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Mammography

About: Mammography is a research topic. Over the lifetime, 20643 publications have been published within this topic receiving 513679 citations.


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Journal ArticleDOI
TL;DR: Initial breast CT images do appear promising and it is likely that breast CT will play some role in breast cancer imaging.
Abstract: Despite the success of screening mammography contributing to the reduction of cancer mortality, a number of other imaging techniques are being studied for breast cancer screening. In our laboratory, a dedicated breast computed tomography (CT) system has been developed and is currently undergoing patient testing. The breast CT system is capable of scanning the breast with the woman lying prone on a tabletop, with the breast in the pendant position. A 360° scan currently requires 16.6 s, and a second scanner with a 9-second scan time is nearly operational. Extensive effort was placed on computing the radiation dose to the breast under CT geometry, and the scan parameters are selected to utilize the same radiation dose levels as two-view mammography. A total of 55 women have been scanned, ten healthy volunteers in a Phase I trial, and 45 women with a high likelihood of having breast cancer in a Phase II trial. The breast CT process leads to the production of approximately three hundred 512 × 512 images for each breast. Subjective evaluation of the breast CT images reveals excellent anatomical detail, good depiction of microcalcifications, and exquisite visualization of the soft tissue components of the tumor when contrasted against adipose tissues. The use of iodine contrast injection dramatically enhances the visualization of tumors. While a thorough scientific investigation based upon observer performance studies is in progress, initial breast CT images do appear promising and it is likely that breast CT will play some role in breast cancer imaging.

161 citations

Journal ArticleDOI
TL;DR: The results suggest that the imaging findings of triple-negative breast cancer might be useful in diagnosing triple- negative breast cancer.
Abstract: This study was designed to investigate the mammography and ultrasound findings of triple-negative breast cancer and to compare the results with characteristics of ER-positive/PR-negative/HER2-negative breast cancer and ER-negative/PR-negative/HER2-positive breast cancer. From January 2007 to October 2008, mammography and ultrasound findings of 245 patients with pathologically confirmed triple-negative (n = 87), ER-positive/PR-negative/HER2-negative (n = 93) or ER-negative/PR-negative/HER2-positive breast cancers (n = 65) were retrospectively reviewed. We also reviewed pathological reports for information on the histological type, histological grade and the status of the biological markers. Triple-negative breast cancers showed a high histological grade. On mammography, triple-negative breast cancers usually presented with a mass (43/87, 49%) or with focal asymmetry (19/87, 22%), and were less associated with calcifications. On ultrasound, the cancers were less frequently seen as non-mass lesions (12/87, 14%), more likely to have circumscribed margins (43/75, 57%), were markedly hypoechoic (36/75, 57%) and less likely to show posterior shadowing (4/75, 5%). Among the three types of breast cancers, ER-negative/PR-negative/HER2-positive breast cancers most commonly had associated calcifications (52/65, 79%) on mammography and were depicted as non-mass lesions (21/65, 32%) on ultrasound. Our results suggest that the imaging findings might be useful in diagnosing triple-negative breast cancer.

161 citations

Journal ArticleDOI
TL;DR: A prospective, population-based study is required to determine the demographic pattern of breast cancer and the factors delaying presentation, which will have important implications in future programmes to promote the early detection of breast Cancer and in understanding geographical as well as racial variations in the incidence of Breast cancer.

161 citations

Journal ArticleDOI
TL;DR: Of 1215 women with elevated breast cancer risk who could, according to protocol guidelines, undergo breast MR imaging, only 57.9% agreed to participate, and reasons for nonparticipation were determined.
Abstract: Our study results suggest that there may be a large group of women at elevated risk of breast cancer for whom MR imaging would not be acceptable; for these women, supplemental screening with US combined with mammography could be considered.

161 citations

Journal ArticleDOI
TL;DR: A new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means 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 is proposed.
Abstract: Purpose: The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screen-film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., “FOR PROCESSING”) and vendor postprocessed (i.e., “FOR PRESENTATION”), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis. Methods: This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely fibroglandular, which 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. Results: Strong association between algorithm-estimated and radiologist-provided breast PD% was detected for both raw (r = 0.82, p 0.1) due to either presentation of the image (raw vs processed) or method of PD% assessment (radiologist vs algorithm). Conclusions: 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.

161 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023970
20221,954
2021847
2020852
2019865
2018852