in the case of OS nestin was an independent prognostic factor. Finally, we further confirmed that nestin expression in endothelial cells reflects a progenitor nature of newly forming vessels.

  19. Brain Tissue Compartment Density Estimated Using Diffusion-Weighted MRI Yields Tissue Parameters Consistent With Histology

    OpenAIRE

    Sepehrband, Farshid; Clark, Kristi A.; Ullmann, Jeremy F. P.; Kurniawan, Nyoman D; Leanage, Gayeshika; Reutens, David C.; Yang, Zhengyi

    2015-01-01

    We examined whether quantitative density measures of cerebral tissue consistent with histology can be obtained from diffusion magnetic resonance imaging (MRI). By incorporating prior knowledge of myelin and cell membrane densities, absolute tissue density values were estimated from relative intra-cellular and intra-neurite density values obtained from diffusion MRI. The NODDI (neurite orientation distribution and density imaging) technique, which can be applied clinically, was used. Myelin de...

  20. Estimation of current density distribution under electrodes for external defibrillation

    Directory of Open Access Journals (Sweden)

    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.

  1. Menstrual Cycle–related Fluctuations in Breast Density Measured by Using Three-dimensional MR Imaging

    Science.gov (United States)

    Chan, Siwa; Su, Min-Ying L.; Lei, Fu-Ju; Wu, Jia-Pei; Lin, Muqing; Nalcioglu, Orhan; Feig, Stephen A.

    2011-01-01

    Purpose: To investigate the fluctuation of fibroglandular tissue volume (FV) and percentage of breast density (PD) during the menstrual cycle and compare with postmenopausal women by using three-dimensional magnetic resonance (MR)–based segmentation methods. Materials and Methods: This study was approved by the Institutional Review Board and was HIPAA compliant. Written informed consent was obtained. Thirty healthy female subjects, 24 premenopausal and six postmenopausal, were recruited. All subjects underwent MR imaging examination each week for 4 consecutive weeks. The breast volume (BV), FV, and PD were measured by two operators to evaluate interoperator variation. The fluctuation of each parameter measured over the course of the four examinations was evaluated on the basis of the coefficient of variation (CV). Results: The results from two operators showed a high Pearson correlation for BV (R2 = 0.99), FV (R2 = 0.98), and PD (R2 = 0.96). The interoperator variation was 3% for BV and around 5%–6% for FV and PD. In the respective premenopausal and postmenopausal groups, the mean CV was 5.0% and 5.6% for BV, 7.6% and 4.2% for FV, and 7.1% and 6.0% for PD. The difference between premenopausal and postmenopausal groups was not significant (all P values > .05). Conclusion: The fluctuation of breast density measured at MR imaging during a menstrual cycle was around 7%. The results may help the design and interpretation of future studies by using the change of breast density as a surrogate marker to evaluate the efficacy of hormone-modifying drugs for cancer treatment or cancer prevention. © RSNA, 2011 Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11110506/-/DC1 PMID:21878616

  2. EXACT MINIMAX ESTIMATION OF THE PREDICTIVE DENSITY IN SPARSE GAUSSIAN MODELS.

    Science.gov (United States)

    Mukherjee, Gourab; Johnstone, Iain M

    We consider estimating the predictive density under Kullback-Leibler loss in an ℓ0 sparse Gaussian sequence model. Explicit expressions of the first order minimax risk along with its exact constant, asymptotically least favorable priors and optimal predictive density estimates are derived. Compared to the sparse recovery results involving point estimation of the normal mean, new decision theoretic phenomena are seen. Suboptimal performance of the class of plug-in density estimates reflects the predictive nature of the problem and optimal strategies need diversification of the future risk. We find that minimax optimal strategies lie outside the Gaussian family but can be constructed with threshold predictive density estimates. Novel minimax techniques involving simultaneous calibration of the sparsity adjustment and the risk diversification mechanisms are used to design optimal predictive density estimates.

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

    Directory of Open Access Journals (Sweden)

    Rongda Chen

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

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

    Directory of Open Access Journals (Sweden)

    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

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

    Science.gov (United States)

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

    2017-06-21

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

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

    Science.gov (United States)

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

    2017-06-01

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

  7. Brain Tissue Compartment Density Estimated Using Diffusion-Weighted MRI Yields Tissue Parameters Consistent With Histology

    Science.gov (United States)

    Sepehrband, Farshid; Clark, Kristi A.; Ullmann, Jeremy F.P.; Kurniawan, Nyoman D.; Leanage, Gayeshika; Reutens, David C.; Yang, Zhengyi

    2015-01-01

    We examined whether quantitative density measures of cerebral tissue consistent with histology can be obtained from diffusion magnetic resonance imaging (MRI). By incorporating prior knowledge of myelin and cell membrane densities, absolute tissue density values were estimated from relative intra-cellular and intra-neurite density values obtained from diffusion MRI. The NODDI (neurite orientation distribution and density imaging) technique, which can be applied clinically, was used. Myelin density estimates were compared with the results of electron and light microscopy in ex vivo mouse brain and with published density estimates in a healthy human brain. In ex vivo mouse brain, estimated myelin densities in different sub-regions of the mouse corpus callosum were almost identical to values obtained from electron microscopy (Diffusion MRI: 42±6%, 36±4% and 43±5%; electron microscopy: 41±10%, 36±8% and 44±12% in genu, body and splenium, respectively). In the human brain, good agreement was observed between estimated fiber density measurements and previously reported values based on electron microscopy. Estimated density values were unaffected by crossing fibers. PMID:26096639

  8. Reliability and precision of pellet-group counts for estimating landscape-level deer density

    Science.gov (United States)

    David S. deCalesta

    2013-01-01

    This study provides hitherto unavailable methodology for reliably and precisely estimating deer density within forested landscapes, enabling quantitative rather than qualitative deer management. Reliability and precision of the deer pellet-group technique were evaluated in 1 small and 2 large forested landscapes. Density estimates, adjusted to reflect deer harvest and...

  9. Divisive Latent Class Modeling as a Density Estimation Method for Categorical Data

    NARCIS (Netherlands)

    van der Palm, D.W.; van der Ark, L.A.; Vermunt, J.K.

    Traditionally latent class (LC) analysis is used by applied researchers as a tool for identifying substantively meaningful clusters. More recently, LC models have also been used as a density estimation tool for categorical variables. We introduce a divisive LC (DLC) model as a density estimation

  10. Divisive latent class modeling as a density estimation method for categorical data

    NARCIS (Netherlands)

    van der Palm, D.W.; van der Ark, L.A.; Vermunt, J.K.

    2016-01-01

    Traditionally latent class (LC) analysis is used by applied researchers as a tool for identifying substantively meaningful clusters. More recently, LC models have also been used as a density estimation tool for categorical variables. We introduce a divisive LC (DLC) model as a density estimation

  11. Comparison of methods for estimating density of forest songbirds from point counts

    Science.gov (United States)

    Jennifer L. Reidy; Frank R. Thompson; J. Wesley. Bailey

    2011-01-01

    New analytical methods have been promoted for estimating the probability of detection and density of birds from count data but few studies have compared these methods using real data. We compared estimates of detection probability and density from distance and time-removal models and survey protocols based on 5- or 10-min counts and outer radii of 50 or 100 m. We...

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

    Science.gov (United States)

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

    2017-03-01

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

  13. Optimisation of in-situ dry density estimation

    Directory of Open Access Journals (Sweden)

    Morvan Mathilde

    2016-01-01

    Full Text Available Nowadays, field experiments are mostly used to determine the resistance and settlements of a soil before building. The needed devices were heavy so they cannot be used in every situation. It is the reason why Gourves et al (1998 developed a light dynamic penetrometer called Panda. For this penetrometer, a standardized hammer has to be blown on the head of the piston. For each blow, it measures the driving energy as well as the driving depth of the cone into the soil. The obtained penetrogram gives us the cone resistance variation with depth. For homogeneous soils, three parameters can determined: the critical depth zc, the initial cone resistance qd0 and the cone resistance in depth qd1. In parallel to the improvement of this apparatus, some researches were lead to obtain a relationship between the dry density of soil and the cone resistance in depth qd1. Knowing dry density of soil can allow to evaluate compaction efficiency for example. To achieve this point, a database of soils was initiated. Each of these soils was tested and classified using laboratory tests, among others, grain size distribution, proctor results, Atterberg limits. Penetrometer tests were also performed for three to five densities and for three to five water contents. Using this database, Chaigneau managed to obtain a logarithmic relation linking qd1 and dry density. But this relation varies with the water content. This article presents our recent researches on a mean to obtain a unified relation using water content, saturation degree or suction. To achieve this point, at first we studied the CNR silt responses with saturation degree and water content. Its water retention curve was realised using filter paper method so we can obtain suction. Then we verified the conclusion of this study to seven soils of the database to validate our hypotheses.

  14. Constructing valid density matrices on an NMR quantum information processor via maximum likelihood estimation

    Energy Technology Data Exchange (ETDEWEB)

    Singh, Harpreet; Arvind; Dorai, Kavita, E-mail: kavita@iisermohali.ac.in

    2016-09-07

    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. - Highlights: • State estimation using maximum likelihood method was performed on an NMR quantum information processor. • Physically valid density matrices were obtained every time in contrast to standard quantum state tomography. • Density matrices of several different entangled and separable states were reconstructed for two and three qubits.

  15. Age and ethnic differences in volumetric breast density in new zealand women: a cross-sectional study.

    Directory of Open Access Journals (Sweden)

    Lis Ellison-Loschmann

    Full Text Available Breast cancer incidence differs by ethnicity in New Zealand (NZ with Māori (the indigenous people women having the highest rates followed by Pakeha (people primarily of British/European descent, Pacific and Asian women, who experience the lowest rates. The reasons for these differences are unclear. Breast density, an important risk factor for breast cancer, has not previously been studied here. We used an automated system, Volpara™, to measure breast density volume from the medio-lateral oblique view of digital mammograms, by age (≤50 years and >50 years and ethnicity (Pakeha/Māori/Pacific/Asian using routine data from the national screening programme: age; x-ray system and mammography details for 3,091 Pakeha, 716 Māori, 170 Pacific and 662 Asian (total n = 4,239 women. Linear regression of the natural logarithm of absolute and percent density values was used, back-transformed and expressed as the ratio of the geometric means. Covariates were age, x-ray system and, for absolute density, the natural log of the volume of non-dense tissue (a proxy for body mass index. Median age for Pakeha women was 55 years; Māori 53 years; and Pacific and Asian women, 52 years. Compared to Pakeha women (reference, Māori had higher absolute volumetric density (1.09; 95% confidence interval [95% CI] 1.03-1.15 which remained following adjustment (1.06; 95% CI 1.01-1.12 and was stronger for older compared to younger Māori women. Asian women had the greatest risk of high percentage breast density (1.35; 95% CI 1.27-1.43 while Pacific women in both the ≤50 and >50 year age groups (0.78; 95% CI 0.66-0.92 and 0.81; 95% CI 0.71-0.93 respectively had the lowest percentage breast density compared to Pakeha. As well as expected age differences, we found differential patterns of breast density by ethnicity consistent with ethnic differences seen in breast cancer risk. Breast density may be a contributing factor to NZ's well-known, but poorly explained

  16. Computer-based automated estimation of breast vascularity and correlation with breast cancer in DCE-MRI images.

    Science.gov (United States)

    Kostopoulos, Spiros A; Vassiou, Katerina G; Lavdas, Eleftherios N; Cavouras, Dionisis A; Kalatzis, Ioannis K; Asvestas, Pantelis A; Arvanitis, Dimitrios L; Fezoulidis, Ioannis V; Glotsos, Dimitris T

    2017-01-01

    Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) with gadolinium constitutes one of the most promising protocols for boosting up the sensitivity in breast cancer detection. The aim of this study was twofold: first to design an image processing methodology to estimate the vascularity of the breast region in DCE-MRI images and second to investigate whether the differences in the composition/texture and vascularity of normal, benign and malignant breasts may serve as potential indicators regarding the presence of the disease. Clinical material comprised thirty nine cases examined on a 3.0-T MRI system (SIGNA HDx; GE Healthcare). Vessel segmentation was performed using a custom made modification of the Seeded Region Growing algorithm that was designed in order to identify pixels belonging to the breast vascular network. Two families of features were extracted: first, morphological and textural features from segmented images in order to quantify the extent and the properties of the vascular network; second, textural features from the whole breast region in order to investigate whether the nature of the disease causes statistically important changes in the texture of affected breasts. Results have indicated that: (a) the texture of vessels presents statistically significant differences (pbreast region for malignant and non-malignant breasts, produced statistically significant differences (pbreasts may be used for the discrimination of non-malignant from malignant patients, and (d) an area under the receiver operating characteristic curve of 0.908 (AUC) was found when features were combined in a logistic regression prediction rule according to ROC analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

  17. Does Urban Density Promote Social Interaction? Evidence from Instrumental Variable Estimation

    OpenAIRE

    Zackary B. Hawley

    2012-01-01

    To test the common assumption that increased local area population density implies high levels of social interaction, I use instrumental variable estimation to examine a causal link between social interaction and urban population density. I instrument for local population density using earthquake and landslide hazard rates in addition to the presence of hard rock beneath the soil. I find the social interaction type matters when determining the relationship with population density. Social inte...

  18. An approach to parameter estimation for breast tumor by finite element method

    Science.gov (United States)

    Xu, A.-qing; Yang, Hong-qin; Ye, Zhen; Su, Yi-ming; Xie, Shu-sen

    2009-02-01

    The temperature of human body on the surface of the skin depends on the metabolic activity, the blood flow, and the temperature of the surroundings. Any abnormality in the tissue, such as the presence of a tumor, alters the normal temperature on the skin surface due to increased metabolic activity of the tumor. Therefore, abnormal skin temperature profiles are an indication of diseases such as tumor or cancer. This study is to present an approach to detect the female breast tumor and its related parameter estimations by combination the finite element method with infrared thermography for the surface temperature profile. A 2D simplified breast embedded a tumor model based on the female breast anatomical structure and physiological characteristics was first established, and then finite element method was used to analyze the heat diffuse equation for the surface temperature profiles of the breast. The genetic optimization algorithm was used to estimate the tumor parameters such as depth, size and blood perfusion by minimizing a fitness function involving the temperature profiles simulated data by finite element method to the experimental data obtained by infrared thermography. This preliminary study shows it is possible to determine the depth and the heat generation rate of the breast tumor by using infrared thermography and the optimization analysis, which may play an important role in the female breast healthcare and diseases evaluation or early detection. In order to develop the proposed methodology to be used in clinical, more accurate anatomy 3D breast geometry should be considered in further investigations.

  19. METAPHOR: Probability density estimation for machine learning based photometric redshifts

    Science.gov (United States)

    Amaro, V.; Cavuoti, S.; Brescia, M.; Vellucci, C.; Tortora, C.; Longo, G.

    2017-06-01

    We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z's and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF's derived from a traditional SED template fitting method (Le Phare).

  20. Risk estimation for healthy women from breast cancer families : New insights and new strategies

    NARCIS (Netherlands)

    van Asperen, CJ; Jonker, MA; Jacobi, CE; van Diemen-Homan, JEM; Bakker, E; Breuning, MH; van Houwelingen, JC; de Bock, GH

    Risk estimation in breast cancer families is often estimated by use of the Claus tables. We analyzed the family histories of 196 counselees; compared the Claus tables with the Claus, the BRCA1/2, the BRCA1/2/ models; and performed linear regression analysis to extend the Claus tables with

  1. Effects of social organization, trap arrangement and density, sampling scale, and population density on bias in population size estimation using some common mark-recapture estimators.

    Science.gov (United States)

    Gupta, Manan; Joshi, Amitabh; Vidya, T N C

    2017-01-01

    Mark-recapture estimators are commonly used for population size estimation, and typically yield unbiased estimates for most solitary species with low to moderate home range sizes. However, these methods assume independence of captures among individuals, an assumption that is clearly violated in social species that show fission-fusion dynamics, such as the Asian elephant. In the specific case of Asian elephants, doubts have been raised about the accuracy of population size estimates. More importantly, the potential problem for the use of mark-recapture methods posed by social organization in general has not been systematically addressed. We developed an individual-based simulation framework to systematically examine the potential effects of type of social organization, as well as other factors such as trap density and arrangement, spatial scale of sampling, and population density, on bias in population sizes estimated by POPAN, Robust Design, and Robust Design with detection heterogeneity. In the present study, we ran simulations with biological, demographic and ecological parameters relevant to Asian elephant populations, but the simulation framework is easily extended to address questions relevant to other social species. We collected capture history data from the simulations, and used those data to test for bias in population size estimation. Social organization significantly affected bias in most analyses, but the effect sizes were variable, depending on other factors. Social organization tended to introduce large bias when trap arrangement was uniform and sampling effort was low. POPAN clearly outperformed the two Robust Design models we tested, yielding close to zero bias if traps were arranged at random in the study area, and when population density and trap density were not too low. Social organization did not have a major effect on bias for these parameter combinations at which POPAN gave more or less unbiased population size estimates. Therefore, the

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-04-15

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

  3. Dual Approach To Superquantile Estimation And Applications To Density Fitting

    Science.gov (United States)

    2016-06-01

    vs. Exponentially Bounded Tails 0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 x Exponential Pareto (a) Density Functions 3 4 5 6 7 8 0.000 0.005...quantile (left) and DSE (right) functions for the exponential distribution (blue solid) and Pareto (dotted red). Notice that the quantile function ...QX (p), superquantile function Q̄X (p), and DSE function E∗X (p) for the Pareto case. We again reference Figure 1.3 and Figure 1.5 of Chapter 1 for

  4. Breast volume estimation from systematic series of CT scans using the Cavalieri principle and 3D reconstruction.

    Science.gov (United States)

    Erić, Mirela; Anderla, Andraš; Stefanović, Darko; Drapšin, Miodrag

    2014-01-01

    Preoperative breast volume estimation is very important for the success of the breast surgery. In the present study, two different breast volume determination methods, Cavalieri principle and 3D reconstruction were compared. Consecutive sections were taken in slice thickness of 5 mm. Every 2nd breast section in a set of consecutive sections was selected. We marked breast tissue with blue line on each selected section, and so prepared CT scans used for breast volume estimation. The volumes of the 60 breasts were estimated using the Cavalieri principle and 3D reconstruction. The mean breast volume value was established to be 467.79 ± 188.90 cm(3) with Cavalieri method and 465.91 ± 191.41 cm(3) with 3D reconstruction. The mean CE for the estimates in this study was calculated as 0.25%. Skin-sparing volume was about 91.64% of the whole breast volume. Both methods are very accurate and have a strong linear association. Our results suggest that the calculation of breast volume or its part in vivo from systematic series of CT scans using the Cavalieri principle or 3D breast reconstruction is accurate enough to have a significant clinical benefit in planning reconstructive breast surgery. These methods can help the surgeon guide the choice of the most appropriate implant or/and flap preoperatively. Copyright © 2014 Surgical Associates Ltd. Published by Elsevier Ltd. All rights reserved.

  5. Numerical estimate of infinite invariant densities: application to Pesin-type identity

    Science.gov (United States)

    Korabel, Nickolay; Barkai, Eli

    2013-08-01

    Weakly chaotic maps with unstable fixed points are investigated in the regime where the invariant density is non-normalizable. We propose that the infinite invariant density \\overline{\\rho }(x) of these maps can be estimated using \\overline{\\rho }(x)={\\lim }_{t\\rightarrow \\infty }{t}^{1-\\alpha }\\rho (x,t), in agreement with earlier work of Thaler. Here ρ(x,t) is the normalized density of particles. This definition uniquely determines the infinite density and is a valuable tool for numerical estimations. We use this density to estimate the sub-exponential separation λα of nearby trajectories. For a particular map introduced by Thaler we use an analytical expression for the infinite invariant density to calculate λα exactly, which perfectly matches simulations without fitting. Misunderstanding which recently appeared in the literature is removed.

  6. Gamma Kernel Estimators for Density and Hazard Rate of Right-Censored Data

    Directory of Open Access Journals (Sweden)

    T. Bouezmarni

    2011-01-01

    Full Text Available The nonparametric estimation for the density and hazard rate functions for right-censored data using the kernel smoothing techniques is considered. The “classical” fixed symmetric kernel type estimator of these functions performs well in the interior region, but it suffers from the problem of bias in the boundary region. Here, we propose new estimators based on the gamma kernels for the density and the hazard rate functions. The estimators are free of bias and achieve the optimal rate of convergence in terms of integrated mean squared error. The mean integrated squared error, the asymptotic normality, and the law of iterated logarithm are studied. A comparison of gamma estimators with the local linear estimator for the density function and with hazard rate estimator proposed by Müller and Wang (1994, which are free from boundary bias, is investigated by simulations.

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

    KAUST Repository

    Qahtan, Abdulhakim Ali Ali

    2012-01-01

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

  8. Use of spatial capture–recapture to estimate density of Andean bears in northern Ecuador

    Science.gov (United States)

    Molina, Santiago; Fuller, Angela K.; Morin, Dana J.; Royle, J. Andrew

    2017-01-01

    The Andean bear (Tremarctos ornatus) is the only extant species of bear in South America and is considered threatened across its range and endangered in Ecuador. Habitat loss and fragmentation is considered a critical threat to the species, and there is a lack of knowledge regarding its distribution and abundance. The species is thought to occur at low densities, making field studies designed to estimate abundance or density challenging. We conducted a pilot camera-trap study to estimate Andean bear density in a recently identified population of Andean bears northwest of Quito, Ecuador, during 2012. We compared 12 candidate spatial capture–recapture models including covariates on encounter probability and density and estimated a density of 7.45 bears/100 km2 within the region. In addition, we estimated that approximately 40 bears used a recently named Andean bear corridor established by the Secretary of Environment, and we produced a density map for this area. Use of a rub-post with vanilla scent attractant allowed us to capture numerous photographs for each event, improving our ability to identify individual bears by unique facial markings. This study provides the first empirically derived density estimate for Andean bears in Ecuador and should provide direction for future landscape-scale studies interested in conservation initiatives requiring spatially explicit estimates of density.

  9. Fasting insulin and endogenous hormones in relation to premenopausal breast density (Canada).

    Science.gov (United States)

    Borugian, Marilyn J; Spinelli, John J; Gordon, Paula B; Abanto, Zenaida; Brooks-Wilson, Angela; Pollak, Michael N; Warren, Linda J; Hislop, T Gregory; Gallagher, Richard P

    2014-03-01

    Mammographic breast density (BD) is associated with increased risk of breast cancer. This study asks which circulating metabolic and reproductive biomarkers are associated with BD, particularly dense breast area, in premenopausal women not taking exogenous hormones. In a cross-sectional study, 299 premenopausal women aged 40-49 completed questionnaires, provided a fasting blood sample, had height, weight, percentage body fat, waist and hip measurements taken, and attended a screening mammogram. Multivariate linear regression was used to calculate adjusted means for percentage BD, absolute dense and non-dense area, across categories of covariates, adjusted for day of menstrual cycle, age, parity, body mass index, percentage body fat, and ethnicity. Fasting insulin levels were inversely associated, and insulin-like growth factor-binding protein 1 levels directly associated with percentage BD, but lost statistical significance after multivariate adjustment. Sex hormone-binding globulin levels were directly associated with percentage BD, still significant after multivariate adjustment (p = 0.03). A significant inverse dose-response association was observed between progesterone levels and dense area (p cancer risk remains unclear.

  10. Utility of relative and absolute measures of mammographic density vs clinical risk factors in evaluating breast cancer risk at time of screening mammography.

    Science.gov (United States)

    Abdolell, Mohamed; Tsuruda, Kaitlyn M; Lightfoot, Christopher B; Payne, Jennifer I; Caines, Judy S; Iles, Sian E

    2016-01-01

    Various clinical risk factors, including high breast density, have been shown to be associated with breast cancer. The utility of using relative and absolute area-based breast density-related measures was evaluated as an alternative to clinical risk factors in cancer risk assessment at the time of screening mammography. Contralateral mediolateral oblique digital mammography images from 392 females with unilateral breast cancer and 817 age-matched controls were analysed. Information on clinical risk factors was obtained from the provincial breast-imaging information system. Breast density-related measures were assessed using a fully automated breast density measurement software. Multivariable logistic regression was conducted, and area under the receiver-operating characteristic (AUROC) curve was used to evaluate the performance of three cancer risk models: the first using only clinical risk factors, the second using only density-related measures and the third using both clinical risk factors and density-related measures. The risk factor-based model generated an AUROC of 0.535, while the model including only breast density-related measures generated a significantly higher AUROC of 0.622 (p risk factor model (p cancer compared with clinical risk factors. Breast cancer risk models based on density-related measures alone can outperform risk models based on clinical factors. Such models may support the development of personalized breast-screening protocols.

  11. An Efficient Acoustic Density Estimation Method with Human Detectors Applied to Gibbons in Cambodia.

    Directory of Open Access Journals (Sweden)

    Darren Kidney

    Full Text Available Some animal species are hard to see but easy to hear. Standard visual methods for estimating population density for such species are often ineffective or inefficient, but methods based on passive acoustics show more promise. We develop spatially explicit capture-recapture (SECR methods for territorial vocalising species, in which humans act as an acoustic detector array. We use SECR and estimated bearing data from a single-occasion acoustic survey of a gibbon population in northeastern Cambodia to estimate the density of calling groups. The properties of the estimator are assessed using a simulation study, in which a variety of survey designs are also investigated. We then present a new form of the SECR likelihood for multi-occasion data which accounts for the stochastic availability of animals. In the context of gibbon surveys this allows model-based estimation of the proportion of groups that produce territorial vocalisations on a given day, thereby enabling the density of groups, instead of the density of calling groups, to be estimated. We illustrate the performance of this new estimator by simulation. We show that it is possible to estimate density reliably from human acoustic detections of visually cryptic species using SECR methods. For gibbon surveys we also show that incorporating observers' estimates of bearings to detected groups substantially improves estimator performance. Using the new form of the SECR likelihood we demonstrate that estimates of availability, in addition to population density and detection function parameters, can be obtained from multi-occasion data, and that the detection function parameters are not confounded with the availability parameter. This acoustic SECR method provides a means of obtaining reliable density estimates for territorial vocalising species. It is also efficient in terms of data requirements since since it only requires routine survey data. We anticipate that the low-tech field requirements will

  12. A generalized model for estimating the energy density of invertebrates

    Science.gov (United States)

    James, Daniel A.; Csargo, Isak J.; Von Eschen, Aaron; Thul, Megan D.; Baker, James M.; Hayer, Cari-Ann; Howell, Jessica; Krause, Jacob; Letvin, Alex; Chipps, Steven R.

    2012-01-01

    Invertebrate energy density (ED) values are traditionally measured using bomb calorimetry. However, many researchers rely on a few published literature sources to obtain ED values because of time and sampling constraints on measuring ED with bomb calorimetry. Literature values often do not account for spatial or temporal variability associated with invertebrate ED. Thus, these values can be unreliable for use in models and other ecological applications. We evaluated the generality of the relationship between invertebrate ED and proportion of dry-to-wet mass (pDM). We then developed and tested a regression model to predict ED from pDM based on a taxonomically, spatially, and temporally diverse sample of invertebrates representing 28 orders in aquatic (freshwater, estuarine, and marine) and terrestrial (temperate and arid) habitats from 4 continents and 2 oceans. Samples included invertebrates collected in all seasons over the last 19 y. Evaluation of these data revealed a significant relationship between ED and pDM (r2  =  0.96, p joint confidence region included the intercept of 0 (−103.0 ± 707.9) and slope of 1 (1.01 ± 0.12). Use of this model requires that only dry and wet mass measurements be obtained, resulting in significant time, sample size, and cost savings compared to traditional bomb calorimetry approaches. This model should prove useful for a wide range of ecological studies because it is unaffected by taxonomic, seasonal, or spatial variability.

  13. ESTIMATING NUMBER DENSITY NV – A COMPARISON OF AN IMPROVED SALTYKOV ESTIMATOR AND THE DISECTOR METHOD

    Directory of Open Access Journals (Sweden)

    Ashot Davtian

    2011-05-01

    Full Text Available Two methods for the estimation of number per unit volume NV of spherical particles are discussed: the (physical disector (Sterio, 1984 and Saltykov's estimator (Saltykov, 1950; Fullman, 1953. A modification of Saltykov's estimator is proposed which reduces the variance. Formulae for bias and variance are given for both disector and improved Saltykov estimator for the case of randomly positioned particles. They enable the comparison of the two estimators with respect to their precision in terms of mean squared error.

  14. Novel Application of Density Estimation Techniques in Muon Ionization Cooling Experiment

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-10-12

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

  15. Correlation of mammographic density and serum calcium levels in patients with primary breast cancer.

    Science.gov (United States)

    Hack, Carolin C; Stoll, Martin J; Jud, Sebastian M; Heusinger, Katharina; Adler, Werner; Haeberle, Lothar; Ganslandt, Thomas; Heindl, Felix; Schulz-Wendtland, Rüdiger; Cavallaro, Alexander; Uder, Michael; Beckmann, Matthias W; Fasching, Peter A; Bayer, Christian M

    2017-06-01

    Percentage mammographic breast density (PMD) is one of the most important risk factors for breast cancer (BC). Calcium, vitamin D, bisphosphonates, and denosumab have been considered and partly confirmed as factors potentially influencing the risk of BC. This retrospective observational study investigated the association between serum calcium level and PMD. A total of 982 BC patients identified in the research database at the University Breast Center for Franconia with unilateral BC, calcium and albumin values, and mammogram at the time of first diagnosis were included. PMD was assessed, using a semiautomated method by two readers. Linear regression analyses were conducted to investigate the impact on PMD of the parameters of serum calcium level adjusted for albumin level, and well-known clinical predictors such as age, body mass index (BMI), menopausal status and confounder for serum calcium like season in which the BC was diagnosed. Increased calcium levels were associated with reduced PMD (P = 0.024). Furthermore, PMD was inversely associated with BMI (P < 0.001) and age (P < 0.001). There was also an association between PMD and menopausal status (P < 0.001). The goodness-of-fit of the regression model was moderate. This is the first study assessing the association between serum calcium level and PMD. An inverse association with adjusted serum calcium levels was observed. These findings add to previously published data relating to vitamin D, bisphosphonates, denosumab, and the RANK/RANKL signaling pathway in breast cancer risk and prevention. © 2017 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

  16. Glycemic index, glycemic load and mammographic breast density: the EPIC Florence longitudinal study.

    Science.gov (United States)

    Masala, Giovanna; Assedi, Melania; Bendinelli, Benedetta; Ermini, Ilaria; Occhini, Daniela; Sieri, Sabina; Brighenti, Furio; Del Turco, Marco Rosselli; Ambrogetti, Daniela; Palli, Domenico

    2013-01-01

    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 investigations.

  17. Glycemic index, glycemic load and mammographic breast density: the EPIC Florence longitudinal study.

    Directory of Open Access Journals (Sweden)

    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

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

    Science.gov (United States)

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

    2017-09-01

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

  19. Estimating Rangeland Vegetation Frequency & Density Using Low-altitude Aerial Photographs

    Directory of Open Access Journals (Sweden)

    M. Royan

    2016-12-01

    Full Text Available Remote sensing and aerial photography are means of exploring, studying and estimating vegetation variables such as species frequency and density in forests and rangelands. Common remote sensing images usually offer general information about vegetation parameters. For detailed information about vegetation (e.g. estimation of vegetation density and/or frequency, larger scale images are needed. The present research was conducted to estimate the density of rangeland vegetation in Inche Boroon area, north of Gorgan city. Using aerial photographs acquired from digital camera mounted on a tittered balloon in different flight altitudes, density and frequency of the main shrub species of the studied region, Halocnemum strobilaceum, were estimated on photographs at different scales (from 1:50 – to 1:1000 and were compared with field measurements. Results showed no significant difference between the field and image estimation of density below 1:600 (heights lower than 75 m but at lower scales the difference was significant. No significant difference between field and image estimation of shrub frequency was also observed up to the scale of 1:1000. Due to the wider field of view of photographs at smaller scales, flight heights of 75 m and 130 m are thus suggested as the appropriate heights to estimate Halocnemum strobilaceum density and frequency respectively in the study area.

  20. Breast cancer size estimation with MRI in BRCA mutation carriers and other high risk patients

    Energy Technology Data Exchange (ETDEWEB)

    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.

  1. Boundary condition generating large strain on breast tumor for nonlinear elasticity estimation.

    Science.gov (United States)

    Tsukune, Mariko; Hatano, Maya; Kobayashi, Yo; Miyashita, Tomoyuki; Fujie, M G

    2013-01-01

    We describe a robotic palpation system that determines whether a breast tumor is benign or malignant by measuring its nonlinear elasticity. Two indenters compress the breast from different directions to generate sufficient strain on the inner tumor, which simply represents clinical dynamic testing. The nonlinear elasticity of the inner tumor is estimated by correcting the reaction force data of the surrounding soft tissue. Here, we present the basic concept of our study and simulation results considering geometric conditions of the indenters using a finite element breast model. Indenters with variable width are applied to the breast at several contact positions in a simulation for comparison. Our results indicate that when the spring stiffness between the contact position of one indenter and the center of the tumor equals the spring stiffness between the contact position of the other indenter and the center of the tumor, a larger contact area (i.e., larger spring stiffness) provides larger strain acting on the inner tumor.

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

    Science.gov (United States)

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

    2014-10-24

    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 (Pbreast cancer susceptibility loci, and four additional regions were found to be associated with breast cancer (Pbreast cancer and illustrate the power of studying intermediate quantitative phenotypes to identify putative disease-susceptibility loci.

  3. EnviroAtlas - Des Moines, IA - Estimated Intersection Density of Walkable Roads

    Data.gov (United States)

    U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...

  4. EnviroAtlas - Pittsburgh, PA - Estimated Intersection Density of Walkable Roads

    Data.gov (United States)

    U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...

  5. EnviroAtlas - Durham, NC - Estimated Intersection Density of Walkable Roads

    Data.gov (United States)

    U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...

  6. EnviroAtlas - Cleveland, OH - Estimated Intersection Density of Walkable Roads

    Data.gov (United States)

    U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...

  7. EnviroAtlas - Portland, OR - Estimated Intersection Density of Walkable Roads

    Data.gov (United States)

    U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...

  8. EnviroAtlas - Green Bay, WI - Estimated Intersection Density of Walkable Roads

    Data.gov (United States)

    U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...

  9. EnviroAtlas - Austin, TX - Estimated Intersection Density of Walkable Roads

    Data.gov (United States)

    U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...

  10. EnviroAtlas - Tampa, FL - Estimated Intersection Density of Walkable Roads

    Data.gov (United States)

    U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...

  11. EnviroAtlas Estimated Intersection Density of Walkable Roads Web Service

    Data.gov (United States)

    U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in each EnviroAtlas community....

  12. EnviroAtlas - Fresno, CA - Estimated Intersection Density of Walkable Roads

    Data.gov (United States)

    U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...

  13. EnviroAtlas - New York, NY - Estimated Intersection Density of Walkable Roads

    Data.gov (United States)

    U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...

  14. EnviroAtlas - Memphis, TN - Estimated Intersection Density of Walkable Roads

    Data.gov (United States)

    U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...

  15. EnviroAtlas - Milwaukee, WI - Estimated Intersection Density of Walkable Roads

    Data.gov (United States)

    U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...

  16. EnviroAtlas - New Bedford, MA - Estimated Intersection Density of Walkable Roads

    Data.gov (United States)

    U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...

  17. EnviroAtlas - Paterson, NJ - Estimated Intersection Density of Walkable Roads

    Data.gov (United States)

    U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...

  18. EnviroAtlas - Portland, ME - Estimated Intersection Density of Walkable Roads

    Data.gov (United States)

    U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...

  19. EnviroAtlas - Woodbine, IA - Estimated Intersection Density of Walkable Roads

    Data.gov (United States)

    U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...

  20. EnviroAtlas - Phoenix, AZ - Estimated Intersection Density of Walkable Roads

    Data.gov (United States)

    U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...

  1. EnviroAtlas - Minneapolis/St. Paul, MN - Estimated Intersection Density of Walkable Roads

    Data.gov (United States)

    U.S. Environmental Protection Agency — This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections...

  2. A Simple Ground-Based Trap For Estimating Densities of Arboreal Leaf Insects

    Science.gov (United States)

    Robert A. Haack; Richard W. Blank

    1991-01-01

    Describes a trap design to use in collecting larval frass or head capsules for estimating densities of aboveground arthropods. The trap is light, compact, durable, and easily constructed from common inexpensive items.

  3. Importance of tree basic density in biomass estimation and associated uncertainties

    DEFF Research Database (Denmark)

    Njana, Marco Andrew; Meilby, Henrik; Eid, Tron

    2016-01-01

    of sustainable forest management, conservation and enhancement of carbon stocks (REDD+) initiatives offer an opportunity for sustainable management of forests including mangroves. In carbon accounting for REDD+, it is required that carbon estimates prepared for monitoring reporting and verification schemes......Key message Aboveground and belowground tree basic densities varied between and within the three mangrove species. If appropriately determined and applied, basic density may be useful in estimation of tree biomass. Predictive accuracy of the common (i.e. multi-species) models including aboveground....../belowground basic density was better than for common models developed without either basic density. However, species-specific models developed without basic density performed better than common models including basic density. Context Reducing Emissions from Deforestation and forest degradation and the role...

  4. Dose estimation for different skin models in interstitial breast brachytherapy

    Science.gov (United States)

    Kabacińska, Renata; Makarewicz, Roman

    2014-01-01

    Purpose Skin is a major organ at risk in breast-conserving therapy (BCT). The American Brachytherapy Society (ABS) recommendations require monitoring of maximum dose received, however, there is no unambiguous way of skin contouring provided. The purpose of this study was to compare the doses received by the skin in different models. Material and methods Standard treatment plans of 20 patients who underwent interstitial breast brachytherapy were analyzed. Every patient had a new treatment plan prepared according to Paris system and had skin contoured in three different ways. The first model, Skin 2 mm, corresponds to the dermatological breast skin thickness and is reaching 2 mm into an external patient contour. It was rejected in a further analysis, because of distinct discontinuities in contouring. The second model, Skin 4 mm, replaced Skin 2 mm, and is reaching 2 mm inside and 2 mm outside of the External contour. The third model, Skin EXT, is created on the External contour and it expands 4 mm outside. Doses received by the most exposed 0.1 cc, 1 cc, 2 cc, and the maximum doses for Skin 4 mm and Skin EXT were compared. Results Mean, median, maximum, and standard deviation of percentage dose difference between Skin EXT and Skin 4 mm for the most exposed 0.1 cc (D0.1cc) of skin were 18.01%, 17.20%, 27.84%, and 4.01%, respectively. All differences were statistically significant (p skin is necessary to avoid complications and obtain a satisfactory cosmetic effect. It is difficult to assess the compatibility of treatment plans with recommendations, while there is no unambiguous way of skin contouring. Especially, if a mean difference of doses between two models of skin contouring is 18% for the most exposed 0.1 cc and can reach almost 28% in some cases. Differences of this magnitude can result in skin complications during BCT. PMID:25097562

  5. Stock Index Returns' Density Prediction using GARCH Models: Frequentist or Bayesian Estimation?

    NARCIS (Netherlands)

    L.F. Hoogerheide (Lennart); D. David (David); N. Corre (Nienke)

    2011-01-01

    textabstractUsing well-known GARCH models for density prediction of daily S&P 500 and Nikkei 225 index returns, a comparison is provided between frequentist and Bayesian estimation. No significant difference is found between the qualities of the forecasts of the whole density, whereas the Bayesian

  6. Pooling local expert opinions for estimating mammal densities in tropical rainforests.

    NARCIS (Netherlands)

    Hoeven, van der C.A.; Boer, de W.F.; Prins, H.H.T.

    2004-01-01

    Methods currently used for assessing wildlife density in rainforests are time and money consuming. The precision of the most commonly used methods is disputed, but accepted because more exact methods are not available. In this study a new method of wildlife density estimation is explained. The new

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

    KAUST Repository

    Qahtan, Abdulhakim Ali Ali

    2016-11-08

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

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

    Science.gov (United States)

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

    2013-04-01

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

  9. [Female genital and breast cancers in France: geographic distribution and estimation of incidence].

    Science.gov (United States)

    Chaplain, G; Grosclaude, P; Arveux, P; Raverdy, N; Menegoz, F; Henry-Amar, M; Schaffer, P; Daures, J P; de Vathaire, F

    1997-10-01

    The aim of the study was to assess the incident number of female breast and genital tract cancers for the whole of France. The study focused on the 1983-1987 period and on 9.1% of the French population. The incident number of female breast and genital tract cancers was estimated for each site and for each of eight French administrative regions covered by a cancer registry qualified through the National Committee of Registries (Calvados, Côte-d'Or, Doubs, Hérault, Isère, Bas-Rhin, Somme, Tarn). Information on mortality rates was available at a regional level as well as at a nationwide level. The method estimated the national incidence rate modelizing the regional age-specific incidence rate as a function of corresponding mortality rate. Breast cancer was the leading site with 25,277 new cases per year while female genital tract cancers affected about 13,856 women. The cancer risk, estimated in using cumulative rate 0-74 years, was assessed at 7.1% for breast and at 1.2%, 1.4% and 1.1% for cervix uteri, corpus uteri and ovary respectively. Breast and genital tract cancers constituted 49% of the whole of cancers in women. Observed breast incidence rate was stable through French regions. There were pronounced contrasts in cervix uteri cancer risk, and some french regions displayed a high risk close to the observed European maxima. Interregional contrasts in risk of the other genital tract cancers were less striking. This study emphasized the importance of female breast and genital tract cancers for public health in France. The main aim of the French Cancer Registries Network is to provide a comprehensive description of cancer risk in France and to produce pertinent projection to 2005 horizon, combining the present data and the already accumulated 1988-1992 data.

  10. Comparison of Dixon Sequences for Estimation of Percent Breast Fibroglandular Tissue.

    Directory of Open Access Journals (Sweden)

    Araminta E W Ledger

    Full Text Available To evaluate sources of error in the Magnetic Resonance Imaging (MRI measurement of percent fibroglandular tissue (%FGT using two-point Dixon sequences for fat-water separation.Ten female volunteers (median age: 31 yrs, range: 23-50 yrs gave informed consent following Research Ethics Committee approval. Each volunteer was scanned twice following repositioning to enable an estimation of measurement repeatability from high-resolution gradient-echo (GRE proton-density (PD-weighted Dixon sequences. Differences in measures of %FGT attributable to resolution, T1 weighting and sequence type were assessed by comparison of this Dixon sequence with low-resolution GRE PD-weighted Dixon data, and against gradient-echo (GRE or spin-echo (SE based T1-weighted Dixon datasets, respectively.%FGT measurement from high-resolution PD-weighted Dixon sequences had a coefficient of repeatability of ±4.3%. There was no significant difference in %FGT between high-resolution and low-resolution PD-weighted data. Values of %FGT from GRE and SE T1-weighted data were strongly correlated with that derived from PD-weighted data (r = 0.995 and 0.96, respectively. However, both sequences exhibited higher mean %FGT by 2.9% (p < 0.0001 and 12.6% (p < 0.0001, respectively, in comparison with PD-weighted data; the increase in %FGT from the SE T1-weighted sequence was significantly larger at lower breast densities.Although measurement of %FGT at low resolution is feasible, T1 weighting and sequence type impact on the accuracy of Dixon-based %FGT measurements; Dixon MRI protocols for %FGT measurement should be carefully considered, particularly for longitudinal or multi-centre studies.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.)

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

    Science.gov (United States)

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

    2015-01-01

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

  13. A population-based tissue probability map-driven level set method for fully automated mammographic density estimations.

    Science.gov (United States)

    Kim, Youngwoo; Hong, Byung Woo; Kim, Seung Ja; Kim, Jong Hyo

    2014-07-01

    ' visual systems and has potential to be used as an automated and quantitative tool for estimations of mammographic breast density levels.

  14. Estimating bulk density of compacted grains in storage bins and modifications of Janssen's load equations as affected by bulk density.

    Science.gov (United States)

    Haque, Ekramul

    2013-03-01

    Janssen created a classical theory based on calculus to estimate static vertical and horizontal pressures within beds of bulk corn. Even today, his equations are widely used to calculate static loadings imposed by granular materials stored in bins. Many standards such as American Concrete Institute (ACI) 313, American Society of Agricultural and Biological Engineers EP 433, German DIN 1055, Canadian Farm Building Code (CFBC), European Code (ENV 1991-4), and Australian Code AS 3774 incorporated Janssen's equations as the standards for static load calculations on bins. One of the main drawbacks of Janssen's equations is the assumption that the bulk density of the stored product remains constant throughout the entire bin. While for all practical purposes, this is true for small bins; in modern commercial-size bins, bulk density of grains substantially increases due to compressive and hoop stresses. Over pressure factors are applied to Janssen loadings to satisfy practical situations such as dynamic loads due to bin filling and emptying, but there are limited theoretical methods available that include the effects of increased bulk density on the loadings of grain transmitted to the storage structures. This article develops a mathematical equation relating the specific weight as a function of location and other variables of materials and storage. It was found that the bulk density of stored granular materials increased with the depth according to a mathematical equation relating the two variables, and applying this bulk-density function, Janssen's equations for vertical and horizontal pressures were modified as presented in this article. The validity of this specific weight function was tested by using the principles of mathematics. As expected, calculations of loads based on the modified equations were consistently higher than the Janssen loadings based on noncompacted bulk densities for all grain depths and types accounting for the effects of increased bulk densities

  15. An asymptotically unbiased minimum density power divergence estimator for the Pareto-tail index

    DEFF Research Database (Denmark)

    Dierckx, Goedele; Goegebeur, Yuri; Guillou, Armelle

    2013-01-01

    We introduce a robust and asymptotically unbiased estimator for the tail index of Pareto-type distributions. The estimator is obtained by fitting the extended Pareto distribution to the relative excesses over a high threshold with the minimum density power divergence criterion. Consistency...

  16. Improved prediction of hardwood tree biomass derived from wood density estimates and form factors for whole trees

    Science.gov (United States)

    David W. MacFarlane; Neil R. Ver Planck

    2012-01-01

    Data from hardwood trees in Michigan were analyzed to investigate how differences in whole-tree form and wood density between trees of different stem diameter relate to residual error in standard-type biomass equations. The results suggested that whole-tree wood density, measured at breast height, explained a significant proportion of residual error in standard-type...

  17. The effect of preoperative serum triglycerides and high-density lipoprotein-cholesterol levels on the prognosis of breast cancer.

    Science.gov (United States)

    Li, Xing; Tang, Hailin; Wang, Jin; Xie, Xinhua; Liu, Peng; Kong, Yanan; Ye, Feng; Shuang, Zeyu; Xie, Zeming; Xie, Xiaoming

    2017-04-01

    Although dyslipidemia has been documented to be associated with several types of cancer including breast cancer, it remains uncertainty the prognostic value of serum lipid in breast cancer. The purpose of this study is to evaluate the association between the preoperative plasma lipid profile and the prognostic of breast cancer patients. The levels of preoperative serum lipid profile (including cholesterol [CHO], Triglycerides [TG], high-density lipoprotein-cholesterol [HDL-C], low-density lipoprotein-cholesterol [LDL-C], apolipoprotein A-I [ApoAI], and apolipoprotein B [ApoB]) and the clinical data were retrospectively collected and reviewed in 1044 breast cancer patients undergoing operation. Kaplan-Meier method and the Cox proportional hazards regression model were used in analyzing the overall survival [OS] and disease-free survival [DFS]. Combining the receiver-operating characteristic and Kaplan-Meier analysis, we found that preoperative lower TG and HDL-C level were risk factors of breast cancer patients. In multivariate analyses, a decreased HDL-C level showed significant association with worse OS (HR: 0.528; 95% CI: 0.302-0.923; P = 0.025), whereas a decreased TG level showed significant association with worse DFS (HR: 0.569; 95% CI: 0.370-0.873; P = 0.010). Preoperative serum levels of TG and HDL-C may be independent factor to predict outcome in breast cancer patient. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Chalkley estimates of angiogenesis in early breast cancer--relevance to prognosis

    DEFF Research Database (Denmark)

    Offersen, Birgitte V; Sørensen, Flemming Brandt; Yilmaz, Mette

    2002-01-01

    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...

  19. Mammographic density-a review on the current understanding of its association with breast cancer.

    Science.gov (United States)

    Huo, C W; Chew, G L; Britt, K L; Ingman, W V; Henderson, M A; Hopper, J L; Thompson, E W

    2014-04-01

    There has been considerable recent interest in the genetic, biological and epidemiological basis of mammographic density (MD), and the search for causative links between MD and breast cancer (BC) risk. This report will critically review the current literature on MD and summarize the current evidence for its association with BC. Keywords 'mammographic dens*', 'dense mammary tissue' or 'percent dens*' were used to search the existing literature in English on PubMed and Medline. All reports were critically analyzed. The data were assigned to one of the following aspects of MD: general association with BC, its relationship with the breast hormonal milieu, the cellular basis of MD, the generic variations of MD, and its significance in the clinical setting. MD adjusted for age, and BMI is associated with increased risk of BC diagnosis, advanced tumour stage at diagnosis and increased risk of both local recurrence and second primary cancers. The MD measures that predict BC risk have high heritability, and to date several genetic markers associated with BC risk have been found to also be associated with these MD risk predictors. Change in MD could be a predictor of the extent of chemoprevention with tamoxifen. Although the biological and genetic pathways that determine and perhaps modulate MD remain largely unresolved, significant inroads are being made into the understanding of MD, which may lead to benefits in clinical screening, assessment and treatment strategies. This review provides a timely update on the current understanding of MD's association with BC risk.

  20. Collagen density and alignment in responsive and resistant trastuzumab-treated breast cancer xenografts

    Science.gov (United States)

    Walsh, Alex J.; Cook, Rebecca S.; Lee, Jae H.; Arteaga, Carlos L.; Skala, Melissa C.

    2015-02-01

    Tumor collagen characteristics influence tumor malignancy, invasion, and metastasis. This study investigates the effects of trastuzumab (Tz) on the collagen of Tz-responsive (BT474) and Tz-resistant (HR6) breast cancer xenografts. Collagen content was assessed by in vivo second harmonic generation (SHG) imaging and histological trichrome staining of tumor sections. Collagen SHG imaging of control BT474 and HR6 tumors demonstrated increased collagen density after 14 days of treatment (p0.05), consistent with the physically distinctive nature of these measurements. There was also no correlation between tumor size and collagen endpoints (p>0.05). These results identify changes within the collagen compartment of the tumor microenvironment following Tz treatment, which are independent from the tumor cell response to Tz, and demonstrate that intravital collagen SHG imaging is capable of measuring dynamic changes in tumor microenvironment following treatment that complements trichrome staining.

  1. High-density SNP arrays improve detection of HER2 amplification and polyploidy in breast tumors

    DEFF Research Database (Denmark)

    Hansen, Thomas V. O.; Vikesaa, Jonas; Buhl, Sine S

    2015-01-01

    BACKGROUND: Human epidermal growth factor receptor-2 (HER2) overexpression and gene amplification are currently established by immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH), respectively. This study investigates whether high-density single nucleotide polymorphism (SNP......) arrays can provide additional diagnostic power to assess HER2 gene status. METHODS: DNA from 65 breast tumor samples previously diagnosed by HER2 IHC and FISH analysis were blinded and examined for HER2 copy number variation employing SNP array analysis. RESULTS: SNP array analysis identified 24 (37......%) samples with selective amplification or imbalance of the HER2 region in the q-arm of chromosome 17. In contrast, only 15 (23%) tumors were found to have HER2 amplification by IHC and FISH analysis. In total, there was a discrepancy in 19 (29%) samples between SNP array and IHC/FISH analysis. In 12...

  2. Local Linear Density Estimation for Filtered Survival Data with Bias Correction

    DEFF Research Database (Denmark)

    Tanggaard, Carsten; Nielsen, Jens Perch; Jones, M.C.

    A class of local linear kernel density estimators based on weighted least squares kernel estimation is considered within the framework of Aalen's multiplicative intensity model. This model includes the filtered data model that, in turn, allows for truncation and/or censoring in addition to accomm...... within our framework. The multiplicative bias correction method proves to be best in a simulation study comparing the performance of the considered estimators. An example concerning old age mortality demonstrates the importance of the improvements provided....

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

    OpenAIRE

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

    2016-01-01

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

  4. Improvement of displacement estimation of breast tissue in ultrasound elastography using the monogenic signal.

    Science.gov (United States)

    Slimi, Taher; Moussa, Ines Marzouk; Kraiem, Tarek; Mahjoubi, Halima

    2017-01-17

    In breast ultrasound elastography, tissues displacements estimation is obtained through a technique that follows the evolution of tissues under stress. However, during the acquisition of B-mode images, tissue displacements are often contaminated with multiplicative noise caused by changes in the speckle pattern in the tissue. Thus, the application of monogenic signal technique on the B-mode image in order to estimate displacement tissue, result in a presence of amplified noise in the deformation tissue image, which severely obscures the useful information. In this paper, we propose a new method based on the monogenic features, that is to improve the old monogenic signal (OMS) technique by improving the filtering step, so that the use of an effective denoising technique is enough to ensure a good estimation of displacement tissue. Our proposed method is based on the use of a robust filtering technique combined with the monogenic model. Two models of phantom elasticity are used in our test validation sold by CIRS company. In-vivo testing was also performed on the sets of clinical B-mode images to 20 patients including malignant breast tumors. Shrinkage wavelets has been used to eliminate the noise according to the threshold, then a guided filter is introduced to completely filter the image, the monogenic model is used after excerpting the image feature and estimating analytically the displacement tissue. Accurate and excellent displacement estimation for breast tissue was observed in proposed method results. By adapting our proposed approach to breast B-mode images, we have shown that it demonstrated a higher performance for displacement estimation; it gives better values in term of standard deviation, higher contrast to noise ratio, greater peak signal-to-noise ratio, excellent structural similarity and much faster speed than OMS and B-spline techniques. The results of the proposed model are encouraging, allowing quick and reliable estimations. Although the proposed

  5. Estimation of heavy metal concentration in FBR reprocessing solvent streams by density measurement

    Energy Technology Data Exchange (ETDEWEB)

    Brown, M.L.; Savage, D.J.

    1986-04-15

    The application of density measurement to heavy metal monitoring in the solvent phase is described, including practical experience gained during three fast reactor fuel reprocessing campaigns. An experimental algorithm relating heavy metal concentration and sample density was generated from laboratory-measured density data, for uranyl nitrate dissolved in nitric acid loaded tri-butyl phosphate in odorless kerosene. Differences in odorless kerosene batch densities are mathematically interpolated, and the algorithm can be used to estimate heavy metal concentrations from the density to within +1.5 g/l. An Anton Paar calculating digital densimeter with remote cell operation was used for all density measurements, but the algorithm will give similar accuracy with any density measuring device capable of a precision of better than 0.0005 g/cm/sup 3/. For plant control purposes, the algorithm was simplified using a density referencing system, whereby the density of solvent not yet loaded with heavy metal is subtracted from the sample density. This simplified algorithm compares very favorably with empirical algorithms, derived from numerical analysis of density data and chemically measured uranium and plutonium data obtained during fuel reprocessing campaigns, particularly when differences in the acidity of the solvent are considered before and after loading with heavy metal. This simplified algorithm has been successfully used for plant control of heavy metal loaded solvent during four fast reactor fuel reprocessing campaigns.

  6. Cetacean population density estimation from single fixed sensors using passive acoustics.

    Science.gov (United States)

    Küsel, Elizabeth T; Mellinger, David K; Thomas, Len; Marques, Tiago A; Moretti, David; Ward, Jessica

    2011-06-01

    Passive acoustic methods are increasingly being used to estimate animal population density. Most density estimation methods are based on estimates of the probability of detecting calls as functions of distance. Typically these are obtained using receivers capable of localizing calls or from studies of tagged animals. However, both approaches are expensive to implement. The approach described here uses a MonteCarlo model to estimate the probability of detecting calls from single sensors. The passive sonar equation is used to predict signal-to-noise ratios (SNRs) of received clicks, which are then combined with a detector characterization that predicts probability of detection as a function of SNR. Input distributions for source level, beam pattern, and whale depth are obtained from the literature. Acoustic propagation modeling is used to estimate transmission loss. Other inputs for density estimation are call rate, obtained from the literature, and false positive rate, obtained from manual analysis of a data sample. The method is applied to estimate density of Blainville's beaked whales over a 6-day period around a single hydrophone located in the Tongue of the Ocean, Bahamas. Results are consistent with those from previous analyses, which use additional tag data. © 2011 Acoustical Society of America

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

    Science.gov (United States)

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

    2016-01-01

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

  8. A comparison of plotless density estimators using Monte Carlo simulation on totally enumerated field data sets

    Directory of Open Access Journals (Sweden)

    Sugihara Robert T

    2008-04-01

    Full Text Available Abstract Background Plotless density estimators are those that are based on distance measures rather than counts per unit area (quadrats or plots to estimate the density of some usually stationary event, e.g. burrow openings, damage to plant stems, etc. These estimators typically use distance measures between events and from random points to events to derive an estimate of density. The error and bias of these estimators for the various spatial patterns found in nature have been examined using simulated populations only. In this study we investigated eight plotless density estimators to determine which were robust across a wide range of data sets from fully mapped field sites. They covered a wide range of situations including animal damage to rice and corn, nest locations, active rodent burrows and distribution of plants. Monte Carlo simulations were applied to sample the data sets, and in all cases the error of the estimate (measured as relative root mean square error was reduced with increasing sample size. The method of calculation and ease of use in the field were also used to judge the usefulness of the estimator. Estimators were evaluated in their original published forms, although the variable area transect (VAT and ordered distance methods have been the subjects of optimization studies. Results An estimator that was a compound of three basic distance estimators was found to be robust across all spatial patterns for sample sizes of 25 or greater. The same field methodology can be used either with the basic distance formula or the formula used with the Kendall-Moran estimator in which case a reduction in error may be gained for sample sizes less than 25, however, there is no improvement for larger sample sizes. The variable area transect (VAT method performed moderately well, is easy to use in the field, and its calculations easy to undertake. Conclusion Plotless density estimators can provide an estimate of density in situations where it

  9. Estimating population density and connectivity of American mink using spatial capture-recapture

    Science.gov (United States)

    Fuller, Angela K.; Sutherland, Christopher S.; Royle, Andy; Hare, Matthew P.

    2016-01-01

    Estimating the abundance or density of populations is fundamental to the conservation and management of species, and as landscapes become more fragmented, maintaining landscape connectivity has become one of the most important challenges for biodiversity conservation. Yet these two issues have never been formally integrated together in a model that simultaneously models abundance while accounting for connectivity of a landscape. We demonstrate an application of using capture–recapture to develop a model of animal density using a least-cost path model for individual encounter probability that accounts for non-Euclidean connectivity in a highly structured network. We utilized scat detection dogs (Canis lupus familiaris) as a means of collecting non-invasive genetic samples of American mink (Neovison vison) individuals and used spatial capture–recapture models (SCR) to gain inferences about mink population density and connectivity. Density of mink was not constant across the landscape, but rather increased with increasing distance from city, town, or village centers, and mink activity was associated with water. The SCR model allowed us to estimate the density and spatial distribution of individuals across a 388 km2 area. The model was used to investigate patterns of space usage and to evaluate covariate effects on encounter probabilities, including differences between sexes. This study provides an application of capture–recapture models based on ecological distance, allowing us to directly estimate landscape connectivity. This approach should be widely applicable to provide simultaneous direct estimates of density, space usage, and landscape connectivity for many species.

  10. Camera traps and activity signs to estimate wild boar density and derive abundance indices.

    Science.gov (United States)

    Massei, G; Coats, J; Lambert, M; Pietravalle, S; Gill, R; Cowan, D

    2017-10-12

    Populations of wild boar and feral pigs are increasing worldwide, in parallel with their significant environmental and economic impact. Reliable methods of monitoring trends and estimating abundance are needed to measure the effects of interventions on population size. The main aims of this study, carried out in five English woodlands were 1. to compare wild boar abundance indexed obtained from camera trap surveys and from activity signs and 2. to assess the precision of density estimates in relation to different densities of camera traps. For each woodland, we calculated a Passive Activity Index (PAI) based on camera trap surveys, rooting activity and wild boar trails on transects and estimated absolute densities based on camera trap surveys. PAIs obtained from different methods showed similar patterns. We found significant between-year differences in abundance of wild boar using PAIs based on camera trap surveys and on trails on transects but not on signs of rooting on transects. The density of wild boar from camera trap surveys varied between 0.7 and 7 animals/km2 . Increasing the density of camera traps above 9/km2 did not increase the precision of the estimate of wild boar density. PAIs based on number of wild boar trails and on camera trap data appear to be more sensitive to changes in population size than PAIs based on signs of rooting. For wild boar densities similar to those recorded in this study, 9 camera traps /km2 are sufficient to estimate the mean density of wild boar. This article is protected by copyright. All rights reserved.

  11. Spatial capture-recapture models for jointly estimating population density and landscape connectivity

    Science.gov (United States)

    Royle, J. Andrew; Chandler, Richard B.; Gazenski, Kimberly D.; Graves, Tabitha A.

    2013-01-01

    Population size and landscape connectivity are key determinants of population viability, yet no methods exist for simultaneously estimating density and connectivity parameters. Recently developed spatial capture–recapture (SCR) models provide a framework for estimating density of animal populations but thus far have not been used to study connectivity. Rather, all applications of SCR models have used encounter probability models based on the Euclidean distance between traps and animal activity centers, which implies that home ranges are stationary, symmetric, and unaffected by landscape structure. In this paper we devise encounter probability models based on “ecological distance,” i.e., the least-cost path between traps and activity centers, which is a function of both Euclidean distance and animal movement behavior in resistant landscapes. We integrate least-cost path models into a likelihood-based estimation scheme for spatial capture–recapture models in order to estimate population density and parameters of the least-cost encounter probability model. Therefore, it is possible to make explicit inferences about animal density, distribution, and landscape connectivity as it relates to animal movement from standard capture–recapture data. Furthermore, a simulation study demonstrated that ignoring landscape connectivity can result in negatively biased density estimators under the naive SCR model.

  12. Spatial capture--recapture models for jointly estimating population density and landscape connectivity.

    Science.gov (United States)

    Royle, J Andrew; Chandler, Richard B; Gazenski, Kimberly D; Graves, Tabitha A

    2013-02-01

    Population size and landscape connectivity are key determinants of population viability, yet no methods exist for simultaneously estimating density and connectivity parameters. Recently developed spatial capture--recapture (SCR) models provide a framework for estimating density of animal populations but thus far have not been used to study connectivity. Rather, all applications of SCR models have used encounter probability models based on the Euclidean distance between traps and animal activity centers, which implies that home ranges are stationary, symmetric, and unaffected by landscape structure. In this paper we devise encounter probability models based on "ecological distance," i.e., the least-cost path between traps and activity centers, which is a function of both Euclidean distance and animal movement behavior in resistant landscapes. We integrate least-cost path models into a likelihood-based estimation scheme for spatial capture-recapture models in order to estimate population density and parameters of the least-cost encounter probability model. Therefore, it is possible to make explicit inferences about animal density, distribution, and landscape connectivity as it relates to animal movement from standard capture-recapture data. Furthermore, a simulation study demonstrated that ignoring landscape connectivity can result in negatively biased density estimators under the naive SCR model.

  13. Estimation of tiger densities in India using photographic captures and recaptures

    Science.gov (United States)

    Karanth, U.; Nichols, J.D.

    1998-01-01

    Previously applied methods for estimating tiger (Panthera tigris) abundance using total counts based on tracks have proved unreliable. In this paper we use a field method proposed by Karanth (1995), combining camera-trap photography to identify individual tigers based on stripe patterns, with capture-recapture estimators. We developed a sampling design for camera-trapping and used the approach to estimate tiger population size and density in four representative tiger habitats in different parts of India. The field method worked well and provided data suitable for analysis using closed capture-recapture models. The results suggest the potential for applying this methodology for estimating abundances, survival rates and other population parameters in tigers and other low density, secretive animal species with distinctive coat patterns or other external markings. Estimated probabilities of photo-capturing tigers present in the study sites ranged from 0.75 - 1.00. The estimated mean tiger densities ranged from 4.1 (SE hat= 1.31) to 11.7 (SE hat= 1.93) tigers/100 km2. The results support the previous suggestions of Karanth and Sunquist (1995) that densities of tigers and other large felids may be primarily determined by prey community structure at a given site.

  14. Estimating detection and density of the Andean cat in the high Andes

    Science.gov (United States)

    Reppucci, Juan; Gardner, Beth; Lucherini, Mauro

    2011-01-01

    The Andean cat (Leopardus jacobita) is one of the most endangered, yet least known, felids. Although the Andean cat is considered at risk of extinction, rigorous quantitative population studies are lacking. Because physical observations of the Andean cat are difficult to make in the wild, we used a camera-trapping array to photo-capture individuals. The survey was conducted in northwestern Argentina at an elevation of approximately 4,200 m during October–December 2006 and April–June 2007. In each year we deployed 22 pairs of camera traps, which were strategically placed. To estimate detection probability and density we applied models for spatial capture–recapture using a Bayesian framework. Estimated densities were 0.07 and 0.12 individual/km2 for 2006 and 2007, respectively. Mean baseline detection probability was estimated at 0.07. By comparison, densities of the Pampas cat (Leopardus colocolo), another poorly known felid that shares its habitat with the Andean cat, were estimated at 0.74–0.79 individual/km2 in the same study area for 2006 and 2007, and its detection probability was estimated at 0.02. Despite having greater detectability, the Andean cat is rarer in the study region than the Pampas cat. Properly accounting for the detection probability is important in making reliable estimates of density, a key parameter in conservation and management decisions for any species.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-01-15

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

  16. Estimation of T2 relaxation time of breast cancer: Correlation with clinical, imaging and pathological features

    Energy Technology Data Exchange (ETDEWEB)

    Seo, Mirinae; Sohn, Yu Mee [Dept. of Radiology, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, Seoul (Korea, Republic of); Ryu, Jung Kyu; Jahng, Geon Ho; Rhee, Sun Jung; Oh, Jang Hoon; Won, Kyu Yeoun [Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul (Korea, Republic of)

    2017-01-15

    The purpose of this study was to estimate the T2* relaxation time in breast cancer, and to evaluate the association between the T2* value with clinical-imaging-pathological features of breast cancer. Between January 2011 and July 2013, 107 consecutive women with 107 breast cancers underwent multi-echo T2*-weighted imaging on a 3T clinical magnetic resonance imaging system. The Student's t test and one-way analysis of variance were used to compare the T2* values of cancer for different groups, based on the clinical-imaging-pathological features. In addition, multiple linear regression analysis was performed to find independent predictive factors associated with the T2* values. Of the 107 breast cancers, 92 were invasive and 15 were ductal carcinoma in situ (DCIS). The mean T2* value of invasive cancers was significantly longer than that of DCIS (p = 0.029). Signal intensity on T2-weighted imaging (T2WI) and histologic grade of invasive breast cancers showed significant correlation with T2* relaxation time in univariate and multivariate analysis. Breast cancer groups with higher signal intensity on T2WI showed longer T2* relaxation time (p = 0.005). Cancer groups with higher histologic grade showed longer T2* relaxation time (p = 0.017). The T2* value is significantly longer in invasive cancer than in DCIS. In invasive cancers, T2* relaxation time is significantly longer in higher histologic grades and high signal intensity on T2WI. Based on these preliminary data, quantitative T2* mapping has the potential to be useful in the characterization of breast cancer.

  17. Bulk density estimation using a 3-dimensional image acquisition and analysis system

    Directory of Open Access Journals (Sweden)

    Heyduk Adam

    2016-01-01

    Full Text Available The paper presents a concept of dynamic bulk density estimation of a particulate matter stream using a 3-d image analysis system and a conveyor belt scale. A method of image acquisition should be adjusted to the type of scale. The paper presents some laboratory results of static bulk density measurements using the MS Kinect time-of-flight camera and OpenCV/Matlab software. Measurements were made for several different size classes.

  18. Estimating peer density effects on oral health for community-based older adults.

    Science.gov (United States)

    Chakraborty, Bibhas; Widener, Michael J; Mirzaei Salehabadi, Sedigheh; Northridge, Mary E; Kum, Susan S; Jin, Zhu; Kunzel, Carol; Palmer, Harvey D; Metcalf, Sara S

    2017-12-29

    As part of a long-standing line of research regarding how peer density affects health, researchers have sought to understand the multifaceted ways that the density of contemporaries living and interacting in proximity to one another influence social networks and knowledge diffusion, and subsequently health and well-being. This study examined peer density effects on oral health for racial/ethnic minority older adults living in northern Manhattan and the Bronx, New York, NY. Peer age-group density was estimated by smoothing US Census data with 4 kernel bandwidths ranging from 0.25 to 1.50 mile. Logistic regression models were developed using these spatial measures and data from the ElderSmile oral and general health screening program that serves predominantly racial/ethnic minority older adults at community centers in northern Manhattan and the Bronx. The oral health outcomes modeled as dependent variables were ordinal dentition status and binary self-rated oral health. After construction of kernel density surfaces and multiple imputation of missing data, logistic regression analyses were performed to estimate the effects of peer density and other sociodemographic characteristics on the oral health outcomes of dentition status and self-rated oral health. Overall, higher peer density was associated with better oral health for older adults when estimated using smaller bandwidths (0.25 and 0.50 mile). That is, statistically significant relationships (p density and improved dentition status were found when peer density was measured assuming a more local social network. As with dentition status, a positive significant association was found between peer density and fair or better self-rated oral health when peer density was measured assuming a more local social network. This study provides novel evidence that the oral health of community-based older adults is affected by peer density in an urban environment. To the extent that peer density signifies the potential for

  19. PEDO-TRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY IN CENTRAL AMAZONIA

    Directory of Open Access Journals (Sweden)

    Henrique Seixas Barros

    2015-04-01

    Full Text Available Under field conditions in the Amazon forest, soil bulk density is difficult to measure. Rigorous methodological criteria must be applied to obtain reliable inventories of C stocks and soil nutrients, making this process expensive and sometimes unfeasible. This study aimed to generate models to estimate soil bulk density based on parameters that can be easily and reliably measured in the field and that are available in many soil-related inventories. Stepwise regression models to predict bulk density were developed using data on soil C content, clay content and pH in water from 140 permanent plots in terra firme (upland forests near Manaus, Amazonas State, Brazil. The model results were interpreted according to the coefficient of determination (R2 and Akaike information criterion (AIC and were validated with a dataset consisting of 125 plots different from those used to generate the models. The model with best performance in estimating soil bulk density under the conditions of this study included clay content and pH in water as independent variables and had R2 = 0.73 and AIC = -250.29. The performance of this model for predicting soil density was compared with that of models from the literature. The results showed that the locally calibrated equation was the most accurate for estimating soil bulk density for upland forests in the Manaus region.

  20. Mid-latitude Ionospheric Storms Density Gradients, Winds, and Drifts Estimated from GPS TEC Imaging

    Science.gov (United States)

    Datta-Barua, S.; Bust, G. S.

    2012-12-01

    Ionospheric storm processes at mid-latitudes stand in stark contrast to the typical quiescent behavior. Storm enhanced density (SED) on the dayside affects continent-sized regions horizontally and are often associated with a plume that extends poleward and upward into the nightside. One proposed cause of this behavior is the sub-auroral polarization stream (SAPS) acting on the SED, and neutral wind effects. The electric field and its effect connecting mid-latitude and polar regions are just beginning to be understood and modeled. Another possible coupling effect is due to neutral winds, particularly those generated at high latitudes by joule heating effects. Of particular interest are electric fields and winds along the boundaries of the SED and plume, because these may be at least partly a cause of sharp horizontal electron density gradients. Thus, it is important to understand what bearing the drifts and winds, and any spatial variations in them (e.g., shear), have on the structure of the enhancement, particularly at its boundaries. Imaging techniques based on GPS TEC play a significant role in study of mid-latitude storm dynamics, particularly at mid-latitudes, where sampling of the ionosphere with ground-based GPS lines of sight is most dense. Ionospheric Data Assimilation 4-Dimensional (IDA4D) is a plasma density estimation algorithm that has been used in a number of scientific investigations over several years. Recently, efforts to estimate drivers of the mid-latitude ionosphere, focusing on electric-field-induced drifts and neutral winds, based on GPS TEC high-resolution imaging have shown promise. Estimating Ionospheric Parameters from Ionospheric Reverse Engineering (EMPIRE) is a tool developed that addresses this kind of investigation. In this work electron density and driver estimates are presented for an ionospheric storm using IDA4D in conjunction with EMPIRE. The IDA4D estimates resolve F-region electron densities at 1-degree resolution at the region

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

    Directory of Open Access Journals (Sweden)

    Shouyang Liu

    2017-05-01

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

  2. Estimation of current density distribution of PAFC by analysis of cell exhaust gas

    Energy Technology Data Exchange (ETDEWEB)

    Kato, S.; Seya, A. [Fuji Electric Co., Ltd., Ichihara-shi (Japan); Asano, A. [Fuji Electric Corporate, Ltd., Yokosuka-shi (Japan)

    1996-12-31

    To estimate distributions of Current densities, voltages, gas concentrations, etc., in phosphoric acid fuel cell (PAFC) stacks, is very important for getting fuel cells with higher quality. In this work, we leave developed a numerical simulation tool to map out the distribution in a PAFC stack. And especially to Study Current density distribution in the reaction area of the cell, we analyzed gas composition in several positions inside a gas outlet manifold of the PAFC stack. Comparing these measured data with calculated data, the current density distribution in a cell plane calculated by the simulation, was certified.

  3. [Correlation with MRI features, cell density and the expression of immunohistochemistry of pure mucinous breast carcinoma].

    Science.gov (United States)

    Guo, Y; Kong, Q C; Zhu, Y Q; Liu, C L; He, H; Zhang, J E; Yang, R M; Jiang, X Q

    2017-05-09

    Objective: To explore the MRI features of the pure mucinous breast carcinoma(PMBC) and the correlation with cell density and the expression of immunohistochemistry. Methods: MRI features of 35 pure mucinous carcinomas were retrospectively analyzed from January 2011 to May 2016 in Guangdong General Hospital. MR images were reviewed for shape, margin, the signal intensity, enhancement patterns of tumors and diffusion weighted imaging (DWI) features and apparent diffusion coefficient (ADC) value. All the patients were detected by immunohistochemical staining with expression of ER, PR, CerbB-2, Ki-67 and Her-2. Correlations between MRI features of PMBC and cell density and the expression of immunohistochemistry were analyzed. Results: A total of 16 oval masses(16/35, 45.7%) and 10 round masses(10/35, 28.6%)were found in 35 PMBC with clear boundary(26/35, 74.3%) and lobulated shape(31/35, 88.6%). Very high signal intensity on T(2)-weighted images was found in 33 PMBC (33/35, 94.3%) and early enhancement rate was 115%±9% for PMBC. 28 PMBC demonstrated persistent enhancing pattern on time-signal intensity curve and 7 PMBC demonstrated plateau pattern.Mean ADC value was (1.91±0.06)×10(-3)mm(2)/s for PMBC. There was significant difference with early enhancement rate and ADC value between PMBC with more or less quantities of cellular mucin (P2, Her-2 and Ki-67 expression between PMBC with more or less quantities of cellular mucin (all P>0.05). Conclusions: PMBC has distinctive MRI features. The prognosis of PMBC is better from correlation between MRI features, cell density and the expression of immunohistochemistry.

  4. Method for Estimating the Charge Density Distribution on a Dielectric Surface.

    Science.gov (United States)

    Nakashima, Takuya; Suhara, Hiroyuki; Murata, Hidekazu; Shimoyama, Hiroshi

    2017-06-01

    High-quality color output from digital photocopiers and laser printers is in strong demand, motivating attempts to achieve fine dot reproducibility and stability. The resolution of a digital photocopier depends on the charge density distribution on the organic photoconductor surface; however, directly measuring the charge density distribution is impossible. In this study, we propose a new electron optical instrument that can rapidly measure the electrostatic latent image on an organic photoconductor surface, which is a dielectric surface, as well as a novel method to quantitatively estimate the charge density distribution on a dielectric surface by combining experimental data obtained from the apparatus via a computer simulation. In the computer simulation, an improved three-dimensional boundary charge density method (BCM) is used for electric field analysis in the vicinity of the dielectric material with a charge density distribution. This method enables us to estimate the profile and quantity of the charge density distribution on a dielectric surface with a resolution of the order of microns. Furthermore, the surface potential on the dielectric surface can be immediately calculated using the obtained charge density. This method enables the relation between the charge pattern on the organic photoconductor surface and toner particle behavior to be studied; an understanding regarding the same may lead to the development of a new generation of higher resolution photocopiers.

  5. Estimation of density-dependent mortality of juvenile bivalves in the Wadden Sea.

    Directory of Open Access Journals (Sweden)

    Henrike Andresen

    Full Text Available We investigated density-dependent mortality within the early months of life of the bivalves Macoma balthica (Baltic tellin and Cerastoderma edule (common cockle in the Wadden Sea. Mortality is thought to be density-dependent in juvenile bivalves, because there is no proportional relationship between the size of the reproductive adult stocks and the numbers of recruits for both species. It is not known however, when exactly density dependence in the pre-recruitment phase occurs and how prevalent it is. The magnitude of recruitment determines year class strength in bivalves. Thus, understanding pre-recruit mortality will improve the understanding of population dynamics. We analyzed count data from three years of temporal sampling during the first months after bivalve settlement at ten transects in the Sylt-Rømø-Bay in the northern German Wadden Sea. Analyses of density dependence are sensitive to bias through measurement error. Measurement error was estimated by bootstrapping, and residual deviances were adjusted by adding process error. With simulations the effect of these two types of error on the estimate of the density-dependent mortality coefficient was investigated. In three out of eight time intervals density dependence was detected for M. balthica, and in zero out of six time intervals for C. edule. Biological or environmental stochastic processes dominated over density dependence at the investigated scale.

  6. Hierarchical models for estimating density from DNA mark-recapture studies.

    Science.gov (United States)

    Gardner, Beth; Royle, J Andrew; Wegan, Michael T

    2009-04-01

    Genetic sampling is increasingly used as a tool by wildlife biologists and managers to estimate abundance and density of species. Typically, DNA is used to identify individuals captured in an array of traps (e.g., baited hair snares) from which individual encounter histories are derived. Standard methods for estimating the size of a closed population can be applied to such data. However, due to the movement of individuals on and off the trapping array during sampling, the area over which individuals are exposed to trapping is unknown, and so obtaining unbiased estimates of density has proved difficult. We propose a hierarchical spatial capture-recapture model which contains explicit models for the spatial point process governing the distribution of individuals and their exposure to (via movement) and detection by traps. Detection probability is modeled as a function of each individual's distance to the trap. We applied this model to a black bear (Ursus americanus) study conducted in 2006 using a hair-snare trap array in the Adirondack region of New York, USA. We estimated the density of bears to be 0.159 bears/km2, which is lower than the estimated density (0.410 bears/km2) based on standard closed population techniques. A Bayesian analysis of the model is fully implemented in the software program WinBUGS.

  7. Estimating cetacean population density using fixed passive acoustic sensors: an example with Blainville's beaked whales.

    Science.gov (United States)

    Marques, Tiago A; Thomas, Len; Ward, Jessica; DiMarzio, Nancy; Tyack, Peter L

    2009-04-01

    Methods are developed for estimating the size/density of cetacean populations using data from a set of fixed passive acoustic sensors. The methods convert the number of detected acoustic cues into animal density by accounting for (i) the probability of detecting cues, (ii) the rate at which animals produce cues, and (iii) the proportion of false positive detections. Additional information is often required for estimation of these quantities, for example, from an acoustic tag applied to a sample of animals. Methods are illustrated with a case study: estimation of Blainville's beaked whale density over a 6 day period in spring 2005, using an 82 hydrophone wide-baseline array located in the Tongue of the Ocean, Bahamas. To estimate the required quantities, additional data are used from digital acoustic tags, attached to five whales over 21 deep dives, where cues recorded on some of the dives are associated with those received on the fixed hydrophones. Estimated density was 25.3 or 22.5 animals/1000 km(2), depending on assumptions about false positive detections, with 95% confidence intervals 17.3-36.9 and 15.4-32.9. These methods are potentially applicable to a wide variety of marine and terrestrial species that are hard to survey using conventional visual methods.

  8. A hierarchical model for estimating density in camera-trap studies

    Science.gov (United States)

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

    2009-01-01

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

  9. Trapping Elusive Cats: Using Intensive Camera Trapping to Estimate the Density of a Rare African Felid.

    Directory of Open Access Journals (Sweden)

    Eléanor Brassine

    Full Text Available Camera trapping studies have become increasingly popular to produce population estimates of individually recognisable mammals. Yet, monitoring techniques for rare species which occur at extremely low densities are lacking. Additionally, species which have unpredictable movements may make obtaining reliable population estimates challenging due to low detectability. Our study explores the effectiveness of intensive camera trapping for estimating cheetah (Acinonyx jubatus numbers. Using both a more traditional, systematic grid approach and pre-determined, targeted sites for camera placement, the cheetah population of the Northern Tuli Game Reserve, Botswana was sampled between December 2012 and October 2013. Placement of cameras in a regular grid pattern yielded very few (n = 9 cheetah images and these were insufficient to estimate cheetah density. However, pre-selected cheetah scent-marking posts provided 53 images of seven adult cheetahs (0.61 ± 0.18 cheetahs/100 km². While increasing the length of the camera trapping survey from 90 to 130 days increased the total number of cheetah images obtained (from 53 to 200, no new individuals were recorded and the estimated population density remained stable. Thus, our study demonstrates that targeted camera placement (irrespective of survey duration is necessary for reliably assessing cheetah densities where populations are naturally very low or dominated by transient individuals. Significantly our approach can easily be applied to other rare predator species.

  10. High-order ionospheric effects on electron density estimation from Fengyun-3C GPS radio occultation

    Science.gov (United States)

    Li, Junhai; Jin, Shuanggen

    2017-03-01

    GPS radio occultation can estimate ionospheric electron density and total electron content (TEC) with high spatial resolution, e.g., China's recent Fengyun-3C GPS radio occultation. However, high-order ionospheric delays are normally ignored. In this paper, the high-order ionospheric effects on electron density estimation from the Fengyun-3C GPS radio occultation data are estimated and investigated using the NeQuick2 ionosphere model and the IGRF12 (International Geomagnetic Reference Field, 12th generation) geomagnetic model. Results show that the high-order ionospheric delays have large effects on electron density estimation with up to 800 el cm-3, which should be corrected in high-precision ionospheric density estimation and applications. The second-order ionospheric effects are more significant, particularly at 250-300 km, while third-order ionospheric effects are much smaller. Furthermore, the high-order ionospheric effects are related to the location, the local time, the radio occultation azimuth and the solar activity. The large high-order ionospheric effects are found in the low-latitude area and in the daytime as well as during strong solar activities. The second-order ionospheric effects have a maximum positive value when the radio occultation azimuth is around 0-20°, and a maximum negative value when the radio occultation azimuth is around -180 to -160°. Moreover, the geomagnetic storm also affects the high-order ionospheric delay, which should be carefully corrected.

  11. Estimation of the optimal number of radiotherapy fractions for breast cancer: A review of the evidence.

    Science.gov (United States)

    Wong, Karen; Delaney, Geoff P; Barton, Michael B

    2015-08-01

    There is variation in radiotherapy fractionation practice, however, there is no evidence-based benchmark for appropriate activity. An evidence-based model was constructed to estimate the optimal number of fractions for the first course of radiotherapy for breast cancer to aid in services planning and performance benchmarking. The published breast cancer radiotherapy utilisation model was adapted. Evidence-based number of fractions was added to each radiotherapy indication. The overall optimal number of fractions was calculated based on the frequency of specific clinical conditions where radiotherapy is indicated and the recommended number of fractions for each condition. Sensitivity analysis was performed to assess the impact of uncertainties on the model. For the entire Australian breast cancer patient population, the estimated optimal number of fractions per patient was 16.8, 14.6, 13.7 and 0.8 for ductal carcinoma in situ, early, advanced and metastatic breast cancer respectively. Overall, the optimal number of fractions per patient was 14.4 (range 14.4-18.7). These results allow comparison with actual practices, and workload prediction to aid in services planning. The model can be easily adapted to other countries by inserting population-specific epidemiological data, and to future changes in cancer incidence, stage distribution and fractionation recommendations. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  12. Etiology and early detection of breast cancer : Biomarkers, lifestyle and mammographic density

    NARCIS (Netherlands)

    Emaus, M.J.

    2015-01-01

    In the Netherlands, one in seven women will develop breast cancer at some point during her lifetime. Although breast cancer mortality has declined, still 23% of the breast cancer patients dies from the disease within 10 years after initial diagnosis. This emphasizes the importance of research on

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

    Directory of Open Access Journals (Sweden)

    Boutayeb Saber

    2010-09-01

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

  14. Behavioral Context of Blue and Fin Whale Calling for Density Estimation

    Science.gov (United States)

    2015-09-30

    1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Behavioral Context of Blue and Fin Whale Calling for...in which we will determine the context-appropriate call production rates for blue and fin whales in the Southern California Bight, with the end...goal of facilitating density estimation from passive acoustic data. OBJECTIVES Before a reliable estimate of blue and fin whale call production

  15. Pedotransfer functions to estimate soil bulk density for Northern Africa : Tunisia case

    OpenAIRE

    Brahim, N.; Bernoux, Martial; Gallali, T. (ed.)

    2012-01-01

    Countries should provide regularly national inventories of greenhouse gas emissions and sinks and, and for the agriculture and forestry sectors this comprise national estimates of soil organic carbon (C) stocks. Estimation of soil C stock requires soil bulk density (D-b) values. However, direct measurement of D-b is often lacking mainly for soils in arid and semi-arid conditions. Much effort has been made in finding alternative solution to predict D-b, either improving in situ determinations,...

  16. The importance of spatial models for estimating the strength of density dependence

    DEFF Research Database (Denmark)

    Thorson, James T.; Skaug, Hans J.; Kristensen, Kasper

    2014-01-01

    the California Coast. In this case, the nonspatial model estimates implausible oscillatory dynamics on an annual time scale, while the spatial model estimates strong autocorrelation and is supported by model selection tools. We conclude by discussing the importance of improved data archiving techniques, so...... that spatial models can be used to re-examine classic questions regarding the presence and strength of density dependence in wild populations Read More: http://www.esajournals.org/doi/abs/10.1890/14-0739.1...

  17. Scent Lure Effect on Camera-Trap Based Leopard Density Estimates.

    Directory of Open Access Journals (Sweden)

    Alexander Richard Braczkowski

    Full Text Available Density estimates for large carnivores derived from camera surveys often have wide confidence intervals due to low detection rates. Such estimates are of limited value to authorities, which require precise population estimates to inform conservation strategies. Using lures can potentially increase detection, improving the precision of estimates. However, by altering the spatio-temporal patterning of individuals across the camera array, lures may violate closure, a fundamental assumption of capture-recapture. Here, we test the effect of scent lures on the precision and veracity of density estimates derived from camera-trap surveys of a protected African leopard population. We undertook two surveys (a 'control' and 'treatment' survey on Phinda Game Reserve, South Africa. Survey design remained consistent except a scent lure was applied at camera-trap stations during the treatment survey. Lures did not affect the maximum movement distances (p = 0.96 or temporal activity of female (p = 0.12 or male leopards (p = 0.79, and the assumption of geographic closure was met for both surveys (p >0.05. The numbers of photographic captures were also similar for control and treatment surveys (p = 0.90. Accordingly, density estimates were comparable between surveys (although estimates derived using non-spatial methods (7.28-9.28 leopards/100km2 were considerably higher than estimates from spatially-explicit methods (3.40-3.65 leopards/100km2. The precision of estimates from the control and treatment surveys, were also comparable and this applied to both non-spatial and spatial methods of estimation. Our findings suggest that at least in the context of leopard research in productive habitats, the use of lures is not warranted.

  18. ALTERNATIVE METHODOLOGIES FOR THE ESTIMATION OF LOCAL POINT DENSITY INDEX: MOVING TOWARDS ADAPTIVE LIDAR DATA PROCESSING

    Directory of Open Access Journals (Sweden)

    Z. Lari

    2012-07-01

    Full Text Available Over the past few years, LiDAR systems have been established as a leading technology for the acquisition of high density point clouds over physical surfaces. These point clouds will be processed for the extraction of geo-spatial information. Local point density is one of the most important properties of the point cloud that highly affects the performance of data processing techniques and the quality of extracted information from these data. Therefore, it is necessary to define a standard methodology for the estimation of local point density indices to be considered for the precise processing of LiDAR data. Current definitions of local point density indices, which only consider the 2D neighbourhood of individual points, are not appropriate for 3D LiDAR data and cannot be applied for laser scans from different platforms. In order to resolve the drawbacks of these methods, this paper proposes several approaches for the estimation of the local point density index which take the 3D relationship among the points and the physical properties of the surfaces they belong to into account. In the simplest approach, an approximate value of the local point density for each point is defined while considering the 3D relationship among the points. In the other approaches, the local point density is estimated by considering the 3D neighbourhood of the point in question and the physical properties of the surface which encloses this point. The physical properties of the surfaces enclosing the LiDAR points are assessed through eigen-value analysis of the 3D neighbourhood of individual points and adaptive cylinder methods. This paper will discuss these approaches and highlight their impact on various LiDAR data processing activities (i.e., neighbourhood definition, region growing, segmentation, boundary detection, and classification. Experimental results from airborne and terrestrial LiDAR data verify the efficacy of considering local point density variation for

  19. Estimating malaria transmission intensity from Plasmodium falciparum serological data using antibody density models.

    Science.gov (United States)

    Pothin, Emilie; Ferguson, Neil M; Drakeley, Chris J; Ghani, Azra C

    2016-02-09

    Serological data are increasingly being used to monitor malaria transmission intensity and have been demonstrated to be particularly useful in areas of low transmission where traditional measures such as EIR and parasite prevalence are limited. The seroconversion rate (SCR) is usually estimated using catalytic models in which the measured antibody levels are used to categorize individuals as seropositive or seronegative. One limitation of this approach is the requirement to impose a fixed cut-off to distinguish seropositive and negative individuals. Furthermore, the continuous variation in antibody levels is ignored thereby potentially reducing the precision of the estimate. An age-specific density model which mimics antibody acquisition and loss was developed to make full use of the information provided by serological measures of antibody levels. This was fitted to blood-stage antibody density data from 12 villages at varying transmission intensity in Northern Tanzania to estimate the exposure rate as an alternative measure of transmission intensity. The results show a high correlation between the exposure rate estimates obtained and the estimated SCR obtained from a catalytic model (r = 0.95) and with two derived measures of EIR (r = 0.74 and r = 0.81). Estimates of exposure rate obtained with the density model were also more precise than those derived from catalytic models. This approach, if validated across different epidemiological settings, could be a useful alternative framework for quantifying transmission intensity, which makes more complete use of serological data.

  20. Regression estimators for late-instar gypsy moth larvae at low pupulation densities

    Science.gov (United States)

    W.E. Wallnr; A.S. Devito; Stanley J. Zarnoch

    1989-01-01

    Two regression estimators were developed for determining densities of late-instar gypsy moth, Lymantria dispar (Lepidoptera: Lymantriidae), larvae from burlap band and pyrethrin spray counts on oak trees in Vermont, Massachusetts, Connecticut, and New York. Studies were conducted by marking larvae on individual burlap banded trees within 15...

  1. The Wegner estimate and the integrated density of states for some ...

    Indian Academy of Sciences (India)

    The integrated density of states (IDS) for random operators is an important function describing many physical characteristics of a random system. Properties of the IDS are derived from the Wegner estimate that describes the influence of finite-volume perturbations on a background system. In this paper, we present a simple ...

  2. How bandwidth selection algorithms impact exploratory data analysis using kernel density estimation.

    Science.gov (United States)

    Harpole, Jared K; Woods, Carol M; Rodebaugh, Thomas L; Levinson, Cheri A; Lenze, Eric J

    2014-09-01

    Exploratory data analysis (EDA) can reveal important features of underlying distributions, and these features often have an impact on inferences and conclusions drawn from data. Graphical analysis is central to EDA, and graphical representations of distributions often benefit from smoothing. A viable method of estimating and graphing the underlying density in EDA is kernel density estimation (KDE). This article provides an introduction to KDE and examines alternative methods for specifying the smoothing bandwidth in terms of their ability to recover the true density. We also illustrate the comparison and use of KDE methods with 2 empirical examples. Simulations were carried out in which we compared 8 bandwidth selection methods (Sheather-Jones plug-in [SJDP], normal rule of thumb, Silverman's rule of thumb, least squares cross-validation, biased cross-validation, and 3 adaptive kernel estimators) using 5 true density shapes (standard normal, positively skewed, bimodal, skewed bimodal, and standard lognormal) and 9 sample sizes (15, 25, 50, 75, 100, 250, 500, 1,000, 2,000). Results indicate that, overall, SJDP outperformed all methods. However, for smaller sample sizes (25 to 100) either biased cross-validation or Silverman's rule of thumb was recommended, and for larger sample sizes the adaptive kernel estimator with SJDP was recommended. Information is provided about implementing the recommendations in the R computing language. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  3. Workshop on the Detection, Classification, Localization and Density Estimation of Marine Mammals Using Passive Acoustics - 2015

    Science.gov (United States)

    2015-09-30

    1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Workshop on the Detection, Classification, Localization...OBJECTIVES The Seventh International Workshop on Detection, Classification, Localization, and Density Estimation (DCLDE) of Marine Mammals Using... workshop was to facilitate its organization and to increase the level of particapation by students and others working in the field. APPROACH The

  4. A hybrid approach to crowd density estimation using statistical leaning and texture classification

    Science.gov (United States)

    Li, Yin; Zhou, Bowen

    2013-12-01

    Crowd density estimation is a hot topic in computer vision community. Established algorithms for crowd density estimation mainly focus on moving crowds, employing background modeling to obtain crowd blobs. However, people's motion is not obvious in most occasions such as the waiting hall in the airport or the lobby in the railway station. Moreover, conventional algorithms for crowd density estimation cannot yield desirable results for all levels of crowding due to occlusion and clutter. We propose a hybrid method to address the aforementioned problems. First, statistical learning is introduced for background subtraction, which comprises a training phase and a test phase. The crowd images are grided into small blocks which denote foreground or background. Then HOG features are extracted and are fed into a binary SVM for each block. Hence, crowd blobs can be obtained by the classification results of the trained classifier. Second, the crowd images are treated as texture images. Therefore, the estimation problem can be formulated as texture classification. The density level can be derived according to the classification results. We validate the proposed algorithm on some real scenarios where the crowd motion is not so obvious. Experimental results demonstrate that our approach can obtain the foreground crowd blobs accurately and work well for different levels of crowding.

  5. DNA-based population density estimation of black bear at northern ...

    African Journals Online (AJOL)

    The analysis of deoxyribonucleic acid (DNA) microsatellites from hair samples obtained by the non-invasive method of traps was used to estimate the population density of black bears (Ursus americanus eremicus) in a mountain located at the county of Lampazos, Nuevo Leon, Mexico. The genotyping of bears was ...

  6. Estimation of bone mineral density by digital X-ray radiogrammetry: theoretical background and clinical testing

    DEFF Research Database (Denmark)

    Rosholm, A; Hyldstrup, L; Backsgaard, L

    2002-01-01

    A new automated radiogrammetric method to estimate bone mineral density (BMD) from a single radiograph of the hand and forearm is described. Five regions of interest in radius, ulna and the three middle metacarpal bones are identified and approximately 1800 geometrical measurements from these bones...

  7. Camera trapping estimates of density and survival of fishers (Martes pennanti)

    Science.gov (United States)

    Mark J. Jordan; Reginald H. Barrett; Kathryn L. Purcell

    2011-01-01

    Developing efficient monitoring strategies for species of conservation concern is critical to ensuring their persistence. We have developed a method using camera traps to estimate density and survival in mesocarnivores and tested it on a population of fishers Martes pennanti in an area of approximately 300 km2 of the southern...

  8. Estimating and Testing Continuous-Time Models in Finance: The Role of Transition Densities

    OpenAIRE

    Yacine Aït-Sahalia

    2009-01-01

    This article surveys recent developments to estimate and test continuous-time models in finance using discrete observations on the underlying asset price or derivative securities' prices. Both parametric and nonparametric methods are described. All these methods share a common focus on the transition density as the central object for inference and testing of the model.

  9. Estimating group size and population density of Eurasian badgers Meles meles by quantifying latrine use

    NARCIS (Netherlands)

    Tuyttens, F.A.M.; Long, B.; Fawcett, T.W.; Skinner, A.; Brown, J.A.; Cheeseman, C.L.; Roddam, A.W.; MacDonald, D.W.

    2001-01-01

    1. Conservation issues and a potential role in disease transmission generate the continued need to census Eurasian badgers Meles metes, but direct counts and sett counts present difficulties. The feasibility of estimating social group size and population density of badgers by quantifying their use

  10. Estimating abundance and density of Amur tigers along the Sino-Russian border.

    Science.gov (United States)

    Xiao, Wenhong; Feng, Limin; Mou, Pu; Miquelle, Dale G; Hebblewhite, Mark; Goldberg, Joshua F; Robinson, Hugh S; Zhao, Xiaodan; Zhou, Bo; Wang, Tianming; Ge, Jianping

    2016-07-01

    As an apex predator the Amur tiger (Panthera tigris altaica) could play a pivotal role in maintaining the integrity of forest ecosystems in Northeast Asia. Due to habitat loss and harvest over the past century, tigers rapidly declined in China and are now restricted to the Russian Far East and bordering habitat in nearby China. To facilitate restoration of the tiger in its historical range, reliable estimates of population size are essential to assess effectiveness of conservation interventions. Here we used camera trap data collected in Hunchun National Nature Reserve from April to June 2013 and 2014 to estimate tiger density and abundance using both maximum likelihood and Bayesian spatially explicit capture-recapture (SECR) methods. A minimum of 8 individuals were detected in both sample periods and the documentation of marking behavior and reproduction suggests the presence of a resident population. Using Bayesian SECR modeling within the 11 400 km(2) state space, density estimates were 0.33 and 0.40 individuals/100 km(2) in 2013 and 2014, respectively, corresponding to an estimated abundance of 38 and 45 animals for this transboundary Sino-Russian population. In a maximum likelihood framework, we estimated densities of 0.30 and 0.24 individuals/100 km(2) corresponding to abundances of 34 and 27, in 2013 and 2014, respectively. These density estimates are comparable to other published estimates for resident Amur tiger populations in the Russian Far East. This study reveals promising signs of tiger recovery in Northeast China, and demonstrates the importance of connectivity between the Russian and Chinese populations for recovering tigers in Northeast China. © 2016 International Society of Zoological Sciences, Institute of Zoology/Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

  11. New density estimates derived using accelerometers on board the CHAMP and GRACE satellites

    Science.gov (United States)

    Mehta, Piyush M.; Walker, Andrew C.; Sutton, Eric K.; Godinez, Humberto C.

    2017-04-01

    Atmospheric mass density estimates derived from accelerometers onboard satellites such as CHAllenging Minisatellite Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE) are crucial in gaining insight into open science questions about the dynamic coupling between space weather events and the upper atmosphere. Recent advances in physics-based satellite drag coefficient modeling allow derivation of new density data sets. This paper uses physics-based satellite drag coefficient models for CHAMP and GRACE to derive new estimates for the neutral atmospheric density. Results show an average difference of 14-18% for CHAMP and 10-24% for GRACE between the new and existing data sets depending on the space weather conditions (i.e., solar and geomagnetic activity levels). The newly derived densities are also compared with existing models, and results are presented. These densities are expected to be useful to the wider scientific community for validating the development of physics-based models and helping to answer open scientific questions regarding our understanding of upper atmosphere dynamics such as the sensitivity of temporal and global density variations to solar and geomagnetic forcing.

  12. Estimations of bulk geometrically necessary dislocation density using high resolution EBSD.

    Science.gov (United States)

    Ruggles, T J; Fullwood, D T

    2013-10-01

    Characterizing the content of geometrically necessary dislocations (GNDs) in crystalline materials is crucial to understanding plasticity. Electron backscatter diffraction (EBSD) effectively recovers local crystal orientation, which is used to estimate the lattice distortion, components of the Nye dislocation density tensor (α), and subsequently the local bulk GND density of a material. This paper presents a complementary estimate of bulk GND density using measurements of local lattice curvature and strain gradients from more recent high resolution EBSD (HR-EBSD) methods. A continuum adaptation of classical equations for the distortion around a dislocation are developed and used to simulate random GND fields to validate the various available approximations of GND content. Copyright © 2013 Elsevier B.V. All rights reserved.

  13. Local linear density estimation for filtered survival data, with bias correction

    DEFF Research Database (Denmark)

    Nielsen, Jens Perch; Tanggaard, Carsten; Jones, M.C.

    2009-01-01

    A class of local linear kernel density estimators based on weighted least-squares kernel estimation is considered within the framework of Aalen's multiplicative intensity model. This model includes the filtered data model that, in turn, allows for truncation and/or censoring in addition to accomm...... within our framework. The multiplicative bias-correction method proves to be the best in a simulation study comparing the performance of the considered estimators. An example concerning old-age mortality demonstrates the importance of the improvements provided....

  14. Using zero-norm constraint for sparse probability density function estimation

    Science.gov (United States)

    Hong, X.; Chen, S.; Harris, C. J.

    2012-11-01

    A new sparse kernel probability density function (pdf) estimator based on zero-norm constraint is constructed using the classical Parzen window (PW) estimate as the target function. The so-called zero-norm of the parameters is used in order to achieve enhanced model sparsity, and it is suggested to minimize an approximate function of the zero-norm. It is shown that under certain condition, the kernel weights of the proposed pdf estimator based on the zero-norm approximation can be updated using the multiplicative nonnegative quadratic programming algorithm. Numerical examples are employed to demonstrate the efficacy of the proposed approach.

  15. Estimating primate population densities: the systematic use of playbacks along transects in population surveys.

    Science.gov (United States)

    Gestich, Carla C; Caselli, Christini B; Nagy-Reis, Mariana B; Setz, Eleonore Z F; da Cunha, Rogério G T

    2017-02-01

    Accurate measures of animal population densities are essential to assess their status, demography, and answer ecological questions. Among several methods proposed to collect abundance data, line transect sampling is used the most. The assumptions required to obtain accurate density estimates through this method, however, are rarely met when studying primates. As most primate species are vocally active, density estimates can be improved by associating transect sampling with playback point counts to scan the entire study area. Yet, attention to playback procedure and data collection design is necessary. Here, we describe a protocol to assess primate densities using playback and test its application on surveys of Callicebus nigrifrons, a small Neotropical primate that shows site fidelity and active vocal behavior. We list important steps and discuss precautions that should be considered, from the adjustments in the recordings in the lab to field procedures in the playback broadcasting sessions. Prior to the surveys, we conducted playback trials with three habituated wild groups at three forest remnants to test their response to the playback stimuli at different distances. Based on these trials, we defined the radius distance covered by the playback sessions. Then, we conducted two surveys in 12 forest remnants, in the northeast of São Paulo State Brazil. The results of density estimates were consistent between the two surveys. As the playback survey protocol we described has proved to be a simple and useful tool for surveying vocal primate and generated reliable data, we suggest that it is a good alternative method to estimate density of species, particularly for those that are responsive to playbacks and show site fidelity. © 2016 Wiley Periodicals, Inc.

  16. An analytical framework for estimating aquatic species density from environmental DNA

    Science.gov (United States)

    Chambert, Thierry; Pilliod, David S.; Goldberg, Caren S.; Doi, Hideyuki; Takahara, Teruhiko

    2018-01-01

    Environmental DNA (eDNA) analysis of water samples is on the brink of becoming a standard monitoring method for aquatic species. This method has improved detection rates over conventional survey methods and thus has demonstrated effectiveness for estimation of site occupancy and species distribution. The frontier of eDNA applications, however, is to infer species density. Building upon previous studies, we present and assess a modeling approach that aims at inferring animal density from eDNA. The modeling combines eDNA and animal count data from a subset of sites to estimate species density (and associated uncertainties) at other sites where only eDNA data are available. As a proof of concept, we first perform a cross-validation study using experimental data on carp in mesocosms. In these data, fish densities are known without error, which allows us to test the performance of the method with known data. We then evaluate the model using field data from a study on a stream salamander species to assess the potential of this method to work in natural settings, where density can never be known with absolute certainty. Two alternative distributions (Normal and Negative Binomial) to model variability in eDNA concentration data are assessed. Assessment based on the proof of concept data (carp) revealed that the Negative Binomial model provided much more accurate estimates than the model based on a Normal distribution, likely because eDNA data tend to be overdispersed. Greater imprecision was found when we applied the method to the field data, but the Negative Binomial model still provided useful density estimates. We call for further model development in this direction, as well as further research targeted at sampling design optimization. It will be important to assess these approaches on a broad range of study systems.

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

    Science.gov (United States)

    2014-01-01

    Introduction Interval cancers are tumors arising after a negative screening episode and before the next screening invitation. They can be classified into true interval cancers, false-negatives, minimal-sign cancers, and occult tumors based on mammographic findings in screening and diagnostic mammograms. This study aimed to describe tumor-related characteristics and the association of breast density and tumor phenotype within four interval cancer categories. Methods We included 2,245 invasive tumors (1,297 screening-detected and 948 interval cancers) diagnosed from 2000 to 2009 among 645,764 women aged 45 to 69 who underwent biennial screening in Spain. Interval cancers were classified by a semi-informed retrospective review into true interval cancers (n = 455), false-negatives (n = 224), minimal-sign (n = 166), and occult tumors (n = 103). Breast density was evaluated using Boyd’s scale and was conflated into: 75%. Tumor-related information was obtained from cancer registries and clinical records. Tumor phenotype was defined as follows: luminal A: ER+/HER2- or PR+/HER2-; luminal B: ER+/HER2+ or PR+/HER2+; HER2: ER-/PR-/HER2+; triple-negative: ER-/PR-/HER2-. The association of tumor phenotype and breast density was assessed using a multinomial logistic regression model. Adjusted odds ratios (OR) and 95% confidence intervals (95% CI) were calculated. All statistical tests were two-sided. Results Forty-eight percent of interval cancers were true interval cancers and 23.6% false-negatives. True interval cancers were associated with HER2 and triple-negative phenotypes (OR = 1.91 (95% CI:1.22-2.96), OR = 2.07 (95% CI:1.42-3.01), respectively) and extremely dense breasts (>75%) (OR = 1.67 (95% CI:1.08-2.56)). However, among true interval cancers a higher proportion of triple-negative tumors was observed in predominantly fatty breasts (breasts (28.7%, 21.4%, 11.3% and 14.3%, respectively; screening-detected cancers, extreme breast density

  18. Population density estimated from locations of individuals on a passive detector array

    Science.gov (United States)

    Efford, Murray G.; Dawson, Deanna K.; Borchers, David L.

    2009-01-01

    The density of a closed population of animals occupying stable home ranges may be estimated from detections of individuals on an array of detectors, using newly developed methods for spatially explicit capture–recapture. Likelihood-based methods provide estimates for data from multi-catch traps or from devices that record presence without restricting animal movement ("proximity" detectors such as camera traps and hair snags). As originally proposed, these methods require multiple sampling intervals. We show that equally precise and unbiased estimates may be obtained from a single sampling interval, using only the spatial pattern of detections. This considerably extends the range of possible applications, and we illustrate the potential by estimating density from simulated detections of bird vocalizations on a microphone array. Acoustic detection can be defined as occurring when received signal strength exceeds a threshold. We suggest detection models for binary acoustic data, and for continuous data comprising measurements of all signals above the threshold. While binary data are often sufficient for density estimation, modeling signal strength improves precision when the microphone array is small.

  19. Statistical computation of Boltzmann entropy and estimation of the optimal probability density function from statistical sample

    Science.gov (United States)

    Sui, Ning; Li, Min; He, Ping

    2014-12-01

    In this work, we investigate the statistical computation of the Boltzmann entropy of statistical samples. For this purpose, we use both histogram and kernel function to estimate the probability density function of statistical samples. We find that, due to coarse-graining, the entropy is a monotonic increasing function of the bin width for histogram or bandwidth for kernel estimation, which seems to be difficult to select an optimal bin width/bandwidth for computing the entropy. Fortunately, we notice that there exists a minimum of the first derivative of entropy for both histogram and kernel estimation, and this minimum point of the first derivative asymptotically points to the optimal bin width or bandwidth. We have verified these findings by large amounts of numerical experiments. Hence, we suggest that the minimum of the first derivative of entropy be used as a selector for the optimal bin width or bandwidth of density estimation. Moreover, the optimal bandwidth selected by the minimum of the first derivative of entropy is purely data-based, independent of the unknown underlying probability density distribution, which is obviously superior to the existing estimators. Our results are not restricted to one-dimensional, but can also be extended to multivariate cases. It should be emphasized, however, that we do not provide a robust mathematical proof of these findings, and we leave these issues with those who are interested in them.

  20. Density of Jatropha curcas Seed Oil and its Methyl Esters: Measurement and Estimations

    Science.gov (United States)

    Veny, Harumi; Baroutian, Saeid; Aroua, Mohamed Kheireddine; Hasan, Masitah; Raman, Abdul Aziz; Sulaiman, Nik Meriam Nik

    2009-04-01

    Density data as a function of temperature have been measured for Jatropha curcas seed oil, as well as biodiesel jatropha methyl esters at temperatures from above their melting points to 90 ° C. The data obtained were used to validate the method proposed by Spencer and Danner using a modified Rackett equation. The experimental and estimated density values using the modified Rackett equation gave almost identical values with average absolute percent deviations less than 0.03% for the jatropha oil and 0.04% for the jatropha methyl esters. The Janarthanan empirical equation was also employed to predict jatropha biodiesel densities. This equation performed equally well with average absolute percent deviations within 0.05%. Two simple linear equations for densities of jatropha oil and its methyl esters are also proposed in this study.

  1. Image enhancement via MMSE estimation of Gaussian scale mixture with Maxwell density in AWGN

    Directory of Open Access Journals (Sweden)

    Pichid Kittisuwan

    2016-03-01

    Full Text Available In optical techniques, noise signal is a classical problem in medical image processing. Recently, there has been considerable interest in using the wavelet transform with Bayesian estimation as a powerful tool for recovering image from noisy data. In wavelet domain, if Bayesian estimator is used for denoising problem, the solution requires a prior knowledge about the distribution of wavelet coefficients. Indeed, wavelet coefficients might be better modeled by super Gaussian density. The super Gaussian density can be generated by Gaussian scale mixture (GSM. So, we present new minimum mean square error (MMSE estimator for spherically-contoured GSM with Maxwell distribution in additive white Gaussian noise (AWGN. We compare our proposed method to current state-of-the-art method applied on standard test image and we quantify achieved performance improvement.

  2. Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches

    Science.gov (United States)

    Fotin, Sergei V.; Yin, Yin; Haldankar, Hrishikesh; Hoffmeister, Jeffrey W.; Periaswamy, Senthil

    2016-03-01

    Computer-aided detection (CAD) has been used in screening mammography for many years and is likely to be utilized for digital breast tomosynthesis (DBT). Higher detection performance is desirable as it may have an impact on radiologist's decisions and clinical outcomes. Recently the algorithms based on deep convolutional architectures have been shown to achieve state of the art performance in object classification and detection. Similarly, we trained a deep convolutional neural network directly on patches sampled from two-dimensional mammography and reconstructed DBT volumes and compared its performance to a conventional CAD algorithm that is based on computation and classification of hand-engineered features. The detection performance was evaluated on the independent test set of 344 DBT reconstructions (GE SenoClaire 3D, iterative reconstruction algorithm) containing 328 suspicious and 115 malignant soft tissue densities including masses and architectural distortions. Detection sensitivity was measured on a region of interest (ROI) basis at the rate of five detection marks per volume. Moving from conventional to deep learning approach resulted in increase of ROI sensitivity from 0:832 +/- 0:040 to 0:893 +/- 0:033 for suspicious ROIs; and from 0:852 +/- 0:065 to 0:930 +/- 0:046 for malignant ROIs. These results indicate the high utility of deep feature learning in the analysis of DBT data and high potential of the method for broader medical image analysis tasks.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2012-07-15

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

  4. The effect of exercise on body composition and bone mineral density in breast cancer survivors taking aromatase inhibitors.

    Science.gov (United States)

    Thomas, Gwendolyn A; Cartmel, Brenda; Harrigan, Maura; Fiellin, Martha; Capozza, Scott; Zhou, Yang; Ercolano, Elizabeth; Gross, Cary P; Hershman, Dawn; Ligibel, Jennifer; Schmitz, Kathryn; Li, Fang-Yong; Sanft, Tara; Irwin, Melinda L

    2017-02-01

    This study examined the effect of 12 months of aerobic and resistance exercise versus usual care on changes in body composition in postmenopausal breast cancer survivors taking aromatase inhibitors (AIs). The Hormones and Physical Exercise study enrolled 121 breast cancer survivors and randomized them to either supervised twice-weekly resistance exercise training and 150 min/wk of aerobic exercise (N = 61) or a usual care (N = 60) group. Dual-energy X-ray absorptiometry scans were conducted at baseline, 6 months, and 12 months to assess changes in body mass index, percent body fat, lean body mass, and bone mineral density. At 12 months, the exercise group relative to the usual care group had a significant increase in lean body mass (0.32 vs. -0.88 kg, P = 0.03), a decrease in percent body fat (-1.4% vs. 0.48%, P = 0.03), and a decrease in body mass index (-0.73 vs. 0.17 kg/m 2 , P = 0.03). Change in bone mineral density was not significantly different between groups at 12 months (0.001 vs. -0.006 g/cm 2 , P = 0.37). A combined resistance and aerobic exercise intervention improved body composition in breast cancer survivors taking AIs. Exercise interventions may help to mitigate the negative side effects of AIs and improve health outcomes in breast cancer survivors. © 2016 The Obesity Society.

  5. Estimation of energy density of Li-S batteries with liquid and solid electrolytes

    Science.gov (United States)

    Li, Chunmei; Zhang, Heng; Otaegui, Laida; Singh, Gurpreet; Armand, Michel; Rodriguez-Martinez, Lide M.

    2016-09-01

    With the exponential growth of technology in mobile devices and the rapid expansion of electric vehicles into the market, it appears that the energy density of the state-of-the-art Li-ion batteries (LIBs) cannot satisfy the practical requirements. Sulfur has been one of the best cathode material choices due to its high charge storage (1675 mAh g-1), natural abundance and easy accessibility. In this paper, calculations are performed for different cell design parameters such as the active material loading, the amount/thickness of electrolyte, the sulfur utilization, etc. to predict the energy density of Li-S cells based on liquid, polymeric and ceramic electrolytes. It demonstrates that Li-S battery is most likely to be competitive in gravimetric energy density, but not volumetric energy density, with current technology, when comparing with LIBs. Furthermore, the cells with polymer and thin ceramic electrolytes show promising potential in terms of high gravimetric energy density, especially the cells with the polymer electrolyte. This estimation study of Li-S energy density can be used as a good guidance for controlling the key design parameters in order to get desirable energy density at cell-level.

  6. Calcium and vitamin D supplementation and loss of bone mineral density in women undergoing breast cancer therapy.

    Science.gov (United States)

    Datta, Mridul; Schwartz, Gary G

    2013-12-01

    An unintended consequence of breast cancer therapies is an increased risk of osteoporosis due to accelerated bone loss. We conducted a systematic review of calcium and/or vitamin D (Ca±D) supplementation trials for maintaining bone mineral density (BMD) in women with breast cancer using the "before-after" data from the Ca±D supplemented comparison group of trials evaluating the effect of drugs such as bisphosphonates on BMD. Whether Ca±D supplements increase BMD in women undergoing breast cancer therapy has never been tested against an unsupplemented control group. However, results from 16 trials indicate that the Ca±D doses tested (500-1500mg calcium; 200-1000IU vitamin D) were inadequate to prevent BMD loss in these women. Cardiovascular disease is the main cause of mortality in women with breast cancer. Because calcium supplements may increase cardiovascular disease risk, future trials should evaluate the safety and efficacy of Ca±D supplementation in women undergoing breast cancer therapy. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  7. Chalkley estimates of angiogenesis in early breast cancer--relevance to prognosis

    DEFF Research Database (Denmark)

    Offersen, Birgitte V; Sørensen, Flemming Brandt; Yilmaz, Mette

    2002-01-01

    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...

  8. Transmit B1+ Field Inhomogeneity and T1 Estimation Errors in Breast DCE-MRI at 3T

    Science.gov (United States)

    Sung, Kyunghyun; Daniel, Bruce L; Hargreaves, Brian A

    2012-01-01

    Purpose To quantify B1+ variation across the breasts and to evaluate the accuracy of pre-contrast T1 estimation with and without B1+ variation in breast MRI patients at 3T. Materials and Methods B1+ and variable flip angle (VFA) T1 mapping were included in our dynamic contrast-enhanced (DCE) breast imaging protocol to study a total of 25 patients on a 3.0T GE MR 750 system. We computed pre-contrast T1 relaxation in fat, which we assumed to be consistent across a cohort of breast imaging subjects, with and without compensation for B1+ variation. The mean and standard deviation of B1+ and T1 values were calculated for statistical data analysis. Results Our measurements showed a consistent B1+ field difference between the left and right breasts. The left breast has an average 15.4% higher flip angle than the prescribed flip angle, and the right breast has an average 17.6% lower flip angle than the prescribed flip angle. This average 33% flip angle difference, which can be vendor and model specific, creates a 52% T1 estimation bias in fat between breasts using the VFA T1 mapping technique. The T1 variation is reduced to 7% by including B1+ correction. Conclusion We have shown that severe B1+ variation over the breasts can cause a substantial error in T1 estimation between the breasts, in VFA T1 maps at 3T, but that compensating for these variations can considerably improve accuracy of T1 measurements, which can directly benefit quantitative breast DCE-MRI at 3T. PMID:23292822

  9. Transmit B1+ field inhomogeneity and T1 estimation errors in breast DCE-MRI at 3 tesla.

    Science.gov (United States)

    Sung, Kyunghyun; Daniel, Bruce L; Hargreaves, Brian A

    2013-08-01

    To quantify B1+ variation across the breasts and to evaluate the accuracy of precontrast T1 estimation with and without B1+ variation in breast MRI patients at 3 Tesla (T). B1+ and variable flip angle (VFA) T1 mapping were included in our dynamic contrast-enhanced (DCE) breast imaging protocol to study a total of 25 patients on a 3.0T GE MR 750 system. We computed precontrast T1 relaxation in fat, which we assumed to be consistent across a cohort of breast imaging subjects, with and without compensation for B1+ variation. The mean and standard deviation of B1+ and T1 values were calculated for statistical data analysis. Our measurements showed a consistent B1+ field difference between the left and right breasts. The left breast has an average 15.4% higher flip angle than the prescribed flip angle, and the right breast has an average 17.6% lower flip angle than the prescribed flip angle. This average 33% flip angle difference, which can be vendor and model specific, creates a 52% T1 estimation bias in fat between breasts using the VFA T1 mapping technique. The T1 variation is reduced to 7% by including B1+ correction. We have shown that severe B1+ variation over the breasts can cause a substantial error in T1 estimation between the breasts, in VFA T1 maps at 3T, but that compensating for these variations can considerably improve accuracy of T1 measurements, which can directly benefit quantitative breast DCE-MRI at 3T. Copyright © 2012 Wiley Periodicals, Inc.

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

    Science.gov (United States)

    Hack, Carolin C; Emons, Julius; Jud, Sebastian M; Heusinger, Katharina; Adler, Werner; Gass, Paul; Haeberle, Lothar; Heindl, Felix; Hein, Alexander; Schulz-Wendtland, Rüdiger; Uder, Michael; Hartmann, Arndt; Beckmann, Matthias W; Fasching, Peter A; Pöhls, Uwe G

    2017-12-01

    Percentage mammographic density (PMD) is a major risk factor for breast cancer (BC). It is strongly associated with body mass index (BMI) and age, which are themselves risk factors for breast cancer. This analysis investigated the association between the number of full-term pregnancies and PMD in different subgroups relative to age and BMI. Patients were identified in the breast cancer database of the University Breast Center for Franconia. A total of 2410 patients were identified, for whom information on parity, age, and BMI, and a mammogram from the time of first diagnosis were available for assessing PMD. Linear regression analyses were conducted to investigate the influence on PMD of the number of full-term pregnancies (FTPs), age, BMI, and interaction terms between them. As in previous studies, age, number of FTPs, and BMI were found to be associated with PMD in the expected direction. However, including the