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Sample records for breast density estimation

  1. Mammography density estimation with automated volumetic breast density measurement

    International Nuclear Information System (INIS)

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

    2014-01-01

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

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

    2000-06-01

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

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

    Science.gov (United States)

    Lee, Juhun; Nishikawa, Robert M

    2018-03-01

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

  5. Semiautomatic estimation of breast density with DM-Scan software.

    Science.gov (United States)

    Martínez Gómez, I; Casals El Busto, M; Antón Guirao, J; Ruiz Perales, F; Llobet Azpitarte, R

    2014-01-01

    To evaluate the reproducibility of the calculation of breast density with DM-Scan software, which is based on the semiautomatic segmentation of fibroglandular tissue, and to compare it with the reproducibility of estimation by visual inspection. The study included 655 direct digital mammograms acquired using craniocaudal projections. Three experienced radiologists analyzed the density of the mammograms using DM-Scan, and the inter- and intra-observer agreement between pairs of radiologists for the Boyd and BI-RADS® scales were calculated using the intraclass correlation coefficient. The Kappa index was used to compare the inter- and intra-observer agreements with those obtained previously for visual inspection in the same set of images. For visual inspection, the mean interobserver agreement was 0,876 (95% CI: 0,873-0,879) on the Boyd scale and 0,823 (95% CI: 0,818-0,829) on the BI-RADS® scale. The mean intraobserver agreement was 0,813 (95% CI: 0,796-0,829) on the Boyd scale and 0,770 (95% CI: 0,742-0,797) on the BI-RADS® scale. For DM-Scan, the mean inter- and intra-observer agreement was 0,92, considerably higher than the agreement for visual inspection. The semiautomatic calculation of breast density using DM-Scan software is more reliable and reproducible than visual estimation and reduces the subjectivity and variability in determining breast density. Copyright © 2012 SERAM. Published by Elsevier Espana. All rights reserved.

  6. 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.

  7. 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

  8. Automated volumetric breast density estimation: A comparison with visual assessment

    International Nuclear Information System (INIS)

    Seo, J.M.; Ko, E.S.; Han, B.-K.; Ko, E.Y.; Shin, J.H.; Hahn, S.Y.

    2013-01-01

    Aim: To compare automated volumetric breast density (VBD) measurement with visual assessment according to Breast Imaging Reporting and Data System (BI-RADS), and to determine the factors influencing the agreement between them. Materials and methods: One hundred and ninety-three consecutive screening mammograms reported as negative were included in the study. Three radiologists assigned qualitative BI-RADS density categories to the mammograms. An automated volumetric breast-density method was used to measure VBD (% breast density) and density grade (VDG). Each case was classified into an agreement or disagreement group according to the comparison between visual assessment and VDG. The correlation between visual assessment and VDG was obtained. Various physical factors were compared between the two groups. Results: Agreement between visual assessment by the radiologists and VDG was good (ICC value = 0.757). VBD showed a highly significant positive correlation with visual assessment (Spearman's ρ = 0.754, p < 0.001). VBD and the x-ray tube target was significantly different between the agreement group and the disagreement groups (p = 0.02 and 0.04, respectively). Conclusion: Automated VBD is a reliable objective method to measure breast density. The agreement between VDG and visual assessment by radiologist might be influenced by physical factors

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

    Science.gov (United States)

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

    2006-03-01

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

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

    Science.gov (United States)

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

    2009-01-01

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

  11. 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, p<0.001). Paired t-test comparison of breast PD% between the raw and the post-processed images showed a statistically significant difference equal to 1.2% (p = 0.006). Our results suggest that the relatively small magnitude of the absolute difference in PD% between raw and post-processed DM images is unlikely to be clinically significant in breast cancer risk stratification. Therefore, it may be feasible to use post-processed DM images for breast PD% estimation in clinical settings. Since most breast imaging clinics routinely use and store only the post-processed DM images, breast PD% estimation from post-processed data may accelerate the integration of breast density in breast cancer risk assessment models used in clinical practice.

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

    Science.gov (United States)

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

    2012-01-01

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

  13. 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.

  14. Digital Breast Tomosynthesis guided Near Infrared Spectroscopy: Volumetric estimates of fibroglandular fraction and breast density from tomosynthesis reconstructions.

    Science.gov (United States)

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

    A multimodality system combining a clinical prototype digital breast tomosynthesis with its imaging geometry modified to facilitate near-infrared spectroscopic imaging has been developed. The accuracy of parameters recovered from near-infrared spectroscopy is dependent on fibroglandular tissue content. Hence, in this study, volumetric estimates of fibroglandular tissue from tomosynthesis reconstructions were determined. A kernel-based fuzzy c-means algorithm was implemented to segment tomosynthesis reconstructed slices in order to estimate fibroglandular content and to provide anatomic priors for near-infrared spectroscopy. This algorithm was used to determine volumetric breast density (VBD), defined as the ratio of fibroglandular tissue volume to the total breast volume, expressed as percentage, from 62 tomosynthesis reconstructions of 34 study participants. For a subset of study participants who subsequently underwent mammography, VBD from mammography matched for subject, breast laterality and mammographic view was quantified using commercial software and statistically analyzed to determine if it differed from tomosynthesis. Summary statistics of the VBD from all study participants were compared with prior independent studies. The fibroglandular volume from tomosynthesis and mammography were not statistically different ( p =0.211, paired t-test). After accounting for the compressed breast thickness, which were different between tomosynthesis and mammography, the VBD from tomosynthesis was correlated with ( r =0.809, p 0.99, paired t-test), and was linearly related to, the VBD from mammography. Summary statistics of the VBD from tomosynthesis were not statistically different from prior studies using high-resolution dedicated breast computed tomography. The observation of correlation and linear association in VBD between mammography and tomosynthesis suggests that breast density associated risk measures determined for mammography are translatable to tomosynthesis

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2013-11-01

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

  17. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

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

    Science.gov (United States)

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

    2010-03-01

    We performed a study to compare methods for volumetric breast density estimation in digital mammography (DM) and magnetic resonance imaging (MRI) for a high-risk population of women. DM and MRI images of the unaffected breast from 32 women with recently detected abnormalities and/or previously diagnosed breast cancer (age range 31-78 yrs, mean 50.3 yrs) were retrospectively analyzed. DM images were analyzed using QuantraTM (Hologic Inc). The MRI images were analyzed using a fuzzy-C-means segmentation algorithm on the T1 map. Both methods were compared to Cumulus (Univ. Toronto). Volumetric breast density estimates from DM and MRI are highly correlated (r=0.90, pwomen with very low-density breasts (peffects in MRI and differences in the computational aspects of the image analysis methods in MRI and DM. The good correlation between the volumetric and the area-based measures, shown to correlate with breast cancer risk, suggests that both DM and MRI volumetric breast density measures can aid in breast cancer risk assessment. Further work is underway to fully-investigate the association between volumetric breast density measures and breast cancer risk.

  20. 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.

  1. 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

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

    Science.gov (United States)

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

    2012-03-01

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

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

  4. 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.

  5. 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...

  6. Variable Kernel Density Estimation

    OpenAIRE

    Terrell, George R.; Scott, David W.

    1992-01-01

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

  7. 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

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

    International Nuclear Information System (INIS)

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

    2013-01-01

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

  9. 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.

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

  11. 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)

  12. Histogram Estimators of Bivariate Densities

    National Research Council Canada - National Science Library

    Husemann, Joyce A

    1986-01-01

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

  13. 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.

  14. Mammographic Breast Density in Malaysian Women with Breast Cancer

    International Nuclear Information System (INIS)

    Noriah Jamal; Humairah Samad Cheung

    2016-01-01

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

  15. Evaluation of breast parenchymal density with QUANTRA software

    International Nuclear Information System (INIS)

    Pahwa, Shivani; Hari, Smriti; Thulkar, Sanjay; Angraal, Suveen

    2015-01-01

    To evaluate breast parenchymal density using QUANTRA software and to correlate numerical breast density values obtained from QUANTRA with ACR BI-RADS breast density categories. Two-view digital mammograms of 545 consecutive women (mean age - 47.7 years) were categorized visually by three independent radiologists into one of the four ACR BI-RADS categories (D1-D4). Numerical breast density values as obtained by QUANTRA software were then used to establish the cutoff values for each category using receiver operator characteristic (ROC) analysis. Numerical breast density values obtained by QUANTRA (range - 7-42%) were systematically lower than visual estimates. QUANTRA breast density value of less than 14.5% could accurately differentiate category D1 from the categories D2, D3, and D4 [area under curve (AUC) on ROC analysis - 94.09%, sensitivity - 85.71%, specificity - 84.21%]. QUANTRA density values of <19.5% accurately differentiated categories D1 and D2 from D3 and D4 (AUC - 94.4%, sensitivity - 87.50%, specificity - 84.60%); QUANTRA density values of <26.5% accurately differentiated categories D1, D2, and D3 from category D4 (AUC - 90.75%, sensitivity - 88.89%, specificity - 88.621%). Breast density values obtained by QUANTRA software can be used to obtain objective cutoff values for each ACR BI-RADS breast density category. Although the numerical density values obtained by QUANTRA are lower than visual estimates, they correlate well with the BI-RADS breast density categories assigned visually to the mammograms

  16. 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.

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

  18. Quantitative assessment of breast density: comparison of different methods

    International Nuclear Information System (INIS)

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

    2011-01-01

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

  19. 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

  20. Quantitative assessment of breast density from mammograms

    International Nuclear Information System (INIS)

    Jamal, N.; Ng, K.H.

    2004-01-01

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

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

    Science.gov (United States)

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

    2015-10-01

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

  2. Optimization of Barron density estimates

    Czech Academy of Sciences Publication Activity Database

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

    2001-01-01

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

  3. Comparison of density estimators. [Estimation of probability density functions

    Energy Technology Data Exchange (ETDEWEB)

    Kao, S.; Monahan, J.F.

    1977-09-01

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

  4. 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.

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

    NARCIS (Netherlands)

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

    2017-01-01

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

  6. 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 ...

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

    Science.gov (United States)

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

    2013-07-17

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

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

    Science.gov (United States)

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

    2013-06-01

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

  9. Cumulative sum quality control for calibrated breast density measurements

    International Nuclear Information System (INIS)

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

    2009-01-01

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

  10. Cumulative sum quality control for calibrated breast density measurements

    Energy Technology Data Exchange (ETDEWEB)

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

    2009-12-15

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

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

    OpenAIRE

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

    2014-01-01

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

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

    International Nuclear Information System (INIS)

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

    2017-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Asim Jamal Shaikh

    2018-01-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2018-04-23

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

  16. 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.

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

    International Nuclear Information System (INIS)

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

    2016-01-01

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

  18. 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

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

    Science.gov (United States)

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

    2018-01-01

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

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

    OpenAIRE

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

    2017-01-01

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

  1. 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

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

    International Nuclear Information System (INIS)

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

    2013-01-01

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

  3. 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

  4. 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.

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

    International Nuclear Information System (INIS)

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

    2013-01-01

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

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

    Science.gov (United States)

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

    2018-05-01

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

  7. 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...

  8. 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.

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

  10. 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.

  11. 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...

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

    International Nuclear Information System (INIS)

    Ng, Kwan-Hoong; Lau, Susie

    2015-01-01

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

  13. 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.

  14. 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.

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

    OpenAIRE

    Stone, Jennifer; Thompson, Deborah J.; dos-Santos-Silva, Isabel; Scott, Christopher; Tamimi, Rulla M.; Lindstrom, Sara; Kraft, Peter; Hazra, Aditi; Li, Jingmei; Eriksson, Louise; Czene, Kamila; Hall, Per; Jensen, Matt; Cunningham, Julie; Olson, Janet E.

    2015-01-01

    Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute non-dense area adjusted for study, age and BMI using mixed linear modeling. We found strong suppo...

  16. 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...

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

    International Nuclear Information System (INIS)

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

    2010-01-01

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

  18. Nonparametric methods for volatility density estimation

    NARCIS (Netherlands)

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

    2009-01-01

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

  19. ADN* Density log estimation Using Rockcell*

    International Nuclear Information System (INIS)

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

    2003-01-01

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

  20. 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.

  1. 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...

  2. 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.

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2013-12-01

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

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

    International Nuclear Information System (INIS)

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

    2013-01-01

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

  6. 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.

  7. 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  

  8. 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.

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

    Science.gov (United States)

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

    2013-01-01

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

  10. High throughput nonparametric probability density estimation.

    Science.gov (United States)

    Farmer, Jenny; Jacobs, Donald

    2018-01-01

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

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

    Science.gov (United States)

    2015-12-01

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

  12. 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

  13. Nonparametric Collective Spectral Density Estimation and Clustering

    KAUST Repository

    Maadooliat, Mehdi; Sun, Ying; Chen, Tianbo

    2017-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

    To investigate the probability of breast cancer among women recalled due to abnormal findings on the screening mammograms (PPV-1) and among women who underwent an invasive procedure (PPV-2) by mammographic density (MD), screening mode and age. We used information about 28,826 recall examinations from 26,951 subsequently screened women in the Norwegian Breast Cancer Screening Program, 1996-2010. The radiologists who performed the recall examinations subjectively classified MD on the mammograms into three categories: fatty (70%). Screening mode was defined as screen-film mammography (SFM) and full-field digital mammography (FFDM). We examined trends of PPVs by MD, screening mode and age. We used logistic regression to estimate odds ratio (OR) of screen-detected breast cancer associated with MD among women recalled, adjusting for screening mode and age. PPV-1 and PPV-2 decreased by increasing MD, regardless of screening mode (p for trend breasts. Among women recalled, the adjusted OR of breast cancer decreased with increasing MD. Compared with women with fatty breasts, the OR was 0.90 (95% CI: 0.84-0.96) for those with medium dense breasts and 0.85 (95% CI: 0.76-0.95) for those with dense breasts. PPVs decreased by increasing MD. Fewer women needed to be recalled or undergo an invasive procedure to detect one breast cancer among those with fatty versus dense breasts in the screening program in Norway, 1996-2010. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  15. 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.

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

    Science.gov (United States)

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

    2013-02-01

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

  17. 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

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

    NARCIS (Netherlands)

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

    2015-01-01

    The objective of this study is to compare different methods for measuring breast density, both visual assessments and automated volumetric density, in a breast cancer screening setting. These measures could potentially be implemented in future screening programmes, in the context of personalised

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

    NARCIS (Netherlands)

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

    2015-01-01

    INTRODUCTION: The objective of this study is to compare different methods for measuring breast density, both visual assessments and automated volumetric density, in a breast cancer screening setting. These measures could potentially be implemented in future screening programmes, in the context of

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

    NARCIS (Netherlands)

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

    2015-01-01

    Introduction The objective of this study is to compare different methods for measuring breast density, both visual assessments and automated volumetric density, in a breast cancer screening setting. These measures could potentially be implemented in future screening programmes, in the context of

  1. Estimation and display of beam density profiles

    Energy Technology Data Exchange (ETDEWEB)

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

    1989-03-15

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

  2. 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...

  3. Density estimation by maximum quantum entropy

    International Nuclear Information System (INIS)

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

    1993-01-01

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

  4. A New Model for the Estimation of Breast Cancer Risk

    National Research Council Canada - National Science Library

    Giger, Maryellen Lissak

    2001-01-01

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

  5. 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.

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

    DEFF Research Database (Denmark)

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

    2014-01-01

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

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

    International Nuclear Information System (INIS)

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

    2011-01-01

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

  8. 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.

  9. 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...

  10. 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.

  11. 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.

  12. Infrared thermography for wood density estimation

    Science.gov (United States)

    López, Gamaliel; Basterra, Luis-Alfonso; Acuña, Luis

    2018-03-01

    Infrared thermography (IRT) is becoming a commonly used technique to non-destructively inspect and evaluate wood structures. Based on the radiation emitted by all objects, this technique enables the remote visualization of the surface temperature without making contact using a thermographic device. The process of transforming radiant energy into temperature depends on many parameters, and interpreting the results is usually complicated. However, some works have analyzed the operation of IRT and expanded its applications, as found in the latest literature. This work analyzes the effect of density on the thermodynamic behavior of timber to be determined by IRT. The cooling of various wood samples has been registered, and a statistical procedure that enables one to quantitatively estimate the density of timber has been designed. This procedure represents a new method to physically characterize this material.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-12-15

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

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

    Science.gov (United States)

    French, David P; Southworth, Jake; Howell, Anthony; Harvie, Michelle; Stavrinos, Paula; Watterson, Donna; Sampson, Sarah; Evans, D Gareth; Donnelly, Louise S

    2018-05-08

    The Predicting Risk of Cancer at Screening (PROCAS) study estimated 10-year breast cancer risk for 53,596 women attending NHS Breast Screening Programme. The present study, nested within the PROCAS study, aimed to assess the psychological impact of receiving breast cancer risk estimates, based on: (a) the Tyrer-Cuzick (T-C) algorithm including breast density or (b) T-C including breast density plus single-nucleotide polymorphisms (SNPs), versus (c) comparison women awaiting results. A sample of 2138 women from the PROCAS study was stratified by testing groups: T-C only, T-C(+SNPs) and comparison women; and by 10-year risk estimates received: 'moderate' (5-7.99%), 'average' (2-4.99%) or 'below average' (<1.99%) risk. Postal questionnaires were returned by 765 (36%) women. Overall state anxiety and cancer worry were low, and similar for women in T-C only and T-C(+SNPs) groups. Women in both T-C only and T-C(+SNPs) groups showed lower-state anxiety but slightly higher cancer worry than comparison women awaiting results. Risk information had no consistent effects on intentions to change behaviour. Most women were satisfied with information provided. There was considerable variation in understanding. No major harms of providing women with 10-year breast cancer risk estimates were detected. Research to establish the feasibility of risk-stratified breast screening is warranted.

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

    CSIR Research Space (South Africa)

    Van der Walt, Christiaan M

    2017-02-01

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

  16. 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

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

    NARCIS (Netherlands)

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

    2008-01-01

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

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2016-11-15

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

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

    Science.gov (United States)

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

    2017-04-01

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

  2. 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.

  3. 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\\

  4. 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

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

    Science.gov (United States)

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

    2017-12-01

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

  6. 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.

  7. 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.

  8. 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.

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

    Science.gov (United States)

    Rayner, Millicent; Harkness, Elaine F.; Foden, Philip; Wilson, Mary; Gadde, Soujanya; Beetles, Ursula; Lim, Yit Y.; Jain, Anil; Bundred, Sally; Barr, Nicky; Evans, D. Gareth; Howell, Anthony; Maxwell, Anthony; Astley, Susan M.

    2018-03-01

    Mammographic breast density is one of the strongest risk factors for breast cancer, and is used in risk prediction and for deciding appropriate imaging strategies. In the Predicting Risk Of Cancer At Screening (PROCAS) study, percent density estimated by two readers on Visual Analogue Scales (VAS) has shown a strong relationship with breast cancer risk when assessed against automated methods. However, this method suffers from reader variability. This study aimed to assess the performance of PROCAS readers using VAS, and to identify those most predictive of breast cancer. We selected the seven readers who had estimated density on over 6,500 women including at least 100 cancer cases, analysing their performance using multivariable logistic regression and Receiver Operator Characteristic (ROC) analysis. All seven readers showed statistically significant odds ratios (OR) for cancer risk according to VAS score after adjusting for classical risk factors. The OR was greatest for reader 18 at 1.026 (95% Cl 1.018-1.034). Adjusted Area Under the ROC Curves (AUCs) were statistically significant for all readers, but greatest for reader 14 at 0.639. Further analysis of the VAS scores for these two readers showed reader 14 had higher sensitivity (78.0% versus 42.2%), whereas reader 18 had higher specificity (78.0% versus 46.0%). Our results demonstrate individual differences when assigning VAS scores; one better identified those with increased risk, whereas another better identified low risk individuals. However, despite their different strengths, both readers showed similar predictive abilities overall. Standardised training for VAS may improve reader variability and consistency of VAS scoring.

  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. 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.

  12. Mammographic Breast Density Patterns in Asymptomatic Mexican Women

    International Nuclear Information System (INIS)

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

    2012-01-01

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

  13. Mammographic Breast Density Patterns in Asymptomatic Mexican Women

    International Nuclear Information System (INIS)

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

    2012-01-01

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

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2017-07-01

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

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

    Science.gov (United States)

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

    2010-01-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2017-09-01

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

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

    International Nuclear Information System (INIS)

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

    2006-01-01

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

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

    Science.gov (United States)

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

    2015-05-01

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

  1. Concrete density estimation by rebound hammer method

    International Nuclear Information System (INIS)

    Ismail, Mohamad Pauzi bin; Masenwat, Noor Azreen bin; Sani, Suhairy bin; Mohd, Shukri; Jefri, Muhamad Hafizie Bin; Abdullah, Mahadzir Bin; Isa, Nasharuddin bin; Mahmud, Mohamad Haniza bin

    2016-01-01

    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

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

    Science.gov (United States)

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

    2018-04-06

    Increased breast density is a significant independent risk factor for breast cancer, and recent studies show that this risk is modifiable. Hence, breast density measures sensitive to small changes are desired. Utilizing fat-water decomposition MRI, we propose an automated, reproducible breast density measurement, which is nonionizing and directly comparable to mammographic density (MD). Retrospective study. The study included two sample sets of breast cancer patients enrolled in a clinical trial, for concordance analysis with MD (40 patients) and reproducibility analysis (10 patients). The majority of MRI scans (59 scans) were performed with a 1.5T GE Signa scanner using radial IDEAL-GRASE sequence, while the remaining (seven scans) were performed with a 3T Siemens Skyra using 3D Cartesian 6-echo GRE sequence with a similar fat-water separation technique. After automated breast segmentation, breast density was calculated using FraGW, a new measure developed to reliably reflect the amount of fibroglandular tissue and total water content in the entire breast. Based on its concordance with MD, FraGW was calibrated to MR-based breast density (MRD) to be comparable to MD. A previous breast density measurement, Fra80-the ratio of breast voxels with density changes and treatment response. 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018. © 2018 International Society for Magnetic Resonance in Medicine.

  3. Graphs to estimate an individualized risk of breast cancer.

    Science.gov (United States)

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

    1996-01-01

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

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

    International Nuclear Information System (INIS)

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

    2004-01-01

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

  5. On Improving Convergence Rates for Nonnegative Kernel Density Estimators

    OpenAIRE

    Terrell, George R.; Scott, David W.

    1980-01-01

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

  6. Estimating diurnal primate densities using distance sampling ...

    African Journals Online (AJOL)

    SARAH

    2016-03-31

    Mar 31, 2016 ... In the second session, we used 10 transect adjusted to transect (Grid 17 ... session transect was visited 20 times while at the second session transect ... probability, the density of the group and the group size of each species ...

  7. 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.

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

    Science.gov (United States)

    Bae, Jong-Myon; Kim, Eun Hee

    2016-11-01

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

  9. 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.

  10. 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.

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

    NARCIS (Netherlands)

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

    2015-01-01

    textabstractBackground. Mammographic breast density is one of the strongest independent risk factors for developing breast cancer. We examined the effect of exemestane and tamoxifen on breast density in Dutch postmenopausal early breast cancer patients participating in the Tamoxifen Exemestane

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

    DEFF Research Database (Denmark)

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

    2018-01-01

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

  13. 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.)

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

    International Nuclear Information System (INIS)

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

    2017-01-01

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

  15. Nonparametric volatility density estimation for discrete time models

    NARCIS (Netherlands)

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

    2005-01-01

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

  16. 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.

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

    Science.gov (United States)

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

    2018-05-01

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

  18. Information geometry of density matrices and state estimation

    International Nuclear Information System (INIS)

    Brody, Dorje C

    2011-01-01

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

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

    Science.gov (United States)

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

    2015-04-01

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

  20. Association between mammogram density and background parenchymal enhancement of breast MRI

    Science.gov (United States)

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

    2018-02-01

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

  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. Current Source Density Estimation for Single Neurons

    Directory of Open Access Journals (Sweden)

    Dorottya Cserpán

    2014-03-01

    Full Text Available Recent developments of multielectrode technology made it possible to measure the extracellular potential generated in the neural tissue with spatial precision on the order of tens of micrometers and on submillisecond time scale. Combining such measurements with imaging of single neurons within the studied tissue opens up new experimental possibilities for estimating distribution of current sources along a dendritic tree. In this work we show that if we are able to relate part of the recording of extracellular potential to a specific cell of known morphology we can estimate the spatiotemporal distribution of transmembrane currents along it. We present here an extension of the kernel CSD method (Potworowski et al., 2012 applicable in such case. We test it on several model neurons of progressively complicated morphologies from ball-and-stick to realistic, up to analysis of simulated neuron activity embedded in a substantial working network (Traub et al, 2005. We discuss the caveats and possibilities of this new approach.

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

    OpenAIRE

    Thomas Jerry A; Cao Ke; Heine John J

    2010-01-01

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

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

    OpenAIRE

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

    2008-01-01

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

  5. 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...

  6. 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.

  7. 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

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Park B

    2017-12-01

    Full Text Available Boyoung Park,1,2 Hye Mi Cho,2 Eun Hye Lee,3 Seunghoon Song,2 Mina Suh,2 Kui Son Choi,1,2 Bong Joo Kang,4 Kyungran Ko,5 Ann Yi,6 Hae Kyoung Jung,7 Joo Hee Cha,8 Jae Kwan Jun,1,2 1National Cancer Center Graduate School of Cancer Science and Policy, Goyang, Republic of Korea; 2National Cancer Control Institute, National Cancer Center, Goyang, Republic of Korea; 3Department of Radiology, Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Republic of Korea; 4Department of Radiology, Seoul St Mary’s Hospital, Catholic University of Korea College of Medicine, Seoul, Republic of Korea; 5Center for Breast Cancer, National Cancer Center Hospital, National Cancer Center, Goyang, Republic of Korea; 6Department of Radiology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea; 7Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea; 8Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea Purpose: The purpose of this study was to investigate the effects of breast density on breast cancer risk among women screened via a nationwide mammographic screening program. Patients and methods: We conducted a nested case–control study for a randomly selected population of 1,561 breast cancer patients and 6,002 matched controls from the National Cancer Screening Program. Breast density was measured and recorded by two independent radiologists using the Breast Imaging Reporting and Data System (BI-RADS. Associations between BI-RADS density and breast cancer risk were evaluated according to screening results, time elapsed since receiving non-recall results, age, and menopausal status after adjusting for possible covariates. Results: Breast cancer risk for women with extremely dense breasts was five times higher (adjusted odds ratio [aOR] =5.0; 95% confidence interval [CI] =3

  10. 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.

  11. Regularized Regression and Density Estimation based on Optimal Transport

    KAUST Repository

    Burger, M.; Franek, M.; Schonlieb, C.-B.

    2012-01-01

    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

  12. Improved Variable Window Kernel Estimates of Probability Densities

    OpenAIRE

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

    1995-01-01

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

  13. On Improving Density Estimators which are not Bona Fide Functions

    OpenAIRE

    Gajek, Leslaw

    1986-01-01

    In order to improve the rate of decrease of the IMSE for nonparametric kernel density estimators with nonrandom bandwidth beyond $O(n^{-4/5})$ all current methods must relax the constraint that the density estimate be a bona fide function, that is, be nonnegative and integrate to one. In this paper we show how to achieve similar improvement without relaxing any of these constraints. The method can also be applied for orthogonal series, adaptive orthogonal series, spline, jackknife, and other ...

  14. 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.

  15. 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.

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

    International Nuclear Information System (INIS)

    Soares, Deanne; Reid, Marvin; James, Michael

    2002-01-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

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

    International Nuclear Information System (INIS)

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

    2004-01-01

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

  19. 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.

  20. Optimal Bandwidth Selection for Kernel Density Functionals Estimation

    Directory of Open Access Journals (Sweden)

    Su Chen

    2015-01-01

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

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

    Science.gov (United States)

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

    2003-01-01

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

  2. Probability Density Estimation Using Neural Networks in Monte Carlo Calculations

    International Nuclear Information System (INIS)

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

    2008-01-01

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

  3. 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.

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

    Science.gov (United States)

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

    2014-12-01

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

  5. 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

  6. 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

  7. Gradient-based stochastic estimation of the density matrix

    Science.gov (United States)

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

    2018-03-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Soares, Deanne; Reid, Marvin; James, Michael

    2002-06-01

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

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

    Science.gov (United States)

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

    2017-12-16

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

  10. 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

  11. 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

  12. A new approach for estimating the density of liquids.

    Science.gov (United States)

    Sakagami, T; Fuchizaki, K; Ohara, K

    2016-10-05

    We propose a novel approach with which to estimate the density of liquids. The approach is based on the assumption that the systems would be structurally similar when viewed at around the length scale (inverse wavenumber) of the first peak of the structure factor, unless their thermodynamic states differ significantly. The assumption was implemented via a similarity transformation to the radial distribution function to extract the density from the structure factor of a reference state with a known density. The method was first tested using two model liquids, and could predict the densities within an error of several percent unless the state in question differed significantly from the reference state. The method was then applied to related real liquids, and satisfactory results were obtained for predicted densities. The possibility of applying the method to amorphous materials is discussed.

  13. 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

  14. 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.

  15. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2006-11-21

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

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

    International Nuclear Information System (INIS)

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

    2006-01-01

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

  18. 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...

  19. Estimate of energy density on CYCLOPS spatial filter pinhole structure

    International Nuclear Information System (INIS)

    Guch, S. Jr.

    1974-01-01

    The inclusion of a spatial filter between the B and C stages in CYCLOPS to reduce the effects of small-scale beam self-focusing is discussed. An estimate is made of the energy density to which the pinhole will be subjected, and the survivability of various pinhole materials and designs is discussed

  20. State of the Art in Photon-Density Estimation

    DEFF Research Database (Denmark)

    Hachisuka, Toshiya; Jarosz, Wojciech; Georgiev, Iliyan

    2013-01-01

    scattering. Since its introduction, photon-density estimation has been significantly extended in computer graphics with the introduction of: specialized techniques that intelligently modify the positions or bandwidths to reduce visual error using a small number of photons, approaches that eliminate error...

  1. State of the Art in Photon Density Estimation

    DEFF Research Database (Denmark)

    Hachisuka, Toshiya; Jarosz, Wojciech; Bouchard, Guillaume

    2012-01-01

    scattering. Since its introduction, photon-density estimation has been significantly extended in computer graphics with the introduction of: specialized techniques that intelligently modify the positions or bandwidths to reduce visual error using a small number of photons, approaches that eliminate error...

  2. 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 ...

  3. 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...

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

    African Journals Online (AJOL)

    Even though data on soil bulk density (BD) and total nitrogen (TN) are essential for planning modern farming techniques, their data availability is limited for many applications in the developing word. This study is designed to estimate BD and TN from soil properties, land-use systems, soil types and landforms in the ...

  5. 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.

  6. 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

  7. 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.

  8. 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).

  9. 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

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

    Science.gov (United States)

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

    2018-01-01

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

  11. Evaluating lidar point densities for effective estimation of aboveground biomass

    Science.gov (United States)

    Wu, Zhuoting; Dye, Dennis G.; Stoker, Jason M.; Vogel, John M.; Velasco, Miguel G.; Middleton, Barry R.

    2016-01-01

    The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) was recently established to provide airborne lidar data coverage on a national scale. As part of a broader research effort of the USGS to develop an effective remote sensing-based methodology for the creation of an operational biomass Essential Climate Variable (Biomass ECV) data product, we evaluated the performance of airborne lidar data at various pulse densities against Landsat 8 satellite imagery in estimating above ground biomass for forests and woodlands in a study area in east-central Arizona, U.S. High point density airborne lidar data, were randomly sampled to produce five lidar datasets with reduced densities ranging from 0.5 to 8 point(s)/m2, corresponding to the point density range of 3DEP to provide national lidar coverage over time. Lidar-derived aboveground biomass estimate errors showed an overall decreasing trend as lidar point density increased from 0.5 to 8 points/m2. Landsat 8-based aboveground biomass estimates produced errors larger than the lowest lidar point density of 0.5 point/m2, and therefore Landsat 8 observations alone were ineffective relative to airborne lidar for generating a Biomass ECV product, at least for the forest and woodland vegetation types of the Southwestern U.S. While a national Biomass ECV product with optimal accuracy could potentially be achieved with 3DEP data at 8 points/m2, our results indicate that even lower density lidar data could be sufficient to provide a national Biomass ECV product with accuracies significantly higher than that from Landsat observations alone.

  12. Estimating the effect of urban density on fuel demand

    Energy Technology Data Exchange (ETDEWEB)

    Karathodorou, Niovi; Graham, Daniel J. [Imperial College London, London, SW7 2AZ (United Kingdom); Noland, Robert B. [Rutgers University, New Brunswick, NJ 08901 (United States)

    2010-01-15

    Much of the empirical literature on fuel demand presents estimates derived from national data which do not permit any explicit consideration of the spatial structure of the economy. Intuitively we would expect the degree of spatial concentration of activities to have a strong link with transport fuel consumption. The present paper addresses this theme by estimating a fuel demand model for urban areas to provide a direct estimate of the elasticity of demand with respect to urban density. Fuel demand per capita is decomposed into car stock per capita, fuel consumption per kilometre and annual distance driven per car per year. Urban density is found to affect fuel consumption, mostly through variations in the car stock and in the distances travelled, rather than through fuel consumption per kilometre. (author)

  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. 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

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2018-06-05

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

  17. Dual-Layer Density Estimation for Multiple Object Instance Detection

    Directory of Open Access Journals (Sweden)

    Qiang Zhang

    2016-01-01

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

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

    Science.gov (United States)

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

    2018-05-02

    Texture patterns have been shown to improve breast cancer risk segregation in addition to area-based mammographic density. The additional value of texture pattern scores on top of volumetric mammographic density measures in a large screening cohort has never been studied. Volumetric mammographic density and texture pattern scores were assessed automatically for the first available digital mammography (DM) screening examination of 51,400 women (50-75 years of age) participating in the Dutch biennial breast cancer screening program between 2003 and 2011. The texture assessment method was developed in a previous study and validated in the current study. Breast cancer information was obtained from the screening registration system and through linkage with the Netherlands Cancer Registry. All screen-detected breast cancers diagnosed at the first available digital screening examination were excluded. During a median follow-up period of 4.2 (interquartile range (IQR) 2.0-6.2) years, 301 women were diagnosed with breast cancer. The associations between texture pattern scores, volumetric breast density measures and breast cancer risk were determined using Cox proportional hazard analyses. Discriminatory performance was assessed using c-indices. The median age of the women at the time of the first available digital mammography examination was 56 years (IQR 51-63). Texture pattern scores were positively associated with breast cancer risk (hazard ratio (HR) 3.16 (95% CI 2.16-4.62) (p value for trend <0.001), for quartile (Q) 4 compared to Q1). The c-index of texture was 0.61 (95% CI 0.57-0.64). Dense volume and percentage dense volume showed positive associations with breast cancer risk (HR 1.85 (95% CI 1.32-2.59) (p value for trend <0.001) and HR 2.17 (95% CI 1.51-3.12) (p value for trend <0.001), respectively, for Q4 compared to Q1). When adding texture measures to models with dense volume or percentage dense volume, c-indices increased from 0.56 (95% CI 0.53-0.59) to 0

  19. 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.

  20. Covariance and correlation estimation in electron-density maps.

    Science.gov (United States)

    Altomare, Angela; Cuocci, Corrado; Giacovazzo, Carmelo; Moliterni, Anna; Rizzi, Rosanna

    2012-03-01

    Quite recently two papers have been published [Giacovazzo & Mazzone (2011). Acta Cryst. A67, 210-218; Giacovazzo et al. (2011). Acta Cryst. A67, 368-382] which calculate the variance in any point of an electron-density map at any stage of the phasing process. The main aim of the papers was to associate a standard deviation to each pixel of the map, in order to obtain a better estimate of the map reliability. This paper deals with the covariance estimate between points of an electron-density map in any space group, centrosymmetric or non-centrosymmetric, no matter the correlation between the model and target structures. The aim is as follows: to verify if the electron density in one point of the map is amplified or depressed as an effect of the electron density in one or more other points of the map. High values of the covariances are usually connected with undesired features of the map. The phases are the primitive random variables of our probabilistic model; the covariance changes with the quality of the model and therefore with the quality of the phases. The conclusive formulas show that the covariance is also influenced by the Patterson map. Uncertainty on measurements may influence the covariance, particularly in the final stages of the structure refinement; a general formula is obtained taking into account both phase and measurement uncertainty, valid at any stage of the crystal structure solution.

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

  2. 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...

  3. Improving Frozen Precipitation Density Estimation in Land Surface Modeling

    Science.gov (United States)

    Sparrow, K.; Fall, G. M.

    2017-12-01

    The Office of Water Prediction (OWP) produces high-value water supply and flood risk planning information through the use of operational land surface modeling. Improvements in diagnosing frozen precipitation density will benefit the NWS's meteorological and hydrological services by refining estimates of a significant and vital input into land surface models. A current common practice for handling the density of snow accumulation in a land surface model is to use a standard 10:1 snow-to-liquid-equivalent ratio (SLR). Our research findings suggest the possibility of a more skillful approach for assessing the spatial variability of precipitation density. We developed a 30-year SLR climatology for the coterminous US from version 3.22 of the Daily Global Historical Climatology Network - Daily (GHCN-D) dataset. Our methods followed the approach described by Baxter (2005) to estimate mean climatological SLR values at GHCN-D sites in the US, Canada, and Mexico for the years 1986-2015. In addition to the Baxter criteria, the following refinements were made: tests were performed to eliminate SLR outliers and frequent reports of SLR = 10, a linear SLR vs. elevation trend was fitted to station SLR mean values to remove the elevation trend from the data, and detrended SLR residuals were interpolated using ordinary kriging with a spherical semivariogram model. The elevation values of each station were based on the GMTED 2010 digital elevation model and the elevation trend in the data was established via linear least squares approximation. The ordinary kriging procedure was used to interpolate the data into gridded climatological SLR estimates for each calendar month at a 0.125 degree resolution. To assess the skill of this climatology, we compared estimates from our SLR climatology with observations from the GHCN-D dataset to consider the potential use of this climatology as a first guess of frozen precipitation density in an operational land surface model. The difference in

  4. 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.

  5. Estimating black bear density using DNA data from hair snares

    Science.gov (United States)

    Gardner, B.; Royle, J. Andrew; Wegan, M.T.; Rainbolt, R.E.; Curtis, P.D.

    2010-01-01

    DNA-based mark-recapture has become a methodological cornerstone of research focused on bear species. The objective of such studies is often to estimate population size; however, doing so is frequently complicated by movement of individual bears. Movement affects the probability of detection and the assumption of closure of the population required in most models. To mitigate the bias caused by movement of individuals, population size and density estimates are often adjusted using ad hoc methods, including buffering the minimum polygon of the trapping array. We used a hierarchical, spatial capturerecapture model that contains explicit components for the spatial-point process that governs the distribution of individuals and their exposure to (via movement), and detection by, traps. We modeled detection probability as a function of each individual's distance to the trap and an indicator variable for previous capture to account for possible behavioral responses. We applied our model to a 2006 hair-snare study of a black bear (Ursus americanus) population in northern New York, USA. Based on the microsatellite marker analysis of collected hair samples, 47 individuals were identified. We estimated mean density at 0.20 bears/km2. A positive estimate of the indicator variable suggests that bears are attracted to baited sites; therefore, including a trap-dependence covariate is important when using bait to attract individuals. Bayesian analysis of the model was implemented in WinBUGS, and we provide the model specification. The model can be applied to any spatially organized trapping array (hair snares, camera traps, mist nests, etc.) to estimate density and can also account for heterogeneity and covariate information at the trap or individual level. ?? The Wildlife Society.

  6. 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.

  7. Structural Reliability Using Probability Density Estimation Methods Within NESSUS

    Science.gov (United States)

    Chamis, Chrisos C. (Technical Monitor); Godines, Cody Ric

    2003-01-01

    A reliability analysis studies a mathematical model of a physical system taking into account uncertainties of design variables and common results are estimations of a response density, which also implies estimations of its parameters. Some common density parameters include the mean value, the standard deviation, and specific percentile(s) of the response, which are measures of central tendency, variation, and probability regions, respectively. Reliability analyses are important since the results can lead to different designs by calculating the probability of observing safe responses in each of the proposed designs. All of this is done at the expense of added computational time as compared to a single deterministic analysis which will result in one value of the response out of many that make up the density of the response. Sampling methods, such as monte carlo (MC) and latin hypercube sampling (LHS), can be used to perform reliability analyses and can compute nonlinear response density parameters even if the response is dependent on many random variables. Hence, both methods are very robust; however, they are computationally expensive to use in the estimation of the response density parameters. Both methods are 2 of 13 stochastic methods that are contained within the Numerical Evaluation of Stochastic Structures Under Stress (NESSUS) program. NESSUS is a probabilistic finite element analysis (FEA) program that was developed through funding from NASA Glenn Research Center (GRC). It has the additional capability of being linked to other analysis programs; therefore, probabilistic fluid dynamics, fracture mechanics, and heat transfer are only a few of what is possible with this software. The LHS method is the newest addition to the stochastic methods within NESSUS. Part of this work was to enhance NESSUS with the LHS method. The new LHS module is complete, has been successfully integrated with NESSUS, and been used to study four different test cases that have been

  8. Comparison of Breast Density Between Synthesized Versus Standard Digital Mammography.

    Science.gov (United States)

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

    2018-06-12

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

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

  10. 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.

  11. 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

  12. Estimating Cardiac Exposure From Breast Cancer Radiotherapy in Clinical Practice

    International Nuclear Information System (INIS)

    Taylor, C.W.; McGale, P.; Povall, J.M.; Thomas, E.; Kumar, S.; Dodwell, D.; Darby, S.C.

    2009-01-01

    Purpose: To assess the value of maximum heart distance (MHD) in predicting the dose and biologically effective dose (BED) to the heart and the left anterior descending (LAD) coronary artery for left-tangential breast or chest wall irradiation. Methods and Materials: A total of 50 consecutive breast cancer patients given adjuvant left-tangential irradiation at a large U.K. radiotherapy center during 2006 were selected. For each patient, the following were derived using three-dimensional computed tomography (CT) planning: (1) mean dose and BED to the heart, (2) mean dose and BED to the LAD coronary artery, (3) MHD, (4) position of the CT slice showing the maximum area of the irradiated heart relative to the mid-plane slice, and (5) sternal and contralateral breast thickness (measures of body fat). Results: A strong linear correlation was found between the MHD and the mean heart dose. For every 1-cm increase in MHD, the mean heart dose increased by 2.9% on average (95% confidence interval 2.5-3.3). A strong linear-quadratic relationship was seen between the MHD and the mean heart BED. The mean LAD coronary artery dose and BED were also correlated with the MHD but the associations were weaker. These relationships were not affected by body fat. The mid-plane CT slice did not give a reliable assessment of cardiac irradiation. Conclusion: The MHD is a reliable predictor of the mean heart dose and BED and gives an approximate estimate of the mean LAD coronary artery dose and BED. Doses predicted by the MHD could help assess the risk of radiation-induced cardiac toxicity where individual CT-based cardiac dosimetry is not possible

  13. 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.

  14. Volumetric breast density affects performance of digital screening mammography

    OpenAIRE

    Wanders, JO; Holland, K; Veldhuis, WB; Mann, RM; Pijnappel, RM; Peeters, PH; Van Gils, CH; Karssemeijer, N

    2016-01-01

    PURPOSE: To determine to what extent automatically measured volumetric mammographic density influences screening performance when using digital mammography (DM). METHODS: We collected a consecutive series of 111,898 DM examinations (2003-2011) from one screening unit of the Dutch biennial screening program (age 50-75 years). Volumetric mammographic density was automatically assessed using Volpara. We determined screening performance measures for four density categories comparable to the Ameri...

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

    International Nuclear Information System (INIS)

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

    1978-01-01

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

  16. 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.

  17. Function of Maximal Microvessel Density in Breast Tumor Metastasis

    National Research Council Canada - National Science Library

    McLeskey, Sandra

    2000-01-01

    .... These data are gained by quantitating the number of microvessels in "hot spots" of high-density tumor vasculature, implying that such hot spots have functional significance in the process of metastasis...

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

    International Nuclear Information System (INIS)

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

    2005-01-01

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

  19. 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

  20. 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.)

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

    International Nuclear Information System (INIS)

    Destounis, Stamatia; Hanson, Sarah; Roehrig, Jimmy

    2007-01-01

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

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

    Science.gov (United States)

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

    2013-02-01

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

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

    Science.gov (United States)

    Othman, Khairulnizam; Ahmad, Afandi

    2016-11-01

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

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

    Science.gov (United States)

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

    2008-01-01

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

  5. Review of methods for level density estimation from resonance parameters

    International Nuclear Information System (INIS)

    Froehner, F.H.

    1983-01-01

    A number of methods are available for statistical analysis of resonance parameter sets, i.e. for estimation of level densities and average widths with account of missing levels. The main categories are (i) methods based on theories of level spacings (orthogonal-ensemble theory, Dyson-Mehta statistics), (ii) methods based on comparison with simulated cross section curves (Monte Carlo simulation, Garrison's autocorrelation method), (iii) methods exploiting the observed neutron width distribution by means of Bayesian or more approximate procedures such as maximum-likelihood, least-squares or moment methods, with various recipes for the treatment of detection thresholds and resolution effects. The present review will concentrate on (iii) with the aim of clarifying the basic mathematical concepts and the relationship between the various techniques. Recent theoretical progress in the treatment of resolution effects, detectability thresholds and p-wave admixture is described. (Auth.)

  6. HEDPIN: a computer program to estimate pinwise power density

    International Nuclear Information System (INIS)

    Cappiello, M.W.

    1976-05-01

    A description is given of the digital computer program, HEDPIN. This program, modeled after a previously developed program, POWPIN, provides a means of estimating the pinwise power density distribution in fast reactor triangular pitched pin bundles. The capability also exists for computing any reaction rate of interest at the respective pin positions within an assembly. HEDPIN was developed in support of FTR fuel and test management as well as fast reactor core design and core characterization planning and analysis. The results of a test devised to check out HEDPIN's computational method are given, and the realm of application is discussed. Nearly all programming is in FORTRAN IV. Variable dimensioning is employed to make efficient use of core memory and maintain short running time for small problems. Input instructions, sample problem, and a program listing are also given

  7. Cortical cell and neuron density estimates in one chimpanzee hemisphere.

    Science.gov (United States)

    Collins, Christine E; Turner, Emily C; Sawyer, Eva Kille; Reed, Jamie L; Young, Nicole A; Flaherty, David K; Kaas, Jon H

    2016-01-19

    The density of cells and neurons in the neocortex of many mammals varies across cortical areas and regions. This variability is, perhaps, most pronounced in primates. Nonuniformity in the composition of cortex suggests regions of the cortex have different specializations. Specifically, regions with densely packed neurons contain smaller neurons that are activated by relatively few inputs, thereby preserving information, whereas regions that are less densely packed have larger neurons that have more integrative functions. Here we present the numbers of cells and neurons for 742 discrete locations across the neocortex in a chimpanzee. Using isotropic fractionation and flow fractionation methods for cell and neuron counts, we estimate that neocortex of one hemisphere contains 9.5 billion cells and 3.7 billion neurons. Primary visual cortex occupies 35 cm(2) of surface, 10% of the total, and contains 737 million densely packed neurons, 20% of the total neurons contained within the hemisphere. Other areas of high neuron packing include secondary visual areas, somatosensory cortex, and prefrontal granular cortex. Areas of low levels of neuron packing density include motor and premotor cortex. These values reflect those obtained from more limited samples of cortex in humans and other primates.

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

    International Nuclear Information System (INIS)

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

    2010-01-01

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

  9. 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.

  10. 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.

  11. Parameter estimation in stochastic mammogram model by heuristic optimization techniques.

    NARCIS (Netherlands)

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

    2006-01-01

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

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

    Science.gov (United States)

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

    2018-03-19

    To assess feasibility and diagnostic accuracy of a novel hand-held ultrasound (US) method for breast density assessment that measures the speed of sound (SoS), in comparison to the ACR mammographic (MG) categories. ACR-MG density (a=fatty to d=extremely dense) and SoS-US were assessed in the retromamillary, inner and outer segments of 106 women by two radiographers. A conventional US system was used for SoS-US. A reflector served as timing reference for US signals transmitted through the breasts. Four blinded readers assessed average SoS (m/s), ΔSoS (segment-variation SoS; m/s) and the ACR-MG density. The highest SoS and ΔSoS values of the three segments were used for MG-ACR whole breast comparison. SoS-US breasts were examined in densities a-d were 1,421 m/s (SD 14), 1,432 m/s (SD 17), 1,448 m/s (SD 20) and 1,500 m/s (SD 31), with significant differences between all groups (pdensity was evident (r s =0.622, p=density without discomfort, readers evaluated measurements with high inter-reader agreement, and SoS-US correlated significantly with ACR-MG breast-density categories. • The novel speed-of-sound ultrasound correlated significantly with mammographic ACR breast density categories. • Radiographers measured breast density without women discomfort or radiation. • SoS-US can be implemented on a standard US machine. • SoS-US shows potential for a quantifiable, cost-effective assessment of breast density.

  13. 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.

  14. 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

  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

    2018-01-09

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

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

    Science.gov (United States)

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

    2015-03-01

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

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

    NARCIS (Netherlands)

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

    2005-01-01

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

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

    DEFF Research Database (Denmark)

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

    2003-01-01

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

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

    Directory of Open Access Journals (Sweden)

    N.A. Belykh

    2013-08-01

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

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

    International Nuclear Information System (INIS)

    Noriah Jamal; Cheung, H.S.

    2014-01-01

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

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2012-11-15

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

  3. 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.

  4. Object characterization simulator for estimating compressed breast during mammography

    International Nuclear Information System (INIS)

    Pinheiro, Luciana de J.S.; Rio, Margarita Chevalier del

    2011-01-01

    The measurement of the thickness of a compressed breast during the mammography test is necessary in order to calculate the glandular dose in mammography procedures, in an analysis of risk/benefit, given that the target organ in these procedures is highly sensitive to ionising radiation. However, mammography is a test of utmost importance in diagnosis. In theory, it may be possible to calculate the thickness of the compressed breast through the measurements of the focus object distance by using projections of radio opaque objects fixed to the compression tray. The facilities of the Laboratory of Applied Radioprotection to Mammography - LARAM were used for this study, as well as breast simulators with well defined thickness, in the assembly of the techniques for the measurement of the thickness of the compressed breast. The results showed that it is possible to determine this thickness through calculations and simulators through this method which is susceptible to be adequate to the dosimetry. (author)

  5. 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.

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

    DEFF Research Database (Denmark)

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

    2016-01-01

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

  7. Spatial pattern corrections and sample sizes for forest density estimates of historical tree surveys

    Science.gov (United States)

    Brice B. Hanberry; Shawn Fraver; Hong S. He; Jian Yang; Dan C. Dey; Brian J. Palik

    2011-01-01

    The U.S. General Land Office land surveys document trees present during European settlement. However, use of these surveys for calculating historical forest density and other derived metrics is limited by uncertainty about the performance of plotless density estimators under a range of conditions. Therefore, we tested two plotless density estimators, developed by...

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

    Science.gov (United States)

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

    2016-01-01

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

  9. 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.

  10. 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.

  11. 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

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

    Science.gov (United States)

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

    2016-10-01

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

  13. Multivariate density estimation using dimension reducing information and tail flattening transformations for truncated or censored data

    DEFF Research Database (Denmark)

    Buch-Kromann, Tine; Nielsen, Jens

    2012-01-01

    This paper introduces a multivariate density estimator for truncated and censored data with special emphasis on extreme values based on survival analysis. A local constant density estimator is considered. We extend this estimator by means of tail flattening transformation, dimension reducing prior...

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

    International Nuclear Information System (INIS)

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

    2000-01-01

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

  15. 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...

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

    Science.gov (United States)

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

    2018-03-01

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

  17. 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.    

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

    Science.gov (United States)

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

    2011-01-01

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

  19. 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.

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

    International Nuclear Information System (INIS)

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

    2009-01-01

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

  1. Rigorous home range estimation with movement data: a new autocorrelated kernel density estimator.

    Science.gov (United States)

    Fleming, C H; Fagan, W F; Mueller, T; Olson, K A; Leimgruber, P; Calabrese, J M

    2015-05-01

    Quantifying animals' home ranges is a key problem in ecology and has important conservation and wildlife management applications. Kernel density estimation (KDE) is a workhorse technique for range delineation problems that is both statistically efficient and nonparametric. KDE assumes that the data are independent and identically distributed (IID). However, animal tracking data, which are routinely used as inputs to KDEs, are inherently autocorrelated and violate this key assumption. As we demonstrate, using realistically autocorrelated data in conventional KDEs results in grossly underestimated home ranges. We further show that the performance of conventional KDEs actually degrades as data quality improves, because autocorrelation strength increases as movement paths become more finely resolved. To remedy these flaws with the traditional KDE method, we derive an autocorrelated KDE (AKDE) from first principles to use autocorrelated data, making it perfectly suited for movement data sets. We illustrate the vastly improved performance of AKDE using analytical arguments, relocation data from Mongolian gazelles, and simulations based upon the gazelle's observed movement process. By yielding better minimum area estimates for threatened wildlife populations, we believe that future widespread use of AKDE will have significant impact on ecology and conservation biology.

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

    International Nuclear Information System (INIS)

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

    2009-01-01

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

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

    Science.gov (United States)

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

    2018-01-01

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

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

    International Nuclear Information System (INIS)

    Uematsu, Takayoshi; Kasami, Masako; Watanabe, Junichiro

    2012-01-01

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

  5. Density Estimation in Several Populations With Uncertain Population Membership

    KAUST Repository

    Ma, Yanyuan; Hart, Jeffrey D.; Carroll, Raymond J.

    2011-01-01

    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

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

    Science.gov (United States)

    Kim, Won Hwa; Cho, Nariya; Kim, Young-Seon; Yi, Ann

    2018-04-06

    To evaluate the changes in mammographic density after tamoxifen discontinuation in premenopausal women with oestrogen receptor-positive breast cancers and the underlying factors METHODS: A total of 213 consecutive premenopausal women with breast cancer who received tamoxifen treatment after curative surgery and underwent three mammograms (baseline, after tamoxifen treatment, after tamoxifen discontinuation) were included. Changes in mammographic density after tamoxifen discontinuation were assessed qualitatively (decrease, no change, or increase) by two readers and measured quantitatively by semi-automated software. The association between % density change and clinicopathological factors was evaluated using univariate and multivariate regression analyses. After tamoxifen discontinuation, a mammographic density increase was observed in 31.9% (68/213, reader 1) to 22.1% (47/213, reader 2) by qualitative assessment, with a mean density increase of 1.8% by quantitative assessment compared to density before tamoxifen discontinuation. In multivariate analysis, younger age (≤ 39 years) and greater % density decline after tamoxifen treatment (≥ 17.0%) were independent factors associated with density change after tamoxifen discontinuation (p density change with a mean density increase of 1.8%, which was associated with younger age and greater density change after tamoxifen treatment. • Increased mammographic density after tamoxifen discontinuation can occur in premenopausal women. • Mean density increase after tamoxifen discontinuation was 1.8%. • Density increase is associated with age and density decrease after tamoxifen.

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

    Science.gov (United States)

    Chen, Rongda; Wang, Ze

    2013-01-01

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

  8. 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.

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

    Science.gov (United States)

    Chen, Rongda; Wang, Ze

    2013-01-01

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

  10. Application of Density Estimation Methods to Datasets from a Glider

    Science.gov (United States)

    2014-09-30

    humpback and sperm whales as well as different dolphin species. OBJECTIVES The objective of this research is to extend existing methods for cetacean...collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources...estimation from single sensor datasets. Required steps for a cue counting approach, where a cue has been defined as a clicking event (Küsel et al., 2011), to

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

    International Nuclear Information System (INIS)

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

    2008-01-01

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

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

    Directory of Open Access Journals (Sweden)

    R. M. Paltuev

    2017-01-01

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

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

    Science.gov (United States)

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

    2015-02-01

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

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

    Science.gov (United States)

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

    2016-05-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

  16. 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

  17. Antineoplastic treatment effect on bone mineral density in Mexican breast cancer patients

    International Nuclear Information System (INIS)

    Monroy-Cisneros, Karina; Esparza-Romero, Julián; Valencia, Mauro E.; Guevara-Torres, Alfonso G.; Méndez-Estrada, Rosa O.; Anduro-Corona, Iván; Astiazarán-García, Humberto

    2016-01-01

    Breast cancer is the most deadly malignancy in Mexican women. Although treatment has improved, it may significantly affect bone mineral status in those who receive it. The aim of this study was to assess the impact of cancer treatment on bone mineral density (BMD) and bone mineral content (BMC), in patients with breast cancer and explore the interaction of menopausal status and clinical stage with cancer treatment on such changes. A quasi-experimental design was applied with measurements before and after a chemotherapy treatment in 40 patients with primary diagnosis of invasive breast cancer. BMD and body composition measurements were taken by dual X-ray absorptiometry (DXA) and changes in these variables due to therapy were analyzed using mixed regression for repeated measurements. Significant loss was found in femoral neck and L2-L4 BMD (p < 0.001). Patients diagnosed with osteopenia or osteoporosis received calcium + vitamin D supplementation (600 mg/200 IU day). It showed a protective effect in the decrease of femoral neck BMD and total BMC. BMD loss in both femoral neck and L2-L4 BMD was higher in premenopausal women: 0.023 g/cm 2 in femoral neck and 0.063 g/cm 2 in L2-L4 (p < 0.001), while in postmenopausal women BMD loss was 0.015 g/cm 2 in femoral neck and 0.035 g/cm 2 in L2-L4 (p = 0.021 and p = 0.001 respectively). Change in lumbar spine BMD was prominent in premenopausal women with advanced clinical stage (IIB, IIIA, IIIB): 0.066 g/cm 2 (p = 0.003). The antineoplastic breast cancer treatment with chemotherapy had a negative impact on BMD, in premenopausal women overall, although a differential effect was found according to clinical stage and calcium supplementation status

  18. Adjusting forest density estimates for surveyor bias in historical tree surveys

    Science.gov (United States)

    Brice B. Hanberry; Jian Yang; John M. Kabrick; Hong S. He

    2012-01-01

    The U.S. General Land Office surveys, conducted between the late 1700s to early 1900s, provide records of trees prior to widespread European and American colonial settlement. However, potential and documented surveyor bias raises questions about the reliability of historical tree density estimates and other metrics based on density estimated from these records. In this...

  19. 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...

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

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

    Science.gov (United States)

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

    2010-01-01

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

  2. 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.

  3. 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.

  4. 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 a set of coincident time series handling mixed frequencies of observation and ragged–edged data structures. They reflect both parameter and filtering uncertainty and are obtained by implementing a bootstrap algorithm for simulating from the distribution of the maximum likelihood estimators of the model...

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

    Science.gov (United States)

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

    2018-05-01

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

  6. 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).

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

    Science.gov (United States)

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

    2014-01-10

    Interval cancers are tumors arising after a negative screening episode and before the next screening invitation. They can be classified into true interval cancers, false-negatives, minimal-sign cancers, and occult tumors based on mammographic findings in screening and diagnostic mammograms. This study aimed to describe tumor-related characteristics and the association of breast density and tumor phenotype within four interval cancer categories. We included 2,245 invasive tumors (1,297 screening-detected and 948 interval cancers) diagnosed from 2000 to 2009 among 645,764 women aged 45 to 69 who underwent biennial screening in Spain. Interval cancers were classified by a semi-informed retrospective review into true interval cancers (n = 455), false-negatives (n = 224), minimal-sign (n = 166), and occult tumors (n = 103). Breast density was evaluated using Boyd's scale and was conflated into: 75%. Tumor-related information was obtained from cancer registries and clinical records. Tumor phenotype was defined as follows: luminal A: ER+/HER2- or PR+/HER2-; luminal B: ER+/HER2+ or PR+/HER2+; HER2: ER-/PR-/HER2+; triple-negative: ER-/PR-/HER2-. The association of tumor phenotype and breast density was assessed using a multinomial logistic regression model. Adjusted odds ratios (OR) and 95% confidence intervals (95% CI) were calculated. All statistical tests were two-sided. Forty-eight percent of interval cancers were true interval cancers and 23.6% false-negatives. True interval cancers were associated with HER2 and triple-negative phenotypes (OR = 1.91 (95% CI:1.22-2.96), OR = 2.07 (95% CI:1.42-3.01), respectively) and extremely dense breasts (>75%) (OR = 1.67 (95% CI:1.08-2.56)). However, among true interval cancers a higher proportion of triple-negative tumors was observed in predominantly fatty breasts (breasts (28.7%, 21.4%, 11.3% and 14.3%, respectively; cancers, extreme breast density being strongly associated with occult tumors (OR

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

    Science.gov (United States)

    2014-09-30

    No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing...will be applied also to other species such as sperm whale (Physeter macrocephalus) (whose high source level assures long range detection and amplifies...improve the accuracy of marine mammal density estimation based on counting echolocation clicks, and will be applicable to density estimates obtained

  10. Light element nucleosynthesis and estimates of the universal baryon density

    International Nuclear Information System (INIS)

    Mathews, G.J.; Viola, V.E.

    1978-01-01

    The present mean universal baryon density rho/sub b/, is of interest because it and the Hubble constant determine the curvature of the Universe. The available indicators of rho/sub b/ come from the present deuterium abundance, if it is assumed that ''big-bang'' nucleosynthesis must produce enough D to at least match the abundance of this nuclide in the interstellar medium. An alternative method utilizing the 7 Li/D ratio is used to evaluate rho/sub b/. With this method the difficulty associated with the astration process can be essentially canceled from the problem. The results obtained indicate an open Universe with a best guess for rho/sub b/ of 7.1 x 10 -31 g/cm 3 . 1 figure, 1 table

  11. 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.

    Directory of Open Access Journals (Sweden)

    Manan Gupta

    Full Text Available 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

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

    DEFF Research Database (Denmark)

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

    2016-01-01

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

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

    International Nuclear Information System (INIS)

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

    2016-01-01

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

  14. 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

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

    Science.gov (United States)

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

    2017-09-01

    We examined the associations of mammographic breast density with breast cancer risk by tumor aggressiveness and by menopausal status and current postmenopausal hormone therapy. This study included 2596 invasive breast cancer cases and 4059 controls selected from participants of four nested case-control studies within four established cohorts: the Mayo Mammography Health Study, the Nurses' Health Study, Nurses' Health Study II, and San Francisco Mammography Registry. Percent breast density (PD), absolute dense (DA), and non-dense areas (NDA) were assessed from digitized film-screen mammograms using a computer-assisted threshold technique and standardized across studies. We used polytomous logistic regression to quantify the associations of breast density with breast cancer risk by tumor aggressiveness (defined as presence of at least two of the following tumor characteristics: size ≥2 cm, grade 2/3, ER-negative status, or positive nodes), stratified by menopausal status and current hormone therapy. Overall, the positive association of PD and borderline inverse association of NDA with breast cancer risk was stronger in aggressive vs. non-aggressive tumors (≥51 vs. 11-25% OR 2.50, 95% CI 1.94-3.22 vs. OR 2.03, 95% CI 1.70-2.43, p-heterogeneity = 0.03; NDA 4th vs. 2nd quartile OR 0.54, 95% CI 0.41-0.70 vs. OR 0.71, 95% CI 0.59-0.85, p-heterogeneity = 0.07). However, there were no differences in the association of DA with breast cancer by aggressive status. In the stratified analysis, there was also evidence of a stronger association of PD and NDA with aggressive tumors among postmenopausal women and, in particular, current estrogen+progesterone users (≥51 vs. 11-25% OR 3.24, 95% CI 1.75-6.00 vs. OR 1.93, 95% CI 1.25-2.98, p-heterogeneity = 0.01; NDA 4th vs. 2nd quartile OR 0.43, 95% CI 0.21-0.85 vs. OR 0.56, 95% CI 0.35-0.89, p-heterogeneity = 0.01), even though the interaction was not significant. Our findings suggest that associations of mammographic

  16. 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.)

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

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

    KAUST Repository

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

    2012-01-01

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

  19. 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.

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

    DEFF Research Database (Denmark)

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

    2016-01-01

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

  1. 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 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.

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

    Directory of Open Access Journals (Sweden)

    Patrícia Magda Soares

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

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

    Science.gov (United States)

    Chung, ChaeWeon; Lee, Suk Jeong

    2013-11-01

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

  4. 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

  5. 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.

  6. Estimation of dislocations density and distribution of dislocations during ECAP-Conform process

    Science.gov (United States)

    Derakhshan, Jaber Fakhimi; Parsa, Mohammad Habibi; Ayati, Vahid; Jafarian, Hamidreza

    2018-01-01

    Dislocation density of coarse grain aluminum AA1100 alloy (140 µm) that was severely deformed by Equal Channel Angular Pressing-Conform (ECAP-Conform) are studied at various stages of the process by electron backscattering diffraction (EBSD) method. The geometrically necessary dislocations (GNDs) density and statistically stored dislocations (SSDs) densities were estimate. Then the total dislocations densities are calculated and the dislocation distributions are presented as the contour maps. Estimated average dislocations density for annealed of about 2×1012 m-2 increases to 4×1013 m-2 at the middle of the groove (135° from the entrance), and they reach to 6.4×1013 m-2 at the end of groove just before ECAP region. Calculated average dislocations density for one pass severely deformed Al sample reached to 6.2×1014 m-2. At micrometer scale the behavior of metals especially mechanical properties largely depend on the dislocation density and dislocation distribution. So, yield stresses at different conditions were estimated based on the calculated dislocation densities. Then estimated yield stresses were compared with experimental results and good agreements were found. Although grain size of material did not clearly change, yield stress shown intensive increase due to the development of cell structure. A considerable increase in dislocations density in this process is a good justification for forming subgrains and cell structures during process which it can be reason of increasing in yield stress.

  7. 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...

  8. A note on the conditional density estimate in single functional index model

    OpenAIRE

    2010-01-01

    Abstract In this paper, we consider estimation of the conditional density of a scalar response variable Y given a Hilbertian random variable X when the observations are linked with a single-index structure. We establish the pointwise and the uniform almost complete convergence (with the rate) of the kernel estimate of this model. As an application, we show how our result can be applied in the prediction problem via the conditional mode estimate. Finally, the estimation of the funct...

  9. 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...

  10. 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...

  11. 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...

  12. 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...

  13. 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...

  14. 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...

  15. 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...

  16. 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...

  17. 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...

  18. 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...

  19. 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...

  20. 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...

  1. 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....

  2. 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...

  3. 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...

  4. 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...

  5. 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...

  6. 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...

  7. 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...

  8. Density meter algorithm and system for estimating sampling/mixing uncertainty

    International Nuclear Information System (INIS)

    Shine, E.P.

    1986-01-01

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

  9. Density meter algorithm and system for estimating sampling/mixing uncertainty

    International Nuclear Information System (INIS)

    Shine, E.P.

    1986-01-01

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

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

    International Nuclear Information System (INIS)

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

    2013-01-01

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

  11. 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.

  12. The Visualization and Analysis of POI Features under Network Space Supported by Kernel Density Estimation

    Directory of Open Access Journals (Sweden)

    YU Wenhao

    2015-01-01

    Full Text Available The distribution pattern and the distribution density of urban facility POIs are of great significance in the fields of infrastructure planning and urban spatial analysis. The kernel density estimation, which has been usually utilized for expressing these spatial characteristics, is superior to other density estimation methods (such as Quadrat analysis, Voronoi-based method, for that the Kernel density estimation considers the regional impact based on the first law of geography. However, the traditional kernel density estimation is mainly based on the Euclidean space, ignoring the fact that the service function and interrelation of urban feasibilities is carried out on the network path distance, neither than conventional Euclidean distance. Hence, this research proposed a computational model of network kernel density estimation, and the extension type of model in the case of adding constraints. This work also discussed the impacts of distance attenuation threshold and height extreme to the representation of kernel density. The large-scale actual data experiment for analyzing the different POIs' distribution patterns (random type, sparse type, regional-intensive type, linear-intensive type discusses the POI infrastructure in the city on the spatial distribution of characteristics, influence factors, and service functions.

  13. Effects of stand density on top height estimation for ponderosa pine

    Science.gov (United States)

    Martin Ritchie; Jianwei Zhang; Todd Hamilton

    2012-01-01

    Site index, estimated as a function of dominant-tree height and age, is often used as an expression of site quality. This expression is assumed to be effectively independent of stand density. Observation of dominant height at two different ponderosa pine levels-of-growing-stock studies revealed that top height stability with respect to stand density depends on the...

  14. 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 canc...

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

    International Nuclear Information System (INIS)

    Tadayyon, Hadi; Sadeghi-Naini, Ali; Czarnota, Gregory; Wirtzfeld, Lauren; Wright, Frances C.

    2014-01-01

    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

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

    KAUST Repository

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

    2016-01-01

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

  18. 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.

  19. 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.

  20. 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...

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

    OpenAIRE

    Gunawan, Hendra; Micheldiament, Micheldiament; Mikhailov, Valentin

    2008-01-01

    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 ...

  2. Investigating the impact of uneven magnetic flux density distribution on core loss estimation

    DEFF Research Database (Denmark)

    Niroumand, Farideh Javidi; Nymand, Morten; Wang, Yiren

    2017-01-01

    is calculated according to an effective flux density value and the macroscopic dimensions of the cores. However, the flux distribution in the core can alter by core shapes and/or operating conditions due to nonlinear material properties. This paper studies the element-wise estimation of the loss in magnetic......There are several approaches for loss estimation in magnetic cores, and all these approaches highly rely on accurate information about flux density distribution in the cores. It is often assumed that the magnetic flux density evenly distributes throughout the core and the overall core loss...

  3. 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...

  4. 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.

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

    International Nuclear Information System (INIS)

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

    1997-01-01

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

  6. 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

  7. Kernel and wavelet density estimators on manifolds and more general metric spaces

    DEFF Research Database (Denmark)

    Cleanthous, G.; Georgiadis, Athanasios; Kerkyacharian, G.

    We consider the problem of estimating the density of observations taking values in classical or nonclassical spaces such as manifolds and more general metric spaces. Our setting is quite general but also sufficiently rich in allowing the development of smooth functional calculus with well localized...... spectral kernels, Besov regularity spaces, and wavelet type systems. Kernel and both linear and nonlinear wavelet density estimators are introduced and studied. Convergence rates for these estimators are established, which are analogous to the existing results in the classical setting of real...

  8. Large Scale Density Estimation of Blue and Fin Whales: Utilizing Sparse Array Data to Develop and Implement a New Method for Estimating Blue and Fin Whale Density

    Science.gov (United States)

    2015-09-30

    titled “Ocean Basin Impact of Ambient Noise on Marine Mammal Detectability, Distribution, and Acoustic Communication ”. Patterns and trends of ocean... mammals in response to potentially negative interactions with human activity requires knowledge of how many animals are present in an area during a...specific time period. Many marine mammal species are relatively hard to sight, making standard visual methods of density estimation difficult and

  9. 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

  10. Controlled breast cancer microarrays for the deconvolution of cellular multilayering and density effects upon drug responses.

    Directory of Open Access Journals (Sweden)

    Maria Håkanson

    Full Text Available Increasing evidence shows that the cancer microenvironment affects both tumorigenesis and the response of cancer to drug treatment. Therefore in vitro models that selectively reflect characteristics of the in vivo environment are greatly needed. Current methods allow us to screen the effect of extrinsic parameters such as matrix composition and to model the complex and three-dimensional (3D cancer environment. However, 3D models that reflect characteristics of the in vivo environment are typically too complex and do not allow the separation of discrete extrinsic parameters.In this study we used a poly(ethylene glycol (PEG hydrogel-based microwell array to model breast cancer cell behavior in multilayer cell clusters that allows a rigorous control of the environment. The innovative array fabrication enables different matrix proteins to be integrated into the bottom surface of microwells. Thereby, extrinsic parameters including dimensionality, type of matrix coating and the extent of cell-cell adhesion could be independently studied. Our results suggest that cell to matrix interactions and increased cell-cell adhesion, at high cell density, induce independent effects on the response to Taxol in multilayer breast cancer cell clusters. In addition, comparing the levels of apoptosis and proliferation revealed that drug resistance mediated by cell-cell adhesion can be related to altered cell cycle regulation. Conversely, the matrix-dependent response to Taxol did not correlate with proliferation changes suggesting that cell death inhibition may be responsible for this effect.The application of the PEG hydrogel platform provided novel insight into the independent role of extrinsic parameters controlling drug response. The presented platform may not only become a useful tool for basic research related to the role of the cancer microenvironment but could also serve as a complementary platform for in vitro drug development.

  11. The estimation of heavy metal concentration in FBR reprocessing solvent streams by density measurement

    International Nuclear Information System (INIS)

    Brown, M.L.; Savage, D.J.

    1986-04-01

    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 odourless kerosene. Differences in odourless 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 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 favourably 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 had been successfully used for plant control of heavy metal loaded solvent during four fast reactor fuel reprocessing campaigns. (author)

  12. Breast Tomosynthesis

    Science.gov (United States)

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

  13. Mathematical simulation for estimating reduction of breast cancer mortality in mass screening using mammography

    International Nuclear Information System (INIS)

    Iinuma, Takeshi; Matsumoto, Tohru; Tateno, Yukio

    1999-01-01

    In Japan it is considered that mammography should be introduced with physical examination for the mass screening of breast cancer instead of physical examination alone, which is performed at present. Before the introduction of mammography, a mathematical simulation should be performed to show the reduction in breast cancer mortality by mass screening compared with an unscreened population. A mathematical model of cancer screening devised by the authors was used to estimate the number of deaths due to breast cancer (A) in the screened group and those (B) in the unscreened group within the same population. Then the relative risk (RR) and attributable risk (RD) were calculated as (A/B) and (B-A) respectively. Three methods of mass screening were compared: (1) physical examination (1-year interval), (2) mammography with physical examination (1-year interval), (3) mammography with physical examination (2-year interval). The calculated RR values were 0.85 for (1), 0.60 for (2) and 0.69 for (3). Assuming that the incidence of breast cancer was 100/10 5 person-years, the calculated RD values were 3.0, 8.1 and 6.2 persons/10 5 person-years for (1), (2) and (3), respectively. The 95% confidence interval of RR for three methods was over 1.0, and thus the reduction of breast cancer mortality was not statistically significant in the present population. In conclusion, mammography with physical examination may reduce breast cancer mortality in comparison with physical examination alone, but a larger number of women must be screened in order to obtain a significant RR value. (author)

  14. Estimating population density and connectivity of American mink using spatial capture-recapture.

    Science.gov (United States)

    Fuller, Angela K; Sutherland, Chris S; Royle, J Andrew; Hare, Matthew P

    2016-06-01

    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.

  15. 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.

  16. High Densities of Tumor-Associated Plasma Cells Predict Improved Prognosis in Triple Negative Breast Cancer

    Directory of Open Access Journals (Sweden)

    Joe Yeong

    2018-05-01

    Full Text Available Breast cancer is the most common malignancy affecting women, but the heterogeneity of the condition is a significant obstacle to effective treatment. Triple negative breast cancers (TNBCs do not express HER2 or the receptors for estrogen or progesterone, and so often have a poor prognosis. Tumor-infiltrating T cells have been well-characterized in TNBC, and increased numbers are associated with better outcomes; however, the potential roles of B cells and plasma cells have been large. Here, we conducted a retrospective correlative study on the expression of B cell/plasma cell-related genes, and the abundance and localization of B cells and plasma cells within TNBCs, and clinical outcome. We analyzed 269 TNBC samples and used immunohistochemistry to quantify tumor-infiltrating B cells and plasma cells, coupled with NanoString measurement of expression of immunoglobulin metagenes. Multivariate analysis revealed that patients bearing TNBCs with above-median densities of CD38+ plasma cells had significantly better disease-free survival (DFS (HR = 0.44; 95% CI 0.26–0.77; p = 0.004 but not overall survival (OS, after adjusting for the effects of known prognostic factors. In contrast, TNBCs with higher immunoglobulin gene expression exhibited improved prognosis (OS p = 0.029 and DFS p = 0.005. The presence of B cells and plasma cells was positively correlated (p < 0.0001, R = 0.558, while immunoglobulin gene IGKC, IGHM, and IGHG1 mRNA expression correlated specifically with the density of CD38+ plasma cells (IGKC p < 0.0001, R = 0.647; IGHM p < 0.0001, R = 0.580; IGHG1 p < 0.0001, R = 0.655. Interestingly, after adjusting the multivariate analysis for the effect of intratumoral CD38+ plasma cell density, the expression levels of all three genes lost significant prognostic value, suggesting a biologically important role of plasma cells. Last but not least, the addition of intratumoral CD38+ plasma cell

  17. Counselees’ expressed level of understanding of the risk estimate and surveillance recommendation are not associated with breast cancer surveillance adherence.

    NARCIS (Netherlands)

    Albada, A.; Dulmen, S. van; Dijkstra, H.; Wieffer, I.; Witkamp, A.; Ausems, M.G.E.M.

    2016-01-01

    We studied counselees’ expressed understanding of the risk estimate and surveillance recommendation in the final consultation for breast cancer genetic counseling in relation with their risk perception, worry and cancer surveillance adherence 1 year post-counseling. Consecutive counselees were

  18. Lack of association between level of Plasminogen Activator Inhibitor-1 and estimates of tumor angiogenesis in early breast cancer

    DEFF Research Database (Denmark)

    Offersen, Birgitte Vrou; Riisbro, Rikke; Knoop, Ann

    2007-01-01

    Plasminogen Activator Inhibitor type-1 (PAI-1) is involved in tumor invasion and progression. High levels of PAI-1 are associated with poor prognosis in breast cancer, and PAI-1 has been shown to play a role in angiogenic processes. Since estimates of tumor angiogenesis may predict poor prognosis...... we studied the relationship between PAI-1 and estimates of angiogenesis in breast cancer. Tumor tissue specimens from 438 breast cancer patients were included. Median follow-up was 10.3 years. Protein levels of PAI-1 were measured using an ELISA. Angiogenesis scores were performed using a Chalkley.......009) were independent markers of death from breast cancer. This study confirms high PAI-1 or high Chalkley counts as markers of poor prognosis in breast cancer patients, and suggests that the prognostic impact of PAI-1 is independent of its supposed involvement in tumor angiogenesis. Udgivelsesdato: 2007...

  19. 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

  20. 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...

  1. Use of health plan data to estimate cost and outcomes of a breast cancer population

    International Nuclear Information System (INIS)

    Konski, Andre

    1997-01-01

    Purpose/Objective: To compare insurance billing data with tumor registry data for estimating date of diagnosis and date of recurrence. To collect and estimate cost of treatment from billing data as a step towards performing cost-effective or cost-utility analysis. To correlate first year treatment cost first year with overall cost to enable the former to serve as a proxy for the latter for patients migrating out of insurance plans. Materials and Methods: Billing data for patients(pts.) diagnosed with breast cancer between 1990-1992 was obtained from Paramount Health Plans, a NCQA accredited health plan in Northwest Ohio. Tumor registry and hospital records were surveyed for the clinical data. Total cost of care received by pts., cost of care associated with treatment of breast cancer, and cost of care billed as breast cancer care was collected for each 12 month period from the date of diagnosis. Costs were measured from a payers, i.e. health plans, perspective. Net present value (NPV) costs discounted at a rate of 3% to the year of diagnosis are reported. Pts. were considered in the plan for the entire duration of the study if they were in the plan from the time of diagnosis to the end of the analysis, (12(96)). Students t-test was used to determined statistical differences between groups analyzed. Results: Paramount Health Plan was a small health plan with approximately 10,000-13,000 female enrolees during the study period. Breast cancer was diagnosed in 21 women during 1990-1992 with 18 pts. diagnosed while in the insurance plan and 3 diagnosed prior to entry into the plan. (12(18)) pts. were in the plan for the entire duration of the study. The mean deviation for the date of diagnosis as recorded from tumor registry data, compared to the first date that a diagnosis of breast cancer appears on the billing record is 18 days (range:0-158). Four pts. experienced a recurrence. A determination of a recurrence from insurance records was only possible in (1(4)) pts. who

  2. [Estimation of Hunan forest carbon density based on spectral mixture analysis of MODIS data].

    Science.gov (United States)

    Yan, En-ping; Lin, Hui; Wang, Guang-xing; Chen, Zhen-xiong

    2015-11-01

    With the fast development of remote sensing technology, combining forest inventory sample plot data and remotely sensed images has become a widely used method to map forest carbon density. However, the existence of mixed pixels often impedes the improvement of forest carbon density mapping, especially when low spatial resolution images such as MODIS are used. In this study, MODIS images and national forest inventory sample plot data were used to conduct the study of estimation for forest carbon density. Linear spectral mixture analysis with and without constraint, and nonlinear spectral mixture analysis were compared to derive the fractions of different land use and land cover (LULC) types. Then sequential Gaussian co-simulation algorithm with and without the fraction images from spectral mixture analyses were employed to estimate forest carbon density of Hunan Province. Results showed that 1) Linear spectral mixture analysis with constraint, leading to a mean RMSE of 0.002, more accurately estimated the fractions of LULC types than linear spectral and nonlinear spectral mixture analyses; 2) Integrating spectral mixture analysis model and sequential Gaussian co-simulation algorithm increased the estimation accuracy of forest carbon density to 81.5% from 74.1%, and decreased the RMSE to 5.18 from 7.26; and 3) The mean value of forest carbon density for the province was 30.06 t · hm(-2), ranging from 0.00 to 67.35 t · hm(-2). This implied that the spectral mixture analysis provided a great potential to increase the estimation accuracy of forest carbon density on regional and global level.

  3. The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables

    OpenAIRE

    Ambrogioni, Luca; Güçlü, Umut; van Gerven, Marcel A. J.; Maris, Eric

    2017-01-01

    This paper introduces the kernel mixture network, a new method for nonparametric estimation of conditional probability densities using neural networks. We model arbitrarily complex conditional densities as linear combinations of a family of kernel functions centered at a subset of training points. The weights are determined by the outer layer of a deep neural network, trained by minimizing the negative log likelihood. This generalizes the popular quantized softmax approach, which can be seen ...

  4. 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.

  5. Trap array configuration influences estimates and precision of black bear density and abundance.

    Directory of Open Access Journals (Sweden)

    Clay M Wilton

    Full Text Available Spatial capture-recapture (SCR models have advanced our ability to estimate population density for wide ranging animals by explicitly incorporating individual movement. Though these models are more robust to various spatial sampling designs, few studies have empirically tested different large-scale trap configurations using SCR models. We investigated how extent of trap coverage and trap spacing affects precision and accuracy of SCR parameters, implementing models using the R package secr. We tested two trapping scenarios, one spatially extensive and one intensive, using black bear (Ursus americanus DNA data from hair snare arrays in south-central Missouri, USA. We also examined the influence that adding a second, lower barbed-wire strand to snares had on quantity and spatial distribution of detections. We simulated trapping data to test bias in density estimates of each configuration under a range of density and detection parameter values. Field data showed that using multiple arrays with intensive snare coverage produced more detections of more individuals than extensive coverage. Consequently, density and detection parameters were more precise for the intensive design. Density was estimated as 1.7 bears per 100 km2 and was 5.5 times greater than that under extensive sampling. Abundance was 279 (95% CI = 193-406 bears in the 16,812 km2 study area. Excluding detections from the lower strand resulted in the loss of 35 detections, 14 unique bears, and the largest recorded movement between snares. All simulations showed low bias for density under both configurations. Results demonstrated that in low density populations with non-uniform distribution of population density, optimizing the tradeoff among snare spacing, coverage, and sample size is of critical importance to estimating parameters with high precision and accuracy. With limited resources, allocating available traps to multiple arrays with intensive trap spacing increased the amount of

  6. 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

  7. A citizen science based survey method for estimating the density of urban carnivores

    Science.gov (United States)

    Baker, Rowenna; Charman, Naomi; Karlsson, Heidi; Yarnell, Richard W.; Mill, Aileen C.; Smith, Graham C.; Tolhurst, Bryony A.

    2018-01-01

    Globally there are many examples of synanthropic carnivores exploiting growth in urbanisation. As carnivores can come into conflict with humans and are potential vectors of zoonotic disease, assessing densities in suburban areas and identifying factors that influence them are necessary to aid management and mitigation. However, fragmented, privately owned land restricts the use of conventional carnivore surveying techniques in these areas, requiring development of novel methods. We present a method that combines questionnaire distribution to residents with field surveys and GIS, to determine relative density of two urban carnivores in England, Great Britain. We determined the density of: red fox (Vulpes vulpes) social groups in 14, approximately 1km2 suburban areas in 8 different towns and cities; and Eurasian badger (Meles meles) social groups in three suburban areas of one city. Average relative fox group density (FGD) was 3.72 km-2, which was double the estimates for cities with resident foxes in the 1980’s. Density was comparable to an alternative estimate derived from trapping and GPS-tracking, indicating the validity of the method. However, FGD did not correlate with a national dataset based on fox sightings, indicating unreliability of the national data to determine actual densities or to extrapolate a national population estimate. Using species-specific clustering units that reflect social organisation, the method was additionally applied to suburban badgers to derive relative badger group density (BGD) for one city (Brighton, 2.41 km-2). We demonstrate that citizen science approaches can effectively obtain data to assess suburban carnivore density, however publicly derived national data sets need to be locally validated before extrapolations can be undertaken. The method we present for assessing densities of foxes and badgers in British towns and cities is also adaptable to other urban carnivores elsewhere. However this transferability is contingent on

  8. A group contribution method to estimate the densities of ionic liquids

    International Nuclear Information System (INIS)

    Qiao Yan; Ma Youguang; Huo Yan; Ma Peisheng; Xia Shuqian

    2010-01-01

    Densities of ionic liquids at different temperature and pressure were collected from 84 references. The collection contains 7381 data points derived from 123 pure ionic liquids and 13 kinds of binary ionic liquids mixtures. In terms of the collected database, a group contribution method based on 51 groups was used to predict the densities of ionic liquids. In group partition, the effect of interaction among several substitutes on the same center was considered. The same structure in different substitutes may have different group values. According to the estimation of pure ionic liquids' densities, the results show that the average relative error is 0.88% and the standard deviation (S) is 0.0181. Using the set of group values three pure ionic liquids densities were predicted, the average relative error is 0.27% and the S is 0.0048. For ionic liquid mixtures, they are thought considered as idea mixtures, so the group contribution method was used to estimate their densities and the average relative error is 1.22% with S is 0.0607. And the method can also be used to estimate the densities of MCl x type ionic liquids which are produced by mixing an ionic liquid with a Cl - anion and a kind of metal chloride.

  9. Estimating the amount and distribution of radon flux density from the soil surface in China

    International Nuclear Information System (INIS)

    Zhuo Weihai; Guo Qiuju; Chen Bo; Cheng Guan

    2008-01-01

    Based on an idealized model, both the annual and the seasonal radon ( 222 Rn) flux densities from the soil surface at 1099 sites in China were estimated by linking a database of soil 226 Ra content and a global ecosystems database. Digital maps of the 222 Rn flux density in China were constructed in a spatial resolution of 25 km x 25 km by interpolation among the estimated data. An area-weighted annual average 222 Rn flux density from the soil surface across China was estimated to be 29.7 ± 9.4 mBq m -2 s -1 . Both regional and seasonal variations in the 222 Rn flux densities are significant in China. Annual average flux densities in the southeastern and northwestern China are generally higher than those in other regions of China, because of high soil 226 Ra content in the southeastern area and high soil aridity in the northwestern one. The seasonal average flux density is generally higher in summer/spring than winter, since relatively higher soil temperature and lower soil water saturation in summer/spring than other seasons are common in China

  10. 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.

  11. 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.

  12. 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.

  13. Distributed Noise Generation for Density Estimation Based Clustering without Trusted Third Party

    Science.gov (United States)

    Su, Chunhua; Bao, Feng; Zhou, Jianying; Takagi, Tsuyoshi; Sakurai, Kouichi

    The rapid growth of the Internet provides people with tremendous opportunities for data collection, knowledge discovery and cooperative computation. However, it also brings the problem of sensitive information leakage. Both individuals and enterprises may suffer from the massive data collection and the information retrieval by distrusted parties. In this paper, we propose a privacy-preserving protocol for the distributed kernel density estimation-based clustering. Our scheme applies random data perturbation (RDP) technique and the verifiable secret sharing to solve the security problem of distributed kernel density estimation in [4] which assumed a mediate party to help in the computation.

  14. 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%.

  15. 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.

  16. Trapping Elusive Cats: Using Intensive Camera Trapping to Estimate the Density of a Rare African Felid.

    Science.gov (United States)

    Brassine, Eléanor; Parker, Daniel

    2015-01-01

    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.

  17. 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.

  18. Trapping Elusive Cats: Using Intensive Camera Trapping to Estimate the Density of a Rare African Felid

    Science.gov (United States)

    Brassine, Eléanor; Parker, Daniel

    2015-01-01

    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/100km²). 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. PMID:26698574

  19. 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.

  20. An automatic iris occlusion estimation method based on high-dimensional density estimation.

    Science.gov (United States)

    Li, Yung-Hui; Savvides, Marios

    2013-04-01

    Iris masks play an important role in iris recognition. They indicate which part of the iris texture map is useful and which part is occluded or contaminated by noisy image artifacts such as eyelashes, eyelids, eyeglasses frames, and specular reflections. The accuracy of the iris mask is extremely important. The performance of the iris recognition system will decrease dramatically when the iris mask is inaccurate, even when the best recognition algorithm is used. Traditionally, people used the rule-based algorithms to estimate iris masks from iris images. However, the accuracy of the iris masks generated this way is questionable. In this work, we propose to use Figueiredo and Jain's Gaussian Mixture Models (FJ-GMMs) to model the underlying probabilistic distributions of both valid and invalid regions on iris images. We also explored possible features and found that Gabor Filter Bank (GFB) provides the most discriminative information for our goal. Finally, we applied Simulated Annealing (SA) technique to optimize the parameters of GFB in order to achieve the best recognition rate. Experimental results show that the masks generated by the proposed algorithm increase the iris recognition rate on both ICE2 and UBIRIS dataset, verifying the effectiveness and importance of our proposed method for iris occlusion estimation.

  1. Analytical Plug-In Method for Kernel Density Estimator Applied to Genetic Neutrality Study

    Science.gov (United States)

    Troudi, Molka; Alimi, Adel M.; Saoudi, Samir

    2008-12-01

    The plug-in method enables optimization of the bandwidth of the kernel density estimator in order to estimate probability density functions (pdfs). Here, a faster procedure than that of the common plug-in method is proposed. The mean integrated square error (MISE) depends directly upon [InlineEquation not available: see fulltext.] which is linked to the second-order derivative of the pdf. As we intend to introduce an analytical approximation of [InlineEquation not available: see fulltext.], the pdf is estimated only once, at the end of iterations. These two kinds of algorithm are tested on different random variables having distributions known for their difficult estimation. Finally, they are applied to genetic data in order to provide a better characterisation in the mean of neutrality of Tunisian Berber populations.

  2. Analytical Plug-In Method for Kernel Density Estimator Applied to Genetic Neutrality Study

    Directory of Open Access Journals (Sweden)

    Samir Saoudi

    2008-07-01

    Full Text Available The plug-in method enables optimization of the bandwidth of the kernel density estimator in order to estimate probability density functions (pdfs. Here, a faster procedure than that of the common plug-in method is proposed. The mean integrated square error (MISE depends directly upon J(f which is linked to the second-order derivative of the pdf. As we intend to introduce an analytical approximation of J(f, the pdf is estimated only once, at the end of iterations. These two kinds of algorithm are tested on different random variables having distributions known for their difficult estimation. Finally, they are applied to genetic data in order to provide a better characterisation in the mean of neutrality of Tunisian Berber populations.

  3. Nonparametric Bayesian density estimation on manifolds with applications to planar shapes.

    Science.gov (United States)

    Bhattacharya, Abhishek; Dunson, David B

    2010-12-01

    Statistical analysis on landmark-based shape spaces has diverse applications in morphometrics, medical diagnostics, machine vision and other areas. These shape spaces are non-Euclidean quotient manifolds. To conduct nonparametric inferences, one may define notions of centre and spread on this manifold and work with their estimates. However, it is useful to consider full likelihood-based methods, which allow nonparametric estimation of the probability density. This article proposes a broad class of mixture models constructed using suitable kernels on a general compact metric space and then on the planar shape space in particular. Following a Bayesian approach with a nonparametric prior on the mixing distribution, conditions are obtained under which the Kullback-Leibler property holds, implying large support and weak posterior consistency. Gibbs sampling methods are developed for posterior computation, and the methods are applied to problems in density estimation and classification with shape-based predictors. Simulation studies show improved estimation performance relative to existing approaches.

  4. Using mortality data to estimate radiation effects on breast cancer incidence

    International Nuclear Information System (INIS)

    Hoel, D.G.; Dinse, G.E.

    1990-01-01

    In this paper we combine Japanese data on radiation exposure and cancer mortality with U.S. data on cancer incidence and lethality to estimate the effects of ionizing radiation on cancer incidence. The analysis is based on the mathematical relationship between the mortality rate and the incidence and lethality rates, as well as on statistical models that relate Japanese incidence rates to U.S. incidence rates and radiation risk factors. Our approach assumes that the risk of death from causes other than the cancer does not depend on whether or not the cancer is present, and among individuals with the cancer, the risk of death attributable to the cancer is the same in Japan and the U.S. and is not affected by radiation exposure. In particular, we focus on the incidence of breast cancer in Japanese women and how this incidence is affected by radiation risk factors. The analysis uses Japanese exposure and mortality data from the Radiation Effects Research Foundation study of atomic bomb survivors and U.S. incidence and lethality data from the Surveillance, Epidemiology, and End Results Registry. Even without Japanese incidence data, we obtain reasonable estimates of the incidence of breast cancer in unexposed Japanese women and identify the radiation risk factors that affect this incidence. Our analysis demonstrates that the age at exposure is an important risk factor, but that the incidence of breast cancer is not affected by the city of residence (Nagasaki versus Hiroshima) or the time since exposure

  5. 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...

  6. Features of the normal choriocapillaris with OCT-angiography: Density estimation and textural properties.

    Science.gov (United States)

    Montesano, Giovanni; Allegrini, Davide; Colombo, Leonardo; Rossetti, Luca M; Pece, Alfredo

    2017-01-01

    The main objective of our work is to perform an in depth analysis of the structural features of normal choriocapillaris imaged with OCT Angiography. Specifically, we provide an optimal radius for a circular Region of Interest (ROI) to obtain a stable estimate of the subfoveal choriocapillaris density and characterize its textural properties using Markov Random Fields. On each binarized image of the choriocapillaris OCT Angiography we performed simulated measurements of the subfoveal choriocapillaris densities with circular Regions of Interest (ROIs) of different radii and with small random displacements from the center of the Foveal Avascular Zone (FAZ). We then calculated the variability of the density measure with different ROI radii. We then characterized the textural features of choriocapillaris binary images by estimating the parameters of an Ising model. For each image we calculated the Optimal Radius (OR) as the minimum ROI radius required to obtain a standard deviation in the simulation below 0.01. The density measured with the individual OR was 0.52 ± 0.07 (mean ± STD). Similar density values (0.51 ± 0.07) were obtained using a fixed ROI radius of 450 μm. The Ising model yielded two parameter estimates (β = 0.34 ± 0.03; γ = 0.003 ± 0.012; mean ± STD), characterizing pixel clustering and white pixel density respectively. Using the estimated parameters to synthetize new random textures via simulation we obtained a good reproduction of the original choriocapillaris structural features and density. In conclusion, we developed an extensive characterization of the normal subfoveal choriocapillaris that might be used for flow analysis and applied to the investigation pathological alterations.

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

    Science.gov (United States)

    Hack, Carolin C; Emons, Julius; Jud, Sebastian M; Heusinger, Katharina; Adler, Werner; Gass, Paul; Haeberle, Lothar; Heindl, Felix; Hein, Alexander; Schulz-Wendtland, Rüdiger; Uder, Michael; Hartmann, Arndt; Beckmann, Matthias W; Fasching, Peter A; Pöhls, Uwe G

    2017-12-01

    Percentage mammographic density (PMD) is a major risk factor for breast cancer (BC). It is strongly associated with body mass index (BMI) and age, which are themselves risk factors for breast cancer. This analysis investigated the association between the number of full-term pregnancies and PMD in different subgroups relative to age and BMI. Patients were identified in the breast cancer database of the University Breast Center for Franconia. A total of 2410 patients were identified, for whom information on parity, age, and BMI, and a mammogram from the time of first diagnosis were available for assessing PMD. Linear regression analyses were conducted to investigate the influence on PMD of the number of full-term pregnancies (FTPs), age, BMI, and interaction terms between them. As in previous studies, age, number of FTPs, and BMI were found to be associated with PMD in the expected direction. However, including the respective interaction terms improved the prediction of PMD even further. Specifically, the association between PMD and the number of FTPs differed in young patients under the age of 45 (mean decrease of 0.37 PMD units per pregnancy) from the association in older age groups (mean decrease between 2.29 and 2.39 PMD units). BMI did not alter the association between PMD and the number of FTPs. The effect of pregnancies on mammographic density does not appear to become apparent before the age of menopause. The mechanism that drives the effect of pregnancies on mammographic density appears to be counter-regulated by other influences on mammographic density in younger patients.

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

    Science.gov (United States)

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

    2015-11-01

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

  9. 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.

  10. A self-directed learning intervention for radiographers rating mammographic breast density

    International Nuclear Information System (INIS)

    Ekpo, E.U.; Hogg, P.; Wasike, E.; McEntee, M.F.

    2017-01-01

    Purpose: Subjective methods of mammographic breast density (MBD) assessment are prone to inter-reader variability. This work aims to assess the impact of a short self-directed, experiential learning intervention on radiographers' reproducibility of MBD assessment. Method: The study used two sets of images (test and learning intervention) containing left craniocaudal and left mediolateral oblique views. The test set had MBD ratings from Volpara™ and radiologists using the fourth edition Breast Imaging and Data Systems (BI-RADS ® ). Seven radiographers rated the MBD of the test set before and after a self-directed learning intervention using the percentage descriptors in the fourth edition BI-RADS ® Atlas. The inter-reader agreement, the agreement between radiographers and Volpara™ as well as radiologists, was assessed using a Weighted Kappa (κ w ). Results: Overall, radiographers' inter-reader agreement (κ w ) was substantial (0.79; 95% CI: 0.70–0.87) before the intervention and almost perfect (0.84; 95% CI: 0.77–0.90) after the intervention. Before the intervention, radiographers demonstrated fair agreement with radiologists (0.24; 95% CI: −0.46–0.61) and Volpara™ (0.24; 95% CI: −0.41–0.59). A fair but slightly improved agreement was also observed between radiographers and radiologists (0.31; 95% CI: −0.33–0.64) as well as Volpara™ (0.28; 95% CI: −0.34–0.61) after the intervention. Conclusion: Findings demonstrate that a short duration self-directed, experiential learning intervention reduces inter-reader differences in MBD classification, but has a negligible impact on improving the agreement between inexperienced and expert readers. - Highlights: • Radiographers' MBD rating show fair agreement with radiologists and Volpara™. • Experiential learning intervention improved reproducibility of MBD classification. • Enhanced MBD rating training may be required to develop a standard fit for practice.

  11. 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

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

    Science.gov (United States)

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

    2014-10-01

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

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

    Directory of Open Access Journals (Sweden)

    Sandi L. Pruitt

    2016-12-01

    Full Text Available To test the Hispanic and Immigrant Paradoxes—i.e., survival advantages despite a worse risk factor profile—and the modifying role of neighborhood context, we examined associations between patient ethnicity, birthplace, neighborhood Hispanic density and neighborhood poverty among 166,254 female breast cancer patients diagnosed 1995–2009 in Texas, U.S. Of all, 79.9% were non-Hispanic White, 15.8% Hispanic U.S.-born, and 4.2% Hispanic foreign-born. We imputed birthplace for the 60.7% of Hispanics missing birthplace data using multiple imputation. Shared frailty Cox proportional hazard models (patients nested within census tracts adjusted for age, diagnosis year, stage, grade, histology, urban/rural residence, and local mammography capacity. Whites (vs. U.S.-born Hispanics had increased all-cause and breast cancer mortality. Foreign-born (vs. U.S.-born Hispanics had increased all-cause and breast cancer mortality. Living in higher Hispanic density neighborhoods was generally associated with increased mortality, although associations differed slightly in magnitude and significance by ethnicity, birthplace, and neighborhood poverty. We found no evidence of an Immigrant Paradox and some evidence of a Hispanic Paradox where protective effects were limited to U.S.-born Hispanics. Contrary to prior studies, foreign birthplace and residence in higher Hispanic density neighborhoods were associated with increased mortality. More research on intersections between ethnicity, birthplace and neighborhood context are needed.

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

    Science.gov (United States)

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

    2010-04-01

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

  15. Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks.

    Science.gov (United States)

    Bandeira Diniz, João Otávio; Bandeira Diniz, Pedro Henrique; Azevedo Valente, Thales Levi; Corrêa Silva, Aristófanes; de Paiva, Anselmo Cardoso; Gattass, Marcelo

    2018-03-01

    The processing of medical image is an important tool to assist in minimizing the degree of uncertainty of the specialist, while providing specialists with an additional source of detect and diagnosis information. Breast cancer is the most common type of cancer that affects the female population around the world. It is also the most deadly type of cancer among women. It is the second most common type of cancer among all others. The most common examination to diagnose breast cancer early is mammography. In the last decades, computational techniques have been developed with the purpose of automatically detecting structures that maybe associated with tumors in mammography examination. This work presents a computational methodology to automatically detection of mass regions in mammography by using a convolutional neural network. The materials used in this work is the DDSM database. The method proposed consists of two phases: training phase and test phase. The training phase has 2 main steps: (1) create a model to classify breast tissue into dense and non-dense (2) create a model to classify regions of breast into mass and non-mass. The test phase has 7 step: (1) preprocessing; (2) registration; (3) segmentation; (4) first reduction of false positives; (5) preprocessing of regions segmented; (6) density tissue classification (7) second reduction of false positives where regions will be classified into mass and non-mass. The proposed method achieved 95.6% of accuracy in classify non-dense breasts tissue and 97,72% accuracy in classify dense breasts. To detect regions of mass in non-dense breast, the method achieved a sensitivity value of 91.5%, and specificity value of 90.7%, with 91% accuracy. To detect regions in dense breasts, our method achieved 90.4% of sensitivity and 96.4% of specificity, with accuracy of 94.8%. According to the results achieved by CNN, we demonstrate the feasibility of using convolutional neural networks on medical image processing techniques for

  16. Estimation of Mesospheric Densities at Low Latitudes Using the Kunming Meteor Radar Together With SABER Temperatures

    Science.gov (United States)

    Yi, Wen; Xue, Xianghui; Reid, Iain M.; Younger, Joel P.; Chen, Jinsong; Chen, Tingdi; Li, Na

    2018-04-01

    Neutral mesospheric densities at a low latitude have been derived during April 2011 to December 2014 using data from the Kunming meteor radar in China (25.6°N, 103.8°E). The daily mean density at 90 km was estimated using the ambipolar diffusion coefficients from the meteor radar and temperatures from the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument. The seasonal variations of the meteor radar-derived density are consistent with the density from the Mass Spectrometer and Incoherent Scatter (MSIS) model, show a dominant annual variation, with a maximum during winter, and a minimum during summer. A simple linear model was used to separate the effects of atmospheric density and the meteor velocity on the meteor radar peak detection height. We find that a 1 km/s difference in the vertical meteor velocity yields a change of approximately 0.42 km in peak height. The strong correlation between the meteor radar density and the velocity-corrected peak height indicates that the meteor radar density estimates accurately reflect changes in neutral atmospheric density and that meteor peak detection heights, when adjusted for meteoroid velocity, can serve as a convenient tool for measuring density variations around the mesopause. A comparison of the ambipolar diffusion coefficient and peak height observed simultaneously by two co-located meteor radars indicates that the relative errors of the daily mean ambipolar diffusion coefficient and peak height should be less than 5% and 6%, respectively, and that the absolute error of the peak height is less than 0.2 km.

  17. Assessing different parameters estimation methods of Weibull distribution to compute wind power density

    International Nuclear Information System (INIS)

    Mohammadi, Kasra; Alavi, Omid; Mostafaeipour, Ali; Goudarzi, Navid; Jalilvand, Mahdi

    2016-01-01

    Highlights: • Effectiveness of six numerical methods is evaluated to determine wind power density. • More appropriate method for computing the daily wind power density is estimated. • Four windy stations located in the south part of Alberta, Canada namely is investigated. • The more appropriate parameters estimation method was not identical among all examined stations. - Abstract: In this study, the effectiveness of six numerical methods is evaluated to determine the shape (k) and scale (c) parameters of Weibull distribution function for the purpose of calculating the wind power density. The selected methods are graphical method (GP), empirical method of Justus (EMJ), empirical method of Lysen (EML), energy pattern factor method (EPF), maximum likelihood method (ML) and modified maximum likelihood method (MML). The purpose of this study is to identify the more appropriate method for computing the wind power density in four stations distributed in Alberta province of Canada namely Edmonton City Center Awos, Grande Prairie A, Lethbridge A and Waterton Park Gate. To provide a complete analysis, the evaluations are performed on both daily and monthly scales. The results indicate that the precision of computed wind power density values change when different parameters estimation methods are used to determine the k and c parameters. Four methods of EMJ, EML, EPF and ML present very favorable efficiency while the GP method shows weak ability for all stations. However, it is found that the more effective method is not similar among stations owing to the difference in the wind characteristics.

  18. Urban birds in the Sonoran Desert: estimating population density from point counts

    Directory of Open Access Journals (Sweden)

    Karina Johnston López

    2015-01-01

    Full Text Available We conducted bird surveys in Hermosillo, Sonora using distance sampling to characterize detection functions at point-transects for native and non-native urban birds in a desert environment. From March to August 2013 we sampled 240 plots in the city and its surroundings; each plot was visited three times. Our purpose was to provide information for a rapid assessment of bird density in this region by using point counts. We identified 72 species, including six non-native species. Sixteen species had sufficient detections to accurately estimate the parameters of the detection functions. To illustrate the estimation of density from bird count data using our inferred detection functions, we estimated the density of the Eurasian Collared-Dove (Streptopelia decaocto under two different levels of urbanization: highly urbanized (90-100% of urban impact and moderately urbanized zones (39-50% of urban impact. Density of S. decaocto in the highly-urbanized and moderately-urbanized zones was 3.97±0.52 and 2.92±0.52 individuals/ha, respectively. By using our detection functions, avian ecologists can efficiently relocate time and effort that is regularly used for the estimation of detection distances, to increase the number of sites surveyed and to collect other relevant ecological information.

  19. 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 ...

  20. Multi-objective mixture-based iterated density estimation evolutionary algorithms

    NARCIS (Netherlands)

    Thierens, D.; Bosman, P.A.N.

    2001-01-01

    We propose an algorithm for multi-objective optimization using a mixture-based iterated density estimation evolutionary algorithm (MIDEA). The MIDEA algorithm is a prob- abilistic model building evolutionary algo- rithm that constructs at each generation a mixture of factorized probability

  1. Eurasian otter (Lutra lutra) density estimate based on radio tracking and other data sources

    Czech Academy of Sciences Publication Activity Database

    Quaglietta, L.; Hájková, Petra; Mira, A.; Boitani, L.

    2015-01-01

    Roč. 60, č. 2 (2015), s. 127-137 ISSN 2199-2401 R&D Projects: GA AV ČR KJB600930804 Institutional support: RVO:68081766 Keywords : Lutra lutra * Density estimation * Edge effect * Known-to-be-alive * Linear habitats * Sampling scale Subject RIV: EG - Zoology

  2. 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 ...

  3. 3D depth-to-basement and density contrast estimates using gravity and borehole data

    Science.gov (United States)

    Barbosa, V. C.; Martins, C. M.; Silva, J. B.

    2009-05-01

    We present a gravity inversion method for simultaneously estimating the 3D basement relief of a sedimentary basin and the parameters defining the parabolic decay of the density contrast with depth in a sedimentary pack assuming the prior knowledge about the basement depth at a few points. The sedimentary pack is approximated by a grid of 3D vertical prisms juxtaposed in both horizontal directions, x and y, of a right-handed coordinate system. The prisms' thicknesses represent the depths to the basement and are the parameters to be estimated from the gravity data. To produce stable depth-to-basement estimates we impose smoothness on the basement depths through minimization of the spatial derivatives of the parameters in the x and y directions. To estimate the parameters defining the parabolic decay of the density contrast with depth we mapped a functional containing prior information about the basement depths at a few points. We apply our method to synthetic data from a simulated complex 3D basement relief with two sedimentary sections having distinct parabolic laws describing the density contrast variation with depth. Our method retrieves the true parameters of the parabolic law of density contrast decay with depth and produces good estimates of the basement relief if the number and the distribution of boreholes are sufficient. We also applied our method to real gravity data from the onshore and part of the shallow offshore Almada Basin, on Brazil's northeastern coast. The estimated 3D Almada's basement shows geologic structures that cannot be easily inferred just from the inspection of the gravity anomaly. The estimated Almada relief presents steep borders evidencing the presence of gravity faults. Also, we note the existence of three terraces separating two local subbasins. These geologic features are consistent with Almada's geodynamic origin (the Mesozoic breakup of Gondwana and the opening of the South Atlantic Ocean) and they are important in understanding

  4. 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.

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

    Science.gov (United States)

    Boyd, Norman F; Li, Qing; Melnichouk, Olga; Huszti, Ella; Martin, Lisa J; Gunasekara, Anoma; Mawdsley, Gord; Yaffe, Martin J; Minkin, Salomon

    2014-01-01

    Evidence from animal models shows that tissue stiffness increases the invasion and progression of cancers, including mammary cancer. We here use measurements of the volume and the projected area of the compressed breast during mammography to derive estimates of breast tissue stiffness and examine the relationship of stiffness to risk of breast cancer. Mammograms were used to measure the volume and projected areas of total and radiologically dense breast tissue in the unaffected breasts of 362 women with newly diagnosed breast cancer (cases) and 656 women of the same age who did not have breast cancer (controls). Measures of breast tissue volume and the projected area of the compressed breast during mammography were used to calculate the deformation of the breast during compression and, with the recorded compression force, to estimate the stiffness of breast tissue. Stiffness was compared in cases and controls, and associations with breast cancer risk examined after adjustment for other risk factors. After adjustment for percent mammographic density by area measurements, and other risk factors, our estimate of breast tissue stiffness was significantly associated with breast cancer (odds ratio = 1.21, 95% confidence interval = 1.03, 1.43, p = 0.02) and improved breast cancer risk prediction in models with percent mammographic density, by both area and volume measurements. An estimate of breast tissue stiffness was associated with breast cancer risk and improved risk prediction based on mammographic measures and other risk factors. Stiffness may provide an additional mechanism by which breast tissue composition is associated with risk of breast cancer and merits examination using more direct methods of measurement.

  6. Fog Density Estimation and Image Defogging Based on Surrogate Modeling for Optical Depth.

    Science.gov (United States)

    Jiang, Yutong; Sun, Changming; Zhao, Yu; Yang, Li

    2017-05-03

    In order to estimate fog density correctly and to remove fog from foggy images appropriately, a surrogate model for optical depth is presented in this paper. We comprehensively investigate various fog-relevant features and propose a novel feature based on the hue, saturation, and value color space which correlate well with the perception of fog density. We use a surrogate-based method to learn a refined polynomial regression model for optical depth with informative fog-relevant features such as dark-channel, saturation-value, and chroma which are selected on the basis of sensitivity analysis. Based on the obtained accurate surrogate model for optical depth, an effective method for fog density estimation and image defogging is proposed. The effectiveness of our proposed method is verified quantitatively and qualitatively by the experimental results on both synthetic and real-world foggy images.

  7. NEW CONCEPTS AND TEST METHODS OF CURVE PROFILE AREA DENSITY IN SURFACE: ESTIMATION OF AREAL DENSITY ON CURVED SPATIAL SURFACE

    OpenAIRE

    Hong Shen

    2011-01-01

    The concepts of curve profile, curve intercept, curve intercept density, curve profile area density, intersection density in containing intersection (or intersection density relied on intersection reference), curve profile intersection density in surface (or curve intercept intersection density relied on intersection of containing curve), and curve profile area density in surface (AS) were defined. AS expressed the amount of curve profile area of Y phase in the unit containing surface area, S...

  8. 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.

  9. Using bremsstrahlung for electron density estimation and correction in EAST tokamak

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Yingjie, E-mail: bestfaye@gmail.com; Wu, Zhenwei; Gao, Wei; Jie, Yinxian; Zhang, Jizong; Huang, Juan; Zhang, Ling; Zhao, Junyu

    2013-11-15

    Highlights: • The visible bremsstrahlung diagnostic provides a simple and effective tool for electron density estimation in steady state discharges. • This method can make up some disadvantages of present FIR and TS diagnostics in EAST tokamak. • Line averaged electron density has been deduced from central VB signal. The results can also be used for FIR n{sub e} correction. • Typical n{sub e} profiles have been obtained with T{sub e} and reconstructed bremsstrahlung profiles. -- Abstract: In EAST electron density (n{sub e}) is measured by the multi-channel far-infrared (FIR) hydrogen cyanide (HCN) interferometer and Thomson scattering (TS) diagnostics. However, it is difficult to obtain accurate n{sub e} profile for that there are many problems existing in current electron density diagnostics. Since the visible bremsstrahlung (VB) emission coefficient has a strong dependence on electron density, the visible bremsstrahlung measurement system developed to determine the ion effective charge (Z{sub eff}) may also be used for n{sub e} estimation via inverse operations. With assumption that Z{sub eff} has a flat profile and does not change significantly in steady state discharges, line averaged electron density (n{sup ¯}{sub e}) has been deduced from VB signals in L-mode and H-mode discharges in EAST. The results are in good coincidence with n{sup ¯}{sub e} from FIR, which proves that VB measurement is an effective tool for n{sub e} estimation. VB diagnostic is also applied to n{sup ¯}{sub e} correction when FIR n{sup ¯}{sub e} is wrong for the laser phase shift reversal together with noise causes errors when electron density changed rapidly in the H-mode discharges. Typical n{sub e} profiles in L-mode and H-mode phase are also deduced with reconstructed bremsstrahlung profiles.

  10. 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.

  11. 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

    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...... 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...... and examine uncertainties in estimation of tree biomass using indirect methods. Methods This study focused on three dominant mangrove species (Avicennia marina (Forssk.) Vierh, Sonneratia alba J. Smith and Rhizophora mucronata Lam.) in Tanzania. A total of 120 trees were destructively sampled for aboveground...

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

    Science.gov (United States)

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

    2016-03-01

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

  13. Theoretical epidemiology applied to health physics: estimation of the risk of radiation-induced breast cancer

    International Nuclear Information System (INIS)

    Sutherland, J.V.

    1983-01-01

    Indirect estimation of low-dose radiation hazards is possible using the multihit model of carcinogenesis. This model is based on cancer incidence data collected over many decades on tens of millions of people. Available data on human radiation effects can be introduced into the modeling process without the requirement that these data precisely define the model to be used. This reduction in the information demanded from the limited data on human radiation effects allows a more rational approach to estimation of low-dose radiation hazards and helps to focus attention on research directed towards understanding the process of carcinogenesis, rather than on repeating human or animal experiments that cannot provide sufficient data to resolve the low-dose estimation problem. Assessment of the risk of radiation-induced breast cancer provides an excellent example of the utility of multihit modeling procedures

  14. 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.

  15. Significant improvement of bone mineral density by denosumab treatment in Japanese osteoporotic patients following breast cancer treatment

    Directory of Open Access Journals (Sweden)

    Nakamura Y

    2018-03-01

    Full Text Available Yukio Nakamura,1,2 Mikio Kamimura,3 Akio Morikawa,4 Akira Taguchi,5 Takako Suzuki,1 Hiroyuki Kato1 1Department of Orthopaedic Surgery, Shinshu University School of Medicine, Matsumoto, 2Department of Orthopedic Surgery, Showa-Inan General Hospital, Komagane, 3Center for Osteoporosis and Spinal Disorders, Kamimura Orthopaedic Clinic, Matsumoto, 4Department of Surgery, Showa-Inan General Hospital, Komagane, 5Department of Oral and Maxillofacial Radiology, School of Dentistry, Matsumoto Dental University, Shiojiri, Japan Background: The aim of this study was to evaluate the effects of denosumab in patients with osteoporosis (OP and non-metastatic breast cancer following treatment of 1 surgery, 2 surgery and aromatase inhibitors, and 3 surgery, aromatase inhibitors, and anti-cancer agents, compared with those in primary OP patients. Patients and methods: In this retrospective 24-month study, patients were divided into the primary OP group (34 cases or OP receiving breast cancer treatment group (breast cancer group; 17 cases. We measured serum calcium, whole parathyroid hormone (PTH, 1,25OH2D3, bone alkaline phosphatase (BAP, tartrate-resistant acid phosphatase-5b (TRACP-5b, and bone mineral density (BMD of the lumbar 1–4 vertebrae (L-BMD and bilateral total hips (H-BMD for 24 months. Results: The percent changes of serum calcium in the breast cancer group were significantly lower than those in the primary OP group at 1 week, 1 and 12 months. The percent changes of whole PTH in the primary OP group were significantly lower than those in the breast cancer group at 2 and 4 months. Significant differences were found between the groups at 18 months (-34.5% in the primary OP group and -52.6% in the breast cancer group, respectively for the percent changes of BAP. Significant differences were found between the groups at 12, 18, and 24 months (-39.7% in the primary OP group and -64.0% in the breast cancer group at 24 months, respectively for the percent

  16. The method of estimating the irradiated lung volume in primary breast irradiation

    International Nuclear Information System (INIS)

    Leite, Miguel Torres Teixeira; Marques, Iara Silva; Geraldo, Jony Marques

    1999-01-01

    Tangential breast fields irradiation usually includes some volume of lung and it is occasionally associated with pneumonitis. The estimation of the amount of lung irradiated can be determined measuring the central lung distance (CLD) by the port films, and it must be inferior to 2.5 cm. The purpose of this study was to determine through a linear regression analysis the relationship between CLD and the geometrical parameters of the treatment, and to develop an equation to predict this volume. The studied population consisted of 100 patients who received definitive radiation for clinical stage I and II breast cancer between January, 1996 and June, 1997. According to the contour of the breast and thorax was determined the angle of the tangential fields. In 71% of the patients the CLD measured by the portal films were superior to 2.5 cm, requiring a new beam arrangement. We develop a simple and convenient quantitative model to predict the irradiated lung volume based on portal films. We need further analysis in order to include variables and antomical variations. (author)

  17. Reliability of different sampling densities for estimating and mapping lichen diversity in biomonitoring studies

    International Nuclear Information System (INIS)

    Ferretti, M.; Brambilla, E.; Brunialti, G.; Fornasier, F.; Mazzali, C.; Giordani, P.; Nimis, P.L.

    2004-01-01

    Sampling requirements related to lichen biomonitoring include optimal sampling density for obtaining precise and unbiased estimates of population parameters and maps of known reliability. Two available datasets on a sub-national scale in Italy were used to determine a cost-effective sampling density to be adopted in medium-to-large-scale biomonitoring studies. As expected, the relative error in the mean Lichen Biodiversity (Italian acronym: BL) values and the error associated with the interpolation of BL values for (unmeasured) grid cells increased as the sampling density decreased. However, the increase in size of the error was not linear and even a considerable reduction (up to 50%) in the original sampling effort led to a far smaller increase in errors in the mean estimates (<6%) and in mapping (<18%) as compared with the original sampling densities. A reduction in the sampling effort can result in considerable savings of resources, which can then be used for a more detailed investigation of potentially problematic areas. It is, however, necessary to decide the acceptable level of precision at the design stage of the investigation, so as to select the proper sampling density. - An acceptable level of precision must be decided before determining a sampling design

  18. Joint estimation of crown of thorns (Acanthaster planci densities on the Great Barrier Reef

    Directory of Open Access Journals (Sweden)

    M. Aaron MacNeil

    2016-08-01

    Full Text Available Crown-of-thorns starfish (CoTS; Acanthaster spp. are an outbreaking pest among many Indo-Pacific coral reefs that cause substantial ecological and economic damage. Despite ongoing CoTS research, there remain critical gaps in observing CoTS populations and accurately estimating their numbers, greatly limiting understanding of the causes and sources of CoTS outbreaks. Here we address two of these gaps by (1 estimating the detectability of adult CoTS on typical underwater visual count (UVC surveys using covariates and (2 inter-calibrating multiple data sources to estimate CoTS densities within the Cairns sector of the Great Barrier Reef (GBR. We find that, on average, CoTS detectability is high at 0.82 [0.77, 0.87] (median highest posterior density (HPD and [95% uncertainty intervals], with CoTS disc width having the greatest influence on detection. Integrating this information with coincident surveys from alternative sampling programs, we estimate CoTS densities in the Cairns sector of the GBR averaged 44 [41, 48] adults per hectare in 2014.

  19. Technical factors influencing cone packing density estimates in adaptive optics flood illuminated retinal images.

    Directory of Open Access Journals (Sweden)

    Marco Lombardo

    Full Text Available PURPOSE: To investigate the influence of various technical factors on the variation of cone packing density estimates in adaptive optics flood illuminated retinal images. METHODS: Adaptive optics images of the photoreceptor mosaic were obtained in fifteen healthy subjects. The cone density and Voronoi diagrams were assessed in sampling windows of 320×320 µm, 160×160 µm and 64×64 µm at 1.5 degree temporal and superior eccentricity from the preferred locus of fixation (PRL. The technical factors that have been analyzed included the sampling window size, the corrected retinal magnification factor (RMFcorr, the conversion from radial to linear distance from the PRL, the displacement between the PRL and foveal center and the manual checking of cone identification algorithm. Bland-Altman analysis was used to assess the agreement between cone density estimated within the different sampling window conditions. RESULTS: The cone density declined with decreasing sampling area and data between areas of different size showed low agreement. A high agreement was found between sampling areas of the same size when comparing density calculated with or without using individual RMFcorr. The agreement between cone density measured at radial and linear distances from the PRL and between data referred to the PRL or the foveal center was moderate. The percentage of Voronoi tiles with hexagonal packing arrangement was comparable between sampling areas of different size. The boundary effect, presence of any retinal vessels, and the manual selection of cones missed by the automated identification algorithm were identified as the factors influencing variation of cone packing arrangements in Voronoi diagrams. CONCLUSIONS: The sampling window size is the main technical factor that influences variation of cone density. Clear identification of each cone in the image and the use of a large buffer zone are necessary to minimize factors influencing variation of Voronoi

  20. Efficient Estimation of Dynamic Density Functions with Applications in Streaming Data

    KAUST Repository

    Qahtan, Abdulhakim

    2016-05-11

    Recent advances in computing technology allow for collecting vast amount of data that arrive continuously in the form of streams. Mining data streams is challenged by the speed and volume of the arriving data. Furthermore, the underlying distribution of the data changes over the time in unpredicted scenarios. To reduce the computational cost, data streams are often studied in forms of condensed representation, e.g., Probability Density Function (PDF). This thesis aims at developing an online density estimator that builds a model called KDE-Track for characterizing the dynamic density of the data streams. KDE-Track estimates the PDF of the stream at a set of resampling points and uses interpolation to estimate the density at any given point. To reduce the interpolation error and computational complexity, we introduce adaptive resampling where more/less resampling points are used in high/low curved regions of the PDF. The PDF values at the resampling points are updated online to provide up-to-date model of the data stream. Comparing with other existing online density estimators, KDE-Track is often more accurate (as reflected by smaller error values) and more computationally efficient (as reflected by shorter running time). The anytime available PDF estimated by KDE-Track can be applied for visualizing the dynamic density of data streams, outlier detection and change detection in data streams. In this thesis work, the first application is to visualize the taxi traffic volume in New York city. Utilizing KDE-Track allows for visualizing and monitoring the traffic flow on real time without extra overhead and provides insight analysis of the pick up demand that can be utilized by service providers to improve service availability. The second application is to detect outliers in data streams from sensor networks based on the estimated PDF. The method detects outliers accurately and outperforms baseline methods designed for detecting and cleaning outliers in sensor data. The

  1. Dense Breasts: Answers to Commonly Asked Questions

    Science.gov (United States)

    ... Cancer Prevention Genetics of Breast & Gynecologic Cancers Breast Cancer Screening Research Dense Breasts: Answers to Commonly Asked Questions What are dense breasts? Breasts contain glandular, connective, and fat tissue. Breast density is a term that describes the ...

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

    International Nuclear Information System (INIS)

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

    2016-01-01

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

  3. A new estimation method for nuclide number densities in equilibrium cycle

    International Nuclear Information System (INIS)

    Seino, Takeshi; Sekimoto, Hiroshi; Ando, Yoshihira.

    1997-01-01

    A new method is proposed for estimating nuclide number densities of LWR equilibrium cycle by multi-recycling calculation. Conventionally, it is necessary to spend a large computation time for attaining the ultimate equilibrium state. Hence, the cycle in nearly constant fuel composition has been considered as an equilibrium state which can be achieved by a few of recycling calculations on a simulated cycle operation under a specific fuel core design. The present method uses steady state fuel nuclide number densities as the initial guess for multi-recycling burnup calculation obtained by a continuously fuel supplied core model. The number densities are modified to be the initial number densities for nuclides of a batch supplied fuel. It was found that the calculated number densities could attain to more precise equilibrium state than that of a conventional multi-recycling calculation with a small number of recyclings. In particular, the present method could give the ultimate equilibrium number densities of the nuclides with the higher mass number than 245 Cm and 244 Pu which were not able to attain to the ultimate equilibrium state within a reasonable number of iterations using a conventional method. (author)