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

PubMed

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

2012-08-01

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

Breast Mass Detection in Digital Mammogram Based on Gestalt Psychology

PubMed Central

Bu, Qirong; Liu, Feihong; Zhang, Min; Ren, Yu; Lv, Yi

2018-01-01

Inspired by gestalt psychology, we combine human cognitive characteristics with knowledge of radiologists in medical image analysis. In this paper, a novel framework is proposed to detect breast masses in digitized mammograms. It can be divided into three modules: sensation integration, semantic integration, and verification. After analyzing the progress of radiologist's mammography screening, a series of visual rules based on the morphological characteristics of breast masses are presented and quantified by mathematical methods. The framework can be seen as an effective trade-off between bottom-up sensation and top-down recognition methods. This is a new exploratory method for the automatic detection of lesions. The experiments are performed on Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) data sets. The sensitivity reached to 92% at 1.94 false positive per image (FPI) on MIAS and 93.84% at 2.21 FPI on DDSM. Our framework has achieved a better performance compared with other algorithms. PMID:29854359

Staging of breast cancer and the advanced applications of digital mammogram: what the physician needs to know?

PubMed

Helal, Maha H; Mansour, Sahar M; Zaglol, Mai; Salaleldin, Lamia A; Nada, Omniya M; Haggag, Marwa A

2017-03-01

To study the role of advanced applications of digital mammogram, whether contrast-enhanced spectral mammography (CESM) or digital breast tomosynthesis (DBT), in the "T" staging of histologically proven breast cancer before planning for treatment management. In this prospective analysis, we evaluated 98 proved malignant breast masses regarding their size, multiplicity and the presence of associated clusters of microcalcifications. Evaluation methods included digital mammography (DM), 3D tomosynthesis and CESM. Traditional DM was first performed then in a period of 10-14-day interval; breast tomosynthesis and contrast-based mammography were performed for the involved breast only. Views at tomosynthesis were acquired in a "step-and-shoot" tube motion mode to produce multiple (11-15), low-dose images and in contrast-enhanced study, low-energy (22-33 kVp) and high-energy (44-49 kVp) exposures were taken after the i.v. injection of the contrast agent. Operative data were the gold standard reference. Breast tomosynthesis showed the highest accuracy in size assessment (n = 69, 70.4%) than contrast-enhanced (n = 49, 50%) and regular mammography (n = 59, 60.2%). Contrast-enhanced mammography presented the least performance in assessing calcifications, yet it was most sensitive in the detection of multiplicity (92.3%), followed by tomosynthesis (77%) and regular mammography (53.8%). The combined analysis of the three modalities provided an accuracy of 74% in the "T" staging of breast cancer. The combined application of tomosynthesis and contrast-enhanced digital mammogram enhanced the performance of the traditional DM and presented an informative method in the staging of breast cancer. Advances in knowledge: Staging and management planning of breast cancer can divert according to tumour size, multiplicity and the presence of microcalcifications. DBT shows sharp outlines of the tumour with no overlap tissue and spots microcalcifications. Contrast-enhanced spectral mammogram shows the extent of abnormal contrast uptake and detects multiplicity. Integrated analysis provides optimal findings for proper "T" staging of breast cancer.

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Comparative visualization of digital mammograms on clinical 2K monitor workstations and hardcopy: a contrast detail analysis

NASA Astrophysics Data System (ADS)

Torbica, Pavle; Buchberger, Wolfgang; Bernathova, M.; Mallouhi, Ammar; Peer, Siegfried; Bosmans, Hilde; Faulkner, Keith

2003-05-01

The purpose of this study was to compare the radiologist`s performance in detecting small low-contrast objects with hardcopy and softcopy reading of digital mammograms. 12 images of a contrast-detail (CD) phantom without and with 25.4 mm, 50.8 mm, and 76.2 mm additional polymethylmetacrylate (PMMA) attenuation were acquired with a caesium iodid/amorphous silicon flat panel detector under standard exposure conditions. The phantom images were read by three independent observers, by conducting a four-alternative forced-choice experiment. Reading of the hardcopy was done on a mammography viewbox under standardized reading conditions. For soft copy reading, a dedicated workstation with two 2K monitors was used. CD-curves and image quality figure (IQF) values were calculated from the correct detection rates of randomly located gold disks in the phantom. The figures were compared for both reading conditions and for different PMMA layers. For all types of exposures, soft copy reading resulted in significantly better contrast-detail characteristics and IQF values, as compared to hard copy reading of laser printouts. (p< 0.01). The authors conclude that the threshold contrast characteristics of digital mammograms displayed on high-resolution monitors are sufficient to make soft copy reading of digital mammograms feasible.

A deep learning method for classifying mammographic breast density categories.

PubMed

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

2018-01-01

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

Modeling of digital mammograms using bicubic spline functions and additive noise

NASA Astrophysics Data System (ADS)

Graffigne, Christine; Maintournam, Aboubakar; Strauss, Anne

1998-09-01

The purpose of our work is the microcalcifications detection on digital mammograms. In order to do so, we model the grey levels of digital mammograms by the sum of a surface trend (bicubic spline function) and an additive noise or texture. We also introduce a robust estimation method in order to overcome the bias introduced by the microcalcifications. After the estimation we consider the subtraction image values as noise. If the noise is not correlated, we adjust its distribution probability by the Pearson's system of densities. It allows us to threshold accurately the images of subtraction and therefore to detect the microcalcifications. If the noise is correlated, a unilateral autoregressive process is used and its coefficients are again estimated by the least squares method. We then consider non overlapping windows on the residues image. In each window the texture residue is computed and compared with an a priori threshold. This provides correct localization of the microcalcifications clusters. However this technique is definitely more time consuming that then automatic threshold assuming uncorrelated noise and does not lead to significantly better results. As a conclusion, even if the assumption of uncorrelated noise is not correct, the automatic thresholding based on the Pearson's system performs quite well on most of our images.

Computer-aided mass detection in mammography: False positive reduction via gray-scale invariant ranklet texture features

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

Masotti, Matteo; Lanconelli, Nico; Campanini, Renato

In this work, gray-scale invariant ranklet texture features are proposed for false positive reduction (FPR) in computer-aided detection (CAD) of breast masses. Two main considerations are at the basis of this proposal. First, false positive (FP) marks surviving our previous CAD system seem to be characterized by specific texture properties that can be used to discriminate them from masses. Second, our previous CAD system achieves invariance to linear/nonlinear monotonic gray-scale transformations by encoding regions of interest into ranklet images through the ranklet transform, an image transformation similar to the wavelet transform, yet dealing with pixels' ranks rather than with theirmore » gray-scale values. Therefore, the new FPR approach proposed herein defines a set of texture features which are calculated directly from the ranklet images corresponding to the regions of interest surviving our previous CAD system, hence, ranklet texture features; then, a support vector machine (SVM) classifier is used for discrimination. As a result of this approach, texture-based information is used to discriminate FP marks surviving our previous CAD system; at the same time, invariance to linear/nonlinear monotonic gray-scale transformations of the new CAD system is guaranteed, as ranklet texture features are calculated from ranklet images that have this property themselves by construction. To emphasize the gray-scale invariance of both the previous and new CAD systems, training and testing are carried out without any in-between parameters' adjustment on mammograms having different gray-scale dynamics; in particular, training is carried out on analog digitized mammograms taken from a publicly available digital database, whereas testing is performed on full-field digital mammograms taken from an in-house database. Free-response receiver operating characteristic (FROC) curve analysis of the two CAD systems demonstrates that the new approach achieves a higher reduction of FP marks when compared to the previous one. Specifically, at 60%, 65%, and 70% per-mammogram sensitivity, the new CAD system achieves 0.50, 0.68, and 0.92 FP marks per mammogram, whereas at 70%, 75%, and 80% per-case sensitivity it achieves 0.37, 0.48, and 0.71 FP marks per mammogram, respectively. Conversely, at the same sensitivities, the previous CAD system reached 0.71, 0.87, and 1.15 FP marks per mammogram, and 0.57, 0.73, and 0.92 FPs per mammogram. Also, statistical significance of the difference between the two per-mammogram and per-case FROC curves is demonstrated by the p-value<0.001 returned by jackknife FROC analysis performed on the two CAD systems.« less

Multi-scales region segmentation for ROI separation in digital mammograms

NASA Astrophysics Data System (ADS)

Zhang, Dapeng; Zhang, Di; Li, Yue; Wang, Wei

2017-02-01

Mammography is currently the most effective imaging modality used by radiologists for the screening of breast cancer. Segmentation is one of the key steps in the process of developing anatomical models for calculation of safe medical dose of radiation. This paper explores the potential of the statistical region merging segmentation technique for Breast segmentation in digital mammograms. First, the mammograms are pre-processing for regions enhancement, then the enhanced images are segmented using SRM with multi scales, finally these segmentations are combined for region of interest (ROI) separation and edge detection. The proposed algorithm uses multi-scales region segmentation in order to: separate breast region from background region, region edge detection and ROIs separation. The experiments are performed using a data set of mammograms from different patients, demonstrating the validity of the proposed criterion. Results show that, the statistical region merging segmentation algorithm actually can work on the segmentation of medical image and more accurate than another methods. And the outcome shows that the technique has a great potential to become a method of choice for segmentation of mammograms.

The effects of gray scale image processing on digital mammography interpretation performance.

PubMed

Cole, Elodia B; Pisano, Etta D; Zeng, Donglin; Muller, Keith; Aylward, Stephen R; Park, Sungwook; Kuzmiak, Cherie; Koomen, Marcia; Pavic, Dag; Walsh, Ruth; Baker, Jay; Gimenez, Edgardo I; Freimanis, Rita

2005-05-01

To determine the effects of three image-processing algorithms on diagnostic accuracy of digital mammography in comparison with conventional screen-film mammography. A total of 201 cases consisting of nonprocessed soft copy versions of the digital mammograms acquired from GE, Fischer, and Trex digital mammography systems (1997-1999) and conventional screen-film mammograms of the same patients were interpreted by nine radiologists. The raw digital data were processed with each of three different image-processing algorithms creating three presentations-manufacturer's default (applied and laser printed to film by each of the manufacturers), MUSICA, and PLAHE-were presented in soft copy display. There were three radiologists per presentation. Area under the receiver operating characteristic curve for GE digital mass cases was worse than screen-film for all digital presentations. The area under the receiver operating characteristic for Trex digital mass cases was better, but only with images processed with the manufacturer's default algorithm. Sensitivity for GE digital mass cases was worse than screen film for all digital presentations. Specificity for Fischer digital calcifications cases was worse than screen film for images processed in default and PLAHE algorithms. Specificity for Trex digital calcifications cases was worse than screen film for images processed with MUSICA. Specific image-processing algorithms may be necessary for optimal presentation for interpretation based on machine and lesion type.

Hexagonal wavelet processing of digital mammography

NASA Astrophysics Data System (ADS)

Laine, Andrew F.; Schuler, Sergio; Huda, Walter; Honeyman-Buck, Janice C.; Steinbach, Barbara G.

1993-09-01

This paper introduces a novel approach for accomplishing mammographic feature analysis through overcomplete multiresolution representations. We show that efficient representations may be identified from digital mammograms and used to enhance features of importance to mammography within a continuum of scale-space. We present a method of contrast enhancement based on an overcomplete, non-separable multiscale representation: the hexagonal wavelet transform. Mammograms are reconstructed from transform coefficients modified at one or more levels by local and global non-linear operators. Multiscale edges identified within distinct levels of transform space provide local support for enhancement. We demonstrate that features extracted from multiresolution representations can provide an adaptive mechanism for accomplishing local contrast enhancement. We suggest that multiscale detection and local enhancement of singularities may be effectively employed for the visualization of breast pathology without excessive noise amplification.

iPixel: a visual content-based and semantic search engine for retrieving digitized mammograms by using collective intelligence.

PubMed

Alor-Hernández, Giner; Pérez-Gallardo, Yuliana; Posada-Gómez, Rubén; Cortes-Robles, Guillermo; Rodríguez-González, Alejandro; Aguilar-Laserre, Alberto A

2012-09-01

Nowadays, traditional search engines such as Google, Yahoo and Bing facilitate the retrieval of information in the format of images, but the results are not always useful for the users. This is mainly due to two problems: (1) the semantic keywords are not taken into consideration and (2) it is not always possible to establish a query using the image features. This issue has been covered in different domains in order to develop content-based image retrieval (CBIR) systems. The expert community has focussed their attention on the healthcare domain, where a lot of visual information for medical analysis is available. This paper provides a solution called iPixel Visual Search Engine, which involves semantics and content issues in order to search for digitized mammograms. iPixel offers the possibility of retrieving mammogram features using collective intelligence and implementing a CBIR algorithm. Our proposal compares not only features with similar semantic meaning, but also visual features. In this sense, the comparisons are made in different ways: by the number of regions per image, by maximum and minimum size of regions per image and by average intensity level of each region. iPixel Visual Search Engine supports the medical community in differential diagnoses related to the diseases of the breast. The iPixel Visual Search Engine has been validated by experts in the healthcare domain, such as radiologists, in addition to experts in digital image analysis.

Pattern Recognition and Size Prediction of Microcalcification Based on Physical Characteristics by Using Digital Mammogram Images.

PubMed

Jothilakshmi, G R; Raaza, Arun; Rajendran, V; Sreenivasa Varma, Y; Guru Nirmal Raj, R

2018-06-05

Breast cancer is one of the life-threatening cancers occurring in women. In recent years, from the surveys provided by various medical organizations, it has become clear that the mortality rate of females is increasing owing to the late detection of breast cancer. Therefore, an automated algorithm is needed to identify the early occurrence of microcalcification, which would assist radiologists and physicians in reducing the false predictions via image processing techniques. In this work, we propose a new algorithm to detect the pattern of a microcalcification by calculating its physical characteristics. The considered physical characteristics are the reflection coefficient and mass density of the binned digital mammogram image. The calculation of physical characteristics doubly confirms the presence of malignant microcalcification. Subsequently, by interpolating the physical characteristics via thresholding and mapping techniques, a three-dimensional (3D) projection of the region of interest (RoI) is obtained in terms of the distance in millimeter. The size of a microcalcification is determined using this 3D-projected view. This algorithm is verified with 100 abnormal mammogram images showing microcalcification and 10 normal mammogram images. In addition to the size calculation, the proposed algorithm acts as a good classifier that is used to classify the considered input image as normal or abnormal with the help of only two physical characteristics. This proposed algorithm exhibits a classification accuracy of 99%.

Automatic localization of the nipple in mammograms using Gabor filters and the Radon transform

NASA Astrophysics Data System (ADS)

Chakraborty, Jayasree; Mukhopadhyay, Sudipta; Rangayyan, Rangaraj M.; Sadhu, Anup; Azevedo-Marques, P. M.

2013-02-01

The nipple is an important landmark in mammograms. Detection of the nipple is useful for alignment and registration of mammograms in computer-aided diagnosis of breast cancer. In this paper, a novel approach is proposed for automatic detection of the nipple based on the oriented patterns of the breast tissues present in mammograms. The Radon transform is applied to the oriented patterns obtained by a bank of Gabor filters to detect the linear structures related to the tissue patterns. The detected linear structures are then used to locate the nipple position using the characteristics of convergence of the tissue patterns towards the nipple. The performance of the method was evaluated with 200 scanned-film images from the mini-MIAS database and 150 digital radiography (DR) images from a local database. Average errors of 5:84 mm and 6:36 mm were obtained with respect to the reference nipple location marked by a radiologist for the mini-MIAS and the DR images, respectively.

Mesh-free based variational level set evolution for breast region segmentation and abnormality detection using mammograms.

PubMed

Kashyap, Kanchan L; Bajpai, Manish K; Khanna, Pritee; Giakos, George

2018-01-01

Automatic segmentation of abnormal region is a crucial task in computer-aided detection system using mammograms. In this work, an automatic abnormality detection algorithm using mammographic images is proposed. In the preprocessing step, partial differential equation-based variational level set method is used for breast region extraction. The evolution of the level set method is done by applying mesh-free-based radial basis function (RBF). The limitation of mesh-based approach is removed by using mesh-free-based RBF method. The evolution of variational level set function is also done by mesh-based finite difference method for comparison purpose. Unsharp masking and median filtering is used for mammogram enhancement. Suspicious abnormal regions are segmented by applying fuzzy c-means clustering. Texture features are extracted from the segmented suspicious regions by computing local binary pattern and dominated rotated local binary pattern (DRLBP). Finally, suspicious regions are classified as normal or abnormal regions by means of support vector machine with linear, multilayer perceptron, radial basis, and polynomial kernel function. The algorithm is validated on 322 sample mammograms of mammographic image analysis society (MIAS) and 500 mammograms from digital database for screening mammography (DDSM) datasets. Proficiency of the algorithm is quantified by using sensitivity, specificity, and accuracy. The highest sensitivity, specificity, and accuracy of 93.96%, 95.01%, and 94.48%, respectively, are obtained on MIAS dataset using DRLBP feature with RBF kernel function. Whereas, the highest 92.31% sensitivity, 98.45% specificity, and 96.21% accuracy are achieved on DDSM dataset using DRLBP feature with RBF kernel function. Copyright © 2017 John Wiley & Sons, Ltd.

Using multiscale texture and density features for near-term breast cancer risk analysis

PubMed Central

Sun, Wenqing; Tseng, Tzu-Liang (Bill); Qian, Wei; Zhang, Jianying; Saltzstein, Edward C.; Zheng, Bin; Lure, Fleming; Yu, Hui; Zhou, Shi

2015-01-01

Purpose: To help improve efficacy of screening mammography by eventually establishing a new optimal personalized screening paradigm, the authors investigated the potential of using the quantitative multiscale texture and density feature analysis of digital mammograms to predict near-term breast cancer risk. Methods: The authors’ dataset includes digital mammograms acquired from 340 women. Among them, 141 were positive and 199 were negative/benign cases. The negative digital mammograms acquired from the “prior” screening examinations were used in the study. Based on the intensity value distributions, five subregions at different scales were extracted from each mammogram. Five groups of features, including density and texture features, were developed and calculated on every one of the subregions. Sequential forward floating selection was used to search for the effective combinations. Using the selected features, a support vector machine (SVM) was optimized using a tenfold validation method to predict the risk of each woman having image-detectable cancer in the next sequential mammography screening. The area under the receiver operating characteristic curve (AUC) was used as the performance assessment index. Results: From a total number of 765 features computed from multiscale subregions, an optimal feature set of 12 features was selected. Applying this feature set, a SVM classifier yielded performance of AUC = 0.729 ± 0.021. The positive predictive value was 0.657 (92 of 140) and the negative predictive value was 0.755 (151 of 200). Conclusions: The study results demonstrated a moderately high positive association between risk prediction scores generated by the quantitative multiscale mammographic image feature analysis and the actual risk of a woman having an image-detectable breast cancer in the next subsequent examinations. PMID:26127038

Mutual information-based template matching scheme for detection of breast masses: from mammography to digital breast tomosynthesis

PubMed Central

Mazurowski, Maciej A; Lo, Joseph Y; Harrawood, Brian P; Tourassi, Georgia D

2011-01-01

Development of a computational decision aid for a new medical imaging modality typically is a long and complicated process. It consists of collecting data in the form of images and annotations, development of image processing and pattern recognition algorithms for analysis of the new images and finally testing of the resulting system. Since new imaging modalities are developed more rapidly than ever before, any effort for decreasing the time and cost of this development process could result in maximizing the benefit of the new imaging modality to patients by making the computer aids quickly available to radiologists that interpret the images. In this paper, we make a step in this direction and investigate the possibility of translating the knowledge about the detection problem from one imaging modality to another. Specifically, we present a computer-aided detection (CAD) system for mammographic masses that uses a mutual information-based template matching scheme with intelligently selected templates. We presented principles of template matching with mutual information for mammography before. In this paper, we present an implementation of those principles in a complete computer-aided detection system. The proposed system, through an automatic optimization process, chooses the most useful templates (mammographic regions of interest) using a large database of previously collected and annotated mammograms. Through this process, the knowledge about the task of detecting masses in mammograms is incorporated in the system. Then we evaluate whether our system developed for screen-film mammograms can be successfully applied not only to other mammograms but also to digital breast tomosynthesis (DBT) reconstructed slices without adding any DBT cases for training. Our rationale is that since mutual information is known to be a robust intermodality image similarity measure, it has high potential of transferring knowledge between modalities in the context of the mass detection task. Experimental evaluation of the system on mammograms showed competitive performance compared to other mammography CAD systems recently published in the literature. When the system was applied “as-is” to DBT, its performance was notably worse than that for mammograms. However, with a simple additional preprocessing step, the performance of the system reached levels similar to that obtained for mammograms. In conclusion, the presented CAD system not only performed competitively on screen-film mammograms but it also performed robustly on DBT showing that direct transfer of knowledge across breast imaging modalities for mass detection is in fact possible. PMID:21554985

Staging of breast cancer and the advanced applications of digital mammogram: what the physician needs to know?

PubMed Central

Helal, Maha H; Zaglol, Mai; Salaleldin, Lamia A; Nada, Omniya M; Haggag, Marwa A

2017-01-01

Objective: To study the role of advanced applications of digital mammogram, whether contrast-enhanced spectral mammography (CESM) or digital breast tomosynthesis (DBT), in the “T” staging of histologically proven breast cancer before planning for treatment management. Methods: In this prospective analysis, we evaluated 98 proved malignant breast masses regarding their size, multiplicity and the presence of associated clusters of microcalcifications. Evaluation methods included digital mammography (DM), 3D tomosynthesis and CESM. Traditional DM was first performed then in a period of 10–14-day interval; breast tomosynthesis and contrast-based mammography were performed for the involved breast only. Views at tomosynthesis were acquired in a “step-and-shoot” tube motion mode to produce multiple (11–15), low-dose images and in contrast-enhanced study, low-energy (22–33 kVp) and high-energy (44–49 kVp) exposures were taken after the i.v. injection of the contrast agent. Operative data were the gold standard reference. Results: Breast tomosynthesis showed the highest accuracy in size assessment (n = 69, 70.4%) than contrast-enhanced (n = 49, 50%) and regular mammography (n = 59, 60.2%). Contrast-enhanced mammography presented the least performance in assessing calcifications, yet it was most sensitive in the detection of multiplicity (92.3%), followed by tomosynthesis (77%) and regular mammography (53.8%). The combined analysis of the three modalities provided an accuracy of 74% in the “T” staging of breast cancer. Conclusion: The combined application of tomosynthesis and contrast-enhanced digital mammogram enhanced the performance of the traditional DM and presented an informative method in the staging of breast cancer. Advances in knowledge: Staging and management planning of breast cancer can divert according to tumour size, multiplicity and the presence of microcalcifications. DBT shows sharp outlines of the tumour with no overlap tissue and spots microcalcifications. Contrast-enhanced spectral mammogram shows the extent of abnormal contrast uptake and detects multiplicity. Integrated analysis provides optimal findings for proper “T” staging of breast cancer. PMID:28055247

Use of volumetric features for temporal comparison of mass lesions in full field digital mammograms

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

Bozek, Jelena, E-mail: jelena.bozek@fer.hr; Grgic, Mislav; Kallenberg, Michiel

2014-02-15

Purpose: Temporal comparison of lesions might improve classification between benign and malignant lesions in full-field digital mammograms (FFDM). The authors compare the use of volumetric features for lesion classification, which are computed from dense tissue thickness maps, to the use of mammographic lesion area. Use of dense tissue thickness maps for lesion characterization is advantageous, since it results in lesion features that are invariant to acquisition parameters. Methods: The dataset used in the analysis consisted of 60 temporal mammogram pairs comprising 120 mediolateral oblique or craniocaudal views with a total of 65 lesions, of which 41 were benign and 24more » malignant. The authors analyzed the performance of four volumetric features, area, and four other commonly used features obtained from temporal mammogram pairs, current mammograms, and prior mammograms. The authors evaluated the individual performance of all features and of different feature sets. The authors used linear discriminant analysis with leave-one-out cross validation to classify different feature sets. Results: Volumetric features from temporal mammogram pairs achieved the best individual performance, as measured by the area under the receiver operating characteristic curve (A{sub z} value). Volume change (A{sub z} = 0.88) achieved higher A{sub z} value than projected lesion area change (A{sub z} = 0.78) in the temporal comparison of lesions. Best performance was achieved with a set that consisted of a set of features extracted from the current exam combined with four volumetric features representing changes with respect to the prior mammogram (A{sub z} = 0.90). This was significantly better (p = 0.005) than the performance obtained using features from the current exam only (A{sub z} = 0.77). Conclusions: Volumetric features from temporal mammogram pairs combined with features from the single exam significantly improve discrimination of benign and malignant lesions in FFDM mammograms compared to using only single exam features. In the comparison with prior mammograms, use of volumetric change may lead to better performance than use of lesion area change.« less

A population-based tissue probability map-driven level set method for fully automated mammographic density estimations.

PubMed

Kim, Youngwoo; Hong, Byung Woo; Kim, Seung Ja; Kim, Jong Hyo

2014-07-01

A major challenge when distinguishing glandular tissues on mammograms, especially for area-based estimations, lies in determining a boundary on a hazy transition zone from adipose to glandular tissues. This stems from the nature of mammography, which is a projection of superimposed tissues consisting of different structures. In this paper, the authors present a novel segmentation scheme which incorporates the learned prior knowledge of experts into a level set framework for fully automated mammographic density estimations. The authors modeled the learned knowledge as a population-based tissue probability map (PTPM) that was designed to capture the classification of experts' visual systems. The PTPM was constructed using an image database of a selected population consisting of 297 cases. Three mammogram experts extracted regions for dense and fatty tissues on digital mammograms, which was an independent subset used to create a tissue probability map for each ROI based on its local statistics. This tissue class probability was taken as a prior in the Bayesian formulation and was incorporated into a level set framework as an additional term to control the evolution and followed the energy surface designed to reflect experts' knowledge as well as the regional statistics inside and outside of the evolving contour. A subset of 100 digital mammograms, which was not used in constructing the PTPM, was used to validate the performance. The energy was minimized when the initial contour reached the boundary of the dense and fatty tissues, as defined by experts. The correlation coefficient between mammographic density measurements made by experts and measurements by the proposed method was 0.93, while that with the conventional level set was 0.47. The proposed method showed a marked improvement over the conventional level set method in terms of accuracy and reliability. This result suggests that the proposed method successfully incorporated the learned knowledge of the experts' visual systems and has potential to be used as an automated and quantitative tool for estimations of mammographic breast density levels.

Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography.

PubMed

Samala, Ravi K; Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A; Wei, Jun; Cha, Kenny

2016-12-01

Develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) volume using a deep convolutional neural network (DCNN) with transfer learning from mammograms. A data set containing 2282 digitized film and digital mammograms and 324 DBT volumes were collected with IRB approval. The mass of interest on the images was marked by an experienced breast radiologist as reference standard. The data set was partitioned into a training set (2282 mammograms with 2461 masses and 230 DBT views with 228 masses) and an independent test set (94 DBT views with 89 masses). For DCNN training, the region of interest (ROI) containing the mass (true positive) was extracted from each image. False positive (FP) ROIs were identified at prescreening by their previously developed CAD systems. After data augmentation, a total of 45 072 mammographic ROIs and 37 450 DBT ROIs were obtained. Data normalization and reduction of non-uniformity in the ROIs across heterogeneous data was achieved using a background correction method applied to each ROI. A DCNN with four convolutional layers and three fully connected (FC) layers was first trained on the mammography data. Jittering and dropout techniques were used to reduce overfitting. After training with the mammographic ROIs, all weights in the first three convolutional layers were frozen, and only the last convolution layer and the FC layers were randomly initialized again and trained using the DBT training ROIs. The authors compared the performances of two CAD systems for mass detection in DBT: one used the DCNN-based approach and the other used their previously developed feature-based approach for FP reduction. The prescreening stage was identical in both systems, passing the same set of mass candidates to the FP reduction stage. For the feature-based CAD system, 3D clustering and active contour method was used for segmentation; morphological, gray level, and texture features were extracted and merged with a linear discriminant classifier to score the detected masses. For the DCNN-based CAD system, ROIs from five consecutive slices centered at each candidate were passed through the trained DCNN and a mass likelihood score was generated. The performances of the CAD systems were evaluated using free-response ROC curves and the performance difference was analyzed using a non-parametric method. Before transfer learning, the DCNN trained only on mammograms with an AUC of 0.99 classified DBT masses with an AUC of 0.81 in the DBT training set. After transfer learning with DBT, the AUC improved to 0.90. For breast-based CAD detection in the test set, the sensitivity for the feature-based and the DCNN-based CAD systems was 83% and 91%, respectively, at 1 FP/DBT volume. The difference between the performances for the two systems was statistically significant (p-value < 0.05). The image patterns learned from the mammograms were transferred to the mass detection on DBT slices through the DCNN. This study demonstrated that large data sets collected from mammography are useful for developing new CAD systems for DBT, alleviating the problem and effort of collecting entirely new large data sets for the new modality.

Digital Image Processing Technique for Breast Cancer Detection

NASA Astrophysics Data System (ADS)

Guzmán-Cabrera, R.; Guzmán-Sepúlveda, J. R.; Torres-Cisneros, M.; May-Arrioja, D. A.; Ruiz-Pinales, J.; Ibarra-Manzano, O. G.; Aviña-Cervantes, G.; Parada, A. González

2013-09-01

Breast cancer is the most common cause of death in women and the second leading cause of cancer deaths worldwide. Primary prevention in the early stages of the disease becomes complex as the causes remain almost unknown. However, some typical signatures of this disease, such as masses and microcalcifications appearing on mammograms, can be used to improve early diagnostic techniques, which is critical for women’s quality of life. X-ray mammography is the main test used for screening and early diagnosis, and its analysis and processing are the keys to improving breast cancer prognosis. As masses and benign glandular tissue typically appear with low contrast and often very blurred, several computer-aided diagnosis schemes have been developed to support radiologists and internists in their diagnosis. In this article, an approach is proposed to effectively analyze digital mammograms based on texture segmentation for the detection of early stage tumors. The proposed algorithm was tested over several images taken from the digital database for screening mammography for cancer research and diagnosis, and it was found to be absolutely suitable to distinguish masses and microcalcifications from the background tissue using morphological operators and then extract them through machine learning techniques and a clustering algorithm for intensity-based segmentation.

INDIAM--an e-learning system for the interpretation of mammograms.

PubMed

Guliato, Denise; Bôaventura, Ricardo S; Maia, Marcelo A; Rangayyan, Rangaraj M; Simedo, Mariângela S; Macedo, Túlio A A

2009-08-01

We propose the design of a teaching system named Interpretation and Diagnosis of Mammograms (INDIAM) for training students in the interpretation of mammograms and diagnosis of breast cancer. The proposed system integrates an illustrated tutorial on radiology of the breast, that is, mammography, which uses education techniques to guide the user (doctors, students, or researchers) through various concepts related to the diagnosis of breast cancer. The user can obtain informative text about specific subjects, access a library of bibliographic references, and retrieve cases from a mammographic database that are similar to a query case on hand. The information of each case stored in the mammographic database includes the radiological findings, the clinical history, the lifestyle of the patient, and complementary exams. The breast cancer tutorial is linked to a module that simulates the analysis and diagnosis of a mammogram. The tutorial incorporates tools for helping the user to evaluate his or her knowledge about a specific subject by using the education system or by simulating a diagnosis with appropriate feedback in case of error. The system also makes available digital image processing tools that allow the user to draw the contour of a lesion, the contour of the breast, or identify a cluster of calcifications in a given mammogram. The contours provided by the user are submitted to the system for evaluation. The teaching system is integrated with AMDI-An Indexed Atlas of Digital Mammograms-that includes case studies, e-learning, and research systems. All the resources are accessible via the Web.

Computer-aided detection and diagnosis of masses and clustered microcalcifications from digital mammograms

NASA Astrophysics Data System (ADS)

Nishikawa, Robert M.; Giger, Maryellen L.; Doi, Kunio; Vyborny, Carl J.; Schmidt, Robert A.; Metz, Charles E.; Wu, Chris Y.; Yin, Fang-Fang; Jiang, Yulei; Huo, Zhimin; Lu, Ping; Zhang, Wei; Ema, Takahiro; Bick, Ulrich; Papaioannou, John; Nagel, Rufus H.

1993-07-01

We are developing an 'intelligent' workstation to assist radiologists in diagnosing breast cancer from mammograms. The hardware for the workstation will consist of a film digitizer, a high speed computer, a large volume storage device, a film printer, and 4 high resolution CRT monitors. The software for the workstation is a comprehensive package of automated detection and classification schemes. Two rule-based detection schemes have been developed, one for breast masses and the other for clustered microcalcifications. The sensitivity of both schemes is 85% with a false-positive rate of approximately 3.0 and 1.5 false detections per image, for the mass and cluster detection schemes, respectively. Computerized classification is performed by an artificial neural network (ANN). The ANN has a sensitivity of 100% with a specificity of 60%. Currently, the ANN, which is a three-layer, feed-forward network, requires as input ratings of 14 different radiographic features of the mammogram that were determined subjectively by a radiologist. We are in the process of developing automated techniques to objectively determine these 14 features. The workstation will be placed in the clinical reading area of the radiology department in the near future, where controlled clinical tests will be performed to measure its efficacy.

Training system for digital mammographic diagnoses of breast cancer

NASA Astrophysics Data System (ADS)

Thomaz, R. L.; Nirschl Crozara, M. G.; Patrocinio, A. C.

2013-03-01

As the technology evolves, the analog mammography systems are being replaced by digital systems. The digital system uses video monitors as the display of mammographic images instead of the previously used screen-film and negatoscope for analog images. The change in the way of visualizing mammographic images may require a different approach for training the health care professionals in diagnosing the breast cancer with digital mammography. Thus, this paper presents a computational approach to train the health care professionals providing a smooth transition between analog and digital technology also training to use the advantages of digital image processing tools to diagnose the breast cancer. This computational approach consists of a software where is possible to open, process and diagnose a full mammogram case from a database, which has the digital images of each of the mammographic views. The software communicates with a gold standard digital mammogram cases database. This database contains the digital images in Tagged Image File Format (TIFF) and the respective diagnoses according to BI-RADSTM, these files are read by software and shown to the user as needed. There are also some digital image processing tools that can be used to provide better visualization of each single image. The software was built based on a minimalist and a user-friendly interface concept that might help in the smooth transition. It also has an interface for inputting diagnoses from the professional being trained, providing a result feedback. This system has been already completed, but hasn't been applied to any professional training yet.

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

NASA Astrophysics Data System (ADS)

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

2012-03-01

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

SU-E-I-59: Investigation of the Usefulness of a Standard Deviation and Mammary Gland Density as Indexes for Mammogram Classification.

PubMed

Takarabe, S; Yabuuchi, H; Morishita, J

2012-06-01

To investigate the usefulness of the standard deviation of pixel values in a whole mammary glands region and the percentage of a high- density mammary glands region to a whole mammary glands region as features for classification of mammograms into four categories based on the ACR BI-RADS breast composition. We used 36 digital mediolateral oblique view mammograms (18 patients) approved by our IRB. These images were classified into the four categories of breast compositions by an experienced breast radiologist and the results of the classification were regarded as a gold standard. First, a whole mammary region in a breast was divided into two regions such as a high-density mammary glands region and a low/iso-density mammary glands region by using a threshold value that was obtained from the pixel values corresponding to a pectoral muscle region. Then the percentage of a high-density mammary glands region to a whole mammary glands region was calculated. In addition, as a new method, the standard deviation of pixel values in a whole mammary glands region was calculated as an index based on the intermingling of mammary glands and fats. Finally, all mammograms were classified by using the combination of the percentage of a high-density mammary glands region and the standard deviation of each image. The agreement rates of the classification between our proposed method and gold standard was 86% (31/36). This result signified that our method has the potential to classify mammograms. The combination of the standard deviation of pixel values in a whole mammary glands region and the percentage of a high-density mammary glands region to a whole mammary glands region was available as features to classify mammograms based on the ACR BI- RADS breast composition. © 2012 American Association of Physicists in Medicine.

Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography

PubMed Central

Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A.; Wei, Jun; Cha, Kenny

2016-01-01

Purpose: Develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) volume using a deep convolutional neural network (DCNN) with transfer learning from mammograms. Methods: A data set containing 2282 digitized film and digital mammograms and 324 DBT volumes were collected with IRB approval. The mass of interest on the images was marked by an experienced breast radiologist as reference standard. The data set was partitioned into a training set (2282 mammograms with 2461 masses and 230 DBT views with 228 masses) and an independent test set (94 DBT views with 89 masses). For DCNN training, the region of interest (ROI) containing the mass (true positive) was extracted from each image. False positive (FP) ROIs were identified at prescreening by their previously developed CAD systems. After data augmentation, a total of 45 072 mammographic ROIs and 37 450 DBT ROIs were obtained. Data normalization and reduction of non-uniformity in the ROIs across heterogeneous data was achieved using a background correction method applied to each ROI. A DCNN with four convolutional layers and three fully connected (FC) layers was first trained on the mammography data. Jittering and dropout techniques were used to reduce overfitting. After training with the mammographic ROIs, all weights in the first three convolutional layers were frozen, and only the last convolution layer and the FC layers were randomly initialized again and trained using the DBT training ROIs. The authors compared the performances of two CAD systems for mass detection in DBT: one used the DCNN-based approach and the other used their previously developed feature-based approach for FP reduction. The prescreening stage was identical in both systems, passing the same set of mass candidates to the FP reduction stage. For the feature-based CAD system, 3D clustering and active contour method was used for segmentation; morphological, gray level, and texture features were extracted and merged with a linear discriminant classifier to score the detected masses. For the DCNN-based CAD system, ROIs from five consecutive slices centered at each candidate were passed through the trained DCNN and a mass likelihood score was generated. The performances of the CAD systems were evaluated using free-response ROC curves and the performance difference was analyzed using a non-parametric method. Results: Before transfer learning, the DCNN trained only on mammograms with an AUC of 0.99 classified DBT masses with an AUC of 0.81 in the DBT training set. After transfer learning with DBT, the AUC improved to 0.90. For breast-based CAD detection in the test set, the sensitivity for the feature-based and the DCNN-based CAD systems was 83% and 91%, respectively, at 1 FP/DBT volume. The difference between the performances for the two systems was statistically significant (p-value < 0.05). Conclusions: The image patterns learned from the mammograms were transferred to the mass detection on DBT slices through the DCNN. This study demonstrated that large data sets collected from mammography are useful for developing new CAD systems for DBT, alleviating the problem and effort of collecting entirely new large data sets for the new modality. PMID:27908154

Wavelet Compression of Satellite-Transmitted Digital Mammograms

NASA Technical Reports Server (NTRS)

Zheng, Yuan F.

2001-01-01

Breast cancer is one of the major causes of cancer death in women in the United States. The most effective way to treat breast cancer is to detect it at an early stage by screening patients periodically. Conventional film-screening mammography uses X-ray films which are effective in detecting early abnormalities of the breast. Direct digital mammography has the potential to improve the image quality and to take advantages of convenient storage, efficient transmission, and powerful computer-aided diagnosis, etc. One effective alternative to direct digital imaging is secondary digitization of X-ray films. This technique may not provide as high an image quality as the direct digital approach, but definitely have other advantages inherent to digital images. One of them is the usage of satellite-transmission technique for transferring digital mammograms between a remote image-acquisition site and a central image-reading site. This technique can benefit a large population of women who reside in remote areas where major screening and diagnosing facilities are not available. The NASA-Lewis Research Center (LeRC), in collaboration with the Cleveland Clinic Foundation (CCF), has begun a pilot study to investigate the application of the Advanced Communications Technology Satellite (ACTS) network to telemammography. The bandwidth of the T1 transmission is limited (1.544 Mbps) while the size of a mammographic image is huge. It takes a long time to transmit a single mammogram. For example, a mammogram of 4k by 4k pixels with 16 bits per pixel needs more than 4 minutes to transmit. Four images for a typical screening exam would take more than 16 minutes. This is too long a time period for a convenient screening. Consequently, compression is necessary for making satellite-transmission of mammographic images practically possible. The Wavelet Research Group of the Department of Electrical Engineering at The Ohio State University (OSU) participated in the LeRC-CCF collaboration by providing advanced compression technology using wavelet transform. OSU developed a time-efficient software package with various wavelets to compress a serious of mammographic images. This documents reports the result of the compression activities.

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

PubMed

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

2013-05-01

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

[Efficacy of storage phosphor-based digital mammography in diagnosis of breast cancer--comparison with film-screen mammography].

PubMed

Kitahama, H

1991-05-25

The aim of this study is to present efficacy of storage phosphor-based digital mammography (CR-mammography) in diagnosis of breast cancer. Ninety-seven cases with breast cancer including 44 cases less than 2 cm in macroscopic size (t1 cases) were evaluated using storage phosphor-based digital mammography (2000 x 2510 pixels by 10 bits). Abnormal findings on CR-mammography were detected in 86 cases (88.7%) of 97 women with breast cancer. Sensitivity of CR-mammography was 88.7%. It was superior to that of film-screen mammography. On t1 breast cancer cases, sensitivity on CR-mammography was 88.6%. False negative rate in t1 breast cancer cases was reduced by image processing using CR-mammography. To evaluate microcalcifications, CR-mammograms and film-screen mammograms were investigated in 22 cases of breast cancer proven pathologically the existence of microcalcifications and 11 paraffin tissue blocks of breast cancer. CR-mammography was superior to film-screen mammography in recognizing of microcalcifications. As regards the detectability for the number and the shape of microcalcifications, CR-mammography was equivalent to film-screen mammography. Receiver operating characteristic (ROC) analysis by eight observers was performed for CR-mammography and film-screen mammography with 54 breast cancer patients and 54 normal cases. The detectability of abnormal findings of breast cancer on CR-mammography (ROC area = 0.91) was better than that on film-screen mammography (ROC area = 0.88) (p less than 0.05). Efficacy of storage phosphor-based digital mammography in diagnosis of breast cancer was discussed and demonstrated in this study.

SU-E-I-58: Objective Models of Breast Shape Undergoing Mammography and Tomosynthesis Using Principal Component Analysis.

PubMed

Feng, Ssj; Sechopoulos, I

2012-06-01

To develop an objective model of the shape of the compressed breast undergoing mammographic or tomosynthesis acquisition. Automated thresholding and edge detection was performed on 984 anonymized digital mammograms (492 craniocaudal (CC) view mammograms and 492 medial lateral oblique (MLO) view mammograms), to extract the edge of each breast. Principal Component Analysis (PCA) was performed on these edge vectors to identify a limited set of parameters and eigenvectors that. These parameters and eigenvectors comprise a model that can be used to describe the breast shapes present in acquired mammograms and to generate realistic models of breasts undergoing acquisition. Sample breast shapes were then generated from this model and evaluated. The mammograms in the database were previously acquired for a separate study and authorized for use in further research. The PCA successfully identified two principal components and their corresponding eigenvectors, forming the basis for the breast shape model. The simulated breast shapes generated from the model are reasonable approximations of clinically acquired mammograms. Using PCA, we have obtained models of the compressed breast undergoing mammographic or tomosynthesis acquisition based on objective analysis of a large image database. Up to now, the breast in the CC view has been approximated as a semi-circular tube, while there has been no objectively-obtained model for the MLO view breast shape. Such models can be used for various breast imaging research applications, such as x-ray scatter estimation and correction, dosimetry estimates, and computer-aided detection and diagnosis. © 2012 American Association of Physicists in Medicine.

Automatic classification for mammogram backgrounds based on bi-rads complexity definition and on a multi content analysis framework

NASA Astrophysics Data System (ADS)

Wu, Jie; Besnehard, Quentin; Marchessoux, Cédric

2011-03-01

Clinical studies for the validation of new medical imaging devices require hundreds of images. An important step in creating and tuning the study protocol is the classification of images into "difficult" and "easy" cases. This consists of classifying the image based on features like the complexity of the background, the visibility of the disease (lesions). Therefore, an automatic medical background classification tool for mammograms would help for such clinical studies. This classification tool is based on a multi-content analysis framework (MCA) which was firstly developed to recognize image content of computer screen shots. With the implementation of new texture features and a defined breast density scale, the MCA framework is able to automatically classify digital mammograms with a satisfying accuracy. BI-RADS (Breast Imaging Reporting Data System) density scale is used for grouping the mammograms, which standardizes the mammography reporting terminology and assessment and recommendation categories. Selected features are input into a decision tree classification scheme in MCA framework, which is the so called "weak classifier" (any classifier with a global error rate below 50%). With the AdaBoost iteration algorithm, these "weak classifiers" are combined into a "strong classifier" (a classifier with a low global error rate) for classifying one category. The results of classification for one "strong classifier" show the good accuracy with the high true positive rates. For the four categories the results are: TP=90.38%, TN=67.88%, FP=32.12% and FN =9.62%.

Detecting mammographically occult cancer in women with dense breasts using Radon Cumulative Distribution Transform: a preliminary analysis

NASA Astrophysics Data System (ADS)

Lee, Juhun; Nishikawa, Robert M.; Rohde, Gustavo K.

2018-02-01

We propose using novel imaging biomarkers for detecting mammographically-occult (MO) cancer in women with dense breast tissue. MO cancer indicates visually occluded, or very subtle, cancer that radiologists fail to recognize as a sign of cancer. We used the Radon Cumulative Distribution Transform (RCDT) as a novel image transformation to project the difference between left and right mammograms into a space, increasing the detectability of occult cancer. We used a dataset of 617 screening full-field digital mammograms (FFDMs) of 238 women with dense breast tissue. Among 238 women, 173 were normal with 2 - 4 consecutive screening mammograms, 552 normal mammograms in total, and the remaining 65 women had an MO cancer with a negative screening mammogram. We used Principal Component Analysis (PCA) to find representative patterns in normal mammograms in the RCDT space. We projected all mammograms to the space constructed by the first 30 eigenvectors of the RCDT of normal cases. Under 10-fold crossvalidation, we conducted quantitative feature analysis to classify normal mammograms and mammograms with MO cancer. We used receiver operating characteristic (ROC) analysis to evaluate the classifier's output using the area under the ROC curve (AUC) as the figure of merit. Four eigenvectors were selected via a feature selection method. The mean and standard deviation of the AUC of the trained classifier on the test set were 0.74 and 0.08, respectively. In conclusion, we utilized imaging biomarkers to highlight differences between left and right mammograms to detect MO cancer using novel imaging transformation.

A new approach to develop computer-aided detection schemes of digital mammograms

NASA Astrophysics Data System (ADS)

Tan, Maxine; Qian, Wei; Pu, Jiantao; Liu, Hong; Zheng, Bin

2015-06-01

The purpose of this study is to develop a new global mammographic image feature analysis based computer-aided detection (CAD) scheme and evaluate its performance in detecting positive screening mammography examinations. A dataset that includes images acquired from 1896 full-field digital mammography (FFDM) screening examinations was used in this study. Among them, 812 cases were positive for cancer and 1084 were negative or benign. After segmenting the breast area, a computerized scheme was applied to compute 92 global mammographic tissue density based features on each of four mammograms of the craniocaudal (CC) and mediolateral oblique (MLO) views. After adding three existing popular risk factors (woman’s age, subjectively rated mammographic density, and family breast cancer history) into the initial feature pool, we applied a sequential forward floating selection feature selection algorithm to select relevant features from the bilateral CC and MLO view images separately. The selected CC and MLO view image features were used to train two artificial neural networks (ANNs). The results were then fused by a third ANN to build a two-stage classifier to predict the likelihood of the FFDM screening examination being positive. CAD performance was tested using a ten-fold cross-validation method. The computed area under the receiver operating characteristic curve was AUC = 0.779   ±   0.025 and the odds ratio monotonically increased from 1 to 31.55 as CAD-generated detection scores increased. The study demonstrated that this new global image feature based CAD scheme had a relatively higher discriminatory power to cue the FFDM examinations with high risk of being positive, which may provide a new CAD-cueing method to assist radiologists in reading and interpreting screening mammograms.

A deep (learning) dive into visual search behaviour of breast radiologists

NASA Astrophysics Data System (ADS)

Mall, Suneeta; Brennan, Patrick C.; Mello-Thoms, Claudia

2018-03-01

Visual search, the process of detecting and identifying objects using the eye movements (saccades) and the foveal vision, has been studied for identification of root causes of errors in the interpretation of mammography. The aim of this study is to model visual search behaviour of radiologists and their interpretation of mammograms using deep machine learning approaches. Our model is based on a deep convolutional neural network, a biologically-inspired multilayer perceptron that simulates the visual cortex, and is reinforced with transfer learning techniques. Eye tracking data obtained from 8 radiologists (of varying experience levels in reading mammograms) reviewing 120 two-view digital mammography cases (59 cancers) have been used to train the model, which was pre-trained with the ImageNet dataset for transfer learning. Areas of the mammogram that received direct (foveally fixated), indirect (peripherally fixated) or no (never fixated) visual attention were extracted from radiologists' visual search maps (obtained by a head mounted eye tracking device). These areas, along with the radiologists' assessment (including confidence of the assessment) of suspected malignancy were used to model: 1) Radiologists' decision; 2) Radiologists' confidence on such decision; and 3) The attentional level (i.e. foveal, peripheral or none) obtained by an area of the mammogram. Our results indicate high accuracy and low misclassification in modelling such behaviours.

Cost-Effectiveness of Double Reading versus Single Reading of Mammograms in a Breast Cancer Screening Programme

PubMed Central

Posso, Margarita; Carles, Misericòrdia; Rué, Montserrat; Puig, Teresa; Bonfill, Xavier

2016-01-01

Objectives The usual practice in breast cancer screening programmes for mammogram interpretation is to perform double reading. However, little is known about its cost-effectiveness in the context of digital mammography. Our purpose was to evaluate the cost-effectiveness of double reading versus single reading of digital mammograms in a population-based breast cancer screening programme. Methods Data from 28,636 screened women was used to establish a decision-tree model and to compare three strategies: 1) double reading; 2) double reading for women in their first participation and single reading for women in their subsequent participations; and 3) single reading. We calculated the incremental cost-effectiveness ratio (ICER), which was defined as the expected cost per one additionally detected cancer. We performed a deterministic sensitivity analysis to test the robustness of the ICER. Results The detection rate of double reading (5.17‰) was similar to that of single reading (4.78‰; P = .768). The mean cost of each detected cancer was €8,912 for double reading and €8,287 for single reading. The ICER of double reading versus single reading was €16,684. The sensitivity analysis showed variations in the ICER according to the sensitivity of reading strategies. The strategy that combines double reading in first participation with single reading in subsequent participations was ruled out due to extended dominance. Conclusions From our results, double reading appears not to be a cost-effective strategy in the context of digital mammography. Double reading would eventually be challenged in screening programmes, as single reading might entail important net savings without significantly changing the cancer detection rate. These results are not conclusive and should be confirmed in prospective studies that investigate long-term outcomes like quality adjusted life years (QALYs). PMID:27459663

A novel approach for detection and classification of mammographic microcalcifications using wavelet analysis and extreme learning machine.

PubMed

Malar, E; Kandaswamy, A; Chakravarthy, D; Giri Dharan, A

2012-09-01

The objective of this paper is to reveal the effectiveness of wavelet based tissue texture analysis for microcalcification detection in digitized mammograms using Extreme Learning Machine (ELM). Microcalcifications are tiny deposits of calcium in the breast tissue which are potential indicators for early detection of breast cancer. The dense nature of the breast tissue and the poor contrast of the mammogram image prohibit the effectiveness in identifying microcalcifications. Hence, a new approach to discriminate the microcalcifications from the normal tissue is done using wavelet features and is compared with different feature vectors extracted using Gray Level Spatial Dependence Matrix (GLSDM) and Gabor filter based techniques. A total of 120 Region of Interests (ROIs) extracted from 55 mammogram images of mini-Mias database, including normal and microcalcification images are used in the current research. The network is trained with the above mentioned features and the results denote that ELM produces relatively better classification accuracy (94%) with a significant reduction in training time than the other artificial neural networks like Bayesnet classifier, Naivebayes classifier, and Support Vector Machine. ELM also avoids problems like local minima, improper learning rate, and over fitting. Copyright © 2012 Elsevier Ltd. All rights reserved.

Mammography image quality and evidence based practice: Analysis of the demonstration of the inframammary angle in the digital setting.

PubMed

Spuur, Kelly; Webb, Jodi; Poulos, Ann; Nielsen, Sharon; Robinson, Wayne

2018-03-01

The aim of this study is to determine the clinical rates of the demonstration of the inframammary angle (IMA) on the mediolateral oblique (MLO) view of the breast on digital mammograms and to compare the outcomes with current accreditation standards for compliance. Relationships between the IMA, age, the posterior nipple line (PNL) and compressed breast thickness will be identified and the study outcomes validated using appropriate analyses of inter-reader and inter-rater reliability and variability. Differences in left versus right data were also investigated. A quantitative retrospective study of 2270 randomly selected paired digital mammograms performed by BreastScreen NSW was undertaken. Data was collected by direct measurement and visual analysis. Intra-class correlation analyses were used to evaluate inter- and intra-rater reliability. The IMA was demonstrated on 52.4% of individual and 42.6% of paired mammograms. A linear relationship was found between the posterior nipple line (PNL) and age (p-value <0.001). The PNL was predicted to increase by 0.48 mm for every one year increment in age. The odds of demonstrating the IMA reduced by 2% for every one year increase in age (p-value = 0.001); are 0.4% higher for every 1 mm increase in PNL (p-value = 0.001) and 1.6% lower for every 1 mm increase in compressed breast thickness, (p-value<0.001). There was high inter- and intra-rater reliability for the PNL while there was 100% agreement for the demonstration of the IMA. Analysis of the demonstration of the IMA indicates clinically achievable rates (42.6%) well below that required for compliance (50%-75%) to known worldwide accreditation standards for screening mammography. These standards should be aligned to the reported evidence base. Visualisation of the IMA is impacted negatively by increasing age and compressed breast thickness but positively by breast size (PNL). Copyright © 2018 Elsevier B.V. All rights reserved.

A controlled phantom study of a noise equalization algorithm for detecting microcalcifications in digital mammograms.

PubMed

Gürün, O O; Fatouros, P P; Kuhn, G M; de Paredes, E S

2001-04-01

We report on some extensions and further developments of a well-known microcalcification detection algorithm based on adaptive noise equalization. Tissue equivalent phantom images with and without labeled microcalcifications were subjected to this algorithm, and analyses of results revealed some shortcomings in the approach. Particularly, it was observed that the method of estimating the width of distributions in the feature space was based on assumptions which resulted in the loss of similarity preservation characteristics. A modification involving a change of estimator statistic was made, and the modified approach was tested on the same phantom images. Other modifications for improving detectability such as downsampling and use of alternate local contrast filters were also tested. The results indicate that these modifications yield improvements in detectability, while extending the generality of the approach. Extensions to real mammograms and further directions of research are discussed.

Development of a Computer-Aided Diagnosis System for Early Detection of Masses Using Retrospectively Detected Cancers on Prior Mammograms

DTIC Science & Technology

2006-06-01

Hadjiiski, and N. Petrick, "Computerized nipple identification for multiple image analysis in computer-aided diagnosis," Medical Physics 31, 2871...candidates, 3 identification of suspicious objects, 4 feature extraction and analysis, and 5 FP reduc- tion by classification of normal tissue...detection of microcalcifi- cations on digitized mammograms.41 An illustration of a La- placian decomposition tree is shown on the left-hand side of Fig. 4

Computer-Assisted Visual Search/Decision Aids as a Training Tool for Mammography

DTIC Science & Technology

1999-07-01

display of a digital mammogram that compensates for the display brightness, the ambient light and the useful range of pixel intensities in the image...described here extends the work of Liu and Nodine (7) to include adjusting the gray-scale transform for ambient illumination and adjusting the mammogram...visible" disk in each band. The observer’s responses are affected by the display contrast and the ambient room lighting. The contrast of each indicated

Temporal Subtraction of Digital Breast Tomosynthesis Images for Improved Mass Detection

DTIC Science & Technology

2008-10-01

K. Fishman and B. M. W. Tsui, "Development of a computer-generated model for the coronary arterial tree based on multislice CT and morphometric data...mathematical models based on geometric primitives8-22. Bakic et al created synthetic x-ray mammograms using a 3D simulated breast tissue model consisting of...utilized a combination of voxel matrices and geometric primitives to create a breast phantom that includes the breast surface, the duct system, and

A new texture descriptor based on local micro-pattern for detection of architectural distortion in mammographic images

NASA Astrophysics Data System (ADS)

de Oliveira, Helder C. R.; Moraes, Diego R.; Reche, Gustavo A.; Borges, Lucas R.; Catani, Juliana H.; de Barros, Nestor; Melo, Carlos F. E.; Gonzaga, Adilson; Vieira, Marcelo A. C.

2017-03-01

This paper presents a new local micro-pattern texture descriptor for the detection of Architectural Distortion (AD) in digital mammography images. AD is a subtle contraction of breast parenchyma that may represent an early sign of breast cancer. Due to its subtlety and variability, AD is more difficult to detect compared to microcalcifications and masses, and is commonly found in retrospective evaluations of false-negative mammograms. Several computer-based systems have been proposed for automatic detection of AD, but their performance are still unsatisfactory. The proposed descriptor, Local Mapped Pattern (LMP), is a generalization of the Local Binary Pattern (LBP), which is considered one of the most powerful feature descriptor for texture classification in digital images. Compared to LBP, the LMP descriptor captures more effectively the minor differences between the local image pixels. Moreover, LMP is a parametric model which can be optimized for the desired application. In our work, the LMP performance was compared to the LBP and four Haralick's texture descriptors for the classification of 400 regions of interest (ROIs) extracted from clinical mammograms. ROIs were selected and divided into four classes: AD, normal tissue, microcalcifications and masses. Feature vectors were used as input to a multilayer perceptron neural network, with a single hidden layer. Results showed that LMP is a good descriptor to distinguish AD from other anomalies in digital mammography. LMP performance was slightly better than the LBP and comparable to Haralick's descriptors (mean classification accuracy = 83%).

A study on mastectomy samples to evaluate breast imaging quality and potential clinical relevance of differential phase contrast mammography.

PubMed

Hauser, Nik; Wang, Zhentian; Kubik-Huch, Rahel A; Trippel, Mafalda; Singer, Gad; Hohl, Michael K; Roessl, Ewald; Köhler, Thomas; van Stevendaal, Udo; Wieberneit, Nataly; Stampanoni, Marco

2014-03-01

Differential phase contrast and scattering-based x-ray mammography has the potential to provide additional and complementary clinically relevant information compared with absorption-based mammography. The purpose of our study was to provide a first statistical evaluation of the imaging capabilities of the new technique compared with digital absorption mammography. We investigated non-fixed mastectomy samples of 33 patients with invasive breast cancer, using grating-based differential phase contrast mammography (mammoDPC) with a conventional, low-brilliance x-ray tube. We simultaneously recorded absorption, differential phase contrast, and small-angle scattering signals that were combined into novel high-frequency-enhanced images with a dedicated image fusion algorithm. Six international, expert breast radiologists evaluated clinical digital and experimental mammograms in a 2-part blinded, prospective independent reader study. The results were statistically analyzed in terms of image quality and clinical relevance. The results of the comparison of mammoDPC with clinical digital mammography revealed the general quality of the images to be significantly superior (P < 0.001); sharpness, lesion delineation, as well as the general visibility of calcifications to be significantly more assessable (P < 0.001); and delineation of anatomic components of the specimens (surface structures) to be significantly sharper (P < 0.001). Spiculations were significantly better identified, and the overall clinically relevant information provided by mammoDPC was judged to be superior (P < 0.001). Our results demonstrate that complementary information provided by phase and scattering enhanced mammograms obtained with the mammoDPC approach deliver images of generally superior quality. This technique has the potential to improve radiological breast diagnostics.

Detection of simulated microcalcifications in fixed mammary tissue: An ROC study of the effect of local versus global histogram equalization.

PubMed

Sund, T; Olsen, J B

2006-09-01

To investigate whether sliding window adaptive histogram equalization (SWAHE) of digital mammograms improves the detection of simulated calcifications, as compared to images normalized by global histogram equalization (GHE). Direct digital mammograms were obtained from mammary tissue phantoms superimposed with different frames. Each frame was divided into forty squares by a wire mesh, and contained granular calcifications randomly positioned in about 50% of the squares. Three radiologists read the mammograms on a display monitor. They classified their confidence in the presence of microcalcifications in each square on a scale of 1 to 5. Images processed with GHE were first read and used as a reference. In a later session, the same images processed with SWAHE were read. The results were compared using ROC methodology. When the total areas AZ were compared, the results were completely equivocal. When comparing the high-specificity partial ROC area AZ,0.2 below false-positive fraction (FPF) 0.20, two of the three observers performed best with the images processed with SWAHE. The difference was not statistically significant. When the reader's confidence threshold in malignancy is set at a high level, increasing the contrast of mammograms with SWAHE may enhance the visibility of microcalcifications without adversely affecting the false-positive rate. When the reader's confidence threshold is set at a low level, the effect of SWAHE is an increase of false positives. Further investigation is needed to confirm the validity of the conclusions.

Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms.

PubMed

Pisano, E D; Zong, S; Hemminger, B M; DeLuca, M; Johnston, R E; Muller, K; Braeuning, M P; Pizer, S M

1998-11-01

The purpose of this project was to determine whether Contrast Limited Adaptive Histogram Equalization (CLAHE) improves detection of simulated spiculations in dense mammograms. Lines simulating the appearance of spiculations, a common marker of malignancy when visualized with masses, were embedded in dense mammograms digitized at 50 micron pixels, 12 bits deep. Film images with no CLAHE applied were compared to film images with nine different combinations of clip levels and region sizes applied. A simulated spiculation was embedded in a background of dense breast tissue, with the orientation of the spiculation varied. The key variables involved in each trial included the orientation of the spiculation, contrast level of the spiculation and the CLAHE settings applied to the image. Combining the 10 CLAHE conditions, 4 contrast levels and 4 orientations gave 160 combinations. The trials were constructed by pairing 160 combinations of key variables with 40 backgrounds. Twenty student observers were asked to detect the orientation of the spiculation in the image. There was a statistically significant improvement in detection performance for spiculations with CLAHE over unenhanced images when the region size was set at 32 with a clip level of 2, and when the region size was set at 32 with a clip level of 4. The selected CLAHE settings should be tested in the clinic with digital mammograms to determine whether detection of spiculations associated with masses detected at mammography can be improved.

Computer aided detection of clusters of microcalcifications on full field digital mammograms

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

Ge Jun; Sahiner, Berkman; Hadjiiski, Lubomir M.

2006-08-15

We are developing a computer-aided detection (CAD) system to identify microcalcification clusters (MCCs) automatically on full field digital mammograms (FFDMs). The CAD system includes six stages: preprocessing; image enhancement; segmentation of microcalcification candidates; false positive (FP) reduction for individual microcalcifications; regional clustering; and FP reduction for clustered microcalcifications. At the stage of FP reduction for individual microcalcifications, a truncated sum-of-squares error function was used to improve the efficiency and robustness of the training of an artificial neural network in our CAD system for FFDMs. At the stage of FP reduction for clustered microcalcifications, morphological features and features derived from themore » artificial neural network outputs were extracted from each cluster. Stepwise linear discriminant analysis (LDA) was used to select the features. An LDA classifier was then used to differentiate clustered microcalcifications from FPs. A data set of 96 cases with 192 images was collected at the University of Michigan. This data set contained 96 MCCs, of which 28 clusters were proven by biopsy to be malignant and 68 were proven to be benign. The data set was separated into two independent data sets for training and testing of the CAD system in a cross-validation scheme. When one data set was used to train and validate the convolution neural network (CNN) in our CAD system, the other data set was used to evaluate the detection performance. With the use of a truncated error metric, the training of CNN could be accelerated and the classification performance was improved. The CNN in combination with an LDA classifier could substantially reduce FPs with a small tradeoff in sensitivity. By using the free-response receiver operating characteristic methodology, it was found that our CAD system can achieve a cluster-based sensitivity of 70, 80, and 90 % at 0.21, 0.61, and 1.49 FPs/image, respectively. For case-based performance evaluation, a sensitivity of 70, 80, and 90 % can be achieved at 0.07, 0.17, and 0.65 FPs/image, respectively. We also used a data set of 216 mammograms negative for clustered microcalcifications to further estimate the FP rate of our CAD system. The corresponding FP rates were 0.15, 0.31, and 0.86 FPs/image for cluster-based detection when negative mammograms were used for estimation of FP rates.« less

Development of a Computer-aided Diagnosis System for Early Detection of Masses Using Retrospectively Detected Cancers on Prior Mammograms

DTIC Science & Technology

2009-06-01

131 cases with 131 biopsy proven masses, of which 27 were malignant and 104 benign. The true locations of the masses were identified by an experi- enced ...two acquisitions would cause differ- ences in the subtlety of the masses on the FFDMs and SFMs. However, assuming that the differences are ran- dom... Lado , M. Souto, and J. J. Vidal, “Computer-aided diagnosis: Automatic detection of malignant masses in digitized mammograms,” Med. Phys. 25, 957–964

Hough transform for clustered microcalcifications detection in full-field digital mammograms

NASA Astrophysics Data System (ADS)

Fanizzi, A.; Basile, T. M. A.; Losurdo, L.; Amoroso, N.; Bellotti, R.; Bottigli, U.; Dentamaro, R.; Didonna, V.; Fausto, A.; Massafra, R.; Moschetta, M.; Tamborra, P.; Tangaro, S.; La Forgia, D.

2017-09-01

Many screening programs use mammography as principal diagnostic tool for detecting breast cancer at a very early stage. Despite the efficacy of the mammograms in highlighting breast diseases, the detection of some lesions is still doubtless for radiologists. In particular, the extremely minute and elongated salt-like particles of microcalcifications are sometimes no larger than 0.1 mm and represent approximately half of all cancer detected by means of mammograms. Hence the need for automatic tools able to support radiologists in their work. Here, we propose a computer assisted diagnostic tool to support radiologists in identifying microcalcifications in full (native) digital mammographic images. The proposed CAD system consists of a pre-processing step, that improves contrast and reduces noise by applying Sobel edge detection algorithm and Gaussian filter, followed by a microcalcification detection step performed by exploiting the circular Hough transform. The procedure performance was tested on 200 images coming from the Breast Cancer Digital Repository (BCDR), a publicly available database. The automatically detected clusters of microcalcifications were evaluated by skilled radiologists which asses the validity of the correctly identified regions of interest as well as the system error in case of missed clustered microcalcifications. The system performance was evaluated in terms of Sensitivity and False Positives per images (FPi) rate resulting comparable to the state-of-art approaches. The proposed model was able to accurately predict the microcalcification clusters obtaining performances (sensibility = 91.78% and FPi rate = 3.99) which favorably compare to other state-of-the-art approaches.

Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms

NASA Astrophysics Data System (ADS)

Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Helvie, Mark A.; Cha, Kenny H.; Richter, Caleb D.

2017-12-01

Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aim of translating the ‘knowledge’ learned from non-medical images to medical diagnostic tasks through supervised training and increasing the generalization capabilities of DCNNs by simultaneously learning auxiliary tasks. We studied this approach in an important application: classification of malignant and benign breast masses. With Institutional Review Board (IRB) approval, digitized screen-film mammograms (SFMs) and digital mammograms (DMs) were collected from our patient files and additional SFMs were obtained from the Digital Database for Screening Mammography. The data set consisted of 2242 views with 2454 masses (1057 malignant, 1397 benign). In single-task transfer learning, the DCNN was trained and tested on SFMs. In multi-task transfer learning, SFMs and DMs were used to train the DCNN, which was then tested on SFMs. N-fold cross-validation with the training set was used for training and parameter optimization. On the independent test set, the multi-task transfer learning DCNN was found to have significantly (p  =  0.007) higher performance compared to the single-task transfer learning DCNN. This study demonstrates that multi-task transfer learning may be an effective approach for training DCNN in medical imaging applications when training samples from a single modality are limited.

Digital mammography: observer performance study of the effects of pixel size on radiologists' characterization of malignant and benign microcalcifications

NASA Astrophysics Data System (ADS)

Chan, Heang-Ping; Helvie, Mark A.; Petrick, Nicholas; Sahiner, Berkman; Adler, Dorit D.; Blane, Caroline E.; Joynt, Lynn K.; Paramagul, Chintana; Roubidoux, Marilyn A.; Wilson, Todd E.; Hadjiiski, Lubomir M.; Goodsitt, Mitchell M.

1999-05-01

A receiver operating characteristic (ROC) experiment was conducted to evaluate the effects of pixel size on the characterization of mammographic microcalcifications. Digital mammograms were obtained by digitizing screen-film mammograms with a laser film scanner. One hundred twelve two-view mammograms with biopsy-proven microcalcifications were digitized at a pixel size of 35 micrometer X 35 micrometer. A region of interest (ROI) containing the microcalcifications was extracted from each image. ROI images with pixel sizes of 70 micrometers, 105 micrometers, and 140 micrometers were derived from the ROI of 35 micrometer pixel size by averaging 2 X 2, 3 X 3, and 4 X 4 neighboring pixels, respectively. The ROI images were printed on film with a laser imager. Seven MQSA-approved radiologists participated as observers. The likelihood of malignancy of the microcalcifications was rated on a 10-point confidence rating scale and analyzed with ROC methodology. The classification accuracy was quantified by the area, Az, under the ROC curve. The statistical significance of the differences in the Az values for different pixel sizes was estimated with the Dorfman-Berbaum-Metz (DBM) method for multi-reader, multi-case ROC data. It was found that five of the seven radiologists demonstrated a higher classification accuracy with the 70 micrometer or 105 micrometer images. The average Az also showed a higher classification accuracy in the range of 70 to 105 micrometer pixel size. However, the differences in A(subscript z/ between different pixel sizes did not achieve statistical significance. The low specificity of image features of microcalcifications an the large interobserver and intraobserver variabilities may have contributed to the relatively weak dependence of classification accuracy on pixel size.

Application of texture analysis method for mammogram density classification

NASA Astrophysics Data System (ADS)

Nithya, R.; Santhi, B.

2017-07-01

Mammographic density is considered a major risk factor for developing breast cancer. This paper proposes an automated approach to classify breast tissue types in digital mammogram. The main objective of the proposed Computer-Aided Diagnosis (CAD) system is to investigate various feature extraction methods and classifiers to improve the diagnostic accuracy in mammogram density classification. Texture analysis methods are used to extract the features from the mammogram. Texture features are extracted by using histogram, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Difference Matrix (GLDM), Local Binary Pattern (LBP), Entropy, Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT), Gabor transform and trace transform. These extracted features are selected using Analysis of Variance (ANOVA). The features selected by ANOVA are fed into the classifiers to characterize the mammogram into two-class (fatty/dense) and three-class (fatty/glandular/dense) breast density classification. This work has been carried out by using the mini-Mammographic Image Analysis Society (MIAS) database. Five classifiers are employed namely, Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). Experimental results show that ANN provides better performance than LDA, NB, KNN and SVM classifiers. The proposed methodology has achieved 97.5% accuracy for three-class and 99.37% for two-class density classification.

Potential Cost Savings of Contrast-Enhanced Digital Mammography.

PubMed

Patel, Bhavika K; Gray, Richard J; Pockaj, Barbara A

2017-06-01

The purpose of this article is to discuss whether the sensitivity and specificity of contrast-enhanced digital mammography (CEDM) render it a viable diagnostic alternative to breast MRI. That CEDM couples low-energy images (comparable to the diagnostic quality of standard mammography) and subtracted contrast-enhanced mammograms make it a cost-effective modality and a realistic substitute for the more costly breast MRI.

Application of the Minkowski-functionals for automated pattern classification of breast parenchyma depicted by digital mammography

NASA Astrophysics Data System (ADS)

Boehm, Holger F.; Fischer, Tanja; Riosk, Dororthea; Britsch, Stefanie; Reiser, Maximilian

2008-03-01

With an estimated life-time-risk of about 10%, breast cancer is the most common cancer among women in western societies. Extensive mammography-screening programs have been implemented for diagnosis of the disease at an early stage. Several algorithms for computer-aided detection (CAD) have been proposed to help radiologists manage the increasing number of mammographic image-data and identify new cases of cancer. However, a major issue with most CAD-solutions is the fact that performance strongly depends on the structure and density of the breast tissue. Prior information about the global tissue quality in a patient would be helpful for selecting the most effective CAD-approach in order to increase the sensitivity of lesion-detection. In our study, we propose an automated method for textural evaluation of digital mammograms using the Minkowski Functionals in 2D. 80 mammograms are consensus-classified by two experienced readers as fibrosis, involution/atrophy, or normal. For each case, the topology of graylevel distribution is evaluated within a retromamillary image-section of 512 x 512 pixels. In addition, we obtain parameters from the graylevel-histogram (20th percentile, median and mean graylevel intensity). As a result, correct classification of the mammograms based on the densitometic parameters is achieved in between 38 and 48%, whereas topological analysis increases the rate to 83%. The findings demonstrate the effectiveness of the proposed algorithm. Compared to features obtained from graylevel histograms and comparable studies, we draw the conclusion that the presented method performs equally good or better. Our future work will be focused on the characterization of the mammographic tissue according to the Breast Imaging Reporting and Data System (BI-RADS). Moreover, other databases will be tested for an in-depth evaluation of the efficiency of our proposal.

Global detection approach for clustered microcalcifications in mammograms using a deep learning network.

PubMed

Wang, Juan; Nishikawa, Robert M; Yang, Yongyi

2017-04-01

In computerized detection of clustered microcalcifications (MCs) from mammograms, the traditional approach is to apply a pattern detector to locate the presence of individual MCs, which are subsequently grouped into clusters. Such an approach is often susceptible to the occurrence of false positives (FPs) caused by local image patterns that resemble MCs. We investigate the feasibility of a direct detection approach to determining whether an image region contains clustered MCs or not. Toward this goal, we develop a deep convolutional neural network (CNN) as the classifier model to which the input consists of a large image window ([Formula: see text] in size). The multiple layers in the CNN classifier are trained to automatically extract image features relevant to MCs at different spatial scales. In the experiments, we demonstrated this approach on a dataset consisting of both screen-film mammograms and full-field digital mammograms. We evaluated the detection performance both on classifying image regions of clustered MCs using a receiver operating characteristic (ROC) analysis and on detecting clustered MCs from full mammograms by a free-response receiver operating characteristic analysis. For comparison, we also considered a recently developed MC detector with FP suppression. In classifying image regions of clustered MCs, the CNN classifier achieved 0.971 in the area under the ROC curve, compared to 0.944 for the MC detector. In detecting clustered MCs from full mammograms, at 90% sensitivity, the CNN classifier obtained an FP rate of 0.69 clusters/image, compared to 1.17 clusters/image by the MC detector. These results indicate that using global image features can be more effective in discriminating clustered MCs from FPs caused by various sources, such as linear structures, thereby providing a more accurate detection of clustered MCs on mammograms.

Mammogram registration using the Cauchy-Navier spline

NASA Astrophysics Data System (ADS)

Wirth, Michael A.; Choi, Christopher

2001-07-01

The process of comparative analysis involves inspecting mammograms for characteristic signs of potential cancer by comparing various analogous mammograms. Factors such as the deformable behavior of the breast, changes in breast positioning, and the amount/geometry of compression may contribute to spatial differences between corresponding structures in corresponding mammograms, thereby significantly complicating comparative analysis. Mammogram registration is a process whereby spatial differences between mammograms can be reduced. Presented in this paper is a nonrigid approach to matching corresponding mammograms based on a physical registration model. Many of the earliest approaches to mammogram registration used spatial transformations which were innately rigid or affine in nature. More recently algorithms have incorporated radial basis functions such as the Thin-Plate Spline to match mammograms. The approach presented here focuses on the use of the Cauchy-Navier Spline, a deformable registration model which offers approximate nonrigid registration. The utility of the Cauchy-Navier Spline is illustrated by matching both temporal and bilateral mammograms.

Deep learning and non-negative matrix factorization in recognition of mammograms

NASA Astrophysics Data System (ADS)

Swiderski, Bartosz; Kurek, Jaroslaw; Osowski, Stanislaw; Kruk, Michal; Barhoumi, Walid

2017-02-01

This paper presents novel approach to the recognition of mammograms. The analyzed mammograms represent the normal and breast cancer (benign and malignant) cases. The solution applies the deep learning technique in image recognition. To obtain increased accuracy of classification the nonnegative matrix factorization and statistical self-similarity of images are applied. The images reconstructed by using these two approaches enrich the data base and thanks to this improve of quality measures of mammogram recognition (increase of accuracy, sensitivity and specificity). The results of numerical experiments performed on large DDSM data base containing more than 10000 mammograms have confirmed good accuracy of class recognition, exceeding the best results reported in the actual publications for this data base.

Automated analysis for microcalcifications in high resolution digital mammograms

DOEpatents

Mascio, Laura N.

1996-01-01

A method for automatically locating microcalcifications indicating breast cancer. The invention assists mammographers in finding very subtle microcalcifications and in recognizing the pattern formed by all the microcalcifications. It also draws attention to microcalcifications that might be overlooked because a more prominent feature draws attention away from an important object. A new filter has been designed to weed out false positives in one of the steps of the method. Previously, iterative selection threshold was used to separate microcalcifications from the spurious signals resulting from texture or other background. A Selective Erosion or Enhancement (SEE) Filter has been invented to improve this step. Since the algorithm detects areas containing potential calcifications on the mammogram, it can be used to determine which areas need be stored at the highest resolution available, while, in addition, the full mammogram can be reduced to an appropriate resolution for the remaining cancer signs.

Automated analysis for microcalcifications in high resolution digital mammograms

DOEpatents

Mascio, L.N.

1996-12-17

A method is disclosed for automatically locating microcalcifications indicating breast cancer. The invention assists mammographers in finding very subtle microcalcifications and in recognizing the pattern formed by all the microcalcifications. It also draws attention to microcalcifications that might be overlooked because a more prominent feature draws attention away from an important object. A new filter has been designed to weed out false positives in one of the steps of the method. Previously, iterative selection threshold was used to separate microcalcifications from the spurious signals resulting from texture or other background. A Selective Erosion or Enhancement (SEE) Filter has been invented to improve this step. Since the algorithm detects areas containing potential calcifications on the mammogram, it can be used to determine which areas need be stored at the highest resolution available, while, in addition, the full mammogram can be reduced to an appropriate resolution for the remaining cancer signs. 8 figs.

Clustering microcalcifications techniques in digital mammograms

NASA Astrophysics Data System (ADS)

Díaz, Claudia. C.; Bosco, Paolo; Cerello, Piergiorgio

2008-11-01

Breast cancer has become a serious public health problem around the world. However, this pathology can be treated if it is detected in early stages. This task is achieved by a radiologist, who should read a large amount of mammograms per day, either for a screening or diagnostic purpose in mammography. However human factors could affect the diagnosis. Computer Aided Detection is an automatic system, which can help to specialists in the detection of possible signs of malignancy in mammograms. Microcalcifications play an important role in early detection, so we focused on their study. The two mammographic features that indicate the microcalcifications could be probably malignant are small size and clustered distribution. We worked with density techniques for automatic clustering, and we applied them on a mammography CAD prototype developed at INFN-Turin, Italy. An improvement of performance is achieved analyzing images from a Perugia-Assisi Hospital, in Italy.

Increasing cancer detection yield of breast MRI using a new CAD scheme of mammograms

NASA Astrophysics Data System (ADS)

Tan, Maxine; Aghaei, Faranak; Hollingsworth, Alan B.; Stough, Rebecca G.; Liu, Hong; Zheng, Bin

2016-03-01

Although breast MRI is the most sensitive imaging modality to detect early breast cancer, its cancer detection yield in breast cancer screening is quite low (< 3 to 4% even for the small group of high-risk women) to date. The purpose of this preliminary study is to test the potential of developing and applying a new computer-aided detection (CAD) scheme of digital mammograms to identify women at high risk of harboring mammography-occult breast cancers, which can be detected by breast MRI. For this purpose, we retrospectively assembled a dataset involving 30 women who had both mammography and breast MRI screening examinations. All mammograms were interpreted as negative, while 5 cancers were detected using breast MRI. We developed a CAD scheme of mammograms, which include a new quantitative mammographic image feature analysis based risk model, to stratify women into two groups with high and low risk of harboring mammography-occult cancer. Among 30 women, 9 were classified into the high risk group by CAD scheme, which included all 5 women who had cancer detected by breast MRI. All 21 low risk women remained negative on the breast MRI examinations. The cancer detection yield of breast MRI applying to this dataset substantially increased from 16.7% (5/30) to 55.6% (5/9), while eliminating 84% (21/25) unnecessary breast MRI screenings. The study demonstrated the potential of applying a new CAD scheme to significantly increase cancer detection yield of breast MRI, while simultaneously reducing the number of negative MRIs in breast cancer screening.

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

NASA Astrophysics Data System (ADS)

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

2008-03-01

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

A database for assessment of effect of lossy compression on digital mammograms

NASA Astrophysics Data System (ADS)

Wang, Jiheng; Sahiner, Berkman; Petrick, Nicholas; Pezeshk, Aria

2018-03-01

With widespread use of screening digital mammography, efficient storage of the vast amounts of data has become a challenge. While lossless image compression causes no risk to the interpretation of the data, it does not allow for high compression rates. Lossy compression and the associated higher compression ratios are therefore more desirable. The U.S. Food and Drug Administration (FDA) currently interprets the Mammography Quality Standards Act as prohibiting lossy compression of digital mammograms for primary image interpretation, image retention, or transfer to the patient or her designated recipient. Previous work has used reader studies to determine proper usage criteria for evaluating lossy image compression in mammography, and utilized different measures and metrics to characterize medical image quality. The drawback of such studies is that they rely on a threshold on compression ratio as the fundamental criterion for preserving the quality of images. However, compression ratio is not a useful indicator of image quality. On the other hand, many objective image quality metrics (IQMs) have shown excellent performance for natural image content for consumer electronic applications. In this paper, we create a new synthetic mammogram database with several unique features. We compare and characterize the impact of image compression on several clinically relevant image attributes such as perceived contrast and mass appearance for different kinds of masses. We plan to use this database to develop a new objective IQM for measuring the quality of compressed mammographic images to help determine the allowed maximum compression for different kinds of breasts and masses in terms of visual and diagnostic quality.

Transient Fourier holography with bacteriorhodopsin films for breast cancer diagnostics

NASA Astrophysics Data System (ADS)

Rao, Devulapalli; Kothapalli, Sri-Rajasekar; Wu, Pengfei; Yelleswarapu, Chandra

X-ray mammography is the current gold standard for breast cancer screening. Microcalcifications and other features which are helpful to the radiologist for early diagnostics are often buried in the noise generated by the surrounding dense tissue. So image processing techniques are required to enhance these important features to improve the sensitivity of detection. An innovative technique is demonstrated for recording a hologram of the mammogram. It is recorded on a thin polymer film of Bacteriorhodopsin (bR) as photo induced isomerization grating containing the interference pattern between the object beam containing the Fourier spatial frequency components of the mammogram and a reference beam. The hologram contains all the enhanced features of the mammogram. A significant innovation of the technique is that the enhanced components in the processed image can be viewed by the radiologist in time scale. A technician can record the movie and when the radiologist looks at the movie at his convenience, freezing the frame as and when desired, he would see the microcalcifications as the brightest and last long in time. He would also observe lesions with intensity decreasing as their size increases. The same bR film can be used repeatedly for recording holograms with different mammograms. The technique is versatile and a different frequency band can be chosen to be optimized by changing the reference beam intensity. The experimental arrangement can be used for mammograms in screen film or digital format.

A similarity learning approach to content-based image retrieval: application to digital mammography.

PubMed

El-Naqa, Issam; Yang, Yongyi; Galatsanos, Nikolas P; Nishikawa, Robert M; Wernick, Miles N

2004-10-01

In this paper, we describe an approach to content-based retrieval of medical images from a database, and provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. Content-based image retrieval (CBIR) refers to the retrieval of images from a database using information derived from the images themselves, rather than solely from accompanying text indices. In the medical-imaging context, the ultimate aim of CBIR is to provide radiologists with a diagnostic aid in the form of a display of relevant past cases, along with proven pathology and other suitable information. CBIR may also be useful as a training tool for medical students and residents. The goal of information retrieval is to recall from a database information that is relevant to the user's query. The most challenging aspect of CBIR is the definition of relevance (similarity), which is used to guide the retrieval machine. In this paper, we pursue a new approach, in which similarity is learned from training examples provided by human observers. Specifically, we explore the use of neural networks and support vector machines to predict the user's notion of similarity. Within this framework we propose using a hierarchal learning approach, which consists of a cascade of a binary classifier and a regression module to optimize retrieval effectiveness and efficiency. We also explore how to incorporate online human interaction to achieve relevance feedback in this learning framework. Our experiments are based on a database consisting of 76 mammograms, all of which contain clustered microcalcifications (MCs). Our goal is to retrieve mammogram images containing similar MC clusters to that in a query. The performance of the retrieval system is evaluated using precision-recall curves computed using a cross-validation procedure. Our experimental results demonstrate that: 1) the learning framework can accurately predict the perceptual similarity reported by human observers, thereby serving as a basis for CBIR; 2) the learning-based framework can significantly outperform a simple distance-based similarity metric; 3) the use of the hierarchical two-stage network can improve retrieval performance; and 4) relevance feedback can be effectively incorporated into this learning framework to achieve improvement in retrieval precision based on online interaction with users; and 5) the retrieved images by the network can have predicting value for the disease condition of the query.

Predicting radiologists' true and false positive decisions in reading mammograms by using gaze parameters and image-based features

NASA Astrophysics Data System (ADS)

Gandomkar, Ziba; Tay, Kevin; Ryder, Will; Brennan, Patrick C.; Mello-Thoms, Claudia

2016-03-01

Radiologists' gaze-related parameters combined with image-based features were utilized to classify suspicious mammographic areas ultimately scored as True Positives (TP) and False Positives (FP). Eight breast radiologists read 120 two-view digital mammograms of which 59 had biopsy proven cancer. Eye tracking data was collected and nearby fixations were clustered together. Suspicious areas on mammograms were independently identified based on thresholding an intensity saliency map followed by automatic segmentation and pruning steps. For each radiologist reported area, radiologist's fixation clusters in the area, as well as neighboring suspicious areas within 2.5° of the center of fixation, were found. A 45-dimensional feature vector containing gaze parameters of the corresponding cluster along with image-based characteristics was constructed. Gaze parameters included total number of fixations in the cluster, dwell time, time to hit the cluster for the first time, maximum number of consecutive fixations, and saccade magnitude of the first fixation in the cluster. Image-based features consisted of intensity, shape, and texture descriptors extracted from the region around the suspicious area, its surrounding tissue, and the entire breast. For each radiologist, a userspecific Support Vector Machine (SVM) model was built to classify the reported areas as TPs or FPs. Leave-one-out cross validation was utilized to avoid over-fitting. A feature selection step was embedded in the SVM training procedure by allowing radial basis function kernels to have 45 scaling factors. The proposed method was compared with the radiologists' performance using the jackknife alternative free-response receiver operating characteristic (JAFROC). The JAFROC figure of merit increased significantly for six radiologists.

Healthy Family 2009: Practicing Healthy Adult Living

MedlinePlus

... doctor the pros and cons of having a prostate-specific antigen (PSA) test or digital rectal examination (DRE) to ... colonoscopies for cancer of the colon, serum prostatin-specific antigen (PSA) tests for prostate cancer, and mammograms for breast cancer. Work out ...

Adaptive multiscale processing for contrast enhancement

NASA Astrophysics Data System (ADS)

Laine, Andrew F.; Song, Shuwu; Fan, Jian; Huda, Walter; Honeyman, Janice C.; Steinbach, Barbara G.

1993-07-01

This paper introduces a novel approach for accomplishing mammographic feature analysis through overcomplete multiresolution representations. We show that efficient representations may be identified from digital mammograms within a continuum of scale space and used to enhance features of importance to mammography. Choosing analyzing functions that are well localized in both space and frequency, results in a powerful methodology for image analysis. We describe methods of contrast enhancement based on two overcomplete (redundant) multiscale representations: (1) Dyadic wavelet transform (2) (phi) -transform. Mammograms are reconstructed from transform coefficients modified at one or more levels by non-linear, logarithmic and constant scale-space weight functions. Multiscale edges identified within distinct levels of transform space provide a local support for enhancement throughout each decomposition. We demonstrate that features extracted from wavelet spaces can provide an adaptive mechanism for accomplishing local contrast enhancement. We suggest that multiscale detection and local enhancement of singularities may be effectively employed for the visualization of breast pathology without excessive noise amplification.

A model based on temporal dynamics of fixations for distinguishing expert radiologists' scanpaths

NASA Astrophysics Data System (ADS)

Gandomkar, Ziba; Tay, Kevin; Brennan, Patrick C.; Mello-Thoms, Claudia

2017-03-01

This study investigated a model which distinguishes expert radiologists from less experienced radiologists based on features describing spatio-temporal dynamics of their eye movement during interpretation of digital mammograms. Eye movements of four expert and four less experienced radiologists were recorded during interpretation of 120 two-view digital mammograms of which 59 had biopsy proven cancers. For each scanpath, a two-dimensional recurrence plot, which represents the radiologist's refixation pattern, was generated. From each plot, six features indicating the spatio-temporal dynamics of fixations were extracted. The first feature measured the percentage of recurrent fixations; the second indicated the percentage of recurrent fixations which was fixated later in several consecutive fixations; the third measured the percentage of recurrent fixations that form a repeated sequence of fixations and the fourth assessed whether the recurrent fixations were occurring sequentially close together. The number of switches between the two mammographic views was also measured, as was the average number of consecutive fixations in each view before switching. These six features along with total time on case and average fixation duration were fed into a support vector machine whose performance was evaluated using 10-fold cross validation. The model achieved a sensitivity of 86.3% and a specificity of 85.2% for distinguishing experts' scanpaths. The obtained result suggests that spatio-temporal dynamics of eye movements can characterize expertise level and has potential applications for monitoring the development of expertise among radiologists as a result of different training regimes and continuing education schemes.

A new idea for visualization of lesions distribution in mammogram based on CPD registration method.

PubMed

Pan, Xiaoguang; Qi, Buer; Yu, Hongfei; Wei, Haiping; Kang, Yan

2017-07-20

Mammography is currently the most effective technique for breast cancer. Lesions distribution can provide support for clinical diagnosis and epidemiological studies. We presented a new idea to help radiologists study breast lesions distribution conveniently. We also developed an automatic tool based on this idea which could show visualization of lesions distribution in a standard mammogram. Firstly, establishing a lesion database to study; then, extracting breast contours and match different women's mammograms to a standard mammogram; finally, showing the lesion distribution in the standard mammogram, and providing the distribution statistics. The crucial process of developing this tool was matching different women's mammograms correctly. We used a hybrid breast contour extraction method combined with coherent point drift method to match different women's mammograms. We tested our automatic tool by four mass datasets of 641 images. The distribution results shown by the tool were consistent with the results counted according to their reports and mammograms by manual. We also discussed the registration error that was less than 3.3 mm in average distance. The new idea is effective and the automatic tool can provide lesions distribution results which are consistent with radiologists simply and conveniently.

Automatic correspondence detection in mammogram and breast tomosynthesis images

NASA Astrophysics Data System (ADS)

Ehrhardt, Jan; Krüger, Julia; Bischof, Arpad; Barkhausen, Jörg; Handels, Heinz

2012-02-01

Two-dimensional mammography is the major imaging modality in breast cancer detection. A disadvantage of mammography is the projective nature of this imaging technique. Tomosynthesis is an attractive modality with the potential to combine the high contrast and high resolution of digital mammography with the advantages of 3D imaging. In order to facilitate diagnostics and treatment in the current clinical work-flow, correspondences between tomosynthesis images and previous mammographic exams of the same women have to be determined. In this paper, we propose a method to detect correspondences in 2D mammograms and 3D tomosynthesis images automatically. In general, this 2D/3D correspondence problem is ill-posed, because a point in the 2D mammogram corresponds to a line in the 3D tomosynthesis image. The goal of our method is to detect the "most probable" 3D position in the tomosynthesis images corresponding to a selected point in the 2D mammogram. We present two alternative approaches to solve this 2D/3D correspondence problem: a 2D/3D registration method and a 2D/2D mapping between mammogram and tomosynthesis projection images with a following back projection. The advantages and limitations of both approaches are discussed and the performance of the methods is evaluated qualitatively and quantitatively using a software phantom and clinical breast image data. Although the proposed 2D/3D registration method can compensate for moderate breast deformations caused by different breast compressions, this approach is not suitable for clinical tomosynthesis data due to the limited resolution and blurring effects perpendicular to the direction of projection. The quantitative results show that the proposed 2D/2D mapping method is capable of detecting corresponding positions in mammograms and tomosynthesis images automatically for 61 out of 65 landmarks. The proposed method can facilitate diagnosis, visual inspection and comparison of 2D mammograms and 3D tomosynthesis images for the physician.

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer.

PubMed

Tamez-Peña, Jose-Gerardo; Rodriguez-Rojas, Juan-Andrés; Gomez-Rueda, Hugo; Celaya-Padilla, Jose-Maria; Rivera-Prieto, Roxana-Alicia; Palacios-Corona, Rebeca; Garza-Montemayor, Margarita; Cardona-Huerta, Servando; Treviño, Victor

2018-01-01

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures.

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer

PubMed Central

Tamez-Peña, Jose-Gerardo; Rodriguez-Rojas, Juan-Andrés; Gomez-Rueda, Hugo; Celaya-Padilla, Jose-Maria; Rivera-Prieto, Roxana-Alicia; Palacios-Corona, Rebeca; Garza-Montemayor, Margarita; Cardona-Huerta, Servando

2018-01-01

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures. PMID:29596496

Mammographic texture synthesis using genetic programming and clustered lumpy background

NASA Astrophysics Data System (ADS)

Castella, Cyril; Kinkel, Karen; Descombes, François; Eckstein, Miguel P.; Sottas, Pierre-Edouard; Verdun, Francis R.; Bochud, François O.

2006-03-01

In this work we investigated the digital synthesis of images which mimic real textures observed in mammograms. Such images could be produced in an unlimited number with tunable statistical properties in order to study human performance and model observer performance in perception experiments. We used the previously developed clustered lumpy background (CLB) technique and optimized its parameters with a genetic algorithm (GA). In order to maximize the realism of the textures, we combined the GA objective approach with psychophysical experiments involving the judgments of radiologists. Thirty-six statistical features were computed and averaged, over 1000 real mammograms regions of interest. The same features were measured for the synthetic textures, and the Mahalanobis distance was used to quantify the similarity of the features between the real and synthetic textures. The similarity, as measured by the Mahalanobis distance, was used as GA fitness function for evolving the free CLB parameters. In the psychophysical approach, experienced radiologists were asked to qualify the realism of synthetic images by considering typical structures that are expected to be found on real mammograms: glandular and fatty areas, and fiber crossings. Results show that CLB images found via optimization with GA are significantly closer to real mammograms than previously published images. Moreover, the psychophysical experiments confirm that all the above mentioned structures are reproduced well on the generated images. This means that we can generate an arbitrary large database of textures mimicking mammograms with traceable statistical properties.

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

PubMed Central

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

2009-01-01

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

Mammographic Breast Density in a Cohort of Medically Underserved Women

DTIC Science & Technology

2014-10-01

chronic diseases, adult weight history, diet , and health literacy. A trained radiologic technician completed full- field digital screening mammograms on... Mediterranean population. Int J Cancer 118:1782-1789 12. El-Bastawissi AY, White E, Mandelson MT, Taplin S (2001) Variation in mammographic breast

Steerable dyadic wavelet transform and interval wavelets for enhancement of digital mammography

NASA Astrophysics Data System (ADS)

Laine, Andrew F.; Koren, Iztok; Yang, Wuhai; Taylor, Fred J.

1995-04-01

This paper describes two approaches for accomplishing interactive feature analysis by overcomplete multiresolution representations. We show quantitatively that transform coefficients, modified by an adaptive non-linear operator, can make more obvious unseen or barely seen features of mammography without requiring additional radiation. Our results are compared with traditional image enhancement techniques by measuring the local contrast of known mammographic features. We design a filter bank representing a steerable dyadic wavelet transform that can be used for multiresolution analysis along arbitrary orientations. Digital mammograms are enhanced by orientation analysis performed by a steerable dyadic wavelet transform. Arbitrary regions of interest (ROI) are enhanced by Deslauriers-Dubuc interpolation representations on an interval. We demonstrate that our methods can provide radiologists with an interactive capability to support localized processing of selected (suspicion) areas (lesions). Features extracted from multiscale representations can provide an adaptive mechanism for accomplishing local contrast enhancement. By improving the visualization of breast pathology can improve changes of early detection while requiring less time to evaluate mammograms for most patients.

Bi-model processing for early detection of breast tumor in CAD system

NASA Astrophysics Data System (ADS)

Mughal, Bushra; Sharif, Muhammad; Muhammad, Nazeer

2017-06-01

Early screening of skeptical masses in mammograms may reduce mortality rate among women. This rate can be further reduced upon developing the computer-aided diagnosis system with decrease in false assumptions in medical informatics. This method highlights the early tumor detection in digitized mammograms. For improving the performance of this system, a novel bi-model processing algorithm is introduced. It divides the region of interest into two parts, the first one is called pre-segmented region (breast parenchyma) and other is the post-segmented region (suspicious region). This system follows the scheme of the preprocessing technique of contrast enhancement that can be utilized to segment and extract the desired feature of the given mammogram. In the next phase, a hybrid feature block is presented to show the effective performance of computer-aided diagnosis. In order to assess the effectiveness of the proposed method, a database provided by the society of mammographic images is tested. Our experimental outcomes on this database exhibit the usefulness and robustness of the proposed method.

Enhancement of breast periphery region in digital mammography

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

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

PubMed

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

2009-01-01

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

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

PubMed Central

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

2018-01-01

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

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

PubMed

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

2018-01-01

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

Transition from film to digital mammography: impact for breast cancer screening through the national breast and cervical cancer early detection program.

PubMed

van Ravesteyn, Nicolien T; van Lier, Lisanne; Schechter, Clyde B; Ekwueme, Donatus U; Royalty, Janet; Miller, Jacqueline W; Near, Aimee M; Cronin, Kathleen A; Heijnsdijk, Eveline A M; Mandelblatt, Jeanne S; de Koning, Harry J

2015-05-01

The National Breast and Cervical Cancer Early Detection Program (NBCCEDP) provides mammograms and diagnostic services for low-income, uninsured women aged 40-64 years. Mammography facilities within the NBCCEDP gradually shifted from plain-film to digital mammography. The purpose of this study is to assess the impact of replacing film with digital mammography on health effects (deaths averted, life-years gained [LYG]); costs (for screening and diagnostics); and number of women reached. NBCCEDP 2010 data and data representative of the program's target population were used in two established microsimulation models. Models simulated observed screening behavior including different screening intervals (annual, biennial, irregular) and starting ages (40, 50 years) for white, black, and Hispanic women. Model runs were performed in 2012. The models predicted 8.0-8.3 LYG per 1,000 film screens for black women, 5.9-7.5 for white women, and 4.0-4.5 for Hispanic women. For all race/ethnicity groups, digital mammography had more LYG than film mammography (2%-4%), but had higher costs (34%-35%). Assuming a fixed budget, 25%-26% fewer women could be served, resulting in 22%-24% fewer LYG if all mammograms were converted to digital. The loss in LYG could be reversed to an 8%-13% increase by only including biennial screening. Digital could result in slightly more LYG than film mammography. However, with a fixed budget, fewer women may be served with fewer LYG. Changes in the program, such as only including biennial screening, will increase LYG/screen and could offset the potential decrease in LYG when shifting to digital mammography. Copyright © 2015 American Journal of Preventive Medicine. All rights reserved.

Transition From Film to Digital Mammography

PubMed Central

van Ravesteyn, Nicolien T.; van Lier, Lisanne; Schechter, Clyde B.; Ekwueme, Donatus U.; Royalty, Janet; Miller, Jacqueline W.; Near, Aimee M.; Cronin, Kathleen A.; Heijnsdijk, Eveline A.M.; Mandelblatt, Jeanne S.; de Koning, Harry J.

2015-01-01

Introduction The National Breast and Cervical Cancer Early Detection Program (NBCCEDP) provides mammograms and diagnostic services for low-income, uninsured women aged 40–64 years. Mammography facilities within the NBCCEDP gradually shifted from plain-film to digital mammography. The purpose of this study is to assess the impact of replacing film with digital mammography on health effects (deaths averted, life-years gained [LYG]), costs (for screening and diagnostics), and number of women reached. Methods NBCCEDP 2010 data and data representative of the program’s target population were used in two established microsimulation models. Models simulated observed screening behavior including different screening intervals (annual, biennial, irregular) and starting ages (40, 50 years) for white, black, and Hispanic women. Model runs were performed in 2012. Results The models predicted 8.0–8.3 LYG per 1,000 film screens for black women, 5.9–7.5 for white women, and 4.0–4.5 for Hispanic women. For all race/ethnicity groups, digital mammography had more LYG than film mammography (2%–4%), but had higher costs (34%–35%). Assuming a fixed budget, 25%–26% fewer women could be served, resulting in 22%–24% fewer LYG if all mammograms were converted to digital. The loss in LYG could be reversed to an 8%–13% increase by only including biennial screening. Conclusions Digital could result in slightly more LYG than film mammography. However, with a fixed budget, fewer women may be served with fewer LYG. Changes in the program, such as only including biennial screening, will increase LYG/screen and could offset the potential decrease in LYG when shifting to digital mammography. PMID:25891052

Mammography screening using independent double reading with consensus: is there a potential benefit for computer-aided detection?

PubMed

Skaane, Per; Kshirsagar, Ashwini; Hofvind, Solveig; Jahr, Gunnar; Castellino, Ronald A

2012-04-01

Double reading improves the cancer detection rate in mammography screening. Single reading with computer-aided detection (CAD) has been considered to be an alternative to double reading. Little is known about the potential benefit of CAD in breast cancer screening with double reading. To compare prospective independent double reading of screen-film (SFM) and full-field digital (FFDM) mammography in population-based screening with retrospective standalone CAD performance on the baseline mammograms of the screen-detected cancers and subsequent cancers diagnosed during the follow-up period. The study had ethics committee approval. A 5-point rating scale for probability of cancer was used for 23,923 (SFM = 16,983; FFDM = 6940) screening mammograms. Of 208 evaluable cancers, 104 were screen-detected and 104 were subsequent (44 interval and 60 next screening round) cancers. Baseline mammograms of subsequent cancers were retrospectively classified in consensus without information about cancer location, histology, or CAD prompting as normal, non-specific minimal signs, significant minimal signs, and false-negatives. The baseline mammograms of the screen-detected cancers and subsequent cancers were evaluated by CAD. Significant minimal signs and false-negatives were considered 'actionable' and potentially diagnosable if correctly prompted by CAD. CAD correctly marked 94% (98/104) of the baseline mammograms of the screen-detected cancers (SFM = 95% [61/64]; FFDM = 93% [37/40]), including 96% (23/24) of those with discordant interpretations. Considering only those baseline examinations of subsequent cancers prospectively interpreted as normal and retrospectively categorized as 'actionable', CAD input at baseline screening had the potential to increase the cancer detection rate from 0.43% to 0.51% (P = 0.13); and to increase cancer detection by 16% ([104 + 17]/104) and decrease interval cancers by 20% (from 44 to 35). CAD may have the potential to increase cancer detection by up to 16%, and to reduce the number of interval cancers by up to 20% in SFM and FFDM screening programs using independent double reading with consensus review. The influence of true- and false-positive CAD marks on decision-making can, however, only be evaluated in a prospective clinical study.

Mammogram registration: a phantom-based evaluation of compressed breast thickness variation effects.

PubMed

Richard, Frédéric J P; Bakić, Predrag R; Maidment, Andrew D A

2006-02-01

The temporal comparison of mammograms is complex; a wide variety of factors can cause changes in image appearance. Mammogram registration is proposed as a method to reduce the effects of these changes and potentially to emphasize genuine alterations in breast tissue. Evaluation of such registration techniques is difficult since ground truth regarding breast deformations is not available in clinical mammograms. In this paper, we propose a systematic approach to evaluate sensitivity of registration methods to various types of changes in mammograms using synthetic breast images with known deformations. As a first step, images of the same simulated breasts with various amounts of simulated physical compression have been used to evaluate a previously described nonrigid mammogram registration technique. Registration performance is measured by calculating the average displacement error over a set of evaluation points identified in mammogram pairs. Applying appropriate thickness compensation and using a preferred order of the registered images, we obtained an average displacement error of 1.6 mm for mammograms with compression differences of 1-3 cm. The proposed methodology is applicable to analysis of other sources of mammogram differences and can be extended to the registration of multimodality breast data.

Nonrigid mammogram registration using mutual information

NASA Astrophysics Data System (ADS)

Wirth, Michael A.; Narhan, Jay; Gray, Derek W. S.

2002-05-01

Of the papers dealing with the task of mammogram registration, the majority deal with the task by matching corresponding control-points derived from anatomical landmark points. One of the caveats encountered when using pure point-matching techniques is their reliance on accurately extracted anatomical features-points. This paper proposes an innovative approach to matching mammograms which combines the use of a similarity-measure and a point-based spatial transformation. Mutual information is a cost-function used to determine the degree of similarity between the two mammograms. An initial rigid registration is performed to remove global differences and bring the mammograms into approximate alignment. The mammograms are then subdivided into smaller regions and each of the corresponding subimages is matched independently using mutual information. The centroids of each of the matched subimages are then used as corresponding control-point pairs in association with the Thin-Plate Spline radial basis function. The resulting spatial transformation generates a nonrigid match of the mammograms. The technique is illustrated by matching mammograms from the MIAS mammogram database. An experimental comparison is made between mutual information incorporating purely rigid behavior, and that incorporating a more nonrigid behavior. The effectiveness of the registration process is evaluated using image differences.

Medical imaging and computers in the diagnosis of breast cancer

NASA Astrophysics Data System (ADS)

Giger, Maryellen L.

2014-09-01

Computer-aided diagnosis (CAD) and quantitative image analysis (QIA) methods (i.e., computerized methods of analyzing digital breast images: mammograms, ultrasound, and magnetic resonance images) can yield novel image-based tumor and parenchyma characteristics (i.e., signatures that may ultimately contribute to the design of patient-specific breast cancer management plans). The role of QIA/CAD has been expanding beyond screening programs towards applications in risk assessment, diagnosis, prognosis, and response to therapy as well as in data mining to discover relationships of image-based lesion characteristics with genomics and other phenotypes; thus, as they apply to disease states. These various computer-based applications are demonstrated through research examples from the Giger Lab.

Automatic segmentation of mammogram and tomosynthesis images

NASA Astrophysics Data System (ADS)

Sargent, Dusty; Park, Sun Young

2016-03-01

Breast cancer is a one of the most common forms of cancer in terms of new cases and deaths both in the United States and worldwide. However, the survival rate with breast cancer is high if it is detected and treated before it spreads to other parts of the body. The most common screening methods for breast cancer are mammography and digital tomosynthesis, which involve acquiring X-ray images of the breasts that are interpreted by radiologists. The work described in this paper is aimed at optimizing the presentation of mammography and tomosynthesis images to the radiologist, thereby improving the early detection rate of breast cancer and the resulting patient outcomes. Breast cancer tissue has greater density than normal breast tissue, and appears as dense white image regions that are asymmetrical between the breasts. These irregularities are easily seen if the breast images are aligned and viewed side-by-side. However, since the breasts are imaged separately during mammography, the images may be poorly centered and aligned relative to each other, and may not properly focus on the tissue area. Similarly, although a full three dimensional reconstruction can be created from digital tomosynthesis images, the same centering and alignment issues can occur for digital tomosynthesis. Thus, a preprocessing algorithm that aligns the breasts for easy side-by-side comparison has the potential to greatly increase the speed and accuracy of mammogram reading. Likewise, the same preprocessing can improve the results of automatic tissue classification algorithms for mammography. In this paper, we present an automated segmentation algorithm for mammogram and tomosynthesis images that aims to improve the speed and accuracy of breast cancer screening by mitigating the above mentioned problems. Our algorithm uses information in the DICOM header to facilitate preprocessing, and incorporates anatomical region segmentation and contour analysis, along with a hidden Markov model (HMM) for processing the multi-frame tomosynthesis images. The output of the algorithm is a new set of images that have been processed to show only the diagnostically relevant region and align the breasts so that they can be easily compared side-by-side. Our method has been tested on approximately 750 images, including various examples of mammogram, tomosynthesis, and scanned images, and has correctly segmented the diagnostically relevant image region in 97% of cases.

Mass type-specific sparse representation for mass classification in computer-aided detection on mammograms

PubMed Central

2013-01-01

Background Breast cancer is the leading cause of both incidence and mortality in women population. For this reason, much research effort has been devoted to develop Computer-Aided Detection (CAD) systems for early detection of the breast cancers on mammograms. In this paper, we propose a new and novel dictionary configuration underpinning sparse representation based classification (SRC). The key idea of the proposed algorithm is to improve the sparsity in terms of mass margins for the purpose of improving classification performance in CAD systems. Methods The aim of the proposed SRC framework is to construct separate dictionaries according to the types of mass margins. The underlying idea behind our method is that the separated dictionaries can enhance the sparsity of mass class (true-positive), leading to an improved performance for differentiating mammographic masses from normal tissues (false-positive). When a mass sample is given for classification, the sparse solutions based on corresponding dictionaries are separately solved and combined at score level. Experiments have been performed on both database (DB) named as Digital Database for Screening Mammography (DDSM) and clinical Full Field Digital Mammogram (FFDM) DBs. In our experiments, sparsity concentration in the true class (SCTC) and area under the Receiver operating characteristic (ROC) curve (AUC) were measured for the comparison between the proposed method and a conventional single dictionary based approach. In addition, a support vector machine (SVM) was used for comparing our method with state-of-the-arts classifier extensively used for mass classification. Results Comparing with the conventional single dictionary configuration, the proposed approach is able to improve SCTC of up to 13.9% and 23.6% on DDSM and FFDM DBs, respectively. Moreover, the proposed method is able to improve AUC with 8.2% and 22.1% on DDSM and FFDM DBs, respectively. Comparing to SVM classifier, the proposed method improves AUC with 2.9% and 11.6% on DDSM and FFDM DBs, respectively. Conclusions The proposed dictionary configuration is found to well improve the sparsity of dictionaries, resulting in an enhanced classification performance. Moreover, the results show that the proposed method is better than conventional SVM classifier for classifying breast masses subject to various margins from normal tissues. PMID:24564973

Seamless lesion insertion in digital mammography: methodology and reader study

NASA Astrophysics Data System (ADS)

Pezeshk, Aria; Petrick, Nicholas; Sahiner, Berkman

2016-03-01

Collection of large repositories of clinical images containing verified cancer locations is costly and time consuming due to difficulties associated with both the accumulation of data and establishment of the ground truth. This problem poses a significant challenge to the development of machine learning algorithms that require large amounts of data to properly train and avoid overfitting. In this paper we expand the methods in our previous publications by making several modifications that significantly increase the speed of our insertion algorithms, thereby allowing them to be used for inserting lesions that are much larger in size. These algorithms have been incorporated into an image composition tool that we have made publicly available. This tool allows users to modify or supplement existing datasets by seamlessly inserting a real breast mass or micro-calcification cluster extracted from a source digital mammogram into a different location on another mammogram. We demonstrate examples of the performance of this tool on clinical cases taken from the University of South Florida Digital Database for Screening Mammography (DDSM). Finally, we report the results of a reader study evaluating the realism of inserted lesions compared to clinical lesions. Analysis of the radiologist scores in the study using receiver operating characteristic (ROC) methodology indicates that inserted lesions cannot be reliably distinguished from clinical lesions.

Mass Detection in Mammographic Images Using Wavelet Processing and Adaptive Threshold Technique.

PubMed

Vikhe, P S; Thool, V R

2016-04-01

Detection of mass in mammogram for early diagnosis of breast cancer is a significant assignment in the reduction of the mortality rate. However, in some cases, screening of mass is difficult task for radiologist, due to variation in contrast, fuzzy edges and noisy mammograms. Masses and micro-calcifications are the distinctive signs for diagnosis of breast cancer. This paper presents, a method for mass enhancement using piecewise linear operator in combination with wavelet processing from mammographic images. The method includes, artifact suppression and pectoral muscle removal based on morphological operations. Finally, mass segmentation for detection using adaptive threshold technique is carried out to separate the mass from background. The proposed method has been tested on 130 (45 + 85) images with 90.9 and 91 % True Positive Fraction (TPF) at 2.35 and 2.1 average False Positive Per Image(FP/I) from two different databases, namely Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM). The obtained results show that, the proposed technique gives improved diagnosis in the early breast cancer detection.

Validation of no-reference image quality index for the assessment of digital mammographic images

NASA Astrophysics Data System (ADS)

de Oliveira, Helder C. R.; Barufaldi, Bruno; Borges, Lucas R.; Gabarda, Salvador; Bakic, Predrag R.; Maidment, Andrew D. A.; Schiabel, Homero; Vieira, Marcelo A. C.

2016-03-01

To ensure optimal clinical performance of digital mammography, it is necessary to obtain images with high spatial resolution and low noise, keeping radiation exposure as low as possible. These requirements directly affect the interpretation of radiologists. The quality of a digital image should be assessed using objective measurements. In general, these methods measure the similarity between a degraded image and an ideal image without degradation (ground-truth), used as a reference. These methods are called Full-Reference Image Quality Assessment (FR-IQA). However, for digital mammography, an image without degradation is not available in clinical practice; thus, an objective method to assess the quality of mammograms must be performed without reference. The purpose of this study is to present a Normalized Anisotropic Quality Index (NAQI), based on the Rényi entropy in the pseudo-Wigner domain, to assess mammography images in terms of spatial resolution and noise without any reference. The method was validated using synthetic images acquired through an anthropomorphic breast software phantom, and the clinical exposures on anthropomorphic breast physical phantoms and patient's mammograms. The results reported by this noreference index follow the same behavior as other well-established full-reference metrics, e.g., the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Reductions of 50% on the radiation dose in phantom images were translated as a decrease of 4dB on the PSNR, 25% on the SSIM and 33% on the NAQI, evidencing that the proposed metric is sensitive to the noise resulted from dose reduction. The clinical results showed that images reduced to 53% and 30% of the standard radiation dose reported reductions of 15% and 25% on the NAQI, respectively. Thus, this index may be used in clinical practice as an image quality indicator to improve the quality assurance programs in mammography; hence, the proposed method reduces the subjectivity inter-observers in the reporting of image quality assessment.

Interval breast cancer characteristics before, during and after the transition from screen-film to full-field digital screening mammography.

PubMed

van Bommel, Rob M G; Weber, Roy; Voogd, Adri C; Nederend, Joost; Louwman, Marieke W J; Venderink, Dick; Strobbe, Luc J A; Rutten, Matthieu J C; Plaisier, Menno L; Lohle, Paul N; Hooijen, Marianne J H; Tjan-Heijnen, Vivianne C G; Duijm, Lucien E M

2017-05-05

To determine the proportion of "true" interval cancers and tumor characteristics of interval breast cancers prior to, during and after the transition from screen-film mammography screening (SFM) to full-field digital mammography screening (FFDM). We included all women with interval cancers detected between January 2006 and January 2014. Breast imaging reports, biopsy results and breast surgery reports of all women recalled at screening mammography and of all women with interval breast cancers were collected. Two experienced screening radiologists reviewed the diagnostic mammograms, on which the interval cancers were diagnosed, as well as the prior screening mammograms and determined whether or not the interval cancer had been missed on the most recent screening mammogram. If not missed, the cancer was considered an occult ("true") interval cancer. A total of 442 interval cancers had been diagnosed, of which 144 at SFM with a prior SFM (SFM-SFM), 159 at FFDM with a prior SFM (FFDM-SFM) and 139 at FFDM with a prior FFDM (FFDM-FFDM). The transition from SFM to FFDM screening resulted in the diagnosis of more occult ("true") interval cancers at FFDM-SFM than at SFM-SFM (65.4% (104/159) versus 49.3% (71/144), P < 0.01), but this increase was no longer statistically significant in women who had been screened digitally for the second time (57.6% (80/139) at FFDM-FFDM versus 49.3% (71/144) at SFM-SFM). Tumor characteristics were comparable for the three interval cancer cohorts, except of a lower porportion (75.7 and 78.0% versus 67.2% af FFDM-FFDM, P < 0.05) of invasive ductal cancers at FFDM with prior FFDM. An increase in the proportion of occult interval cancers is observed during the transition from SFM to FFDM screening mammography. However, this increase seems temporary and is no longer detectable after the second round of digital screening. Tumor characteristics and type of surgery are comparable for interval cancers detected prior to, during and after the transition from SFM to FFDM screening mammography, except of a lower proportion of invasive ductal cancers after the transition.

Effect of spatial noise of medical grade Liquid Crystal Displays (LCD) on the detection of micro-calcification

NASA Astrophysics Data System (ADS)

Roehrig, Hans; Fan, Jiahua; Dallas, William J.; Krupinski, Elizabeth A.; Johnson, Jeffrey

2009-08-01

This presentation describes work in progress that is the result of an NIH SBIR Phase 1 project that addresses the wide- spread concern for the large number of breast-cancers and cancer victims [1,2]. The primary goal of the project is to increase the detection rate of microcalcifications as a result of the decrease of spatial noise of the LCDs used to display the mammograms [3,4]. Noise reduction is to be accomplished with the aid of a high performance CCD camera and subsequent application of local-mean equalization and error diffusion [5,6]. A second goal of the project is the actual detection of breast cancer. Contrary to the approach to mammography, where the mammograms typically have a pixel matrix of approximately 1900 x 2300 pixels, otherwise known as FFDM or Full-Field Digital Mammograms, we will only use sections of mammograms with a pixel matrix of 256 x 256 pixels. This is because at this time, reduction of spatial noise on an LCD can only be done on relatively small areas like 256 x 256 pixels. In addition, judging the efficacy for detection of breast cancer will be done using two methods: One is a conventional ROC study [7], the other is a vision model developed over several years starting at the Sarnoff Research Center and continuing at the Siemens Corporate Research in Princeton NJ [8].

Detection method of visible and invisible nipples on digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Chae, Seung-Hoon; Jeong, Ji-Wook; Lee, Sooyeul; Chae, Eun Young; Kim, Hak Hee; Choi, Young-Wook

2015-03-01

Digital Breast Tomosynthesis(DBT) with 3D breast image can improve detection sensitivity of breast cancer more than 2D mammogram on dense breast. The nipple location information is needed to analyze DBT. The nipple location is invaluable information in registration and as a reference point for classifying mass or micro-calcification clusters. Since there are visible nipple and invisible nipple in 2D mammogram or DBT, the nipple detection of breast must be possible to detect visible and invisible nipple of breast. The detection method of visible nipple using shape information of nipple is simple and highly efficient. However, it is difficult to detect invisible nipple because it doesn't have prominent shape. Mammary glands in breast connect nipple, anatomically. The nipple location is detected through analyzing location of mammary glands in breast. In this paper, therefore, we propose a method to detect the nipple on a breast, which has a visible or invisible nipple using changes of breast area and mammary glands, respectively. The result shows that our proposed method has average error of 2.54+/-1.47mm.

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

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

Normal and abnormal tissue identification system and method for medical images such as digital mammograms

NASA Technical Reports Server (NTRS)

Heine, John J. (Inventor); Clarke, Laurence P. (Inventor); Deans, Stanley R. (Inventor); Stauduhar, Richard Paul (Inventor); Cullers, David Kent (Inventor)

2001-01-01

A system and method for analyzing a medical image to determine whether an abnormality is present, for example, in digital mammograms, includes the application of a wavelet expansion to a raw image to obtain subspace images of varying resolution. At least one subspace image is selected that has a resolution commensurate with a desired predetermined detection resolution range. A functional form of a probability distribution function is determined for each selected subspace image, and an optimal statistical normal image region test is determined for each selected subspace image. A threshold level for the probability distribution function is established from the optimal statistical normal image region test for each selected subspace image. A region size comprising at least one sector is defined, and an output image is created that includes a combination of all regions for each selected subspace image. Each region has a first value when the region intensity level is above the threshold and a second value when the region intensity level is below the threshold. This permits the localization of a potential abnormality within the image.

Neural and Decision Theoretic Approaches for the Automated Segmentation of Radiodense Tissue in Digitized Mammograms

NASA Astrophysics Data System (ADS)

Eckert, R.; Neyhart, J. T.; Burd, L.; Polikar, R.; Mandayam, S. A.; Tseng, M.

2003-03-01

Mammography is the best method available as a non-invasive technique for the early detection of breast cancer. The radiographic appearance of the female breast consists of radiolucent (dark) regions due to fat and radiodense (light) regions due to connective and epithelial tissue. The amount of radiodense tissue can be used as a marker for predicting breast cancer risk. Previously, we have shown that the use of statistical models is a reliable technique for segmenting radiodense tissue. This paper presents improvements in the model that allow for further development of an automated system for segmentation of radiodense tissue. The segmentation algorithm employs a two-step process. In the first step, segmentation of tissue and non-tissue regions of a digitized X-ray mammogram image are identified using a radial basis function neural network. The second step uses a constrained Neyman-Pearson algorithm, developed especially for this research work, to determine the amount of radiodense tissue. Results obtained using the algorithm have been validated by comparing with estimates provided by a radiologist employing previously established methods.

Locally adaptive decision in detection of clustered microcalcifications in mammograms.

PubMed

Sainz de Cea, María V; Nishikawa, Robert M; Yang, Yongyi

2018-02-15

In computer-aided detection or diagnosis of clustered microcalcifications (MCs) in mammograms, the performance often suffers from not only the presence of false positives (FPs) among the detected individual MCs but also large variability in detection accuracy among different cases. To address this issue, we investigate a locally adaptive decision scheme in MC detection by exploiting the noise characteristics in a lesion area. Instead of developing a new MC detector, we propose a decision scheme on how to best decide whether a detected object is an MC or not in the detector output. We formulate the individual MCs as statistical outliers compared to the many noisy detections in a lesion area so as to account for the local image characteristics. To identify the MCs, we first consider a parametric method for outlier detection, the Mahalanobis distance detector, which is based on a multi-dimensional Gaussian distribution on the noisy detections. We also consider a non-parametric method which is based on a stochastic neighbor graph model of the detected objects. We demonstrated the proposed decision approach with two existing MC detectors on a set of 188 full-field digital mammograms (95 cases). The results, evaluated using free response operating characteristic (FROC) analysis, showed a significant improvement in detection accuracy by the proposed outlier decision approach over traditional thresholding (the partial area under the FROC curve increased from 3.95 to 4.25, p-value  <10 -4 ). There was also a reduction in case-to-case variability in detected FPs at a given sensitivity level. The proposed adaptive decision approach could not only reduce the number of FPs in detected MCs but also improve case-to-case consistency in detection.

Locally adaptive decision in detection of clustered microcalcifications in mammograms

NASA Astrophysics Data System (ADS)

Sainz de Cea, María V.; Nishikawa, Robert M.; Yang, Yongyi

2018-02-01

In computer-aided detection or diagnosis of clustered microcalcifications (MCs) in mammograms, the performance often suffers from not only the presence of false positives (FPs) among the detected individual MCs but also large variability in detection accuracy among different cases. To address this issue, we investigate a locally adaptive decision scheme in MC detection by exploiting the noise characteristics in a lesion area. Instead of developing a new MC detector, we propose a decision scheme on how to best decide whether a detected object is an MC or not in the detector output. We formulate the individual MCs as statistical outliers compared to the many noisy detections in a lesion area so as to account for the local image characteristics. To identify the MCs, we first consider a parametric method for outlier detection, the Mahalanobis distance detector, which is based on a multi-dimensional Gaussian distribution on the noisy detections. We also consider a non-parametric method which is based on a stochastic neighbor graph model of the detected objects. We demonstrated the proposed decision approach with two existing MC detectors on a set of 188 full-field digital mammograms (95 cases). The results, evaluated using free response operating characteristic (FROC) analysis, showed a significant improvement in detection accuracy by the proposed outlier decision approach over traditional thresholding (the partial area under the FROC curve increased from 3.95 to 4.25, p-value  <10-4). There was also a reduction in case-to-case variability in detected FPs at a given sensitivity level. The proposed adaptive decision approach could not only reduce the number of FPs in detected MCs but also improve case-to-case consistency in detection.

Method for simulating dose reduction in digital mammography using the Anscombe transformation.

PubMed

Borges, Lucas R; Oliveira, Helder C R de; Nunes, Polyana F; Bakic, Predrag R; Maidment, Andrew D A; Vieira, Marcelo A C

2016-06-01

This work proposes an accurate method for simulating dose reduction in digital mammography starting from a clinical image acquired with a standard dose. The method developed in this work consists of scaling a mammogram acquired at the standard radiation dose and adding signal-dependent noise. The algorithm accounts for specific issues relevant in digital mammography images, such as anisotropic noise, spatial variations in pixel gain, and the effect of dose reduction on the detective quantum efficiency. The scaling process takes into account the linearity of the system and the offset of the detector elements. The inserted noise is obtained by acquiring images of a flat-field phantom at the standard radiation dose and at the simulated dose. Using the Anscombe transformation, a relationship is created between the calculated noise mask and the scaled image, resulting in a clinical mammogram with the same noise and gray level characteristics as an image acquired at the lower-radiation dose. The performance of the proposed algorithm was validated using real images acquired with an anthropomorphic breast phantom at four different doses, with five exposures for each dose and 256 nonoverlapping ROIs extracted from each image and with uniform images. The authors simulated lower-dose images and compared these with the real images. The authors evaluated the similarity between the normalized noise power spectrum (NNPS) and power spectrum (PS) of simulated images and real images acquired with the same dose. The maximum relative error was less than 2.5% for every ROI. The added noise was also evaluated by measuring the local variance in the real and simulated images. The relative average error for the local variance was smaller than 1%. A new method is proposed for simulating dose reduction in clinical mammograms. In this method, the dependency between image noise and image signal is addressed using a novel application of the Anscombe transformation. NNPS, PS, and local noise metrics confirm that this method is capable of precisely simulating various dose reductions.

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

PubMed

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

2017-07-01

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

Utility of adaptive control processing for the interpretation of digital mammograms.

PubMed

Jinnouchi, Mikako; Yabuuchi, Hidetake; Kubo, Makoto; Tokunaga, Eriko; Yamamoto, Hidetaka; Honda, Hiroshi

2016-11-01

Background Adaptive control processing for mammography (ACM) is a novel program that automatically sets up appropriate image-processing parameters for individual mammograms (MMGs) by analyzing the focal and whole breast histogram. Purpose To investigate whether ACM improves the image contrast of digital MMGs and whether it improves radiologists' diagnostic performance in reading of MMGs. Material and Methods One hundred normal cases for image quality assessment and another 100 cases (50 normal and 50 cancers) for observer performance assessment were enrolled. All mammograms were examined with and without ACM. Five radiologists assessed the intra- and extra-mammary contrast of 100 normal MMGs, and the mean scores of the intra- and extra-mammary contrast were compared between MMGs with and without ACM in both the dense and non-dense group. They classified 100 MMGs into BI-RADS categories 1-5, and were asked to rate the images on a scale of 0 to 100 for the likelihood of the presence of category 3-5 lesions in each breast. Detectability of breast cancer, reading time, and frequency of window adjustment were compared between MMGs with and without ACM. Results ACM improved the intra-mammary contrast in both the dense and non-dense group but degraded extra-mammary contrast in the dense group. There was no significant difference in detectability of breast cancer between MMGs with and without ACM. Frequency of window adjustment without ACM was significantly higher than that with ACM. Reading time without ACM was significantly longer than that with ACM. Conclusion ACM improves the image contrast of MMGs and shortens reading time.

Impact of full field digital mammography on the classification and mammographic characteristics of interval breast cancers.

PubMed

Knox, Mark; O'Brien, Angela; Szabó, Endre; Smith, Clare S; Fenlon, Helen M; McNicholas, Michelle M; Flanagan, Fidelma L

2015-06-01

Full field digital mammography (FFDM) is increasingly replacing screen film mammography (SFM) in breast screening programs. Interval breast cancers are an issue in all screening programs and the purpose of our study is to assess the impact of FFDM on the classification of interval breast cancers at independent blind review and to compare the mammographic features of interval cancers at FFDM and SFM. This study included 138 cases of interval breast cancer, 76 following an FFDM screening examination and 62 following screening with SFM. The prior screening mammogram was assessed by each of five consultant breast radiologists who were blinded to the site of subsequent cancer. Subsequent review of the diagnostic mammogram was performed and cases were classified as missed, minimal signs, occult or true interval. Mammographic features of the interval cancer at diagnosis and any abnormality identified on the prior screening mammogram were recorded. The percentages of cancers classified as missed at FFDM and SFM did not differ significantly, 10.5% (8 of 76) at FFDM and 8.1% (5 of 62) at SFM (p=.77). There were significantly less interval cancers presenting as microcalcifications (alone or in association with another abnormality) following screening with FFDM, 16% (12 of 76) than following a SFM examination, 32% (20 of 62) (p=.02). Interval breast cancers continue to pose a problem at FFDM. The switch to FFDM has changed the mammographic presentation of interval breast cancer, with less interval cancers presenting in association with microcalcifications. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm.

PubMed

Pereira, Danilo Cesar; Ramos, Rodrigo Pereira; do Nascimento, Marcelo Zanchetta

2014-04-01

In Brazil, the National Cancer Institute (INCA) reports more than 50,000 new cases of the disease, with risk of 51 cases per 100,000 women. Radiographic images obtained from mammography equipments are one of the most frequently used techniques for helping in early diagnosis. Due to factors related to cost and professional experience, in the last two decades computer systems to support detection (Computer-Aided Detection - CADe) and diagnosis (Computer-Aided Diagnosis - CADx) have been developed in order to assist experts in detection of abnormalities in their initial stages. Despite the large number of researches on CADe and CADx systems, there is still a need for improved computerized methods. Nowadays, there is a growing concern with the sensitivity and reliability of abnormalities diagnosis in both views of breast mammographic images, namely cranio-caudal (CC) and medio-lateral oblique (MLO). This paper presents a set of computational tools to aid segmentation and detection of mammograms that contained mass or masses in CC and MLO views. An artifact removal algorithm is first implemented followed by an image denoising and gray-level enhancement method based on wavelet transform and Wiener filter. Finally, a method for detection and segmentation of masses using multiple thresholding, wavelet transform and genetic algorithm is employed in mammograms which were randomly selected from the Digital Database for Screening Mammography (DDSM). The developed computer method was quantitatively evaluated using the area overlap metric (AOM). The mean ± standard deviation value of AOM for the proposed method was 79.2 ± 8%. The experiments demonstrate that the proposed method has a strong potential to be used as the basis for mammogram mass segmentation in CC and MLO views. Another important aspect is that the method overcomes the limitation of analyzing only CC and MLO views. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Alignment of breast cancer screening guidelines, accountability metrics, and practice patterns.

PubMed

Onega, Tracy; Haas, Jennifer S; Bitton, Asaf; Brackett, Charles; Weiss, Julie; Goodrich, Martha; Harris, Kimberly; Pyle, Steve; Tosteson, Anna N A

2017-01-01

Breast cancer screening guidelines and metrics are inconsistent with each other and may differ from breast screening practice patterns in primary care. This study measured breast cancer screening practice patterns in relation to common evidence-based guidelines and accountability metrics. Cohort study using primary data collected from a regional breast cancer screening research network between 2011 and 2014. Using information on women aged 30 to 89 years within 21 primary care practices of 2 large integrated health systems in New England, we measured the proportion of women screened overall and by age using 2 screening definition categories: any mammogram and screening mammogram. Of the 81,352 women in our cohort, 54,903 (67.5%) had at least 1 mammogram during the time period, 48,314 (59.4%) had a screening mammogram. Women aged 50 to 69 years were the highest proportion screened (82.4% any mammogram, 75% screening indication); 72.6% of women at age 40 had a screening mammogram with a median of 70% (range = 54.3%-84.8%) among the practices. Of women aged at least 75 years, 63.3% had a screening mammogram, with the median of 63.9% (range = 37.2%-78.3%) among the practices. Of women who had 2 or more mammograms, 79.5% were screened annually. Primary care practice patterns for breast cancer screening are not well aligned with some evidence-based guidelines and accountability metrics. Metrics and incentives should be designed with more uniformity and should also include shared decision making when the evidence does not clearly support one single conclusion.

Full-field acoustomammography using an acousto-optic sensor.

PubMed

Sandhu, J S; Schmidt, R A; La Rivière, P J

2009-06-01

In this Letter the authors introduce a wide-field transmission ultrasound approach to breast imaging based on the use of a large area acousto-optic (AO) sensor. Accompanied by a suitable acoustic source, such a detector could be mounted on a traditional mammography system and provide a mammographylike ultrasound projection image of the compressed breast in registration with the x-ray mammogram. The authors call the approach acoustography. The hope is that this additional information could improve the sensitivity and specificity of screening mammography. The AO sensor converts ultrasound directly into a visual image by virtue of the acousto-optic effect of the liquid crystal layer contained in the AO sensor. The image is captured with a digital video camera for processing, analysis, and storage. In this Letter, the authors perform a geometrical resolution analysis and also present images of a multimodality breast phantom imaged with both mammography and acoustography to demonstrate the feasibility of the approach. The geometric resolution analysis suggests that the technique could readily detect tumors of diameter of 3 mm using 8.5 MHz ultrasound, with smaller tumors detectable with higher frequency ultrasound, though depth penetration might then become a limiting factor. The preliminary phantom images show high contrast and compare favorably to digital mammograms of the same phantom. The authors have introduced and established, through phantom imaging, the feasibility of a full-field transmission ultrasound detector for breast imaging based on the use of a large area AO sensor. Of course variations in attenuation of connective, glandular, and fatty tissues will lead to images with more cluttered anatomical background than those of the phantom imaged here. Acoustic coupling to the mammographically compressed breast, particularly at the margins, will also have to be addressed.

Full-field acoustomammography using an acousto-optic sensor

PubMed Central

Sandhu, J. S.; Schmidt, R. A.; La Rivière, P. J.

2009-01-01

In this Letter the authors introduce a wide-field transmission ultrasound approach to breast imaging based on the use of a large area acousto-optic (AO) sensor. Accompanied by a suitable acoustic source, such a detector could be mounted on a traditional mammography system and provide a mammographylike ultrasound projection image of the compressed breast in registration with the x-ray mammogram. The authors call the approach acoustography. The hope is that this additional information could improve the sensitivity and specificity of screening mammography. The AO sensor converts ultrasound directly into a visual image by virtue of the acousto-optic effect of the liquid crystal layer contained in the AO sensor. The image is captured with a digital video camera for processing, analysis, and storage. In this Letter, the authors perform a geometrical resolution analysis and also present images of a multimodality breast phantom imaged with both mammography and acoustography to demonstrate the feasibility of the approach. The geometric resolution analysis suggests that the technique could readily detect tumors of diameter of 3 mm using 8.5 MHz ultrasound, with smaller tumors detectable with higher frequency ultrasound, though depth penetration might then become a limiting factor. The preliminary phantom images show high contrast and compare favorably to digital mammograms of the same phantom. The authors have introduced and established, through phantom imaging, the feasibility of a full-field transmission ultrasound detector for breast imaging based on the use of a large area AO sensor. Of course variations in attenuation of connective, glandular, and fatty tissues will lead to images with more cluttered anatomical background than those of the phantom imaged here. Acoustic coupling to the mammographically compressed breast, particularly at the margins, will also have to be addressed. PMID:19610321

Contrast enhanced dual energy spectral mammogram, an emerging addendum in breast imaging.

PubMed

Kariyappa, Kalpana D; Gnanaprakasam, Francis; Anand, Subhapradha; Krishnaswami, Murali; Ramachandran, Madan

2016-11-01

To assess the role of contrast-enhanced dual-energy spectral mammogram (CEDM) as a problem-solving tool in equivocal cases. 44 consenting females with equivocal findings on full-field digital mammogram underwent CEDM. All the images were interpreted by two radiologists independently. Confidence of presence was plotted on a three-point Likert scale and probability of cancer was assigned on Breast Imaging Reporting and Data System scoring. Histopathology was taken as the gold standard. Statistical analyses of all variables were performed. 44 breast lesions were included in the study, among which 77.3% lesions were malignant or precancerous and 22.7% lesions were benign or inconclusive. 20% of lesions were identified only on CEDM. True extent of the lesion was made out in 15.9% of cases, multifocality was established in 9.1% of cases and ductal extension was demonstrated in 6.8% of cases. Statistical significance for CEDM was p-value <0.05. Interobserver kappa value was 0.837. CEDM has a useful role in identifying occult lesions in dense breasts and in triaging lesions. In a mammographically visible lesion, CEDM characterizes the lesion, affirms the finding and better demonstrates response to treatment. Hence, we conclude that CEDM is a useful complementary tool to standard mammogram. Advances in knowledge: CEDM can detect and demonstrate lesions even in dense breasts with the advantage of feasibility of stereotactic biopsy in the same setting. Hence, it has the potential to be a screening modality with need for further studies and validation.

Mammogram image quality as a potential contributor to disparities in breast cancer stage at diagnosis: an observational study

PubMed Central

2013-01-01

Background In an ongoing study of racial/ethnic disparities in breast cancer stage at diagnosis, we consented patients to allow us to review their mammogram images, in order to examine the potential role of mammogram image quality on this disparity. Methods In a population-based study of urban breast cancer patients, a single breast imaging specialist (EC) performed a blinded review of the index mammogram that prompted diagnostic follow-up, as well as recent prior mammograms performed approximately one or two years prior to the index mammogram. Seven indicators of image quality were assessed on a five-point Likert scale, where 4 and 5 represented good and excellent quality. These included 3 technologist-associated image quality (TAIQ) indicators (positioning, compression, sharpness), and 4 machine associated image quality (MAIQ) indicators (contrast, exposure, noise and artifacts). Results are based on 494 images examined for 268 patients, including 225 prior images. Results Whereas MAIQ was generally high, TAIQ was more variable. In multivariable models of sociodemographic predictors of TAIQ, less income was associated with lower TAIQ (p < 0.05). Among prior mammograms, lower TAIQ was subsequently associated with later stage at diagnosis, even after adjusting for multiple patient and practice factors (OR = 0.80, 95% CI: 0.65, 0.99). Conclusions Considerable gains could be made in terms of increasing image quality through better positioning, compression and sharpness, gains that could impact subsequent stage at diagnosis. PMID:23621946

A fully automatic microcalcification detection approach based on deep convolution neural network

NASA Astrophysics Data System (ADS)

Cai, Guanxiong; Guo, Yanhui; Zhang, Yaqin; Qin, Genggeng; Zhou, Yuanpin; Lu, Yao

2018-02-01

Breast cancer is one of the most common cancers and has high morbidity and mortality worldwide, posing a serious threat to the health of human beings. The emergence of microcalcifications (MCs) is an important signal of early breast cancer. However, it is still challenging and time consuming for radiologists to identify some tiny and subtle individual MCs in mammograms. This study proposed a novel computer-aided MC detection algorithm on the full field digital mammograms (FFDMs) using deep convolution neural network (DCNN). Firstly, a MC candidate detection system was used to obtain potential MC candidates. Then a DCNN was trained using a novel adaptive learning strategy, neutrosophic reinforcement sample learning (NRSL) strategy to speed up the learning process. The trained DCNN served to recognize true MCs. After been classified by DCNN, a density-based regional clustering method was imposed to form MC clusters. The accuracy of the DCNN with our proposed NRSL strategy converges faster and goes higher than the traditional DCNN at same epochs, and the obtained an accuracy of 99.87% on training set, 95.12% on validation set, and 93.68% on testing set at epoch 40. For cluster-based MC cluster detection evaluation, a sensitivity of 90% was achieved at 0.13 false positives (FPs) per image. The obtained results demonstrate that the designed DCNN plays a significant role in the MC detection after being prior trained.

Mammogram segmentation using maximal cell strength updation in cellular automata.

PubMed

Anitha, J; Peter, J Dinesh

2015-08-01

Breast cancer is the most frequently diagnosed type of cancer among women. Mammogram is one of the most effective tools for early detection of the breast cancer. Various computer-aided systems have been introduced to detect the breast cancer from mammogram images. In a computer-aided diagnosis system, detection and segmentation of breast masses from the background tissues is an important issue. In this paper, an automatic segmentation method is proposed to identify and segment the suspicious mass regions of mammogram using a modified transition rule named maximal cell strength updation in cellular automata (CA). In coarse-level segmentation, the proposed method performs an adaptive global thresholding based on the histogram peak analysis to obtain the rough region of interest. An automatic seed point selection is proposed using gray-level co-occurrence matrix-based sum average feature in the coarse segmented image. Finally, the method utilizes CA with the identified initial seed point and the modified transition rule to segment the mass region. The proposed approach is evaluated over the dataset of 70 mammograms with mass from mini-MIAS database. Experimental results show that the proposed approach yields promising results to segment the mass region in the mammograms with the sensitivity of 92.25% and accuracy of 93.48%.

Mammographic enhancement with combining local statistical measures and sliding band filter for improved mass segmentation in mammograms

NASA Astrophysics Data System (ADS)

Kim, Dae Hoe; Choi, Jae Young; Choi, Seon Hyeong; Ro, Yong Man

2012-03-01

In this study, a novel mammogram enhancement solution is proposed, aiming to improve the quality of subsequent mass segmentation in mammograms. It has been widely accepted that characteristics of masses are usually hyper-dense or uniform density with respect to its background. Also, their core parts are likely to have high-intensity values while the values of intensity tend to be decreased as the distance to core parts increases. Based on the aforementioned observations, we develop a new and effective mammogram enhancement method by combining local statistical measurements and Sliding Band Filtering (SBF). By effectively combining local statistical measurements and SBF, we are able to improve the contrast of the bright and smooth regions (which represent potential mass regions), as well as, at the same time, the regions where their surrounding gradients are converging to the centers of regions of interest. In this study, 89 mammograms were collected from the public MAIS database (DB) to demonstrate the effectiveness of the proposed enhancement solution in terms of improving mass segmentation. As for a segmentation method, widely used contour-based segmentation approach was employed. The contour-based method in conjunction with the proposed enhancement solution achieved overall detection accuracy of 92.4% with a total of 85 correct cases. On the other hand, without using our enhancement solution, overall detection accuracy of the contour-based method was only 78.3%. In addition, experimental results demonstrated the feasibility of our enhancement solution for the purpose of improving detection accuracy on mammograms containing dense parenchymal patterns.

Evaluation of hybrids algorithms for mass detection in digitalized mammograms

NASA Astrophysics Data System (ADS)

Cordero, José; Garzón Reyes, Johnson

2011-01-01

The breast cancer remains being a significant public health problem, the early detection of the lesions can increase the success possibilities of the medical treatments. The mammography is an image modality effective to early diagnosis of abnormalities, where the medical image is obtained of the mammary gland with X-rays of low radiation, this allows detect a tumor or circumscribed mass between two to three years before that it was clinically palpable, and is the only method that until now achieved reducing the mortality by breast cancer. In this paper three hybrids algorithms for circumscribed mass detection on digitalized mammograms are evaluated. In the first stage correspond to a review of the enhancement and segmentation techniques used in the processing of the mammographic images. After a shape filtering was applied to the resulting regions. By mean of a Bayesian filter the survivors regions were processed, where the characteristics vector for the classifier was constructed with few measurements. Later, the implemented algorithms were evaluated by ROC curves, where 40 images were taken for the test, 20 normal images and 20 images with circumscribed lesions. Finally, the advantages and disadvantages in the correct detection of a lesion of every algorithm are discussed.

Three-dimensional reconstruction of clustered microcalcifications from two digitized mammograms

NASA Astrophysics Data System (ADS)

Stotzka, Rainer; Mueller, Tim O.; Epper, Wolfgang; Gemmeke, Hartmut

1998-06-01

X-ray mammography is one of the most significant diagnosis methods in early detection of breast cancer. Usually two X- ray images from different angles are taken from each mamma to make even overlapping structures visible. X-ray mammography has a very high spatial resolution and can show microcalcifications of 50 - 200 micron in size. Clusters of microcalcifications are one of the most important and often the only indicator for malignant tumors. These calcifications are in some cases extremely difficult to detect. Computer assisted diagnosis of digitized mammograms may improve detection and interpretation of microcalcifications and cause more reliable diagnostic findings. We build a low-cost mammography workstation to detect and classify clusters of microcalcifications and tissue densities automatically. New in this approach is the estimation of the 3D formation of segmented microcalcifications and its visualization which will put additional diagnostic information at the radiologists disposal. The real problem using only two or three projections for reconstruction is the big loss of volume information. Therefore the arrangement of a cluster is estimated using only the positions of segmented microcalcifications. The arrangement of microcalcifications is visualized to the physician by rotating.

Mapping 3D breast lesions from full-field digital mammograms using subject-specific finite element models

NASA Astrophysics Data System (ADS)

García, E.; Oliver, A.; Diaz, O.; Diez, Y.; Gubern-Mérida, A.; Martí, R.; Martí, J.

2017-03-01

Patient-specific finite element (FE) models of the breast have received increasing attention due to the potential capability of fusing images from different modalities. During the Magnetic Resonance Imaging (MRI) to X-ray mammography registration procedure, the FE model is compressed mimicking the mammographic acquisition. Subsequently, suspicious lesions in the MRI volume can be projected into the 2D mammographic space. However, most registration algorithms do not provide the reverse information, avoiding to obtain the 3D geometrical information from the lesions localized in the mammograms. In this work we introduce a fast method to localize the 3D position of the lesion within the MRI, using both cranio-caudal (CC) and medio-lateral oblique (MLO) mammographic projections, indexing the tetrahedral elements of the biomechanical model by means of an uniform grid. For each marked lesion in the Full-Field Digital Mammogram (FFDM), the X-ray path from source to the marker is calculated. Barycentric coordinates are computed in the tetrahedrons traversed by the ray. The list of elements and coordinates allows to localize two curves within the MRI and the closest point between both curves is taken as the 3D position of the lesion. The registration errors obtained in the mammographic space are 9.89 +/- 3.72 mm in CC- and 8.04 +/- 4.68 mm in MLO-projection and the error in the 3D MRI space is equal to 10.29 +/- 3.99 mm. Regarding the uniform grid, it is computed spending between 0.1 and 0.7 seconds. The average time spent to compute the 3D location of a lesion is about 8 ms.

Comparison of Breast Density Between Synthesized Versus Standard Digital Mammography.

PubMed

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

2018-06-12

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

A breast health educational program for Chinese-American women: 3- to 12-month postintervention effect.

PubMed

Lee-Lin, Frances; Nguyen, Thuan; Pedhiwala, Nisreen; Dieckmann, Nathan; Menon, Usha

2015-01-01

To test the efficacy of a culturally targeted breast cancer screening educational program in increasing mammogram completion in Chinese-American immigrant women. Randomized controlled study. Chinese communities, Portland, Oregon. From April 2010 to September 2011, 300 women were randomized to receive a theory-based, culturally targeted breast cancer screening educational intervention (n = 147) or a mammography screening brochure published by the National Cancer Institute (n = 153). The two-part intervention consisted of group teaching with targeted, theory-based messages followed by individual counseling sessions. Mammography completion, perceived susceptibility, perceived benefits, perceived barriers, perceived cultural barriers, and demographic variables. A 2 × 3 mixed logistic model was applied to determine odds ratio of mammogram completion. Behavior changed in both groups, with a total of 170 participants (56.7%) reporting a mammogram at 12 months. The logistic model indicated increased odds of mammogram completion in the intervention compared to the control group at 3, 6, and 12 months. When controlling for marital status, age, and age moved to the United States, the intervention group was nine times more likely to complete mammograms than the control group. The culturally targeted educational program significantly increased mammogram use among Chinese immigrant women. Further testing of effectiveness in larger community settings is needed. The intervention may also serve as a foundation from which to develop education to increase cancer screening among other minority subgroups.

Telemammography Using Satellite Communications

NASA Technical Reports Server (NTRS)

1996-01-01

Telemammography, the electronic transmission of digitized mammograms, can connect patients with timely, critical medical expertise; howev er, an adequate terrestrial communications infrastructure does not exist in these areas. NASA Lewis Research Center's Advanced Space Commu nications Laboratory is now working with leading breast cancer resear ch hospitals, including the Cleveland Clinic and the University of Virginia, to perform the critical research necessary to allow new satell ite networks to support telemammography.

Contrast enhanced dual energy spectral mammogram, an emerging addendum in breast imaging

PubMed Central

Gnanaprakasam, Francis; Anand, Subhapradha; Krishnaswami, Murali; Ramachandran, Madan

2016-01-01

Objective: To assess the role of contrast-enhanced dual-energy spectral mammogram (CEDM) as a problem-solving tool in equivocal cases. Methods: 44 consenting females with equivocal findings on full-field digital mammogram underwent CEDM. All the images were interpreted by two radiologists independently. Confidence of presence was plotted on a three-point Likert scale and probability of cancer was assigned on Breast Imaging Reporting and Data System scoring. Histopathology was taken as the gold standard. Statistical analyses of all variables were performed. Results: 44 breast lesions were included in the study, among which 77.3% lesions were malignant or precancerous and 22.7% lesions were benign or inconclusive. 20% of lesions were identified only on CEDM. True extent of the lesion was made out in 15.9% of cases, multifocality was established in 9.1% of cases and ductal extension was demonstrated in 6.8% of cases. Statistical significance for CEDM was p-value <0.05. Interobserver kappa value was 0.837. Conclusion: CEDM has a useful role in identifying occult lesions in dense breasts and in triaging lesions. In a mammographically visible lesion, CEDM characterizes the lesion, affirms the finding and better demonstrates response to treatment. Hence, we conclude that CEDM is a useful complementary tool to standard mammogram. Advances in knowledge: CEDM can detect and demonstrate lesions even in dense breasts with the advantage of feasibility of stereotactic biopsy in the same setting. Hence, it has the potential to be a screening modality with need for further studies and validation. PMID:27610475

Towards the use of computationally inserted lesions for mammographic CAD assessment

NASA Astrophysics Data System (ADS)

Ghanian, Zahra; Pezeshk, Aria; Petrick, Nicholas; Sahiner, Berkman

2018-03-01

Computer-aided detection (CADe) devices used for breast cancer detection on mammograms are typically first developed and assessed for a specific "original" acquisition system, e.g., a specific image detector. When CADe developers are ready to apply their CADe device to a new mammographic acquisition system, they typically assess the CADe device with images acquired using the new system. Collecting large repositories of clinical images containing verified cancer locations and acquired by the new image acquisition system is costly and time consuming. Our goal is to develop a methodology to reduce the clinical data burden in the assessment of a CADe device for use with a different image acquisition system. We are developing an image blending technique that allows users to seamlessly insert lesions imaged using an original acquisition system into normal images or regions acquired with a new system. In this study, we investigated the insertion of microcalcification clusters imaged using an original acquisition system into normal images acquired with that same system utilizing our previously-developed image blending technique. We first performed a reader study to assess whether experienced observers could distinguish between computationally inserted and native clusters. For this purpose, we applied our insertion technique to clinical cases taken from the University of South Florida Digital Database for Screening Mammography (DDSM) and the Breast Cancer Digital Repository (BCDR). Regions of interest containing microcalcification clusters from one breast of a patient were inserted into the contralateral breast of the same patient. The reader study included 55 native clusters and their 55 inserted counterparts. Analysis of the reader ratings using receiver operating characteristic (ROC) methodology indicated that inserted clusters cannot be reliably distinguished from native clusters (area under the ROC curve, AUC=0.58±0.04). Furthermore, CADe sensitivity was evaluated on mammograms with native and inserted microcalcification clusters using a commercial CADe system. For this purpose, we used full field digital mammograms (FFDMs) from 68 clinical cases, acquired at the University of Michigan Health System. The average sensitivities for native and inserted clusters were equal, 85.3% (58/68). These results demonstrate the feasibility of using the inserted microcalcification clusters for assessing mammographic CAD devices.

Method for simulating dose reduction in digital mammography using the Anscombe transformation

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

Borges, Lucas R., E-mail: lucas.rodrigues.borges@usp.br; Oliveira, Helder C. R. de; Nunes, Polyana F.

2016-06-15

Purpose: This work proposes an accurate method for simulating dose reduction in digital mammography starting from a clinical image acquired with a standard dose. Methods: The method developed in this work consists of scaling a mammogram acquired at the standard radiation dose and adding signal-dependent noise. The algorithm accounts for specific issues relevant in digital mammography images, such as anisotropic noise, spatial variations in pixel gain, and the effect of dose reduction on the detective quantum efficiency. The scaling process takes into account the linearity of the system and the offset of the detector elements. The inserted noise is obtainedmore » by acquiring images of a flat-field phantom at the standard radiation dose and at the simulated dose. Using the Anscombe transformation, a relationship is created between the calculated noise mask and the scaled image, resulting in a clinical mammogram with the same noise and gray level characteristics as an image acquired at the lower-radiation dose. Results: The performance of the proposed algorithm was validated using real images acquired with an anthropomorphic breast phantom at four different doses, with five exposures for each dose and 256 nonoverlapping ROIs extracted from each image and with uniform images. The authors simulated lower-dose images and compared these with the real images. The authors evaluated the similarity between the normalized noise power spectrum (NNPS) and power spectrum (PS) of simulated images and real images acquired with the same dose. The maximum relative error was less than 2.5% for every ROI. The added noise was also evaluated by measuring the local variance in the real and simulated images. The relative average error for the local variance was smaller than 1%. Conclusions: A new method is proposed for simulating dose reduction in clinical mammograms. In this method, the dependency between image noise and image signal is addressed using a novel application of the Anscombe transformation. NNPS, PS, and local noise metrics confirm that this method is capable of precisely simulating various dose reductions.« less

Method for simulating dose reduction in digital mammography using the Anscombe transformation

PubMed Central

Borges, Lucas R.; de Oliveira, Helder C. R.; Nunes, Polyana F.; Bakic, Predrag R.; Maidment, Andrew D. A.; Vieira, Marcelo A. C.

2016-01-01

Purpose: This work proposes an accurate method for simulating dose reduction in digital mammography starting from a clinical image acquired with a standard dose. Methods: The method developed in this work consists of scaling a mammogram acquired at the standard radiation dose and adding signal-dependent noise. The algorithm accounts for specific issues relevant in digital mammography images, such as anisotropic noise, spatial variations in pixel gain, and the effect of dose reduction on the detective quantum efficiency. The scaling process takes into account the linearity of the system and the offset of the detector elements. The inserted noise is obtained by acquiring images of a flat-field phantom at the standard radiation dose and at the simulated dose. Using the Anscombe transformation, a relationship is created between the calculated noise mask and the scaled image, resulting in a clinical mammogram with the same noise and gray level characteristics as an image acquired at the lower-radiation dose. Results: The performance of the proposed algorithm was validated using real images acquired with an anthropomorphic breast phantom at four different doses, with five exposures for each dose and 256 nonoverlapping ROIs extracted from each image and with uniform images. The authors simulated lower-dose images and compared these with the real images. The authors evaluated the similarity between the normalized noise power spectrum (NNPS) and power spectrum (PS) of simulated images and real images acquired with the same dose. The maximum relative error was less than 2.5% for every ROI. The added noise was also evaluated by measuring the local variance in the real and simulated images. The relative average error for the local variance was smaller than 1%. Conclusions: A new method is proposed for simulating dose reduction in clinical mammograms. In this method, the dependency between image noise and image signal is addressed using a novel application of the Anscombe transformation. NNPS, PS, and local noise metrics confirm that this method is capable of precisely simulating various dose reductions. PMID:27277017

Mass detection with digitized screening mammograms by using Gabor features

NASA Astrophysics Data System (ADS)

Zheng, Yufeng; Agyepong, Kwabena

2007-03-01

Breast cancer is the leading cancer among American women. The current lifetime risk of developing breast cancer is 13.4% (one in seven). Mammography is the most effective technology presently available for breast cancer screening. With digital mammograms computer-aided detection (CAD) has proven to be a useful tool for radiologists. In this paper, we focus on mass detection that is a common category of breast cancers relative to calcification and architecture distortion. We propose a new mass detection algorithm utilizing Gabor filters, termed as "Gabor Mass Detection" (GMD). There are three steps in the GMD algorithm, (1) preprocessing, (2) generating alarms and (3) classification (reducing false alarms). Down-sampling, quantization, denoising and enhancement are done in the preprocessing step. Then a total of 30 Gabor filtered images (along 6 bands by 5 orientations) are produced. Alarm segments are generated by thresholding four Gabor images of full orientations (Stage-I classification) with image-dependent thresholds computed via histogram analysis. Next a set of edge histogram descriptors (EHD) are extracted from 24 Gabor images (6 by 4) that will be used for Stage-II classification. After clustering EHD features with fuzzy C-means clustering method, a k-nearest neighbor classifier is used to reduce the number of false alarms. We initially analyzed 431 digitized mammograms (159 normal images vs. 272 cancerous images, from the DDSM project, University of South Florida) with the proposed GMD algorithm. And a ten-fold cross validation was used for testing the GMD algorithm upon the available data. The GMD performance is as follows: sensitivity (true positive rate) = 0.88 at false positives per image (FPI) = 1.25, and the area under the ROC curve = 0.83. The overall performance of the GMD algorithm is satisfactory and the accuracy of locating masses (highlighting the boundaries of suspicious areas) is relatively high. Furthermore, the GMD algorithm can successfully detect early-stage (with small values of Assessment & low Subtlety) malignant masses. In addition, Gabor filtered images are used in both stages of classifications, which greatly simplifies the GMD algorithm.

Maintaining quality in the UK breast screening program

NASA Astrophysics Data System (ADS)

Gale, Alastair

2010-02-01

Breast screening in the UK has been implemented for over 20 years and annually nearly two million women are now screened with an estimated 1,400 lives saved. Nationally, some 700 individuals interpret screening mammograms in almost 110 screening centres. Currently, women aged 50 to 70 are invited for screening every three years and by 2012 this age range will increase to 47 - 73 years. There is a rapid ongoing transition from using film mammograms to full field digital mammography such that in 2010 every screening centre will be partly digital. An early, and long running, concern has been how to ensure the highest quality of imaging interpretation across the UK, an issue enhanced by the use of a three year screening interval. To partly address this question a self assessment scheme was developed in 1988 and subsequently implemented nationally in the UK as a virtually mandatory activity. The scheme is detailed from its beginnings, through its various developments to current incarnation and future plans. This encompasses both radiological (single view screening, two view screening, mammographic film and full field digital mammography) as well as design changes (cases reported by means of: form filling; PDA; tablet PC; iPhone, and the internet). The scheme provides a rich data source which is regularly studied to examine different aspects of radiological performance. Overall it aids screening radiologists by giving them regular access to a range of difficult exemplar cases together with feedback on their performance as compared to their peers.

Automated recognition of microcalcification clusters in mammograms

NASA Astrophysics Data System (ADS)

Bankman, Isaac N.; Christens-Barry, William A.; Kim, Dong W.; Weinberg, Irving N.; Gatewood, Olga B.; Brody, William R.

1993-07-01

The widespread and increasing use of mammographic screening for early breast cancer detection is placing a significant strain on clinical radiologists. Large numbers of radiographic films have to be visually interpreted in fine detail to determine the subtle hallmarks of cancer that may be present. We developed an algorithm for detecting microcalcification clusters, the most common and useful signs of early, potentially curable breast cancer. We describe this algorithm, which utilizes contour map representations of digitized mammographic films, and discuss its benefits in overcoming difficulties often encountered in algorithmic approaches to radiographic image processing. We present experimental analyses of mammographic films employing this contour-based algorithm and discuss practical issues relevant to its use in an automated film interpretation instrument.

The Efficacy of Mammography Boot Camp to Improve the Performance of Radiologists

PubMed Central

Lee, Eun Hye; Jung, Seung Eun; Kim, You Me; Choi, Nami

2014-01-01

Objective To evaluate the efficacy of a mammography boot camp (MBC) to improve radiologists' performance in interpreting mammograms in the National Cancer Screening Program (NCSP) in Korea. Materials and Methods Between January and July of 2013, 141 radiologists were invited to a 3-day educational program composed of lectures and group practice readings using 250 digital mammography cases. The radiologists' performance in interpreting mammograms were evaluated using a pre- and post-camp test set of 25 cases validated prior to the camp by experienced breast radiologists. Factors affecting the radiologists' performance, including age, type of attending institution, and type of test set cases, were analyzed. Results The average scores of the pre- and post-camp tests were 56.0 ± 12.2 and 78.3 ± 9.2, respectively (p < 0.001). The post-camp test scores were higher than the pre-camp test scores for all age groups and all types of attending institutions (p < 0.001). The rate of incorrect answers in the post-camp test decreased compared to the pre-camp test for all suspicious cases, but not for negative cases (p > 0.05). Conclusion The MBC improves radiologists' performance in interpreting mammograms irrespective of age and type of attending institution. Improved interpretation is observed for suspicious cases, but not for negative cases. PMID:25246818

Consistency of visual assessments of mammographic breast density from vendor-specific "for presentation" images.

PubMed

Abdolell, Mohamed; Tsuruda, Kaitlyn; Lightfoot, Christopher B; Barkova, Eva; McQuaid, Melanie; Caines, Judy; Iles, Sian E

2016-01-01

Discussions of percent breast density (PD) and breast cancer risk implicitly assume that visual assessments of PD are comparable between vendors despite differences in technology and display algorithms. This study examines the extent to which visual assessments of PD differ between mammograms acquired from two vendors. Pairs of "for presentation" digital mammography images were obtained from two mammography units for 146 women who had a screening mammogram on one vendor unit followed by a diagnostic mammogram on a different vendor unit. Four radiologists independently visually assessed PD from single left mediolateral oblique view images from the two vendors. Analysis of variance, intra-class correlation coefficients (ICC), scatter plots, and Bland-Altman plots were used to evaluate PD assessments between vendors. The mean radiologist PD for each image was used as a consensus PD measure. Overall agreement of the PD assessments was excellent between the two vendors with an ICC of 0.95 (95% confidence interval: 0.93 to 0.97). Bland-Altman plots demonstrated narrow upper and lower limits of agreement between the vendors with only a small bias (2.3 percentage points). The results of this study support the assumption that visual assessment of PD is consistent across mammography vendors despite vendor-specific appearances of "for presentation" images.

Assessment of automatic exposure control performance in digital mammography using a no-reference anisotropic quality index

NASA Astrophysics Data System (ADS)

Barufaldi, Bruno; Borges, Lucas R.; Bakic, Predrag R.; Vieira, Marcelo A. C.; Schiabel, Homero; Maidment, Andrew D. A.

2017-03-01

Automatic exposure control (AEC) is used in mammography to obtain acceptable radiation dose and adequate image quality regardless of breast thickness and composition. Although there are physics methods for assessing the AEC, it is not clear whether mammography systems operate with optimal dose and image quality in clinical practice. In this work, we propose the use of a normalized anisotropic quality index (NAQI), validated in previous studies, to evaluate the quality of mammograms acquired using AEC. The authors used a clinical dataset that consists of 561 patients and 1,046 mammograms (craniocaudal breast views). The results show that image quality is often maintained, even at various radiation levels (mean NAQI = 0.14 +/- 0.02). However, a more careful analysis of NAQI reveals that the average image quality decreases as breast thickness increases. The NAQI is reduced by 32% on average, when the breast thickness increases from 31 to 71 mm. NAQI also decreases with lower breast density. The variation in breast parenchyma alone cannot fully account for the decrease of NAQI with thickness. Examination of images shows that images of large, fatty breasts are often inadequately processed. This work shows that NAQI can be applied in clinical mammograms to assess mammographic image quality, and highlights the limitations of the automatic exposure control for some images.

Performance of a fail-safe system to follow up abnormal mammograms in primary care.

PubMed

Grossman, Ellie; Phillips, Russell S; Weingart, Saul N

2010-09-01

Missed and delayed breast cancer diagnoses are major sources of potential harm to patients and medical malpractice liability in the United States. Follow-up of abnormal mammogram results is an essential but challenging component of safe breast care. To explore the value of an inexpensive method to follow up abnormal test results, we examined a paper-based fail-safe system. We examined a fail-safe system used to follow up abnormal mammograms at a primary care practice at an urban teaching hospital. We analyzed all abnormal mammogram reports and clinicians' responses to follow-up reminders. We characterized potential lapses identified in this system and used regression models to identify patient, provider, and test result characteristics associated with such lapses. Clinicians responded to fail-safe reminders for 92% of 948 abnormal mammograms. Clinicians reported that they were unaware of the abnormal result in 8% of cases and that there was no follow-up plan in place for 3% of cases. Clinicians with more years of experience were more likely to be aware of the abnormal result (odds of being unaware per incremental year in practice, 0.92; 95% confidence interval, 0.88-0.97) and were more likely to have a follow-up plan. A paper-based fail-safe system for abnormal mammograms is feasible in a primary care practice. However, special care is warranted to ensure full clinician adherence and address staff transitions and trainee-related issues.

A new full-field digital mammography system with and without the use of an advanced post-processing algorithm: comparison of image quality and diagnostic performance.

PubMed

Ahn, Hye Shin; Kim, Sun Mi; Jang, Mijung; Yun, Bo La; Kim, Bohyoung; Ko, Eun Sook; Han, Boo-Kyung; Chang, Jung Min; Yi, Ann; Cho, Nariya; Moon, Woo Kyung; Choi, Hye Young

2014-01-01

To compare new full-field digital mammography (FFDM) with and without use of an advanced post-processing algorithm to improve image quality, lesion detection, diagnostic performance, and priority rank. During a 22-month period, we prospectively enrolled 100 cases of specimen FFDM mammography (Brestige®), which was performed alone or in combination with a post-processing algorithm developed by the manufacturer: group A (SMA), specimen mammography without application of "Mammogram enhancement ver. 2.0"; group B (SMB), specimen mammography with application of "Mammogram enhancement ver. 2.0". Two sets of specimen mammographies were randomly reviewed by five experienced radiologists. Image quality, lesion detection, diagnostic performance, and priority rank with regard to image preference were evaluated. Three aspects of image quality (overall quality, contrast, and noise) of the SMB were significantly superior to those of SMA (p < 0.05). SMB was significantly superior to SMA for visualizing calcifications (p < 0.05). Diagnostic performance, as evaluated by cancer score, was similar between SMA and SMB. SMB was preferred to SMA by four of the five reviewers. The post-processing algorithm may improve image quality with better image preference in FFDM than without use of the software.

Wavelet processing techniques for digital mammography

NASA Astrophysics Data System (ADS)

Laine, Andrew F.; Song, Shuwu

1992-09-01

This paper introduces a novel approach for accomplishing mammographic feature analysis through multiresolution representations. We show that efficient (nonredundant) representations may be identified from digital mammography and used to enhance specific mammographic features within a continuum of scale space. The multiresolution decomposition of wavelet transforms provides a natural hierarchy in which to embed an interactive paradigm for accomplishing scale space feature analysis. Similar to traditional coarse to fine matching strategies, the radiologist may first choose to look for coarse features (e.g., dominant mass) within low frequency levels of a wavelet transform and later examine finer features (e.g., microcalcifications) at higher frequency levels. In addition, features may be extracted by applying geometric constraints within each level of the transform. Choosing wavelets (or analyzing functions) that are simultaneously localized in both space and frequency, results in a powerful methodology for image analysis. Multiresolution and orientation selectivity, known biological mechanisms in primate vision, are ingrained in wavelet representations and inspire the techniques presented in this paper. Our approach includes local analysis of complete multiscale representations. Mammograms are reconstructed from wavelet representations, enhanced by linear, exponential and constant weight functions through scale space. By improving the visualization of breast pathology we can improve the chances of early detection of breast cancers (improve quality) while requiring less time to evaluate mammograms for most patients (lower costs).

Computer-aided classification of breast masses using contrast-enhanced digital mammograms

NASA Astrophysics Data System (ADS)

Danala, Gopichandh; Aghaei, Faranak; Heidari, Morteza; Wu, Teresa; Patel, Bhavika; Zheng, Bin

2018-02-01

By taking advantages of both mammography and breast MRI, contrast-enhanced digital mammography (CEDM) has emerged as a new promising imaging modality to improve efficacy of breast cancer screening and diagnosis. The primary objective of study is to develop and evaluate a new computer-aided detection and diagnosis (CAD) scheme of CEDM images to classify between malignant and benign breast masses. A CEDM dataset consisting of 111 patients (33 benign and 78 malignant) was retrospectively assembled. Each case includes two types of images namely, low-energy (LE) and dual-energy subtracted (DES) images. First, CAD scheme applied a hybrid segmentation method to automatically segment masses depicting on LE and DES images separately. Optimal segmentation results from DES images were also mapped to LE images and vice versa. Next, a set of 109 quantitative image features related to mass shape and density heterogeneity was initially computed. Last, four multilayer perceptron-based machine learning classifiers integrated with correlationbased feature subset evaluator and leave-one-case-out cross-validation method was built to classify mass regions depicting on LE and DES images, respectively. Initially, when CAD scheme was applied to original segmentation of DES and LE images, the areas under ROC curves were 0.7585+/-0.0526 and 0.7534+/-0.0470, respectively. After optimal segmentation mapping from DES to LE images, AUC value of CAD scheme significantly increased to 0.8477+/-0.0376 (p<0.01). Since DES images eliminate overlapping effect of dense breast tissue on lesions, segmentation accuracy was significantly improved as compared to regular mammograms, the study demonstrated that computer-aided classification of breast masses using CEDM images yielded higher performance.

Theorizing the Pathways From Seeking and Scanning to Mammography Screening.

PubMed

Lee, Chul-Joo; Zhao, Xiaoquan; Pena-y-Lillo, Macarena

2016-01-01

This study combines insights from existing theories in mass communication and health communication, and builds an integrated model accounting for the mechanisms by which an individual's acquisition of mammogram-related media information becomes associated with intentions to obtain a mammogram. Our model was largely supported by a survey with a nationally representative sample of American females between the ages of 40 and 70 years. As expected, seeking and scanning mammogram-related information from the media were both positively associated with reflective integration of media health information, which in turn was positively related to behavioral attitudes and perceived normative pressures. Attitudes and normative pressures were then positively linked to the intention to get a mammogram. Based on these findings, we offer some suggestions for future research in this area.

Computer-aided Classification of Mammographic Masses Using Visually Sensitive Image Features

PubMed Central

Wang, Yunzhi; Aghaei, Faranak; Zarafshani, Ali; Qiu, Yuchen; Qian, Wei; Zheng, Bin

2017-01-01

Purpose To develop a new computer-aided diagnosis (CAD) scheme that computes visually sensitive image features routinely used by radiologists to develop a machine learning classifier and distinguish between the malignant and benign breast masses detected from digital mammograms. Methods An image dataset including 301 breast masses was retrospectively selected. From each segmented mass region, we computed image features that mimic five categories of visually sensitive features routinely used by radiologists in reading mammograms. We then selected five optimal features in the five feature categories and applied logistic regression models for classification. A new CAD interface was also designed to show lesion segmentation, computed feature values and classification score. Results Areas under ROC curves (AUC) were 0.786±0.026 and 0.758±0.027 when to classify mass regions depicting on two view images, respectively. By fusing classification scores computed from two regions, AUC increased to 0.806±0.025. Conclusion This study demonstrated a new approach to develop CAD scheme based on 5 visually sensitive image features. Combining with a “visual aid” interface, CAD results may be much more easily explainable to the observers and increase their confidence to consider CAD generated classification results than using other conventional CAD approaches, which involve many complicated and visually insensitive texture features. PMID:27911353

Follow-Up of Abnormal Breast and Colorectal Cancer Screening by Race/Ethnicity.

PubMed

McCarthy, Anne Marie; Kim, Jane J; Beaber, Elisabeth F; Zheng, Yingye; Burnett-Hartman, Andrea; Chubak, Jessica; Ghai, Nirupa R; McLerran, Dale; Breen, Nancy; Conant, Emily F; Geller, Berta M; Green, Beverly B; Klabunde, Carrie N; Inrig, Stephen; Skinner, Celette Sugg; Quinn, Virginia P; Haas, Jennifer S; Schnall, Mitchell; Rutter, Carolyn M; Barlow, William E; Corley, Douglas A; Armstrong, Katrina; Doubeni, Chyke A

2016-10-01

Timely follow-up of abnormal tests is critical to the effectiveness of cancer screening, but may vary by screening test, healthcare system, and sociodemographic group. Timely follow-up of abnormal mammogram and fecal occult blood testing or fecal immunochemical tests (FOBT/FIT) were compared by race/ethnicity using Population-Based Research Optimizing Screening through Personalized Regimens consortium data. Participants were women with an abnormal mammogram (aged 40-75 years) or FOBT/FIT (aged 50-75 years) in 2010-2012. Analyses were performed in 2015. Timely follow-up was defined as colonoscopy ≤3 months following positive FOBT/FIT; additional imaging or biopsy ≤3 months following Breast Imaging Reporting and Data System Category 0, 4, or 5 mammograms; or ≤9 months following Category 3 mammograms. Logistic regression was used to model receipt of timely follow-up adjusting for study site, age, year, insurance, and income. Among 166,602 mammograms, 10.7% were abnormal; among 566,781 FOBT/FITs, 4.3% were abnormal. Nearly 96% of patients with abnormal mammograms received timely follow-up versus 68% with abnormal FOBT/FIT. There was greater variability in receipt of follow-up across healthcare systems for positive FOBT/FIT than for abnormal mammograms. For mammography, black women were less likely than whites to receive timely follow-up (91.8% vs 96.0%, OR=0.71, 95% CI=0.51, 0.97). For FOBT/FIT, Hispanics were more likely than whites to receive timely follow-up than whites (70.0% vs 67.6%, OR=1.12, 95% CI=1.04, 1.21). Timely follow-up among women was more likely for abnormal mammograms than FOBT/FITs, with small variations in follow-up rates by race/ethnicity and larger variation across healthcare systems. Copyright © 2016 American Journal of Preventive Medicine. All rights reserved.

A Community-Based Partnership to Successfully Implement and Maintain a Breast Health Navigation Program.

PubMed

Drake, Bettina F; Tannan, Shivon; Anwuri, Victoria V; Jackson, Sherrill; Sanford, Mark; Tappenden, Jennifer; Goodman, Melody S; Colditz, Graham A

2015-12-01

Breast cancer screening combined with follow-up and treatment reduces breast cancer mortality. However, in the study clinic, only 12 % of eligible women ≥40 years received a mammogram in the previous year. The objective of this project was to implement patient navigation, in our partner health clinic to (1) identify women overdue for a mammogram; and (2) increase mammography utilization in this population over a 2-year period. Women overdue for a mammogram were identified. One patient navigator made navigation attempts over a 2-year period (2009-2011). Navigation included working around systems- and individual-level barriers to receive a mammogram as well as the appropriate follow-up post screening. Women were contacted up to three times to initiate navigation. The proportion of women navigated and who received a mammogram during the study period were compared to women who did not receive a mammogram using Chi square tests for categorical variables and t tests for continuous variables with an α = 0.05. Barriers to previous mammography were also assessed. With 94.8 % of eligible women navigated and 94 % of these women completing mammography, the implementation project reached 89 % of the target population. This project was a successful implementation of an evidence-based patient navigation program that continues to provide significant impact in a high-need area. Cost was the most commonly cite barrier to mammography. Increasing awareness of resources in the community for mammography and follow-up care remains a necessary adjunct to removing structural and financial barriers to accessing preventive services.

Effect of the Availability of Prior Full-Field Digital Mammography and Digital Breast Tomosynthesis Images on the Interpretation of Mammograms

PubMed Central

Catullo, Victor J.; Chough, Denise M.; Ganott, Marie A.; Kelly, Amy E.; Shinde, Dilip D.; Sumkin, Jules H.; Wallace, Luisa P.; Bandos, Andriy I.; Gur, David

2015-01-01

Purpose To assess the effect of and interaction between the availability of prior images and digital breast tomosynthesis (DBT) images in decisions to recall women during mammogram interpretation. Materials and Methods Verbal informed consent was obtained for this HIPAA-compliant institutional review board–approved protocol. Eight radiologists independently interpreted twice deidentified mammograms obtained in 153 women (age range, 37–83 years; mean age, 53.7 years ± 9.3 [standard deviation]) in a mode by reader by case-balanced fully crossed study. Each study consisted of current and prior full-field digital mammography (FFDM) images and DBT images that were acquired in our facility between June 2009 and January 2013. For one reading, sequential ratings were provided by using (a) current FFDM images only, (b) current FFDM and DBT images, and (c) current FFDM, DBT, and prior FFDM images. The other reading consisted of (a) current FFDM images only, (b) current and prior FFDM images, and (c) current FFDM, prior FFDM, and DBT images. Fifty verified cancer cases, 60 negative and benign cases (clinically not recalled), and 43 benign cases (clinically recalled) were included. Recall recommendations and interaction between the effect of prior FFDM and DBT images were assessed by using a generalized linear model accounting for case and reader variability. Results Average recall rates in noncancer cases were significantly reduced with the addition of prior FFDM images by 34% (145 of 421) and 32% (106 of 333) without and with DBT images, respectively (P < .001). However, this recall reduction was achieved at the cost of a corresponding 7% (23 of 345) and 4% (14 of 353) reduction in sensitivity (P = .006). In contrast, availability of DBT images resulted in a smaller reduction in recall rates (false-positive interpretations) of 19% (76 of 409) and 26% (71 of 276) without and with prior FFDM images, respectively (P = .001). Availability of DBT images resulted in 4% (15 of 338) and 8% (25 of 322) increases in sensitivity, respectively (P = .007). The effects of the availability of prior FFDM images or DBT images did not significantly change regardless of the sequence in presentation (P = .81 and P = .47 for specificity and sensitivity, respectively). Conclusion The availability of prior FFDM or DBT images is a largely independent contributing factor in reducing recall recommendations during mammographic interpretation. © RSNA, 2015 PMID:25768673

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

PubMed Central

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

2009-01-01

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

Advanced Signal Processing Methods Applied to Digital Mammography

NASA Technical Reports Server (NTRS)

Stauduhar, Richard P.

1997-01-01

The work reported here is on the extension of the earlier proposal of the same title, August 1994-June 1996. The report for that work is also being submitted. The work reported there forms the foundation for this work from January 1997 to September 1997. After the earlier work was completed there were a few items that needed to be completed prior to submission of a new and more comprehensive proposal for further research. Those tasks have been completed and two new proposals have been submitted, one to NASA, and one to Health & Human Services WS). The main purpose of this extension was to refine some of the techniques that lead to automatic large scale evaluation of full mammograms. Progress on each of the proposed tasks follows. Task 1: A multiresolution segmentation of background from breast has been developed and tested. The method is based on the different noise characteristics of the two different fields. The breast field has more power in the lower octaves and the off-breast field behaves similar to a wideband process, where more power is in the high frequency octaves. After the two fields are separated by lowpass filtering, a region labeling routine is used to find the largest contiguous region, the breast. Task 2: A wavelet expansion that can decompose the image without zero padding has been developed. The method preserves all properties of the power-of-two wavelet transform and does not add appreciably to computation time or storage. This work is essential for analysis of the full mammogram, as opposed to selecting sections from the full mammogram. Task 3: A clustering method has been developed based on a simple counting mechanism. No ROC analysis has been performed (and was not proposed), so we cannot finally evaluate this work without further support. Task 4: Further testing of the filter reveals that different wavelet bases do yield slightly different qualitative results. We cannot provide quantitative conclusions about this for all possible bases without further support. Task 5: Better modeling does indeed make an improvement in the detection output. After the proposal ended, we came up with some new theoretical explanations that helps in understanding when the D4 filter should be better. This work is currently in the review process. Task 6: N/A. This no longer applies in view of Tasks 4-5. Task 7: Comprehensive plans for further work have been completed. These plans are the subject of two proposals, one to NASA and one to HHS. These proposals represent plans for a complete evaluation of the methods for identifying normal mammograms, augmented with significant further theoretical work.

Genetic Plymorphisms, Estrogens, and Breast Density

DTIC Science & Technology

2005-01-01

Babych N et al. Investigations on the genetic polymorphism in the region of CYP17 gene encoding 5’-UTR in patients with polycystic ovarian syndrome ...digitizing cranio- suppression of ovarian function through a gonadotropin-releasing caudal views of the mammograms, we performed computer- hormone...McDuffie K, Kolonel LN, Terada K, Donlon TA, 2002;4:R5. Wilkens LR, Guo C, Le Marchand L. Case-control study of ovarian 22. Haiman CA, Hankinson SE

Automated System for Early Breast Cancer Detection in Mammograms

NASA Technical Reports Server (NTRS)

Bankman, Isaac N.; Kim, Dong W.; Christens-Barry, William A.; Weinberg, Irving N.; Gatewood, Olga B.; Brody, William R.

1993-01-01

The increasing demand on mammographic screening for early breast cancer detection, and the subtlety of early breast cancer signs on mammograms, suggest an automated image processing system that can serve as a diagnostic aid in radiology clinics. We present a fully automated algorithm for detecting clusters of microcalcifications that are the most common signs of early, potentially curable breast cancer. By using the contour map of the mammogram, the algorithm circumvents some of the difficulties encountered with standard image processing methods. The clinical implementation of an automated instrument based on this algorithm is also discussed.

Evaluation of a New Ensemble Learning Framework for Mass Classification in Mammograms.

PubMed

Rahmani Seryasat, Omid; Haddadnia, Javad

2018-06-01

Mammography is the most common screening method for diagnosis of breast cancer. In this study, a computer-aided system for diagnosis of benignity and malignity of the masses was implemented in mammogram images. In the computer aided diagnosis system, we first reduce the noise in the mammograms using an effective noise removal technique. After the noise removal, the mass in the region of interest must be segmented and this segmentation is done using a deformable model. After the mass segmentation, a number of features are extracted from it. These features include: features of the mass shape and border, tissue properties, and the fractal dimension. After extracting a large number of features, a proper subset must be chosen from among them. In this study, we make use of a new method on the basis of a genetic algorithm for selection of a proper set of features. After determining the proper features, a classifier is trained. To classify the samples, a new architecture for combination of the classifiers is proposed. In this architecture, easy and difficult samples are identified and trained using different classifiers. Finally, the proposed mass diagnosis system was also tested on mini-Mammographic Image Analysis Society and digital database for screening mammography databases. The obtained results indicate that the proposed system can compete with the state-of-the-art methods in terms of accuracy. Copyright © 2017 Elsevier Inc. All rights reserved.

Medical Advocacy and Supportive Environments for African-Americans Following Abnormal Mammograms.

PubMed

Molina, Yamile; Hempstead, Bridgette H; Thompson-Dodd, Jacci; Weatherby, Shauna Rae; Dunbar, Claire; Hohl, Sarah D; Malen, Rachel C; Ceballos, Rachel M

2015-09-01

African-American women experience disproportionately adverse outcomes relative to non-Latina White women after an abnormal mammogram result. Research has suggested medical advocacy and staff support may improve outcomes among this population. The purpose of the study was to understand reasons African-American women believe medical advocacy to be important and examine if and how staff can encourage and be supportive of medical advocacy. A convenience-based sample of 30-74-year-old women who self-identified as African-American/Black/of African descent and who had received an abnormal mammogram result was recruited from community-based organizations, mobile mammography services, and the local department of health. This qualitative study included semi-structured interviews. Patients perceived medical advocacy to be particularly important for African-Americans, given mistrust and discrimination present in medical settings and their own familiarity with their bodies and symptoms. Respondents emphasized that staff can encourage medical advocacy through offering information in general in a clear, informative, and empathic style. Cultural competency interventions that train staff how to foster medical advocacy may be a strategy to improve racial disparities following an abnormal mammogram.

Utilization of screening mammography in New Hampshire: a population-based assessment.

PubMed

Carney, Patricia A; Goodrich, Martha E; Mackenzie, Todd; Weiss, Julia E; Poplack, Steven P; Wells, Wendy S; Titus-Ernstoff, Linda

2005-10-15

The objective of screening mammography is to identify breast carcinoma early, which requires routine screening. Although self-report data indicate that screening utilization is high, the results of this population-based assessment indicated that utilization is lower than reported previously. The authors compared New Hampshire population data from the 2000 Census with clinical encounter data for the corresponding time obtained from the New Hampshire Mammography Network, a mammography registry that captures approximately 90% of the mammograms performed in participating New Hampshire facilities. The results showed that approximately 36% of New Hampshire women either never had a mammogram or had not had a mammogram in > 27 months (irregular screenees), and older women (80 yrs and older) were less likely to be screened (79% unscreened/underscreened) compared with younger women (ages 40-69 yrs; 28-32% unscreened/underscreened). Of the screened women, 44% were adhering to an interval of 14 months, and 21% were adhering within 15 months and 26 months. The remaining 35% of the women had 1 or 2 mammograms and did not return within 27 months. Routine mammography screening may be occurring less often than believed when survey data alone are used. An important, compelling concern is the reason women had one or two mammograms only and then did not return for additional screening. This area deserves additional research. Copyright 2005 American Cancer Society

Predictors and patterns of fear of cancer recurrence in breast cancer survivors.

PubMed

McGinty, Heather L; Small, Brent J; Laronga, Christine; Jacobsen, Paul B

2016-01-01

This prospective, longitudinal study examined fear of cancer recurrence (FCR) among breast cancer survivors having mammograms. FCR was hypothesized to increase prior to the mammogram, decrease from immediately pre- to immediately post-mammogram with negative results, and then increase following the mammogram. The possible presence of different trajectories of FCR was also examined. Based on the cognitive-behavioral model (CBM) of health anxiety, greater perceived risk of recurrence, worse perceived consequences of recurrence, lower treatment efficacy beliefs, lower coping self-efficacy, and more engagement in reassurance-seeking behaviors were hypothesized to be associated with greater FCR across all study time points. Following treatment completion for Stage 0-IIIA breast cancer, 161 women completed the following measures: perceived risk and perceived consequences of recurrence, treatment efficacy beliefs, coping self-efficacy, and reassurance-seeking behaviors. Participants reported FCR at 3 time points before and 3 after the mammogram. Growth curve analysis was used to test for changes in FCR over time and growth mixture modeling examined different trajectories in FCR and the ability of the CBM to predict these trajectories. As hypothesized, FCR significantly changed over time; scores increased prior to the mammogram, decreased immediately following receipt of negative mammography results, and increased during the month following the mammogram. Growth mixture models revealed 2 classes, higher-FCR and lower-FCR, which were predicted by the CBM. These study findings support the use of the CBM in predicting which cancer survivors experience greater FCR and indicates that CBM-driven interventions may prove beneficial for reducing distressing FCR. (c) 2015 APA, all rights reserved).

Towards an in-plane methodology to track breast lesions using mammograms and patient-specific finite-element simulations

NASA Astrophysics Data System (ADS)

Lapuebla-Ferri, Andrés; Cegoñino-Banzo, José; Jiménez-Mocholí, Antonio-José; Pérez del Palomar, Amaya

2017-11-01

In breast cancer screening or diagnosis, it is usual to combine different images in order to locate a lesion as accurately as possible. These images are generated using a single or several imaging techniques. As x-ray-based mammography is widely used, a breast lesion is located in the same plane of the image (mammogram), but tracking it across mammograms corresponding to different views is a challenging task for medical physicians. Accordingly, simulation tools and methodologies that use patient-specific numerical models can facilitate the task of fusing information from different images. Additionally, these tools need to be as straightforward as possible to facilitate their translation to the clinical area. This paper presents a patient-specific, finite-element-based and semi-automated simulation methodology to track breast lesions across mammograms. A realistic three-dimensional computer model of a patient’s breast was generated from magnetic resonance imaging to simulate mammographic compressions in cranio-caudal (CC, head-to-toe) and medio-lateral oblique (MLO, shoulder-to-opposite hip) directions. For each compression being simulated, a virtual mammogram was obtained and posteriorly superimposed to the corresponding real mammogram, by sharing the nipple as a common feature. Two-dimensional rigid-body transformations were applied, and the error distance measured between the centroids of the tumors previously located on each image was 3.84 mm and 2.41 mm for CC and MLO compression, respectively. Considering that the scope of this work is to conceive a methodology translatable to clinical practice, the results indicate that it could be helpful in supporting the tracking of breast lesions.

Performance characteristics of digital vs film screen mammography in community practice.

PubMed

Dabbous, Firas; Dolecek, Therese A; Friedewald, Sarah M; Tossas-Milligan, Katherine Y; Macarol, Tere; Summerfelt, Wm Thomas; Rauscher, Garth H

2018-05-01

We compared the performance characteristics of 297 629 full field digital (FFDM) and 416 791 screen film mammograms (SFM). Sensitivity increased with age, decreased with breast density, and was lower for more aggressive and lobular tumors. While sensitivity did not differ significantly by modality, specificity was generally 1%-2% points higher for FFDM than for SFM across age and breast density categories. The lower recall rate for FFDM vs SFM in our study may partially explain performance differences by modality. In this large health care organization, modest gains in performance were achieved with the introduction of FFDM as a replacement for SFM. © 2017 Wiley Periodicals, Inc.

Automated detection of microcalcification clusters in mammograms

NASA Astrophysics Data System (ADS)

Karale, Vikrant A.; Mukhopadhyay, Sudipta; Singh, Tulika; Khandelwal, Niranjan; Sadhu, Anup

2017-03-01

Mammography is the most efficient modality for detection of breast cancer at early stage. Microcalcifications are tiny bright spots in mammograms and can often get missed by the radiologist during diagnosis. The presence of microcalcification clusters in mammograms can act as an early sign of breast cancer. This paper presents a completely automated computer-aided detection (CAD) system for detection of microcalcification clusters in mammograms. Unsharp masking is used as a preprocessing step which enhances the contrast between microcalcifications and the background. The preprocessed image is thresholded and various shape and intensity based features are extracted. Support vector machine (SVM) classifier is used to reduce the false positives while preserving the true microcalcification clusters. The proposed technique is applied on two different databases i.e DDSM and private database. The proposed technique shows good sensitivity with moderate false positives (FPs) per image on both databases.

Three-Class Mammogram Classification Based on Descriptive CNN Features

PubMed Central

Zhang, Qianni; Jadoon, Adeel

2017-01-01

In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE). In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT), while in the second method discrete curvelet transform (DCT) is used. In both methods, dense scale invariant feature (DSIFT) for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN). Softmax layer and support vector machine (SVM) layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques. PMID:28191461

Three-Class Mammogram Classification Based on Descriptive CNN Features.

PubMed

Jadoon, M Mohsin; Zhang, Qianni; Haq, Ihsan Ul; Butt, Sharjeel; Jadoon, Adeel

2017-01-01

In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE). In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT), while in the second method discrete curvelet transform (DCT) is used. In both methods, dense scale invariant feature (DSIFT) for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN). Softmax layer and support vector machine (SVM) layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques.

MPGD for breast cancer prevention: a high resolution and low dose radiation medical imaging

NASA Astrophysics Data System (ADS)

Gutierrez, R. M.; Cerquera, E. A.; Mañana, G.

2012-07-01

Early detection of small calcifications in mammograms is considered the best preventive tool of breast cancer. However, existing digital mammography with relatively low radiation skin exposure has limited accessibility and insufficient spatial resolution for small calcification detection. Micro Pattern Gaseous Detectors (MPGD) and associated technologies, increasingly provide new information useful to generate images of microscopic structures and make more accessible cutting edge technology for medical imaging and many other applications. In this work we foresee and develop an application for the new information provided by a MPGD camera in the form of highly controlled images with high dynamical resolution. We present a new Super Detail Image (S-DI) that efficiently profits of this new information provided by the MPGD camera to obtain very high spatial resolution images. Therefore, the method presented in this work shows that the MPGD camera with SD-I, can produce mammograms with the necessary spatial resolution to detect microcalcifications. It would substantially increase efficiency and accessibility of screening mammography to highly improve breast cancer prevention.

Multiscale wavelet representations for mammographic feature analysis

NASA Astrophysics Data System (ADS)

Laine, Andrew F.; Song, Shuwu

1992-12-01

This paper introduces a novel approach for accomplishing mammographic feature analysis through multiresolution representations. We show that efficient (nonredundant) representations may be identified from digital mammography and used to enhance specific mammographic features within a continuum of scale space. The multiresolution decomposition of wavelet transforms provides a natural hierarchy in which to embed an interactive paradigm for accomplishing scale space feature analysis. Choosing wavelets (or analyzing functions) that are simultaneously localized in both space and frequency, results in a powerful methodology for image analysis. Multiresolution and orientation selectivity, known biological mechanisms in primate vision, are ingrained in wavelet representations and inspire the techniques presented in this paper. Our approach includes local analysis of complete multiscale representations. Mammograms are reconstructed from wavelet coefficients, enhanced by linear, exponential and constant weight functions localized in scale space. By improving the visualization of breast pathology we can improve the changes of early detection of breast cancers (improve quality) while requiring less time to evaluate mammograms for most patients (lower costs).

A Standard Mammography Unit - Standard 3D Ultrasound Probe Fusion Prototype: First Results.

PubMed

Schulz-Wendtland, Rüdiger; Jud, Sebastian M; Fasching, Peter A; Hartmann, Arndt; Radicke, Marcus; Rauh, Claudia; Uder, Michael; Wunderle, Marius; Gass, Paul; Langemann, Hanna; Beckmann, Matthias W; Emons, Julius

2017-06-01

The combination of different imaging modalities through the use of fusion devices promises significant diagnostic improvement for breast pathology. The aim of this study was to evaluate image quality and clinical feasibility of a prototype fusion device (fusion prototype) constructed from a standard tomosynthesis mammography unit and a standard 3D ultrasound probe using a new method of breast compression. Imaging was performed on 5 mastectomy specimens from patients with confirmed DCIS or invasive carcinoma (BI-RADS ™ 6). For the preclinical fusion prototype an ABVS system ultrasound probe from an Acuson S2000 was integrated into a MAMMOMAT Inspiration (both Siemens Healthcare Ltd) and, with the aid of a newly developed compression plate, digital mammogram and automated 3D ultrasound images were obtained. The quality of digital mammogram images produced by the fusion prototype was comparable to those produced using conventional compression. The newly developed compression plate did not influence the applied x-ray dose. The method was not more labour intensive or time-consuming than conventional mammography. From the technical perspective, fusion of the two modalities was achievable. In this study, using only a few mastectomy specimens, the fusion of an automated 3D ultrasound machine with a standard mammography unit delivered images of comparable quality to conventional mammography. The device allows simultaneous ultrasound - the second important imaging modality in complementary breast diagnostics - without increasing examination time or requiring additional staff.

Analysis of percent density estimates from digital breast tomosynthesis projection images

NASA Astrophysics Data System (ADS)

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

2007-03-01

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

PubMed Central

Schulz-Wendtland, Rüdiger; Jud, Sebastian M.; Fasching, Peter A.; Hartmann, Arndt; Radicke, Marcus; Rauh, Claudia; Uder, Michael; Wunderle, Marius; Gass, Paul; Langemann, Hanna; Beckmann, Matthias W.; Emons, Julius

2017-01-01

Aim The combination of different imaging modalities through the use of fusion devices promises significant diagnostic improvement for breast pathology. The aim of this study was to evaluate image quality and clinical feasibility of a prototype fusion device (fusion prototype) constructed from a standard tomosynthesis mammography unit and a standard 3D ultrasound probe using a new method of breast compression. Materials and Methods Imaging was performed on 5 mastectomy specimens from patients with confirmed DCIS or invasive carcinoma (BI-RADS ™ 6). For the preclinical fusion prototype an ABVS system ultrasound probe from an Acuson S2000 was integrated into a MAMMOMAT Inspiration (both Siemens Healthcare Ltd) and, with the aid of a newly developed compression plate, digital mammogram and automated 3D ultrasound images were obtained. Results The quality of digital mammogram images produced by the fusion prototype was comparable to those produced using conventional compression. The newly developed compression plate did not influence the applied x-ray dose. The method was not more labour intensive or time-consuming than conventional mammography. From the technical perspective, fusion of the two modalities was achievable. Conclusion In this study, using only a few mastectomy specimens, the fusion of an automated 3D ultrasound machine with a standard mammography unit delivered images of comparable quality to conventional mammography. The device allows simultaneous ultrasound – the second important imaging modality in complementary breast diagnostics – without increasing examination time or requiring additional staff. PMID:28713173

Breast cancer risk assessment and diagnosis model using fuzzy support vector machine based expert system

NASA Astrophysics Data System (ADS)

Dheeba, J.; Jaya, T.; Singh, N. Albert

2017-09-01

Classification of cancerous masses is a challenging task in many computerised detection systems. Cancerous masses are difficult to detect because these masses are obscured and subtle in mammograms. This paper investigates an intelligent classifier - fuzzy support vector machine (FSVM) applied to classify the tissues containing masses on mammograms for breast cancer diagnosis. The algorithm utilises texture features extracted using Laws texture energy measures and a FSVM to classify the suspicious masses. The new FSVM treats every feature as both normal and abnormal samples, but with different membership. By this way, the new FSVM have more generalisation ability to classify the masses in mammograms. The classifier analysed 219 clinical mammograms collected from breast cancer screening laboratory. The tests made on the real clinical mammograms shows that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and Laws texture features, the area under the Receiver operating characteristic curve reached .95, which corresponds to a sensitivity of 93.27% with a specificity of 87.17%. The results suggest that detecting masses using FSVM contribute to computer-aided detection of breast cancer and as a decision support system for radiologists.

Getting started with protocol for quality assurance of digital mammography in the clinical centre of Montenegro.

PubMed

Ivanovic, S; Bosmans, H; Mijovic, S

2015-07-01

The purpose of this work is (i) to work out a test procedure for quality assurance (QA) in digital mammography with newly released test equipment, including the MagicMax mam multimeter (IBA, Germany) and the anthropomorphic tissue equivalent phantom Mammo AT (IBA, Germany), and (ii) to determine whether a first digital computer radiography (CR) system in Montenegro meets the current European standards. Tested parameters were tube output (µGy mAs(-1)) and output rate (mGy s(-1)), reproducibility and accuracy of tube voltage, half value layer, reproducibility and accuracy of the AEC system, exposure control steps, image receptor's response function, image quality and printer stability test. The evaluated dosimetric quantity is the average glandular dose (AGD) as evaluated from PMMA slabs simulating breast tissue. The main findings are that QA can be organised in Montenegro. (1) All measured parameters are within the range described in European protocols except the tube voltage which deviated more than ± 1 kV. The automatic determination of the HVL was satisfactorily. AGD ranged from 0.66 to 7.02 mGy for PMMA thicknesses from 20 to 70 mm, and is in accordance with literature data. (2) The image quality score as obtained with the anthropomorphic tissue equivalent phantom Mammo AT for the CR system was similar to findings on the authors' conventional screen-film mammography. (3) In clinical practice the mammograms are printed. The CR reader produces images with a pixel size of 43.75 µm, which is compatible with the laser printer (39 µm laser spot spacing). The image processing algorithm embedded in the reader successfully processes mammograms with desirable image brightness and contrast in the printed image. The authors conclude that this first digital mammography system seems a good candidate for breast cancer screening applications. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features.

PubMed

Shi, Bibo; Grimm, Lars J; Mazurowski, Maciej A; Baker, Jay A; Marks, Jeffrey R; King, Lorraine M; Maley, Carlo C; Hwang, E Shelley; Lo, Joseph Y

2018-03-01

The aim of this study was to determine whether deep features extracted from digital mammograms using a pretrained deep convolutional neural network are prognostic of occult invasive disease for patients with ductal carcinoma in situ (DCIS) on core needle biopsy. In this retrospective study, digital mammographic magnification views were collected for 99 subjects with DCIS at biopsy, 25 of which were subsequently upstaged to invasive cancer. A deep convolutional neural network model that was pretrained on nonmedical images (eg, animals, plants, instruments) was used as the feature extractor. Through a statistical pooling strategy, deep features were extracted at different levels of convolutional layers from the lesion areas, without sacrificing the original resolution or distorting the underlying topology. A multivariate classifier was then trained to predict which tumors contain occult invasive disease. This was compared with the performance of traditional "handcrafted" computer vision (CV) features previously developed specifically to assess mammographic calcifications. The generalization performance was assessed using Monte Carlo cross-validation and receiver operating characteristic curve analysis. Deep features were able to distinguish DCIS with occult invasion from pure DCIS, with an area under the receiver operating characteristic curve of 0.70 (95% confidence interval, 0.68-0.73). This performance was comparable with the handcrafted CV features (area under the curve = 0.68; 95% confidence interval, 0.66-0.71) that were designed with prior domain knowledge. Despite being pretrained on only nonmedical images, the deep features extracted from digital mammograms demonstrated comparable performance with handcrafted CV features for the challenging task of predicting DCIS upstaging. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Semiautomatic estimation of breast density with DM-Scan software.

PubMed

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

2014-01-01

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

The Minnesota Green Tea Trial (MGTT), a randomized controlled trial of the efficacy of green tea extract on biomarkers of breast cancer risk: Study rationale, design, methods, and participant characteristics

PubMed Central

Samavat, Hamed; Dostal, Allison M.; Wang, Renwei; Bedell, Sarah; Emory, Tim H.; Ursin, Giske; Torkelson, Carolyn J.; Gross, Myron D.; Le, Chap T.; Yu, Mimi C.; Yang, Chung S.; Yee, Douglas; Wu, Anna H.; Yuan, Jian-Min; Kurzer, Mindy S.

2015-01-01

Purpose The Minnesota Green Tea Trial (MGTT) was a randomized, placebo-controlled, double-blinded trial investigating the effect of daily green tea extract consumption for 12 months on biomarkers of breast cancer risk. Methods Participants were healthy postmenopausal women at high risk of breast cancer due to dense breast tissue with differing catechol-O-methyltransferase (COMT) genotypes. The intervention was a green tea catechin extract containing 843.0 ± 44.0 mg/day epigallocatechin gallate or placebo capsules for one year. Annual digital screening mammograms were obtained at baseline and month 12, and fasting blood and 24-hour urine samples were provided at baseline, months 6, and 12. Primary endpoints included changes in percent mammographic density, circulating endogenous sex hormones and insulin-like growth factor axis proteins; secondary endpoints were changes in urinary estrogens and estrogen metabolites and circulating F2-isoprostanes, a biomarker of oxidative stress. Results The MGTT screened more than 100,000 mammograms and randomized 1075 participants based on treatment (green tea extract vs. placebo), stratified by COMT genotype activity (high COMT vs. low/intermediate COMT genotype activity). 937 women successfully completed the study and 138 dropped out (overall dropout rate= 12.8%). Conclusions In this paper we report the rationale, design, recruitment, participant characteristics, and methods for biomarker and statistical analyses. PMID:26206423

The Immediate Impact of the 2009 USPSTF Screening Guideline Change on Physician Recommendation of a Screening Mammogram: Findings from a National Ambulatory and Medical Care Survey-Based Study.

PubMed

Rajan, Suja S; Suryavanshi, Manasi S; Karanth, Siddharth; Lairson, David R

2017-04-01

Regular screening is considered the most effective method to reduce the mortality and morbidity associated with breast cancer. Nevertheless, contradictory evidence about screening mammograms has led to periodic changes and considerable variations among different screening guidelines. This study is the first to examine the immediate impact of the 2009 US Preventive Services Task Force (USPSTF) guideline modification on physician recommendation of mammograms. The study included visits by women aged 40 years and older without prior breast cancer from the National Ambulatory and Medical Care Survey 2008-2010. Bivariate and multiple logistic regressions were used to determine the factors associated with mammography recommendation. Approximately 29,395 visits were included and mammography was recommended during 1350 visits; 50-64-year-old women had 72% higher odds, and 65-74-year-old women had twice the odds of getting a mammogram recommendation compared with 40-49-year-old women in 2009. However, there was no difference in recommendation by age groups in 2008 and 2010. Obstetricians and gynecologists did not modify their recommendation behavior in 2009, unlike all other specialists who reduced their recommendation for 40-49-year-old women in 2009. Other characteristics associated with mammogram recommendations were certain patient comorbidities, physician specialty and primary care physician status, health maintenance organization status of the clinic, and certain visit characteristics. This study demonstrated a temporary effect of the USPSTF screening guideline change on mammogram recommendation. However, in light of conflicting recommendations by different guidelines, the physicians erred toward the more rigorous guidelines and did not permanently reduce their mammogram recommendation for women aged 40-49 years.

Community Partnerships, Food Pantries, and an Evidence-Based Intervention to Increase Mammography among Rural Women

ERIC Educational Resources Information Center

Bencivenga, Marcyann; DeRubis, Susan; Leach, Patricia; Lotito, Lisa; Shoemaker, Charles; Lengerich, Eugene J.

2008-01-01

Context: Multiple national agencies and organizations recommend that women age 40 years and older have an annual screening mammogram. Women who are poor, less educated, lack a usual source of care, and reside in rural Appalachia are less likely to have had a recent mammogram. Purpose: To increase use of mammography among a rural Appalachian…

Initial Image Quality and Clinical Experience with New CR Digital Mammography System: A Phantom and Clinical Study

NASA Astrophysics Data System (ADS)

Gaona, Enrique; Alfonso, Beatriz Y. Álvarez; Castellanos, Gustavo Casian; Enríquez, Jesús Gabriel Franco

2008-08-01

The goal of the study was to evaluate the first CR digital mammography system (® Konica-Minolta) in Mexico in clinical routine for cancer detection in a screening population and to determine if high resolution CR digital imaging is equivalent to state-of-the-art screen-film imaging. The mammograms were evaluated by two observers with cytological or histological confirmation for BIRADS 3, 4 and 5. Contrast, exposure and artifacts of the images were evaluated. Different details like skin, retromamillary space and parenchymal structures were judged. The detectability of microcalcifications and lesions were compared and correlated to histology. The difference in sensitivity of CR Mammography (CRM) and Screen Film Mammography (SFM) was not statistically significant. However, CRM had a significantly lower recall rate, and the lesion detection was equal or superior to conventional images. There is no significant difference in the number of microcalcifications and highly suspicious calcifications were equally detected on both film-screen and digital images. Different anatomical regions were better detectable in digital than in conventional mammography.

Initial Image Quality and Clinical Experience with New CR Digital Mammography System: A Phantom and Clinical Study

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

Gaona, Enrique; Enriquez, Jesus Gabriel Franco; Alfonso, Beatriz Y. Alvarez

2008-08-11

The goal of the study was to evaluate the first CR digital mammography system ( registered Konica-Minolta) in Mexico in clinical routine for cancer detection in a screening population and to determine if high resolution CR digital imaging is equivalent to state-of-the-art screen-film imaging. The mammograms were evaluated by two observers with cytological or histological confirmation for BIRADS 3, 4 and 5. Contrast, exposure and artifacts of the images were evaluated. Different details like skin, retromamillary space and parenchymal structures were judged. The detectability of microcalcifications and lesions were compared and correlated to histology. The difference in sensitivity of CRmore » Mammography (CRM) and Screen Film Mammography (SFM) was not statistically significant. However, CRM had a significantly lower recall rate, and the lesion detection was equal or superior to conventional images. There is no significant difference in the number of microcalcifications and highly suspicious calcifications were equally detected on both film-screen and digital images. Different anatomical regions were better detectable in digital than in conventional mammography.« less

The influence of software filtering in digital mammography image quality

NASA Astrophysics Data System (ADS)

Michail, C.; Spyropoulou, V.; Kalyvas, N.; Valais, I.; Dimitropoulos, N.; Fountos, G.; Kandarakis, I.; Panayiotakis, G.

2009-05-01

Breast cancer is one of the most frequently diagnosed cancers among women. Several techniques have been developed to help in the early detection of breast cancer such as conventional and digital x-ray mammography, positron and single-photon emission mammography, etc. A key advantage in digital mammography is that images can be manipulated as simple computer image files. Thus non-dedicated commercially available image manipulation software can be employed to process and store the images. The image processing tools of the Photoshop (CS 2) software usually incorporate digital filters which may be used to reduce image noise, enhance contrast and increase spatial resolution. However, improving an image quality parameter may result in degradation of another. The aim of this work was to investigate the influence of three sharpening filters, named hereafter sharpen, sharpen more and sharpen edges on image resolution and noise. Image resolution was assessed by means of the Modulation Transfer Function (MTF).In conclusion it was found that the correct use of commercial non-dedicated software on digital mammograms may improve some aspects of image quality.

A network-based training environment: a medical image processing paradigm.

PubMed

Costaridou, L; Panayiotakis, G; Sakellaropoulos, P; Cavouras, D; Dimopoulos, J

1998-01-01

The capability of interactive multimedia and Internet technologies is investigated with respect to the implementation of a distance learning environment. The system is built according to a client-server architecture, based on the Internet infrastructure, composed of server nodes conceptually modelled as WWW sites. Sites are implemented by customization of available components. The environment integrates network-delivered interactive multimedia courses, network-based tutoring, SIG support, information databases of professional interest, as well as course and tutoring management. This capability has been demonstrated by means of an implemented system, validated with digital image processing content, specifically image enhancement. Image enhancement methods are theoretically described and applied to mammograms. Emphasis is given to the interactive presentation of the effects of algorithm parameters on images. The system end-user access depends on available bandwidth, so high-speed access can be achieved via LAN or local ISDN connections. Network based training offers new means of improved access and sharing of learning resources and expertise, as promising supplements in training.

Availability and accessibility of subsidized mammogram screening program in peninsular Malaysia: A preliminary study using travel impedance approach.

PubMed

Mahmud, Aidalina; Aljunid, Syed Mohamed

2018-01-01

Access to healthcare is essential in the pursuit of universal health coverage. Components of access are availability, accessibility (spatial and non-spatial), affordability and acceptability. Measuring spatial accessibility is common approach to evaluating access to health care. This study aimed to determine the availability and spatial accessibility of subsidised mammogram screening in Peninsular Malaysia. Availability was determined from the number and distribution of facilities. Spatial accessibility was determined using the travel impedance approach to represent the revealed access as opposed to potential access measured by other spatial measurement methods. The driving distance of return trips from the respondent's residence to the facilities was determined using a mapping application. The travel expenditure was estimated by multiplying the total travel distance by a standardised travel allowance rate, plus parking fees. Respondents in this study were 344 breast cancer patients who received treatment at 4 referral hospitals between 2015 and 2016. In terms of availability, there were at least 6 major entities which provided subsidised mammogram programs. Facilities with mammogram involved with these programs were located more densely in the central and west coast region of the Peninsula. The ratio of mammogram facility to the target population of women aged 40-74 years ranged between 1: 10,000 and 1:80,000. In terms of accessibility, of the 3.6% of the respondents had undergone mammogram screening, their mean travel distance was 53.4 km (SD = 34.5, range 8-112 km) and the mean travel expenditure was RM 38.97 (SD = 24.00, range RM7.60-78.40). Among those who did not go for mammogram screening, the estimated travel distance and expenditure had a skewed distribution with median travel distance of 22.0 km (IQR 12.0, 42.0, range 2.0-340.0) and the median travel cost of RM 17.40 (IQR 10.40, 30.00, range 3.40-240.00). Higher travel impedance was noted among those who lived in sub-urban and rural areas. In summary, availability of mammogram facilities was good in the central and west coast of the peninsula. The overall provider-to-population ratio was lower than recommended. Based on the travel impedance approach used, accessibility to subsidised mammogram screening among the respondents was good in urban areas but deprived in other areas. This study was a preliminary study with limitations. Nonetheless, the evidence suggests that actions have to be taken to improve the accessibility to opportunistic mammogram screening in Malaysia in pursuit of universal health coverage.

Impact of a two-city community cancer prevention intervention on African Americans.

PubMed Central

Blumenthal, Daniel S.; Fort, Jane G.; Ahmed, Nasar U.; Semenya, Kofi A.; Schreiber, George B.; Perry, Shelley; Guillory, Joyce

2005-01-01

We report the first multisite, multicomponent community intervention trial to focus on cancer prevention in African Americans. The project explored the potential of historically black medical schools to deliver health information to their local communities and used a community-based participatory research approach. The intervention consisted of culturally sensitive messages at appropriate educational levels delivered over an 18-month period and tested in predominantly black census tracts in Nashville, TN and Atlanta, GA. Chattanooga, TN and Decatur, GA served as comparison cities. Results were evaluated by pre- and postintervention random-digit dial telephone surveys. The intervention cities showed an increase in reported contact with or knowledge of the project. There was little or no effect on knowledge or attitudes in the intervention cities. Compared to Chattanooga, Nashville showed an increase in percentage of women receiving Pap smears. Compared to Decatur, Atlanta showed an increase in percentage of age-appropriate populations receiving digital rectal exams, colorectal cancer screenings and mammograms. The results of this community intervention trial demonstrated modest success and are encouraging for future efforts of longer duration. PMID:16334495

Mammographic compression in Asian women.

PubMed

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

2017-01-01

To investigate: (1) the variability of mammographic compression parameters amongst Asian women; and (2) the effects of reducing compression force on image quality and mean glandular dose (MGD) in Asian women based on phantom study. We retrospectively collected 15818 raw digital mammograms from 3772 Asian women aged 35-80 years who underwent screening or diagnostic mammography between Jan 2012 and Dec 2014 at our center. The mammograms were processed using a volumetric breast density (VBD) measurement software (Volpara) to assess compression force, compression pressure, compressed breast thickness (CBT), breast volume, VBD and MGD against breast contact area. The effects of reducing compression force on image quality and MGD were also evaluated based on measurement obtained from 105 Asian women, as well as using the RMI156 Mammographic Accreditation Phantom and polymethyl methacrylate (PMMA) slabs. Compression force, compression pressure, CBT, breast volume, VBD and MGD correlated significantly with breast contact area (p<0.0001). Compression parameters including compression force, compression pressure, CBT and breast contact area were widely variable between [relative standard deviation (RSD)≥21.0%] and within (p<0.0001) Asian women. The median compression force should be about 8.1 daN compared to the current 12.0 daN. Decreasing compression force from 12.0 daN to 9.0 daN increased CBT by 3.3±1.4 mm, MGD by 6.2-11.0%, and caused no significant effects on image quality (p>0.05). Force-standardized protocol led to widely variable compression parameters in Asian women. Based on phantom study, it is feasible to reduce compression force up to 32.5% with minimal effects on image quality and MGD.

Breast mass segmentation in mammograms combining fuzzy c-means and active contours

NASA Astrophysics Data System (ADS)

Hmida, Marwa; Hamrouni, Kamel; Solaiman, Basel; Boussetta, Sana

2018-04-01

Segmentation of breast masses in mammograms is a challenging issue due to the nature of mammography and the characteristics of masses. In fact, mammographic images are poor in contrast and breast masses have various shapes and densities with fuzzy and ill-defined borders. In this paper, we propose a method based on a modified Chan-Vese active contour model for mass segmentation in mammograms. We conduct the experiment on mass Regions of Interest (ROI) extracted from the MIAS database. The proposed method consists of mainly three stages: Firstly, the ROI is preprocessed to enhance the contrast. Next, two fuzzy membership maps are generated from the preprocessed ROI based on fuzzy C-Means algorithm. These fuzzy membership maps are finally used to modify the energy of the Chan-Vese model and to perform the final segmentation. Experimental results indicate that the proposed method yields good mass segmentation results.

A new breast cancer risk analysis approach using features extracted from multiple sub-regions on bilateral mammograms

NASA Astrophysics Data System (ADS)

Sun, Wenqing; Tseng, Tzu-Liang B.; Zheng, Bin; Zhang, Jianying; Qian, Wei

2015-03-01

A novel breast cancer risk analysis approach is proposed for enhancing performance of computerized breast cancer risk analysis using bilateral mammograms. Based on the intensity of breast area, five different sub-regions were acquired from one mammogram, and bilateral features were extracted from every sub-region. Our dataset includes 180 bilateral mammograms from 180 women who underwent routine screening examinations, all interpreted as negative and not recalled by the radiologists during the original screening procedures. A computerized breast cancer risk analysis scheme using four image processing modules, including sub-region segmentation, bilateral feature extraction, feature selection, and classification was designed to detect and compute image feature asymmetry between the left and right breasts imaged on the mammograms. The highest computed area under the curve (AUC) is 0.763 ± 0.021 when applying the multiple sub-region features to our testing dataset. The positive predictive value and the negative predictive value were 0.60 and 0.73, respectively. The study demonstrates that (1) features extracted from multiple sub-regions can improve the performance of our scheme compared to using features from whole breast area only; (2) a classifier using asymmetry bilateral features can effectively predict breast cancer risk; (3) incorporating texture and morphological features with density features can boost the classification accuracy.

Association between mammogram density and background parenchymal enhancement of breast MRI

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Do pre-trained deep learning models improve computer-aided classification of digital mammograms?

NASA Astrophysics Data System (ADS)

Aboutalib, Sarah S.; Mohamed, Aly A.; Zuley, Margarita L.; Berg, Wendie A.; Luo, Yahong; Wu, Shandong

2018-02-01

Digital mammography screening is an important exam for the early detection of breast cancer and reduction in mortality. False positives leading to high recall rates, however, results in unnecessary negative consequences to patients and health care systems. In order to better aid radiologists, computer-aided tools can be utilized to improve distinction between image classifications and thus potentially reduce false recalls. The emergence of deep learning has shown promising results in the area of biomedical imaging data analysis. This study aimed to investigate deep learning and transfer learning methods that can improve digital mammography classification performance. In particular, we evaluated the effect of pre-training deep learning models with other imaging datasets in order to boost classification performance on a digital mammography dataset. Two types of datasets were used for pre-training: (1) a digitized film mammography dataset, and (2) a very large non-medical imaging dataset. By using either of these datasets to pre-train the network initially, and then fine-tuning with the digital mammography dataset, we found an increase in overall classification performance in comparison to a model without pre-training, with the very large non-medical dataset performing the best in improving the classification accuracy.

Assessment of Mammography Experiences and Satisfaction among American Indian/Alaska Native Women

PubMed Central

Ndikum-Moffor, Florence M.; Braiuca, Stacy; Daley, Christine Makosky; Gajewski, Byron J.; Engelman, Kimberly K.

2013-01-01

BACKGROUND American Indian/Alaska Native (AI/AN) women have lower breast cancer (BCA) screening and 5-year survival rates than non-Hispanic Whites. Understanding reasons for low screening rates is important to combat later stage diagnoses. The purpose of this study was to assess mammography experiences and satisfaction among AI/AN women. METHODS Nine focus groups were held with rural (N=15) and urban (N=38) AI/AN women 40 years and older in Kansas and Kansas City, Missouri, living both near and far from Indian Health Service (IHS) and tribal facilities, to examine experiences and satisfaction with mammography. Transcripts were coded and themes identified using a community-based participatory research approach. FINDINGS Themes were classified under knowledge, communication, and awareness of breast cancer, barriers to mammography, mammogram facility size, impressions of mammogram technologist, motivations to getting a mammogram, and how to improve the mammogram experience. Participants had knowledge of prevention, but described cultural reasons for not discussing it and described better experiences in smaller facilities. Participants indicated having a mammogram technologist who was friendly, knowledgeable, respectful, competent, and explained the test was a determining factor in satisfaction. Other factors included family history, physician recommendation, and financial incentives. Barriers included transportation, cost, perceptions of prejudice, and time constraints. Participants on reservations or near IHS facilities preferred IHS over mainstream providers. Suggestions for improvement included caring technologists, better machines with less discomfort, and education. CONCLUSIONS Interventions to enhance the professionalism, empathy, and cultural awareness of mammogram technologists, reduce barriers, and provide positive expectations and incentives could improve satisfaction and compliance with screening mammography. PMID:24183414

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Medical radiologic technologist review: effects on a population-based breast cancer screening program.

PubMed

Tonita, J M; Hillis, J P; Lim, C H

1999-05-01

To evaluate the effects of medical radiologic technologist review of mammograms in a population-based breast cancer screening program. A technologist review pilot project was incorporated into the Regina, Saskatchewan, Canada, reading center. Technologists received special training in mammographic interpretation. They reviewed all 27,863 mammograms obtained at the center from July 1995 to September 1996 that were reviewed by a radiologist and selected cases for second blind reading by another radiologist. When the two radiologists' readings were in agreement, the report was sent. When the readings differed, a third opinion was obtained from the program's consulting radiologist. Changes in the number of mammograms interpreted as abnormal and the number of cancers detected were assessed. The technologist review was responsible for the detection of nine cancers missed at the first radiologist's interpretation. Technologists were very discriminating; only 391 cases (1.4%) were sent for double reading. The positive predictive value of screening did not change significantly (7.5% without review, 8.1% with review; P > .20). A substantial number of cancers were found with the technologist review. The number of mammograms interpreted as abnormal was reduced slightly. The technologist review proved to be a cost-effective alternative to double reading by two radiologists.

Analysis of framelets for breast cancer diagnosis.

PubMed

Thivya, K S; Sakthivel, P; Venkata Sai, P M

2016-01-01

Breast cancer is the second threatening tumor among the women. The effective way of reducing breast cancer is its early detection which helps to improve the diagnosing process. Digital mammography plays a significant role in mammogram screening at earlier stage of breast carcinoma. Even though, it is very difficult to find accurate abnormality in prevalent screening by radiologists. But the possibility of precise breast cancer screening is encouraged by predicting the accurate type of abnormality through Computer Aided Diagnosis (CAD) systems. The two most important indicators of breast malignancy are microcalcifications and masses. In this study, framelet transform, a multiresolutional analysis is investigated for the classification of the above mentioned two indicators. The statistical and co-occurrence features are extracted from the framelet decomposed mammograms with different resolution levels and support vector machine is employed for classification with k-fold cross validation. This system achieves 94.82% and 100% accuracy in normal/abnormal classification (stage I) and benign/malignant classification (stage II) of mass classification system and 98.57% and 100% for microcalcification system when using the MIAS database.

Availability and accessibility of subsidized mammogram screening program in peninsular Malaysia: A preliminary study using travel impedance approach

PubMed Central

Aljunid, Syed Mohamed

2018-01-01

Access to healthcare is essential in the pursuit of universal health coverage. Components of access are availability, accessibility (spatial and non-spatial), affordability and acceptability. Measuring spatial accessibility is common approach to evaluating access to health care. This study aimed to determine the availability and spatial accessibility of subsidised mammogram screening in Peninsular Malaysia. Availability was determined from the number and distribution of facilities. Spatial accessibility was determined using the travel impedance approach to represent the revealed access as opposed to potential access measured by other spatial measurement methods. The driving distance of return trips from the respondent’s residence to the facilities was determined using a mapping application. The travel expenditure was estimated by multiplying the total travel distance by a standardised travel allowance rate, plus parking fees. Respondents in this study were 344 breast cancer patients who received treatment at 4 referral hospitals between 2015 and 2016. In terms of availability, there were at least 6 major entities which provided subsidised mammogram programs. Facilities with mammogram involved with these programs were located more densely in the central and west coast region of the Peninsula. The ratio of mammogram facility to the target population of women aged 40–74 years ranged between 1: 10,000 and 1:80,000. In terms of accessibility, of the 3.6% of the respondents had undergone mammogram screening, their mean travel distance was 53.4 km (SD = 34.5, range 8–112 km) and the mean travel expenditure was RM 38.97 (SD = 24.00, range RM7.60–78.40). Among those who did not go for mammogram screening, the estimated travel distance and expenditure had a skewed distribution with median travel distance of 22.0 km (IQR 12.0, 42.0, range 2.0–340.0) and the median travel cost of RM 17.40 (IQR 10.40, 30.00, range 3.40–240.00). Higher travel impedance was noted among those who lived in sub-urban and rural areas. In summary, availability of mammogram facilities was good in the central and west coast of the peninsula. The overall provider-to-population ratio was lower than recommended. Based on the travel impedance approach used, accessibility to subsidised mammogram screening among the respondents was good in urban areas but deprived in other areas. This study was a preliminary study with limitations. Nonetheless, the evidence suggests that actions have to be taken to improve the accessibility to opportunistic mammogram screening in Malaysia in pursuit of universal health coverage. PMID:29389972

Automated detection scheme of architectural distortion in mammograms using adaptive Gabor filter

NASA Astrophysics Data System (ADS)

Yoshikawa, Ruriha; Teramoto, Atsushi; Matsubara, Tomoko; Fujita, Hiroshi

2013-03-01

Breast cancer is a serious health concern for all women. Computer-aided detection for mammography has been used for detecting mass and micro-calcification. However, there are challenges regarding the automated detection of the architectural distortion about the sensitivity. In this study, we propose a novel automated method for detecting architectural distortion. Our method consists of the analysis of the mammary gland structure, detection of the distorted region, and reduction of false positive results. We developed the adaptive Gabor filter for analyzing the mammary gland structure that decides filter parameters depending on the thickness of the gland structure. As for post-processing, healthy mammary glands that run from the nipple to the chest wall are eliminated by angle analysis. Moreover, background mammary glands are removed based on the intensity output image obtained from adaptive Gabor filter. The distorted region of the mammary gland is then detected as an initial candidate using a concentration index followed by binarization and labeling. False positives in the initial candidate are eliminated using 23 types of characteristic features and a support vector machine. In the experiments, we compared the automated detection results with interpretations by a radiologist using 50 cases (200 images) from the Digital Database of Screening Mammography (DDSM). As a result, true positive rate was 82.72%, and the number of false positive per image was 1.39. There results indicate that the proposed method may be useful for detecting architectural distortion in mammograms.

Computerized image analysis: estimation of breast density on mammograms

NASA Astrophysics Data System (ADS)

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

2000-06-01

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

The interplay of attention economics and computer-aided detection marks in screening mammography

NASA Astrophysics Data System (ADS)

Schwartz, Tayler M.; Sridharan, Radhika; Wei, Wei; Lukyanchenko, Olga; Geiser, William; Whitman, Gary J.; Haygood, Tamara Miner

2016-03-01

Introduction: According to attention economists, overabundant information leads to decreased attention for individual pieces of information. Computer-aided detection (CAD) alerts radiologists to findings potentially associated with breast cancer but is notorious for creating an abundance of false-positive marks. We suspected that increased CAD marks do not lengthen mammogram interpretation time, as radiologists will selectively disregard these marks when present in larger numbers. We explore the relevance of attention economics in mammography by examining how the number of CAD marks affects interpretation time. Methods: We performed a retrospective review of bilateral digital screening mammograms obtained between January 1, 2011 and February 28, 2014, using only weekend interpretations to decrease distractions and the likelihood of trainee participation. We stratified data according to reader and used ANOVA to assess the relationship between number of CAD marks and interpretation time. Results: Ten radiologists, with median experience after residency of 12.5 years (range 6 to 24,) interpreted 1849 mammograms. When accounting for number of images, Breast Imaging Reporting and Data System category, and breast density, increasing numbers of CAD marks was correlated with longer interpretation time only for the three radiologists with the fewest years of experience (median 7 years.) Conclusion: For the 7 most experienced readers, increasing CAD marks did not lengthen interpretation time. We surmise that as CAD marks increase, the attention given to individual marks decreases. Experienced radiologists may rapidly dismiss larger numbers of CAD marks as false-positive, having learned that devoting extra attention to such marks does not improve clinical detection.

Automated selection of BI-RADS lesion descriptors for reporting calcifications in mammograms

NASA Astrophysics Data System (ADS)

Paquerault, Sophie; Jiang, Yulei; Nishikawa, Robert M.; Schmidt, Robert A.; D'Orsi, Carl J.; Vyborny, Carl J.; Newstead, Gillian M.

2003-05-01

We are developing an automated computer technique to describe calcifications in mammograms according to the BI-RADS lexicon. We evaluated this technique by its agreement with radiologists' description of the same lesions. Three expert mammographers reviewed our database of 90 cases of digitized mammograms containing clustered microcalcifications and described the calcifications according to BI-RADS. In our study, the radiologists used only 4 of the 5 calcification distribution descriptors and 5 of the 14 calcification morphology descriptors contained in BI-RADS. Our computer technique was therefore designed specifically for these 4 calcification distribution descriptors and 5 calcification morphology descriptors. For calcification distribution, 4 linear discriminant analysis (LDA) classifiers were developed using 5 computer-extracted features to produce scores of how well each descriptor describes a cluster. Similarly, for calcification morphology, 5 LDAs were designed using 10 computer-extracted features. We trained the LDAs using only the BI-RADS data reported by the first radiologist and compared the computer output to the descriptor data reported by all 3 radiologists (for the first radiologist, the leave-one-out method was used). The computer output consisted of the best calcification distribution descriptor and the best 2 calcification morphology descriptors. The results of the comparison with the data from each radiologist, respectively, were: for calcification distribution, percent agreement, 74%, 66%, and 73%, kappa value, 0.44, 0.36, and 0.46; for calcification morphology, percent agreement, 83%, 77%, and 57%, kappa value, 0.78, 0.70, and 0.44. These results indicate that the proposed computer technique can select BI-RADS descriptors in good agreement with radiologists.

Applying a 2D based CAD scheme for detecting micro-calcification clusters using digital breast tomosynthesis images: an assessment

NASA Astrophysics Data System (ADS)

Park, Sang Cheol; Zheng, Bin; Wang, Xiao-Hui; Gur, David

2008-03-01

Digital breast tomosynthesis (DBT) has emerged as a promising imaging modality for screening mammography. However, visually detecting micro-calcification clusters depicted on DBT images is a difficult task. Computer-aided detection (CAD) schemes for detecting micro-calcification clusters depicted on mammograms can achieve high performance and the use of CAD results can assist radiologists in detecting subtle micro-calcification clusters. In this study, we compared the performance of an available 2D based CAD scheme with one that includes a new grouping and scoring method when applied to both projection and reconstructed DBT images. We selected a dataset involving 96 DBT examinations acquired on 45 women. Each DBT image set included 11 low dose projection images and a varying number of reconstructed image slices ranging from 18 to 87. In this dataset 20 true-positive micro-calcification clusters were visually detected on the projection images and 40 were visually detected on the reconstructed images, respectively. We first applied the CAD scheme that was previously developed in our laboratory to the DBT dataset. We then tested a new grouping method that defines an independent cluster by grouping the same cluster detected on different projection or reconstructed images. We then compared four scoring methods to assess the CAD performance. The maximum sensitivity level observed for the different grouping and scoring methods were 70% and 88% for the projection and reconstructed images with a maximum false-positive rate of 4.0 and 15.9 per examination, respectively. This preliminary study demonstrates that (1) among the maximum, the minimum or the average CAD generated scores, using the maximum score of the grouped cluster regions achieved the highest performance level, (2) the histogram based scoring method is reasonably effective in reducing false-positive detections on the projection images but the overall CAD sensitivity is lower due to lower signal-to-noise ratio, and (3) CAD achieved higher sensitivity and higher false-positive rate (per examination) on the reconstructed images. We concluded that without changing the detection threshold or performing pre-filtering to possibly increase detection sensitivity, current CAD schemes developed and optimized for 2D mammograms perform relatively poorly and need to be re-optimized using DBT datasets and new grouping and scoring methods need to be incorporated into the schemes if these are to be used on the DBT examinations.

Investigating the link between the radiological experience and the allocation of an 'equivocal finding'

NASA Astrophysics Data System (ADS)

Rawashdeh, Mohammad A.; Vidotti, Camila; Lee, Warwick; Lewis, Sarah J.; Mello-Thoms, Claudia; Reed, Warren M.; Tapia, Kriscia; Brennan, Patrick C.

2016-03-01

Rationale and Objectives: This study will investigate the link between radiologists' experience in reporting mammograms, their caseloads and the decision to give a classification of Royal Australian and New Zealand College of Radiologists (RANZCR) category `3' (indeterminate or equivocal finding). Methods: A test set of 60 mammograms comprising of 20 abnormal and 40 normal cases were shown to 92 radiologists. Each radiologist was asked to identify and localize abnormalities and provide a RANZCR assessment category. Details were obtained from each reader regarding their experience, qualifications and breast reading activities. `Equivocal fractions' were calculated by dividing the number of `equivocal findings' given by each radiologist in the abnormal and normal cases by the total number of cases analyzed: 20 and 40 respectively. The `equivocal fractions' for each of the groups (normal vs abnormal) were calculated and independently correlated with age, number of years since qualification as a radiologist, number of years reading mammograms, number of mammograms read per year, number of hours reading mammograms per week and number of mammograms read over lifetime (the number of years reading mammograms multiplied by the number of mammograms read per year). The non-parametric Spearman test was used. Results: Statistically negative correlations were noted between `equivocal fractions' for the following groups: • For abnormal cases: hours per week (r= -0.38 P= 0.0001) • For normal cases: total number of mammograms read per year (r= -0.29, P= 0.006); number of mammograms read over lifetime (r= -0.21, P= 0.049)); hours reading mammograms per week (r= - 0.20, P= 0.05). Conclusion: Radiologists with greater reading experience assign fewer RANZCR category 3 or equivocal classifications. The findings have implications for screening program efficacy and recall rates. This work is still in progress and further data will be presented at the conference.

Accuracy of Screening Mammography Interpretation by Characteristics of Radiologists

PubMed Central

Barlow, William E.; Chi, Chen; Carney, Patricia A.; Taplin, Stephen H.; D’Orsi, Carl; Cutter, Gary; Hendrick, R. Edward; Elmore, Joann G.

2011-01-01

Background Radiologists differ in their ability to interpret screening mammograms accurately. We investigated the relationship of radiologist characteristics to actual performance from 1996 to 2001. Methods Screening mammograms (n = 469 512) interpreted by 124 radiologists were linked to cancer outcome data. The radiologists completed a survey that included questions on demographics, malpractice concerns, years of experience interpreting mammograms, and the number of mammograms read annually. We used receiver operating characteristics (ROC) analysis to analyze variables associated with sensitivity, specificity, and the combination of the two, adjusting for patient variables that affect performance. All P values are two-sided. Results Within 1 year of the mammogram, 2402 breast cancers were identified. Relative to low annual interpretive volume (≤1000 mammograms), greater interpretive volume was associated with higher sensitivity (P = .001; odds ratio [OR] for moderate volume [1001–2000] = 1.68, 95% CI = 1.18 to 2.39; OR for high volume [>2000] = 1.89, 95% CI = 1.36 to 2.63). Specificity decreased with volume (OR for 1001–2000 = 0.65, 95% CI = 0.52 to 0.83; OR for more than 2000 = 0.76, 95% CI = 0.60 to 0.96), compared with 1000 or less (P = .002). Greater number of years of experience interpreting mammograms was associated with lower sensitivity (P = .001), but higher specificity (P = .003). ROC analysis using the ordinal BI-RADS interpretation showed an association between accuracy and both previous mammographic history (P = .012) and breast density (P<.001). No association was observed between accuracy and years interpreting mammograms (P = .34) or mammography volume (P = .94), after adjusting for variables that affect the threshold for calling a mammogram positive. Conclusions We found no evidence that greater volume or experience at interpreting mammograms is associated with better performance. However, they may affect sensitivity and specificity, possibly by determining the threshold for calling a mammogram positive. Increasing volume requirements is unlikely to improve overall mammography performance. PMID:15601640

TU-F-18C-09: Mammogram Surveillance Using Texture Analysis for Breast Cancer Patients After Radiation Therapy

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

Kuo, H; Tome, W; FOX, J

2014-06-15

Purpose: To study the feasibility of applying cancer risk model established from treated patients to predict the risk of recurrence on follow-up mammography after radiation therapy for both ipsilateral and contralateral breast. Methods: An extensive set of textural feature functions was applied to a set of 196 Mammograms from 50 patients. 56 Mammograms from 28 patients were used as training set, 44 mammograms from 22 patients were used as test set and the rest were used for prediction. Feature functions include Histogram, Gradient, Co-Occurrence Matrix, Run-Length Matrix and Wavelet Energy. An optimum subset of the feature functions was selected bymore » Fisher Coefficient (FO) or Mutual Information (MI) (up to top 10 features) or a method combined FO, MI and Principal Component (FMP) (up to top 30 features). One-Nearest Neighbor (1-NN), Linear Discriminant Analysis (LDA) and Nonlinear Discriminant Analysis (NDA) were utilized to build a risk model of breast cancer from the training set of mammograms at the time of diagnosis. The risk model was then used to predict the risk of recurrence from mammogram taken one year and three years after RT. Results: FPM with NDA has the best classification power in classifying the training set of the mammogram with lesions versus those without lesions. The model of FPM with NDA achieved a true positive (TP) rate of 82% compared to 45.5% of using FO with 1-NN. The best false positive (FP) rates were 0% and 3.6% in contra-lateral breast of 1-year and 3-years after RT, and 10.9% in ipsi-lateral breast of 3-years after RT. Conclusion: Texture analysis offers high dimension to differentiate breast tissue in mammogram. Using NDA to classify mammogram with lesion from mammogram without lesion, it can achieve rather high TP and low FP in the surveillance of mammogram for patient with conservative surgery combined RT.« less

DynAMITe: a prototype large area CMOS APS for breast cancer diagnosis using x-ray diffraction measurements

NASA Astrophysics Data System (ADS)

Konstantinidis, A.; Anaxagoras, T.; Esposito, M.; Allinson, N.; Speller, R.

2012-03-01

X-ray diffraction studies are used to identify specific materials. Several laboratory-based x-ray diffraction studies were made for breast cancer diagnosis. Ideally a large area, low noise, linear and wide dynamic range digital x-ray detector is required to perform x-ray diffraction measurements. Recently, digital detectors based on Complementary Metal-Oxide- Semiconductor (CMOS) Active Pixel Sensor (APS) technology have been used in x-ray diffraction studies. Two APS detectors, namely Vanilla and Large Area Sensor (LAS), were developed by the Multidimensional Integrated Intelligent Imaging (MI-3) consortium to cover a range of scientific applications including x-ray diffraction. The MI-3 Plus consortium developed a novel large area APS, named as Dynamically Adjustable Medical Imaging Technology (DynAMITe), to combine the key characteristics of Vanilla and LAS with a number of extra features. The active area (12.8 × 13.1 cm2) of DynaMITe offers the ability of angle dispersive x-ray diffraction (ADXRD). The current study demonstrates the feasibility of using DynaMITe for breast cancer diagnosis by identifying six breast-equivalent plastics. Further work will be done to optimize the system in order to perform ADXRD for identification of suspicious areas of breast tissue following a conventional mammogram taken with the same sensor.

Evaluation of clinical image processing algorithms used in digital mammography.

PubMed

Zanca, Federica; Jacobs, Jurgen; Van Ongeval, Chantal; Claus, Filip; Celis, Valerie; Geniets, Catherine; Provost, Veerle; Pauwels, Herman; Marchal, Guy; Bosmans, Hilde

2009-03-01

Screening is the only proven approach to reduce the mortality of breast cancer, but significant numbers of breast cancers remain undetected even when all quality assurance guidelines are implemented. With the increasing adoption of digital mammography systems, image processing may be a key factor in the imaging chain. Although to our knowledge statistically significant effects of manufacturer-recommended image processings have not been previously demonstrated, the subjective experience of our radiologists, that the apparent image quality can vary considerably between different algorithms, motivated this study. This article addresses the impact of five such algorithms on the detection of clusters of microcalcifications. A database of unprocessed (raw) images of 200 normal digital mammograms, acquired with the Siemens Novation DR, was collected retrospectively. Realistic simulated microcalcification clusters were inserted in half of the unprocessed images. All unprocessed images were subsequently processed with five manufacturer-recommended image processing algorithms (Agfa Musica 1, IMS Raffaello Mammo 1.2, Sectra Mamea AB Sigmoid, Siemens OPVIEW v2, and Siemens OPVIEW v1). Four breast imaging radiologists were asked to locate and score the clusters in each image on a five point rating scale. The free-response data were analyzed by the jackknife free-response receiver operating characteristic (JAFROC) method and, for comparison, also with the receiver operating characteristic (ROC) method. JAFROC analysis revealed highly significant differences between the image processings (F = 8.51, p < 0.0001), suggesting that image processing strongly impacts the detectability of clusters. Siemens OPVIEW2 and Siemens OPVIEW1 yielded the highest and lowest performances, respectively. ROC analysis of the data also revealed significant differences between the processing but at lower significance (F = 3.47, p = 0.0305) than JAFROC. Both statistical analysis methods revealed that the same six pairs of modalities were significantly different, but the JAFROC confidence intervals were about 32% smaller than ROC confidence intervals. This study shows that image processing has a significant impact on the detection of microcalcifications in digital mammograms. Objective measurements, such as described here, should be used by the manufacturers to select the optimal image processing algorithm.

National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium.

PubMed

Lehman, Constance D; Arao, Robert F; Sprague, Brian L; Lee, Janie M; Buist, Diana S M; Kerlikowske, Karla; Henderson, Louise M; Onega, Tracy; Tosteson, Anna N A; Rauscher, Garth H; Miglioretti, Diana L

2017-04-01

Purpose To establish performance benchmarks for modern screening digital mammography and assess performance trends over time in U.S. community practice. Materials and Methods This HIPAA-compliant, institutional review board-approved study measured the performance of digital screening mammography interpreted by 359 radiologists across 95 facilities in six Breast Cancer Surveillance Consortium (BCSC) registries. The study included 1 682 504 digital screening mammograms performed between 2007 and 2013 in 792 808 women. Performance measures were calculated according to the American College of Radiology Breast Imaging Reporting and Data System, 5th edition, and were compared with published benchmarks by the BCSC, the National Mammography Database, and performance recommendations by expert opinion. Benchmarks were derived from the distribution of performance metrics across radiologists and were presented as 50th (median), 10th, 25th, 75th, and 90th percentiles, with graphic presentations using smoothed curves. Results Mean screening performance measures were as follows: abnormal interpretation rate (AIR), 11.6 (95% confidence interval [CI]: 11.5, 11.6); cancers detected per 1000 screens, or cancer detection rate (CDR), 5.1 (95% CI: 5.0, 5.2); sensitivity, 86.9% (95% CI: 86.3%, 87.6%); specificity, 88.9% (95% CI: 88.8%, 88.9%); false-negative rate per 1000 screens, 0.8 (95% CI: 0.7, 0.8); positive predictive value (PPV) 1, 4.4% (95% CI: 4.3%, 4.5%); PPV2, 25.6% (95% CI: 25.1%, 26.1%); PPV3, 28.6% (95% CI: 28.0%, 29.3%); cancers stage 0 or 1, 76.9%; minimal cancers, 57.7%; and node-negative invasive cancers, 79.4%. Recommended CDRs were achieved by 92.1% of radiologists in community practice, and 97.1% achieved recommended ranges for sensitivity. Only 59.0% of radiologists achieved recommended AIRs, and only 63.0% achieved recommended levels of specificity. Conclusion The majority of radiologists in the BCSC surpass cancer detection recommendations for screening mammography; however, AIRs continue to be higher than the recommended rate for almost half of radiologists interpreting screening mammograms. © RSNA, 2016 Online supplemental material is available for this article.

National Performance Benchmarks for Modern Diagnostic Digital Mammography: Update from the Breast Cancer Surveillance Consortium.

PubMed

Sprague, Brian L; Arao, Robert F; Miglioretti, Diana L; Henderson, Louise M; Buist, Diana S M; Onega, Tracy; Rauscher, Garth H; Lee, Janie M; Tosteson, Anna N A; Kerlikowske, Karla; Lehman, Constance D

2017-04-01

Purpose To establish contemporary performance benchmarks for diagnostic digital mammography with use of recent data from the Breast Cancer Surveillance Consortium (BCSC). Materials and Methods Institutional review board approval was obtained for active or passive consenting processes or to obtain a waiver of consent to enroll participants, link data, and perform analyses. Data were obtained from six BCSC registries (418 radiologists, 92 radiology facilities). Mammogram indication and assessments were prospectively collected for women undergoing diagnostic digital mammography and linked with cancer diagnoses from state cancer registries. The study included 401 548 examinations conducted from 2007 to 2013 in 265 360 women. Results Overall diagnostic performance measures were as follows: cancer detection rate, 34.7 per 1000 (95% confidence interval [CI]: 34.1, 35.2); abnormal interpretation rate, 12.6% (95% CI: 12.5%, 12.7%); positive predictive value (PPV) of a biopsy recommendation (PPV 2 ), 27.5% (95% CI: 27.1%, 27.9%); PPV of biopsies performed (PPV 3 ), 30.4% (95% CI: 29.9%, 30.9%); false-negative rate, 4.8 per 1000 (95% CI: 4.6, 5.0); sensitivity, 87.8% (95% CI: 87.3%, 88.4%); and specificity, 90.5% (95% CI: 90.4%, 90.6%). Among cancers detected, 63.4% were stage 0 or 1 cancers, 45.6% were minimal cancers, the mean size of invasive cancers was 21.2 mm, and 69.6% of invasive cancers were node negative. Performance metrics varied widely across diagnostic indications, with cancer detection rate (64.5 per 1000) and abnormal interpretation rate (18.7%) highest for diagnostic mammograms obtained to evaluate a breast problem with a lump. Compared with performance during the screen-film mammography era, diagnostic digital performance showed increased abnormal interpretation and cancer detection rates and decreasing PPVs, with less than 70% of radiologists within acceptable ranges for PPV 2 and PPV 3 . Conclusion These performance measures can serve as national benchmarks that may help transform the marked variation in radiologists' diagnostic performance into targeted quality improvement efforts. © RSNA, 2017 Online supplemental material is available for this article.

Effectiveness and cost-effectiveness of double reading in digital mammography screening: A systematic review and meta-analysis.

PubMed

Posso, Margarita; Puig, Teresa; Carles, Misericòrdia; Rué, Montserrat; Canelo-Aybar, Carlos; Bonfill, Xavier

2017-11-01

Double reading is the strategy of choice for mammogram interpretation in screening programmes. It remains, however, unknown whether double reading is still the strategy of choice in the context of digital mammography. Our aim was to determine the effectiveness and cost-effectiveness of double reading versus single reading of digital mammograms in screening programmes. We performed a systematic review by searching the PubMed, Embase, and Cochrane Library databases up to April 2017. We used the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) tool and CHEERS (Consolidated Health Economic Evaluation Reporting Standards) checklist to assess the methodological quality of the diagnostic studies and economic evaluations, respectively. A proportion's meta-analysis approach, 95% Confidence Intervals (95% CI) and test of heterogeneity (P values) were used for pooled results. Costs are expressed US$ PPP (United States Dollar purchasing power parities). The PROSPERO ID of this Systematic Review's protocol is CRD42014013804. Of 1473 potentially relevant hits, four high-quality studies were included. The pooled cancer detection rate of double reading was 6.01 per 1000 screens (CI: 4.47‰-7.77‰), and it was 5.65 per 1000 screens (CI: 3.95‰-7.65‰) for single reading (P=0.76). The pooled proportion of false-positives of double reading was 47.03 per 1000 screens (CI: 39.13‰-55.62‰) and it was 40.60 per 1000 screens (CI: 38.58‰-42.67‰) for single reading (P=0.12). One study reported, for double reading, an ICER (Incremental Cost-Effectiveness Ratio) of 16,684 Euros (24,717 US$ PPP; 2015 value) per detected cancer. Single reading+CAD (computer-aided-detection) was cost-effective in Japan. The evidence of benefit for double reading compared to single reading for digital mammography interpretation is scarce. Double reading seems to increase operational costs, have a not significantly higher false-positive rate, and a similar cancer detection rate. Copyright © 2017 Elsevier B.V. All rights reserved.

A New Approach to Develop Computer-aided Diagnosis Scheme of Breast Mass Classification Using Deep Learning Technology

PubMed Central

Qiu, Yuchen; Yan, Shiju; Gundreddy, Rohith Reddy; Wang, Yunzhi; Cheng, Samuel; Liu, Hong; Zheng, Bin

2017-01-01

PURPOSE To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. METHODS An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. RESULTS The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. CONCLUSIONS This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process. PMID:28436410

A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.

PubMed

Qiu, Yuchen; Yan, Shiju; Gundreddy, Rohith Reddy; Wang, Yunzhi; Cheng, Samuel; Liu, Hong; Zheng, Bin

2017-01-01

To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process.

Image Based Biomarker of Breast Cancer Risk: Analysis of Risk Disparity Among Minority Populations

DTIC Science & Technology

2014-03-01

attenuation coefficient of calcium hydroxyapatite ; is a parameter controlling the contrast of MCs in synthetic images (0< ə); and is the linear...effect of acquisition parameters and quantum noise," Med Phys, 37, 1591-600 (2010). [13] M. J. Yaffe, J. M. Boone, N. Packard, O. Alonzo-Proulx, S. Y...the acquisition of individual mammograms, the use of linear transformations does not seem appropriate for mammogram registration. Non-linear

Using BIRADS categories in ROC experiments

NASA Astrophysics Data System (ADS)

Kallergi, Maria; Hersh, Marla R.; Thomas, Jerry A.

2002-04-01

This paper investigated the use of the Breast Imaging Reporting And Data System (BIRADS) Lexicon in ROC mammography experiments. Analysis was based on data from parallel ROC experiments performed at two Institutions with different readers and databases to compare film to digitized mammography. Seven readers participated in the studies and read approximately 200 cases each in two formats: film and digital or softcopy. Reporting was done using BIRADS categories 1 through 5. Training was done with a separate set of cases and included detailed review of the relationship between BIRADS and a standard ROC discrete 5-point rating scale. The results from both sites showed equivalency between film and softcopy mammography. Decisions using the BIRADS categories showed no unsampled ROC regions and no degenerate data. Fits yielded smooth ROC curves that correlated to clinical practice. In a qualitative evaluation, all observers indicated preference in using the BIRADS classes instead of a discrete or continuous rating scheme. Familiarity with the rating process seems to relieve some of the bias associated with the interpretation of digitized mammograms from computer monitors (softcopy reading). Our results suggested that BIRADS categories can be used in comparative ROC studies because they represent a scale familiar to the reader that can be followed consistently and they provide a rating approach that accounts for both positive and negative cases to be evaluated and categorized.

Analysis of a mammography teaching program based on an affordance design model.

PubMed

Luo, Ping; Eikman, Edward A; Kealy, William; Qian, Wei

2006-12-01

The wide use of computer technology in education, particularly in mammogram reading, asks for e-learning evaluation. The existing media comparative studies, learner attitude evaluations, and performance tests are problematic. Based on an affordance design model, this study examined an existing e-learning program on mammogram reading. The selection criteria include content relatedness, representativeness, e-learning orientation, image quality, program completeness, and accessibility. A case study was conducted to examine the affordance features, functions, and presentations of the selected software. Data collection and analysis methods include interviews, protocol-based document analysis, and usability tests and inspection. Also some statistics were calculated. The examination of PBE identified that this educational software designed and programmed some tools. The learner can use these tools in the process of optimizing displays, scanning images, comparing different projections, marking the region of interests, constructing a descriptive report, assessing one's learning outcomes, and comparing one's decisions with the experts' decisions. Further, PBE provides some resources for the learner to construct one's knowledge and skills, including a categorized image library, a term-searching function, and some teaching links. Besides, users found it easy to navigate and carry out tasks. The users also reacted positively toward PBE's navigation system, instructional aids, layout, pace and flow of information, graphics, and other presentation design. The software provides learners with some cognitive tools, supporting their perceptual problem-solving processes and extending their capabilities. Learners can internalize the mental models in mammogram reading through multiple perceptual triangulations, sensitization of related features, semantic description of mammogram findings, and expert-guided semantic report construction. The design of these cognitive tools and the software interface matches the findings and principles in human learning and instructional design. Working with PBE's case-based simulations and categorized gallery, learners can enrich and transfer their experience to their jobs.

Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms.

PubMed

Li, Hui; Giger, Maryellen L; Huynh, Benjamin Q; Antropova, Natalia O

2017-10-01

To evaluate deep learning in the assessment of breast cancer risk in which convolutional neural networks (CNNs) with transfer learning are used to extract parenchymal characteristics directly from full-field digital mammographic (FFDM) images instead of using computerized radiographic texture analysis (RTA), 456 clinical FFDM cases were included: a "high-risk" BRCA1/2 gene-mutation carriers dataset (53 cases), a "high-risk" unilateral cancer patients dataset (75 cases), and a "low-risk dataset" (328 cases). Deep learning was compared to the use of features from RTA, as well as to a combination of both in the task of distinguishing between high- and low-risk subjects. Similar classification performances were obtained using CNN [area under the curve [Formula: see text]; standard error [Formula: see text

Psychological distress, social withdrawal, and coping following receipt of an abnormal mammogram among different ethnicities: a mediation model.

PubMed

Molina, Yamile; Beresford, Shirley A A; Espinoza, Noah; Thompson, Beti

2014-09-01

To explore ethnic differences in psychological distress and social withdrawal after receiving an abnormal mammogram result and to assess if coping strategies mediate ethnic differences. Descriptive correlational. Two urban mobile mammography units and a rural community hospital in the state of Washington. 41 Latina and 41 non-Latina Caucasian (NLC) women who had received an abnormal mammogram result. Women completed standard sociodemographic questions, Impact of Event Scale-Revised, the social dimension of the Psychological Consequences Questionnaire, and the Brief COPE. Ethnicity, psychological distress, social withdrawal, and coping. Latinas experienced greater psychological distress and social withdrawal compared to NLC counterparts. Denial as a coping strategy mediated ethnic differences in psychological distress. Religious coping mediated ethnic differences in social withdrawal. Larger population-based studies are necessary to understand how ethnic differences in coping strategies can influence psychological outcomes. This is an important finding that warrants additional study among women who are and are not diagnosed with breast cancer following an abnormal mammogram. Nurses may be able to work with Latina patients to diminish denial coping and consequent distress. Nurses may be particularly effective, given cultural values concerning strong interpersonal relationships and respect for authority figures.

Malpractice issues in radiology: medicare compliance versus standard of care conformance--real or imaginary conflict?

PubMed

Duszak, Richard; Berlin, Leonard

2010-06-01

Plaintiff's Attorney (Pl Att:: Doctor, the record shows that the patient was referred to the hospital's radiology department by her gynecologist for a screening mammogram. The record also shows that when completing the mammography information form, the patient wrote that she had a lump in her left breast, correct? Defendant Radiologist (Df Ra:): Yes. Pl Att: But your technologist performed, and you interpreted, a screening mammogram. Doesn't the radiology standard of care require you to do a diagnostic mammogram when the patient has a breast lump? Df Ra:: Well, normally yes, but if it's going to be a diagnostic mammogram, then the referring physician has to order it. In this case our tech called the gynecologist and asked him whether he wanted to order a diagnostic study, and he said no, he didn't feel the lump, and that we should only do a plain screening mammogram. Pl Att:: Please explain something. You're agreeing that a woman with a breast lump should have a diagnostic mammogram, but you are saying that you didn't do one because the patient's physician wouldn't order it? Don't you have a duty to do the diagnostic mammogram in a case like this on your own, without having to ask permission from the patient's gynecologist? Df Ra:: Only the treating physician can change a screening mammogram into a diagnostic mammogram, and I am not the treating physician. If I went ahead and did a diagnostic mammography examination on my own, it would be Medicare fraud, and our hospital's compliance officer says it could result in our hospital being fined and thrown out of the Medicare program. Pl Atty: What prevents you then from recommending-not ordering, but just recommending-a diagnostic mammogram in your report, because the patient says she's got a lump? Df Rad: Well, according to our hospital's compliance officer, that would also be fraud.

Experience with a proposed teleradiology system for digital mammography

NASA Astrophysics Data System (ADS)

Saulnier, Emilie T.; Mitchell, Robert J.; Abdel-Malek, Aiman A.; Dudding, Kathryn E.

1995-05-01

Teleradiology offers significant improvement in efficiency and effectiveness over current practices in traditional film/screen-based diagnosis. In the context of digital mammography, the increasing number of women who need to be screened for breast cancer, including those in remote rural regions, make the advantages of teleradiology especially attractive for digital mammography. At the same time, the size and resolution of digital mammograms are among the most challenging to support in a cost effective teleradiology system. This paper describes a teleradiology architecture developed for use with digital mammography by GE Corporate Research and Development in collaboration with Massachusetts General Hospital under National Cancer Institute (NCI/NIH) grant number R01 CA60246-01. Experience with a testbed prototype is described. The telemammography architecture is intended to consist of a main mammography diagnostic site serving several remote screening sites. As patient exams become available, they are forwarded by an image server to the diagnostic site over a WAN communications link. A radiologist at the diagnostic site views a patient exam as it arrives, interprets it, and then relays a report back to the technician at the remote site. A secondary future scenario consists of mobile units which forward images to a remote site, which then forwards them to the main diagnostic site. The testbed architecture is based on the Digital Imaging and Communications in Medicine (DICOM) standard, created by the American College of Radiology (ACR) and National Electrical Manufacturers Association (NEMA). A specification of vendor-independent data formats and data transfer services for digital medical images, DICOM specifies a protocol suite starting at the application layer downward, including the TCP/IP layers. The current DICOM definition does not provide an information element that is specifically tailored to mammography, so we have used the DICOM secondary capture data format for the mammography images. In conclusion, experience with the testbed is described, as is performance analysis related to selection of network components needed to extend this architecture to clinical evaluation. Recommendations are made as to the critical areas for future work.

Effect of protocol-related variables and women's characteristics on the cumulative false-positive risk in breast cancer screening

PubMed Central

Román, R.; Sala, M.; Salas, D.; Ascunce, N.; Zubizarreta, R.; Castells, X.

2012-01-01

Background: Reducing the false-positive risk in breast cancer screening is important. We examined how the screening-protocol and women's characteristics affect the cumulative false-positive risk. Methods: This is a retrospective cohort study of 1 565 364 women aged 45–69 years who underwent 4 739 498 screening mammograms from 1990 to 2006. Multilevel discrete hazard models were used to estimate the cumulative false-positive risk over 10 sequential mammograms under different risk scenarios. Results: The factors affecting the false-positive risk for any procedure and for invasive procedures were double mammogram reading [odds ratio (OR) = 2.06 and 4.44, respectively], two mammographic views (OR = 0.77 and 1.56, respectively), digital mammography (OR = 0.83 for invasive procedures), premenopausal status (OR = 1.31 and 1.22, respectively), use of hormone replacement therapy (OR = 1.03 and 0.84, respectively), previous invasive procedures (OR = 1.52 and 2.00, respectively), and a familial history of breast cancer (OR = 1.18 and 1.21, respectively). The cumulative false-positive risk for women who started screening at age 50–51 was 20.39% [95% confidence interval (CI) 20.02–20.76], ranging from 51.43% to 7.47% in the highest and lowest risk profiles, respectively. The cumulative risk for invasive procedures was 1.76% (95% CI 1.66–1.87), ranging from 12.02% to 1.58%. Conclusions: The cumulative false-positive risk varied widely depending on the factors studied. These findings are relevant to provide women with accurate information and to improve the effectiveness of screening programs. PMID:21430183

Detectability of BI-RADS category 3 or higher breast lesions and reading time on mammography: comparison between 5-MP and 8-MP LCD monitors.

PubMed

Yabuuchi, Hidetake; Kawanami, Satoshi; Kamitani, Takeshi; Matsumura, Tomomi; Yamasaki, Yuzo; Morishita, Junji; Honda, Hiroshi

2017-04-01

Background Five-megapixel (MP) displays are recommended as soft copy devices for digital mammogram. An 8-MP liquid crystal display (LCD) (two 4-MP displays within one display) might offer the advantage of being able to view biplane mammography more easily than the dual planes of 5-MP LCDs. Purpose To compare detectability of Breast Imaging Reporting and Data System (BI-RADS) category 3 or higher lesions and reading time on mammography between 5- MP and 8-MP LCDs. Material and Methods The mammograms of 240 breasts of 120 patients including 60 breasts with BI-RADS category 3 or higher lesions and 180 breasts with normal or category 2 lesions were enrolled. All bilateral mammograms were displayed on bifacial 5-MP LCDs or an 8-MP LCD (two 4-MP displays within one display). Six radiologists assessed 240 breasts on each display. The observations were analyzed using receiver operating characteristic (ROC) analysis. A jack-knife method was used for statistical analysis. We employed a paired t-test to determine whether any significant differences existed in the reading time between two different displays. A P value < 0.05 was considered significant. Results The mean areas under the ROC curve obtained using 5-MP and 8-MP LCDs were 0.925 and 0.915, respectively, and there was no significant difference ( P = 0.46). There was also no significant difference in the reading time between two types of displays (57.8 min. vs. 51.5 min, P = 0.39). Conclusion The detectability of BI-RADS category 3 or higher lesions and reading time using an 8-MP LCD were comparable to those using a 5-MP LCD.

Conflicting national recommendations and the use of screening mammography: does the physician's recommendation matter?

PubMed

Taplin, S H; Urban, N; Taylor, V M; Savarino, J

1997-01-01

This study evaluated whether women's perceptions of the conflicting recommendations for breast cancer screening were associated with decreased use of mammography. We conducted a random-digit-dial telephone survey of 1024 women in four communities of western Washington State. In addition to collecting data for demographics, beliefs about mammography, and insurance coverage, we inquired whether the respondents were aware of any conflicting recommendations about when to begin or how frequently to perform screening mammography, whether their physicians had recommended a mammogram, and whether they were likely to do what their physicians recommended. After grouping women according to whether they perceived conflicting recommendations, we used chi-square statistics to compare the distribution of proportions of women by age, race, household income, education, and insurance coverage. To estimate the odds of their having a mammogram in the previous 2 years (yes or no), we used multivariate logistic regression and included the above variables as covariates. Sixty-two percent of eligible women completed the survey, and 49 percent (479 of 985) perceived conflicting recommendations. The association between perceiving conflict and mammography use was not significant. Eighty-three percent of women who perceived conflicting recommendations reported being more comfortable using their own judgment about getting the procedure. After controlling for whether women perceived conflicting recommendations and all other factors, women who said they followed their physician's advice but did not recall their physician recommending mammography were 71 percent less likely to have received a recent mammogram than were women who reported their physician did recommend it (odds ratio 0.29, confidence interval 0.16-0.51). The conflicting recommendations surrounding breast cancer screening are not influencing women's choices about mammography. The physician recommendation and women's self-reported likeliness to follow it are the most important factors associated with mammography use.

[Estimated mammogram coverage in Goiás State, Brazil].

PubMed

Corrêa, Rosangela da Silveira; Freitas-Júnior, Ruffo; Peixoto, João Emílio; Rodrigues, Danielle Cristina Netto; Lemos, Maria Eugênia da Fonseca; Marins, Lucy Aparecida Parreira; Silveira, Erika Aparecida da

2011-09-01

This cross-sectional study aimed to estimate mammogram coverage in the State of Goiás, Brazil, describing the supply, demand, and variations in different age groups, evaluating 98 mammography services as observational units. We estimated the mammogram rates by age group and type of health service, as well as the number of tests required to cover 70% and 100% of the target population. We assessed the association between mammograms, geographical distribution of mammography machines, type of service, and age group. Full coverage estimates, considering 100% of women in the 40-69 and 50-69-year age brackets, were 61% and 66%, of which the Brazilian Unified National Health System provided 13% and 14%, respectively. To achieve 70% coverage, 43,424 additional mammograms would be needed. All the associations showed statistically significant differences (p < 0.001). We conclude that mammogram coverage is unevenly distributed in the State of Goiás and that fewer tests are performed than required.

Classification of breast tissue in mammograms using efficient coding.

PubMed

Costa, Daniel D; Campos, Lúcio F; Barros, Allan K

2011-06-24

Female breast cancer is the major cause of death by cancer in western countries. Efforts in Computer Vision have been made in order to improve the diagnostic accuracy by radiologists. Some methods of lesion diagnosis in mammogram images were developed based in the technique of principal component analysis which has been used in efficient coding of signals and 2D Gabor wavelets used for computer vision applications and modeling biological vision. In this work, we present a methodology that uses efficient coding along with linear discriminant analysis to distinguish between mass and non-mass from 5090 region of interest from mammograms. The results show that the best rates of success reached with Gabor wavelets and principal component analysis were 85.28% and 87.28%, respectively. In comparison, the model of efficient coding presented here reached up to 90.07%. Altogether, the results presented demonstrate that independent component analysis performed successfully the efficient coding in order to discriminate mass from non-mass tissues. In addition, we have observed that LDA with ICA bases showed high predictive performance for some datasets and thus provide significant support for a more detailed clinical investigation.

Automatic detection of anomalies in screening mammograms

PubMed Central

2013-01-01

Background Diagnostic performance in breast screening programs may be influenced by the prior probability of disease. Since breast cancer incidence is roughly half a percent in the general population there is a large probability that the screening exam will be normal. That factor may contribute to false negatives. Screening programs typically exhibit about 83% sensitivity and 91% specificity. This investigation was undertaken to determine if a system could be developed to pre-sort screening-images into normal and suspicious bins based on their likelihood to contain disease. Wavelets were investigated as a method to parse the image data, potentially removing confounding information. The development of a classification system based on features extracted from wavelet transformed mammograms is reported. Methods In the multi-step procedure images were processed using 2D discrete wavelet transforms to create a set of maps at different size scales. Next, statistical features were computed from each map, and a subset of these features was the input for a concerted-effort set of naïve Bayesian classifiers. The classifier network was constructed to calculate the probability that the parent mammography image contained an abnormality. The abnormalities were not identified, nor were they regionalized. The algorithm was tested on two publicly available databases: the Digital Database for Screening Mammography (DDSM) and the Mammographic Images Analysis Society’s database (MIAS). These databases contain radiologist-verified images and feature common abnormalities including: spiculations, masses, geometric deformations and fibroid tissues. Results The classifier-network designs tested achieved sensitivities and specificities sufficient to be potentially useful in a clinical setting. This first series of tests identified networks with 100% sensitivity and up to 79% specificity for abnormalities. This performance significantly exceeds the mean sensitivity reported in literature for the unaided human expert. Conclusions Classifiers based on wavelet-derived features proved to be highly sensitive to a range of pathologies, as a result Type II errors were nearly eliminated. Pre-sorting the images changed the prior probability in the sorted database from 37% to 74%. PMID:24330643

Clinical evaluation of a mobile digital specimen radiography system for intraoperative specimen verification.

PubMed

Wang, Yingbing; Ebuoma, Lilian; Saksena, Mansi; Liu, Bob; Specht, Michelle; Rafferty, Elizabeth

2014-08-01

Use of mobile digital specimen radiography systems expedites intraoperative verification of excised breast specimens. The purpose of this study was to evaluate the performance of a such a system for verifying targets. A retrospective review included 100 consecutive pairs of breast specimen radiographs. Specimens were imaged in the operating room with a mobile digital specimen radiography system and then with a conventional digital mammography system in the radiology department. Two expert reviewers independently scored each image for image quality on a 3-point scale and confidence in target visualization on a 5-point scale. A target was considered confidently verified only if both reviewers declared the target to be confidently detected. The 100 specimens contained a total of 174 targets, including 85 clips (49%), 53 calcifications (30%), 35 masses (20%), and one architectural distortion (1%). Although a significantly higher percentage of mobile digital specimen radiographs were considered poor quality by at least one reviewer (25%) compared with conventional digital mammograms (1%), 169 targets (97%), were confidently verified with mobile specimen radiography; 172 targets (98%) were verified with conventional digital mammography. Three faint masses were not confidently verified with mobile specimen radiography, and conventional digital mammography was needed for confirmation. One faint mass and one architectural distortion were not confidently verified with either method. Mobile digital specimen radiography allows high diagnostic confidence for verification of target excision in breast specimens across target types, despite lower image quality. Substituting this modality for conventional digital mammography can eliminate delays associated with specimen transport, potentially decreasing surgical duration and increasing operating room throughput.

Mammography: an update of the EUSOBI recommendations on information for women.

PubMed

Sardanelli, Francesco; Fallenberg, Eva M; Clauser, Paola; Trimboli, Rubina M; Camps-Herrero, Julia; Helbich, Thomas H; Forrai, Gabor

2017-02-01

This article summarises the information to be offered to women about mammography. After a delineation of the aim of early diagnosis of breast cancer, the difference between screening mammography and diagnostic mammography is explained. The need to bring images and reports from the previous mammogram (and from other recent breast imaging examinations) is highlighted. Mammography technique and procedure are described with particular attention to discomfort and pain experienced by a small number of women who undergo the test. Information is given on the recall during a screening programme and on the request for further work-up after a diagnostic mammography. The logic of the mammography report and of classification systems such as R1-R5 and BI-RADS is illustrated, and brief but clear information is given about the diagnostic performance of the test, with particular reference to interval cancers, i.e., those cancers that are missed at screening mammography. Moreover, the breast cancer risk due to radiation exposure from mammography is compared to the reduction in mortality obtained with the test, and the concept of overdiagnosis is presented with a reliable estimation of its extent. Information about new mammographic technologies (tomosynthesis and contrast-enhanced spectral mammography) is also given. Finally, frequently asked questions are answered. • Direct digital mammography should be preferred to film-screen or phosphor plates. • Screening (in asymptomatic women) should be distinguished from diagnosis (in symptomatic women). • A breast symptom has to be considered even after a negative mammogram. • Digital breast tomosynthesis increases cancer detection and decreases the recall rate. • Contrast-enhanced spectral mammography can help in cancer detection and lesion characterisation.

Hospitalized women's willingness to pay for an inpatient screening mammogram.

PubMed

Khaliq, Waseem; Harris, Ché Matthew; Landis, Regina; Bridges, John F P; Wright, Scott M

2014-01-01

Lower rates for breast cancer screening persist among low income and uninsured women. Although Medicare and many other insurance plans would pay for screening mammograms done during hospital stays, breast cancer screening has not been part of usual hospital care. This study explores the mean amount of money that hospitalized women were willing to contribute towards the cost of a screening mammogram. Of the 193 enrolled patients, 72% were willing to pay a mean of $83.41 (95% CI, $71.51-$95.31) in advance towards inpatient screening mammogram costs. The study's findings suggest that hospitalized women value the prospect of screening mammography during the hospitalization. It may be wise policy to offer mammograms to nonadherent hospitalized women, especially those who are at high risk for developing breast cancer. © 2014 Annals of Family Medicine, Inc.

Satellite teleradiology test bed for digital mammography

NASA Astrophysics Data System (ADS)

Barnett, Bruce G.; Dudding, Kathryn E.; Abdel-Malek, Aiman A.; Mitchell, Robert J.

1996-05-01

Teleradiology offers significant improvement in efficiency and patient compliance over current practices in traditional film/screen-based diagnosis. The increasing number of women who need to be screened for breast cancer, including those in remote rural regions, make the advantages of teleradiology especially attractive for digital mammography. At the same time, the size and resolution of digital mammograms are among the most challenging to support in a cost effective teleradiology system. This paper will describe a teleradiology architecture developed for use with digital mammography by GE Corporate Research and Development in collaboration with Massachusetts General Hospital under National Cancer Institute (NCI/NIH) grant number R01 CA60246-01. The testbed architecture is based on the Digital Imaging and Communications in Medicine (DICOM) standard, created by the American College of Radiology and National Electrical Manufacturers Association. The testbed uses several Sun workstations running SunOS, which emulate a rural examination facility connected to a central diagnostic facility, and uses a TCP-based DICOM application to transfer images over a satellite link. Network performance depends on the product of the bandwidth times the round- trip time. A satellite link has a round trip of 513 milliseconds, making the bandwidth-delay a significant problem. This type of high bandwidth, high delay network is called a Long Fat Network, or LFN. The goal of this project was to quantify the performance of the satellite link, and evaluate the effectiveness of TCP over an LFN. Four workstations have Sun's HSI/S (High Speed Interface) option. Two are connected by a cable, and two are connected through a satellite link. Both interfaces have the same T1 bandwidth (1.544 Megabits per second). The only difference was the round trip time. Even with large window buffers, the time to transfer a file over the satellite link was significantly longer, due to the bandwidth-delay. To compensate for this, TCP extensions for LFNs such as the Window Scaling Option (described in RFC1323) were necessary to optimize the use of the link. A high level analysis of throughput, with and without these TCP extensions, will be discussed. Recommendations will be made as to the critical areas for future work.

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

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

PubMed

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

2018-01-25

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

Use of the Persuasive Health Message framework in the development of a community-based mammography promotion campaign

PubMed Central

Johnson-Turbes, Ashani

2015-01-01

Purpose We describe how the Persuasive Health Message (PHM) framework was used to guide the formative evaluation informing development of messages and materials used in a community-based multi-media campaign intended to motivate low-income African American women to obtain low- or no-cost mammograms through the CDC’s National Breast and Cervical Cancer Early Detection Program. Methods Seventy-eight African American women were recruited for eight focus groups that discussed breast cancer screening. The moderator guide was developed in accordance with the PHM framework and solicited information on perceived threat and efficacy, cues, salient beliefs and referents, and barriers to self-efficacy. Results We created persuasive messages to emphasize that African American women are susceptible to the threat of breast cancer, but that their personal action in obtaining regular mammograms may lead to early detection, subsequent treatment, and reduced cancer mortality. The messages addressed concerns of self-efficacy by emphasizing that uninsured women can also obtain high-quality low- or no-cost mammograms. In an attempt to combat the sentiment that breast cancer is a death sentence, the messages indicated that breast cancer can be successfully treated, especially when detected early. Conclusions The PHM framework consists of three steps: (1) determine information about threat and efficacy; (2) develop an audience profile; and (3) construct a persuasive message. It offered our team easy-to-follow, flexible steps to create a persuasive and effective campaign promoting awareness and use of mammogram screening among low-income African American women. PMID:25724414

Testing novel patient financial incentives to increase breast cancer screening.

PubMed

Merrick, Elizabeth Levy; Hodgkin, Dominic; Horgan, Constance M; Lorenz, Laura S; Panas, Lee; Ritter, Grant A; Kasuba, Paul; Poskanzer, Debra; Nefussy, Renee Altman

2015-11-01

To examine the effects of 3 types of low-cost financial incentives for patients, including a novel "person-centered" approach on breast cancer screening (mammogram) rates. Randomized controlled trial with 4 arms: 3 types of financial incentives ($15 gift card, entry into lottery for $250 gift card, and a person-centered incentive with choice of $15 gift card or lottery) and a control group. Sample included privately insured Tufts Health Plan members in Massachusetts who were women aged 42 to 69 years with no mammogram claim in ≥ 2.6 years. A sample of 4700 eligible members were randomized to 4 study arms. The control group received a standard reminder letter and the incentive groups received a reminder letter plus an incentive offer for obtaining a mammogram within the next 4 months. Bivariate tests and multivariate logistic regression were used to assess the incentives' impact on mammogram receipt. Data were analyzed for 4427 members (after exclusions such as undeliverable mail). The percent of members receiving a mammogram during the study was 11.7% (gift card), 12.1% (lottery), 13.4% (person-centered/choice), and 11.9% (controls). Differences were not statistically significant in bivariate or multivariate full-sample analyses. In exploratory subgroup analyses of members with a mammogram during the most recent year prior to the study-defined gap, person-centered incentives were associated with a higher likelihood of mammogram receipt. None of the low-cost incentives tested had a statistically significant effect on mammogram rates in the full sample. Exploratory findings for members who were more recently screened suggest that they may be more responsive to person-centered incentives.

Breast Cancer Risk Prediction and Mammography Biopsy Decisions

PubMed Central

Armstrong, Katrina; Handorf, Elizabeth A.; Chen, Jinbo; Demeter, Mirar N. Bristol

2012-01-01

Background Controversy continues about screening mammography, in part because of the risk of false-negative and false-positive mammograms. Pre-test breast cancer risk factors may improve the positive and negative predictive value of screening. Purpose To create a model that estimates the potential impact of pre-test risk prediction using clinical and genomic information on the reclassification of women with abnormal mammograms (BI-RADS3 and BI-RADS4 [Breast Imaging-Reporting and Data System]) above and below the threshold for breast biopsy. Methods The current study modeled 1-year breast cancer risk in women with abnormal screening mammograms using existing data on breast cancer risk factors, 12 validated breast cancer single nucleotide polymorphisms (SNPs), and probability of cancer given the BI-RADS category. Examination was made of reclassification of women above and below biopsy thresholds of 1%, 2%, and 3% risk. The Breast Cancer Surveillance Consortium data were collected from 1996 to 2002. Data analysis was conducted in 2010 and 2011. Results Using a biopsy risk threshold of 2% and the standard risk factor model, 5% of women with a BI-RADS3 mammogram had a risk above the threshold, and 3% of women with BIRADS4A mammograms had a risk below the threshold. The addition of 12 SNPs in the model resulted in 8% of women with a BI-RADS3 mammogram above the threshold for biopsy and 7% of women with BI-RADS4A mammograms below the threshold. Conclusions The incorporation of pre-test breast cancer risk factors could change biopsy decisions for a small proportion of women with abnormal mammograms. The greatest impact comes from standard breast cancer risk factors. PMID:23253645

Volumetric breast density affects performance of digital screening mammography.

PubMed

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

2017-02-01

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

Population of 224 realistic human subject-based computational breast phantoms

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

Erickson, David W.; Wells, Jered R., E-mail: jered.wells@duke.edu; Sturgeon, Gregory M.

Purpose: To create a database of highly realistic and anatomically variable 3D virtual breast phantoms based on dedicated breast computed tomography (bCT) data. Methods: A tissue classification and segmentation algorithm was used to create realistic and detailed 3D computational breast phantoms based on 230 + dedicated bCT datasets from normal human subjects. The breast volume was identified using a coarse three-class fuzzy C-means segmentation algorithm which accounted for and removed motion blur at the breast periphery. Noise in the bCT data was reduced through application of a postreconstruction 3D bilateral filter. A 3D adipose nonuniformity (bias field) correction was thenmore » applied followed by glandular segmentation using a 3D bias-corrected fuzzy C-means algorithm. Multiple tissue classes were defined including skin, adipose, and several fractional glandular densities. Following segmentation, a skin mask was produced which preserved the interdigitated skin, adipose, and glandular boundaries of the skin interior. Finally, surface modeling was used to produce digital phantoms with methods complementary to the XCAT suite of digital human phantoms. Results: After rejecting some datasets due to artifacts, 224 virtual breast phantoms were created which emulate the complex breast parenchyma of actual human subjects. The volume breast density (with skin) ranged from 5.5% to 66.3% with a mean value of 25.3% ± 13.2%. Breast volumes ranged from 25.0 to 2099.6 ml with a mean value of 716.3 ± 386.5 ml. Three breast phantoms were selected for imaging with digital compression (using finite element modeling) and simple ray-tracing, and the results show promise in their potential to produce realistic simulated mammograms. Conclusions: This work provides a new population of 224 breast phantoms based on in vivo bCT data for imaging research. Compared to previous studies based on only a few prototype cases, this dataset provides a rich source of new cases spanning a wide range of breast types, volumes, densities, and parenchymal patterns.« less

Population of 224 realistic human subject-based computational breast phantoms

PubMed Central

Erickson, David W.; Wells, Jered R.; Sturgeon, Gregory M.; Dobbins, James T.; Segars, W. Paul; Lo, Joseph Y.

2016-01-01

Purpose: To create a database of highly realistic and anatomically variable 3D virtual breast phantoms based on dedicated breast computed tomography (bCT) data. Methods: A tissue classification and segmentation algorithm was used to create realistic and detailed 3D computational breast phantoms based on 230 + dedicated bCT datasets from normal human subjects. The breast volume was identified using a coarse three-class fuzzy C-means segmentation algorithm which accounted for and removed motion blur at the breast periphery. Noise in the bCT data was reduced through application of a postreconstruction 3D bilateral filter. A 3D adipose nonuniformity (bias field) correction was then applied followed by glandular segmentation using a 3D bias-corrected fuzzy C-means algorithm. Multiple tissue classes were defined including skin, adipose, and several fractional glandular densities. Following segmentation, a skin mask was produced which preserved the interdigitated skin, adipose, and glandular boundaries of the skin interior. Finally, surface modeling was used to produce digital phantoms with methods complementary to the XCAT suite of digital human phantoms. Results: After rejecting some datasets due to artifacts, 224 virtual breast phantoms were created which emulate the complex breast parenchyma of actual human subjects. The volume breast density (with skin) ranged from 5.5% to 66.3% with a mean value of 25.3% ± 13.2%. Breast volumes ranged from 25.0 to 2099.6 ml with a mean value of 716.3 ± 386.5 ml. Three breast phantoms were selected for imaging with digital compression (using finite element modeling) and simple ray-tracing, and the results show promise in their potential to produce realistic simulated mammograms. Conclusions: This work provides a new population of 224 breast phantoms based on in vivo bCT data for imaging research. Compared to previous studies based on only a few prototype cases, this dataset provides a rich source of new cases spanning a wide range of breast types, volumes, densities, and parenchymal patterns. PMID:26745896

The association of social support and education with breast and cervical cancer screening.

PubMed

Documet, Patricia; Bear, Todd M; Flatt, Jason D; Macia, Laura; Trauth, Jeanette; Ricci, Edmund M

2015-02-01

Disparities in breast and cervical cancer screening by socioeconomic status persist in the United States. It has been suggested that social support may facilitate screening, especially among women of low socioeconomic status. However, at present, it is unclear whether social support enables mammogram and Pap test compliance. This study examines the association between social support and compliance with mammogram and Pap test screening guidelines, and whether social support provides added value for women of low education. Data were from a countywide 2009-2010 population-based survey, which included records of 2,588 women 40 years and older (mammogram) and 2,123 women 21 to 65 years old (Pap test). Compliance was determined using the guidelines in effect at the time of data collection. Social support was significantly related to mammogram (adjusted odds ratio = 1.43; 95% confidence interval [1.16, 1.77]) and Pap test (adjusted odds ratio = 1.71; 95% confidence interval [1.27, 2.29]) compliance after controlling for age, race, having a regular health care provider, and insurance status. The interaction between social support and education had a significant effect on Pap test compliance only among women younger than 40; the effect was not significant for mammogram compliance. Social support is associated with breast and cervical cancer screening compliance. The association between education and cancer screening behavior may be moderated by social support; however, results hold only for Pap tests among younger women. Practitioners and researchers should focus on interventions that activate social support networks as they may help increase both breast and cervical cancer screening compliance among women with low educational attainment. © 2014 Society for Public Health Education.

Method for inserting noise in digital mammography to simulate reduction in radiation dose

NASA Astrophysics Data System (ADS)

Borges, Lucas R.; de Oliveira, Helder C. R.; Nunes, Polyana F.; Vieira, Marcelo A. C.

2015-03-01

The quality of clinical x-ray images is closely related to the radiation dose used in the imaging study. The general principle for selecting the radiation is ALARA ("as low as reasonably achievable"). The practical optimization, however, remains challenging. It is well known that reducing the radiation dose increases the quantum noise, which could compromise the image quality. In order to conduct studies about dose reduction in mammography, it would be necessary to acquire repeated clinical images, from the same patient, with different dose levels. However, such practice would be unethical due to radiation related risks. One solution is to simulate the effects of dose reduction in clinical images. This work proposes a new method, based on the Anscombe transformation, which simulates dose reduction in digital mammography by inserting quantum noise into clinical mammograms acquired with the standard radiation dose. Thus, it is possible to simulate different levels of radiation doses without exposing the patient to new levels of radiation. Results showed that the achieved quality of simulated images generated with our method is the same as when using other methods found in the literature, with the novelty of using the Anscombe transformation for converting signal-independent Gaussian noise into signal-dependent quantum noise.

Self-Report Versus Medical Record for Mammography Screening Among Minority Women.

PubMed

Nandy, Karabi; Menon, Usha; Szalacha, Laura A; Park, HanJong; Lee, Jongwon; Lee, Eunice E

2016-12-01

Self-report is the most common means of obtaining mammography screening data. The purpose of this study was to assess the accuracy of minority women's self-reported mammography by comparing their self-reported dates of mammograms with those in their medical records from a community-based randomized control trial. We found that out of 192 women, 116 signed the Health Information Portability and Accountability Act form and, among these, 97 had medical records that could be verified (97 / 116 = 83.6%). Ninety-two records matched where both sources confirmed a mammogram; 48 of 92 (52.2%) matched perfectly on self-reported date of mammogram. Complexities in the verification process warrant caution when verifying self-reported mammography screening in minority populations. In spite of some limitations, our findings support the usage of self-reported data on mammography as a validated tool for other researchers investigating mammography screening among minority women who continue to have low screening rates. © The Author(s) 2016.

Correlates of mammography screening among Hispanic women living in lower Rio Grande Valley farmworker communities.

PubMed

Palmer, Richard C; Fernandez, Maria E; Tortolero-Luna, Guillermo; Gonzales, Alicia; Mullen, Patricia Dolan

2005-08-01

Factors contributing to the underuse of mammography screening by female Hispanic farmworkers aged 50 years and older in the Lower Rio Grande Valley were determined through home-based, Spanish-language personal interviews (N = 200). Questions covered adherence to screening mammography guidelines (mammogram within 2 years), healthcare access, sociodemographic characteristics, and theoretical constructs related to breast cancer screening in the literature. Multivariate findings indicated that adherent women were 3.6 times more likely to have health insurance. Self-efficacy for obtaining a mammogram and decisional balance were also significantly related to adherence; age, income, and education variables were not associated, perhaps because of restricted variation. Results indicate continuing efforts are needed to ensure that medically underserved migrant farmworker women have access to health care services. In addition, efforts to increase their self-efficacy in obtaining a mammogram and to counter negative attitudes and opinions by stressing the positive prognosis associated with early detection are warranted.

Evaluation of a "loss-framed" minimal intervention to increase mammography utilization among medically un- and under-insured women.

PubMed

Abood, Doris A; Coster, Daniel C; Mullis, Ann K; Black, David R

2002-01-01

This study was conducted because mammography is under-utilized, even though it is the most effective early detection screening device for breast cancer. A loss-framed telephonic message based on prospect theory was evaluated for the effects on mammography utilization among medically un- and under-insured women living in demographically similar rural counties in Florida. The sample consisted mostly of White women (approximately 89%) 50-64 years old. Experimental group participants received the loss-framed message telephonically and those in the comparison group received the "usual telephone procedure." Logistic regression analyses revealed that women who received the loss-framed message were six times more likely to obtain a mammogram (OR = 6.6, P < 0.0001). The impact of the loss-framed message persisted even after adjustment for initial versus re-screen mammogram effects. This in-reach, loss-framed, minimal intervention seems to have viability and may serve as an alternative or adjunct program for encouraging women to receive mammograms.

B-Spline Filtering for Automatic Detection of Calcification Lesions in Mammograms

NASA Astrophysics Data System (ADS)

Bueno, G.; Sánchez, S.; Ruiz, M.

2006-10-01

Breast cancer continues to be an important health problem between women population. Early detection is the only way to improve breast cancer prognosis and significantly reduce women mortality. It is by using CAD systems that radiologist can improve their ability to detect, and classify lesions in mammograms. In this study the usefulness of using B-spline based on a gradient scheme and compared to wavelet and adaptative filtering has been investigated for calcification lesion detection and as part of CAD systems. The technique has been applied to different density tissues. A qualitative validation shows the success of the method.

Comparative effectiveness of mailed reminder letters on mammography screening compliance.

PubMed

Romaire, Melissa A; Bowles, Erin J Aiello; Anderson, Melissa L; Buist, Diana S M

2012-08-01

Reminder letters are effective at prompting women to schedule mammograms. Less well studied are reminders addressing multiple preventive service recommendations. We compared the effectiveness of a mammogram-specific reminder sent when a woman was due for a mammogram to a reminder letter addressing multiple preventive services and sent on a woman's birthday on mammography receipt. The study included 48,583 women 52-74 years enrolled in Group Health Cooperative, a health plan in Washington State. From 2005 to 2009, women were mailed 88,605 mammogram-specific or birthday letters. In this one group pretest-posttest study, we modeled the odds of obtaining a screening mammogram after receiving a letter by reminder type using logistic regression, controlling for demographic and healthcare use characteristics and stratifying by whether women were overdue or up-to-date with mammography at the mailing. Among women up-to-date with screening, birthday letters were negatively associated with mammography receipt compared to mammogram-specific letters (birthday letters with 1-2 recommendations: OR=0.73; 95% CI:0.68-0.79; 3 recommendations: OR=0.74; 95% CI:0.69-0.78; 4-8 recommendations: OR=0.62 95% CI:0.55-0.68) after. Among overdue women, birthday letters with 4-8 recommendations were negatively associated with mammography receipt. Transitioning from mammogram-specific reminder letters to multiple preventive service birthday letters was associated with decreased mammography receipt. Copyright © 2012 Elsevier Inc. All rights reserved.

A novel image toggle tool for comparison of serial mammograms: automatic density normalization and alignment-development of the tool and initial experience.

PubMed

Honda, Satoshi; Tsunoda, Hiroko; Fukuda, Wataru; Saida, Yukihisa

2014-12-01

The purpose is to develop a new image toggle tool with automatic density normalization (ADN) and automatic alignment (AA) for comparing serial digital mammograms (DMGs). We developed an ADN and AA process to compare the images of serial DMGs. In image density normalization, a linear interpolation was applied by taking two points of high- and low-brightness areas. The alignment was calculated by determining the point of the greatest correlation while shifting the alignment between the current and prior images. These processes were performed on a PC with a 3.20-GHz Xeon processor and 8 GB of main memory. We selected 12 suspected breast cancer patients who had undergone screening DMGs in the past. Automatic processing was retrospectively performed on these images. Two radiologists subjectively evaluated them. The process of the developed algorithm took approximately 1 s per image. In our preliminary experience, two images could not be aligned approximately. When they were aligned, image toggling allowed detection of differences between examinations easily. We developed a new tool to facilitate comparative reading of DMGs on a mammography viewing system. Using this tool for toggling comparisons might improve the interpretation efficiency of serial DMGs.

Relationship between breast sound speed and mammographic percent density

NASA Astrophysics Data System (ADS)

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

2011-03-01

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

A completely automated CAD system for mass detection in a large mammographic database.

PubMed

Bellotti, R; De Carlo, F; Tangaro, S; Gargano, G; Maggipinto, G; Castellano, M; Massafra, R; Cascio, D; Fauci, F; Magro, R; Raso, G; Lauria, A; Forni, G; Bagnasco, S; Cerello, P; Zanon, E; Cheran, S C; Lopez Torres, E; Bottigli, U; Masala, G L; Oliva, P; Retico, A; Fantacci, M E; Cataldo, R; De Mitri, I; De Nunzio, G

2006-08-01

Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing second-order spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. (c) ROI classification by means of a neural network, with supervision provided by the radiologist's diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images [2307 negative, 1062 pathological (or positive), containing at least one confirmed mass, as diagnosed by an expert radiologist]. To assess the performance of the system, receiver operating characteristic (ROC) and free-response ROC analysis were employed. The area under the ROC curve was found to be Az = 0.783 +/- 0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.

Do mammographic technologists affect radiologists’ diagnostic mammography interpretative performance?

PubMed Central

Henderson, Louise M.; Benefield, Thad; Bowling, J. Michael; Durham, Danielle; Marsh, Mary W.; Schroeder, Bruce F.; Yankaskas, Bonnie C.

2015-01-01

Objective The purpose of this study was to determine whether the technologist has an effect on the radiologists’ interpretative performance of diagnostic mammography. Materials and Methods Using data from a community based mammography registry from 1994 to 2009, we identified 162,755 diagnostic mammograms interpreted by 286 radiologists and performed by 303 mammographic technologists. We calculated sensitivity, false positive rate, and positive predictive value of biopsy (PPV2) for examinations performed (images taken) by each mammographic technologist, separately for film and digital modalities. We assessed the variability of these performance measures among mammographic technologists, using mixed effects logistic regression and taking into account the clustering of examinations within women, radiologists, and radiology practice. Results Among the 291 technologists performing film examinations, mean sensitivity of the examinations they performed was 83.0% (95% Confidence Interval (CI)=80.8–85.2%), mean false positive rate was 8.5 per 1000 examinations (95%CI: 8.0–9.0%), and mean PPV2 was 27.1% (95%CI: 24.8–29.4). For the 45 technologists performing digital examinations, mean sensitivity of the examinations they performed was 79.6% (95%CI: 73.1–86.2%), mean false positive rate was 8.8 (95%CI: 7.5–10.0%), and mean PPV2 was 23.6% (95%CI: 18.8–28.4%). We found significant variation by technologist in the sensitivity, false positive rate, and PPV2 for film but not digital mammography (p<0.0001 for all 3 film performance measures). Conclusions Our results suggest that the technologist has an influence on radiologists’ performance of diagnostic film mammography but not digital. Future work should examine why this difference by modality exists and determine if similar patterns are observed for screening mammography. PMID:25794085

Mammographic features of isolated tuberculous mastitis.

PubMed

Al-Marri, Mohammed R; Aref, Essam; Omar, Ahamed J

2005-04-01

To present the mammography findings in 8 patients with tuberculosis (TB) of the breast, with a review of the literature. This study is a retrospective data collection. Each chart with confirmed breast TB based on bacteriology or pathologic findings was analyzed for clinical presentation, gender, nationality, demographic data, prior history of TB, investigation, management, mammographic findings and ultrasound, when available. Mammograms were reviewed by 2 consultant radiologists without knowing the previous diagnosis or the nature of the study. The study was carried out at The State Tuberculosis Registry and Radiology Department, Hamad General Hospital, State of Qatar, from 1990 to 2002. Out of 13 females with TB mastitis, only 8 cases had mammograms preoperatively. The incidence of breast TB in Qatar is rare (1/1000 mammograms per year). Three types of TB mastitis were identified in our study; the nodular (50%), the diffuse (37.5%) of which 77% were limited to one sector of the breast and the sclerosing (12.5%) mastitis. Three patients (43%) were reported as carcinoma. Although mammography identified 3 types of TB, it was not helpful in differentiating TB from carcinoma of the breast. However, the careful evaluation of the degree of density and trabecular thickening of the mass in relation to it size might reduce the number of false positive cases of carcinoma diagnosed with mammograms. Biopsy specimen remains the best diagnostic tool in TB mastitis.

Mammography use among women ages 40-49 after the 2009 U.S. Preventive Services Task Force recommendation.

PubMed

Block, Lauren D; Jarlenski, Marian P; Wu, Albert W; Bennett, Wendy L

2013-11-01

In 2009, the U.S. Preventive Service Task Force changed its recommendation regarding screening mammography in average-risk women aged 40-49 years. To evaluate the effects of the 2009 recommendation on reported mammogram use in a population-based survey. Secondary data analysis of data collected in the 2006, 2008, and 2010 Behavioral Risk Factor Surveillance System surveys. Women ages 40-74 years in the 50 states and Washington, DC who were not pregnant at time of survey and reported data on mammogram use during the 2006, 2008, or 2010 survey. Mammogram use was compared between women ages 40-49 and women ages 50-74 before and after the recommendation. We performed a difference-in-difference estimation adjusted for access to care, education, race, and health status, and stratified analyses by whether women reported having a routine checkup in the prior year. Reported prevalence of mammogram use in the past year among women ages 40-49 and 50-74 was 53.2 % and 65.2 %, respectively in 2008, and 51.7 % and 62.4 % in 2010. In 2010, mammography use did not significantly decline from 2006-2008 in women ages 40-49 relative to women ages 50-74. There was no reduction in mammography use among younger women in 2010 compared to older women and previous years. Patients and providers may have been hesitant to comply with the 2009 recommendation.

Effect of Radiologists’ Diagnostic Work-up Volume on Interpretive Performance

PubMed Central

Anderson, Melissa L.; Smith, Robert A.; Carney, Patricia A.; Miglioretti, Diana L.; Monsees, Barbara S.; Sickles, Edward A.; Taplin, Stephen H.; Geller, Berta M.; Yankaskas, Bonnie C.; Onega, Tracy L.

2014-01-01

Purpose To examine radiologists’ screening performance in relation to the number of diagnostic work-ups performed after abnormal findings are discovered at screening mammography by the same radiologist or by different radiologists. Materials and Methods In an institutional review board–approved HIPAA-compliant study, the authors linked 651 671 screening mammograms interpreted from 2002 to 2006 by 96 radiologists in the Breast Cancer Surveillance Consortium to cancer registries (standard of reference) to evaluate the performance of screening mammography (sensitivity, false-positive rate [FPRfalse-positive rate], and cancer detection rate [CDRcancer detection rate]). Logistic regression was used to assess the association between the volume of recalled screening mammograms (“own” mammograms, where the radiologist who interpreted the diagnostic image was the same radiologist who had interpreted the screening image, and “any” mammograms, where the radiologist who interpreted the diagnostic image may or may not have been the radiologist who interpreted the screening image) and screening performance and whether the association between total annual volume and performance differed according to the volume of diagnostic work-up. Results Annually, 38% of radiologists performed the diagnostic work-up for 25 or fewer of their own recalled screening mammograms, 24% performed the work-up for 0–50, and 39% performed the work-up for more than 50. For the work-up of recalled screening mammograms from any radiologist, 24% of radiologists performed the work-up for 0–50 mammograms, 32% performed the work-up for 51–125, and 44% performed the work-up for more than 125. With increasing numbers of radiologist work-ups for their own recalled mammograms, the sensitivity (P = .039), FPRfalse-positive rate (P = .004), and CDRcancer detection rate (P < .001) of screening mammography increased, yielding a stepped increase in women recalled per cancer detected from 17.4 for 25 or fewer mammograms to 24.6 for more than 50 mammograms. Increases in work-ups for any radiologist yielded significant increases in FPRfalse-positive rate (P = .011) and CDRcancer detection rate (P = .001) and a nonsignificant increase in sensitivity (P = .15). Radiologists with a lower annual volume of any work-ups had consistently lower FPRfalse-positive rate, sensitivity, and CDRcancer detection rate at all annual interpretive volumes. Conclusion These findings support the hypothesis that radiologists may improve their screening performance by performing the diagnostic work-up for their own recalled screening mammograms and directly receiving feedback afforded by means of the outcomes associated with their initial decision to recall. Arranging for radiologists to work up a minimum number of their own recalled cases could improve screening performance but would need systems to facilitate this workflow. © RSNA, 2014 Online supplemental material is available for this article. PMID:24960110

Effect of radiologists' diagnostic work-up volume on interpretive performance.

PubMed

Buist, Diana S M; Anderson, Melissa L; Smith, Robert A; Carney, Patricia A; Miglioretti, Diana L; Monsees, Barbara S; Sickles, Edward A; Taplin, Stephen H; Geller, Berta M; Yankaskas, Bonnie C; Onega, Tracy L

2014-11-01

To examine radiologists' screening performance in relation to the number of diagnostic work-ups performed after abnormal findings are discovered at screening mammography by the same radiologist or by different radiologists. In an institutional review board-approved HIPAA-compliant study, the authors linked 651 671 screening mammograms interpreted from 2002 to 2006 by 96 radiologists in the Breast Cancer Surveillance Consortium to cancer registries (standard of reference) to evaluate the performance of screening mammography (sensitivity, false-positive rate [ FPR false-positive rate ], and cancer detection rate [ CDR cancer detection rate ]). Logistic regression was used to assess the association between the volume of recalled screening mammograms ("own" mammograms, where the radiologist who interpreted the diagnostic image was the same radiologist who had interpreted the screening image, and "any" mammograms, where the radiologist who interpreted the diagnostic image may or may not have been the radiologist who interpreted the screening image) and screening performance and whether the association between total annual volume and performance differed according to the volume of diagnostic work-up. Annually, 38% of radiologists performed the diagnostic work-up for 25 or fewer of their own recalled screening mammograms, 24% performed the work-up for 0-50, and 39% performed the work-up for more than 50. For the work-up of recalled screening mammograms from any radiologist, 24% of radiologists performed the work-up for 0-50 mammograms, 32% performed the work-up for 51-125, and 44% performed the work-up for more than 125. With increasing numbers of radiologist work-ups for their own recalled mammograms, the sensitivity (P = .039), FPR false-positive rate (P = .004), and CDR cancer detection rate (P < .001) of screening mammography increased, yielding a stepped increase in women recalled per cancer detected from 17.4 for 25 or fewer mammograms to 24.6 for more than 50 mammograms. Increases in work-ups for any radiologist yielded significant increases in FPR false-positive rate (P = .011) and CDR cancer detection rate (P = .001) and a nonsignificant increase in sensitivity (P = .15). Radiologists with a lower annual volume of any work-ups had consistently lower FPR false-positive rate , sensitivity, and CDR cancer detection rate at all annual interpretive volumes. These findings support the hypothesis that radiologists may improve their screening performance by performing the diagnostic work-up for their own recalled screening mammograms and directly receiving feedback afforded by means of the outcomes associated with their initial decision to recall. Arranging for radiologists to work up a minimum number of their own recalled cases could improve screening performance but would need systems to facilitate this workflow.

Wavelet-based 3D reconstruction of microcalcification clusters from two mammographic views: new evidence that fractal tumors are malignant and Euclidean tumors are benign.

PubMed

Batchelder, Kendra A; Tanenbaum, Aaron B; Albert, Seth; Guimond, Lyne; Kestener, Pierre; Arneodo, Alain; Khalil, Andre

2014-01-01

The 2D Wavelet-Transform Modulus Maxima (WTMM) method was used to detect microcalcifications (MC) in human breast tissue seen in mammograms and to characterize the fractal geometry of benign and malignant MC clusters. This was done in the context of a preliminary analysis of a small dataset, via a novel way to partition the wavelet-transform space-scale skeleton. For the first time, the estimated 3D fractal structure of a breast lesion was inferred by pairing the information from two separate 2D projected mammographic views of the same breast, i.e. the cranial-caudal (CC) and mediolateral-oblique (MLO) views. As a novelty, we define the "CC-MLO fractal dimension plot", where a "fractal zone" and "Euclidean zones" (non-fractal) are defined. 118 images (59 cases, 25 malignant and 34 benign) obtained from a digital databank of mammograms with known radiologist diagnostics were analyzed to determine which cases would be plotted in the fractal zone and which cases would fall in the Euclidean zones. 92% of malignant breast lesions studied (23 out of 25 cases) were in the fractal zone while 88% of the benign lesions were in the Euclidean zones (30 out of 34 cases). Furthermore, a Bayesian statistical analysis shows that, with 95% credibility, the probability that fractal breast lesions are malignant is between 74% and 98%. Alternatively, with 95% credibility, the probability that Euclidean breast lesions are benign is between 76% and 96%. These results support the notion that the fractal structure of malignant tumors is more likely to be associated with an invasive behavior into the surrounding tissue compared to the less invasive, Euclidean structure of benign tumors. Finally, based on indirect 3D reconstructions from the 2D views, we conjecture that all breast tumors considered in this study, benign and malignant, fractal or Euclidean, restrict their growth to 2-dimensional manifolds within the breast tissue.

Visualizing the Diffusion of Digital Mammography in New York State.

PubMed

Boscoe, Francis P; Zhang, Xiuling

2017-04-01

Background: Digital mammography saw rapid adoption during the first decade of the 2000s. We were interested in identifying the times and locations where the technology was introduced within the state of New York as a way of illustrating the uneven introduction of this technology. Methods: Using a sample of Medicare claims data from the period 2004 to 2012 from women ages 65 and over without cancer, we calculated the percentage of mammograms that were digital by zip code of residence and illustrated them with a series of smoothed maps. Results: Maps for three of the years (2005, 2008, and 2011) show the conversion from almost no digital mammography to nearly all digital mammography. The 2008 map reveals sharp disparities between areas that had and had not yet adopted the technology. Socioeconomic differences explain some of this pattern. Conclusions: Geographic disparities in access to medical technology are underappreciated relative to other sources of disparities. Our method provides a way of measuring and communicating this phenomenon. Impact: Our method could be applied to illuminate current examples, where access to medical technology is highly uneven, such as 3D tomography and robotic surgery. Cancer Epidemiol Biomarkers Prev; 26(4); 490-4. ©2017 AACR See all the articles in this CEBP Focus section, "Geospatial Approaches to Cancer Control and Population Sciences." ©2017 American Association for Cancer Research.

Depression and anxiety diagnoses are not associated with delayed resolution of abnormal mammograms and pap tests among vulnerable women.

PubMed

Kronman, Andrea C; Freund, Karen M; Heeren, Tim; Beaver, Kristine A; Flynn, Mary; Battaglia, Tracy A

2012-04-01

Delays in care after abnormal cancer screening contribute to disparities in cancer outcomes. Women with psychiatric disorders are less likely to receive cancer screening and may also have delays in diagnostic resolution after an abnormal screening test. To determine if depression and anxiety are associated with delays in resolution after abnormal mammograms and Pap tests in a vulnerable population of urban women. We conducted retrospective chart reviews of electronic medical records to identify women who had a diagnosis of depression or anxiety in the year prior to the abnormal mammogram or Pap test. We used time-to-event analysis to analyze the outcome of time to resolution after abnormal cancer screening, and Cox proportional hazards regression modeling to control for confounding. Women receiving care in six Boston-area community health centers 2004-2005: 523 with abnormal mammograms, 474 with abnormal Pap tests. Of the women with abnormal mammogram and pap tests, 19% and 16%, respectively, had co-morbid depression. There was no difference in time to diagnostic resolution between depressed and not-depressed women for those with abnormal mammograms (aHR = 0.9, 95 CI 0.7,1.1) or Pap tests (aHR = 0.9, 95 CI 0.7,1.3). An active diagnosis of depression and/or anxiety in the year prior to an abnormal mammogram or Pap test was not associated with a prolonged time to diagnostic resolution. Our findings imply that documented mood disorders do not identify an additional barrier to resolution after abnormal cancer screening in a vulnerable population of women.

Quantitative comparison of clustered microcalcifications in for-presentation and for-processing mammograms in full-field digital mammography.

PubMed

Wang, Juan; Nishikawa, Robert M; Yang, Yongyi

2017-07-01

Mammograms acquired with full-field digital mammography (FFDM) systems are provided in both "for-processing'' and "for-presentation'' image formats. For-presentation images are traditionally intended for visual assessment by the radiologists. In this study, we investigate the feasibility of using for-presentation images in computerized analysis and diagnosis of microcalcification (MC) lesions. We make use of a set of 188 matched mammogram image pairs of MC lesions from 95 cases (biopsy proven), in which both for-presentation and for-processing images are provided for each lesion. We then analyze and characterize the MC lesions from for-presentation images and compare them with their counterparts in for-processing images. Specifically, we consider three important aspects in computer-aided diagnosis (CAD) of MC lesions. First, we quantify each MC lesion with a set of 10 image features of clustered MCs and 12 textural features of the lesion area. Second, we assess the detectability of individual MCs in each lesion from the for-presentation images by a commonly used difference-of-Gaussians (DoG) detector. Finally, we study the diagnostic accuracy in discriminating between benign and malignant MC lesions from the for-presentation images by a pretrained support vector machine (SVM) classifier. To accommodate the underlying background suppression and image enhancement in for-presentation images, a normalization procedure is applied. The quantitative image features of MC lesions from for-presentation images are highly consistent with that from for-processing images. The values of Pearson's correlation coefficient between features from the two formats range from 0.824 to 0.961 for the 10 MC image features, and from 0.871 to 0.963 for the 12 textural features. In detection of individual MCs, the FROC curve from for-presentation is similar to that from for-processing. In particular, at sensitivity level of 80%, the average number of false-positives (FPs) per image region is 9.55 for both for-presentation and for-processing images. Finally, for classifying MC lesions as malignant or benign, the area under the ROC curve is 0.769 in for-presentation, compared to 0.761 in for-processing (P = 0.436). The quantitative results demonstrate that MC lesions in for-presentation images are highly consistent with that in for-processing images in terms of image features, detectability of individual MCs, and classification accuracy between malignant and benign lesions. These results indicate that for-presentation images can be compatible with for-processing images for use in CAD algorithms for MC lesions. © 2017 American Association of Physicists in Medicine.

Mammograms

MedlinePlus

... mammography facility know about breast implants when scheduling a mammogram. The technologist and radiologist must be experienced in performing mammography on women who have breast implants. If the technologist ...

Impact of the Introduction of Digital Mammography in an Organized Screening Program on the Recall and Detection Rate.

PubMed

Campari, Cinzia; Giorgi Rossi, Paolo; Mori, Carlo Alberto; Ravaioli, Sara; Nitrosi, Andrea; Vacondio, Rita; Mancuso, Pamela; Cattani, Antonella; Pattacini, Pierpaolo

2016-04-01

In 2012, the Reggio Emilia Breast Cancer Screening Program introduced digital mammography in all its facilities at the same time. The aim of this work is to analyze the impact of digital mammography introduction on the recall rate, detection rate, and positive predictive value. The program actively invites women aged 45-74 years. We included women screened in 2011, all of whom underwent film-screen mammography, and all women screened in 2012, all of whom underwent digital mammography. Double reading was used for all mammograms, with arbitration in the event of disagreement. A total of 42,240 women underwent screen-film mammography and 45,196 underwent digital mammography. The recall rate increased from 3.3 to 4.4% in the first year of digital mammography (relative recall adjusted by age and round 1.46, 95% CI = 1.37-1.56); the positivity rate for each individual reading, before arbitration, rose from 3 to 5.7%. The digital mammography recall rate decreased during 2012: after 12 months, it was similar to the recall rate with screen-film mammography. The detection rate was similar: 5.9/1000 and 5.2/1000 with screen-film and digital mammography, respectively (adjusted relative detection rate 0.95, 95% CI = 0.79-1.13). The relative detection rate for ductal carcinoma in situ remained the same. The introduction of digital mammography to our organized screening program had a negative impact on specificity, thereby increasing the recall rate. The effect was limited to the first 12 months after introduction and was attenuated by the double reading with arbitration. We did not observe any relevant effects on the detection rate.

Improved visualization of breast cancer features in multifocal carcinoma using phase-contrast and dark-field mammography: an ex vivo study.

PubMed

Grandl, Susanne; Scherer, Kai; Sztrókay-Gaul, Anikó; Birnbacher, Lorenz; Willer, Konstantin; Chabior, Michael; Herzen, Julia; Mayr, Doris; Auweter, Sigrid D; Pfeiffer, Franz; Bamberg, Fabian; Hellerhoff, Karin

2015-12-01

Conventional X-ray attenuation-based contrast is inherently low for the soft-tissue components of the female breast. To overcome this limitation, we investigate the diagnostic merits arising from dark-field mammography by means of certain tumour structures enclosed within freshly dissected mastectomy samples. We performed grating-based absorption, absolute phase and dark-field mammography of three freshly dissected mastectomy samples containing bi- and multifocal carcinoma using a compact, laboratory Talbot-Lau interferometer. Preoperative in vivo imaging (digital mammography, ultrasound, MRI), postoperative histopathological analysis and ex vivo digital mammograms of all samples were acquired for the diagnostic verification of our results. In the diagnosis of multifocal tumour growth, dark-field mammography seems superior to standard breast imaging modalities, providing a better resolution of small, calcified tumour nodules, demarcation of tumour boundaries with desmoplastic stromal response and spiculated soft-tissue strands extending from an invasive ductal breast cancer. On the basis of selected cases, we demonstrate that dark-field mammography is capable of outperforming conventional mammographic imaging of tumour features in both calcified and non-calcified tumours. Presuming dose optimization, our results encourage further studies on larger patient cohorts to identify those patients that will benefit the most from this promising additional imaging modality. • X-ray dark-field mammography provides significantly improved visualization of tumour features • X-ray dark-field mammography is capable of outperforming conventional mammographic imaging • X-ray dark-field mammography provides imaging sensitivity towards highly dispersed calcium grains.

Mammogram

MedlinePlus

Mammography; Breast cancer - mammography; Breast cancer - screening mammography; Breast lump - mammogram; Breast tomosynthesis ... images. This does not always mean you have breast cancer. Your health care provider may simply need to ...

Comparison of digital mammography and digital breast tomosynthesis in the detection of architectural distortion.

PubMed

Dibble, Elizabeth H; Lourenco, Ana P; Baird, Grayson L; Ward, Robert C; Maynard, A Stanley; Mainiero, Martha B

2018-01-01

To compare interobserver variability (IOV), reader confidence, and sensitivity/specificity in detecting architectural distortion (AD) on digital mammography (DM) versus digital breast tomosynthesis (DBT). This IRB-approved, HIPAA-compliant reader study used a counterbalanced experimental design. We searched radiology reports for AD on screening mammograms from 5 March 2012-27 November 2013. Cases were consensus-reviewed. Controls were selected from demographically matched non-AD examinations. Two radiologists and two fellows blinded to outcomes independently reviewed images from two patient groups in two sessions. Readers recorded presence/absence of AD and confidence level. Agreement and differences in confidence and sensitivity/specificity between DBT versus DM and attendings versus fellows were examined using weighted Kappa and generalised mixed modeling, respectively. There were 59 AD patients and 59 controls for 1,888 observations (59 × 2 (cases and controls) × 2 breasts × 2 imaging techniques × 4 readers). For all readers, agreement improved with DBT versus DM (0.61 vs. 0.37). Confidence was higher with DBT, p = .001. DBT achieved higher sensitivity (.59 vs. .32), p < .001; specificity remained high (>.90). DBT achieved higher positive likelihood ratio values, smaller negative likelihood ratio values, and larger ROC values. DBT decreases IOV, increases confidence, and improves sensitivity while maintaining high specificity in detecting AD. • Digital breast tomosynthesis decreases interobserver variability in the detection of architectural distortion. • Digital breast tomosynthesis increases reader confidence in the detection of architectural distortion. • Digital breast tomosynthesis improves sensitivity in the detection of architectural distortion.

Associations of coffee consumption and caffeine intake with mammographic breast density.

PubMed

Yaghjyan, Lusine; Colditz, Graham; Rosner, Bernard; Gasparova, Aleksandra; Tamimi, Rulla M

2018-05-01

Previous studies suggest that coffee and caffeine intake may be associated with reduced breast cancer risk. We investigated the association of coffee and caffeine intake with mammographic breast density by woman's menopausal status and, in postmenopausal women, by hormone therapy (HT). This study included 4130 cancer-free women within the Nurses' Health Study and Nurses' Health Study II cohorts. Percent breast density (PD) was measured from digitized film mammograms using a computer-assisted thresholding technique and square root-transformed for the analysis. Average cumulative coffee/caffeine consumption was calculated using data from all food frequency questionnaires preceding the mammogram date. Information regarding breast cancer risk factors was obtained from questionnaires closest to the mammogram date. We used generalized linear regression to quantify associations of regular, decaffeinated, and total coffee, and energy-adjusted caffeine intake with percent density. In multivariable analyses, decaffeinated coffee was positively associated with PD in premenopausal women (2+ cups/day: β = 0.23, p trend = 0.03). In postmenopausal women, decaffeinated and total coffee were inversely associated with PD (decaffeinated 2+ cups/day: β = - 0.24, p trend = 0.04; total 4+ cups/day: β = - 0.16, p trend = 0.02). Interaction of decaffeinated coffee with menopausal status was significant (p-interaction < 0.001). Among current HT users, regular coffee and caffeine were inversely associated with PD (regular coffee 4+ cups/day: β = - 0.29, p trend = 0.01; caffeine 4th vs. 1st quartile: β = - 0.32, p trend = 0.01). Among past users, decaffeinated coffee was inversely associated with PD (2+ cups/day β = - 0.70, p trend = 0.02). Associations of decaffeinated coffee with percent density differ by woman's menopausal status. Associations of regular coffee and caffeine with percent density may differ by HT status.

Risk of Breast Cancer in Women with False-Positive Results according to Mammographic Features.

PubMed

Castells, Xavier; Torá-Rocamora, Isabel; Posso, Margarita; Román, Marta; Vernet-Tomas, Maria; Rodríguez-Arana, Ana; Domingo, Laia; Vidal, Carmen; Baré, Marisa; Ferrer, Joana; Quintana, María Jesús; Sánchez, Mar; Natal, Carmen; Espinàs, Josep A; Saladié, Francina; Sala, María

2016-08-01

Purpose To assess the risk of breast cancer in women with false-positive screening results according to radiologic classification of mammographic features. Materials and Methods Review board approval was obtained, with waiver of informed consent. This retrospective cohort study included 521 200 women aged 50-69 years who underwent screening as part of the Spanish Breast Cancer Screening Program between 1994 and 2010 and who were observed until December 2012. Cox proportional hazards regression analysis was used to estimate the age-adjusted hazard ratio (HR) of breast cancer and the 95% confidence interval (CI) in women with false-positive mammograms as compared with women with negative mammograms. Separate models were adjusted for screen-detected and interval cancers and for screen-film and digital mammography. Time without a breast cancer diagnosis was plotted by using Kaplan-Meier curves. Results When compared with women with negative mammograms, the age-adjusted HR of cancer in women with false-positive results was 1.84 (95% CI: 1.73, 1.95; P < .001). The risk was higher in women who had calcifications, whether they were (HR, 2.73; 95% CI: 2.28, 3.28; P < .001) or were not (HR, 2.24; 95% CI: 2.02, 2.48; P < .001) associated with masses. Women in whom mammographic features showed changes in subsequent false-positive results were those who had the highest risk (HR, 9.13; 95% CI: 8.28, 10.07; P < .001). Conclusion Women with false-positive results had an increased risk of breast cancer, particularly women who had calcifications at mammography. Women who had more than one examination with false-positive findings and in whom the mammographic features changed over time had a highly increased risk of breast cancer. Previous mammographic features might yield useful information for further risk-prediction models and personalized follow-up screening protocols. (©) RSNA, 2016 Online supplemental material is available for this article.

Application of grammar-based codes for lossless compression of digital mammograms

NASA Astrophysics Data System (ADS)

Li, Xiaoli; Krishnan, Srithar; Ma, Ngok-Wah

2006-01-01

A newly developed grammar-based lossless source coding theory and its implementation was proposed in 1999 and 2000, respectively, by Yang and Kieffer. The code first transforms the original data sequence into an irreducible context-free grammar, which is then compressed using arithmetic coding. In the study of grammar-based coding for mammography applications, we encountered two issues: processing time and limited number of single-character grammar G variables. For the first issue, we discover a feature that can simplify the matching subsequence search in the irreducible grammar transform process. Using this discovery, an extended grammar code technique is proposed and the processing time of the grammar code can be significantly reduced. For the second issue, we propose to use double-character symbols to increase the number of grammar variables. Under the condition that all the G variables have the same probability of being used, our analysis shows that the double- and single-character approaches have the same compression rates. By using the methods proposed, we show that the grammar code can outperform three other schemes: Lempel-Ziv-Welch (LZW), arithmetic, and Huffman on compression ratio, and has similar error tolerance capabilities as LZW coding under similar circumstances.

Acceleration of Image Segmentation Algorithm for (Breast) Mammogram Images Using High-Performance Reconfigurable Dataflow Computers

PubMed Central

Filipovic, Nenad D.

2017-01-01

Image segmentation is one of the most common procedures in medical imaging applications. It is also a very important task in breast cancer detection. Breast cancer detection procedure based on mammography can be divided into several stages. The first stage is the extraction of the region of interest from a breast image, followed by the identification of suspicious mass regions, their classification, and comparison with the existing image database. It is often the case that already existing image databases have large sets of data whose processing requires a lot of time, and thus the acceleration of each of the processing stages in breast cancer detection is a very important issue. In this paper, the implementation of the already existing algorithm for region-of-interest based image segmentation for mammogram images on High-Performance Reconfigurable Dataflow Computers (HPRDCs) is proposed. As a dataflow engine (DFE) of such HPRDC, Maxeler's acceleration card is used. The experiments for examining the acceleration of that algorithm on the Reconfigurable Dataflow Computers (RDCs) are performed with two types of mammogram images with different resolutions. There were, also, several DFE configurations and each of them gave a different acceleration value of algorithm execution. Those acceleration values are presented and experimental results showed good acceleration. PMID:28611851

Acceleration of Image Segmentation Algorithm for (Breast) Mammogram Images Using High-Performance Reconfigurable Dataflow Computers.

PubMed

Milankovic, Ivan L; Mijailovic, Nikola V; Filipovic, Nenad D; Peulic, Aleksandar S

2017-01-01

Image segmentation is one of the most common procedures in medical imaging applications. It is also a very important task in breast cancer detection. Breast cancer detection procedure based on mammography can be divided into several stages. The first stage is the extraction of the region of interest from a breast image, followed by the identification of suspicious mass regions, their classification, and comparison with the existing image database. It is often the case that already existing image databases have large sets of data whose processing requires a lot of time, and thus the acceleration of each of the processing stages in breast cancer detection is a very important issue. In this paper, the implementation of the already existing algorithm for region-of-interest based image segmentation for mammogram images on High-Performance Reconfigurable Dataflow Computers (HPRDCs) is proposed. As a dataflow engine (DFE) of such HPRDC, Maxeler's acceleration card is used. The experiments for examining the acceleration of that algorithm on the Reconfigurable Dataflow Computers (RDCs) are performed with two types of mammogram images with different resolutions. There were, also, several DFE configurations and each of them gave a different acceleration value of algorithm execution. Those acceleration values are presented and experimental results showed good acceleration.

Cancer screening among Vietnamese in Hawaii.

PubMed

Nguyen, Ly T; Withy, Kelley; Nguyen, Michelle M; Yamada, Seiji

2003-07-01

To determine the extent of utilization of cancer screening services by Vietnamese in Hawaii, who had sought medical care from 1996 through 2000. A chart review of 952 adult Vietnamese patients was performed. Of all eligible women, 52% and 26% had Papanicolaou test and mammogram, respectively. Among men age 45 and over, 8.4% had prostate-specific antigen test and 3.4% had digital rectal exam. Flexible sigmoidoscopy and colonoscopy were not utilized by patients. This is the first study to examine the use of cancer screening tests by Vietnamese immigrants in Hawaii. Our findings of lower utilization rates in cancer screening by both male and female strongly support efforts to educate and promote preventive health for this population.

Rapid Point-Of-Care Breath Test for Biomarkers of Breast Cancer and Abnormal Mammograms

PubMed Central

Phillips, Michael; Beatty, J. David; Cataneo, Renee N.; Huston, Jan; Kaplan, Peter D.; Lalisang, Roy I.; Lambin, Philippe; Lobbes, Marc B. I.; Mundada, Mayur; Pappas, Nadine; Patel, Urvish

2014-01-01

Background Previous studies have reported volatile organic compounds (VOCs) in breath as biomarkers of breast cancer and abnormal mammograms, apparently resulting from increased oxidative stress and cytochrome p450 induction. We evaluated a six-minute point-of-care breath test for VOC biomarkers in women screened for breast cancer at centers in the USA and the Netherlands. Methods 244 women had a screening mammogram (93/37 normal/abnormal) or a breast biopsy (cancer/no cancer 35/79). A mobile point-of-care system collected and concentrated breath and air VOCs for analysis with gas chromatography and surface acoustic wave detection. Chromatograms were segmented into a time series of alveolar gradients (breath minus room air). Segmental alveolar gradients were ranked as candidate biomarkers by C-statistic value (area under curve [AUC] of receiver operating characteristic [ROC] curve). Multivariate predictive algorithms were constructed employing significant biomarkers identified with multiple Monte Carlo simulations and cross validated with a leave-one-out (LOO) procedure. Results Performance of breath biomarker algorithms was determined in three groups: breast cancer on biopsy versus normal screening mammograms (81.8% sensitivity, 70.0% specificity, accuracy 79% (73% on LOO) [C-statistic value], negative predictive value 99.9%); normal versus abnormal screening mammograms (86.5% sensitivity, 66.7% specificity, accuracy 83%, 62% on LOO); and cancer versus no cancer on breast biopsy (75.8% sensitivity, 74.0% specificity, accuracy 78%, 67% on LOO). Conclusions A pilot study of a six-minute point-of-care breath test for volatile biomarkers accurately identified women with breast cancer and with abnormal mammograms. Breath testing could potentially reduce the number of needless mammograms without loss of diagnostic sensitivity. PMID:24599224

Perceptions of One's Neighborhood and Mammogram Use among a Sample of Low-Income Women at Risk for Human Immunodeficiency Virus and Sexually Transmitted Infections.

PubMed

Davey-Rothwell, Melissa A; Bowie, Janice; Murray, Laura; Latkin, Carl A

2016-01-01

Neighborhood disorder, signs of physical and social disorganization, has been related to a range of poor mental and physical health outcomes. Although individual factors have been widely associated with getting a mammogram, little is known about the impact of the neighborhood environment on a woman's decision to get a mammogram. In a sample of women at risk for human immunodeficiency virus and sexually transmitted infections, we explored the role of perceptions of one's neighborhood on getting a mammogram. The study included two samples: women 40 to 49 years (n = 233) and women 50 years and older (n = 83). Data were collected from May 2006 through June 2008. Women age 50 years and older who lived in a neighborhood with disorder were 72% less likely to get a mammogram compared with women who lived in neighborhoods without disorder. There was no relationship for women age 40 to 49 years. Interventions are needed to increase awareness and encourage women living in neighborhoods with disorder to get a mammogram. In addition to interventions to increase mammography, programs are needed to decrease neighborhood disorder. Increasing neighborhood cohesion, social control, and empowerment could integrate health promotion programs to both reduce disorder and increase health behaviors. Copyright © 2016 Jacobs Institute of Women's Health. Published by Elsevier Inc. All rights reserved.

Mammogram classification scheme using 2D-discrete wavelet and local binary pattern for detection of breast cancer

NASA Astrophysics Data System (ADS)

Adi Putra, Januar

2018-04-01

In this paper, we propose a new mammogram classification scheme to classify the breast tissues as normal or abnormal. Feature matrix is generated using Local Binary Pattern to all the detailed coefficients from 2D-DWT of the region of interest (ROI) of a mammogram. Feature selection is done by selecting the relevant features that affect the classification. Feature selection is used to reduce the dimensionality of data and features that are not relevant, in this paper the F-test and Ttest will be performed to the results of the feature extraction dataset to reduce and select the relevant feature. The best features are used in a Neural Network classifier for classification. In this research we use MIAS and DDSM database. In addition to the suggested scheme, the competent schemes are also simulated for comparative analysis. It is observed that the proposed scheme has a better say with respect to accuracy, specificity and sensitivity. Based on experiments, the performance of the proposed scheme can produce high accuracy that is 92.71%, while the lowest accuracy obtained is 77.08%.

Cancer Cases from ACRIN Digital Mammographic Imaging Screening Trial: Radiologist Analysis with Use of a Logistic Regression Model1

PubMed Central

Pisano, Etta D.; Acharyya, Suddhasatta; Cole, Elodia B.; Marques, Helga S.; Yaffe, Martin J.; Blevins, Meredith; Conant, Emily F.; Hendrick, R. Edward; Baum, Janet K.; Fajardo, Laurie L.; Jong, Roberta A.; Koomen, Marcia A.; Kuzmiak, Cherie M.; Lee, Yeonhee; Pavic, Dag; Yoon, Sora C.; Padungchaichote, Wittaya; Gatsonis, Constantine

2009-01-01

Purpose: To determine which factors contributed to the Digital Mammographic Imaging Screening Trial (DMIST) cancer detection results. Materials and Methods: This project was HIPAA compliant and institutional review board approved. Seven radiologist readers reviewed the film hard-copy (screen-film) and digital mammograms in DMIST cancer cases and assessed the factors that contributed to lesion visibility on both types of images. Two multinomial logistic regression models were used to analyze the combined and condensed visibility ratings assigned by the readers to the paired digital and screen-film images. Results: Readers most frequently attributed differences in DMIST cancer visibility to variations in image contrast—not differences in positioning or compression—between digital and screen-film mammography. The odds of a cancer being more visible on a digital mammogram—rather than being equally visible on digital and screen-film mammograms—were significantly greater for women with dense breasts than for women with nondense breasts, even with the data adjusted for patient age, lesion type, and mammography system (odds ratio, 2.28; P < .0001). The odds of a cancer being more visible at digital mammography—rather than being equally visible at digital and screen-film mammography—were significantly greater for lesions imaged with the General Electric digital mammography system than for lesions imaged with the Fischer (P = .0070) and Fuji (P = .0070) devices. Conclusion: The significantly better diagnostic accuracy of digital mammography, as compared with screen-film mammography, in women with dense breasts demonstrated in the DMIST was most likely attributable to differences in image contrast, which were most likely due to the inherent system performance improvements that are available with digital mammography. The authors conclude that the DMIST results were attributable primarily to differences in the display and acquisition characteristics of the mammography devices rather than to reader variability. PMID:19703878

Two-view information fusion for improvement of computer-aided detection (CAD) of breast masses on mammograms

NASA Astrophysics Data System (ADS)

Wei, Jun; Sahiner, Berkman; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Helvie, Mark A.; Roubidoux, Marilyn A.; Zhou, Chuan; Ge, Jun; Zhang, Yiheng

2006-03-01

We are developing a two-view information fusion method to improve the performance of our CAD system for mass detection. Mass candidates on each mammogram were first detected with our single-view CAD system. Potential object pairs on the two-view mammograms were then identified by using the distance between the object and the nipple. Morphological features, Hessian feature, correlation coefficients between the two paired objects and texture features were used as input to train a similarity classifier that estimated a similarity scores for each pair. Finally, a linear discriminant analysis (LDA) classifier was used to fuse the score from the single-view CAD system and the similarity score. A data set of 475 patients containing 972 mammograms with 475 biopsy-proven masses was used to train and test the CAD system. All cases contained the CC view and the MLO or LM view. We randomly divided the data set into two independent sets of 243 cases and 232 cases. The training and testing were performed using the 2-fold cross validation method. The detection performance of the CAD system was assessed by free response receiver operating characteristic (FROC) analysis. The average test FROC curve was obtained from averaging the FP rates at the same sensitivity along the two corresponding test FROC curves from the 2-fold cross validation. At the case-based sensitivities of 90%, 85% and 80% on the test set, the single-view CAD system achieved an FP rate of 2.0, 1.5, and 1.2 FPs/image, respectively. With the two-view fusion system, the FP rates were reduced to 1.7, 1.3, and 1.0 FPs/image, respectively, at the corresponding sensitivities. The improvement was found to be statistically significant (p<0.05) by the AFROC method. Our results indicate that the two-view fusion scheme can improve the performance of mass detection on mammograms.

A similarity measure method combining location feature for mammogram retrieval.

PubMed

Wang, Zhiqiong; Xin, Junchang; Huang, Yukun; Li, Chen; Xu, Ling; Li, Yang; Zhang, Hao; Gu, Huizi; Qian, Wei

2018-05-28

Breast cancer, the most common malignancy among women, has a high mortality rate in clinical practice. Early detection, diagnosis and treatment can reduce the mortalities of breast cancer greatly. The method of mammogram retrieval can help doctors to find the early breast lesions effectively and determine a reasonable feature set for image similarity measure. This will improve the accuracy effectively for mammogram retrieval. This paper proposes a similarity measure method combining location feature for mammogram retrieval. Firstly, the images are pre-processed, the regions of interest are detected and the lesions are segmented in order to get the center point and radius of the lesions. Then, the method, namely Coherent Point Drift, is used for image registration with the pre-defined standard image. The center point and radius of the lesions after registration are obtained and the standard location feature of the image is constructed. This standard location feature can help figure out the location similarity between the image pair from the query image to each dataset image in the database. Next, the content feature of the image is extracted, including the Histogram of Oriented Gradients, the Edge Direction Histogram, the Local Binary Pattern and the Gray Level Histogram, and the image pair content similarity can be calculated using the Earth Mover's Distance. Finally, the location similarity and content similarity are fused to form the image fusion similarity, and the specified number of the most similar images can be returned according to it. In the experiment, 440 mammograms, which are from Chinese women in Northeast China, are used as the database. When fusing 40% lesion location feature similarity and 60% content feature similarity, the results have obvious advantages. At this time, precision is 0.83, recall is 0.76, comprehensive indicator is 0.79, satisfaction is 96.0%, mean is 4.2 and variance is 17.7. The results show that the precision and recall of this method have obvious advantage, compared with the content-based image retrieval.

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

PubMed

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

2016-11-15

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

Study of the Effects of Total Modulation Transfer Function Changes on Observer Performance Using Clinical Mammograms.

NASA Astrophysics Data System (ADS)

Bencomo, Jose Antonio Fagundez

The main goal of this study was to relate physical changes in image quality measured by Modulation Transfer Function (MTF) to diagnostic accuracy. One Hundred and Fifty Kodak Min-R screen/film combination conventional craniocaudal mammograms obtained with the Pfizer Microfocus Mammographic system were selected from the files of the Department of Radiology, at M.D. Anderson Hospital and Tumor Institute. The mammograms included 88 cases with a variety of benign diagnosis and 62 cases with a variety of malignant biopsy diagnosis. The average age of the patient population was 55 years old. 70 cases presented calcifications with 30 cases having calcifications smaller than 0.5mm. 46 cases presented irregular bordered masses larger than 1 cm. 30 cases presented smooth bordered masses with 20 larger than 1 cm. Four separated copies of the original images were made each having a different change in the MTF using a defocusing technique whereby copies of the original were obtained by light exposure through different thicknesses (spacing) of transparent film base. The mammograms were randomized, and evaluated by three experienced mammographers for the degree of visibility of various anatomical breast structures and pathological lesions (masses and calicifications), subjective image quality, and mammographic interpretation. 3,000 separate evaluations were anayzed by several statistical techniques including Receiver Operating Characteristic curve analysis, McNemar test for differences between proportions and the Landis et al. method of agreement weighted kappa for ordinal categorical data. Results from the statistical analysis show: (1) There were no statistical significant differences in the diagnostic accuracy of the observers when diagnosing from mammograms with the same MTF. (2) There were no statistically significant differences in diagnostic accuracy for each observer when diagnosing from mammograms with the different MTF's used in the study. (3) There statistical significant differences in detail visibility between the copies and the originals. Detail visibility was better in the originals. (4) Feature interpretations were not significantly different between the originals and the copies. (5) Perception of image quality did not affect image interpretation. Continuation and improvement of this research ca be accomplished by: using a case population more sensitive to MTF changes, i.e., asymptomatic women with minimum breast cancer, more observers (including less experienced radiologists and experienced technologists) must collaborate in the study, and using a minimum of 200 benign and 200 malignant cases.

Optical Demonstration of a Medical Imaging System with an EMCCD-Sensor Array for Use in a High Resolution Dynamic X-ray Imager

PubMed Central

Qu, Bin; Huang, Ying; Wang, Weiyuan; Sharma, Prateek; Kuhls-Gilcrist, Andrew T.; Cartwright, Alexander N.; Titus, Albert H.; Bednarek, Daniel R.; Rudin, Stephen

2011-01-01

Use of an extensible array of Electron Multiplying CCDs (EMCCDs) in medical x-ray imager applications was demonstrated for the first time. The large variable electronic-gain (up to 2000) and small pixel size of EMCCDs provide effective suppression of readout noise compared to signal, as well as high resolution, enabling the development of an x-ray detector with far superior performance compared to conventional x-ray image intensifiers and flat panel detectors. We are developing arrays of EMCCDs to overcome their limited field of view (FOV). In this work we report on an array of two EMCCD sensors running simultaneously at a high frame rate and optically focused on a mammogram film showing calcified ducts. The work was conducted on an optical table with a pulsed LED bar used to provide a uniform diffuse light onto the film to simulate x-ray projection images. The system can be selected to run at up to 17.5 frames per second or even higher frame rate with binning. Integration time for the sensors can be adjusted from 1 ms to 1000 ms. Twelve-bit correlated double sampling AD converters were used to digitize the images, which were acquired by a National Instruments dual-channel Camera Link PC board in real time. A user-friendly interface was programmed using LabVIEW to save and display 2K × 1K pixel matrix digital images. The demonstration tiles a 2 × 1 array to acquire increased-FOV stationary images taken at different gains and fluoroscopic-like videos recorded by scanning the mammogram simultaneously with both sensors. The results show high resolution and high dynamic range images stitched together with minimal adjustments needed. The EMCCD array design allows for expansion to an M×N array for arbitrarily larger FOV, yet with high resolution and large dynamic range maintained. PMID:23505330

Toward a standard reference database for computer-aided mammography

NASA Astrophysics Data System (ADS)

Oliveira, Júlia E. E.; Gueld, Mark O.; de A. Araújo, Arnaldo; Ott, Bastian; Deserno, Thomas M.

2008-03-01

Because of the lack of mammography databases with a large amount of codified images and identified characteristics like pathology, type of breast tissue, and abnormality, there is a problem for the development of robust systems for computer-aided diagnosis. Integrated to the Image Retrieval in Medical Applications (IRMA) project, we present an available mammography database developed from the union of: The Mammographic Image Analysis Society Digital Mammogram Database (MIAS), The Digital Database for Screening Mammography (DDSM), the Lawrence Livermore National Laboratory (LLNL), and routine images from the Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen. Using the IRMA code, standardized coding of tissue type, tumor staging, and lesion description was developed according to the American College of Radiology (ACR) tissue codes and the ACR breast imaging reporting and data system (BI-RADS). The import was done automatically using scripts for image download, file format conversion, file name, web page and information file browsing. Disregarding the resolution, this resulted in a total of 10,509 reference images, and 6,767 images are associated with an IRMA contour information feature file. In accordance to the respective license agreements, the database will be made freely available for research purposes, and may be used for image based evaluation campaigns such as the Cross Language Evaluation Forum (CLEF). We have also shown that it can be extended easily with further cases imported from a picture archiving and communication system (PACS).

The simulation of 3D mass models in 2D digital mammography and breast tomosynthesis

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

Shaheen, Eman, E-mail: eman.shaheen@uzleuven.be; De Keyzer, Frederik; Bosmans, Hilde

2014-08-15

Purpose: This work proposes a new method of building 3D breast mass models with different morphological shapes and describes the validation of the realism of their appearance after simulation into 2D digital mammograms and breast tomosynthesis images. Methods: Twenty-five contrast enhanced MRI breast lesions were collected and each mass was manually segmented in the three orthogonal views: sagittal, coronal, and transversal. The segmented models were combined, resampled to have isotropic voxel sizes, triangularly meshed, and scaled to different sizes. These masses were referred to as nonspiculated masses and were then used as nuclei onto which spicules were grown with anmore » iterative branching algorithm forming a total of 30 spiculated masses. These 55 mass models were projected into 2D projection images to obtain mammograms after image processing and into tomographic sequences of projection images, which were then reconstructed to form 3D tomosynthesis datasets. The realism of the appearance of these mass models was assessed by five radiologists via receiver operating characteristic (ROC) analysis when compared to 54 real masses. All lesions were also given a breast imaging reporting and data system (BIRADS) score. The data sets of 2D mammography and tomosynthesis were read separately. The Kendall's coefficient of concordance was used for the interrater observer agreement assessment for the BIRADS scores per modality. Further paired analysis, using the Wilcoxon signed rank test, of the BIRADS assessment between 2D and tomosynthesis was separately performed for the real masses and for the simulated masses. Results: The area under the ROC curves, averaged over all observers, was 0.54 (95% confidence interval [0.50, 0.66]) for the 2D study, and 0.67 (95% confidence interval [0.55, 0.79]) for the tomosynthesis study. According to the BIRADS scores, the nonspiculated and the spiculated masses varied in their degrees of malignancy from normal (BIRADS 1) to highly suggestive for malignancy (BIRADS 5) indicating the required variety of shapes and margins of these models. The assessment of the BIRADS scores for all observers indicated good agreement based on Kendall's coefficient for both the 2D and the tomosynthesis evaluations. The paired analysis of the BIRADS scores between 2D and tomosynthesis for each observer revealed consistent behavior for the real and simulated masses. Conclusions: A database of 3D mass models, with variety of shapes and margins, was validated for the realism of their appearance for 2D digital mammography and for breast tomosynthesis. This database is suitable for use in future observer performance studies whether in virtual clinical trials or in patient images with simulated lesions.« less

Witnessing to Save Lives!!

ERIC Educational Resources Information Center

Spatz, Thea S.; And Others

1996-01-01

A statewide effort in Arkansas to promote low-cost mammograms required community-based education for adult learners. It combined health education, learning styles, brain hemisphericity, anthropology, and the presentation of culturally appropriate role models. (Author/JOW)

Abnormality detection of mammograms by discriminative dictionary learning on DSIFT descriptors.

PubMed

Tavakoli, Nasrin; Karimi, Maryam; Nejati, Mansour; Karimi, Nader; Reza Soroushmehr, S M; Samavi, Shadrokh; Najarian, Kayvan

2017-07-01

Detection and classification of breast lesions using mammographic images are one of the most difficult studies in medical image processing. A number of learning and non-learning methods have been proposed for detecting and classifying these lesions. However, the accuracy of the detection/classification still needs improvement. In this paper we propose a powerful classification method based on sparse learning to diagnose breast cancer in mammograms. For this purpose, a supervised discriminative dictionary learning approach is applied on dense scale invariant feature transform (DSIFT) features. A linear classifier is also simultaneously learned with the dictionary which can effectively classify the sparse representations. Our experimental results show the superior performance of our method compared to existing approaches.

The personal costs and convenience of screening mammography.

PubMed

Suter, Lisa Gale; Nakano, Connie Y; Elmore, Joann G

2002-09-01

Few studies have examined the impact of women's personal costs on obtaining a screening mammogram in the United States. All women obtaining screening mammograms at nine Connecticut mammography facilities during a 2-week study period were asked to complete a questionnaire. Facilities included urban and rural fixed sites and mobile sites. The survey included questions about insurance coverage, mammogram payment, and personal costs in terms of transportation, family care, parking, and lost work time from the women's perspective. The response rate was 62% (731 of 1189). Thirty-two percent of respondents incurred some type of personal cost, including lost work time, family care, and parking. Women incurring personal costs were more likely than those without personal costs to attend an urban facility (46% vs. 23%, p < 0.01) and be under the age of 50 (40% vs. 26%, p < 0.01). Overall, 61% of women listed convenience and 17% listed cost as a reason for choosing a mammography facility; 23% reported that cost might prevent them from obtaining a future mammogram. One third of women obtaining mammograms may be incurring personal costs. These personal costs should be considered in future cost-effectiveness analyses.

Breast Cancers Between Mammograms Have Aggressive Features

Cancer.gov

Breast cancers that are discovered in the period between regular screening mammograms—known as interval cancers—are more likely to have features associated with aggressive behavior and a poor prognosis than cancers found via screening mammograms.

Impact of Immediate Interpretation of Screening Tomosynthesis Mammography on Performance Metrics.

PubMed

Winkler, Nicole S; Freer, Phoebe; Anzai, Yoshimi; Hu, Nan; Stein, Matthew

2018-05-07

This study aimed to compare performance metrics for immediate and delayed batch interpretation of screening tomosynthesis mammograms. This HIPAA compliant study was approved by institutional review board with a waiver of consent. A retrospective analysis of screening performance metrics for tomosynthesis mammograms interpreted in 2015 when mammograms were read immediately was compared to historical controls from 2013 to 2014 when mammograms were batch interpreted after the patient had departed. A total of 5518 screening tomosynthesis mammograms (n = 1212 for batch interpretation and n = 4306 for immediate interpretation) were evaluated. The larger sample size for the latter group reflects a group practice shift to performing tomosynthesis for the majority of patients. Age, breast density, comparison examinations, and high-risk status were compared. An asymptotic proportion test and multivariable analysis were used to compare performance metrics. There was no statistically significant difference in recall or cancer detection rates for the batch interpretation group compared to immediate interpretation group with respective recall rate of 6.5% vs 5.3% = +1.2% (95% confidence interval -0.3 to 2.7%; P = .101) and cancer detection rate of 6.6 vs 7.2 per thousand = -0.6 (95% confidence interval -5.9 to 4.6; P = .825). There was no statistically significant difference in positive predictive values (PPVs) including PPV1 (screening recall), PPV2 (biopsy recommendation), or PPV 3 (biopsy performed) with batch interpretation (10.1%, 42.1%, and 40.0%, respectively) and immediate interpretation (13.6%, 39.2%, and 39.7%, respectively). After adjusting for age, breast density, high-risk status, and comparison mammogram, there was no difference in the odds of being recalled or cancer detection between the two groups. There is no statistically significant difference in interpretation performance metrics for screening tomosynthesis mammograms interpreted immediately compared to those interpreted in a delayed fashion. Copyright © 2018. Published by Elsevier Inc.

Mammography decision making: Trends and predictors of provider communication in the Health Information National Trends Survey, 2011 to 2014.

PubMed

Spring, Laura M; Marshall, Megan R; Warner, Erica T

2017-02-01

In 2009, the US Preventive Services Task Force recommended that the decision to initiate screening mammography before age 50 years should be individualized. Herein, the authors examined whether health care providers are communicating regarding mammography decision making with women and whether communication is associated with screening behavior. Data were drawn from the 2011 to 2014 Health Information National Trends Survey (HINTS). A total of 5915 female respondents aged ≥ 40 years who responded to the following question were included: "Has a doctor or other health professional ever told you that you could choose whether or not to have a mammogram?" We used logistic regression to generate odds ratios (ORs) and 95% confidence intervals (95% CIs) for predictors of provider communication and assessed whether provider communication was associated with mammography in the previous 2 years overall and stratified by age. Fewer than 50% of the women reported provider communication regarding mammogram choice. Women who reported provider communication were not found to be more likely to report no mammogram within the past 2 years (OR, 1.07; 95% CI, 0.87-1.31) compared with those who did not. When stratified by 10-year age group, provider communication was associated with a higher likelihood of no mammogram only among women age ≥70 years (OR, 1.64; 95% CI, 1.15-2.34), and was associated with a lower likelihood of no mammogram only among women aged 40 to 49 years (OR, 0.63; 95% CI, 0.43-0.92). Between 2011 and 2014, less than one-half of women received communication regarding mammogram choice despite recommendations from the US Preventive Services Task Force. Provider communication regarding mammogram choice can influence screening behavior, particularly for younger and older women. Cancer 2017;123:401-409. © 2016 American Cancer Society. © 2016 American Cancer Society.

Breast dosimetry in clinical mammography

NASA Astrophysics Data System (ADS)

Benevides, Luis Alberto Do Rego

The objective of this study was show that a clinical dosimetry protocol that utilizes a dosimetric breast phantom series based on population anthropometric measurements can reliably predict the average glandular dose (AGD) imparted to the patient during a routine screening mammogram. In the study, AGD was calculated using entrance skin exposure and dose conversion factors based on fibroglandular content, compressed breast thickness, mammography unit parameters and modifying parameters for homogeneous phantom (phantom factor), compressed breast lateral dimensions (volume factor) and anatomical features (anatomical factor). The protocol proposes the use of a fiber-optic coupled (FOCD) or Metal Oxide Semiconductor Field Effect Transistor (MOSFET) dosimeter to measure the entrance skin exposure at the time of the mammogram without interfering with diagnostic information of the mammogram. The study showed that FOCD had sensitivity with less than 7% energy dependence, linear in all tube current-time product stations, and was reproducible within 2%. FOCD was superior to MOSFET dosimeter in sensitivity, reusability, and reproducibility. The patient fibroglandular content was evaluated using a calibrated modified breast tissue equivalent homogeneous phantom series (BRTES-MOD) designed from anthropomorphic measurements of a screening mammography population and whose elemental composition was referenced to International Commission on Radiation Units and Measurements Report 44 tissues. The patient fibroglandular content, compressed breast thickness along with unit parameters and spectrum half-value layer were used to derive the currently used dose conversion factor (DgN). The study showed that the use of a homogeneous phantom, patient compressed breast lateral dimensions and patient anatomical features can affect AGD by as much as 12%, 3% and 1%, respectively. The protocol was found to be superior to existing methodologies. In addition, the study population anthropometric measurements enabled the development of analytical equations to calculate the whole breast area, estimate for the skin layer thickness and optimal location for automatic exposure control ionization chamber. The clinical dosimetry protocol developed in this study can reliably predict the AGD imparted to an individual patient during a routine screening mammogram.

What Is a Mammogram and When Should I Get One?

MedlinePlus

... Statistics What CDC Is Doing Research African American Women and Mass Media Campaign Public Service Announcements Print Materials Buttons and Badges Stay Informed Cancer Home What Is a Mammogram? Language: English (US) Español ( ...

Computerized multiple image analysis on mammograms: performance improvement of nipple identification for registration of multiple views using texture convergence analyses

NASA Astrophysics Data System (ADS)

Zhou, Chuan; Chan, Heang-Ping; Sahiner, Berkman; Hadjiiski, Lubomir M.; Paramagul, Chintana

2004-05-01

Automated registration of multiple mammograms for CAD depends on accurate nipple identification. We developed two new image analysis techniques based on geometric and texture convergence analyses to improve the performance of our previously developed nipple identification method. A gradient-based algorithm is used to automatically track the breast boundary. The nipple search region along the boundary is then defined by geometric convergence analysis of the breast shape. Three nipple candidates are identified by detecting the changes along the gray level profiles inside and outside the boundary and the changes in the boundary direction. A texture orientation-field analysis method is developed to estimate the fourth nipple candidate based on the convergence of the tissue texture pattern towards the nipple. The final nipple location is determined from the four nipple candidates by a confidence analysis. Our training and test data sets consisted of 419 and 368 randomly selected mammograms, respectively. The nipple location identified on each image by an experienced radiologist was used as the ground truth. For 118 of the training and 70 of the test images, the radiologist could not positively identify the nipple, but provided an estimate of its location. These were referred to as invisible nipple images. In the training data set, 89.37% (269/301) of the visible nipples and 81.36% (96/118) of the invisible nipples could be detected within 1 cm of the truth. In the test data set, 92.28% (275/298) of the visible nipples and 67.14% (47/70) of the invisible nipples were identified within 1 cm of the truth. In comparison, our previous nipple identification method without using the two convergence analysis techniques detected 82.39% (248/301), 77.12% (91/118), 89.93% (268/298) and 54.29% (38/70) of the nipples within 1 cm of the truth for the visible and invisible nipples in the training and test sets, respectively. The results indicate that the nipple on mammograms can be detected accurately. This will be an important step towards automatic multiple image analysis for CAD techniques.

Sampling probability distributions of lesions in mammograms

NASA Astrophysics Data System (ADS)

Looney, P.; Warren, L. M.; Dance, D. R.; Young, K. C.

2015-03-01

One approach to image perception studies in mammography using virtual clinical trials involves the insertion of simulated lesions into normal mammograms. To facilitate this, a method has been developed that allows for sampling of lesion positions across the cranio-caudal and medio-lateral radiographic projections in accordance with measured distributions of real lesion locations. 6825 mammograms from our mammography image database were segmented to find the breast outline. The outlines were averaged and smoothed to produce an average outline for each laterality and radiographic projection. Lesions in 3304 mammograms with malignant findings were mapped on to a standardised breast image corresponding to the average breast outline using piecewise affine transforms. A four dimensional probability distribution function was found from the lesion locations in the cranio-caudal and medio-lateral radiographic projections for calcification and noncalcification lesions. Lesion locations sampled from this probability distribution function were mapped on to individual mammograms using a piecewise affine transform which transforms the average outline to the outline of the breast in the mammogram. The four dimensional probability distribution function was validated by comparing it to the two dimensional distributions found by considering each radiographic projection and laterality independently. The correlation of the location of the lesions sampled from the four dimensional probability distribution function across radiographic projections was shown to match the correlation of the locations of the original mapped lesion locations. The current system has been implemented as a web-service on a server using the Python Django framework. The server performs the sampling, performs the mapping and returns the results in a javascript object notation format.

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors: Automated measurement development for full field digital mammography

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

Fowler, E. E.; Sellers, T. A.; Lu, B.

Purpose: The Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors are used for standardized mammographic reporting and are assessed visually. This reporting is clinically relevant because breast composition can impact mammographic sensitivity and is a breast cancer risk factor. New techniques are presented and evaluated for generating automated BI-RADS breast composition descriptors using both raw and calibrated full field digital mammography (FFDM) image data.Methods: A matched case-control dataset with FFDM images was used to develop three automated measures for the BI-RADS breast composition descriptors. Histograms of each calibrated mammogram in the percent glandular (pg) representation were processed tomore » create the new BR{sub pg} measure. Two previously validated measures of breast density derived from calibrated and raw mammograms were converted to the new BR{sub vc} and BR{sub vr} measures, respectively. These three measures were compared with the radiologist-reported BI-RADS compositions assessments from the patient records. The authors used two optimization strategies with differential evolution to create these measures: method-1 used breast cancer status; and method-2 matched the reported BI-RADS descriptors. Weighted kappa (κ) analysis was used to assess the agreement between the new measures and the reported measures. Each measure's association with breast cancer was evaluated with odds ratios (ORs) adjusted for body mass index, breast area, and menopausal status. ORs were estimated as per unit increase with 95% confidence intervals.Results: The three BI-RADS measures generated by method-1 had κ between 0.25–0.34. These measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.87 (1.34, 2.59) for BR{sub pg}; (b) OR = 1.93 (1.36, 2.74) for BR{sub vc}; and (c) OR = 1.37 (1.05, 1.80) for BR{sub vr}. The measures generated by method-2 had κ between 0.42–0.45. Two of these measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.95 (1.24, 3.09) for BR{sub pg}; (b) OR = 1.42 (0.87, 2.32) for BR{sub vc}; and (c) OR = 2.13 (1.22, 3.72) for BR{sub vr}. The radiologist-reported measures from the patient records showed a similar association, OR = 1.49 (0.99, 2.24), although only borderline statistically significant.Conclusions: A general framework was developed and validated for converting calibrated mammograms and continuous measures of breast density to fully automated approximations for the BI-RADS breast composition descriptors. The techniques are general and suitable for a broad range of clinical and research applications.« less

21 CFR 900.2 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-04-01

... radiographic image of a phantom. (ll) Physical science means physics, chemistry, radiation science (including medical physics and health physics), and engineering. (mm) Positive mammogram means a mammogram that has... FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MAMMOGRAPHY QUALITY...

Women with Disabilities and Breast Cancer Screening

MedlinePlus

... Likely to Have Received a Mammogram During the Past Two Years 1 Breast Cancer Screening Recommendations 2 If you ... of Age Who Received a Mammogram During the Past 2 Years, By Disability Status – 2010 National Household Interview Survey( ...

Engaging Immigrant and Refugee Women in Breast Health Education.

PubMed

Gondek, Matthew; Shogan, May; Saad-Harfouche, Frances G; Rodriguez, Elisa M; Erwin, Deborah O; Griswold, Kim; Mahoney, Martin C

2015-09-01

This project assessed the impact of a community-based educational program on breast cancer knowledge and screening among Buffalo (NY) immigrant and refugee females. Program participants completed language-matched pre- and post-test assessments during a single session educational program; breast cancer screening information was obtained from the mobile mammography unit to which participants were referred. Pre- and post-test knowledge scores were compared to assess changes in responses to each of the six individual knowledge items, as well as overall. Mammogram records were reviewed to identify Breast Imaging Reporting and Data System (BI-RADS) scores. The proportion of correct responses to each of the six knowledge items increased significantly on the post-program assessments; 33 % of women >40 years old completed mammograms. The findings suggest that a health education program for immigrant and refugee women, delivered in community-based settings and involving interpreters, can enhance breast cancer knowledge and lead to improvements in mammography completion.

Improving the accuracy in detection of clustered microcalcifications with a context-sensitive classification model.

PubMed

Wang, Juan; Nishikawa, Robert M; Yang, Yongyi

2016-01-01

In computer-aided detection of microcalcifications (MCs), the detection accuracy is often compromised by frequent occurrence of false positives (FPs), which can be attributed to a number of factors, including imaging noise, inhomogeneity in tissue background, linear structures, and artifacts in mammograms. In this study, the authors investigated a unified classification approach for combating the adverse effects of these heterogeneous factors for accurate MC detection. To accommodate FPs caused by different factors in a mammogram image, the authors developed a classification model to which the input features were adapted according to the image context at a detection location. For this purpose, the input features were defined in two groups, of which one group was derived from the image intensity pattern in a local neighborhood of a detection location, and the other group was used to characterize how a MC is different from its structural background. Owing to the distinctive effect of linear structures in the detector response, the authors introduced a dummy variable into the unified classifier model, which allowed the input features to be adapted according to the image context at a detection location (i.e., presence or absence of linear structures). To suppress the effect of inhomogeneity in tissue background, the input features were extracted from different domains aimed for enhancing MCs in a mammogram image. To demonstrate the flexibility of the proposed approach, the authors implemented the unified classifier model by two widely used machine learning algorithms, namely, a support vector machine (SVM) classifier and an Adaboost classifier. In the experiment, the proposed approach was tested for two representative MC detectors in the literature [difference-of-Gaussians (DoG) detector and SVM detector]. The detection performance was assessed using free-response receiver operating characteristic (FROC) analysis on a set of 141 screen-film mammogram (SFM) images (66 cases) and a set of 188 full-field digital mammogram (FFDM) images (95 cases). The FROC analysis results show that the proposed unified classification approach can significantly improve the detection accuracy of two MC detectors on both SFM and FFDM images. Despite the difference in performance between the two detectors, the unified classifiers can reduce their FP rate to a similar level in the output of the two detectors. In particular, with true-positive rate at 85%, the FP rate on SFM images for the DoG detector was reduced from 1.16 to 0.33 clusters/image (unified SVM) and 0.36 clusters/image (unified Adaboost), respectively; similarly, for the SVM detector, the FP rate was reduced from 0.45 clusters/image to 0.30 clusters/image (unified SVM) and 0.25 clusters/image (unified Adaboost), respectively. Similar FP reduction results were also achieved on FFDM images for the two MC detectors. The proposed unified classification approach can be effective for discriminating MCs from FPs caused by different factors (such as MC-like noise patterns and linear structures) in MC detection. The framework is general and can be applicable for further improving the detection accuracy of existing MC detectors.

Reduction of false-positives in a CAD scheme for automated detection of architectural distortion in digital mammography

NASA Astrophysics Data System (ADS)

de Oliveira, Helder C. R.; Mencattini, Arianna; Casti, Paola; Martinelli, Eugenio; di Natale, Corrado; Catani, Juliana H.; de Barros, Nestor; Melo, Carlos F. E.; Gonzaga, Adilson; Vieira, Marcelo A. C.

2018-02-01

This paper proposes a method to reduce the number of false-positives (FP) in a computer-aided detection (CAD) scheme for automated detection of architectural distortion (AD) in digital mammography. AD is a subtle contraction of breast parenchyma that may represent an early sign of breast cancer. Due to its subtlety and variability, AD is more difficult to detect compared to microcalcifications and masses, and is commonly found in retrospective evaluations of false-negative mammograms. Several computer-based systems have been proposed for automated detection of AD in breast images. The usual approach is automatically detect possible sites of AD in a mammographic image (segmentation step) and then use a classifier to eliminate the false-positives and identify the suspicious regions (classification step). This paper focus on the optimization of the segmentation step to reduce the number of FPs that is used as input to the classifier. The proposal is to use statistical measurements to score the segmented regions and then apply a threshold to select a small quantity of regions that should be submitted to the classification step, improving the detection performance of a CAD scheme. We evaluated 12 image features to score and select suspicious regions of 74 clinical Full-Field Digital Mammography (FFDM). All images in this dataset contained at least one region with AD previously marked by an expert radiologist. The results showed that the proposed method can reduce the false positives of the segmentation step of the CAD scheme from 43.4 false positives (FP) per image to 34.5 FP per image, without increasing the number of false negatives.

Diffusion of digital breast tomosynthesis among women in primary care: associations with insurance type.

PubMed

Clark, Cheryl R; Tosteson, Tor D; Tosteson, Anna N A; Onega, Tracy; Weiss, Julie E; Harris, Kimberly A; Haas, Jennifer S

2017-05-01

Digital breast tomosynthesis (DBT) has shown potential to improve breast cancer screening and diagnosis compared to digital mammography (DM). The FDA approved DBT use in conjunction with conventional DM in 2011, but coverage was approved by CMS recently in 2015. Given changes in coverage policies, it is important to monitor diffusion of DBT by insurance type. This study examined DBT trends and estimated associations with insurance type. From June 2011 to September 2014, DBT use in 22 primary care centers in the Dartmouth -Brigham and Women's Hospital Population-based Research Optimizing Screening through Personalized Regimens research center (PROSPR) was examined among women aged 40-89. A longitudinal repeated measures analysis estimated the proportion of DBT performed for screening or diagnostic indications over time and by insurance type. During the study period, 93,182 mammograms were performed on 48,234 women. Of these exams, 16,506 DBT tests were performed for screening (18.1%) and 2537 were performed for diagnosis (15.7%). Between 2011 and 2014, DBT utilization increased in all insurance groups. However, by the latest observed period, screening DBT was used more frequently under private insurance (43.4%) than Medicaid (36.2%), Medicare (37.8%), other (38.6%), or no insurance (32.9%; P < 0.0001). No sustained differences in use of DBT for diagnostic testing were seen by insurance type. DBT is increasingly used for breast cancer screening and diagnosis. Use of screening DBT may be associated with insurance type. Surveillance is required to ensure that disparities in breast cancer screening are minimized as DBT becomes more widely available. © 2017 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

21 CFR 900.2 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-04-01

... radiographic image of a phantom. (ll) Physical science means physics, chemistry, radiation science (including medical physics and health physics), and engineering. (mm) Positive mammogram means a mammogram that has... 50 percent adipose tissue. (vv) Survey means an onsite physics consultation and evaluation of a...

21 CFR 900.2 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-04-01

... radiographic image of a phantom. (ll) Physical science means physics, chemistry, radiation science (including medical physics and health physics), and engineering. (mm) Positive mammogram means a mammogram that has... 50 percent adipose tissue. (vv) Survey means an onsite physics consultation and evaluation of a...

21 CFR 900.2 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-04-01

... radiographic image of a phantom. (ll) Physical science means physics, chemistry, radiation science (including medical physics and health physics), and engineering. (mm) Positive mammogram means a mammogram that has... 50 percent adipose tissue. (vv) Survey means an onsite physics consultation and evaluation of a...

Breast cancer screening practices among Asian Americans and Pacific Islanders.

PubMed

Oh, Kyeung Mi; Zhou, Qiuping Pearl; Kreps, Gary L; Ryu, Shin Kue

2012-09-01

To compare the breast cancer screening practices and related factors between Asian Americans and Pacific Islanders (PIs) and non-Hispanic whites. Using 2008 Behavioral Risk Factor Surveillance System data, reported mammogram usage among women aged 40+ were compared. Covariates included demographics, risk behaviors, health perception, care access, and general health practice behavior. PIs had higher rates of screening mammogram usage than did Asian Americans. Most covariates had different levels of influence on mammogram screening for the 2 groups, with a few in opposite directions. Understanding the magnitude and predictors of these disparities for racial/ethnic groups can help inform targeted interventions.

Application of adaptive boosting to EP-derived multilayer feed-forward neural networks (MLFN) to improve benign/malignant breast cancer classification

NASA Astrophysics Data System (ADS)

Land, Walker H., Jr.; Masters, Timothy D.; Lo, Joseph Y.; McKee, Dan

2001-07-01

A new neural network technology was developed for improving the benign/malignant diagnosis of breast cancer using mammogram findings. A new paradigm, Adaptive Boosting (AB), uses a markedly different theory in solutioning Computational Intelligence (CI) problems. AB, a new machine learning paradigm, focuses on finding weak learning algorithm(s) that initially need to provide slightly better than random performance (i.e., approximately 55%) when processing a mammogram training set. Then, by successive development of additional architectures (using the mammogram training set), the adaptive boosting process improves the performance of the basic Evolutionary Programming derived neural network architectures. The results of these several EP-derived hybrid architectures are then intelligently combined and tested using a similar validation mammogram data set. Optimization focused on improving specificity and positive predictive value at very high sensitivities, where an analysis of the performance of the hybrid would be most meaningful. Using the DUKE mammogram database of 500 biopsy proven samples, on average this hybrid was able to achieve (under statistical 5-fold cross-validation) a specificity of 48.3% and a positive predictive value (PPV) of 51.8% while maintaining 100% sensitivity. At 97% sensitivity, a specificity of 56.6% and a PPV of 55.8% were obtained.

Factors related to noncompliance with screening mammogram appointments among low-income African-American women.

PubMed Central

Crump, S. R.; Mayberry, R. M.; Taylor, B. D.; Barefield, K. P.; Thomas, P. E.

2000-01-01

Despite current mammography recommendations, screening rates among African-American women are suboptimal. The purpose of this case-control study was to identify the psychological, demographic, and health care system barriers to screening mammography use among low-income African-American women. A total of 574 women with screening mammogram appointments at an urban hospital were interviewed to determine the predictors of mammogram appointment noncompliance. Predictor variables included: demographics; breast cancer knowledge, attitudes, beliefs, and screening practices; and type of health care provider making the referral. Age was inversely related to mammogram appointment noncompliance. Relative to women 40 to 49 years old, women 70 years of age and older were the least likely to miss their appointments (odds ratio [OR], 0.3; 95% confidence interval [CI], 0.2, 0.5). Women referred for mammography by a physician's assistant or nurse practitioner were less likely to miss their appointments than women referred by a physician (OR, 0.3; 95% CI, 0.1, 0.8). Embarrassment, lack of breast symptoms, and forgetfulness also contributed to noncompliance. Key demographic, attitudinal, and health care system factors hinder low-income African-American women from obtaining screening mammograms. These findings have significant health education and policy implications for health care delivery to women in this population. PMID:10881473

Fully automated breast density assessment from low-dose chest CT

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

The Design of a Multi-component Intervention to Promote Screening Mammography in an American Indian Community: The Native Women's Health Project

PubMed Central

Tolma, Eleni L.; Engelman, Kimberly; Stoner, Julie A.; Thomas, Cara; Joseph, Stephanie; Li, Ji; Blackwater, Cecily; Henderson, J. Neil; Carson, L. D.; Neely, Norma; Edwards, Tewanna

2016-01-01

Background Breast cancer is an important public health issue among American Indian/Alaska Native (AI/AN) women in the US. This article describes the design and implementation of a culturally sensitive intervention to promote breast health among AI/AN women through a hybrid model that incorporates clinical and community-based approaches. This is one of the first studies using this model addressing breast cancer disparities among AI/AN populations in the US. Methods The Theory of Planned Behavior was used as the guiding framework of the intervention and Community Based Participatory Research was the primary vehicle for the intervention planning and implementation. Three preliminary studies took place that aimed to identify qualitatively and quantitatively what deterred or encouraged AI women to get past or future mammograms. The research results were shared with community members who, through a prioritization process, identified the theoretical focus of the intervention and its corresponding activities. The priority population consisted of AI women ages 40–74, with no recent mammogram, and no breast cancer history. Results The intervention centered on the promotion of social modeling and physician recommendation. The main corresponding activities included enhancing patient-physician communication about screening mammography through a structured dialogue, receipt of a breast cancer brochure, participation in an inter-generational discussion group, and a congratulatory bracelet upon receipt of a mammogram. Environmental and policy related changes also were developed. Conclusion Creating a theory-based, culturally-sensitive intervention through tribal participatory research is a challenging approach towards eliminating breast cancer disparities among hard-to-reach populations. PMID:29546205

A qualitative study on Singaporean women's views towards breast cancer screening and Single Nucleotide Polymorphisms (SNPs) gene testing to guide personalised screening strategies.

PubMed

Wong, Xin Yi; Chong, Kok Joon; van Til, Janine A; Wee, Hwee Lin

2017-11-21

Breast cancer is the top cancer by incidence and mortality in Singaporean women. Mammography is by far its best screening tool, but current recommended age and interval may not yield the most benefit. Recent studies have demonstrated the potential of single nucleotide polymorphisms (SNPs) to improve discriminatory accuracy of breast cancer risk assessment models. This study was conducted to understand Singaporean women's views towards breast cancer screening and SNPs gene testing to guide personalised screening strategies. Focus group discussions were conducted among English-speaking women (n = 27) between 40 to 65 years old, both current and lapsed mammogram users. Women were divided into four groups based on age and mammogram usage. Discussions about breast cancer and screening experience, as well as perception and attitude towards SNPs gene testing were conducted by an experienced moderator. Women were also asked for factors that will influence their uptake of the test. Transcripts were analysed using thematic analysis to captured similarities and differences in views expressed. Barriers to repeat mammogram attendance include laziness to make appointment and painful and uncomfortable screening process. However, the underlying reason may be low perceived susceptibility to breast cancer. Facilitators to repeat mammogram attendance include ease of making appointment and timely reminders. Women were generally receptive towards SNPs gene testing, but required information on accuracy, cost, invasiveness, and side effects before they decide whether to go for it. Other factors include waiting time for results and frequency interval. On average, women gave a rating of 7.5 (range 5 to 10) when asked how likely they will go for the test. Addressing concerns such as pain and discomfort during mammogram, providing timely reminders and debunking breast cancer myths can help to improve screening uptake. Women demonstrated a spectrum of responses towards a novel test like SNPs gene testing, but need more information to make an informed decision. Future public health education on predictive genetic testing should adequately address both benefits and risks. Findings from this study is used to inform a discrete choice experiment to empirically quantify women preferences and willingness-to-pay for SNPs gene testing.

Improving mass candidate detection in mammograms via feature maxima propagation and local feature selection.

PubMed

Melendez, Jaime; Sánchez, Clara I; van Ginneken, Bram; Karssemeijer, Nico

2014-08-01

Mass candidate detection is a crucial component of multistep computer-aided detection (CAD) systems. It is usually performed by combining several local features by means of a classifier. When these features are processed on a per-image-location basis (e.g., for each pixel), mismatching problems may arise while constructing feature vectors for classification, which is especially true when the behavior expected from the evaluated features is a peaked response due to the presence of a mass. In this study, two of these problems, consisting of maxima misalignment and differences of maxima spread, are identified and two solutions are proposed. The first proposed method, feature maxima propagation, reproduces feature maxima through their neighboring locations. The second method, local feature selection, combines different subsets of features for different feature vectors associated with image locations. Both methods are applied independently and together. The proposed methods are included in a mammogram-based CAD system intended for mass detection in screening. Experiments are carried out with a database of 382 digital cases. Sensitivity is assessed at two sets of operating points. The first one is the interval of 3.5-15 false positives per image (FPs/image), which is typical for mass candidate detection. The second one is 1 FP/image, which allows to estimate the quality of the mass candidate detector's output for use in subsequent steps of the CAD system. The best results are obtained when the proposed methods are applied together. In that case, the mean sensitivity in the interval of 3.5-15 FPs/image significantly increases from 0.926 to 0.958 (p < 0.0002). At the lower rate of 1 FP/image, the mean sensitivity improves from 0.628 to 0.734 (p < 0.0002). Given the improved detection performance, the authors believe that the strategies proposed in this paper can render mass candidate detection approaches based on image location classification more robust to feature discrepancies and prove advantageous not only at the candidate detection level, but also at subsequent steps of a CAD system.

Mammography performance in Oman: Review of factors influencing cancer yield and positive predictive value.

PubMed

Taif, Sawsan; Tufail, Fatma; Alnuaimi, Ahmed Sameer

2016-06-01

The aim of this study is to assess mammography performance in Oman by estimating the breast cancer rate and the positive predictive value (PPV) with the influence of some variables. This cross-sectional study was conducted on mammograms done in one of the three main breast imaging centers in Oman between January 2008 and July 2012. Diagnostic and screening groups were identified and assessed separately. Rate of abnormal mammograms, rate of breast cancer and the PPV were estimated according to Breast Imaging Reporting and Data System (BIRADS) score, presence of breast lump and patient's age. Total of 653 mammograms were included, 254 diagnostic and 399 screening. Abnormal mammograms (BIRADS 4 and 5) form 31.9% of the diagnostic examinations compared with 6.8% of screening examinations. Breast cancer was present in 17.9% of the diagnostic compared with 1.0% of the screening group. The PPV of BIRADS 5 was 94.1%, and for BIRADS 4 was 37.1 and 26.7% for diagnostic and screening studies. Overall PPV for abnormal mammograms was 65.2% in the diagnostic and 26.7% in the screening group. Mammography PPV shows positive association with age (P = 0.039) while presence of breast lump has no significant effect on the PPV (P = 0.38). BIRADS 5 score was found to have a high cancer yield making it a strong predictor of cancer. Different results were obtained in the diagnostic compared with screening mammography with higher rates of abnormal mammograms and breast cancer. Mammography performance should be better in the older women. © 2014 Wiley Publishing Asia Pty Ltd.

Psychological distress in U.S. women who have experienced false-positive mammograms.

PubMed

Jatoi, Ismail; Zhu, Kangmin; Shah, Mona; Lawrence, William

2006-11-01

In the United States, approximately 10.7% of all screening mammograms lead to a false-positive result, but the overall impact of false-positives on psychological well-being is poorly understood. Data were analyzed from the 2000 U.S. National Health Interview Survey (NHIS), the most recent national survey that included a cancer control module. Study subjects were 9,755 women who ever had a mammogram, of which 1,450 had experienced a false-positive result. Psychological distress was assessed using the validated K6 questionnaire and logistic regression was used to discern any association with previous false-positive mammograms. In a multivariate analysis, women who had indicated a previous false-positive mammogram were more likely to report feeling sad (OR = 1.18, 95% CI, 1.03-1.35), restless (OR = 1.23, 95% CI, 1.08-1.40), worthless (OR = 1.27, 95% CI, 1.04-1.54), and finding that everything was an effort (OR = 1.27, 95% CI, 1.10-1.47). These women were also more likely to have seen a mental health professional in the 12 months preceding the survey (OR = 1.28, 95% CI, 1.03-1.58) and had a higher composite score on all items of the K6 scale (P < 0.0001), a reflection of increased psychological distress. Analyses by age and race revealed that, among women who had experienced false-positives, younger women were more likely to feel that everything was an effort, and blacks were more likely to feel restless. In a random sampling of the U.S. population, women who had previously experienced false-positive mammograms were more likely to report symptoms of anxiety and depression.

Finding the minimal intervention needed for sustained mammography adherence.

PubMed

Gierisch, Jennifer M; DeFrank, Jessica T; Bowling, J Michael; Rimer, Barbara K; Matuszewski, Jeanine M; Farrell, David; Skinner, Celette Sugg

2010-10-01

Regular adherence to mammography screening saves lives, yet few women receive regular mammograms. RCT. Participants were recruited through a state employee health plan. All were women aged 40-75 years and had recent mammograms prior to enrollment (n=3547). Data were collected from 2004 to 2009. Trial tested efficacy of a two-step adaptively-designed intervention to increase mammography adherence over 4 years. The first intervention step consisted of three reminder types: enhanced usual care reminders (EUCR); enhanced letter reminders (ELR); both delivered by mail, and automated telephone reminders (ATR). After delivery of reminders, women who became off-schedule in any of the 4 years received a second step of supplemental interventions. Three supplemental intervention arms contained priming letters and telephone counseling: barriers only (BarriCall); barriers plus positive consequences of getting mammograms (BarriConCall+); and barriers plus negative consequences of not getting mammograms (BarriConCall-). Average cumulative number of days non-adherent to mammography over 4 years based on annual screening guidelines (analyses conducted in 2009). All reminders performed equally well in reducing number of days of non-adherence. Women randomized to receive supplemental interventions had significantly fewer days of non-adherence compared to women who received EUCR (p=0.0003). BarrConCall+ and BarrConCall- conditions did not significantly differ in days non-adherent compared to women in the barriers-only condition (BarriCon). The minimal intervention needed for sustained mammography use is a combination of a reminder followed by a priming letter and barrier-specific telephone counseling for women who become off-schedule. Additional costs associated with supplemental interventions should be considered by organizations deciding which interventions to use. NCT01148875. Copyright © 2010 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

DICOM organ dose does not accurately represent calculated dose in mammography

NASA Astrophysics Data System (ADS)

Suleiman, Moayyad E.; Brennan, Patrick C.; McEntee, Mark F.

2016-03-01

This study aims to analyze the agreement between the mean glandular dose estimated by the mammography unit (organ dose) and mean glandular dose calculated using Dance et al published method (calculated dose). Anonymised digital mammograms from 50 BreastScreen NSW centers were downloaded and exposure information required for the calculation of dose was extracted from the DICOM header along with the organ dose estimated by the system. Data from quality assurance annual tests for the included centers were collected and used to calculate the mean glandular dose for each mammogram. Bland-Altman analysis and a two-tailed paired t-test were used to study the agreement between calculated and organ dose and the significance of any differences. A total of 27,869 dose points from 40 centers were included in the study, mean calculated dose and mean organ dose (+/- standard deviation) were 1.47 (+/-0.66) and 1.38 (+/-0.56) mGy respectively. A statistically significant 0.09 mGy bias (t = 69.25; p<0.0001) with 95% limits of agreement between calculated and organ doses ranging from -0.34 and 0.52 were shown by Bland-Altman analysis, which indicates a small yet highly significant difference between the two means. The use of organ dose for dose audits is done at the risk of over or underestimating the calculated dose, hence, further work is needed to identify the causal agents for differences between organ and calculated doses and to generate a correction factor for organ dose.

Mammography status using patient self-reports and computerized radiology database.

PubMed

Thompson, B; Taylor, V; Goldberg, H; Mullen, M

1999-10-01

This study sought to compare self-reported mammography use of low-income women utilizing an inner-city public hospital with a computerized hospital database for tracking mammography use. A survey of all age-eligible women using the hospital's internal medicine clinic was done; responses were matched with the radiology database. We examined concordance among the two data sources. Concordance between self-report and the database was high (82%) when using "ever had a mammogram at the hospital," but low (58%) when comparing self-reported last mammogram with the information contained in the database. Disagreements existed between self-reports and the database. Because we sought to ensure that women would know exactly what a mammogram entailed by including a picture of a woman having a mammogram, it is possible that women's responses were accurate, leading to concerns that discrepancies might be present in the database. Physicians and staff must ensure that they understand the full history of a woman's experience with mammography before recommending for or against the procedure.

Design and evaluation of a theory-based, culturally relevant outreach model for breast and cervical cancer screening for Latina immigrants.

PubMed

White, Kari; Garces, Isabel C; Bandura, Lisa; McGuire, Allison A; Scarinci, Isabel C

2012-01-01

Breast and cervical cancer are common among Latinas, but screening rates among foreign-born Latinas are relatively low. In this article we describe the design and implementation of a theory-based (PEN-3) outreach program to promote breast and cervical cancer screening to Latina immigrants, and evaluate the program's effectiveness. We used data from self-administered questionnaires completed at six annual outreach events to examine the sociodemographic characteristics of attendees and evaluate whether the program reached the priority population - foreign-born Latina immigrants with limited access to health care and screening services. To evaluate the program's effectiveness in connecting women to screening, we examined the proportion and characteristics of women who scheduled and attended Pap smear and mammography appointments. Among the 782 Latinas who attended the outreach program, 60% and 83% had not had a Pap smear or mammogram, respectively, in at least a year. Overall, 80% scheduled a Pap smear and 78% scheduled a mammogram. Women without insurance, who did not know where to get screening and had not been screened in the last year were more likely to schedule appointments (P < .05). Among women who scheduled appointments, 65% attended their Pap smear and 79% attended the mammogram. We did not identify significant differences in sociodemographic characteristics associated with appointment attendance. Using a theoretical approach to outreach design and implementation, it is possible to reach a substantial number of Latina immigrants and connect them to cancer screening services.

A crisis of visibility: The psychological consequences of false-positive screening mammograms, an interview study.

PubMed

Bond, Mary; Garside, Ruth; Hyde, Christopher

2015-11-01

To understand the meaning of having a false-positive screening mammogram. Qualitative interview study. Twenty-one women, who had experienced false-positive screening mammograms, took part in semi-structured interviews that were analysed with Interpretive Phenomenological Analysis. This research took place in the United Kingdom. The analysis revealed a wide range of response to having a false-positive mammogram, from nonchalance to extreme fear. These reactions come from the potential for the belief that one is healthy to be challenged by being recalled, as the worst is frequently assumed. For most, the image of the lesion on the X-ray brought the reality of this challenge into sharp focus, as they might soon discover they had breast cancer. Waiting, whether for the appointment, at the clinic or for biopsy results was considered the worst aspect of being recalled. Generally, the uncertainty was quickly resolved with the pronouncement of the 'all-clear', which brought considerable relief and the restoration of belief in the healthy self. However, for some, lack of information, contradictory information, or poor interpersonal communication meant that uncertainty about their health status lingered at least until their next normal screening mammogram. Mammography screening related anxiety lasted for up to 12 years. Breast cancer screening produces a 'crisis of visibility'. Accepting the screening invitation is taking a risk that you may experience unnecessary stress, uncertainty, fear, anxiety, and physical pain. Not accepting the invitation is taking a risk that malignant disease will remain invisible. Statement of contribution What is already known on this subject? More than 50,000 women a year in England have a false-positive mammogram (FPM). Having an FPM can cause anxiety compared with a normal mammogram. The anxiety can last up to 35 months. What does this study add? Refocuses attention from the average response found in quantitative studies to the wide range of individual response. Gives insight into the nature of the anxiety of having FPMs. Highlights the role of uncertainty in provoking distress from an FPM. © 2015 The British Psychological Society.

Inequalities in socioeconomic status and race and the odds of undergoing a mammogram in Brazil.

PubMed

Melo, Enirtes Caetano Prates; de Oliveira, Evangelina Xavier Gouveia; Chor, Dóra; Carvalho, Marilia Sá; Pinheiro, Rejane Sobrino

2016-09-15

Access to mammograms, in common with other diagnostic procedures, is strongly conditioned by socioeconomic disparities. Which aspects of inequality affect the odds of undergoing a mammogram, and whether they are the same in different localities, are relevant issues related to the success of health policies. This study analyzed data from the 2008 PNAD - Brazilian National Household Sample Survey (11.607 million women 40 years of age or older), on having had at least one mammogram over life for women 40 years of age or older in each of Brazil's nine Metropolitan Regions (MR), according to socioeconomic position. The effects of income, schooling, health insurance and race in the different regions were investigated using multivariate logistical regression for each region individually, and for all MRs combined. The age-adjusted odds of a woman having had a mammogram according to race and stratified by two income strata (and two schooling strata) were also analyzed. Having a higher income increases four to seven times a woman's odds of having had at least one mammogram in all MRs except Curitiba. For schooling, the gradient, though less steep, is favorable to women with more years of study. Having health insurance increases two to three times the odds in all MRs. Multivariate analysis did not show differences due to race (except for the Fortaleza MR), but the stratified analysis by income and schooling shows effects of race in most MRs, with greater differences for women with higher socioeconomic status. This study confirms that income and schooling, as well as having health insurance, are still important determinants of inequality in health service use in Brazil. Additionally, race also contributes to the odds of having had a mammogram. The point is not to isolate the effect of each factor, but to evaluate how their interrelations may exacerbate differences, generating patterns of cumulative adversity, a theme that is still little explored in Brazil. This is much more important when we consider that race has only recently started be included in analyses of health outcomes in Brazil.

Breast Cancer Screening (PDQ®)—Patient Version

Cancer.gov

Breast cancer screening is performed using mammogram, clinical breast exam (CBE), and MRI (magnetic resonance imaging) tests. Learn about these and other tests that have been studied to detect or screen for breast cancer in this expert-reviewed and evidence-based summary.

Natural History of Breast Density and Breast Cancer Risk

DTIC Science & Technology

2001-07-01

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

Breast density characterization using texton distributions.

PubMed

Petroudi, Styliani; Brady, Michael

2011-01-01

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

Promoting Breast Cancer Screening through Storytelling by Chamorro Cancer Survivors

PubMed Central

Manglona, Rosa Duenas; Robert, Suzanne; Isaacson, Lucy San Nicolas; Garrido, Marie; Henrich, Faye Babauta; Santos, Lola Sablan; Le, Daisy; Peters, Ruth

2017-01-01

The largest Chamorro population outside of Guam and the Mariana Islands reside in California. Cancer health disparities disproportionally affect Pacific Islander communities, including the Chamorro, and breast cancer is the most common cancer affecting women. To address health concerns such as cancer, Pacific Islander women frequently utilize storytelling to initiate conversations about health and to address sensitive topics such as breast health and cancer. One form of storytelling used in San Diego is a play that conveys the message of breast cancer screening to the community in a culturally and linguistically appropriate way. This play, Nan Nena’s Mammogram, tells the story of an older woman in the community who learns about breast cancer screening from her young niece. The story builds upon the underpinnings of Chamorro culture - family, community, support, and humor - to portray discussing breast health, getting support for breast screening, and visiting the doctor. The story of Nan Nena’s Mammogram reflects the willingness of a few pioneering Chamorro women to use their personal experiences of cancer survivorship to promote screening for others. Through the support of a Chamorro community-based organization, these Chamorro breast cancer survivors have used the success of Nan Nena’s Mammogram to expand their education activities and to form a new cancer survivor organization for Chamorro women in San Diego. PMID:29805328

Investigating the Link Between Radiologists Gaze, Diagnostic Decision, and Image Content

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

Tourassi, Georgia; Voisin, Sophie; Paquit, Vincent C

2013-01-01

Objective: To investigate machine learning for linking image content, human perception, cognition, and error in the diagnostic interpretation of mammograms. Methods: Gaze data and diagnostic decisions were collected from six radiologists who reviewed 20 screening mammograms while wearing a head-mounted eye-tracker. Texture analysis was performed in mammographic regions that attracted radiologists attention and in all abnormal regions. Machine learning algorithms were investigated to develop predictive models that link: (i) image content with gaze, (ii) image content and gaze with cognition, and (iii) image content, gaze, and cognition with diagnostic error. Both group-based and individualized models were explored. Results: By poolingmore » the data from all radiologists machine learning produced highly accurate predictive models linking image content, gaze, cognition, and error. Merging radiologists gaze metrics and cognitive opinions with computer-extracted image features identified 59% of the radiologists diagnostic errors while confirming 96.2% of their correct diagnoses. The radiologists individual errors could be adequately predicted by modeling the behavior of their peers. However, personalized tuning appears to be beneficial in many cases to capture more accurately individual behavior. Conclusions: Machine learning algorithms combining image features with radiologists gaze data and diagnostic decisions can be effectively developed to recognize cognitive and perceptual errors associated with the diagnostic interpretation of mammograms.« less

Can a totally different approach to soft tissue computer aided detection (CADe) result in affecting radiologists' decisions?

NASA Astrophysics Data System (ADS)

Gur, David

2018-03-01

We tested whether a case based CADe scheme, developed only on negatively interpreted screening mammograms, has predictive value for cancer detection during subsequent screening and how this approach may affect radiologists' performances when alerting them to a small subset ( 15%) of exams on which radiologists tend to miss cancers. A series of six parameters case based CADe schemes, using 200 negative mammograms (800 images 100 women with breast cancer at subsequent screening and 100 women who remained negative), carefully matched by age and breast density, were optimized. CADe alone schemes performed at AUC=0.68 (+/- 0.01). Five radiologists and 4 residents interpreted the same cases and performed at AUC =0.71 (experienced radiologists) and AUC= 0.61 (residents). With the "CADe warnings" shown to the interpreters only if they did not recall one of 24 highest CADe scoring cases, assisted performance of radiologists and residents respectively, were 0.71 and 0.63 (p>0.05). However, when the CADe alone performance was raised to an AUC=0.78, by artificially increasing the number of possible warnings from 16 to 24, radiologists' performances significantly improved from an AUC of 0.68 to 0.72 (p<0.05). In conclusion, the use case based information other than breast density could highlight a small fraction of women whose cancers are more likely to be missed by radiologists and later detected during subsequent mammograms, thereby, leading to an assisted approach that improves radiologists' performances. However, to be effective, the performance of the CADe alone should be substantially higher (e.g. ΔAUC >=0.07) than that of the un-assisted radiologist.

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

PubMed

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

2017-09-20

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

Church-based breast cancer screening education: impact of two approaches on Latinas enrolled in public and private health insurance plans.

PubMed

Sauaia, Angela; Min, Sung-joon; Lack, David; Apodaca, Cecilia; Osuna, Diego; Stowe, Angela; MGinnis, Gretchen F; Latts, Lisa M; Byers, Tim

2007-10-01

The Tepeyac Project is a church-based health promotion project that was conducted from 1999 through 2005 to increase breast cancer screening rates among Latinas in Colorado. Previous reports evaluated the project among Medicare and Medicaid enrollees in the state. In this report, we evaluate the program among enrollees in the state's five major insurance plans. We compared the Tepeyac Project's two interventions: the Printed Intervention and the Promotora Intervention. In the first, we mailed culturally tailored education packages to 209 Colorado Catholic churches for their use. In the second, promotoras (peer counselors) in four Catholic churches delivered breast-health education messages personally. We compared biennial mammogram claims from the five insurance plans in the analysis at baseline (1998-1999) and during follow-up (2000-2001) for Latinas who had received the interventions. We used generalized estimating equations (GEE) analysis to adjust rates for confounders. The mammogram rate for Latinas in the Printed Intervention remained the same from baseline to follow-up (58% [2979/5130] vs 58% [3338/5708]). In the Promotora Intervention, the rate was 59% (316/536) at baseline and 61% (359/590) at follow-up. Rates increased modestly over time and varied widely by insurance type. After adjusting for age, income, urban versus rural location, disability, and insurance type, we found that women exposed to the Promotora Intervention had a significantly higher increase in biennial mammograms than did women exposed to the Printed Intervention (GEE parameter estimate = .24 [+/-.11], P = .03). For insured Latinas, personally delivering church-based education through peer counselors appears to be a better breast-health promotion method than mailing printed educational materials to churches.

Comparative Effectiveness of Digital Versus Film-Screen Mammography in Community Practice in the United States

PubMed Central

Kerlikowske, Karla; Hubbard, Rebecca A.; Miglioretti, Diana L.; Geller, Berta M.; Yankaskas, Bonnie C.; Lehman, Constance D.; Taplin, Stephen H.; Sickles, Edward A.

2013-01-01

Background Few studies have examined the comparative effectiveness of digital versus film-screen mammography in U.S. community practice. Objective To determine whether the interpretive performance of digital and film-screen mammography differs. Design Prospective cohort study. Setting Mammography facilities in the Breast Cancer Surveillance Consortium. Participants 329 261 women aged 40 to 79 years underwent 869 286 mammograms (231 034 digital; 638 252 film-screen). Measurements Invasive cancer or ductal carcinoma in situ diagnosed within 12 months of a digital or film-screen examination and calculation of mammography sensitivity, specificity, cancer detection rates, and tumor outcomes. Results Overall, cancer detection rates and tumor characteristics were similar for digital and film-screen mammography, but the sensitivity and specificity of each modality varied by age, tumor characteristics, breast density, and menopausal status. Compared with film-screen mammography, the sensitivity of digital mammography was significantly higher for women aged 60 to 69 years (89.9% vs. 83.0%; P = 0.014) and those with estrogen receptor-negative cancer (78.5% vs. 65.8%; P = 0.016); borderline significantly higher for women aged 40 to 49 years (82.4% vs. 75.6%; P = 0.071), those with extremely dense breasts (83.6% vs. 68.1%; P= 0.051), and pre- or perimenopausal women (87.1% vs. 81.7%; P = 0.057); and borderline significantly lower for women aged 50 to 59 years (80.5% vs. 85.1%; P = 0.097). The specificity of digital and film-screen mammography was similar by decade of age, except for women aged 40 to 49 years (88.0% vs. 89.7%; P< 0.001). Limitation Statistical power for subgroup analyses was limited. Conclusion Overall, cancer detection with digital or film-screen mammography is similar in U.S. women aged 50 to 79 years undergoing screening mammography. Women aged 40 to 49 years are more likely to have extremely dense breasts and estrogen receptor-negative tumors; if they are offered mammography screening, they may choose to undergo digital mammography to optimize cancer detection. Primary Funding Source National Cancer Institute. PMID:22007043

Compositional breast imaging using a dual-energy mammography protocol

PubMed Central

Laidevant, Aurelie D.; Malkov, Serghei; Flowers, Chris I.; Kerlikowske, Karla; Shepherd, John A.

2010-01-01

Purpose: Mammography has a low sensitivity in dense breasts due to low contrast between malignant and normal tissue confounded by the predominant water density of the breast. Water is found in both adipose and fibroglandular tissue and constitutes most of the mass of a breast. However, significant protein mass is mainly found in the fibroglandular tissue where most cancers originate. If the protein compartment in a mammogram could be imaged without the influence of water, the sensitivity and specificity of the mammogram may be improved. This article describes a novel approach to dual-energy mammography, full-field digital compositional mammography (FFDCM), which can independently image the three compositional components of breast tissue: water, lipid, and protein. Methods: Dual-energy attenuation and breast shape measures are used together to solve for the three compositional thicknesses. Dual-energy measurements were performed on breast-mimicking phantoms using a full-field digital mammography unit. The phantoms were made of materials shown to have similar x-ray attenuation properties of the compositional compartments. They were made of two main stacks of thicknesses around 2 and 4 cm. Twenty-six thickness and composition combinations were used to derive the compositional calibration using a least-squares fitting approach. Results: Very high accuracy was achieved with a simple cubic fitting function with root mean square errors of 0.023, 0.011, and 0.012 cm for the water, lipid, and protein thicknesses, respectively. The repeatability (percent coefficient of variation) of these measures was tested using sequential images and was found to be 0.5%, 0.5%, and 3.3% for water, lipid, and protein, respectively. However, swapping the location of the two stacks of the phantom on the imaging plate introduced further errors showing the need for more complete system uniformity corrections. Finally, a preliminary breast image is presented of each of the compositional compartments separately. Conclusions: FFDCM has been derived and exhibited good compositional thickness accuracy on phantoms. Preliminary breast images demonstrated the feasibility of creating individual compositional diagnostic images in a clinical environment. PMID:20175478

Intentions to Maintain Adherence to Mammography

PubMed Central

Bowling, J. Michael; Brewer, Noel T.; Lipkus, Isaac M.; Skinner, Celette Sugg; Strigo, Tara S.; Rimer, Barbara K.

2008-01-01

Abstract Objective Recent attention has focused on moving women from having initial mammograms to maintaining adherence to regular mammography schedules. We examined behavioral intentions to maintain mammography adherence, which include the likelihood of performing a behavior, and implementation intentions, specific action plans to obtain mammograms. Potential predictors were Theory of Planned Behavior constructs, previous barriers, previous mammography maintenance, and age. Methods Respondents were 2062 currently adherent women due for their next mammograms in 3–4 months according to American Cancer Society recommendations for annual screening. Statistical models were used to examine predictors of behavioral and two implementation intentions, including having thought about where women would get their next mammograms and having thought about making appointments. Results With the exception of pros, cons, and subjective norms, all variables predicted behavioral intentions (p ≤ 0.05). Stronger perceived control, previous mammography maintenance, and one barrier (vs. none) predicted being more likely to have thought about where to get their next mammograms. Previous maintenance and no barriers (vs. two) predicted being more likely to have thought about making appointments. Conclusions Our findings suggest that among women currently adherent to mammography, volitional factors, such as barriers, may be better predictors of implementation intentions than motivational factors, such as attitudes. Implementation variables may be useful in understanding how women move from intentions to action. Future research should examine how such factors relate to mammography maintenance behaviors and can be integrated into behavior change interventions. PMID:18657041

Women's views on reminder letters for screening mammography: Mixed methods study of women from 23 family health networks.

PubMed

Kaczorowski, Janusz; Karwalajtys, Tina; Lohfeld, Lynne; Laryea, Stephanie; Anderson, Kelly; Roder, Stefanie; Sebaldt, Rolf J

2009-06-01

To explore women's perspectives on the acceptability and content of reminder letters for screening mammography from their family physicians, as well as such letters' effect on screening intentions. Cross-sectional mailed survey followed by focus groups with a subgroup of respondents. Ontario. One family physician was randomly selected from each of 23 family health networks and primary care networks participating in a demonstration project to increase the delivery of preventive services. From the practice roster of each physician, up to 35 randomly selected women aged 50 to 69 years who were due or overdue for screening mammograms and who had received reminder letters from their family physicians within the past 6 months were surveyed. Recall of having received reminder letters and of their content, influence of the letters on decisions to have mammograms, and interest in receiving future reminder letters. Focus group interviews with survey respondents explored the survey findings in greater depth using a standardized interview guide. The response rate to the survey was 55.7% (384 of 689), and 45.1% (173 of 384) of responding women reported having mammograms in the past 6 months. Among women who recalled receiving letters and either making appointments for or having mammograms, 74.8% (122 of 163) indicated that the letters substantially influenced their decisions. Most respondents (77.1% [296 of 384]) indicated that they would like to continue to receive reminders, and 28.9% (111 of 384) indicated that they would like to receive additional information about mammograms. Participants in 2 focus groups (n = 3 and n = 5) indicated that they thought letters reflected a positive attitude of physicians toward mammography screening. They also commented that newly eligible women had different information needs than women who had had mammograms done in the past. Reminder letters were considered by participants to be useful and appeared to influence women's decisions to undergo mammography screening.

Design and evaluation of a theory-based, culturally relevant outreach model for breast and cervical cancer screening for Latina immigrants

PubMed Central

White, Kari; Garces, Isabel C.; Bandura, Lisa; McGuire, Allison A.; Scarinci, Isabel C.

2013-01-01

Objectives Breast and cervical cancer are common among Latinas, but screening rates among foreign-born Latinas are relatively low. In this article we describe the design and implementation of a theory-based (PEN-3) outreach program to promote breast and cervical cancer screening to Latina immigrants, and evaluate the program’s effectiveness. Methods We used data from self-administered questionnaires completed at six annual outreach events to examine the sociodemographic characteristics of attendees and evaluate whether the program reached the priority population – foreign-born Latina immigrants with limited access to health care and screening services. To evaluate the program’s effectiveness in connecting women to screening, we examined the proportion and characteristics of women who scheduled and attended Pap smear and mammography appointments. Results Among the 782 Latinas who attended the outreach program, 60% and 83% had not had a Pap smear or mammogram, respectively, in at least a year. Overall, 80% scheduled a Pap smear and 78% scheduled a mammogram. Women without insurance, who did not know where to get screening and had not been screened in the last year were more likely to schedule appointments (p < 0.05). Among women who scheduled appointments, 65% attended their Pap smear and 79% attended the mammogram. We did not identify significant differences in sociodemographic characteristics associated with appointment attendance. Conclusions Using a theoretical approach to outreach design and implementation, it is possible to reach a substantial number of Latina immigrants and connect them to cancer screening services. PMID:22870569

Breast cancer screening practices among first-generation immigrant muslim women.

PubMed

Hasnain, Memoona; Menon, Usha; Ferrans, Carol Estwing; Szalacha, Laura

2014-07-01

The purpose of this study was to identify beliefs about breast cancer, screening practices, and factors associated with mammography use among first-generation immigrant Muslim women in Chicago, IL. A convenience sample of 207 first-generation immigrant Muslim women (Middle Eastern 51%; South Asian 49%) completed a culturally adapted questionnaire developed from established instruments. The questionnaire was administered in Urdu, Hindi, Arabic, or English, based on participant preference. Internal-consistency reliability was demonstrated for all scales (alpha coefficients ranged from 0.64 to 0.91). Associations between enabling, predisposing, and need variables and the primary outcome of mammography use were explored by fitting logistic regression models. Although 70% of the women reported having had a mammogram at least once, only 52% had had one within the past 2 years. Four factors were significant predictors of ever having had a mammogram: years in the United States, self-efficacy, perceived importance of mammography, and intent to be screened. Five factors were significant predictors of adherence (having had a mammogram in the past 2 years): years in the United States, having a primary care provider, perceived importance of mammography, barriers, and intent to be screened. This article sheds light on current screening practices and identifies theory-based constructs that facilitate and hinder Muslim women's participation in mammography screening. Our findings provide insights for reaching out particularly to new immigrants, developing patient education programs grounded in culturally appropriate approaches to address perceived barriers and building women's self-efficacy, as well as systems-level considerations for ensuring access to primary care providers.

Mobile Phone Multilevel and Multimedia Messaging Intervention for Breast Cancer Screening: Pilot Randomized Controlled Trial.

PubMed

Lee, Hee; Ghebre, Rahel; Le, Chap; Jang, Yoo Jeong; Sharratt, Monica; Yee, Douglas

2017-11-07

Despite the increasing breast cancer incidence and mortality rates, Korean American immigrant women have one of the lowest rates of breast cancer screening across racial groups in the United States. Mobile health (mHealth), defined as the delivery of health care information or services through mobile communication devices, has been utilized to successfully improve a variety of health outcomes. This study adapted the principles of mHealth to advance breast cancer prevention efforts among Korean American immigrant women, an underserved community. Using a randomized controlled trial design, 120 Korean American women aged 40 to 77 years were recruited and randomly assigned to either the mMammogram intervention group (n=60) to receive culturally and personally tailored multilevel and multimedia messages through a mobile phone app along with health navigator services or the usual care control group (n=60) to receive a printed brochure. Outcome measures included knowledge, attitudes, and beliefs about breast cancer screening, readiness for mammography, and mammogram receipt. The feasibility and acceptability of the mMammogram intervention was also assessed. The intervention group showed significantly greater change on scores of knowledge of breast cancer and screening guidelines (P=.01). The intervention group also showed significantly greater readiness for mammography use after the intervention compared with the control group. A significantly higher proportion of women who received the mMammogram intervention (75%, 45/60) completed mammograms by the 6-month follow-up compared with the control group (30%, 18/60; P<.001). In addition, the intervention group rated satisfaction with the intervention (P=.003), effectiveness of the intervention (P<.001), and increase of knowledge on breast cancer and screenings (P=.001) significantly higher than the control group. A mobile phone app-based intervention combined with health navigator service was a feasible, acceptable, and effective intervention mechanism to promote breast cancer screening in Korean American immigrant women. A flexible, easily tailored approach that relies on recent technological advancements can reach underserved and hard-to-recruit populations that bear disproportionate cancer burdens. Clinicaltrials.gov NCT01972048; https://clinicaltrials.gov/show/NCT01972048 (Archived by WebCite at https://clinicaltrials.gov/archive/NCT01972048/2013_10_29). ©Hee Lee, Rahel Ghebre, Chap Le, Yoo Jeong Jang, Monica Sharratt, Douglas Yee. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 07.11.2017.

Effect of filter on average glandular dose and image quality in digital mammography

NASA Astrophysics Data System (ADS)

Songsaeng, C.; Krisanachinda, A.; Theerakul, K.

2016-03-01

To determine the average glandular dose and entrance surface air kerma in both phantoms and patients to assess image quality for different target-filters (W/Rh and W/Ag) in digital mammography system. The compressed breast thickness, compression force, average glandular dose, entrance surface air kerma, peak kilovoltage and tube current time were recorded and compared between W/Rh and W/Ag target filter. The CNR and the figure of merit were used to determine the effect of target filter on image quality. The mean AGD of the W/Rh target filter was 1.75 mGy, the mean ESAK was 6.67 mGy, the mean CBT was 54.1 mm, the mean CF was 14 1bs. The mean AGD of W/Ag target filter was 2.7 mGy, the mean ESAK was 12.6 mGy, the mean CBT was 75.5 mm, the mean CF was 15 1bs. In phantom study, the AGD was 1.2 mGy at 4 cm, 3.3 mGy at 6 cm and 3.83 mGy at 7 cm thickness. The FOM was 24.6, CNR was 9.02 at thickness 6 cm. The FOM was 18.4, CNR was 8.6 at thickness 7 cm. The AGD from Digital Mammogram system with W/Rh of thinner CBT was lower than the AGD from W/Ag target filter.

Computer-aided detection (CAD) of breast cancer on full field digital and screening film mammograms

NASA Astrophysics Data System (ADS)

Sun, Xuejun; Qian, Wei; Song, Xiaoshan; Qian, Yuyan; Song, Dansheng; Clark, Robert A.

2003-05-01

Full-field digital mammography (FFDM) as a new breast imaging modality has potential to detect more breast cancers or to detect them at smaller sizes and earlier stages compared with screening film mammography (SFM). However, its performance needs verification, and it would pose new problems for the development of CAD methods for breast cancer detection and diagnosis. Performance evaluation of CAD systems on FFDM and SFM has been conducted in this study, respectively. First, an adaptive CAD system employing a series of advanced modules has been developed on FFDM. Second, a standardization approach has been developed to make the CAD system independent of characteristics of digitizer or imaging modalities for mammography. CAD systems developed previously for SFM and developed in this study for FFDM have been evaluated on FFDM and SFM images without and with standardization, respectively, to examine the performance improvement of the CAD system developed in this study. Computerized free-response receiver operating characteristic (FROC) analysis has been adopted as performance evaluation method. Compared with previous one, the CAD system developed in this study demonstrated significantly performance improvements. However, the comparison results have shown that the performances of final CAD system in this study are not significantly different on FFDM and on SFM after standardization. It needs further study on the assessment of CAD system performance on FFDM and SFM modalities.

Using component technologies for web based wavelet enhanced mammographic image visualization.

PubMed

Sakellaropoulos, P; Costaridou, L; Panayiotakis, G

2000-01-01

The poor contrast detectability of mammography can be dealt with by domain specific software visualization tools. Remote desktop client access and time performance limitations of a previously reported visualization tool are addressed, aiming at more efficient visualization of mammographic image resources existing in web or PACS image servers. This effort is also motivated by the fact that at present, web browsers do not support domain-specific medical image visualization. To deal with desktop client access the tool was redesigned by exploring component technologies, enabling the integration of stand alone domain specific mammographic image functionality in a web browsing environment (web adaptation). The integration method is based on ActiveX Document Server technology. ActiveX Document is a part of Object Linking and Embedding (OLE) extensible systems object technology, offering new services in existing applications. The standard DICOM 3.0 part 10 compatible image-format specification Papyrus 3.0 is supported, in addition to standard digitization formats such as TIFF. The visualization functionality of the tool has been enhanced by including a fast wavelet transform implementation, which allows for real time wavelet based contrast enhancement and denoising operations. Initial use of the tool with mammograms of various breast structures demonstrated its potential in improving visualization of diagnostic mammographic features. Web adaptation and real time wavelet processing enhance the potential of the previously reported tool in remote diagnosis and education in mammography.

The influence of mammographic technologists on radiologists’ ability to interpret screening mammograms in community practice

PubMed Central

Henderson, Louise M.; Benefield, Thad; Marsh, Mary W.; Schroeder, Bruce F.; Durham, Danielle; Yankaskas, Bonnie C.; Bowling, J. Michael

2014-01-01

Purpose To determine whether the mammographic technologist has an effect on the radiologists’ interpretative performance of screening mammography in community practice. Materials and Methods In this institutional review board approved retrospective cohort study, we included Carolina Mammography Registry (CMR) data from 372 radiologists and 356 mammographic technologists from 1994 to 2009 who performed 1,003,276 screening mammograms. Measures of interpretative performance (recall rate, sensitivity, specificity, positive predictive value (PPV1), and cancer detection rate (CDR)) were ascertained prospectively with cancer outcomes collected from the state cancer registry and pathology reports. To determine if the mammographic technologist influenced the radiologists’ performance, we employed mixed effects logistic regression models, including a radiologist-specific random effect and taking into account the clustering of examinations across women, separately for screen-film mammography (SFM) and full field digital mammography (FFDM). Results Of the 356 mammographic technologists included, 343 performed 889,347 SFM examinations and 51 performed 113,929 FFDM examinations, and 38 performed both SFM and FFDM. A total of 4,328 cancers were reported for SFM and 564 cancers for FFDM. The technologists had a statistically significant effect on the radiologists’ recall rate, sensitivity, specificity and CDR for both SFM and FFDM (p-values<0.01). For PPV1, variability by technologist was observed for SFM (p-value<0.0001) but not for FFDM (p-value=0.088). Conclusion The interpretative performance of radiologists in screening mammography varies substantially by the technologist performing the examination. Additional studies should aim to identify technologist characteristics that may explain this variation. PMID:25435185

Digital mammography: more microcalcifications, more columnar cell lesions without atypia.

PubMed

Verschuur-Maes, Anoek H J; van Gils, Carla H; van den Bosch, Maurice A A J; De Bruin, Peter C; van Diest, Paul J

2011-09-01

The incidence of columnar cell lesions in breast core needle biopsies since full-field digital mammography in comparison with screen-filmed mammography was analyzed. As tiny microcalcifications characterize columnar cell lesions at mammography, we hypothesized that more columnar cell lesions are diagnosed since full-field digital mammography due to its higher sensitivity for microcalcifications. In all, 3437 breast core needle biopsies performed in three hospitals and resulting from in total 55 159 mammographies were revised: 1424 taken in the screen-filmed mammography and 2013 in the full-field digital mammography period. Between the screen-filmed mammography and full-field digital mammography periods, we compared the proportion of mammographies that led to core needle biopsies, the mammographic indication for core needle biopsies (density, microcalcifications, or both) and the proportion of columnar cell lesions with or without atypia. The columnar cell lesions were graded according to Schnitt, and we included atypical ductal hyperplasia arising in the context of columnar cell lesions. Proportions were compared using χ(2) tests and prevalence ratios were adjusted for age and hospital. We found that more core needle biopsies per mammogram were taken in the full-field digital mammography period (7.6%) compared with the screen-filmed mammography period (5.0%, P<0.0001). Microcalcifications were more often diagnosed with full-field digital mammography than with screen-filmed mammography (adjusted prevalence ratio: 1.14, confidence interval 95%: 1.01-1.28). Core needle biopsies from the full-field digital mammography era showed more columnar cell lesions (10.8%) than those from the screen-filmed mammography era (4.9%; adjusted prevalence ratio: 1.93, confidence interval 95%: 1.48-2.51), particularly due to more columnar cell lesions without atypia (8.2% respectively 2.8%) while the proportion of columnar cell lesions with atypia remained nearly constant (2.0 vs 2.6%). In conclusion, since the implementation of full-field digital mammography, more microcalcifications are seen at mammography, more often resulting in core needle biopsies, which especially yields more columnar cell lesions without atypia.

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

PubMed

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

2014-10-01

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

Inequity of healthcare utilization on mammography examination and Pap smear screening in Thailand: Analysis of a population-based household survey

PubMed Central

2017-01-01

Healthcare in Thailand is not equally distributed, and not all people can equally access healthcare resources even if they are covered by health insurance. To examine factors associated with the utilization of mammography examination for breast cancer and Pap smear screening for cervical cancer, data from the national reproductive health survey conducted by the National Statistical Office of Thailand in 2009 was examined. The survey was carried out on 15,074,126 women aged 30–59 years. The results showed that the wealthier respondents had more mammograms than did the lower-income groups. The concentration index was 0.144. The data on Pap smears for cervical cancer also showed that the wealthier respondents were more likely to have had a Pap smear than their lower-income counterparts. The concentration index was 0.054. Determinants of mammography examination were education, followed by health welfare and wealth index, whereas the determinants of Pap smear screening were wealth index, followed by health welfare and education. The government should support greater education for women because education was associated with socioeconomic status and wealth. There should be an increase in the number of screening campaigns, mobile clinics, and low-cost mammograms and continued support for accessibility to mammograms, especially in rural areas and low-income communities. PMID:28282430

Breast Cancer Screening in Patients With Newly Diagnosed Lung and Colorectal Cancer: A Population-Based Study of Utilization

PubMed Central

Sadigh, Gelareh; Carlos, Ruth C.; Ward, Kevin C.; Switchenko, Jeffrey M.; Jiang, Renjian; Applegate, Kimberly E.; Duszak, Richard

2017-01-01

Purpose To assess breast cancer screening utilization in Medicare beneficiaries with colorectal and lung cancer versus cancer-free controls. Methods Female fee-for-service Medicare beneficiaries who were ≥67 years old and diagnosed with lung or colorectal cancer between 2000 and 2011 and who reported to a Surveillance, Epidemiology, and End Results (SEER) registry (case group) were followed for 2 years after their diagnoses, unless death, a diagnosis of breast cancer, or the end of 2013 came first. A similar number of cancer-free controls were individually matched to cases by age, race, registry region, and follow-up time. Screening utilization was defined as the percentage of women with ≥1 screening mammogram during follow-up. Results Overall, 104,164 cases (48% colorectal, 52% lung; 30% advanced cancer) and 104,164 controls were included. Among women with lung or colorectal cancer, 22% underwent ≥1 screening mammogram versus 26% of controls (odds ratio [OR] 0.80; 95% confidence interval [CI] 0.78–0.82). Stratified by cancer type, 28% of colorectal cancer cases versus 29% of controls (OR 0.98; 95% CI 0.95–1.01) and 17% of lung cancer cases versus 23% of controls (OR 0.63; 95% CI 0.60–0.65) received ≥1 mammogram. When stratified by stage, 8% with advanced cancer versus 18% of controls (OR 0.33; 95% CI 0.31–0.35) and 30% with early-stage cancer versus 30% of controls (OR 1; 95% CI 0.97–1.02) underwent ≥1 mammogram. Conclusion Screening mammography utilization rates are similar between Medicare beneficiaries with early-stage cancer versus controls. Although the majority of patients with advanced-stage cancer appropriately do not pursue screening mammography, a small number (8%) continue with screening. PMID:28325489

Breast Cancer Screening in Patients With Newly Diagnosed Lung and Colorectal Cancer: A Population-Based Study of Utilization.

PubMed

Sadigh, Gelareh; Carlos, Ruth C; Ward, Kevin C; Switchenko, Jeffrey M; Jiang, Renjian; Applegate, Kimberly E; Duszak, Richard

2017-07-01

To assess breast cancer screening utilization in Medicare beneficiaries with colorectal and lung cancer versus cancer-free controls. Female fee-for-service Medicare beneficiaries who were ≥67 years old and diagnosed with lung or colorectal cancer between 2000 and 2011 and who reported to a Surveillance, Epidemiology, and End Results (SEER) registry (case group) were followed for 2 years after their diagnoses, unless death, a diagnosis of breast cancer, or the end of 2013 came first. A similar number of cancer-free controls were individually matched to cases by age, race, registry region, and follow-up time. Screening utilization was defined as the percentage of women with ≥1 screening mammogram during follow-up. Overall, 104,164 cases (48% colorectal, 52% lung; 30% advanced cancer) and 104,164 controls were included. Among women with lung or colorectal cancer, 22% underwent ≥1 screening mammogram versus 26% of controls (odds ratio [OR] 0.80; 95% confidence interval [CI] 0.78-0.82). Stratified by cancer type, 28% of colorectal cancer cases versus 29% of controls (OR 0.98; 95% CI 0.95-1.01) and 17% of lung cancer cases versus 23% of controls (OR 0.63; 95% CI 0.60-0.65) received ≥1 mammogram. When stratified by stage, 8% with advanced cancer versus 18% of controls (OR 0.33; 95% CI 0.31-0.35) and 30% with early-stage cancer versus 30% of controls (OR 1; 95% CI 0.97-1.02) underwent ≥1 mammogram. Screening mammography utilization rates are similar between Medicare beneficiaries with early-stage cancer versus controls. Although the majority of patients with advanced-stage cancer appropriately do not pursue screening mammography, a small number (8%) continue with screening. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Effect of cost-sharing reductions on preventive service use among Medicare fee-for-service beneficiaries.

PubMed

Goodwin, Suzanne M; Anderson, Gerard F

2012-01-01

Section 4104 of the Patient Protection and Affordable Care Act (ACA) waives previous cost-sharing requirements for many Medicare-covered preventive services. In 1997, Congress passed similar legislation waiving the deductible only for mammograms and Pap smears. The purpose of this study is to examine the effect of the deductible waiver on mammogram and Pap smear utilization rates. Using 1995-2003 Medicare claims from a sample of female, elderly Medicare fee-for-service beneficiaries, two pre/post analyses were conducted comparing mammogram and Pap smear utilization rates before and after implementation of the deductible waiver. Receipt of screening mammograms and Pap smears served as the outcome measures, and two time measures, representing two post-test observation periods, were used to examine the short- and long-term impacts on utilization. There was a 20 percent short-term and a 25 percent longer term increase in the probability of having had a mammogram in the four years following the 1997 deductible waiver. Beneficiaries were no more likely to receive a Pap smear following the deductible waiver. Elimination of cost sharing may be an effective strategy for increasing preventive service use, but the impact could depend on the characteristics of the procedure, its cost, and the disease and populations it targets. These historical findings suggest that, with implementation of Section 4104, the greatest increases in utilization will be seen for preventive services that screen for diseases with high incidence or prevalence rates that increase with age, that are expensive, and that are performed on a frequent basis.

Mammography screening in single older African-American women: a study of related factors.

PubMed

Zhu, K; Hunter, S; Bernard, L J; Payne-Wilks, K; Roland, C L; Levine, R S

2000-01-01

Using baseline data from an intervention study, we examined cognitive, psychological, social and medical care factors in relation to the use of a mammogram in the preceding year among single African-American women aged 65 and older. Study subjects were 325 African-American women aged 65 and older who were divorced, widowed, separated or never-married, and lived in ten public housing complexes in Nashville, Tennessee. In-person interviews were conducted to collect information on breast screening behavior, knowledge and attitude, social network and activities, emotional and psychological symptoms and signs, and medical care use. Compared with those who had not had a mammogram in the preceding year, women who had had a mammogram in the preceding year were three times more likely to have a regular doctor (95% confidence interval [CI] 1.4-5.0) and about six times more likely to have a doctor's recommendation for a mammogram (95%CI 3.4-11.1). In addition, they were more likely to: (a) have attended a meeting on breast health or received educational materials on breast cancer; (b) agree that a woman needs a mammogram even though she has no breast problem; (c) agree that a woman can have breast cancer without having symptoms; (d) have living children and grandchildren; and (e) attend social activities more frequently. While access to regular medical care and receiving a physician's recommendation are strongly associated with mammography among these older, single African-American women, education on breast health and social networks also appear to be influential.

Do cultural factors predict mammography behaviour among Korean immigrants in the USA?

PubMed

Lee, Hanju; Kim, Jiyun; Han, Hae-Ra

2009-12-01

This paper is a report of a study of the correlates of mammogram use among Korean American women. Despite the increasing incidence of and mortality from breast cancer, Asian women in the United States of America report consistently low rates of mammography screening. A number of health beliefs and sociodemographic characteristics have been associated with mammogram participation among these women. However, studies systematically investigating cultural factors in relation to mammogram experience have been scarce. We measured screening-related health beliefs, modesty and use of Eastern medicine in 100 Korean American women in 2006. Hierarchical logistic regression was used to examine the unique contribution of the study variables, after accounting for sociodemographic characteristics. Only 51% reported past mammogram use. Korean American women who had previously had mammograms were statistically significantly older and had higher perceived benefit scores than those who had not. Perceived benefits (odds ratio = 6.3, 95% confidence interval = 2.12, 18.76) and breast cancer susceptibility (odds ratio = 3.18, 95% confidence interval = 1.06, 9.59) were statistically significant correlates of mammography experience, whereas cultural factors did not correlate. Post hoc analysis showed that for women with some or good English skills, cultural factors statistically significantly correlated with health beliefs and breast cancer knowledge (P < 0.05). Nurses should consider the inclusion in culturally tailored interventions of more targeted outreach and healthcare system navigation assistance for promoting mammography screening in Korean American women. Further research is needed to unravel the interplay between acculturation, cultural factors and health beliefs related to cancer screening behaviours of Korean American women.

Understanding the patient-provider communication needs and experiences of Latina and non-Latina White women following an abnormal mammogram.

PubMed

Molina, Yamile; Hohl, Sarah D; Ko, Linda K; Rodriguez, Edgar A; Thompson, Beti; Beresford, Shirley A A

2014-12-01

Latinas are more likely to delay recommended follow-up care than non-Latina White (NLW) women after an abnormal mammogram result. Ethnic differences in communication needs and experiences with health-care staff and providers may contribute to these delays as well as satisfaction with care. Nonetheless, little research has explored the aspects of communication that may contribute to patient comprehension, adherence to follow-up care, and satisfaction across ethnicity. The purpose of this exploratory, qualitative study was to identify patients' communication needs and experiences with follow-up care among Latina and NLW women who received an abnormal mammogram. We conducted 41 semi-structured interviews with 19 Latina and 22 NLW women between the ages of 40 and 74 who had received an abnormal mammogram. Communication themes indicated that women's needs and experiences concerning abnormal mammograms and follow-up care varied across ethnicity. Latinas and NLW women appeared to differ in their comprehension of abnormal results and follow-up care as a result of language barriers and health literacy. Both groups of women identified clear, empathic communication as being important in patient-provider communication; however, Latinas underscored the need for warm communicative styles, and NLW women emphasized the importance of providing more information. Women with high levels of satisfaction with patient-provider interactions appeared to have positive perspectives of subsequent screening and cancer treatment. To improve patient satisfaction and adherence to follow-up care among Latinas, educational programs are necessary to counsel health-care professionals with regard to language, health literacy, and empathic communication needs in health-care service delivery.

Accurate 3D Modeling of Breast Deformation for Temporal Mammogram Registration

DTIC Science & Technology

2009-09-01

U.S. Army Medical Research and Materiel Command Fort Detrick, Maryland 21702-5012 DISTRIBUTION...MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR’S ACRONYM(S) U.S. Army Medical Research and Materiel Command Fort Detrick, Maryland...Towards Registration of Temporal Mammograms by Finite Element Simulation of MR Breast Volumes”, Proceedings of SPIE Medical Imaging 2008 2. Qiu Y

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

PubMed

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

2017-08-14

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

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

NASA Astrophysics Data System (ADS)

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

2011-03-01

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

Insurance-Based Differences in Time to Diagnostic Follow-up after Positive Screening Mammography.

PubMed

Durham, Danielle D; Robinson, Whitney R; Lee, Sheila S; Wheeler, Stephanie B; Reeder-Hayes, Katherine E; Bowling, J Michael; Olshan, Andrew F; Henderson, Louise M

2016-11-01

Insurance may lengthen or inhibit time to follow-up after positive screening mammography. We assessed the association between insurance status and time to initial diagnostic follow-up after a positive screening mammogram. Using 1995-2010 data from a North Carolina population-based registry of breast imaging and cancer outcomes, we identified women with a positive screening mammogram. We compared receipt of follow-up within 60 days of screening using logistic regression and evaluated time to follow-up initiation using Cox proportional hazards regression. Among 43,026 women included in the study, 73% were <65 years and 27% were 65+ years. Median time until initial diagnostic follow-up was similar by age group and insurance status. In the adjusted model for women <65, uninsured women experienced a longer time to initiation of diagnostic follow-up [HR, 0.47; 95% confidence interval (CI), 0.25-0.89] versus women with private insurance. There were increased odds of these uninsured women not meeting the Centers for Disease Control and Prevention guideline for follow-up within 60 days (OR, 1.59; 95% CI, 1.31-1.94). Among women ages 65+, women with private insurance experienced a faster time to follow-up (adjusted HR, 2.09; 95% CI, 1.27-3.44) than women with Medicare and private insurance. Approximately 10% of women had no follow-up by 365 days. We found differences in time to initial diagnostic follow-up after a positive screening mammogram by insurance status and age group. Uninsured women younger than 65 years at a positive screening event had delayed follow-up. Replication of these findings and examination of their clinical significance warrant additional investigation. Cancer Epidemiol Biomarkers Prev; 25(11); 1474-82. ©2016 AACR. ©2016 American Association for Cancer Research.

A cloud-based multimodality case file for mobile devices.

PubMed

Balkman, Jason D; Loehfelm, Thomas W

2014-01-01

Recent improvements in Web and mobile technology, along with the widespread use of handheld devices in radiology education, provide unique opportunities for creating scalable, universally accessible, portable image-rich radiology case files. A cloud database and a Web-based application for radiologic images were developed to create a mobile case file with reasonable usability, download performance, and image quality for teaching purposes. A total of 75 radiology cases related to breast, thoracic, gastrointestinal, musculoskeletal, and neuroimaging subspecialties were included in the database. Breast imaging cases are the focus of this article, as they best demonstrate handheld display capabilities across a wide variety of modalities. This case subset also illustrates methods for adapting radiologic content to cloud platforms and mobile devices. Readers will gain practical knowledge about storage and retrieval of cloud-based imaging data, an awareness of techniques used to adapt scrollable and high-resolution imaging content for the Web, and an appreciation for optimizing images for handheld devices. The evaluation of this software demonstrates the feasibility of adapting images from most imaging modalities to mobile devices, even in cases of full-field digital mammograms, where high resolution is required to represent subtle pathologic features. The cloud platform allows cases to be added and modified in real time by using only a standard Web browser with no application-specific software. Challenges remain in developing efficient ways to generate, modify, and upload radiologic and supplementary teaching content to this cloud-based platform. Online supplemental material is available for this article. ©RSNA, 2014.

Closing the Gap in Mammogram Screening: An Experimental Intervention among Low-Income Hispanic Women in Community Health Clinics

ERIC Educational Resources Information Center

Deavenport, Alexis; Modeste, Naomi; Marshak, Helen Hopp; Neish, Christine

2011-01-01

A low rate of mammogram screening exists among low-income Hispanic women. To address this disparity, an experimental intervention containing audiovisual and written media was conducted using the health belief model as a framework. The purpose of this study was to determine if low-income Hispanic women, more than 40 years of age, who received…

Mammographic screening for breast cancer in a resource-restricted environment.

PubMed

Apffelstaedt, S P; Dalmayer, L; Baatjes, K

2014-04-01

Mammographic screening is carried out at public sector hospitals as part of clinical practice. We report the experience of such screening at Tygerberg Academic Hospital (TBAH), a tertiary referral hospital in the Western Cape Province, South Africa. All mammograms performed between 2003 and 2012 at TBAH were analysed regarding patient demographics, clinical data, indication and outcome according to the American College of Radiology Breast Imaging Reporting and Data System (BIRADS). Screening mammography was offered to patients > 40 years of age and mammograms were read by experienced breast surgeons. Patients with BIRADS 3 and 4 lesions were recalled for short-term follow-up, further imaging or tissue acquisition. Patients with BIRADS 5 lesions were recalled for tissue acquisition. Further imaging, method of tissue acquisition, histology results and use of neo-adjuvant therapy were also recorded. Of 16 105 mammograms, 3 774 (23.4%) were carried out for screening purposes. The median age of patients undergoing screening was 54 years. Of 407 women with mammograms that were reported as BIRADS 3 - 5 (10.8% of screening mammograms), 187 (46% of recalled women) went on to have further imaging only. Tissue was acquired in 175 patients (43% of recalled women), comprising a biopsy rate of 4.6% of the total series. The malignancy rate in cases in which tissue acquisition was done was 25%. Forty-three breast cancers were diagnosed (11.4/1 000 examinations). Of the cancers, nine (31%) were ductal carcinomas in situ. Of 20 invasive cancers, nine (45%) were < 10 mm in size. Of the invasive cancers, 40% were node-positive. The cancer diagnosis rate indicates a high breast cancer load in an urbanised population.

An Assessment of the Likelihood, Frequency, and Content of Verbal Communication Between Radiologists and Women Receiving Screening and Diagnostic Mammography

PubMed Central

Carney, Patricia A.; Kettler, Mark; Cook, Andrea J.; Geller, Berta M.; Karliner, Leah; Miglioretti, Diana L.; Bowles, Erin Aiello; Buist, Diana S.; Gallagher, Thomas H.; Elmore, Joann G.

2009-01-01

Rationale & Objective Research on communication between radiologists and women undergoing screening and diagnostic mammography is limited. We describe community radiologists’ communication practices with patients regarding screening and diagnostic mammogram results and factors associated with frequency of communication. Materials & Methods We received surveys from 257 radiologists (70% of those eligible) about the extent to which they talk to women as part of their healthcare visit for either screening or diagnostic mammograms, whether this occurs if the exam assessment is positive or negative, and how they use estimates of patient risk to convey information about an abnormal exam where the specific finding of cancer is not yet known. We also assessed characteristics of the radiologists to identify associations with more or less frequent communication at the time of the mammogram. Results Two hundred and forty-three radiologists provided complete data (95%). Very few (<6%) reported routinely communicating with women when screening mammograms were either normal or abnormal. Less than half (47%) routinely communicated with women when their diagnostic mammograms were normal, while 77% often or always communicated with women when their diagnostic exams were abnormal. For positive diagnostic exams, female radiologists were more likely to be frequent communicators compared to males (87.1% to 72.8%; p-value = 0.02) and those who spend 40-79% of their time in breast imaging (94.6%) were more likely to be frequent communicators compared to those who spend less time (67.2%-78.9%; p-value = 0.02). Most radiologists convey risk information using general rather than numeric statements (57.7% vs. 28.5%). Conclusions Radiologists are most likely to convey information about diagnostic mammographic findings when results are abnormal. Most radiologists convey risk information using general rather than numeric statements. PMID:19442539

Deep learning and three-compartment breast imaging in breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Drukker, Karen; Huynh, Benjamin Q.; Giger, Maryellen L.; Malkov, Serghei; Avila, Jesus I.; Fan, Bo; Joe, Bonnie; Kerlikowske, Karla; Drukteinis, Jennifer S.; Kazemi, Leila; Pereira, Malesa M.; Shepherd, John

2017-03-01

We investigated whether deep learning has potential to aid in the diagnosis of breast cancer when applied to mammograms and biologic tissue composition images derived from three-compartment (3CB) imaging. The dataset contained diagnostic mammograms and 3CB images (water, lipid, and protein content) of biopsy-sampled BIRADS 4 and 5 lesions in 195 patients. In 58 patients, the lesion manifested as a mass (13 malignant vs. 45 benign), in 87 as microcalcifications (19 vs. 68), and in 56 as (focal) asymmetry or architectural distortion (11 vs. 45). Six patients had both a mass and calcifications. For each mammogram and corresponding 3CB images, a 128x128 region of interest containing the lesion was selected by an expert radiologist and used directly as input to a deep learning method pretrained on a very large independent set of non-medical images. We used a nested leave-one-out-by-case (patient) model selection and classification protocol. The area under the ROC curve (AUC) for the task of distinguishing between benign and malignant lesions was used as performance metric. For the cases with mammographic masses, the AUC increased from 0.83 (mammograms alone) to 0.89 (mammograms+3CB, p=.162). For the microcalcification and asymmetry/architectural distortion cases the AUC increased from 0.84 to 0.91 (p=.116) and from 0.61 to 0.87 (p=.006), respectively. Our results indicate great potential for the application of deep learning methods in the diagnosis of breast cancer and additional knowledge of the biologic tissue composition appeared to improve performance, especially for lesions mammographically manifesting as asymmetries or architectural distortions.

Frequency and Determinants of a Short-Interval Follow-up Recommendation After an Abnormal Screening Mammogram.

PubMed

Pelletier, Eric; Daigle, Jean-Marc; Defay, Fannie; Major, Diane; Guertin, Marie-Hélène; Brisson, Jacques

2016-11-01

After imaging assessment of an abnormal screening mammogram, a follow-up examination 6 months later is recommended to some women. Our aim was to identify which characteristics of lesions, women, and physicians are associated to such short-interval follow-up recommendation in the Quebec Breast Cancer Screening Program. Between 1998 and 2008, 1,839,396 screening mammograms were performed and a total of 114,781 abnormal screens were assessed by imaging only. Multivariate analysis was done with multilevel Poisson regression models with robust variance and generalized linear mixed models. A short-interval follow-up was recommended in 26.7% of assessments with imaging only, representing 2.3% of all screens. Case-mix adjusted proportion of short-interval follow-up recommendations varied substantially across physicians (range: 4%-64%). Radiologists with high recall rates (≥15%) had a high proportion of short-interval follow-up recommendation (risk ratio: 1.82; 95% confidence interval: 1.35-2.45) compared to radiologists with low recall rates (<5%). The adjusted proportion of short-interval follow-up was high (22.8%) even when a previous mammogram was usually available. Short-interval follow-up recommendation at assessment is frequent in this Canadian screening program, even when a previous mammogram is available. Characteristics related to radiologists appear to be key determinants of short-interval follow-up recommendation, rather than characteristics of lesions or patient mix. Given that it can cause anxiety to women and adds pressure on the health system, it appears important to record and report short-interval follow-up and to identify ways to reduce its frequency. Short-interval follow-up recommendations should be considered when assessing the burden of mammography screening. Copyright © 2016 Canadian Association of Radiologists. Published by Elsevier Inc. All rights reserved.

Recommendations for breast imaging follow-up of women with a previous history of breast cancer: position paper from the Italian Group for Mammography Screening (GISMa) and the Italian College of Breast Radiologists (ICBR) by SIRM.

PubMed

Bucchi, Lauro; Belli, Paolo; Benelli, Eva; Bernardi, Daniela; Brancato, Beniamino; Calabrese, Massimo; Carbonaro, Luca A; Caumo, Francesca; Cavallo-Marincola, Beatrice; Clauser, Paola; Fedato, Chiara; Frigerio, Alfonso; Galli, Vania; Giordano, Livia; Golinelli, Paola; Mariscotti, Giovanna; Martincich, Laura; Montemezzi, Stefania; Morrone, Doralba; Naldoni, Carlo; Paduos, Adriana; Panizza, Pietro; Pediconi, Federica; Querci, Fiammetta; Rizzo, Antonio; Saguatti, Gianni; Tagliafico, Alberto; Trimboli, Rubina M; Zuiani, Chiara; Sardanelli, Francesco

2016-12-01

Women who were previously treated for breast cancer (BC) are an important particular subgroup of women at intermediate BC risk. Their breast follow-up should be planned taking in consideration a 1.0-1.5 % annual rate of loco-regional recurrences and new ipsilateral or contralateral BCs during 15-20 years, and be based on a regional/district invitation system. This activity should be carried out by a Department of Radiology integrating screening and diagnostics in the context of a Breast Unit. We recommend the adoption of protocols dedicated to women previously treated for BC, with a clear definition of responsibilities, methods for invitation, site(s) of visits, methods for clinical and radiological evaluation, follow-up duration, role and function of family doctors and specialists. These women will be invited to get a mammogram in dedicated sessions starting from the year after the end of treatment. The planned follow-up duration will be at least 10 years and will be defined on the basis of patient's age and preferences, taking into consideration organizational matters. Special agreements can be defined in the case of women who have their follow-up planned at other qualified centers. Dedicated screening sessions should include: evaluation of familial/personal history (if previously not done) for identifying high-risk conditions which could indicate a different screening strategy; immediate evaluation of mammograms by one or, when possible, two breast radiologists with possible addition of supplemental mammographic views, digital breast tomosynthesis, clinical breast examination, breast ultrasound; and prompt planning of possible further workup. Results of these screening sessions should be set apart from those of general female population screening and presented in dedicated reports. The following research issues are suggested: further risk stratification and effectiveness of follow-up protocols differentiated also for BC pathologic subtype and molecular classification, and evaluation of different models of survivorship care, also in terms of cost-effectiveness.

Using x-ray mammograms to assist in microwave breast image interpretation.

PubMed

Curtis, Charlotte; Frayne, Richard; Fear, Elise

2012-01-01

Current clinical breast imaging modalities include ultrasound, magnetic resonance (MR) imaging, and the ubiquitous X-ray mammography. Microwave imaging, which takes advantage of differing electromagnetic properties to obtain image contrast, shows potential as a complementary imaging technique. As an emerging modality, interpretation of 3D microwave images poses a significant challenge. MR images are often used to assist in this task, and X-ray mammograms are readily available. However, X-ray mammograms provide 2D images of a breast under compression, resulting in significant geometric distortion. This paper presents a method to estimate the 3D shape of the breast and locations of regions of interest from standard clinical mammograms. The technique was developed using MR images as the reference 3D shape with the future intention of using microwave images. Twelve breast shapes were estimated and compared to ground truth MR images, resulting in a skin surface estimation accurate to within an average Euclidean distance of 10 mm. The 3D locations of regions of interest were estimated to be within the same clinical area of the breast as corresponding regions seen on MR imaging. These results encourage investigation into the use of mammography as a source of information to assist with microwave image interpretation as well as validation of microwave imaging techniques.

Family/friend recommendations and mammography intentions: the roles of perceived mammography norms and support

PubMed Central

Molina, Yamile; Ornelas, India J.; Doty, Sarah L.; Bishop, Sonia; Beresford, Shirley A. A.; Coronado, Gloria D.

2015-01-01

Identifying factors that increase mammography use among Latinas is an important public health priority. Latinas are more likely to report mammography intentions and use, if a family member or friend recommends that they get a mammogram. Little is known about the mechanisms underlying the relationship between social interactions and mammography intentions. Theory suggests that family/friend recommendations increase perceived mammography norms (others believe a woman should obtain a mammogram) and support (others will help her obtain a mammogram), which in turn increase mammography intentions and use. We tested these hypotheses with data from the ¡Fortaleza Latina! study, a randomized controlled trial including 539 Latinas in Washington State. Women whose family/friend recommended they get a mammogram within the last year were more likely to report mammography intentions, norms and support. Perceived mammography norms mediated the relationship between family/friend recommendations and intentions, Mediated Effect = 0.38, 95%CI [0.20, 0.61], but not support, Mediated Effect = 0.002, 95%CI [−0.07, 0.07]. Our findings suggest perceived mammography norms are a potential mechanism underlying the effect of family/friend recommendations on mammography use among Latinas. Our findings make an important contribution to theory about the associations of social interactions, perceptions and health behaviors. PMID:26324395

Mammographic density, breast cancer risk and risk prediction

PubMed Central

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

2007-01-01

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

Similarity estimation for reference image retrieval in mammograms using convolutional neural network

NASA Astrophysics Data System (ADS)

Muramatsu, Chisako; Higuchi, Shunichi; Morita, Takako; Oiwa, Mikinao; Fujita, Hiroshi

2018-02-01

Periodic breast cancer screening with mammography is considered effective in decreasing breast cancer mortality. For screening programs to be successful, an intelligent image analytic system may support radiologists' efficient image interpretation. In our previous studies, we have investigated image retrieval schemes for diagnostic references of breast lesions on mammograms and ultrasound images. Using a machine learning method, reliable similarity measures that agree with radiologists' similarity were determined and relevant images could be retrieved. However, our previous method includes a feature extraction step, in which hand crafted features were determined based on manual outlines of the masses. Obtaining the manual outlines of masses is not practical in clinical practice and such data would be operator-dependent. In this study, we investigated a similarity estimation scheme using a convolutional neural network (CNN) to skip such procedure and to determine data-driven similarity scores. By using CNN as feature extractor, in which extracted features were employed in determination of similarity measures with a conventional 3-layered neural network, the determined similarity measures were correlated well with the subjective ratings and the precision of retrieving diagnostically relevant images was comparable with that of the conventional method using handcrafted features. By using CNN for determination of similarity measure directly, the result was also comparable. By optimizing the network parameters, results may be further improved. The proposed method has a potential usefulness in determination of similarity measure without precise lesion outlines for retrieval of similar mass images on mammograms.

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

PubMed Central

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

2015-01-01

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

Local breast density assessment using reacquired mammographic images.

PubMed

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

2017-08-01

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

Incorporation of the technologist's opinion for arbitration of discrepant assessments among radiologists at screening mammography.

PubMed

Coolen, Angela M P; Lameijer, Joost R C; Voogd, Adri C; Strobbe, Luc J; Louwman, Marieke W J; Tjan-Heijnen, Vivianne C G; Duijm, Lucien E M

2018-05-05

We determined whether the addition of the technologist's opinion may be helpful in deciding if discordant readings at blinded double reading should be recalled. A consecutive series of 99,013 digital screening mammograms, obtained between July 2013 and January 2015, were included. All mammograms were first interpreted by a technologist and then double read in a blinded fashion by a team of 13 screening radiologists. All concordant and discordant positive readings among radiologists were recalled. Out of 3562 recalls, 998 women were recalled after a discordant reading. Of these women, 337 (33.8%) had a positive technologist assessment, of which 40 (11.9%) were diagnosed with breast cancer. Sixty women with a negative technologist assessment (60/661, 9.1%) were diagnosed with breast cancer (p = 0.16). Recall rate would have decreased with technologist arbitration (3.6% vs. 2.9%, p < 0.001). Cancer detection rate decreased with 8.5%, from 7.1/1000 screens to 6.5/1000 screens (p = 0.10). Among women with a positive technologist assessment, the probability of breast cancer was highest in case of suspicious microcalcifications and lowest for suspicious masses (30.4% (17/56) versus 7.0% (16/212), p < 0.001). Breast cancers were diagnosed in all groups of mammographic abnormalities, except in women with a suspicious asymmetry and a negative technologist assessment. Assessment by a technologist does not provide a significant discriminating ability in case of a discordant radiologist reading and, taking into account the decrease in cancer detection rate, does not appear to be a suitable arbitration strategy for discordant recalls at blinded double reading.

Interactions of alcohol and postmenopausal hormone use in regards to mammographic breast density.

PubMed

Yaghjyan, Lusine; Colditz, Graham; Eliassen, Heather; Rosner, Bernard; Gasparova, Aleksandra; Tamimi, Rulla M

2018-06-25

We investigated the association of alcohol intake with mammographic breast density in postmenopausal women by their hormone therapy (HT) status. This study included 2,100 cancer-free postmenopausal women within the Nurses' Health Study and Nurses' Health Study II cohorts. Percent breast density (PD), absolute dense (DA), and non-dense areas (NDA) were measured from digitized film mammograms using a computer-assisted thresholding technique; all measures were square root transformed. Alcohol consumption was assessed with a food frequency questionnaire (0, < 5, and ≥ 5 g/day). Information regarding breast cancer risk factors was obtained from baseline or biennial questionnaires closest to the mammogram date. We used generalized linear regression to examine associations between alcohol and breast density measures in women with no HT history, current, and past HT users. In multivariable analyses, we found no associations of alcohol consumption with PD (p trend = 0.32) and DA (p trend = 0.53) and an inverse association with NDA (β = - 0.41, 95% CI - 0.73, - 0.09 for ≥ 5 g/day, p trend < 0.01). In the stratified analysis by HT status, alcohol was not associated with PD in any of the strata. We found a significant inverse association of alcohol with NDA among past HT users (β = - 0.79, 95% CI - 1.51, - 0.07 for ≥ 5 g/day, p trend = 0.02). There were no significant interactions between alcohol and HT in relation to PD, DA, and NDA (p interaction = 0.19, 0.42, and 0.43, respectively). Our findings suggest that associations of alcohol with breast density do not vary by HT status.

iPads in Breast Imaging – A Phantom Study

PubMed Central

Hammon, M.; Schlechtweg, P. M.; Schulz-Wendtland, R.; Uder, M.; Schwab, S. A.

2014-01-01

Introduction: Modern tablet PCs as the iPad are becoming more and more integrated into medicine. The aim of this study was to evaluate the display quality of iPads regarding digital mammography. Materials and Methods: Three experienced readers compared the display quality of the iPad 2 and 3 with a dedicated 10 megapixel (MP) mammography liquid crystal display (LCD) screen in consensus using the standardized Contrast Detail Mammography (CDMAM) phantom. Phantom fields without agreement between the readers were classified as “uncertain”, correct 2 : 1 decisions were classified as “uncertain/readable”. In a second step display quality of the three reading devices was judged subjectively in a side by side comparison. Results: The 10 MP screen was superior to both iPads in 4 (phantom-)fields and inferior in 2 fields. Comparing the iPads, version 3 was superior in 4 fields and version 2 was superior in 1 field. However these differences were not significant. Total number of “uncertain” fields did not show significant differences. The number of “uncertain” fields was 15 with the 10 MP screen, 16 with the iPad 2 and 17 with the iPad 3 (p > 0.05), the number of “uncertain/readable” fields was 4, 7 and 8, respectively. Subjective image quality of the iPad 3 and the 10 MP screen was rated superior to the iPad 2. Conclusion: The evaluated iPads, especially in version 3, seem to be adequate to display mammograms in a diagnostic quality and thus could be useful e.g. for patient consultation, clinical demonstration or educational and teaching purposes. However primary mammogram reading should still be performed on dedicated large sized reading screens. PMID:24741126

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

PubMed

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

2016-01-01

To investigate the probability of breast cancer among women recalled due to abnormal findings on the screening mammograms (PPV-1) and among women who underwent an invasive procedure (PPV-2) by mammographic density (MD), screening mode and age. We used information about 28,826 recall examinations from 26,951 subsequently screened women in the Norwegian Breast Cancer Screening Program, 1996-2010. The radiologists who performed the recall examinations subjectively classified MD on the mammograms into three categories: fatty (<30% fibroglandular tissue); medium dense (30-70%) and dense (>70%). Screening mode was defined as screen-film mammography (SFM) and full-field digital mammography (FFDM). We examined trends of PPVs by MD, screening mode and age. We used logistic regression to estimate odds ratio (OR) of screen-detected breast cancer associated with MD among women recalled, adjusting for screening mode and age. PPV-1 and PPV-2 decreased by increasing MD, regardless of screening mode (p for trend <0.05 for both PPVs). PPV-1 and PPV-2 were statistically significantly higher for FFDM compared with SFM for women with fatty breasts. Among women recalled, the adjusted OR of breast cancer decreased with increasing MD. Compared with women with fatty breasts, the OR was 0.90 (95% CI: 0.84-0.96) for those with medium dense breasts and 0.85 (95% CI: 0.76-0.95) for those with dense breasts. PPVs decreased by increasing MD. Fewer women needed to be recalled or undergo an invasive procedure to detect one breast cancer among those with fatty versus dense breasts in the screening program in Norway, 1996-2010. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A comprehensive tool for measuring mammographic density changes over time.

PubMed

Eriksson, Mikael; Li, Jingmei; Leifland, Karin; Czene, Kamila; Hall, Per

2018-06-01

Mammographic density is a marker of breast cancer risk and diagnostics accuracy. Density change over time is a strong proxy for response to endocrine treatment and potentially a stronger predictor of breast cancer incidence. We developed STRATUS to analyse digital and analogue images and enable automated measurements of density changes over time. Raw and processed images from the same mammogram were randomly sampled from 41,353 healthy women. Measurements from raw images (using FDA approved software iCAD) were used as templates for STRATUS to measure density on processed images through machine learning. A similar two-step design was used to train density measures in analogue images. Relative risks of breast cancer were estimated in three unique datasets. An alignment protocol was developed using images from 11,409 women to reduce non-biological variability in density change. The protocol was evaluated in 55,073 women having two regular mammography screens. Differences and variances in densities were compared before and after image alignment. The average relative risk of breast cancer in the three datasets was 1.6 [95% confidence interval (CI) 1.3-1.8] per standard deviation of percent mammographic density. The discrimination was AUC 0.62 (CI 0.60-0.64). The type of image did not significantly influence the risk associations. Alignment decreased the non-biological variability in density change and re-estimated the yearly overall percent density decrease from 1.5 to 0.9%, p < 0.001. The quality of STRATUS density measures was not influenced by mammogram type. The alignment protocol reduced the non-biological variability between images over time. STRATUS has the potential to become a useful tool for epidemiological studies and clinical follow-up.

Improving the performance of lesion-based computer-aided detection schemes of breast masses using a case-based adaptive cueing method

NASA Astrophysics Data System (ADS)

Tan, Maxine; Aghaei, Faranak; Wang, Yunzhi; Qian, Wei; Zheng, Bin

2016-03-01

Current commercialized CAD schemes have high false-positive (FP) detection rates and also have high correlations in positive lesion detection with radiologists. Thus, we recently investigated a new approach to improve the efficacy of applying CAD to assist radiologists in reading and interpreting screening mammograms. Namely, we developed a new global feature based CAD approach/scheme that can cue the warning sign on the cases with high risk of being positive. In this study, we investigate the possibility of fusing global feature or case-based scores with the local or lesion-based CAD scores using an adaptive cueing method. We hypothesize that the information from the global feature extraction (features extracted from the whole breast regions) are different from and can provide supplementary information to the locally-extracted features (computed from the segmented lesion regions only). On a large and diverse full-field digital mammography (FFDM) testing dataset with 785 cases (347 negative and 438 cancer cases with masses only), we ran our lesion-based and case-based CAD schemes "as is" on the whole dataset. To assess the supplementary information provided by the global features, we used an adaptive cueing method to adaptively adjust the original CAD-generated detection scores (Sorg) of a detected suspicious mass region based on the computed case-based score (Scase) of the case associated with this detected region. Using the adaptive cueing method, better sensitivity results were obtained at lower FP rates (<= 1 FP per image). Namely, increases of sensitivities (in the FROC curves) of up to 6.7% and 8.2% were obtained for the ROI and Case-based results, respectively.

Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions

PubMed Central

Tan, Maxine; Aghaei, Faranak; Wang, Yunzhi; Zheng, Bin

2017-01-01

The purpose of this study is to evaluate a new method to improve performance of computer-aided detection (CAD) schemes of screening mammograms with two approaches. In the first approach, we developed a new case based CAD scheme using a set of optimally selected global mammographic density, texture, spiculation, and structural similarity features computed from all four full-field digital mammography (FFDM) images of the craniocaudal (CC) and mediolateral oblique (MLO) views by using a modified fast and accurate sequential floating forward selection feature selection algorithm. Selected features were then applied to a “scoring fusion” artificial neural network (ANN) classification scheme to produce a final case based risk score. In the second approach, we combined the case based risk score with the conventional lesion based scores of a conventional lesion based CAD scheme using a new adaptive cueing method that is integrated with the case based risk scores. We evaluated our methods using a ten-fold cross-validation scheme on 924 cases (476 cancer and 448 recalled or negative), whereby each case had all four images from the CC and MLO views. The area under the receiver operating characteristic curve was AUC = 0.793±0.015 and the odds ratio monotonically increased from 1 to 37.21 as CAD-generated case based detection scores increased. Using the new adaptive cueing method, the region based and case based sensitivities of the conventional CAD scheme at a false positive rate of 0.71 per image increased by 2.4% and 0.8%, respectively. The study demonstrated that supplementary information can be derived by computing global mammographic density image features to improve CAD-cueing performance on the suspicious mammographic lesions. PMID:27997380

Breast density estimation from high spectral and spatial resolution MRI

PubMed Central

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

2016-01-01

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

Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms.

PubMed

Tourassi, Georgia D; Harrawood, Brian; Singh, Swatee; Lo, Joseph Y; Floyd, Carey E

2007-01-01

The purpose of this study was to evaluate image similarity measures employed in an information-theoretic computer-assisted detection (IT-CAD) scheme. The scheme was developed for content-based retrieval and detection of masses in screening mammograms. The study is aimed toward an interactive clinical paradigm where physicians query the proposed IT-CAD scheme on mammographic locations that are either visually suspicious or indicated as suspicious by other cuing CAD systems. The IT-CAD scheme provides an evidence-based, second opinion for query mammographic locations using a knowledge database of mass and normal cases. In this study, eight entropy-based similarity measures were compared with respect to retrieval precision and detection accuracy using a database of 1820 mammographic regions of interest. The IT-CAD scheme was then validated on a separate database for false positive reduction of progressively more challenging visual cues generated by an existing, in-house mass detection system. The study showed that the image similarity measures fall into one of two categories; one category is better suited to the retrieval of semantically similar cases while the second is more effective with knowledge-based decisions regarding the presence of a true mass in the query location. In addition, the IT-CAD scheme yielded a substantial reduction in false-positive detections while maintaining high detection rate for malignant masses.

Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms

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

Tourassi, Georgia D.; Harrawood, Brian; Singh, Swatee

The purpose of this study was to evaluate image similarity measures employed in an information-theoretic computer-assisted detection (IT-CAD) scheme. The scheme was developed for content-based retrieval and detection of masses in screening mammograms. The study is aimed toward an interactive clinical paradigm where physicians query the proposed IT-CAD scheme on mammographic locations that are either visually suspicious or indicated as suspicious by other cuing CAD systems. The IT-CAD scheme provides an evidence-based, second opinion for query mammographic locations using a knowledge database of mass and normal cases. In this study, eight entropy-based similarity measures were compared with respect to retrievalmore » precision and detection accuracy using a database of 1820 mammographic regions of interest. The IT-CAD scheme was then validated on a separate database for false positive reduction of progressively more challenging visual cues generated by an existing, in-house mass detection system. The study showed that the image similarity measures fall into one of two categories; one category is better suited to the retrieval of semantically similar cases while the second is more effective with knowledge-based decisions regarding the presence of a true mass in the query location. In addition, the IT-CAD scheme yielded a substantial reduction in false-positive detections while maintaining high detection rate for malignant masses.« less

Residential particulate matter and distance to roadways in relation to mammographic density: results from the Nurses' Health Studies.

PubMed

DuPre, Natalie C; Hart, Jaime E; Bertrand, Kimberly A; Kraft, Peter; Laden, Francine; Tamimi, Rulla M

2017-11-23

High mammographic density is a strong, well-established breast cancer risk factor. Three studies conducted in various smaller geographic settings reported inconsistent findings between air pollution and mammographic density. We assessed whether particulate matter (PM) exposures (PM 2.5 , PM 2.5-10 , and PM 10 ) and distance to roadways were associated with mammographic density among women residing across the United States. The Nurses' Health Studies are prospective cohorts for whom a subset has screening mammograms from the 1990s (interquartile range 1990-1999). PM was estimated using spatio-temporal models linked to residential addresses. Among 3258 women (average age at mammogram 52.7 years), we performed multivariable linear regression to assess associations between square-root-transformed mammographic density and PM within 1 and 3 years before the mammogram. For linear regression estimates of PM in relation to untransformed mammographic density outcomes, bootstrapped robust standard errors are used to calculate 95% confidence intervals (CIs). Analyses were stratified by menopausal status and region of residence. Recent PM and distance to roadways were not associated with mammographic density in premenopausal women (PM 2.5 within 3 years before mammogram β = 0.05, 95% CI -0.16, 0.27; PM 2.5-10 β = 0, 95%, CI -0.15, 0.16; PM 10 β = 0.02, 95% CI -0.10, 0.13) and postmenopausal women (PM 2.5 within 3 years before mammogram β = -0.05, 95% CI -0.27, 0.17; PM 2.5-10 β = -0.01, 95% CI -0.16, 0.14; PM 10 β = -0.02, 95% CI -0.13, 0.09). Largely null associations were observed within regions. Suggestive associations were observed among postmenopausal women in the Northeast (n = 745), where a 10-μg/m 3 increase in PM 2.5 within 3 years before the mammogram was associated with 3.4 percentage points higher percent mammographic density (95% CI -0.5, 7.3). These findings do not support that recent PM or roadway exposures influence mammographic density. Although PM was largely not associated with mammographic density, we cannot rule out the role of PM during earlier exposure time windows and possible associations among northeastern postmenopausal women.

Assembly and evaluation of a training module and dataset with feedback for improved interpretation of digital breast tomosynthesis examinations

NASA Astrophysics Data System (ADS)

Gur, David; Zuley, Margarita L.; Sumkin, Jules H.; Hakim, Christiane M.; Chough, Denise M.; Lovy, Linda; Sobran, Cynthia; Logue, Durwin; Zheng, Bin; Klym, Amy H.

2012-02-01

The FDA recently approved Digital Breast Tomosynthesis (DBT) for use in screening for the early detection of breast cancer. However, MQSA qualification for interpreting DBT through training was noted as important. Performance issues related to training are largely unknown. Therefore, we assembled a unique computerized training module to assess radiologists' performances before and after using the training module. Seventy-one actual baseline mammograms (no priors) with FFDM and DBT images were assembled to be read before and after training with the developed module. Fifty examinations of FFDM and DBT images enriched with positive findings were assembled for the training module. Depicted findings were carefully reviewed, summarized, and entered into a specially designed training database where findings were identified by case number and synchronized to the display of the related FFDM plus DBT examinations on a clinical workstation. Readers reported any findings using screening BIRADS (0, 1, or 2) followed by instantaneous feedback of the verified truth. Six radiologists participated in the study and reader average sensitivity and specificity were compared before and after training. Average sensitivity improved and specificity remained relatively the same after training. Performance changes may be affected by disease prevalence in the training set.

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

PubMed

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

2017-04-01

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

Using a Tracking System to Improve Prostate Cancer Screening and Follow-up in a Small Urban Community

DTIC Science & Technology

2001-05-01

clinical practice, attention is directed toward informing the general public about them. In the late 1980 ’s, the Prostate Cancer Education Council...impact of abnormal mammograms on psychosocial outcomes and subsequent screening. PsychoOncology, 9, 402-410. Myers, R.E., Hyslop T., Jennings-Dozier, K...impact of abnormal mammograms on psychosocial outcomes and subsequent screening. PsychoOncology, 9, 402- 410. Myers, R.E., Hyslop T., Jennings-Dozier, K

Diagnosing Diagnosis Errors: Lessons From A Multi-Institutional Collaborative Project

DTIC Science & Technology

2005-01-01

Breast Cancer Inappropriately reassured to have benign lesions - 21/435 (5%); 14 (3%) misread mammogram, 4 (1%) misread pathologic finding, 5 (1...diagnostic tests they are using. It is well known that a normal mammogram in a woman with a breast lump does not rule out the diagnosis of breast cancer ...physician delay in the diagnosis of breast cancer . Arch Intern Med 2002;162:1343–8. 27. Clark S. Spinal infections go undetected. Lancet 1998;351

Frequency-Domain Optical Mammogram

DTIC Science & Technology

2002-10-01

have performed the proposed analysis of frequency-domain optical mammograms for a clinical population of about 150 patients. This analysis has led to...model the propagation of light in tissue14-20 have led to new approaches to optical mammography. As The authors are with the Department of Electrical...Modulation Methods, and Signal Detection /406 7.2.1 Lasers and arc lamps / 407’ 7.2.2 Pulsed sources / 407 7.2.3 Laser diodes and light-emitting diodes ( LEDs

Mammography Screening Among African-American Women With a Family History of Breast Cancer

DTIC Science & Technology

1999-02-01

Women into Mammography Stages of C hange ................................................................... Page 101-102 Appendix F: Poster Presentations...Agree 5. Strongly agree 7. Refused 8. Don’t know 2.8 Once you have a couple of mammograms that are normal, you dont need any more for a few years...mammogram is off schedule but is not thinking or planning to get another and has not scheduled an appointment. 102 Appendix F Poster Presentations and

U.S. Preventive Services Task Force recommendations and cancer screening among female Medicare beneficiaries.

PubMed

Salloum, Ramzi G; Kohler, Racquel E; Jensen, Gail A; Sheridan, Stacey L; Carpenter, William R; Biddle, Andrea K

2014-03-01

Medicare covers several cancer screening tests not currently recommended by the U.S. Preventive Services Task Force (Task Force). In September 2002, the Task Force relaxed the upper age limit of 70 years for breast cancer screening recommendations, and in March 2003 an upper age limit of 65 years was introduced for cervical cancer screening recommendations. We assessed whether mammogram and Pap test utilization among women with Medicare coverage is influenced by changes in the Task Force's recommendations for screening. We identified female Medicare beneficiaries aged 66-80 years and used bivariate probit regression to examine the receipt of breast (mammogram) and cervical (Pap test) cancer screening reflecting changes in the Task Force recommendations. We analyzed 9,760 Medicare Current Beneficiary Survey responses from 2001 to 2007. More than two-thirds reported receiving a mammogram and more than one-third a Pap test in the previous 2 years. Lack of recommendation was given as a reason for not getting screened among the majority (51% for mammogram and 75% for Pap). After controlling for beneficiary-level socioeconomic characteristics and access to care factors, we did not observe a significant change in breast and cervical cancer screening patterns following the changes in Task Force recommendations. Although there is evidence that many Medicare beneficiaries adhere to screening guidelines, some women may be receiving non-recommended screening services covered by Medicare.

Ethnic differences in social support after initial receipt of an abnormal mammogram.

PubMed

Molina, Yamile; Hohl, Sarah D; Nguyen, Michelle; Hempstead, Bridgette H; Weatherby, Shauna Rae; Dunbar, Claire; Beresford, Shirley A A; Ceballos, Rachel M

2016-10-01

We examine access to and type of social support after initial receipt of an abnormal mammogram across non-Latina White (NLW), African American, and Latina women. This cross-sectional study used a mixed method design, with quantitative and qualitative measures. Women were recruited through 2 community advocates and 3 breast-health-related care organizations. With regard to access, African American women were less likely to access social support relative to NLW counterparts. Similar nonsignificant differences were found for Latinas. Women did not discuss results with family and friends to avoid burdening social networks and negative reactions. Networks' geographic constraints and medical mistrust influenced Latina and African American women's decisions to discuss results. With regard to type of social support, women reported emotional support across ethnicity. Latina and African American women reported more instrumental support, whereas NLW women reported more informational support in the context of their well-being. There are shared and culturally unique aspects of women's experiences with social support after initially receiving an abnormal mammogram. Latina and African American women may particularly benefit from informational support from health care professionals. Communitywide efforts to mitigate mistrust and encourage active communication about cancer may improve ethnic disparities in emotional well-being and diagnostic resolution during initial receipt of an abnormal mammogram. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Family/friend recommendations and mammography intentions: the roles of perceived mammography norms and support.

PubMed

Molina, Yamile; Ornelas, India J; Doty, Sarah L; Bishop, Sonia; Beresford, Shirley A A; Coronado, Gloria D

2015-10-01

Identifying factors that increase mammography use among Latinas is an important public health priority. Latinas are more likely to report mammography intentions and use, if a family member or friend recommends that they get a mammogram. Little is known about the mechanisms underlying the relationship between social interactions and mammography intentions. Theory suggests that family/friend recommendations increase perceived mammography norms (others believe a woman should obtain a mammogram) and support (others will help her obtain a mammogram), which in turn increase mammography intentions and use. We tested these hypotheses with data from the ¡Fortaleza Latina! study, a randomized controlled trial including 539 Latinas in Washington State. Women whose family/friend recommended they get a mammogram within the last year were more likely to report mammography intentions, norms and support. Perceived mammography norms mediated the relationship between family/friend recommendations and intentions, Mediated Effect = 0.38, 95%CI [0.20, 0.61], but not support, Mediated Effect = 0.002, 95%CI [-0.07, 0.07]. Our findings suggest perceived mammography norms are a potential mechanism underlying the effect of family/friend recommendations on mammography use among Latinas. Our findings make an important contribution to theory about the associations of social interactions, perceptions and health behaviors. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Computer-aided classification of mammographic masses using the deep learning technology: a preliminary study

NASA Astrophysics Data System (ADS)

Qiu, Yuchen; Yan, Shiju; Tan, Maxine; Cheng, Samuel; Liu, Hong; Zheng, Bin

2016-03-01

Although mammography is the only clinically acceptable imaging modality used in the population-based breast cancer screening, its efficacy is quite controversy. One of the major challenges is how to help radiologists more accurately classify between benign and malignant lesions. The purpose of this study is to investigate a new mammographic mass classification scheme based on a deep learning method. In this study, we used an image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms, which includes 280 malignant and 280 benign mass ROIs, respectively. An eight layer deep learning network was applied, which employs three pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perception (MLP) classifier for feature categorization. In order to improve robustness of selected features, each convolution layer is connected with a max-pooling layer. A number of 20, 10, and 5 feature maps were utilized for the 1st, 2nd and 3rd convolution layer, respectively. The convolution networks are followed by a MLP classifier, which generates a classification score to predict likelihood of a ROI depicting a malignant mass. Among 560 ROIs, 420 ROIs were used as a training dataset and the remaining 140 ROIs were used as a validation dataset. The result shows that the new deep learning based classifier yielded an area under the receiver operation characteristic curve (AUC) of 0.810+/-0.036. This study demonstrated the potential superiority of using a deep learning based classifier to distinguish malignant and benign breast masses without segmenting the lesions and extracting the pre-defined image features.

Location- and lesion-dependent estimation of background tissue complexity for anthropomorphic model observer

NASA Astrophysics Data System (ADS)

Avanaki, Ali R. N.; Espig, Kathryn; Knippel, Eddie; Kimpe, Tom R. L.; Xthona, Albert; Maidment, Andrew D. A.

2016-03-01

In this paper, we specify a notion of background tissue complexity (BTC) as perceived by a human observer that is suited for use with model observers. This notion of BTC is a function of image location and lesion shape and size. We propose four unsupervised BTC estimators based on: (i) perceived pre- and post-lesion similarity of images, (ii) lesion border analysis (LBA; conspicuous lesion should be brighter than its surround), (iii) tissue anomaly detection, and (iv) mammogram density measurement. The latter two are existing methods we adapt for location- and lesion-dependent BTC estimation. To validate the BTC estimators, we ask human observers to measure BTC as the visibility threshold amplitude of an inserted lesion at specified locations in a mammogram. Both human-measured and computationally estimated BTC varied with lesion shape (from circular to oval), size (from small circular to larger circular), and location (different points across a mammogram). BTCs measured by different human observers are correlated (ρ=0.67). BTC estimators are highly correlated to each other (0.84

Using population-based data to examine preventive services by disability type among dually eligible (Medicare/Medicaid) adults.

PubMed

Reichard, Amanda; Fox, Michael H

2013-04-01

Individuals dually eligible for Medicaid and Medicare constitute a small percentage of these program's populations but account for a disproportionately large percent of their total costs. While much work has examined high expenditures, little is known about their health and details of their health care utilization. Utilize an important public health surveillance tool to better understand preventive service use among the dual eligible population. This study involved descriptive and regression analyses of dual eligibles in the Medical Expenditure Panel Survey data from pooled alternate years 2000-2008. We classified the sample into 4 mutually exclusive groups: cognitive limitations, physical disabilities, double diagnosis (cognitive limitations and physical disability), or neither cognitive limitations nor physical disability. For most groups, age was significantly associated with preventive services, though direction varies. Older age was linked to greater receipt of flu shots while younger age was associated with greater receipt of Pap tests, mammograms and dental services. Black women in all groups (except cognitive limitations) had an increased likelihood of receiving a Pap test and a mammogram. A subset of dual eligibles drives the majority of expenditures. People with physical disabilities, regardless of whether they also have a cognitive limitation, are among the highest costing and sickest of our non-institutionalized dual eligible population. Efforts to understand and address the challenges faced by women with physical disabilities in accessing Pap tests or mammograms may be helpful in improving the overall health status for this disability group, but also for all dual eligibles. Published by Elsevier Inc.

A Promotora-administered group education intervention to promote breast and cervical cancer screening in a rural community along the U.S.-Mexico border: a randomized controlled trial.

PubMed

Nuño, Tomas; Martinez, Maria Elena; Harris, Robin; García, Francisco

2011-03-01

Breast cancer is the most common neoplasm among Hispanic women. Cervical cancer has a higher incidence and mortality among Hispanic women compared with non-Hispanic White women. To assess the effectiveness of a promotora-administered educational intervention to promote breast and cervical cancer screening among post-reproductive age, medically underserved Hispanic women residing along the U.S.-Mexico border. Women age 50 or older were eligible to participate in this intervention study. A total of 381 subjects agreed to participate. Women were randomly assigned into one of two groups, educational intervention or usual care. The primary outcomes were self-reported mammogram and Pap smear screening. Logistic regression analysis was used to compute odds ratios for comparisons between intervention and control groups. Women in the intervention group were 2.0 times more likely to report having had a mammogram within the last year when compared with the usual care group (95% CI = 1.3-3.1). Likewise, women in the intervention group were 1.5 times more likely to report having a Pap smear within the last year when compared with the usual care group, although this was not statistically significant (95% CI = 0.9-2.6). In a secondary analysis, the intervention suggests a stronger effect on those that had not had a mammogram or Pap smear within the past year at baseline. A promotora-based educational intervention can be used to increase breast and cervical cancer screening utilization among Hispanic women.

Improving Cancer Detection and Dose Efficiency in Dedicated Breast Cancer CT

DTIC Science & Technology

2011-02-01

17. A. E. Burgess, F. L. Jacobson, and P. F. Judy , “ Human observer detection experiments with mammograms and power-law noise,” Med. Phys., Vol. 28...Jacobson F L and Judy P F 2001 Human observer detection experiments with mammograms and power-law noise Med. Phys. 28 419–37 Crawford C R and Kak A C 1979...anthropomorphic head phantom was designed for realistically simulating human head [12], it features not only a natural human skeleton but also contrast

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

PubMed

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

2018-05-02

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

Web-based versus in-person methods for training lay community health advisors to implement health promotion workshops: participant outcomes from a cluster-randomized trial.

PubMed

Holt, Cheryl L; Tagai, Erin K; Santos, Sherie Lou Zara; Scheirer, Mary Ann; Bowie, Janice; Haider, Muhiuddin; Slade, Jimmie

2018-06-28

Project HEAL (Health through Early Awareness and Learning) is an implementation trial that compared two methods of training lay peer community health advisors (CHAs)-in-person ("Traditional") versus web-based ("Technology")-to conduct a series of three evidence-based cancer educational workshops in African American churches. This analysis reports on participant outcomes from Project HEAL. Fifteen churches were randomized to the two CHA training methods and the intervention impact was examined over 24 months. This study was conducted in Prince George's County, MD, and enrolled 375 church members age 40-75. Participants reported on knowledge and screening behaviors for breast, prostate, and colorectal cancer. Overall, cancer knowledge in all areas increased during the study period (p < .001). There were significant increases in digital rectal exam (p < .05), fecal occult blood test (p < .001), and colonoscopy (p < .01) at 24 months; however, this did not differ by study group. Mammography maintenance (56% overall) was evidenced by women reporting multiple mammograms within the study period. Participants attending all three workshops were more likely to report a fecal occult blood test or colonoscopy at 24 months (p < .05) than those who attended only one. These findings suggest that lay individuals can receive web-based training to successfully implement an evidence-based health promotion intervention that results in participant-level outcomes comparable with (a) people trained using the traditional classroom method and (b) previous efficacy trials. Findings have implications for resources and use of technology to increase widespread dissemination of evidence-based health promotion interventions through training lay persons in community settings.

Selection of examples in case-based computer-aided decision systems

PubMed Central

Mazurowski, Maciej A.; Zurada, Jacek M.; Tourassi, Georgia D.

2013-01-01

Case-based computer-aided decision (CB-CAD) systems rely on a database of previously stored, known examples when classifying new, incoming queries. Such systems can be particularly useful since they do not need retraining every time a new example is deposited in the case base. The adaptive nature of case-based systems is well suited to the current trend of continuously expanding digital databases in the medical domain. To maintain efficiency, however, such systems need sophisticated strategies to effectively manage the available evidence database. In this paper, we discuss the general problem of building an evidence database by selecting the most useful examples to store while satisfying existing storage requirements. We evaluate three intelligent techniques for this purpose: genetic algorithm-based selection, greedy selection and random mutation hill climbing. These techniques are compared to a random selection strategy used as the baseline. The study is performed with a previously presented CB-CAD system applied for false positive reduction in screening mammograms. The experimental evaluation shows that when the development goal is to maximize the system’s diagnostic performance, the intelligent techniques are able to reduce the size of the evidence database to 37% of the original database by eliminating superfluous and/or detrimental examples while at the same time significantly improving the CAD system’s performance. Furthermore, if the case-base size is a main concern, the total number of examples stored in the system can be reduced to only 2–4% of the original database without a decrease in the diagnostic performance. Comparison of the techniques shows that random mutation hill climbing provides the best balance between the diagnostic performance and computational efficiency when building the evidence database of the CB-CAD system. PMID:18854606

Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography.

PubMed

Zyout, Imad; Czajkowska, Joanna; Grzegorzek, Marcin

2015-12-01

The high number of false positives and the resulting number of avoidable breast biopsies are the major problems faced by current mammography Computer Aided Detection (CAD) systems. False positive reduction is not only a requirement for mass but also for calcification CAD systems which are currently deployed for clinical use. This paper tackles two problems related to reducing the number of false positives in the detection of all lesions and masses, respectively. Firstly, textural patterns of breast tissue have been analyzed using several multi-scale textural descriptors based on wavelet and gray level co-occurrence matrix. The second problem addressed in this paper is the parameter selection and performance optimization. For this, we adopt a model selection procedure based on Particle Swarm Optimization (PSO) for selecting the most discriminative textural features and for strengthening the generalization capacity of the supervised learning stage based on a Support Vector Machine (SVM) classifier. For evaluating the proposed methods, two sets of suspicious mammogram regions have been used. The first one, obtained from Digital Database for Screening Mammography (DDSM), contains 1494 regions (1000 normal and 494 abnormal samples). The second set of suspicious regions was obtained from database of Mammographic Image Analysis Society (mini-MIAS) and contains 315 (207 normal and 108 abnormal) samples. Results from both datasets demonstrate the efficiency of using PSO based model selection for optimizing both classifier hyper-parameters and parameters, respectively. Furthermore, the obtained results indicate the promising performance of the proposed textural features and more specifically, those based on co-occurrence matrix of wavelet image representation technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

Comparison of tissue equalization, and premium view post-processing methods in full field digital mammography.

PubMed

Chen, Baoying; Wang, Wei; Huang, Jin; Zhao, Ming; Cui, Guangbin; Xu, Jing; Guo, Wei; Du, Pang; Li, Pei; Yu, Jun

2010-10-01

To retrospectively evaluate the diagnostic abilities of 2 post-processing methods provided by GE Senographe DS system, tissue equalization (TE) and premium view (PV) in full field digital mammography (FFDM). In accordance with the ethical standards of the World Medical Association, this study was approved by regional ethics committee and signed informed patient consents were obtained. We retrospectively reviewed digital mammograms from 101 women (mean age, 47 years; range, 23-81 years) in the modes of TE and PV, respectively. Three radiologists, fully blinded to the post-processing methods, all patient clinical information and histologic results, read images by using objective image interpretation criteria for diagnostic information end points such as lesion border delineation, definition of disease extent, visualization of internal and surrounding morphologic features of the lesions. Also, overall diagnostic impression in terms of lesion conspicuity, detectability and diagnostic confidence was assessed. Between-group comparisons were performed with Wilcoxon signed rank test. Readers 1, 2, and 3 demonstrated significant overall better impression of PV in 29, 27, and 24 patients, compared with that for TE in 12, 13, and 11 patients, respectively (p<0.05). Significant (p<0.05) better impression of PV was also demonstrated for diagnostic information end points. Importantly, PV proved to be more sensitive than TE while detecting malignant lesions in dense breast rather than benign lesions and malignancy in non-dense breast (p<0.01). PV compared with TE provides marked better diagnostic information in FFDM, particularly for patients with malignancy in dense breast. Copyright © 2009 Elsevier Ireland Ltd. All rights reserved.

Cost-effectiveness of targeted versus tailored interventions to promote mammography screening among women military veterans in the United States.

PubMed

Lairson, David R; Chan, Wen; Chang, Yu-Chia; del Junco, Deborah J; Vernon, Sally W

2011-05-01

We conducted an economic evaluation of mammography promotion interventions in a population-based, nationally representative sample of 5500 women veterans. Women 52 years and older were randomly selected from the National Registry of Women Veterans and randomly assigned to a survey-only control group and two intervention groups that varied in the extent of personalization (tailored vs. targeted). Effectiveness measures were the prevalence of at least one self-reported post-intervention mammogram and two post-intervention mammograms 6-15 months apart. Incremental cost-effectiveness ratios (ICERs) were the incremental cost per additional person screened. Uncertainty was examined with sensitivity analysis and bootstrap simulation. The targeted intervention cost $25 per person compared to $52 per person for the tailored intervention. About 27% of the cost was incurred in identifying and recruiting the eligible population. The percent of women reporting at least one mammogram were .447 in the control group, .469 in the targeted group, and .460 in the tailored group. The ICER was $1116 comparing the targeted group to the control group (95% confidence interval (CI)=$493 to dominated). The tailored intervention was dominated (more costly and less effective) by the targeted intervention. Decision-makers should consider effectiveness evidence and the full recruitment and patient time costs associated with the implementation of screening interventions when making investments in mammography screening promotion programs. Identification and recruitment of eligible participants add substantial costs to outreach screening promotion interventions. Tailoring adds substantial cost to the targeted mammography promotion strategy without a commensurate increase in effectiveness. Although cost-effectiveness has been reported to be higher for some in-reach screening promotion interventions, a recent meta-analysis revealed significant heterogeneity in the effect sizes of published health-plan based intervention studies for repeat mammography (i.e., some studies reported null effects compared with control groups). Copyright © 2010 Elsevier Ltd. All rights reserved.

Detection of masses in mammogram images using CNN, geostatistic functions and SVM.

PubMed

Sampaio, Wener Borges; Diniz, Edgar Moraes; Silva, Aristófanes Corrêa; de Paiva, Anselmo Cardoso; Gattass, Marcelo

2011-08-01

Breast cancer occurs with high frequency among the world's population and its effects impact the patients' perception of their own sexuality and their very personal image. This work presents a computational methodology that helps specialists detect breast masses in mammogram images. The first stage of the methodology aims to improve the mammogram image. This stage consists in removing objects outside the breast, reducing noise and highlighting the internal structures of the breast. Next, cellular neural networks are used to segment the regions that might contain masses. These regions have their shapes analyzed through shape descriptors (eccentricity, circularity, density, circular disproportion and circular density) and their textures analyzed through geostatistic functions (Ripley's K function and Moran's and Geary's indexes). Support vector machines are used to classify the candidate regions as masses or non-masses, with sensitivity of 80%, rates of 0.84 false positives per image and 0.2 false negatives per image, and an area under the ROC curve of 0.87. Copyright © 2011 Elsevier Ltd. All rights reserved.

Urban women's preferences for learning of their mammogram result: a qualitative study.

PubMed

Marcus, Erin N; Drummond, Darlene; Dietz, Noella

2012-03-01

Research suggests that communication of mammogram results is flawed for many low-income ethnic minority women. This study conducted four focus groups with low-income inner-city minority women (n = 34). The goals of our project were: (1) to elucidate women's experiences learning of their result; (2) to elicit their preferences as to how this communication could be improved; and (3) to gather information to help inform the development of a new tool for communicating mammogram results. Salient themes included dissatisfaction with result communication; difficulty elucidating the meaning of a typical results notification letter; a preference for direct verbal communication of results and for print materials that included pictures, testimonials, and an action plan including a hotline to call with questions; and a strong interest in advance education about the likelihood of having to return for additional follow up. Video and other programs to inform patients before the test about what happens after may improve patient satisfaction and enhance women's understanding of their personal result and follow up plan.

Online Support: Impact on Anxiety in Women Who Experience an Abnormal Screening Mammogram

PubMed Central

Obadina, Eniola T.; Dubenske, Lori L.; McDowell, Helene E.; Atwood, Amy K.; Mayer, Deborah K.; Woods, Ryan W.; Gustafson, David H.; Burnside, Elizabeth S.

2014-01-01

OBJECTIVES To determine whether an online support tool can impact anxiety in women experiencing an abnormal mammogram. MATERIALS AND METHODS We developed an online support system using the Comprehensive Health Enhancement Support System (CHESS) designed for women experiencing an abnormal mammogram as a model. Our trial randomized 130 of these women to online support (the intervention group) or to a list of five commonly used Internet sites (the comparison group). Surveys assessed anxiety and breast cancer worry, and patient satisfaction at three important clinical time points: when women were notified of their abnormal mammogram, at the time of diagnostic imaging, and at the time of biopsy (if biopsy was recommended). RESULTS Study participants in the intervention group showed a significant decrease in anxiety at the time of biopsy compared to the comparison group (p=0.017). However, there was no significant difference in anxiety between the intervention group and the comparison group at the time of diagnostic work-up. We discontinued assessment of patient satisfaction after finding that many women had substantial difficulty answering the questions that referenced their physician, because they did not understand who their physician was for this process of care. CONCLUSION The combination of the inability to identify the physician providing care during the mammography work-up and anxiety effects seen only after an interaction with the breast imaging team may indicate that online support only decreases the anxiety of women in concert with direct interpersonal support from the healthcare team. PMID:25193424

Exploring potential use of internet, E-mail, and instant text messaging to promote breast health and mammogram use among immigrant Hispanic women in Los Angeles County.

PubMed

Dang, Catherine M; Estrada, Sylvia; Bresee, Catherine; Phillips, Edward H

2013-10-01

Breast cancer is now the leading cause of death in Hispanic women (HW). Internet, e-mail, and instant text messaging may be cost-effective in educating HW about breast health and in reducing breast cancer mortality. We surveyed 905 HW women attending a free health fair about their technology use, acculturation, insurance status, mammography use, and breast cancer knowledge. Data were analyzed by t test or χ(2) tests. Mean age was 51.9 ± 14.2 years (range, 18 to 88 years). Ninety-two per cent were foreign-born. Most had completed some high school (39%) or elementary (38%) education. Most (62%) were uninsured. The majority spoke (67%) and read (66%) only Spanish. Only 60 per cent of HW older than 40 years had a recent mammogram. HW older than 40 years who had not had a recent mammogram were younger (mean 54.9 ± 10.8 vs 58 ± 10.4 years) and less likely to have health insurance (25 vs 44%; P < 0.001). Most HW never use the Internet (58%) or e-mail (64%). However, 70 per cent have mobile phones (66% older than 40 years), and 65 per cent use text messaging daily (58% older than 40 years, P = 0.001). In fact, 45 per cent wish to receive a mammogram reminder by text. Text messaging may be an inexpensive way to promote breast health and screening mammography use among uninsured HW.

Does telephone scheduling assistance increase mammography screening adherence?

PubMed

Payton, Colleen A; Sarfaty, Mona; Beckett, Shirley; Campos, Carmen; Hilbert, Kathleen

2015-11-01

The 2 objectives were: 1) describe the use of a patient navigation process utilized to promote adherence to mammography screening within a primary care practice, and 2) determine the result of the navigation process and estimate the time required to increase mammography screening with this approach in a commercially insured patient population enrolled in a health maintenance organization. An evaluation of a nonrandomized practice improvement intervention. Women eligible for mammography (n = 298) who did not respond to 2 reminder letters were contacted via telephone by a navigator who offered scheduling assistance for mammography screening. The patient navigator scheduled appointments, documented the number of calls, and confirmed completed mammograms in the electronic health record, as well as estimated the time for calls and chart review. Of the 188 participants reached by phone, 112 (59%) scheduled appointments using the patient navigator, 35 (19%) scheduled their own appointments independently prior to the call, and 41 (22%) declined. As a result of the telephone intervention, 78 of the 188 women reached (41%) received a mammogram; also, all 35 women who had independently scheduled a mammogram received one. Chart documentation confirmed that 113 (38%) of the cohort of 298 women completed a mammogram. The estimated time burden for the entire project was 55 hours and 33 minutes, including calling patients, scheduling appointments, and chart review. A patient navigator can increase mammography adherence in a previously nonadherent population by making the screening appointment while the patient is on the phone.

Breast cancers missed by screening radiologists can be detected by reading mammograms at a distance.

PubMed

Schreutelkamp, Ineke L; Kwee, Robert M; Veekmans, Peter; Adriaensen, Miraude E A P M

2018-05-03

During locally organized quality assurance evaluation sessions for screening radiologists, we noticed that individual screening radiologists did miss tumours which in our opinion could be detected at a distance. To determine whether tumours missed by individual screening radiologists can be detected at a distance. Twenty-eight screening mammograms of 28 females (mean age 63 years, range 49-73) with a pathologically proven malignant tumour missed by individual screening radiologists were mixed with 56 normal screening mammograms of 56 females (mean age 63 years, range 53-74). This test set was independently assessed by a senior screening radiologist and by a radiology resident without prior training in screening mammography at 1.5 m distance from the screen display. Readers were unaware of the prevalence of pathologically proven malignant tumours in the test set. Primary outcome was whether the reader would recall the woman. The senior screening radiologist recalled 28 of 28 women with a pathologically proven malignant tumour (sensitivity of 100%) and 16 of 56 women without pathology (specificity of 71%). The radiology resident recalled 25 of 28 women with a pathologically proven malignant tumour (sensitivity of 89%) and 10 of 56 women without pathology (specificity of 82%). Some malignant tumours missed by an individual screening radiologist can be detected from 1.5 m distance. Therefore, we recommend that screening radiologists consciously take a distant view before closely evaluating the mammogram in detail.

Online support: Impact on anxiety in women who experience an abnormal screening mammogram.

PubMed

Obadina, Eniola T; Dubenske, Lori L; McDowell, Helene E; Atwood, Amy K; Mayer, Deborah K; Woods, Ryan W; Gustafson, David H; Burnside, Elizabeth S

2014-12-01

To determine whether an online support tool can impact anxiety in women experiencing an abnormal mammogram. We developed an online support system using the Comprehensive Health Enhancement Support System (CHESS) designed for women experiencing an abnormal mammogram as a model. Our trial randomized 130 of these women to online support (the intervention group) or to a list of five commonly used Internet sites (the comparison group). Surveys assessed anxiety and breast cancer worry, and patient satisfaction at three important clinical time points: when women were notified of their abnormal mammogram, at the time of diagnostic imaging, and at the time of biopsy (if biopsy was recommended). Study participants in the intervention group showed a significant decrease in anxiety at the time of biopsy compared to the comparison group (p = 0.017). However, there was no significant difference in anxiety between the intervention group and the comparison group at the time of diagnostic work-up. We discontinued assessment of patient satisfaction after finding that many women had substantial difficulty answering the questions that referenced their physician, because they did not understand who their physician was for this process of care. The combination of the inability to identify the physician providing care during the mammography work-up and anxiety effects seen only after an interaction with the breast imaging team may indicate that online support only decreases the anxiety of women in concert with direct interpersonal support from the healthcare team. Copyright © 2014 Elsevier Ltd. All rights reserved.

Influencing clinicians and healthcare managers: can ROC be more persuasive?

NASA Astrophysics Data System (ADS)

Taylor-Phillips, S.; Wallis, M. G.; Duncan, A.; Gale, A. G.

2010-02-01

Receiver Operating Characteristic analysis provides a reliable and cost effective performance measurement tool, without using full clinical trials. However, when ROC analysis shows that performance is statistically superior in one condition than another it is difficult to relate this result to effects in practice, or even to determine whether it is clinically significant. In this paper we present two concurrent analyses: using ROC methods alongside single threshold recall rate data, and suggest that reporting both provides complimentary data. Four mammographers read 160 difficult cases (41% malignant) twice, with and without prior mammograms. Lesion location and probability of malignancy was reported for each case and analyzed using JAFROC. Concurrently each participant chose recall or return to screen for each case. JAFROC analysis showed that the presence of prior mammograms improved performance (p<.05). Single threshold data showed a trend towards a 26% increase in the number of false positive recalls without prior mammograms (p=.056). If this trend were present throughout the NHS Breast Screening Programme then discarding prior mammograms would correspond to an increase in recall rate from 4.6% to 5.3%, and 12,414 extra women recalled annually for assessment. Whilst ROC methods account for all possible thresholds of recall and have higher power, providing a single threshold example of false positive, false negative, and recall rates when reporting results could be more influential for clinicians. This paper discusses whether this is a useful additional method of presenting data, or whether it is misleading and inaccurate.

Performance of a subsidised mammographic screening programme in Malaysia, a middle-income Asian country.

PubMed

Lee, Marianne; Mariapun, Shivaani; Rajaram, Nadia; Teo, Soo-Hwang; Yip, Cheng-Har

2017-01-28

The incidence of breast cancer in Asia is increasing because of urbanization and lifestyle changes. In the developing countries in Asia, women present at late stages, and mortality is high. Mammographic screening is the only evidence-based screening modality that reduces breast cancer mortality. To date, only opportunistic screening is offered in the majority of Asian countries because of the lack of justification and funding. Nevertheless, there have been few reports on the effectiveness of such programmes. In this study, we describe the cancer detection rate and challenges experienced in an opportunistic mammographic screening programme in Malaysia. From October 2011 to June 2015, 1,778 asymptomatic women, aged 40-74 years, underwent subsidised mammographic screening. All patients had a clinical breast examination before mammographic screening, and women with mammographic abnormalities were referred to a surgeon. The cancer detection rate and variables associated with a recommendation for adjunct ultrasonography were determined. The mean age for screening was 50.8 years and seven cancers (0.39%) were detected. The detection rate was 0.64% in women aged 50 years and above, and 0.12% in women below 50 years old. Adjunct ultrasonography was recommended in 30.7% of women, and was significantly associated with age, menopausal status, mammographic density and radiologist's experience. The main reasons cited for recommendation of an adjunct ultrasound was dense breasts and mammographic abnormalities. The cancer detection rate is similar to population-based screening mammography programmes in high-income Asian countries. Unlike population-based screening programmes in Caucasian populations where the adjunct ultrasonography rate is 2-4%, we report that 3 out of 10 women attending screening mammography were recommended for adjunct ultrasonography. This could be because Asian women attending screening are likely premenopausal and hence have denser breasts. Radiologists who reported more than 360 mammograms were more confident in reporting a mammogram as normal without adjunct ultrasonography compared to those who reported less than 180 mammograms. Our subsidised opportunistic mammographic screening programme is able to provide equivalent cancer detection rates but the high recall for adjunct ultrasonography would make screening less cost-effective.

Using automated texture features to determine the probability for masking of a tumor on mammography, but not ultrasound.

PubMed

Häberle, Lothar; Hack, Carolin C; Heusinger, Katharina; Wagner, Florian; Jud, Sebastian M; Uder, Michael; Beckmann, Matthias W; Schulz-Wendtland, Rüdiger; Wittenberg, Thomas; Fasching, Peter A

2017-08-30

Tumors in radiologically dense breast were overlooked on mammograms more often than tumors in low-density breasts. A fast reproducible and automated method of assessing percentage mammographic density (PMD) would be desirable to support decisions whether ultrasonography should be provided for women in addition to mammography in diagnostic mammography units. PMD assessment has still not been included in clinical routine work, as there are issues of interobserver variability and the procedure is quite time consuming. This study investigated whether fully automatically generated texture features of mammograms can replace time-consuming semi-automatic PMD assessment to predict a patient's risk of having an invasive breast tumor that is visible on ultrasound but masked on mammography (mammography failure). This observational study included 1334 women with invasive breast cancer treated at a hospital-based diagnostic mammography unit. Ultrasound was available for the entire cohort as part of routine diagnosis. Computer-based threshold PMD assessments ("observed PMD") were carried out and 363 texture features were obtained from each mammogram. Several variable selection and regression techniques (univariate selection, lasso, boosting, random forest) were applied to predict PMD from the texture features. The predicted PMD values were each used as new predictor for masking in logistic regression models together with clinical predictors. These four logistic regression models with predicted PMD were compared among themselves and with a logistic regression model with observed PMD. The most accurate masking prediction was determined by cross-validation. About 120 of the 363 texture features were selected for predicting PMD. Density predictions with boosting were the best substitute for observed PMD to predict masking. Overall, the corresponding logistic regression model performed better (cross-validated AUC, 0.747) than one without mammographic density (0.734), but less well than the one with the observed PMD (0.753). However, in patients with an assigned mammography failure risk >10%, covering about half of all masked tumors, the boosting-based model performed at least as accurately as the original PMD model. Automatically generated texture features can replace semi-automatically determined PMD in a prediction model for mammography failure, such that more than 50% of masked tumors could be discovered.

Simulation of a compact analyzer-based imaging system with a regular x-ray source

NASA Astrophysics Data System (ADS)

Caudevilla, Oriol; Zhou, Wei; Stoupin, Stanislav; Verman, Boris; Brankov, J. G.

2017-03-01

Analyzer-based Imaging (ABI) belongs to a broader family of phase-contrast (PC) X-ray techniques. PC measures X-ray deflection phenomena when interacting with a sample, which is known to provide higher contrast images of soft tissue than other X-ray methods. This is of high interest in the medical field, in particular for mammogram applications. This paper presents a simulation tool for table-top ABI systems using a conventional polychromatic X-ray source.

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

DTIC Science & Technology

2013-10-01

MAMMOGRAM 5 OTHER BREAST IMAGING TEST (E.G., MRI , ULTRASOUND) 6 THEMOGRAPHY 7 NOTHING 8 DK/REF PROBE: ANYTHING ELSE? IF NECESSARY: JUST THE...SKIP IF HOWCHECK = 5] Have you ever had any other breast imaging procedure designed to detect breast cancer (for example, an MRI or ultrasound? 1 YES...SELECT ALL THAT APPLY) 1 Another mammogram 2 Ultrasound of the breast 3 MRI of the breast 4 OTHER [specify:] 31 5 NONE 6 DK/REF {Q: ADDSURG

Multi-image CAD employing features derived from ipsilateral mammographic views

NASA Astrophysics Data System (ADS)

Good, Walter F.; Zheng, Bin; Chang, Yuan-Hsiang; Wang, Xiao Hui; Maitz, Glenn S.; Gur, David

1999-05-01

On mammograms, certain kinds of features related to masses (e.g., location, texture, degree of spiculation, and integrated density difference) tend to be relatively invariant, or at last predictable, with respect to breast compression. Thus, ipsilateral pairs of mammograms may contain information not available from analyzing single views separately. To demonstrate the feasibility of incorporating multi-view features into CAD algorithm, `single-image' CAD was applied to each individual image in a set of 60 ipsilateral studies, after which all possible pairs of suspicious regions, consisting of one from each view, were formed. For these 402 pairs we defined and evaluated `multi-view' features such as: (1) relative position of centers of regions; (2) ratio of lengths of region projections parallel to nipple axis lines; (3) ratio of integrated contrast difference; (4) ratio of the sizes of the suspicious regions; and (5) measure of relative complexity of region boundaries. Each pair was identified as either a `true positive/true positive' (T) pair (i.e., two regions which are projections of the same actual mass), or as a falsely associated pair (F). Distributions for each feature were calculated. A Bayesian network was trained and tested to classify pairs of suspicious regions based exclusively on the multi-view features described above. Distributions for all features were significantly difference for T versus F pairs as indicated by likelihood ratios. Performance of the Bayesian network, which was measured by ROC analysis, indicates a significant ability to distinguish between T pairs and F pairs (Az equals 0.82 +/- 0.03), using information that is attributed to the multi-view content. This study is the first demonstration that there is a significant amount of spatial information that can be derived from ipsilateral pairs of mammograms.

Applying a CAD-generated imaging marker to assess short-term breast cancer risk

NASA Astrophysics Data System (ADS)

Mirniaharikandehei, Seyedehnafiseh; Zarafshani, Ali; Heidari, Morteza; Wang, Yunzhi; Aghaei, Faranak; Zheng, Bin

2018-02-01

Although whether using computer-aided detection (CAD) helps improve radiologists' performance in reading and interpreting mammograms is controversy due to higher false-positive detection rates, objective of this study is to investigate and test a new hypothesis that CAD-generated false-positives, in particular, the bilateral summation of false-positives, is a potential imaging marker associated with short-term breast cancer risk. An image dataset involving negative screening mammograms acquired from 1,044 women was retrospectively assembled. Each case involves 4 images of craniocaudal (CC) and mediolateral oblique (MLO) view of the left and right breasts. In the next subsequent mammography screening, 402 cases were positive for cancer detected and 642 remained negative. A CAD scheme was applied to process all "prior" negative mammograms. Some features from CAD scheme were extracted, which include detection seeds, the total number of false-positive regions, an average of detection scores and the sum of detection scores in CC and MLO view images. Then the features computed from two bilateral images of left and right breasts from either CC or MLO view were combined. In order to predict the likelihood of each testing case being positive in the next subsequent screening, two logistic regression models were trained and tested using a leave-one-case-out based cross-validation method. Data analysis demonstrated the maximum prediction accuracy with an area under a ROC curve of AUC=0.65+/-0.017 and the maximum adjusted odds ratio of 4.49 with a 95% confidence interval of [2.95, 6.83]. The results also illustrated an increasing trend in the adjusted odds ratio and risk prediction scores (p<0.01). Thus, the study showed that CAD-generated false-positives might provide a new quantitative imaging marker to help assess short-term breast cancer risk.

Travel burden to breast MRI and utilization: are risk and sociodemographics related

PubMed Central

Onega, Tracy; Lee, Christoph I.; Benkeser, David; Alford-Teaster, Jennifer; Haas, Jennifer S.; Tosteson, Anna N. A.; Hill, Deirdre; Shi, Xun; Henderson, Louise M.; Hubbard, Rebecca A.

2016-01-01

Background Mammograms, unlike magnetic resonance imaging (MRI), are relatively geographically accessible. Additional travel time is often required to access breast MRI. However, the amount of additional travel time and whether it varies based on sociodemographic or breast cancer risk factors is unknown. Methods We examine screening mammograms and MRIs between 2005 and 2012 in the Breast Cancer Surveillance Consortium (BCSC) by a) travel time to the closest and actual mammography facility used, and the difference between the two; b) woman's breast cancer risk factors and c) socio-demographic characteristics. We used logistic regression to examine the odds of traveling farther than the closest facility in relation to women's characteristics. Results Among 821,683 screening mammograms, 76.6% occurred at the closest facility compared to 51.9% of screening MRIs (N=3,687). The median differential travel time among women not using the closest facility for mammography was 14 minutes (IQR: 8-25) versus 20 minutes (IQR 11-40) for breast MRI. Differential travel time for both imaging modalities did not vary notably by breast cancer risk factors, but was significantly longer for non-urban residents. For non-Hispanic black, compared to non-Hispanic white women, the adjusted odds of traveling farther than the closest facility were 9% lower for mammography (OR 0.91; 95% CI:0.87-0.95), but more than two times higher for MRI (OR 2.64; 95% CI:1.36-5.13). Conclusions Breast cancer risk factors were not related to excess travel time for screening MRI, but sociodemographic factors were, suggesting the possibility that geographic distribution of advanced imaging may exacerbated disparities for some vulnerable populations. PMID:27026577

Patient Navigation Improves Subsequent Breast Cancer Screening After a Noncancerous Result: Evidence from the Patient Navigation in Medically Underserved Areas Study.

PubMed

Molina, Yamile; Kim, Sage J; Berrios, Nerida; Glassgow, Anne Elizabeth; San Miguel, Yazmin; Darnell, Julie S; Pauls, Heather; Vijayasiri, Ganga; Warnecke, Richard B; Calhoun, Elizabeth A

2018-03-01

Past efforts to assess patient navigation on cancer screening utilization have focused on one-time uptake, which may not be sufficient in the long term. This is partially due to limited resources for in-person, longitudinal patient navigation. We examine the effectiveness of a low-intensity phone- and mail-based navigation on multiple screening episodes with a focus on screening uptake after receiving noncancerous results during a previous screening episode. The is a secondary analysis of patients who participated in a randomized controlled patient navigation trial in Chicago. Participants include women referred for a screening mammogram, aged 50-74 years, and with a history of benign/normal screening results. Navigation services focused on identification of barriers and intervention via shared decision-making processes. A multivariable logistic regression intent-to-treat model was used to examine differences in odds of obtaining a screening mammogram within 2 years of the initial mammogram (yes/no) between navigated and non-navigated women. Sensitivity analyses were conducted to explore patterns across subsets of participants (e.g., navigated women successfully contacted before the initial appointment; women receiving care at Hospital C). The final sample included 2,536 women (741 navigated, 1,795 non-navigated). Navigated women exhibited greater odds of obtaining subsequent screenings relative to women in the standard care group in adjusted models and analyses including women who received navigation before the initial appointment. Our findings suggest that low-intensity navigation services can improve follow-up screening among women who receive a noncancerous result. Further investigation is needed to confirm navigation's impacts on longitudinal screening.

Mammography interval and breast cancer mortality in women over the age of 75.

PubMed

Simon, Michael S; Wassertheil-Smoller, Sylvia; Thomson, Cynthia A; Ray, Roberta M; Hubbell, F Allan; Lessin, Lawrence; Lane, Dorothy S; Kuller, Lew H

2014-11-01

The purpose of this study is to evaluate the relationship between mammography interval and breast cancer mortality among older women with breast cancer. The study population included 1,914 women diagnosed with invasive breast cancer at age 75 or later during their participation in the Women's health initiative, with an average follow-up of 4.4 years (3.1 SD). Cause of death was based on medical record review. Mammography interval was defined as the time between the last self-reported mammogram 7 or more months prior to diagnosis, and the date of diagnosis. Multivariable adjusted hazard ratios (HR) and 95 % confidence intervals (CIs) for breast cancer mortality and all-cause mortality were computed from Cox proportional hazards analyses. Prior mammograms were reported by 73.0 % of women from 7 months to ≤2 year of diagnosis (referent group), 19.4 % (>2 to <5 years), and 7.5 % (≥5 years or no prior mammogram). Women with the longest versus shortest intervals had more poorly differentiated (28.5 % vs. 22.7 %), advanced stage (25.7 % vs. 22.9 %), and estrogen receptor negative tumors (20.9 % vs. 13.1 %). Compared to the referent group, women with intervals of >2 to <5 years or ≥5 years had an increased risk of breast cancer mortality (HR 1.62, 95 % CI 1.03-2.54) and (HR 2.80, 95 % CI 1.57-5.00), respectively, p trend = 0.0002. There was no significant relationship between mammography interval and other causes of death. These results suggest a continued role for screening mammography among women 75 years of age and older.

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

PubMed

Sharma, Vipul; Singh, Sukhwinder

2014-06-01

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

Examining the association between possessing a regular source of healthcare and adherence with cancer screenings among Haitian households in Little Haiti, Miami-Dade County, Florida

PubMed Central

Pang, Hauchie; Cataldi, Mariel; Allseits, Emmanuelle; Ward-Peterson, Melissa; de la Vega, Pura Rodríguez; Castro, Grettel; Acuña, Juan Manuel

2017-01-01

Abstract Immigrant minorities regularly experience higher incidence and mortality rates of cancer. Frequently, a variety of social determinants create obstacles for those individuals to get the screenings they need. This is especially true for Haitian immigrants, a particularly vulnerable immigrant population in South Florida, who have been identified as having low cancer screening rates. While Haitian immigrants have some of the lowest cancer screening rates in the country, there is little existing literature that addresses barriers to cancer screenings among the population of Little Haiti in Miami-Dade County, Florida. The objective of this study was to evaluate the association between having a regular source of healthcare and adherence to recommended cancer screenings in the Little Haiti population of Miami. This secondary analysis utilized data collected from a random-sample, population-based household survey conducted from November 2011 to December 2012 among a geographic area approximating Little Haiti in Miami-Dade County, Florida. A total of 421 households identified as Haitian. The main exposure of interest was whether households possessed a regular source of care. Three separate outcomes were considered: adherence with colorectal cancer screening, mammogram adherence, and Pap smear adherence. Analysis was limited to households who met the age criteria for each outcome of interest. Bivariate associations were examined using the chi square test and Fisher exact test. Binary logistic regression was used to estimate unadjusted and adjusted odds ratios (ORs) with 95% confidence intervals (CIs). After adjusting for the head of household's education and household insurance status, households without a regular source of care were significantly less likely to adhere with colorectal cancer screening (OR = 0.33; 95% CI: 0.14–0.80) or mammograms (OR = 0.28; 95% CI: 0.11–0.75). Households with insurance coverage gaps were significantly less likely to adhere with mammograms (OR = 0.40; 95% CI: 0.17–0.97) or Pap smears (OR = 0.28; 95% CI: 0.13–0.58). Our study explored adherence with multiple cancer screenings. We found a strong association between possessing a regular source of care and adherence with colorectal cancer screening and mammogram adherence. Targeted approaches to improving access to regular care may improve adherence to cancer screening adherence among this unique immigrant population. PMID:28796056

Dissemination of periodic mammography and patterns of use, by birth cohort, in Catalonia (Spain)

PubMed Central

Rue, Montserrat; Carles, Misericordia; Vilaprinyo, Ester; Martinez-Alonso, Montserrat; Espinas, Josep-Alfons; Pla, Roger; Brugulat, Pilar

2008-01-01

Background In Catalonia (Spain) breast cancer mortality has declined since the beginning of the 1990s. The dissemination of early detection by mammography and the introduction of adjuvant treatments are among the possible causes of this decrease, and both were almost coincident in time. Thus, understanding how these procedures were incorporated into use in the general population and in women diagnosed with breast cancer is very important for assessing their contribution to the reduction in breast cancer mortality. In this work we have modeled the dissemination of periodic mammography and described repeat mammography behavior in Catalonia from 1975 to 2006. Methods Cross-sectional data from three Catalan Health Surveys for the calendar years 1994, 2002 and 2006 was used. The dissemination of mammography by birth cohort was modeled using a mixed effects model and repeat mammography behavior was described by age and survey year. Results For women born from 1938 to 1952, mammography clearly had a period effect, meaning that they started to have periodic mammograms at the same calendar years but at different ages. The age at which approximately 50% of the women were receiving periodic mammograms went from 57.8 years of age for women born in 1938–1942 to 37.3 years of age for women born in 1963–1967. Women in all age groups experienced an increase in periodic mammography use over time, although women in the 50–69 age group have experienced the highest increase. Currently, the target population of the Catalan Breast Cancer Screening Program, 50–69 years of age, is the group that self-reports the highest utilization of periodic mammograms, followed by the 40–49 age group. A higher proportion of women of all age groups have annual mammograms rather than biennial or irregular ones. Conclusion Mammography in Catalonia became more widely implemented during the 1990s. We estimated when cohorts initiated periodic mammograms and how frequently women are receiving them. These two pieces of information will be entered into a cost-effectiveness model of early detection in Catalonia. PMID:19014679

TRICARE: changes included in the National Defense Authorization Act for Fiscal Year 2007; improvements to descriptions of cancer screening for women. Final rule.

PubMed

2010-08-06

The Department is publishing this final rule to implement section 703 of the National Defense Authorization Act (NDAA) for Fiscal Year 2007 (FY07), Public Law 109-364. Specifically, that legislation authorizes breast cancer screening and cervical cancer screening for female beneficiaries of the Military Health System, instead of constraining such testing to mammograms and Papanicolaou smears. The rule allows coverage for "breast cancer screening" and "cervical cancer screening" for female beneficiaries of the Military Health System, instead of constraining such testing to mammograms and Papanicolaou tests. This rule ensures new breast and cervical cancer screening procedures can be added to the TRICARE benefit as such procedures are proven to be a safe, effective, and nationally accepted medical practice. This amends the cancer specific recommendations for breast and cervical cancer screenings to be brought in line with the processes for updating other cancer screening recommendations. In response to public comment on the proposed rule, this final rule includes a clarification that the benefit encompasses screening based on Health and Human Services guidelines.

An adaptive incremental approach to constructing ensemble classifiers: application in an information-theoretic computer-aided decision system for detection of masses in mammograms.

PubMed

Mazurowski, Maciej A; Zurada, Jacek M; Tourassi, Georgia D

2009-07-01

Ensemble classifiers have been shown efficient in multiple applications. In this article, the authors explore the effectiveness of ensemble classifiers in a case-based computer-aided diagnosis system for detection of masses in mammograms. They evaluate two general ways of constructing subclassifiers by resampling of the available development dataset: Random division and random selection. Furthermore, they discuss the problem of selecting the ensemble size and propose two adaptive incremental techniques that automatically select the size for the problem at hand. All the techniques are evaluated with respect to a previously proposed information-theoretic CAD system (IT-CAD). The experimental results show that the examined ensemble techniques provide a statistically significant improvement (AUC = 0.905 +/- 0.024) in performance as compared to the original IT-CAD system (AUC = 0.865 +/- 0.029). Some of the techniques allow for a notable reduction in the total number of examples stored in the case base (to 1.3% of the original size), which, in turn, results in lower storage requirements and a shorter response time of the system. Among the methods examined in this article, the two proposed adaptive techniques are by far the most effective for this purpose. Furthermore, the authors provide some discussion and guidance for choosing the ensemble parameters.

Mammography Among Women With Severe Mental Illness: Exploring Disparities Through a Large Retrospective Cohort Study.

PubMed

Thomas, Melanie; James, Monique; Vittinghoff, Eric; Creasman, Jennifer M; Schillinger, Dean; Mangurian, Christina

2018-01-01

This study examined mammogram screening rates among women with severe mental illness by using a socioecological framework. Because it has been shown that people with severe mental illness receive less preventive health care overall, the analysis included psychosocial predictors of mammogram screening rates in a cohort of women with severe mental illness. This retrospective cohort study (N=14,651) used existing statewide data for women ages 48-67 in California with Medicaid insurance who received treatment in the specialty mental health care system. The primary outcome of interest was evidence of breast cancer screening via mammogram. The associations of each predictor of interest with mammogram screening were evaluated by using Poisson models with robust standard errors. Across all demographic and diagnostic categories, rates of breast cancer screening in this cohort of women with severe mental illness fell below the national average. Only 26.3% (3,859/14,651) of women in the cohort received breast cancer screening in the past year. This study replicated previous findings that women with schizophrenia spectrum disorder and those with a comorbid substance use disorder are less likely to receive screening than those with other types of mental illness. In this cohort of women with severe mental illness, evidence of nonpsychiatric health care utilization was strongly associated with breast cancer screening (adjusted risk ratio=3.30, 95% confidence interval=2.61-4.16, p<.001). The findings can inform efforts to improve breast cancer screening among women with severe mental illness, such as targeted outreach to population subsets and colocation of primary care services in mental health treatment settings.

A method to determine the mammographic regions that show early changes due to the development of breast cancer

NASA Astrophysics Data System (ADS)

Karemore, Gopal; Nielsen, Mads; Karssemeijer, Nico; Brandt, Sami S.

2014-11-01

It is well understood nowadays that changes in the mammographic parenchymal pattern are an indicator of a risk of breast cancer and we have developed a statistical method that estimates the mammogram regions where the parenchymal changes, due to breast cancer, occur. This region of interest is computed from a score map by utilising the anatomical breast coordinate system developed in our previous work. The method also makes an automatic scale selection to avoid overfitting while the region estimates are computed by a nested cross-validation scheme. In this way, it is possible to recover those mammogram regions that show a significant difference in classification scores between the cancer and the control group. Our experiments suggested that the most significant mammogram region is the region behind the nipple and that can be justified by previous findings from other research groups. This result was conducted on the basis of the cross-validation experiments on independent training, validation and testing sets from the case-control study of 490 women, of which 245 women were diagnosed with breast cancer within a period of 2-4 years after the baseline mammograms. We additionally generalised the estimated region to another, mini-MIAS study and showed that the transferred region estimate gives at least a similar classification result when compared to the case where the whole breast region is used. In all, by following our method, one most likely improves both preclinical and follow-up breast cancer screening, but a larger study population will be required to test this hypothesis.

Postmenopausal hormone therapy and changes in mammographic density.

PubMed

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

2007-04-10

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

Visual adaptation and the amplitude spectra of radiological images.

PubMed

Kompaniez-Dunigan, Elysse; Abbey, Craig K; Boone, John M; Webster, Michael A

2018-01-01

We examined how visual sensitivity and perception are affected by adaptation to the characteristic amplitude spectra of X-ray mammography images. Because of the transmissive nature of X-ray photons, these images have relatively more low-frequency variability than natural images, a difference that is captured by a steeper slope of the amplitude spectrum (~ - 1.5) compared to the ~ 1/f (slope of - 1) spectra common to natural scenes. Radiologists inspecting these images are therefore exposed to a different balance of spectral components, and we measured how this exposure might alter spatial vision. Observers (who were not radiologists) were adapted to images of normal mammograms or the same images sharpened by filtering the amplitude spectra to shallower slopes. Prior adaptation to the original mammograms significantly biased judgments of image focus relative to the sharpened images, demonstrating that the images are sufficient to induce substantial after-effects. The adaptation also induced strong losses in threshold contrast sensitivity that were selective for lower spatial frequencies, though these losses were very similar to the threshold changes induced by the sharpened images. Visual search for targets (Gaussian blobs) added to the images was also not differentially affected by adaptation to the original or sharper images. These results complement our previous studies examining how observers adapt to the textural properties or phase spectra of mammograms. Like the phase spectrum, adaptation to the amplitude spectrum of mammograms alters spatial sensitivity and visual judgments about the images. However, unlike the phase spectrum, adaptation to the amplitude spectra did not confer a selective performance advantage relative to more natural spectra.

[Opinion and Participation in the Regional Early Breast Cancer Detection Program in 2007 on the part of family physicians from a health district in the Autonomous Community of Madrid, Spain].

PubMed

Garrido Elustondo, Sofía; Sánchez Padilla, Elisabeth; Ramírez Alesón, Victoria; González Hernández, Ma José; González Navarro, Andrés; López Gómez, Carlos

2008-01-01

Mammogram screening is the most effective method for the early detection of breast cancer. The objective of this study is to evaluate the degree of knowledge, the opinion and the participation in the early breast cancer detection program on the part of the family physicians of the Autonomous Community of Madrid. The population studied was comprised of family physicians from Madrid Health District Seven. An anonymous, self-administered questionnaire comprised of 30 questions grouped into physicians characteristics and opinion concerning the early breast cancer detection programs. A total of 46% of the physicians replied. A total of 94% of the physicians believed that it is their duty to inform their patients concerning preventive activities, including breast cancer screening, and 95% believed their advice to be useful for convincing women to have a mammogram. A total of 72% believed information to be lacking on this program. During the time when mammograms are being taken at their centres, 24% of the physicians surveyed always or almost always ask the women if they have any doubts or would like further information, 43% having set up appointments for them and 95% advising them to have a mammogram taken. The family physicians have a good opinion of the early breast cancer detection program and feel their advice to be effective for improving the participation in the program. They report lack of information and inform women about the program to only a small degree.

Mammographic screening practices among Chinese-Australian women.

PubMed

Kwok, Cannas; Fethney, Judith; White, Kate

2012-03-01

To report mammographic screening practice among Chinese-Australian women, and to examine the relationship between demographic characteristics, acculturation factors (English proficiency and length of stay in Australia), cultural beliefs, and having a mammogram as recommended. Cross-sectional and descriptive. The study was conducted in 2009 in Sydney, Australia. Of 988 Chinese-Australian women over 18 years of age invited to participate in the study, 785 (79%) completed and returned the questionnaire. Of these women, 320 (40.8%) were in the target age range of 50 to 69 years. The Chinese Breast Cancer Screening Beliefs Questionnaire (CBCSB) was used as a data collection instrument. Analysis included descriptive statistics, bivariate analysis using chi-square and t tests, and logistic regression. Of the 320 women in the targeted age range of 50 to 69 years, 238 (74.4%) had a mammogram as recommended biannually. Being married-de facto, in the 60 to 69 age group, and speaking Cantonese at home were positively associated with women's mammographic screening practice. However, no statistically significant differences in acculturation factors and having a mammogram as recommended were found. In terms of CBCSB score, women who had mammograms as recommended had more positive attitudes toward health checkups and perceived fewer barriers to mammographic screening. Effort should be focused on specific subgroups of Chinese-Australian women in order to fully understand the barriers involved in participating in mammographic screening. Nurses can use the findings from the present study to design culturally sensitive breast cancer screening programs to encourage women's participation in mammography. © 2011 Sigma Theta Tau International.

The classification of normal screening mammograms

NASA Astrophysics Data System (ADS)

Ang, Zoey Z. Y.; Rawashdeh, Mohammad A.; Heard, Robert; Brennan, Patrick C.; Lee, Warwick; Lewis, Sarah J.

2016-03-01

Rationale and objectives: To understand how breast screen readers classify the difficulty of normal screening mammograms using common lexicon describing normal appearances. Cases were also assessed on their suitability for a single reader strategy. Materials and Methods: 15 breast readers were asked to interpret a test set of 29 normal screening mammogram cases and classify them by rating the difficulty of the case on a five-point Likert scale, identifying the salient features and assessing their suitability for single reading. Using the False Positive Fractions from a previous study, the 29 cases were classified into 10 "low", 10 "medium" and nine "high" difficulties. Data was analyzed with descriptive statistics. Spearman's correlation was used to test the strength of association between the difficulty of the cases and the readers' recommendation for single reading strategy. Results: The ratings from readers in this study corresponded to the known difficulty level of cases for the 'low' and 'high' difficulty cases. Uniform ductal pattern and density, symmetrical mammographic features and the absence of micro-calcifications were the main reasons associated with 'low' difficulty cases. The 'high' difficulty cases were described as having `dense breasts'. There was a statistically significant negative correlation between the difficulty of the cases and readers' recommendation for single reading (r = -0.475, P = 0.009). Conclusion: The findings demonstrated potential relationships between certain mammographic features and the difficulty for readers to classify mammograms as 'normal'. The standard Australian practice of double reading was deemed more suitable for most cases. There was an inverse moderate association between the difficulty of the cases and the recommendations for single reading.

Effectiveness of breast cancer screening policies in countries with medium-low incidence rates.

PubMed

Kong, Qingxia; Mondschein, Susana; Pereira, Ana

2018-02-05

Chile has lower breast cancer incidence rates compared to those in developed countries. Our public health system aims to perform 10 biennial screening mammograms in the age group of 50 to 69 years by 2020. Using a dynamic programming model, we have found the optimal ages to perform 10 screening mammograms that lead to the lowest lifetime death rate and we have evaluated a set of fixed inter-screening interval policies. The optimal ages for the 10 mammograms are 43, 47, 51, 54, 57, 61, 65, 68, 72, and 76 years, and the most effective fixed inter-screening is every four years after the 40 years. Both policies respectively reduce lifetime death rate in 6.4% and 5.7% and the cost of saving one life in 17% and 9.3% compared to the 2020 Chilean policy. Our findings show that two-year inter-screening interval policies are less effective in countries with lower breast cancer incidence; thus we recommend screening policies with a wider age range and larger inter-screening intervals for Chile.

Effectiveness of breast cancer screening policies in countries with medium-low incidence rates

PubMed Central

Kong, Qingxia; Mondschein, Susana; Pereira, Ana

2018-01-01

ABSTRACT Chile has lower breast cancer incidence rates compared to those in developed countries. Our public health system aims to perform 10 biennial screening mammograms in the age group of 50 to 69 years by 2020. Using a dynamic programming model, we have found the optimal ages to perform 10 screening mammograms that lead to the lowest lifetime death rate and we have evaluated a set of fixed inter-screening interval policies. The optimal ages for the 10 mammograms are 43, 47, 51, 54, 57, 61, 65, 68, 72, and 76 years, and the most effective fixed inter-screening is every four years after the 40 years. Both policies respectively reduce lifetime death rate in 6.4% and 5.7% and the cost of saving one life in 17% and 9.3% compared to the 2020 Chilean policy. Our findings show that two-year inter-screening interval policies are less effective in countries with lower breast cancer incidence; thus we recommend screening policies with a wider age range and larger inter-screening intervals for Chile. PMID:29412375

The psychological impact of a false-positive screening mammogram in Barcelona.

PubMed

Espasa, Rebecca; Murta-Nascimento, Cristiane; Bayés, Ramón; Sala, Maria; Casamitjana, Montserrat; Macià, Francesc; Castells, Xavier

2012-12-01

The purpose of this study was to ascertain the psychological impact of mammographic screening for women who receive negative results and for those who need additional non-invasive and invasive complementary investigations to exclude breast cancer (false positives). One hundred fifty women who attended a breast cancer screening programme in Barcelona, aged 50-69 years, were included in this study: 50 with negative results and 100 with false positive mammograms (50 underwent non-invasive and 50 underwent invasive complementary investigations). Participants worried little until they underwent mammography, but worries increased when a telephone call notified the women of the need for further testing. A substantial proportion of women requiring further assessment reported that they were at least somewhat worried about having breast cancer throughout the screening process (P < 0.0001). Nevertheless, levels of anxiety and depression, measured by the Hospital Anxiety and Depression Scale, showed no statistically significant differences among the three groups. In conclusion, although the women showed no psychological morbidity, there is a substantial psychological response in those with an abnormal screening mammogram.

The mammography screening employee inreach program.

PubMed

Robinson, Joanne; Seltzer, Vicki; Lawrence, Loretta; Autz, George; Kostroff, Karen; Weiselberg, Lora; Colagiacomo, Maria

2007-02-01

To determine whether our health care employees were undergoing mammography screening according to American Cancer Society guidelines and to determine whether aggressive outreach, education and streamlining of mammography scheduling could improve compliance. All female employees at North Shore University Hospital (NSUH) and several other health system facilities (SF) were sent mailings to their homes that included breast health education and mammography screening guidelines, a questionnaire regarding their own mammography screening history and the opportunity to have their mammography screening scheduled by the Mammography Screening Employee Inreach Program (MSEIP) coordinator. Of the approximately 2,700 female employees aged 40 and over at NSUH and SF, 2,235 (82.7%) responded to the questionnaire, and 1,455 had a mammogram done via the MSEIP. Of the 1,455, 43% either were overdue for a mammogram or had never had one. During a second year of the MSEIP at NSUH and SF, an additional 1,706 mammograms were done. People employed in health care jobs do not necessarily avail themselves of appropriate health care screening. An aggressive program that utilized education, outreach and assistance with scheduling was effective in increasing compliance with mammography screening.

COLLABORATIVE MODELING OF THE BENEFITS AND HARMS ASSOCIATED WITH DIFFERENT U.S. BREAST CANCER SCREENING STRATEGIES

PubMed Central

Mandelblatt, Jeanne S.; Stout, Natasha K.; Schechter, Clyde B.; van den Broek, Jeroen J.; Miglioretti, Diana; Krapcho, Martin; Trentham-Dietz, Amy; Munoz, Diego; Lee, Sandra J.; Berry, Donald A.; van Ravesteyn, Nicolien T.; Alagoz, Oguzhan; Kerlikowske, Karla; Tosteson, Anna N.A.; Near, Aimee M.; Hoeffken, Amanda; Chang, Yaojen; Heijnsdijk, Eveline A.; Chisholm, Gary; Huang, Xuelin; Huang, Hui; Ergun, Mehmet Ali; Gangnon, Ronald; Sprague, Brian L.; Plevritis, Sylvia; Feuer, Eric; de Koning, Harry J.; Cronin, Kathleen A.

2016-01-01

Background Controversy persists about optimal mammography screening strategies. Objective To evaluate mammography strategies considering screening and treatment advances. Design Collaboration of six simulation models. Data Sources National data on incidence, risk, breast density, digital mammography performance, treatment effects, and other-cause mortality. Target Population An average-risk cohort. Time Horizon Lifetime. Perspective Societal. Interventions Mammograms from age 40, 45 or 50 to 74 at annual or biennial intervals, or annually from 40 or 45 to 49 then biennially to 74, assuming 100% screening and treatment adherence. Outcome Measures Screening benefits (vs. no screening) include percent breast cancer mortality reduction, deaths averted, and life-years gained. Harms include number of mammograms, false-positives, benign biopsies, and overdiagnosis. Results for Average-Risk Women Biennial strategies maintain 79.8%-81.3% (range across strategies and models: 68.3–98.9%) of annual screening benefits with almost half the false-positives and fewer overdiagnoses. Screening biennially from ages 50–74 achieves a median 25.8% (range: 24.1%-31.8%) breast cancer mortality reduction; annual screening from ages 40–74 years reduces mortality an additional 12.0% (range: 5.7%-17.2%) vs. no screening, but yields 1988 more false-positives and 7 more overdiagnoses per 1000 women screened. Annual screening from ages 50–74 had similar benefits as other strategies but more harms, so would not be recommended. Sub-population Results Annual screening starting at age 40 for women who have a two- to four-fold increase in risk has a similar balance of harms and benefits as biennial screening of average-risk women from 50–74. Limitations We do not consider other imaging technologies, polygenic risk, or non-adherence. Conclusion These results suggest that biennial screening is efficient for average-risk groups, but decisions on strategies depend on the weight given to the balance of harms and benefits. Primary Funding Source National Institutes of Health PMID:26756606

Limited data tomographic image reconstruction via dual formulation of total variation minimization

NASA Astrophysics Data System (ADS)

Jang, Kwang Eun; Sung, Younghun; Lee, Kangeui; Lee, Jongha; Cho, Seungryong

2011-03-01

The X-ray mammography is the primary imaging modality for breast cancer screening. For the dense breast, however, the mammogram is usually difficult to read due to tissue overlap problem caused by the superposition of normal tissues. The digital breast tomosynthesis (DBT) that measures several low dose projections over a limited angle range may be an alternative modality for breast imaging, since it allows the visualization of the cross-sectional information of breast. The DBT, however, may suffer from the aliasing artifact and the severe noise corruption. To overcome these problems, a total variation (TV) regularized statistical reconstruction algorithm is presented. Inspired by the dual formulation of TV minimization in denoising and deblurring problems, we derived a gradient-type algorithm based on statistical model of X-ray tomography. The objective function is comprised of a data fidelity term derived from the statistical model and a TV regularization term. The gradient of the objective function can be easily calculated using simple operations in terms of auxiliary variables. After a descending step, the data fidelity term is renewed in each iteration. Since the proposed algorithm can be implemented without sophisticated operations such as matrix inverse, it provides an efficient way to include the TV regularization in the statistical reconstruction method, which results in a fast and robust estimation for low dose projections over the limited angle range. Initial tests with an experimental DBT system confirmed our finding.

Convolutional neural network approach for enhanced capture of breast parenchymal complexity patterns associated with breast cancer risk

NASA Astrophysics Data System (ADS)

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

2017-03-01

We assess the feasibility of a parenchymal texture feature fusion approach, utilizing a convolutional neural network (ConvNet) architecture, to benefit breast cancer risk assessment. Hypothesizing that by capturing sparse, subtle interactions between localized motifs present in two-dimensional texture feature maps derived from mammographic images, a multitude of texture feature descriptors can be optimally reduced to five meta-features capable of serving as a basis on which a linear classifier, such as logistic regression, can efficiently assess breast cancer risk. We combine this methodology with our previously validated lattice-based strategy for parenchymal texture analysis and we evaluate the feasibility of this approach in a case-control study with 424 digital mammograms. In a randomized split-sample setting, we optimize our framework in training/validation sets (N=300) and evaluate its descriminatory performance in an independent test set (N=124). The discriminatory capacity is assessed in terms of the the area under the curve (AUC) of the receiver operator characteristic (ROC). The resulting meta-features exhibited strong classification capability in the test dataset (AUC = 0.90), outperforming conventional, non-fused, texture analysis which previously resulted in an AUC=0.85 on the same case-control dataset. Our results suggest that informative interactions between localized motifs exist and can be extracted and summarized via a fairly simple ConvNet architecture.

Population-based breast cancer screening in a primary care network

PubMed Central

Atlas, Steven J.; Ashburner, Jeffrey M.; Chang, Yuchiao; Lester, William T.; Barry, Michael J.; Grant, Richard W.

2013-01-01

Objective To assess up to 3-year follow-up of a health information technology system that facilitated population-based breast cancer screening. Study Design Cohort study with 2-year follow-up after completing a 1-year cluster randomized trial. Methods Women 42-69 years old receiving care within a 12-practice primary care network. The trial tested an integrated, non-visit-based population management informatics system that: 1) identified women overdue for mammograms, 2) connected them to primary care providers using a Web-based tool, 3) created automatically-generated outreach letters for patients specified by providers, 4) monitored for subsequent mammography scheduling and completion, and 5) provided practice delegates a list of women remaining unscreened for reminder phone calls. All practices also provided visit-based cancer screening reminders. Eligible women overdue for a mammogram during a one-year study period included those overdue at study start (prevalent cohort) or becoming overdue during follow-up (incident cohort). The main outcome measure was mammography completion rates over three years. Results Among 32,688 eligible women, 9,795 (30%) were overdue for screening including 4,487 in intervention and 5,308 in control practices. Intervention patients were somewhat younger, more likely to be non-Hispanic white, and have health insurance compared to control patients. Among patients in the prevalent cohort (n=6,697), adjusted completion rates were significantly higher among intervention compared to control patients after 3 years (51.7% vs. 45.8%, p=0.002). For patients in the incident cohort (n=3,098), adjusted completion rates after 2 years were 53.8% vs. 48.7%, p=0.052, respectively. Conclusions Population-based informatics systems can enable sustained increases in mammography screening rates beyond that seen with office-based visit reminders. PMID:23286611

Breast screening: What can the interval cancer review teach us? Are we perhaps being a bit too hard on ourselves?

PubMed

Lekanidi, Katerina; Dilks, Phil; Suaris, Tamara; Kennett, Steffan; Purushothaman, Hema

2017-09-01

The aim of this study was to determine the features that make interval cancers apparent on the preceding screening mammogram and determine whether changes in the ways of performing the interval cancer review will affect the true interval cancer rate. This study was approved by the clinical governance committee. Mammograms of women diagnosed with an interval cancer were included in the study if they had been allocated to either the "suspicious signs" group or "subtle signs" group, during the historic interval cancer review. Three radiologists, individually and blinded to the site of interval cancer, reviewed the mammograms and documented the presence, site, characteristics and classification of any abnormality. Findings were compared with the appearances of the abnormality at the site of subsequent cancer development by a different breast radiologist. The chi-squared test was used in the analysis of the results, seeking associations between recall concordance and cancer mammographic or histological characteristics. 111/590 interval cancers fulfilled the study inclusion criteria. In 17% of the cases none of the readers identified the relevant abnormality on the screening mammogram. 1/3 readers identified the relevant lesion in 22% of the cases, 2/3 readers in 28% of cases and all 3 readers in 33% of cases. The commonest unanimously recalled abnormality was microcalcification and the most challenging mammographic abnormality to detect was asymmetric density. We did not find any statistically significant association between recall concordance and time to interval cancer, position of lesion in the breast, breast density or cancer grade. Even the simple step of performing an independent blinded review of interval cancers reduces the rate of interval cancers classified as missed by up to 39%. Copyright © 2017 Elsevier B.V. All rights reserved.

Temporal assessment of radiomic features on clinical mammography in a high-risk population

NASA Astrophysics Data System (ADS)

Mendel, Kayla R.; Li, Hui; Lan, Li; Chan, Chun-Wai; King, Lauren M.; Tayob, Nabihah; Whitman, Gary; El-Zein, Randa; Bedrosian, Isabelle; Giger, Maryellen L.

2018-02-01

Extraction of high-dimensional quantitative data from medical images has become necessary in disease risk assessment, diagnostics and prognostics. Radiomic workflows for mammography typically involve a single medical image for each patient although medical images may exist for multiple imaging exams, especially in screening protocols. Our study takes advantage of the availability of mammograms acquired over multiple years for the prediction of cancer onset. This study included 841 images from 328 patients who developed subsequent mammographic abnormalities, which were confirmed as either cancer (n=173) or non-cancer (n=155) through diagnostic core needle biopsy. Quantitative radiomic analysis was conducted on antecedent FFDMs acquired a year or more prior to diagnostic biopsy. Analysis was limited to the breast contralateral to that in which the abnormality arose. Novel metrics were used to identify robust radiomic features. The most robust features were evaluated in the task of predicting future malignancies on a subset of 72 subjects (23 cancer cases and 49 non-cancer controls) with mammograms over multiple years. Using linear discriminant analysis, the robust radiomic features were merged into predictive signatures by: (i) using features from only the most recent contralateral mammogram, (ii) change in feature values between mammograms, and (iii) ratio of feature values over time, yielding AUCs of 0.57 (SE=0.07), 0.63 (SE=0.06), and 0.66 (SE=0.06), respectively. The AUCs for temporal radiomics (ratio) statistically differed from chance, suggesting that changes in radiomics over time may be critical for risk assessment. Overall, we found that our two-stage process of robustness assessment followed by performance evaluation served well in our investigation on the role of temporal radiomics in risk assessment.

Screening Mammography Use Among Older Women Before and After the 2009 U.S. Preventive Services Task Force Recommendations.

PubMed

Chang, Chiang-Hua; Bynum, Julie P W; Onega, Tracy; Colla, Carrie H; Lurie, Jon D; Tosteson, Anna N A

2016-10-01

It is uncertain how changes in the U.S. Preventive Services Task Force breast cancer screening recommendations (from annual to biennial mammography screening in women aged 50-74 and grading the evidence as insufficient for screening in women aged 75 and older) have affected mammography use among Medicare beneficiaries. Cohort study of 12 million Medicare fee-for-service women aged 65-74 and 75 and older to measure changes in 3-year screening use, 2007-2009 (before) and 2010-2012 (after), defined by two measures-proportion screened and frequency of screening by age, race/ethnicity, and hospital referral region. Fewer women were screened, but with similar frequency after 2009 for both age groups (after vs. before: age 65-74: 60.1% vs. 60.8% screened, 2.1 vs. 2.1 mammograms per screened woman; age 75 and older: 31.7% vs. 33.6% screened, 1.9 vs. 1.9 mammograms per screened woman; all p < 0.05). Black women were the only subgroup with an increase in screening use, and for both age groups (after vs. before: age 65-74: 55.4% vs. 54.0% screened and 2.0 vs. 1.9 mammograms per screened woman; age 75 and older: 28.5% vs. 27.9% screened and 1.8 vs. 1.8 mammograms per screened woman; all p < 0.05). Regional change patterns in screening were more similar between age groups (Pearson correlation r = 0.781 for proportion screened; r = 0.840 for frequency of screening) than between black versus nonblack women (Pearson correlation r = 0.221 for proportion screened; r = 0.212 for frequency of screening). Changes in screening mammography use for Medicare women are not fully aligned with the 2009 recommendations.

Does mammogram attendance influence participation in cervical and colorectal cancer screening? A prospective study among 1856 French women.

PubMed

Bertaut, Aurélie; Coudert, Julien; Bengrine, Leila; Dancourt, Vincent; Binquet, Christine; Douvier, Serge

2018-01-01

We aimed to determine participation rates and factors associated with participation in colorectal (fecal occul blood test) and cervical cancer (Pap-smear) screening among a population of women participating in breast cancer screening. From August to October 2015, a self-administered questionnaire was sent by post to 2 900 women aged 50-65, living in Côte-d'Or, France, and who were up to date with mammogram screening. Polytomic logistic regression was used to identify correlates of participation in both cervical and colorectal cancer screenings. Participation in all 3 screenings was chosen as the reference. Study participation rate was 66.3% (n = 1856). Besides being compliant with mammogram, respectively 78.3% and 56.6% of respondents were up to date for cervical and colorectal cancer screenings, while 46.2% were compliant with the 3 screenings. Consultation with a gynecologist in the past year was associated with higher chance of undergoing the 3 screenings or female cancer screenings (p<10-4), when consultation with a GP was associated with higher chance of undergoing the 3 screenings or organized cancer screenings (p<0.05). Unemployment, obesity, age>59 and yearly flu vaccine were associated with a lower involvement in cervical cancer screening. Women from high socio-economic classes were more likely to attend only female cancer screenings (p = 0.009). Finally, a low level of physical activity and tobacco use were associated with higher risk of no additional screening participation (p<10-3 and p = 0.027). Among women participating in breast screening, colorectal and cervical cancer screening rates could be improved. Including communication about these 2 cancer screenings in the mammogram invitation could be worth to explore.

Change in Mammography Use Following the Revised Guidelines from the U.S. Preventive Services Task Force.

PubMed

Lee, Jeannette Y; Malak, Sharp F; Klimberg, Vicki Suzanne; Henry-Tillman, Ronda; Kadlubar, Susan

2017-03-01

The U.S. Preventive Services Task Force (USPSTF) recommended screening mammography every 1-2 years for women 40 years and older in 2002, and changed its recommendations in 2009 to no routine screening for women between 40 and 49 years of age; and biennial screening for women between 50 and 74 years of age. This study evaluates the change in mammographic use after the issuance of the revised recommendations. Women who participated in a cross-sectional study of breast cancer risk factors from 2007 to 2013 were asked if they had received a mammogram in the preceding 2 years. All 3442 study participants who enrolled in the study after January 1, 2011 were matched by race, age, and educational level with women enrolled between 2007 and 2010. The proportions of women who stated they had received a mammogram in the past 2 years were compared between the two groups. One fourth of the participants were African American and 39% were 40-49 years of age. Among white women, significant decreases in recent mammogram use from 2007-2010 to 2011-2013 were detected for women 40-49 years of age (-10.3%, p < 0.001) and 50-74 years of age (-8.8%, p < 0.001). Among African-American women, the change in recent mammogram use was not statistically significant for women 40-49 years of age (-2.7%, p = 0.440) or 50-74 years of age (-2.2%, p = 0.398). Following the change in the USPSTF guidelines, mammography use among white women declined; however, no change was observed among African-American women. © 2016 Wiley Periodicals, Inc.

Surveillance for cancer recurrence in long-term young breast cancer survivors randomly selected from a statewide cancer registry.

PubMed

Jones, Tarsha; Duquette, Debra; Underhill, Meghan; Ming, Chang; Mendelsohn-Victor, Kari E; Anderson, Beth; Milliron, Kara J; Copeland, Glenn; Janz, Nancy K; Northouse, Laurel L; Duffy, Sonia M; Merajver, Sofia D; Katapodi, Maria C

2018-05-01

This study examined clinical breast exam (CBE) and mammography surveillance in long-term young breast cancer survivors (YBCS) and identified barriers and facilitators to cancer surveillance practices. Data collected with a self-administered survey from a statewide, randomly selected sample of YBCS diagnosed with invasive breast cancer or ductal carcinoma in situ younger than 45 years old, stratified by race (Black vs. White/Other). Multivariate logistic regression models identified predictors of annual CBEs and mammograms. Among 859 YBCS (n = 340 Black; n = 519 White/Other; mean age = 51.0 ± 5.9; diagnosed 11.0 ± 4.0 years ago), the majority (> 85%) reported an annual CBE and a mammogram. Black YBCS in the study were more likely to report lower rates of annual mammography and more barriers accessing care compared to White/Other YBCS. Having a routine source of care, confidence to use healthcare services, perceived expectations from family members and healthcare providers to engage in cancer surveillance, and motivation to comply with these expectations were significant predictors of having annual CBEs and annual mammograms. Cost-related lack of access to care was a significant barrier to annual mammograms. Routine source of post-treatment care facilitated breast cancer surveillance above national average rates. Persistent disparities regarding access to mammography surveillance were identified for Black YBCS, primarily due to lack of access to routine source of care and high out-of-pocket costs. Public health action targeting cancer surveillance in YBCS should ensure routine source of post-treatment care and address cost-related barriers. Clinical Trials Registration Number: NCT01612338.

Disparities in abnormal mammogram follow-up time for Asian women compared to non-Hispanic Whites and between Asian ethnic groups

PubMed Central

Nguyen, KH; Pasick, RJ; Stewart, SL; Kerlikowske, K; Karliner, LS

2017-01-01

Background Delays in abnormal mammogram follow-up contribute to poor outcomes. We examined abnormal screening mammogram follow-up differences for non-Hispanic Whites (NHW) and Asian women. Methods Prospective cohort of NHW and Asian women with a Breast Imaging Reporting and Data System abnormal result of 0 or 3+ in the San Francisco Mammography Registry between 2000–2010. We performed Kaplan-Meier estimation for median-days to follow-up with a diagnostic radiologic test, and compared proportion with follow-up at 30, 60 and 90 days, and no follow-up at one-year for Asians overall (and Asian ethnic groups) and NHWs. We additionally assessed the relationship between race/ethnicity and time-to-follow-up with adjusted Cox proportional hazards models. Results Among Asian women, Vietnamese and Filipinas had the longest, and Japanese the shortest, median follow-up time (32, 28, 19 days, respectively) compared to NHWs (15 days). The proportion of women receiving follow-up at 30 days was lower for Asians vs NHWs (57% vs 77%, p<0.0001), and these disparities persisted at 60 and 90 days for all Asian ethnic groups except Japanese. Asians had a reduced hazard of follow-up compared with NHWs (aHR 0.70, 95% CI 0.69–0.72). Asians also had a higher rate than NHWs of no follow-up (15% vs 10%; p<0.001); among Asian ethnic groups, Filipinas had the highest percentage of women with no follow-up (18.1%). Conclusion Asian, particularly Filipina and Vietnamese, women were less likely than NHWs to receive timely follow-up after an abnormal screening mammogram. Research should disaggregate Asian ethnicity to better understand and address barriers to effective cancer prevention. PMID:28603859

Efficiency of Core Biopsy for BI-RADS-5 Breast Lesions.

PubMed

Wolf, Ronald; Quan, Glenda; Calhoun, Kris; Soot, Laurel; Skokan, Laurie

2008-01-01

Stereotactic biopsy has proven more cost effective for biopsy of lesions associated with moderately suspicious mammograms. Data regarding selection of stereotactic biopsy (CORE) instead of excisional biopsy (EB) as the first diagnostic procedure in patients with nonpalpable breast lesions and highest suspicion breast imaging-reporting and data system (BI-RADS)-5 mammograms are sparse. Records from a regional health system radiology database were screened for mammograms associated with image-guided biopsy. A total of 182 nonpalpable BI-RADS-5 lesions were sampled in 178 patients over 5 years, using CORE or EB. Initial surgical margins, number of surgeries, time from initial procedure to last related surgical procedure, and hospital and professional charges for related admissions were compared using chi-squared, t-test, and Wilcoxon Mann-Whitney tests. A total of 108 CORE and 74 EB were performed as the first diagnostic procedure. Invasive or in situ carcinoma was diagnosed in 156 (86%) of all biopsies, 95 in CORE and 61 in EB groups. Negative margins of the first surgical procedure were more frequent in CORE (n = 70, 74%) versus EB (n = 17, 28%), p < 0.05. Use of CORE was associated with fewer total surgical procedures per lesion (1.29 +/- 0.05 versus 1.8 +/- 0.05, p < 0.05). Time of initial diagnostic procedure to final treatment did not vary significantly according to group (27 +/- 2 days versus 22 +/- 2 days, CORE versus EB). Mean charges including the diagnostic procedure and all subsequent surgeries were not different between CORE and EB groups ($10,500 +/- 300 versus $11,500 +/- 500, p = 0.08). Use of CORE as the first procedure in patients with highly suspicious mammograms is associated with improved pathologic margins and need for fewer surgical procedures than EB, and should be considered the preferred initial diagnostic approach.

Mobile Versus Fixed Facility: Latinas' Attitudes and Preferences for Obtaining a Mammogram.

PubMed

Scheel, John R; Tillack, Allison A; Mercer, Lauren; Coronado, Gloria D; Beresford, Shirley A A; Molina, Yamile; Thompson, Beti

2018-01-01

Mobile mammographic services have been proposed as a way to reduce Latinas' disproportionate late-stage presentation compared with white women by increasing their access to mammography. The aims of this study were to assess why Latinas may not use mobile mammographic services and to explore their preferences after using these services. Using a mixed-methods approach, a secondary analysis was conducted of baseline survey data (n = 538) from a randomized controlled trial to improve screening mammography rates among Latinas in Washington. Descriptive statistics and bivariate regression were used to characterize mammography location preferences and to test for associations with sociodemographic indices, health care access, and perceived breast cancer risk and beliefs. On the basis of these findings, a qualitative study (n = 18) was used to explore changes in perceptions after using mobile mammographic services. More Latinas preferred obtaining a mammogram at a fixed facility (52.3% [n = 276]) compared with having no preference (46.3% [n = 249]) and preferring mobile mammographic services (1.7% [n = 9]). Concerns about privacy and comfort (15.6% [n = 84]) and about general quality (10.6% [n = 57]) were common reasons for preferring a fixed facility. Those with no history of mammography preferred a fixed facility (P < .05). In the qualitative study, Latinas expressed similar initial concerns but became positive toward the mobile mammographic services after obtaining a mammogram. Although most Latinas preferred obtaining a mammogram at a fixed facility, positive experiences with mobile mammography services changed their attitudes toward them. These findings highlight the need to include community education when using mobile mammographic service to increase screening mammography rates in underserved communities. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

The impact of contrast enhanced spectral mammogram (CESM) and three dimensional breast ultrasound (3DUS) on the characterization of the disease extend in cancer patients.

PubMed

Helal, Maha Hussien; Salem, Dorria Saleh; Salaleldin, Lamia Adel; Mansour, Sahar Mahmoud; Alkalaawy, Basma Mohamed; Mokhtar, Nadia Mahmoud

2018-04-11

The main importance of imaging breast cancer is to guide conservative surgeries. In this study we evaluated the role of CESM in correlation with 3D breast ultrasound in characterizing the extension of the intramammary cancer in view of the: (i) the size of the main tumour, (ii) the multiplicity of the breast cancer, and (iii) the peri-tumoral stromal involvement (i.e. free or intra-ductal extension of the cancer). The study is a prospective analysis that included 300 breast masses proved to be malignant. The masses were evaluated for their size, multiplicity and surrounding stromal involvement. Contrast-based mammography performed with low (22-33 kVp) and high (44-49 kVp) energy exposures that were taken after IV injection of contrast agent and followed by bilateral 3D breast ultrasound. Operative data were the gold standard reference. There was no significant difference between the sizes of the included cancers as measured by CESM and 3DUS and that measured at the pathological analysis. CESM showed higher accuracy (32.7%, n = 98) than 3DUS (24.7%, n = 74) in the size agreement within 5% range. CESM was the most accurate modality (94%, n = 282) in detecting tumor multiplicity, followed by traditional sonomammogram (88%, n = 264), then 3D breast US (84%, n = 252). Intra-ductal extension of the breast cancer was best evaluated by the 3DUS with an accuracy value of 98% (n = 294) compared to only 60% (n = 180) by CESM. CESM is a recommend investigation in breast cancer to increase the accuracy of size measurement and the detection of multiple tumors. The addition of 3DUS can enhance the detection of intra-ductal extension. Advances in knowledge: Choice of conservative breast surgery versus mastectomy is still a debate. We used an advanced, contrast-based, application of the mammogram: contrast enhanced spectral mammogram and a non-invasive three-dimensional breast ultrasound in the assessment of the local extension of the breast cancer regarding size, perifocal stromal infiltration and multiplicity to guide the selection of proper management in proved cases of breast cancer.

A hybrid deep learning approach to predict malignancy of breast lesions using mammograms

NASA Astrophysics Data System (ADS)

Wang, Yunzhi; Heidari, Morteza; Mirniaharikandehei, Seyedehnafiseh; Gong, Jing; Qian, Wei; Qiu, Yuchen; Zheng, Bin

2018-03-01

Applying deep learning technology to medical imaging informatics field has been recently attracting extensive research interest. However, the limited medical image dataset size often reduces performance and robustness of the deep learning based computer-aided detection and/or diagnosis (CAD) schemes. In attempt to address this technical challenge, this study aims to develop and evaluate a new hybrid deep learning based CAD approach to predict likelihood of a breast lesion detected on mammogram being malignant. In this approach, a deep Convolutional Neural Network (CNN) was firstly pre-trained using the ImageNet dataset and serve as a feature extractor. A pseudo-color Region of Interest (ROI) method was used to generate ROIs with RGB channels from the mammographic images as the input to the pre-trained deep network. The transferred CNN features from different layers of the CNN were then obtained and a linear support vector machine (SVM) was trained for the prediction task. By applying to a dataset involving 301 suspicious breast lesions and using a leave-one-case-out validation method, the areas under the ROC curves (AUC) = 0.762 and 0.792 using the traditional CAD scheme and the proposed deep learning based CAD scheme, respectively. An ensemble classifier that combines the classification scores generated by the two schemes yielded an improved AUC value of 0.813. The study results demonstrated feasibility and potentially improved performance of applying a new hybrid deep learning approach to develop CAD scheme using a relatively small dataset of medical images.

Applying a new mammographic imaging marker to predict breast cancer risk

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Danala, Gopichandh; Hollingsworth, Alan B.; Stoug, Rebecca G.; Pearce, Melanie; Liu, Hong; Zheng, Bin

2018-02-01

Identifying and developing new mammographic imaging markers to assist prediction of breast cancer risk has been attracting extensive research interest recently. Although mammographic density is considered an important breast cancer risk, its discriminatory power is lower for predicting short-term breast cancer risk, which is a prerequisite to establish a more effective personalized breast cancer screening paradigm. In this study, we presented a new interactive computer-aided detection (CAD) scheme to generate a new quantitative mammographic imaging marker based on the bilateral mammographic tissue density asymmetry to predict risk of cancer detection in the next subsequent mammography screening. An image database involving 1,397 women was retrospectively assembled and tested. Each woman had two digital mammography screenings namely, the "current" and "prior" screenings with a time interval from 365 to 600 days. All "prior" images were originally interpreted negative. In "current" screenings, these cases were divided into 3 groups, which include 402 positive, 643 negative, and 352 biopsy-proved benign cases, respectively. There is no significant difference of BIRADS based mammographic density ratings between 3 case groups (p < 0.6). When applying the CAD-generated imaging marker or risk model to classify between 402 positive and 643 negative cases using "prior" negative mammograms, the area under a ROC curve is 0.70+/-0.02 and the adjusted odds ratios show an increasing trend from 1.0 to 8.13 to predict the risk of cancer detection in the "current" screening. Study demonstrated that this new imaging marker had potential to yield significantly higher discriminatory power to predict short-term breast cancer risk.

Patterns of cancer screening in primary care from 2005 to 2010.

PubMed

Martires, Kathryn J; Kurlander, David E; Minwell, Gregory J; Dahms, Eric B; Bordeaux, Jeremy S

2014-01-15

Cancer screening recommendations vary widely, especially for breast, prostate, and skin cancer screening. Guidelines are provided by the American Cancer Society, the US Preventive Services Task Force, and various professional organizations. The recommendations often differ with regard to age and frequency of screening. The objective of this study was to determine actual rates of screening in the primary care setting. Data from the National Ambulatory Medical Care Survey were used. Only adult visits to non-federally employed, office-based physicians for preventive care from 2005 through 2010 were examined. Prevalence rates for breast, pelvic, and rectal examinations were calculated, along with the rates for mammograms, Papanicolaou smears, and prostate-specific antigen tests. Factors associated with screening, including age, race, smoking status, and insurance type, were examined using t tests and chi-square tests. In total, 8521 visits were examined. The rates of most screening examinations and tests were stable over time. Clinical breast examinations took place significantly more than mammography was ordered (54.8% vs 34.6%; P<.001). White patients received more mammography (P=.031), skin examinations (P<.010), digital rectal examinations (P<.010), and prostate-specific antigen tests (P=.003) than patients of other races. Patients who paid with Medicare or private insurance received more screening than patients who had Medicaid or no insurance (P<.010). Current cancer screening practices in primary care vary significantly. Cancer screening may not follow evidence-based practices and may not be targeting patients considered most at risk. Racial and socioeconomic disparities are present in cancer screening in primary care. © 2013 American Cancer Society.

Novel multiresolution mammographic density segmentation using pseudo 3D features and adaptive cluster merging

NASA Astrophysics Data System (ADS)

He, Wenda; Juette, Arne; Denton, Erica R. E.; Zwiggelaar, Reyer

2015-03-01

Breast cancer is the most frequently diagnosed cancer in women. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective ways to overcome the disease. Successful mammographic density segmentation is a key aspect in deriving correct tissue composition, ensuring an accurate mammographic risk assessment. However, mammographic densities have not yet been fully incorporated with non-image based risk prediction models, (e.g. the Gail and the Tyrer-Cuzick model), because of unreliable segmentation consistency and accuracy. This paper presents a novel multiresolution mammographic density segmentation, a concept of stack representation is proposed, and 3D texture features were extracted by adapting techniques based on classic 2D first-order statistics. An unsupervised clustering technique was employed to achieve mammographic segmentation, in which two improvements were made; 1) consistent segmentation by incorporating an optimal centroids initialisation step, and 2) significantly reduced the number of missegmentation by using an adaptive cluster merging technique. A set of full field digital mammograms was used in the evaluation. Visual assessment indicated substantial improvement on segmented anatomical structures and tissue specific areas, especially in low mammographic density categories. The developed method demonstrated an ability to improve the quality of mammographic segmentation via clustering, and results indicated an improvement of 26% in segmented image with good quality when compared with the standard clustering approach. This in turn can be found useful in early breast cancer detection, risk-stratified screening, and aiding radiologists in the process of decision making prior to surgery and/or treatment.

A model of primary and scattered photon fluence for mammographic x-ray image quantification

NASA Astrophysics Data System (ADS)

Tromans, Christopher E.; Cocker, Mary R.; Brady, Michael, Sir

2012-10-01

We present an efficient method to calculate the primary and scattered x-ray photon fluence component of a mammographic image. This can be used for a range of clinically important purposes, including estimation of breast density, personalized image display, and quantitative mammogram analysis. The method is based on models of: the x-ray tube; the digital detector; and a novel ray tracer which models the diverging beam emanating from the focal spot. The tube model includes consideration of the anode heel effect, and empirical corrections for wear and manufacturing tolerances. The detector model is empirical, being based on a family of transfer functions that cover the range of beam qualities and compressed breast thicknesses which are encountered clinically. The scatter estimation utilizes optimal information sampling and interpolation (to yield a clinical usable computation time) of scatter calculated using fundamental physics relations. A scatter kernel arising around each primary ray is calculated, and these are summed by superposition to form the scatter image. Beam quality, spatial position in the field (in particular that arising at the air-boundary due to the depletion of scatter contribution from the surroundings), and the possible presence of a grid, are considered, as is tissue composition using an iterative refinement procedure. We present numerous validation results that use a purpose designed tissue equivalent step wedge phantom. The average differences between actual acquisitions and modelled pixel intensities observed across the adipose to fibroglandular attenuation range vary between 5% and 7%, depending on beam quality and, for a single beam quality are 2.09% and 3.36% respectively with and without a grid.

Expert identification of visual primitives used by CNNs during mammogram classification

NASA Astrophysics Data System (ADS)

Wu, Jimmy; Peck, Diondra; Hsieh, Scott; Dialani, Vandana; Lehman, Constance D.; Zhou, Bolei; Syrgkanis, Vasilis; Mackey, Lester; Patterson, Genevieve

2018-02-01

This work interprets the internal representations of deep neural networks trained for classification of diseased tissue in 2D mammograms. We propose an expert-in-the-loop inter- pretation method to label the behavior of internal units in convolutional neural networks (CNNs). Expert radiologists identify that the visual patterns detected by the units are correlated with meaningful medical phenomena such as mass tissue and calcificated vessels. We demonstrate that several trained CNN models are able to produce explanatory descriptions to support the final classification decisions. We view this as an important first step toward interpreting the internal representations of medical classification CNNs and explaining their predictions.

Use of joint two-view information for computerized lesion detection on mammograms: improvement of microcalcification detection accuracy

NASA Astrophysics Data System (ADS)

Sahiner, Berkman; Gurcan, Metin N.; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Petrick, Nicholas; Helvie, Mark A.

2002-05-01

We are developing new techniques to improve the accuracy of computerized microcalcification detection by using the joint two-view information on craniocaudal (CC) and mediolateral-oblique (MLO) views. After cluster candidates were detected using a single-view detection technique, candidates on CC and MLO views were paired using their radial distances from the nipple. Object pairs were classified with a joint two-view classifier that used the similarity of objects in a pair. Each cluster candidate was also classified as a true microcalcification cluster or a false-positive (FP) using its single-view features. The outputs of these two classifiers were fused. A data set of 38 pairs of mammograms from our database was used to train the new detection technique. The independent test set consisted of 77 pairs of mammograms from the University of South Florida public database. At a per-film sensitivity of 70%, the FP rates were 0.17 and 0.27 with the fusion and single-view detection methods, respectively. Our results indicate that correspondence of cluster candidates on two different views provides valuable additional information for distinguishing false from true microcalcification clusters.

Associations Between Religion-Related Factors and Breast Cancer Screening Among American Muslims

PubMed Central

Padela, Aasim I.; Murrar, Sohad; Adviento, Brigid; Liao, Chuanhong; Hosseinian, Zahra; Peek, Monica; Curlin, Farr

2015-01-01

American Muslims have low rates of mammography utilization, and research suggests that religious values influence their health-seeking behaviors. We assessed associations between religion-related factors and breast cancer screening in this population. A diverse group of Muslim women were recruited from mosques and Muslim organization sites in Greater Chicago to self-administer a survey incorporating measures of fatalism, religiosity, discrimination, and Islamic modesty. 254 surveys were collected of which 240 met age inclusion criteria (40 years of age or older). Of the 240, 72 respondents were Arab, 71 South Asian, 59 African American, and 38 identified with another ethnicity. 77 % of respondents had at least one mammogram in their lifetime, yet 37 % had not obtained mammography within the past 2 years. In multivariate models, positive religious coping, and perceived religious discrimination in healthcare were negatively associated with having a mammogram in the past 2 years, while having a PCP was positively associated. Ever having a mammogram was positively associated with increasing age and years of US residency, and knowing someone with breast cancer. Promoting biennial mammography among American Muslims may require addressing ideas about religious coping and combating perceived religious discrimination through tailored interventions. PMID:24700026

Associations between religion-related factors and breast cancer screening among American Muslims.

PubMed

Padela, Aasim I; Murrar, Sohad; Adviento, Brigid; Liao, Chuanhong; Hosseinian, Zahra; Peek, Monica; Curlin, Farr

2015-06-01

American Muslims have low rates of mammography utilization, and research suggests that religious values influence their health-seeking behaviors. We assessed associations between religion-related factors and breast cancer screening in this population. A diverse group of Muslim women were recruited from mosques and Muslim organization sites in Greater Chicago to self-administer a survey incorporating measures of fatalism, religiosity, discrimination, and Islamic modesty. 254 surveys were collected of which 240 met age inclusion criteria (40 years of age or older). Of the 240, 72 respondents were Arab, 71 South Asian, 59 African American, and 38 identified with another ethnicity. 77% of respondents had at least one mammogram in their lifetime, yet 37% had not obtained mammography within the past 2 years. In multivariate models, positive religious coping, and perceived religious discrimination in healthcare were negatively associated with having a mammogram in the past 2 years, while having a PCP was positively associated. Ever having a mammogram was positively associated with increasing age and years of US residency, and knowing someone with breast cancer. Promoting biennial mammography among American Muslims may require addressing ideas about religious coping and combating perceived religious discrimination through tailored interventions.

Analytical optimization of digital subtraction mammography with contrast medium using a commercial unit.

PubMed

Rosado-Méndez, I; Palma, B A; Brandan, M E

2008-12-01

Contrast-medium-enhanced digital mammography (CEDM) is an image subtraction technique which might help unmasking lesions embedded in very dense breasts. Previous works have stated the feasibility of CEDM and the imperative need of radiological optimization. This work presents an extension of a former analytical formalism to predict contrast-to-noise ratio (CNR) in subtracted mammograms. The goal is to optimize radiological parameters available in a clinical mammographic unit (x-ray tube anode/filter combination, voltage, and loading) by maximizing CNR and minimizing total mean glandular dose (D(gT)), simulating the experimental application of an iodine-based contrast medium and the image subtraction under dual-energy nontemporal, and single- or dual-energy temporal modalities. Total breast-entrance air kerma is limited to a fixed 8.76 mGy (1 R, similar to screening studies). Mathematical expressions obtained from the formalism are evaluated using computed mammographic x-ray spectra attenuated by an adipose/glandular breast containing an elongated structure filled with an iodinated solution in various concentrations. A systematic study of contrast, its associated variance, and CNR for different spectral combinations is performed, concluding in the proposal of optimum x-ray spectra. The linearity between contrast in subtracted images and iodine mass thickness is proven, including the determination of iodine visualization limits based on Rose's detection criterion. Finally, total breast-entrance air kerma is distributed between both images in various proportions in order to maximize the figure of merit CNR2/D(gT). Predicted results indicate the advantage of temporal subtraction (either single- or dual-energy modalities) with optimum parameters corresponding to high-voltage, strongly hardened Rh/Rh spectra. For temporal techniques, CNR was found to depend mostly on the energy of the iodinated image, and thus reduction in D(gT) could be achieved if the spectral energy of the noniodinated image is decreased and the breast-entrance air kerma is evenly distributed between both acquisitions. Predicted limits, in terms of iodine concentration, are found to guarantee the visualization of common clinical angiogenic concentrations in the breast.

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

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

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

2012-07-15

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

A comparison study of image features between FFDM and film mammogram images

PubMed Central

Jing, Hao; Yang, Yongyi; Wernick, Miles N.; Yarusso, Laura M.; Nishikawa, Robert M.

2012-01-01

Purpose: This work is to provide a direct, quantitative comparison of image features measured by film and full-field digital mammography (FFDM). The purpose is to investigate whether there is any systematic difference between film and FFDM in terms of quantitative image features and their influence on the performance of a computer-aided diagnosis (CAD) system. Methods: The authors make use of a set of matched film-FFDM image pairs acquired from cadaver breast specimens with simulated microcalcifications consisting of bone and teeth fragments using both a GE digital mammography system and a screen-film system. To quantify the image features, the authors consider a set of 12 textural features of lesion regions and six image features of individual microcalcifications (MCs). The authors first conduct a direct comparison on these quantitative features extracted from film and FFDM images. The authors then study the performance of a CAD classifier for discriminating between MCs and false positives (FPs) when the classifier is trained on images of different types (film, FFDM, or both). Results: For all the features considered, the quantitative results show a high degree of correlation between features extracted from film and FFDM, with the correlation coefficients ranging from 0.7326 to 0.9602 for the different features. Based on a Fisher sign rank test, there was no significant difference observed between the features extracted from film and those from FFDM. For both MC detection and discrimination of FPs from MCs, FFDM had a slight but statistically significant advantage in performance; however, when the classifiers were trained on different types of images (acquired with FFDM or SFM) for discriminating MCs from FPs, there was little difference. Conclusions: The results indicate good agreement between film and FFDM in quantitative image features. While FFDM images provide better detection performance in MCs, FFDM and film images may be interchangeable for the purposes of training CAD algorithms, and a single CAD algorithm may be applied to either type of images. PMID:22830771

A deep learning approach for the analysis of masses in mammograms with minimal user intervention.

PubMed

Dhungel, Neeraj; Carneiro, Gustavo; Bradley, Andrew P

2017-04-01

We present an integrated methodology for detecting, segmenting and classifying breast masses from mammograms with minimal user intervention. This is a long standing problem due to low signal-to-noise ratio in the visualisation of breast masses, combined with their large variability in terms of shape, size, appearance and location. We break the problem down into three stages: mass detection, mass segmentation, and mass classification. For the detection, we propose a cascade of deep learning methods to select hypotheses that are refined based on Bayesian optimisation. For the segmentation, we propose the use of deep structured output learning that is subsequently refined by a level set method. Finally, for the classification, we propose the use of a deep learning classifier, which is pre-trained with a regression to hand-crafted feature values and fine-tuned based on the annotations of the breast mass classification dataset. We test our proposed system on the publicly available INbreast dataset and compare the results with the current state-of-the-art methodologies. This evaluation shows that our system detects 90% of masses at 1 false positive per image, has a segmentation accuracy of around 0.85 (Dice index) on the correctly detected masses, and overall classifies masses as malignant or benign with sensitivity (Se) of 0.98 and specificity (Sp) of 0.7. Copyright © 2017 Elsevier B.V. All rights reserved.

Breast and cervical cancer screening among South Asian immigrants in the United States.

PubMed

Menon, Usha; Szalacha, Laura A; Prabhughate, Abhijit

2012-01-01

South Asian (SA) immigrants (from Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan, and Sri Lanka) constitute the fastest growing of all Asian American immigrants to the United States, with a growth rate of 106% from 1990 to 2001. Data are lacking on health behaviors of this population subgroup, including cancer-related information. : The purpose of this study was to assess rates and correlates of breast and cervical cancer screening in a community sample of SAs. Participants were recruited from among attendees of 3 community-based agency programs. Data were collected in English, Hindi, and Gujarati from a convenience sample of 198 participants. Two-thirds of the sample (n = 127, 65.5%) had ever had a mammogram, whereas only a third (n = 65, 32.8%) had ever had a Papanicolaou smear or vaginal examination. Several predisposing factors (eg, country of birth, years in the United States, acculturation, age, and acknowledged barriers to screening) were significant predictors of breast and cervical screening, whereas the only enabling factor was past screening behavior. Additional study is warranted on cultural aspects of cancer screening behaviors. These data are formative on facilitators and barriers to mammogram and Papanicolaou test completion among these understudied minority women. Nurses who practice in primary care may begin to target health education based on sociodemographics of SA women and emphasize discussion of barriers to screening.

An adaptive incremental approach to constructing ensemble classifiers: Application in an information-theoretic computer-aided decision system for detection of masses in mammograms

PubMed Central

Mazurowski, Maciej A.; Zurada, Jacek M.; Tourassi, Georgia D.

2009-01-01

Ensemble classifiers have been shown efficient in multiple applications. In this article, the authors explore the effectiveness of ensemble classifiers in a case-based computer-aided diagnosis system for detection of masses in mammograms. They evaluate two general ways of constructing subclassifiers by resampling of the available development dataset: Random division and random selection. Furthermore, they discuss the problem of selecting the ensemble size and propose two adaptive incremental techniques that automatically select the size for the problem at hand. All the techniques are evaluated with respect to a previously proposed information-theoretic CAD system (IT-CAD). The experimental results show that the examined ensemble techniques provide a statistically significant improvement (AUC=0.905±0.024) in performance as compared to the original IT-CAD system (AUC=0.865±0.029). Some of the techniques allow for a notable reduction in the total number of examples stored in the case base (to 1.3% of the original size), which, in turn, results in lower storage requirements and a shorter response time of the system. Among the methods examined in this article, the two proposed adaptive techniques are by far the most effective for this purpose. Furthermore, the authors provide some discussion and guidance for choosing the ensemble parameters. PMID:19673196

[Sensitivity and specificity of the breast screening program in the Isere region based on positive results between 1991 and 1999].

PubMed

Garnier, A; Poncet, F; Billette De Villemeur, A; Exbrayat, C; Bon, M F; Chevalier, A; Salicru, B; Tournegros, J M

2009-06-01

The screening program guidelines specify that the call back rate of women for additional imaging (positive mammogram) should not exceed 7% at initial screening, and 5% at subsequent screening. Materials and methods. Results in the Isere region (12%) have prompted a review of the correlation between the call back rate and indicators of quality (detection rate, sensitivity, specificity, positive predictive value) for the radiologists providing interpretations during that time period. Three groups of radiologists were identified: the group with call back rate of 10% achieved the best results (sensitivity: 92%, detection rate: 0.53%, specificity: 90%). The group with lowest call back rate (7.7%) showed insufficient sensitivity (58%). The last group with call back rate of 18.3%, showed no improvement in sensitivity (82%) and detection rate (0.53%), but showed reduced specificity (82%). The protocol update in 2001 does not resolve this problematic situation and national results continue to demonstrate a high percentage of positive screening mammograms. A significant increase in the number of positive screening examinations compared to recommended guidelines is not advantageous and leads to an overall decrease in the quality of the screening.

Development of a stationary digital breast tomosynthesis system for clinical applications

NASA Astrophysics Data System (ADS)

Tucker, Andrew Wallace

Digital breast tomosynthesis (DBT) has been shown to be a very beneficial tool in the fight against breast cancer. However, current DBT systems have poor spatial resolution compared to full field digital mammography (FFDM), the current gold standard for screening mammography. The poor spatial resolution of DBT systems is a result of the single X-ray source design. In DBT systems a single X-ray source is rotated over an angular span in order to acquire the images needed for 3D reconstruction. The rotation of the X-ray source degrades the spatial resolution of the images. DBT systems which are approved for use in the United States for screening mammography are required to also take a full field digital mammogram with every DBT acquisition in order to compensate for the poor spatial resolution. This double exposure essentially doubles the radiation dose to patients. Over the past few years our research group has developed a carbon nanotube (CNT) based X-ray source technology. The unique nature of CNT X-ray sources allows for multiple X-ray focal spots in a single X-ray source. Using this technology we have recently developed a stationary DBT system (s-DBT) system which is capable of producing a full tomosynthesis image dataset with zero motion of the X-ray source. This system has been shown to have increased spatial resolution over other DBT systems in a laboratory setting. The goal of this thesis work was to optimize the s-DBT system, demonstrate its usefulness over other systems, and finally implement it into the clinic for a clinical trial. The s-DBT system was optimized using different image quality measurements. The optimized system was then used in a breast specimen imaging trial which compared s-DBT to magnified 2D mammography and a conventional single source DBT system. Readers preferred s-DBT to magnified 2D mammography for specimen margin delineation and mass detection, these results were not significant. Using physical measures for spatial resolution the s-DBT system was shown to have improved image quality over conventional single source DBT systems in breast tissue. A separate study showed that s-DBT could be a feasible alternative to FFDM for screening patients with breast implants. Finally, a second s-DBT system was constructed and implemented into the Department of Mammography at UNC hospitals. The first patient was imaged on the system in December of 2013.

Digital Subtraction Angiography (DSA) Techniques For The Evaluation Of Breast Lesions

NASA Astrophysics Data System (ADS)

Flynn, Michael J.; Ackerman, Laurens; Wilderman, Scott; Block, Roger; Watt, Christine; Burke, Matt; Shetty, P. C.

1984-08-01

Digital subtraction angiography of the breast may permit the differentiation of benign and malignant breast lesions. We have developed specific techniques for performing DSAB. The patient is examined in an oblique prone position with the involved breast in an immobilization device of our own design. The immobilization device adapts to our angiographic patient table and provides a water bolus with slight compression. The central ray of the x-ray beam is positioned for a lateral view of the breast, similar to the lateral view obtained in a mammogram. Iodinated contrast is injected from a catheter position in the superior vena cava. A kilovoltage of 50 kVp is employed which produces a near optimal signal to noise ratio for iodine contrast. The iodine signal to noise ratio characteristics of breast DSA have been modeled using a computer program which estimates the x-ray spectrum, filtration effects(tube, tissue, iodine, and grid), and image intensifier energy absorption. The energy absorbed in the input phosphor of the image intensifier is determined using a Monte Carlo radiation transport technique. Images are acquired in a 512 x 512 x 10 matrix with a 9" image intensifier using a geometric magnification of approximately 2. Typically, 10 mAs per exposure is required. A maximum of 40 exposures are made in three phases totalling 5 minutes. The average absorbed dose to the breast for a single exposure is 48 millirads (6 cm thickness) as determined by a Monte Carlo radiation transport computation of energy absorbed in breast tissue.

Sensitivity and specificity of mammographic screening as practised in Vermont and Norway

PubMed Central

Hofvind, S; Geller, B M; Skelly, J; Vacek, P M

2012-01-01

Objective The aim of this study was to examine the sensitivity and specificity of screening mammography as performed in Vermont, USA, and Norway. Methods Incident screening data from 1997 to 2003 for female patients aged 50–69 years from the Vermont Breast Cancer Surveillance System (116 996 subsequent screening examinations) and the Norwegian Breast Cancer Screening Program (360 872 subsequent screening examinations) were compared. Sensitivity and specificity estimates for the initial (based on screening mammogram only) and final (screening mammogram plus any further diagnostic imaging) interpretations were directly adjusted for age using 5-year age intervals for the combined Vermont and Norway population, and computed for 1 and 2 years of follow-up, which ended at the time of the next screening mammogram. Results For the 1-year follow-up, sensitivities for initial assessments were 82.0%, 88.2% and 92.5% for 1-, 2- and >2-year screening intervals, respectively, in Vermont (p=0.022). For final assessments, the values were 73.6%, 83.3% and 81.2% (p=0.047), respectively. For Norway, sensitivities for initial assessments were 91.0% and 91.3% (p=0.529) for 2- and >2-year intervals, and 90.7% and 91.3%, respectively, for final assessments (p=0.630). Specificity was lower in Vermont than in Norway for each screening interval and for all screening intervals combined, for both initial (90.6% vs 97.8% for all intervals; p<0.001) and final (98.8% vs 99.5% for all intervals; p<0.001) assessments. Conclusion Our study showed higher sensitivity and specificity in a biennial screening programme with an independent double reading than in a predominantly annual screening program with a single reading. Advances in knowledge This study demonstrates that higher recall rates and lower specificity are not always associated with higher sensitivity of screening mammography. Differences in the screening processes in Norway and Vermont suggest potential areas for improvement in the latter. PMID:22993383

Potential impact of legislation mandating breast density notification: benefits, harms, and cost effectiveness of supplemental ultrasound screening

PubMed Central

Sprague, Brian L.; Stout, Natasha K.; Schechter, Clyde; van Ravesteyn, Nicolien T.; Cevik, Mucahit; Alagoz, Oguzhan; Lee, Christoph I.; van den Broek, Jeroen J.; Miglioretti, Diana L.; Mandelblatt, Jeanne S.; de Koning, Harry J.; Kerlikowske, Karla; Lehman, Constance D.; Tosteson, Anna N. A.

2014-01-01

Background At least nineteen states have laws that require telling women with dense breasts and a negative screening mammogram to consider supplemental screening. The most readily available supplemental screening modality is ultrasound, yet little is known about its effectiveness. Objective To evaluate the benefits, harms, and cost-effectiveness of supplemental ultrasound screening for women with dense breasts. Design Comparative modeling with 3 validated simulation models. Data Sources Surveillance, Epidemiology, and End Results Program; Breast Cancer Surveillance Consortium; the medical literature. Target Population A contemporary cohort of women eligible for routine screening. Time Horizon Lifetime. Perspective Payer. Interventions Supplemental ultrasound screening for women with dense breasts following a negative screening mammogram. Outcome Measures Breast cancer deaths averted, quality-adjusted life years (QALYs) gained, false positive ultrasound biopsy recommendations, costs, costs per QALY gained. Results of Base-Case Analysis Supplemental ultrasound screening after a negative mammogram for women aged 50–74 with heterogeneously or extremely dense breasts averted 0.36 additional breast cancer deaths (range across models: 0.14–0.75), gained 1.7 QALYs (0.9–4.7), and resulted in 354 false-positive ultrasound biopsy recommendations (345–421) per 1000 women with dense breasts compared with biennial screening by mammography alone. The cost-effectiveness ratio was $325,000 per QALY gained ($112,000-$766,000). Restricting supplemental ultrasound screening to women with extremely dense breasts cost $246,000 per QALY gained ($74,000-$535,000). Results of Sensitivity Analysis The conclusions were not sensitive to ultrasound performance characteristics, screening frequency, or starting age. Limitations Provider costs for coordinating supplemental ultrasound were not considered. Conclusions Supplemental ultrasound screening for women with dense breasts undergoing screening mammography would substantially increase costs while producing relatively small benefits in breast cancer deaths averted and QALYs gained. Primary Funding Source National Institutes of Health PMID:25486550

Comparison of breast density measurements made using ultrasound tomography and mammography

NASA Astrophysics Data System (ADS)

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

2015-03-01

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

Large Subpectoral Lipoma on Screening Mammography

PubMed Central

Su, Andres; Margolies, Laurie

2017-01-01

A 61 year-old woman presenting for bilateral screening mammogram was found to have an oval fat-density mass in the posterior right breast, partially visualized, with anterior displacement and thinning of the pectoralis major muscle. This mass was found on CT and MRI correlation to represent a large fat-containing mass, likely a lipoma, deep to the pectoralis major. On subsequent screening mammograms, the visualized portion of the mass remained stable. Subpectoral lipomas and intramuscular lipomas within the pectoralis major are rare, and their appearance on mammography may not be familiar to most radiologists. A review of the literature and a discussion of their appearance on multiple imaging modalities is provided. PMID:29299106

Pre-reading mammograms by specialised breast technologists: legal implications for technologist and radiologist in The Netherlands.

PubMed

van den Biggelaar, F J H M; Flobbe, K; van Engelshoven, J M A; de Bijl, N P Y M

2009-09-01

This paper focuses on the legal implications in terms of duties and responsibilities for radiologists and radiologic technologists of independent pre-reading of mammograms by radiologic technologists, so patients could be discharged without being seen by a radiologist. Pre-reading could be effectuated when preconditions are met to perform reserved procedures by unauthorised professionals as stated in the Individual Health Care Professions (IHCP) Act. Furthermore, compliance with a protocol or code of conduct in combination with adequate training and supervision should be sufficient to disprove potential claims. For a wide implementation, pre-reading should be well-embedded in legal rules and should answer the professional standard of care.

Bilateral Image Subtraction and Multivariate Models for the Automated Triaging of Screening Mammograms

PubMed Central

Celaya-Padilla, José; Martinez-Torteya, Antonio; Rodriguez-Rojas, Juan; Galvan-Tejada, Jorge; Treviño, Victor; Tamez-Peña, José

2015-01-01

Mammography is the most common and effective breast cancer screening test. However, the rate of positive findings is very low, making the radiologic interpretation monotonous and biased toward errors. This work presents a computer-aided diagnosis (CADx) method aimed to automatically triage mammogram sets. The method coregisters the left and right mammograms, extracts image features, and classifies the subjects into risk of having malignant calcifications (CS), malignant masses (MS), and healthy subject (HS). In this study, 449 subjects (197 CS, 207 MS, and 45 HS) from a public database were used to train and evaluate the CADx. Percentile-rank (p-rank) and z-normalizations were used. For the p-rank, the CS versus HS model achieved a cross-validation accuracy of 0.797 with an area under the receiver operating characteristic curve (AUC) of 0.882; the MS versus HS model obtained an accuracy of 0.772 and an AUC of 0.842. For the z-normalization, the CS versus HS model achieved an accuracy of 0.825 with an AUC of 0.882 and the MS versus HS model obtained an accuracy of 0.698 and an AUC of 0.807. The proposed method has the potential to rank cases with high probability of malignant findings aiding in the prioritization of radiologists work list. PMID:26240818

A situational analysis of breast cancer early detection services in Trinidad and Tobago.

PubMed

Badal, Kimberly; Rampersad, Fidel; Warner, Wayne A; Toriola, Adetunji T; Mohammed, Hamish; Scheffel, Harold-Alexis; Ali, Rehanna; Moosoodeen, Murrie; Konduru, Siva; Russel, Adaila; Haraksingh, Rajini

2018-01-01

A situational analysis of breast cancer (BC) early detection services was carried out to investigate whether Trinidad and Tobago (T&T) has the framework for successful organized national screening. An online survey was designed to assess the availability, accessibility, quality control and assurance (QC&A), and monitoring and evaluation (M&E) mechanisms for public and private BC early detection. A focus group with local radiologists (n = 3) was held to identify unaddressed challenges and make recommendations for improvement. Major public hospitals offer free detection services with wait times of 1-6 months for an appointment. Private institutions offer mammograms for TTD$240 (USD$37) at minimum with same day service. Both sectors report a lack of trained staff. Using 1.2 mammograms per 10,000 women ≥40 years as sufficient, the public sector's rate of 0.19 mammograms per 10,000 women ≥40 years for screening and diagnosis is inadequate. Program M&E mechanisms, QC&A guidelines for machinery use, delays in receipt of pathology reports, and unreliable drug access are further unaddressed challenges. T&T must first strengthen its human and physical resources, implement M&E and QC&A measures, strengthen cancer care, and address other impediments to BC early detection before investing in nationally organized BC screening.

Time for a re-evaluation of mammography in the young? Results of an audit of mammography in women younger than 40 in a resource restricted environment.

PubMed

Taylor, Liezel; Basro, Sarinah; Apffelstaedt, Justus P; Baatjes, Karin

2011-08-01

Mammography in younger women is considered to be of limited value. In a resource restricted environment without access to magnetic resonance imaging (MRI) and with a high incidence of breast cancer in the young, mammography remains an important diagnostic tool. Recent technical advances and better regulation of mammography make a reassessment of its value in these conditions necessary. Data of all the mammograms performed at a tertiary hospital and private breast clinic between January 2003 and July 2009 in women less than 40 years of age were collected. Indications were the presence of a mass, follow-up after primary cancer therapy, and screening for patients perceived at high risk due to a family history or the presence of atypical hyperplasia. Data acquired were as follows: Demographics, prior breast surgery, indication for mammography, outcome of mammography, diagnostic procedures, and their results. Of 2,167 mammograms, 393 were performed for a palpable mass, diagnostic mammography. In these, the overall cancer detection rate was 40%. If the mammography was reported as breast imaging reporting and data system (BIRADS(®)) 5 versus BIRADS(®) 3 and 4 versus BIRADS(®) 1 and 2, a final diagnosis of malignancy was established in 96, 48, and 5%, respectively. Of 367 mammograms done for the follow-up after primary treatment of breast cancer, seven cancers were diagnosed for a detection rate of 1.9%. Of 1,312 mammograms performed for screening, the recall rate was 4%; the biopsy rate 2%, and the cancer diagnosis rate 3/1,000 examinations. In contrast to past series, this series has shown that recent advances in mammography have made it a useful tool in the management of breast problems in young women, notably in a resource-restricted environment. Women for screening should be selected carefully.

Tumor Size of Invasive Breast Cancer on Magnetic Resonance Imaging and Conventional Imaging (Mammogram/Ultrasound): Comparison with Pathological Size and Clinical Implications.

PubMed

Haraldsdóttir, K H; Jónsson, Þ; Halldórsdóttir, A B; Tranberg, K-G; Ásgeirsson, K S

2017-03-01

In Landspitali University Hospital, magnetic resonance imaging is used non-selectively in addition to mammogram and ultrasound in the preoperative assessment of breast cancer patients. The aim of this study was to assess invasive tumor size on imaging, compare with pathological size and evaluate the impact of magnetic resonance imaging on the type of surgery performed. All women with invasive breast cancer, diagnosed in Iceland, between 2007 and 2009 were reviewed retrospectively. In all, 438 of 641 (68%) patients diagnosed had preoperative magnetic resonance imaging. Twelve patients treated with neoadjuvant chemotherapy were excluded and 65 patients with multifocal or contralateral disease were assessed separately. Correlations between microscopic and radiologic tumor sizes were relatively weak. All imaging methods were inaccurate especially for large tumors, resulting in an overall underestimation of tumor size for these tumors. Magnetic resonance imaging under- and overestimated pathological tumor size by more than 10 mm in 16/348 (4.6%) and 26/348 patients (7.5%), respectively. In 19 patients (73%), overestimation of size was seen exclusively on magnetic resonance imaging. For tumors under- or overestimated by magnetic resonance imaging, the mastectomy rates were 56% and 65%, respectively, compared to an overall mastectomy rate of 43%. Of 51 patients diagnosed with multifocal disease on pathology, 19 (37%) were diagnosed by mammogram or ultrasound and 40 (78%) by magnetic resonance imaging resulting in a total detection rate of 84% (43 patients). Fourteen (3%) patients were diagnosed preoperatively with contralateral disease. Of those tumors, all were detected on magnetic resonance imaging but seven (50%) were also detected on mammogram or ultrasound or both. Our results suggest that routine use of magnetic resonance imaging may result in both under- and overestimation of tumor size and increase mastectomy rates in a small proportion of patients. Magnetic resonance imaging aids in the diagnosis of contralateral and multifocal disease.

Time to definitive diagnosis of breast cancer in Latina and non-Hispanic white women: the six cities study.

PubMed

Ramirez, Amelie G; Pérez-Stable, Eliseo J; Talavera, Gregory A; Penedo, Frank J; Carrillo, J Emilio; Fernandez, Maria E; Muñoz, Edgar; Long Parma, Dorothy; Holden, Alan Ec; San Miguel de Majors, Sandra; Nápoles, Anna; Castañeda, Sheila F; Gallion, Kipling J

2013-12-01

Time delay after an abnormal screening mammogram may have a critical impact on tumor size, stage at diagnosis, treatment, prognosis, and survival of subsequent breast cancer. This study was undertaken to evaluate disparities between Latina and non-Hispanic white (NHW) women in time to definitive diagnosis of breast cancer after an abnormal screening mammogram, as well as factors contributing to such disparities. As part of the activities of the National Cancer Institute (NCI)-funded Redes En Acción research network, clinical records of 186 Latinas and 74 NHWs who received abnormal screening mammogram results were reviewed to determine the time to obtain a definitive diagnosis. Data was obtained from participating clinics in six U.S. cities and included demographics, clinical history, and mammogram characteristics. Kaplan-Meier estimates and Cox proportional hazards models were used to test differences in median time to definitive diagnosis by ethnicity after adjusting for clinic site, demographics, and clinical characteristics. Time-to-event analysis showed that Latinas took 2.2 times longer to reach 50% definitively diagnosed with breast cancer relative to NHWs, and three times longer to reach 80% diagnosed (p=0.001). Latinas' median time to definitive diagnosis was 60 days compared to 27 for NHWs, a 59% gap in diagnosis rates (adjusted Hazard Ratio [aHR] = 1.59, 95% CI = 1.09, 2.31; p=0.015). BI-RADS-4/5 women's diagnosis rate was more than twice that of BI-RADS-3 (aHR = 2.11, 95% CI = 1.18, 3.78; p=0.011). Disparities in time between receipt of abnormal screening result and definitive diagnosis adversely affect Latinas compared to NHWs, and remain significant after adjusting for demographic and clinical variables. With cancer now the leading cause of mortality among Latinos, a greater need exists for ethnically and culturally appropriate interventions like patient navigation to facilitate Latinas' successful entry into, and progression through, the cancer care system.

Mammograms

MedlinePlus

... gov/widgets/fahc.html" width="243" height="179" title="Find a Health Center Widget" ... M.D., Community Oncology and Prevention Trials Research Group, Division of Cancer Prevention, National Cancer Institute, ...

Mammography screening in six diverse communities in Chicago--a population study.

PubMed

Whitman, Steve; Shah, Ami M; Silva, Abigail; Ansell, David

2007-01-01

Despite the fact that recent studies suggest a narrowing in access to mammography, Black women are much more likely to die from breast cancer than White women. Data at the community level regarding mammography screening can help explain health disparities and inform plans for improved screening efforts. In 2002-2003, a comprehensive household health survey in English or Spanish was conducted in six community areas with 1700 households. The module on mammography was based on a state-based nationwide health survey and included questions on frequency of mammography, repeat screenings, and several demographic variables. The proportion of women >or=40 years (n=482) who received a mammogram in the past 2 years ranged from 74% to 90% across the six communities. The community with the highest screening proportion was predominantly Mexican and included recent immigrants. The screening proportion in the poorest community area, which was all Black, was 77%. Women with health insurance, higher income, and more education were more likely to receive a mammogram. Proportions for women >or=50 years (n=286) were slightly higher but similar. Repeat screening, which is recommended, occurred at lower levels. Access to and utilization of mammography have grown in recent years so that even these vulnerable communities had screening proportions at or even higher than the national average and the Healthy People Year 2010 objective. Nonetheless, repeat screening sequences were lower and may require attention if mammography screening efforts are to have a greater impact on female breast cancer mortality.

Mammographic density measured as changes in tissue structure caused by HRT

NASA Astrophysics Data System (ADS)

Raundahl, Jakob; Loog, Marco; Nielsen, Mads

2006-03-01

Numerous studies have investigated the relation between mammographic density and breast cancer risk. These studies indicate that women with high breast density have a four to six fold risk increase. An investigation of whether or not this relation is causal is important for, e.g., hormone replacement therapy (HRT), which has been shown to actually increase the density. No gold standard for automatic assessment of mammographic density exists. Manual methods such as Wolfe patterns and BI-RADS are helpful for communication of diagnostic sensitivity, but they are both time consuming and crude. They may be sufficient in certain cases and for single measurements, but for serial, temporal analysis it is necessary to be able to detect more subtle changes and, in addition, to be more reproducible. In this work an automated method for measuring the effect of HRT w.r.t. changes in biological density in the breast is presented. This measure is a novel measure, which provides structural information orthogonal to intensity-based methods. Hessian eigenvalues at different scales are used as features and a clustering of these is employed to divide a mammogram into four structurally different areas. Subsequently, based on the relative size of the areas, a density score is determined. In the experiments, two sets of mammograms of 50 patients from a double blind, placebo controlled HRT experiment were used. The change in density for the HRT group, measured with the new method, was significantly higher (p = 0.0002) than the change in the control group.

4D co-registration of X-ray and MR-mammograms: initial clinical results and potential incremental diagnostic value.

PubMed

Dietzel, Matthias; Hopp, Torsten; Ruiter, Nicole V; Kaiser, Clemens G; Kaiser, Werner A; Baltzer, Pascal A

2015-01-01

4D co-registration of X-ray- and MR-mammograms (XM and MM) is a new method of image fusion. The present study aims to evaluate its clinical feasibility, radiological accuracy, and potential clinical value. XM and MM of 25 patients were co-registered. Results were evaluated by a blinded reader. Precision of the 4D co-registration was "very good" (mean-score [ms]=7), and lesions were "easier to delineate" (ms=5). In 88.8%, "relevant additional diagnostic information" was present, accounting for a more "confident diagnosis" in 76% (ms=5). 4D co-registration is feasible, accurate, and of potential clinical value. Copyright © 2015 Elsevier Inc. All rights reserved.

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

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

Vedantham, S; Shi, L; Karellas, A

2015-06-15

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

Does Litigation Influence Medical Practice? The Influence of Community Radiologists’ Medical Malpractice Perceptions and Experience on Screening Mammography1

PubMed Central

Elmore, Joann G.; Taplin, Stephen H.; Barlow, William E.; Cutter, Gary R.; D’Orsi, Carl J.; Hendrick, R. Edward; Abraham, Linn A.; Fosse, Jessica S.; Carney, Patricia A.

2011-01-01

PURPOSE To assess the relationship between radiologists’ perception of and experience with medical malpractice and their patient-recall rates in actual community-based clinical settings. MATERIALS AND METHODS All study activities were approved by the institutional review boards of the involved institutions, and patient and radiologist informed consent was obtained where necessary. This study was performed in three regions of the United States (Washington, Colorado, and New Hampshire). Radiologists who routinely interpret mammograms completed a mailed survey that included questions on demographic data, practice environment, and medical malpractice. Survey responses were linked to interpretive performance for all screening mammography examinations performed between January 1, 1996, and December 31, 2001. The odds of recall were modeled by using logistic regression analysis based on generalized estimating equations that adjust for study region. RESULTS Of 181 eligible radiologists, 139 (76.8%) returned the survey with full consent. The analysis included 124 radiologists who had interpreted a total of 557 143 screening mammograms. Approximately half (64 of 122 [52.4%]) of the radiologists reported a prior malpractice claim, with 18 (14.8%) reporting mammography-related claims. The majority (n = 51 [81.0%]) of the 63 radiologists who responded to a question regarding the degree of stress caused by a medical malpractice claim described the experience as very or extremely stressful. More than three of every four radiologists (ie, 94 [76.4%] of 123) expressed concern about the impact medical malpractice has on mammography practice, with over half (72 [58.5%] of 123) indicating that their concern moderately to greatly increased the number of their recommendations for breast biopsies. Radiologists’ estimates of their future malpractice risk were substantially higher than the actual historical risk. Almost one of every three radiologists (43 of 122 [35.3%]) had considered withdrawing from mammogram interpretation because of malpractice concerns. No significant association was found between recall rates and radiologists’ experiences or perceptions of medical malpractice. CONCLUSION U.S. radiologists are extremely concerned about medical malpractice and report that this concern affects their recall rates and biopsy recommendations. However, medical malpractice experience and concerns were not associated with recall or false-positive rates. Heightened concern of almost all radiologists may be a key reason that recall rates are higher in the United States than in other countries, but this hypothesis requires further study. PMID:15987961

Assessment of mammographic film processor performance in a hospital and mobile screening unit.

PubMed

Murray, J G; Dowsett, D J; Laird, O; Ennis, J T

1992-12-01

In contrast to the majority of mammographic breast screening programmes, film processing at this centre occurs on site in both hospital and mobile trailer units. Initial (1989) quality control (QC) sensitometric tests revealed a large variation in film processor performance in the mobile unit. The clinical significance of these variations was assessed and acceptance limits for processor performance determined. Abnormal mammograms were used as reference material and copied using high definition 35 mm film over a range of exposure settings. The copies were than matched with QC film density variation from the mobile unit. All films were subsequently ranked for spatial and contrast resolution. Optimal values for processing time of 2 min (equivalent to film transit time 3 min and developer time 46 s) and temperature of 36 degrees C were obtained. The widespread anomaly of reporting film transit time as processing time is highlighted. Use of mammogram copies as a means of measuring the influence of film processor variation is advocated. Careful monitoring of the mobile unit film processor performance has produced stable quality comparable with the hospital based unit. The advantages of on site film processing are outlined. The addition of a sensitometric step wedge to all mammography film stock as a means of assessing image quality is recommended.

Breast arterial calcification is associated with reproductive factors in asymptomatic postmenopausal women.

PubMed

Bielak, Lawrence F; Whaley, Dana H; Sheedy, Patrick F; Peyser, Patricia A

2010-09-01

The etiology of breast arterial calcification (BAC) is not well understood. We examined reproductive history and cardiovascular disease (CVD) risk factor associations with the presence of detectable BAC in asymptomatic postmenopausal women. Reproductive history and CVD risk factors were obtained in 240 asymptomatic postmenopausal women from a community-based research study who had a screening mammogram within 2 years of their participation in the study. The mammograms were reviewed for the presence of detectable BAC. Age-adjusted logistic regression models were fit to assess the association between each risk factor and the presence of BAC. Multiple variable logistic regression models were used to identify the most parsimonious model for the presence of BAC. The prevalence of BAC increased with increased age (p < 0.0001). The most parsimonious logistic regression model for BAC presence included age at time of examination, increased parity (p = 0.01), earlier age at first birth (p = 0.002), weight, and an age-by-weight interaction term (p = 0.004). Older women with a smaller body size had a higher probability of having BAC than women of the same age with a larger body size. The presence or absence of BAC at mammography may provide an assessment of a postmenopausal woman's lifetime estrogen exposure and indicate women who could be at risk for hormonally related conditions.

Breast cancer screening

MedlinePlus

Mammogram - breast cancer screening; Breast exam - breast cancer screening; MRI - breast cancer screening ... performed to screen women to detect early breast cancer when it is more likely to be cured. ...

The use of lower resolution viewing devices for mammographic interpretation: implications for education and training.

PubMed

Chen, Yan; James, Jonathan J; Turnbull, Anne E; Gale, Alastair G

2015-10-01

To establish whether lower resolution, lower cost viewing devices have the potential to deliver mammographic interpretation training. On three occasions over eight months, fourteen consultant radiologists and reporting radiographers read forty challenging digital mammography screening cases on three different displays: a digital mammography workstation, a standard LCD monitor, and a smartphone. Standard image manipulation software was available for use on all three devices. Receiver operating characteristic (ROC) analysis and ANOVA (Analysis of Variance) were used to determine the significance of differences in performance between the viewing devices with/without the application of image manipulation software. The effect of reader's experience was also assessed. Performance was significantly higher (p < .05) on the mammography workstation compared to the other two viewing devices. When image manipulation software was applied to images viewed on the standard LCD monitor, performance improved to mirror levels seen on the mammography workstation with no significant difference between the two. Image interpretation on the smartphone was uniformly poor. Film reader experience had no significant effect on performance across all three viewing devices. Lower resolution standard LCD monitors combined with appropriate image manipulation software are capable of displaying mammographic pathology, and are potentially suitable for delivering mammographic interpretation training. • This study investigates potential devices for training in mammography interpretation. • Lower resolution standard LCD monitors are potentially suitable for mammographic interpretation training. • The effect of image manipulation tools on mammography workstation viewing is insignificant. • Reader experience had no significant effect on performance in all viewing devices. • Smart phones are not suitable for displaying mammograms.

Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis.

PubMed

Samala, Ravi K; Chan, Heang-Ping; Hadjiiski, Lubomir M; Helvie, Mark A; Richter, Caleb; Cha, Kenny

2018-05-01

Deep learning models are highly parameterized, resulting in difficulty in inference and transfer learning for image recognition tasks. In this work, we propose a layered pathway evolution method to compress a deep convolutional neural network (DCNN) for classification of masses in digital breast tomosynthesis (DBT). The objective is to prune the number of tunable parameters while preserving the classification accuracy. In the first stage transfer learning, 19 632 augmented regions-of-interest (ROIs) from 2454 mass lesions on mammograms were used to train a pre-trained DCNN on ImageNet. In the second stage transfer learning, the DCNN was used as a feature extractor followed by feature selection and random forest classification. The pathway evolution was performed using genetic algorithm in an iterative approach with tournament selection driven by count-preserving crossover and mutation. The second stage was trained with 9120 DBT ROIs from 228 mass lesions using leave-one-case-out cross-validation. The DCNN was reduced by 87% in the number of neurons, 34% in the number of parameters, and 95% in the number of multiply-and-add operations required in the convolutional layers. The test AUC on 89 mass lesions from 94 independent DBT cases before and after pruning were 0.88 and 0.90, respectively, and the difference was not statistically significant (p  >  0.05). The proposed DCNN compression approach can reduce the number of required operations by 95% while maintaining the classification performance. The approach can be extended to other deep neural networks and imaging tasks where transfer learning is appropriate.

Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Helvie, Mark A.; Richter, Caleb; Cha, Kenny

2018-05-01

Deep learning models are highly parameterized, resulting in difficulty in inference and transfer learning for image recognition tasks. In this work, we propose a layered pathway evolution method to compress a deep convolutional neural network (DCNN) for classification of masses in digital breast tomosynthesis (DBT). The objective is to prune the number of tunable parameters while preserving the classification accuracy. In the first stage transfer learning, 19 632 augmented regions-of-interest (ROIs) from 2454 mass lesions on mammograms were used to train a pre-trained DCNN on ImageNet. In the second stage transfer learning, the DCNN was used as a feature extractor followed by feature selection and random forest classification. The pathway evolution was performed using genetic algorithm in an iterative approach with tournament selection driven by count-preserving crossover and mutation. The second stage was trained with 9120 DBT ROIs from 228 mass lesions using leave-one-case-out cross-validation. The DCNN was reduced by 87% in the number of neurons, 34% in the number of parameters, and 95% in the number of multiply-and-add operations required in the convolutional layers. The test AUC on 89 mass lesions from 94 independent DBT cases before and after pruning were 0.88 and 0.90, respectively, and the difference was not statistically significant (p  >  0.05). The proposed DCNN compression approach can reduce the number of required operations by 95% while maintaining the classification performance. The approach can be extended to other deep neural networks and imaging tasks where transfer learning is appropriate.

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

Bueno, G.; Ruiz, M.; Sanchez, S

Breast cancer continues to be an important health problem between women population. Early detection is the only way to improve breast cancer prognosis and significantly reduce women mortality. It is by using CAD systems that radiologist can improve their ability to detect, and classify lesions in mammograms. In this study the usefulness of using B-spline based on a gradient scheme and compared to wavelet and adaptative filtering has been investigated for calcification lesion detection and as part of CAD systems. The technique has been applied to different density tissues. A qualitative validation shows the success of the method.

Generalized procrustean image deformation for subtraction of mammograms

NASA Astrophysics Data System (ADS)

Good, Walter F.; Zheng, Bin; Chang, Yuan-Hsiang; Wang, Xiao Hui; Maitz, Glenn S.

1999-05-01

This project is a preliminary evaluation of two simple fully automatic nonlinear transformations which can map any mammographic image onto a reference image while guaranteeing registration of specific features. The first method automatically identifies skin lines, after which each pixel is given coordinates in the range [0,1] X [0,1], where the actual value of a coordinate is the fractional distance of the pixel between tissue boundaries in either the horizontal or vertical direction. This insures that skin lines are put in registration. The second method, which is the method of primary interest, automatically detects pectoral muscles, skin lines and nipple locations. For each image, a polar coordinate system is established with its origin at the intersection of the nipple axes line (NAL) and a line indicating the pectoral muscle. Points within a mammogram are identified by the angle of their position vector, relative to the NAL, and by their fractional distance between the origin and the skin line. This deforms mammograms in such a way that their pectoral lines, NALs and skin lines are all in registration. After images are deformed, their grayscales are adjusted by applying linear regression to pixel value pairs for corresponding tissue pixels. In a comparison of these methods to a previously reported 'translation/rotation' technique, evaluation of difference images clearly indicates that the polar coordinates method results in the most accurate registration of the transformations considered.

Lack of nationwide Danish guidelines on mammography before non-oncological breast surgery.

PubMed

Foged, Thomas; Sørensen, Jens Ahm; Søe, Katrine Lydolph; Bille, Camilla

2015-05-01

Non-oncological breast surgery like breast reduction and mastopexy are often performed in younger patients, i.e. in women who have not yet had mammography. Breast cancer is, however, a very frequent disease that is increasingly prevalent in women below 50 years of age. Occult breast cancer may not be recognised before breast surgery, which may result in several disadvantages for the women. Therefore, detecting a breast cancer before a woman undergoes non-oncological breast surgery is of paramount importance. All public plastic surgery and breast surgery departments and all private clinics or hospitals providing plastic surgery were asked two questions: 1) When do you recommend a mammography prior to non-oncological breast surgery? 2) How old must a mammogram be before it needs to be repeated? Answers were received from all plastic surgery and breast surgery departments, and all but three of the private clinics and hospitals. Overall, information was obtained from 95.5% of the respondents (n = 63). Currently, there are no Danish guidelines on mammography before non-oncological breast surgery. A national guideline could recommend a preoperative mammogram from the age of 40 years stipulating that the mammogram should have been made within the past 12 months; however, the final recommendation should be prepared by a multidisciplinary working group counting experts from plastic surgery, breast surgery, pathology and radiology. not relevant. not relevant.

How predictive is breast arterial calcification of cardiovascular disease and risk factors when found at screening mammography?

PubMed

Kataoka, Masako; Warren, Ruth; Luben, Robert; Camus, Joanna; Denton, Erika; Sala, Elvis; Day, Nicholas; Khaw, Kay-Tee

2006-07-01

The purpose of this study was to examine the relationship between breast arterial calcification (BAC), commonly found on mammography, and cardiovascular disease and its risk factors. The study population, nested within the European Prospective Investigation of Cancer-Norfolk (EPIC-Norfolk) cohort study, consisted of 1,590 women older than 55 years, not taking hormone replacement therapy, and with available screening mammograms. Mammograms were coded by three radiologists for presence or absence of BAC. History of coronary heart disease (CHD), stroke, and diabetes and risk factors for cardiovascular disease (including smoking status, body mass index [BMI], blood pressure, diabetes, and glycosylated hemoglobin [HbA1c]) were independently measured from health examinations in the EPIC study. The prevalence of BAC was 16.0%. Women with BAC were significantly older than those without it. BAC was associated with prevalent CHD, but not stroke. The odds ratio of having CHD was 2.54 (95% confidence interval, 1.03-6.30). The sensitivity and specificity were 32.4% and 85.5%, respectively. Except for smoking, which showed an inverse association, there was no consistent significant association of BAC with cardiovascular disease risk factors including BMI, diabetes, HbA1c, or lipids. BAC found on mammograms was associated with prevalent CHD after adjustment for age, but with low sensitivity. BAC may provide additional information toward identifying cardiovascular disease risk among otherwise healthy women.

Is unhealthy substance use associated with failure to receive cancer screening and flu vaccination? A retrospective cross-sectional study

PubMed Central

Kim, Theresa W; Alford, Daniel P; Cabral, Howard; Saitz, Richard; Samet, Jeffrey H

2011-01-01

Objective To compare cancer screening and flu vaccination among persons with and without unhealthy substance use. Design The authors analysed data from 4804 women eligible for mammograms, 4414 eligible for Papanicolou (Pap) smears, 7008 persons eligible for colorectal cancer (CRC) screening and 7017 persons eligible for flu vaccination. All patients were screened for unhealthy substance use. The main outcome was completion of cancer screening and flu vaccination. Results Among the 9995 patients eligible for one or more of the preventive services of interest, 10% screened positive for unhealthy substance use. Compared with women without unhealthy substance use, women with unhealthy substance use received mammograms less frequently (75.4% vs 83.8%; p<0.0001), but Pap smears no less frequently (77.9% vs 78.1%). Persons with unhealthy substance use received CRC screening no less frequently (61.7% vs 63.4%), yet received flu vaccination less frequently (44.7% vs 50.4%; p=0.01). In multivariable analyses, women with unhealthy substance use were less likely to receive mammograms (adjusted odds ratio 0.68; 95% CI 0.52 to 0.89), and persons with unhealthy substance use were less likely to receive flu vaccination (adjusted odds ratio 0.81; 95% CI 0.67 to 0.97). Conclusions Unhealthy substance use is a risk factor for not receiving all appropriate preventive health services. PMID:22021737

The Effect of Breast Implants on Mammogram Outcomes.

PubMed

Kam, Kelli; Lee, Esther; Pairawan, Seyed; Anderson, Kendra; Cora, Cherie; Bae, Won; Senthil, Maheswari; Solomon, Naveenraj; Lum, Sharon

2015-10-01

Breast cancer detection in women with implants has been questioned. We sought to evaluate the impact of breast implants on mammographic outcomes. A retrospective review of women undergoing mammography between March 1 and October 30, 2013 was performed. Demographic characteristics and mammogram results were compared between women with and without breast implants. Overall, 4.8 per cent of 1863 women identified during the study period had breast implants. Median age was 59 years (26-93). Women with implants were younger (53.9 vs 59.2 years, P < 0.0001), had lower body mass index (25.4 vs 28.9, P < 0.0001), and were more likely to have dense breast tissue (72.1% vs 56.4%, P = 0.004) than those without. There were no statistically significant differences with regards to Breast Imaging Recording and Data System 0 score (13.3% with implants vs 21.4% without), call back exam (18.9% with vs 24.1% without), time to resolution of abnormal imaging (58.6 days with vs 43.3 without), or cancer detection rate (0% with implants vs 1.0% without). Because implants did not significantly affect mammogram results, women with implants should be reassured that mammography remains useful in detecting cancer. However, future research is required to determine whether lower call back rates and longer time to resolution of imaging findings contribute to delays in diagnosis in patients with implants.

Breast lift

MedlinePlus

... Planning to have more children Talk with a plastic surgeon if you are considering cosmetic breast surgery. ... before surgery: You may need a mammogram . Your plastic surgeon will do a routine breast exam. You ...

Dense Breasts

MedlinePlus

... fatty tissue. On a mammogram, fatty tissue appears dark (radio-lucent) and the glandular and connective tissues ... white on mammography) and non-dense fatty tissue (dark on mammography) using a visual scale and assign ...

Breast Reference Set Application: Chris Li-FHCRC (2015) — EDRN Public Portal

Cancer.gov

We propose to evaluate nine candidate biomarkers for ER+ breast cancer in samples from the EDRN Breast Cancer Reference Set. These biomarkers have been preliminarily validated in preclinical samples. The intended clinical applications of these markers are to: 1. Inform timing of a subsequent mammogram in women with a negative screening mammogram; 2. Inform continuation of mammographic screening among women 75-79 years; 3. Prioritize women who should be screened with mammography in areas with limited resources. Testing the reference samples would further expedite addressing these intended clinical applications by providing further validation data to support requests for samples from other sources for further Phase 3 evaluation (e.g., WHI, PLCO, and samples collected at the time of mammographic screening from the University of Toronto and UCSF).

Breast biopsy -- stereotactic

MedlinePlus

... sent to a pathologist to be examined. Normal Results A normal result means there is no sign ... follow-up mammogram or other tests. What Abnormal Results Mean If the biopsy shows benign breast tissue ...

Accuracy of Mammograms

MedlinePlus

... Change Become An Advocate In Science Corporate Partners Shop Komen Merchandise Connect With Us Personal Stories Personal ... Conference Support Awards & Recognition Grants Central Memorials & Dedications Shop Komen Merchandise Corporate Partners Corporate Partners Corporate Partners ...

Breast augmentation surgery

MedlinePlus

... the shape of your breasts. Talk with a plastic surgeon if you are considering breast augmentation. Discuss ... mammograms or breast x-rays before surgery. The plastic surgeon will do a routine breast exam. Several ...

Enhanced visualization of abnormalities in digital-mammographic images

NASA Astrophysics Data System (ADS)

Young, Susan S.; Moore, William E.

2002-05-01

This paper describes two new presentation methods that are intended to improve the ability of radiologists to visualize abnormalities in mammograms by enhancing the appearance of the breast parenchyma pattern relative to the fatty-tissue surroundings. The first method, referred to as mountain- view, is obtained via multiscale edge decomposition through filter banks. The image is displayed in a multiscale edge domain that causes the image to have a topographic-like appearance. The second method displays the image in the intensity domain and is referred to as contrast-enhancement presentation. The input image is first passed through a decomposition filter bank to produce a filtered output (Id). The image at the lowest resolution is processed using a LUT (look-up table) to produce a tone scaled image (I'). The LUT is designed to optimally map the code value range corresponding to the parenchyma pattern in the mammographic image into the dynamic range of the output medium. The algorithm uses a contrast weight control mechanism to produce the desired weight factors to enhance the edge information corresponding to the parenchyma pattern. The output image is formed using a reconstruction filter bank through I' and enhanced Id.

Neighborhood Structural Similarity Mapping for the Classification of Masses in Mammograms.

PubMed

Rabidas, Rinku; Midya, Abhishek; Chakraborty, Jayasree

2018-05-01

In this paper, two novel feature extraction methods, using neighborhood structural similarity (NSS), are proposed for the characterization of mammographic masses as benign or malignant. Since gray-level distribution of pixels is different in benign and malignant masses, more regular and homogeneous patterns are visible in benign masses compared to malignant masses; the proposed method exploits the similarity between neighboring regions of masses by designing two new features, namely, NSS-I and NSS-II, which capture global similarity at different scales. Complementary to these global features, uniform local binary patterns are computed to enhance the classification efficiency by combining with the proposed features. The performance of the features are evaluated using the images from the mini-mammographic image analysis society (mini-MIAS) and digital database for screening mammography (DDSM) databases, where a tenfold cross-validation technique is incorporated with Fisher linear discriminant analysis, after selecting the optimal set of features using stepwise logistic regression method. The best area under the receiver operating characteristic curve of 0.98 with an accuracy of is achieved with the mini-MIAS database, while the same for the DDSM database is 0.93 with accuracy .

Cognizance and utilization about breast cancer screening among the health professional female students and staffs of University Kuala Lumpur, Royal College of Medicine Perak, Malaysia

PubMed Central

Haque, A. T. M. Emdadul; Mohd Hisham, Muhammad Afif Bin; Ahmad Adzman, Noor Azwa Laili Binti; Azudin, Nur Atiqah Binti; Shafri, Nursakinah Binti; Haque, Mainul

2016-01-01

Background: Breast cancer (BC) is a major life-threatening problem and a global concern including Malaysia. BC is an equal threat for both developing and developed countries. The aim of this study was to determine the relationship between sociodemographic factors with knowledge, attitude, and perception on BC screening among the females of University Kuala Lumpur, Royal College of Medicine Perak (UniKL RCMP). Materials and Methods: This cross-sectional study was conducted from 2015 to 2016. The populations included were the students and staff of UniKL RCMP. The simple sampling method was used and a set of questionnaire was prepared and distributed to the participants who were willing to participate. The data were analyzed by using the SPSS version 17. Results: Of the 220 only 203 questionnaires were returned. Nearly 87.7% of participants indicated genetic factors as the cause of BC, followed by exposure to carcinogenic and X-ray. Excessive smoking (54.2%) and sedentary lifestyle (52.2%) were the risk factors of the BC. 100% of participants thought that breast self-examination (BSE) is important to detect a breast lump and most of them (76.8%) knew what a mammogram is but only 2.0% went for a mammogram. Chemotherapy (71.9%) and surgery (71.9%) were treatments options according to study participants. Nearly 91.1% agreed that regular mammogram could help to detect BC at an early stage. Nearly 88.2% thought BC is not easily curable. Finally, for the attitude on BC screening, most of them knew how to perform BSE (69.0%) with the frequency of 36.0% doing it once a year. Conclusions: The majority of the participants found the good knowledge on BC and on how to perform BSE. Although most of them knew what a mammogram is, only a few have gone for it since perhaps it is recommended for those who are above 50-year-old. Therefore, researchers believe and trust that there is an urgent need of state-funded multicenter study to prevent and early diagnosis of BC in Malaysia. PMID:28144097

Cognizance and utilization about breast cancer screening among the health professional female students and staffs of University Kuala Lumpur, Royal College of Medicine Perak, Malaysia.

PubMed

Haque, A T M Emdadul; Mohd Hisham, Muhammad Afif Bin; Ahmad Adzman, Noor Azwa Laili Binti; Azudin, Nur Atiqah Binti; Shafri, Nursakinah Binti; Haque, Mainul

2016-01-01

Breast cancer (BC) is a major life-threatening problem and a global concern including Malaysia. BC is an equal threat for both developing and developed countries. The aim of this study was to determine the relationship between sociodemographic factors with knowledge, attitude, and perception on BC screening among the females of University Kuala Lumpur, Royal College of Medicine Perak (UniKL RCMP). This cross-sectional study was conducted from 2015 to 2016. The populations included were the students and staff of UniKL RCMP. The simple sampling method was used and a set of questionnaire was prepared and distributed to the participants who were willing to participate. The data were analyzed by using the SPSS version 17. Of the 220 only 203 questionnaires were returned. Nearly 87.7% of participants indicated genetic factors as the cause of BC, followed by exposure to carcinogenic and X-ray. Excessive smoking (54.2%) and sedentary lifestyle (52.2%) were the risk factors of the BC. 100% of participants thought that breast self-examination (BSE) is important to detect a breast lump and most of them (76.8%) knew what a mammogram is but only 2.0% went for a mammogram. Chemotherapy (71.9%) and surgery (71.9%) were treatments options according to study participants. Nearly 91.1% agreed that regular mammogram could help to detect BC at an early stage. Nearly 88.2% thought BC is not easily curable. Finally, for the attitude on BC screening, most of them knew how to perform BSE (69.0%) with the frequency of 36.0% doing it once a year. The majority of the participants found the good knowledge on BC and on how to perform BSE. Although most of them knew what a mammogram is, only a few have gone for it since perhaps it is recommended for those who are above 50-year-old. Therefore, researchers believe and trust that there is an urgent need of state-funded multicenter study to prevent and early diagnosis of BC in Malaysia.

Disparities in abnormal mammogram follow-up time for Asian women compared with non-Hispanic white women and between Asian ethnic groups.

PubMed

Nguyen, Kim H; Pasick, Rena J; Stewart, Susan L; Kerlikowske, Karla; Karliner, Leah S

2017-09-15

Delays in abnormal mammogram follow-up contribute to poor outcomes. In the current study, the authors examined differences in abnormal screening mammogram follow-up between non-Hispanic white (NHW) and Asian women. The authors used a prospective cohort of NHW and Asian women with a Breast Imaging, Reporting and Data System (BI-RADS) abnormal result of category 0 or 3-plus in the San Francisco Mammography Registry between 2000 and 2010. Kaplan-Meier estimation for the median number of days to follow-up with a diagnostic radiologic test was performed, and the authors compared the percentage of women with follow-up at 30 days, 60 days, and 90 days and no follow-up at 1 year for Asian women overall (and Asian ethnic groups) and NHW women. In addition, the authors assessed the relationship between race/ethnicity and time to follow-up with adjusted Cox proportional hazards models. Among Asian women, Vietnamese and Filipina women had the longest, and Japanese women the shortest, median follow-up (32 days, 28 days, and 19 days, respectively) compared with NHW women (15 days). The percentage of women receiving follow-up at 30 days was lower for Asians versus NHWs (57% vs 77%; P<.0001), and these disparities persisted at 60 days and 90 days for all Asian ethnic groups except Japanese. Asian women had a reduced hazard of follow-up compared with NHW women (adjusted hazard ratio, 0.70; 95% confidence interval, 0.69-0.72). Asian women also had a higher rate of receiving no follow-up compared with NHW women (15% vs 10%; P<.001); among Asian ethnic groups, Filipinas were found to have the highest percentage of women with no follow-up (18.1%). Asian women, particularly Filipina and Vietnamese women, were less likely than NHW women to receive timely follow-up after an abnormal screening mammogram. Research should disaggregate Asian ethnicity to better understand and address barriers to effective cancer prevention. Cancer 2017;123:3468-75. © 2017 American Cancer Society. © 2017 American Cancer Society.

Influence of Computer-Aided Detection on Performance of Screening Mammography

PubMed Central

Fenton, Joshua J.; Taplin, Stephen H.; Carney, Patricia A.; Abraham, Linn; Sickles, Edward A.; D'Orsi, Carl; Berns, Eric A.; Cutter, Gary; Hendrick, R. Edward; Barlow, William E.; Elmore, Joann G.

2011-01-01

Background Computer-aided detection identifies suspicious findings on mammograms to assist radiologists. Since the Food and Drug Administration approved the technology in 1998, it has been disseminated into practice, but its effect on the accuracy of interpretation is unclear. Methods We determined the association between the use of computer-aided detection at mammography facilities and the performance of screening mammography from 1998 through 2002 at 43 facilities in three states. We had complete data for 222,135 women (a total of 429,345 mammograms), including 2351 women who received a diagnosis of breast cancer within 1 year after screening. We calculated the specificity, sensitivity, and positive predictive value of screening mammography with and without computer-aided detection, as well as the rates of biopsy and breast-cancer detection and the overall accuracy, measured as the area under the receiver-operating-characteristic (ROC) curve. Results Seven facilities (16%) implemented computer-aided detection during the study period. Diagnostic specificity decreased from 90.2% before implementation to 87.2% after implementation (P<0.001), the positive predictive value decreased from 4.1% to 3.2% (P = 0.01), and the rate of biopsy increased by 19.7% (P<0.001). The increase in sensitivity from 80.4% before implementation of computer-aided detection to 84.0% after implementation was not significant (P = 0.32). The change in the cancer-detection rate (including invasive breast cancers and ductal carcinomas in situ) was not significant (4.15 cases per 1000 screening mammograms before implementation and 4.20 cases after implementation, P = 0.90). Analyses of data from all 43 facilities showed that the use of computer-aided detection was associated with significantly lower overall accuracy than was nonuse (area under the ROC curve, 0.871 vs. 0.919; P = 0.005). Conclusions The use of computer-aided detection is associated with reduced accuracy of interpretation of screening mammograms. The increased rate of biopsy with the use of computer-aided detection is not clearly associated with improved detection of invasive breast cancer. PMID:17409321

Breast cancer screening practices of African migrant women in Australia: a descriptive cross-sectional study.

PubMed

Ogunsiji, Olayide Oluyemisi; Kwok, Cannas; Fan, Lee Chun

2017-04-17

Breast cancer is the most diagnosed cancer among women and a leading cause of mortality and morbidity, globally. Breast cancer mortality can be improved through routine cancer screening, yet migrant populations have lower participation rates. While African migrants are among the fastest growing migrant population in Australia, their breast cancer screening behaviour is under-studied. The aims of this study were to report breast cancer screening status of African migrant women and factors associated with their breast cancer screening behaviour in Australia. A descriptive, cross-sectional approach was utilised for this study. Two hundred and sixty four African migrant women aged 18-69 years and recruited from a number of organisations responded to a self-reported African version of the Breast Cancer Screening Beliefs Questionnaire (BCSBQ). Main research variables are breast cancer screening practices and demographic characteristics and total scores on each of the BCSBQ subscales. Multivariable logistic regression analyses were performed to investigate the impact of the demographic variables on the likelihood of women in the target age range 50-74 years having screening practices as recommended. While most of the participants heard of breast awareness (76.1%) and mammogram (85.2%), only 11.4% practised monthly breast awareness, whereas 65.9% had ever had a mammogram as frequently as recommended. Age and employment were determining factors for participating in mammogram. Significant different scores were found in the "Practical barriers" between women at the target age who had and had not performed breast awareness (80.4 versus 77.5, p-value = 0.002) and mammogram (77.1 versus 70.3, p-value = 0.009) regularly as recommended. Moreover, attitudes towards general health check-ups subscale scores were significantly higher in women who had performed clinical breast examination as frequently as recommended than those who had not. The research reveals that practical barriers and attitudes towards general health check-ups are important factors to take into account in determining African migrant women's participation in breast cancer screening. Progress in reducing breast cancer deaths through early detection needs to focus on attitudinal change among African migrants.