The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Joann G Elmore - One of the best experts on this subject based on the ideXlab platform.
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mammographic screening for breast cancer
The New England Journal of Medicine, 2003Co-Authors: Suzanne W Fletcher, Joann G ElmoreAbstract:A 44-year-old woman who is a new patient has no known current health problems and no family history of breast or ovarian cancer. Eighteen months ago, she had a normal screening Mammogram. She recently read that Mammograms may not help to prevent death from breast cancer and that “the patient should decide.” But she does not think she knows enough. She worries that there is a breast-cancer epidemic. What should her physician advise?
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effect of false positive Mammograms on interval breast cancer screening in a health maintenance organization
Annals of Internal Medicine, 1999Co-Authors: Marcia L Burman, Stephen H Taplin, Douglas F Herta, Joann G ElmoreAbstract:Among women with no history of breast cancer, having a false-positive Mammogram did not adversely affect screening behavior in the next recommended interval. Women with false-positive Mammograms, e...
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ten year risk of false positive screening Mammograms and clinical breast examinations
The New England Journal of Medicine, 1998Co-Authors: Joann G Elmore, Mary B Barton, Victoria M Moceri, Sarah Polk, Philip J Arena, Suzanne W FletcherAbstract:Background The cumulative risk of a false positive result of a breast-cancer screening test is unknown. Methods We performed a 10-year retrospective cohort study of breast-cancer screening and diagnostic evaluations among 2400 women who were 40 to 69 years old at study entry. Mammograms or clinical breast examinations that were interpreted as indeterminate, aroused a suspicion of cancer, or prompted recommendations for additional workup in women in whom breast cancer was not diagnosed within the next year were considered to be false positive tests. Results A total of 9762 screening Mammograms and 10,905 screening clinical breast examinations were performed, for a median of 4 Mammograms and 5 clinical breast examinations per woman over the 10-year period. Of the women who were screened, 23.8 percent had at least one false positive Mammogram, 13.4 percent had at least one false positive breast examination, and 31.7 percent had at least one false positive result for either test. The estimated cumulative risk...
Keith Humphreys - One of the best experts on this subject based on the ideXlab platform.
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association of microcalcification clusters with short term invasive breast cancer risk and breast cancer risk factors
Scientific Reports, 2019Co-Authors: Kamila Czene, Keith Humphreys, Per HallAbstract:Using for-presentation and for-processing digital Mammograms, the presence of microcalcifications has been shown to be associated with short-term risk of breast cancer. In a previous article we developed an algorithm for microcalcification cluster detection from for-presentation digital Mammograms. Here, we focus on digitised Mammograms and use a three-step algorithm. In total, 253 incident invasive breast cancer cases (with a negative Mammogram between three months and two years before diagnosis, from which we measured microcalcifications) and 728 controls (also with prior Mammograms) were included in a short-term risk study. After adjusting for potential confounding variables, we found evidence of an association between the number of microcalcification clusters and short-term (within 3–24 months) invasive breast cancer risk (per cluster OR = 1.30, 95% CI = (1.11, 1.53)). Using the 728 postmenopausal healthy controls, we also examined association of microcalcification clusters with reproductive factors and other established breast cancer risk factors. Age was positively associated with the presence of microcalcification clusters (p = 4 × 10−04). Of ten other risk factors that we studied, life time breastfeeding duration had the strongest evidence of association with the presence of microcalcifications (positively associated, unadjusted p = 0.001). Developing algorithms, such as ours, which can be applied on both digitised and digital Mammograms (in particular for presentation images), is important because large epidemiological studies, for deriving markers of (clinical) risk prediction of breast cancer and prognosis, can be based on images from these different formats.
Stephen H Taplin - One of the best experts on this subject based on the ideXlab platform.
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changes in breast density associated with initiation discontinuation and continuing use of hormone replacement therapy
JAMA, 2001Co-Authors: Carolyn M Rutter, Margaret T Mandelson, Mary B Laya, Deborah Seger, Stephen H TaplinAbstract:ContextInitiation of hormone replacement therapy (HRT) has been shown to increase breast density. Evidence exists that increased breast density decreases mammographic sensitivity. The effects on breast density of discontinuing and continuing HRT have not been studied systematically.ObjectiveTo examine the effects of initiation, discontinuation, and continued use of HRT on breast density in postmenopausal women.Design, Setting, and ParticipantsObservational cohort study of 5212 naturally postmenopausal women aged 40 to 96 years and enrolled in a large health maintenance organization in western Washington State who had 2 screening Mammograms between 1996 and 1998.Main Outcome MeasuresBreast density, assessed using the clinical radiologists' BI-RADS 4-point scale, compared among women who did not use HRT before either Mammogram (nonusers); who used HRT before the first but not before the second Mammogram (discontinuers); who used HRT before the second but not before the first Mammogram (initiators); and who used HRT prior to both Mammograms (continuing users).ResultsRelative to nonusers, women who initiated HRT were more likely to show increases in breast density (relative risk [RR], 2.57; 95% confidence interval [CI], 2.12-3.08), while women who discontinued HRT use were more likely to show decreases in density (RR, 1.81; 95% CI, 1.06-2.98) and women who continued to use HRT were more likely to show both increases in density (RR, 1.33; 95% CI, 1.13-1.55) and sustained high density (RR, 1.45; 95% CI, 1.33-1.58).ConclusionsThese results indicate that breast density changes associated with HRT are dynamic, increasing with initiation, and decreasing with discontinuation.
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breast density as a predictor of mammographic detection comparison of interval and screen detected cancers
Journal of the National Cancer Institute, 2000Co-Authors: Margaret T Mandelson, Stephen H Taplin, Nina Oestreicher, Peggy L Porter, Donna White, Charles Finder, Emily WhiteAbstract:Background Screening mammography is the best method to reduce mortality from breast cancer, yet some breast cancers cannot be detected by mammography. Cancers diagnosed after a negative Mammogram are known as interval cancers. This study investigated whether mammographic breast density is related to the risk of interval cancer. Methods Subjects were selected from women participating in mammographic screening from 1988 through 1993 in a large health maintenance organization based in Seattle, WA. Women were eligible for the study if they had been diagnosed with a first primary invasive breast cancer within 24 months of a screening Mammogram and before a subsequent one. Interval cancer case subjects (n = 149) were women whose breast cancer occurred after a negative or benign mammographic assessment. Screen-detected control subjects (n = 388) were diagnosed after a positive screening Mammogram. One radiologist, who was blinded to cancer status, assessed breast density by use of the American College of Radiology Breast Imaging Reporting and Data System. Results Mammographic sensitivity (i.e., the ability of mammography to detect a cancer) was 80% among women with predominantly fatty breasts but just 30% in women with extremely dense breasts. The odds ratio (OR) for interval cancer among women with extremely dense breasts was 6.14 (95% confidence interval [CI] = 1.95-19.4), compared with women with extremely fatty breasts, after adjustment for age at index Mammogram, menopausal status, use of hormone replacement therapy, and body mass index. When only those interval cancer cases confirmed by retrospective review of index Mammograms were considered, the OR increased to 9.47 (95% CI = 2.78-32.3). Conclusion Mammographic breast density appears to be a major risk factor for interval cancer.
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effect of false positive Mammograms on interval breast cancer screening in a health maintenance organization
Annals of Internal Medicine, 1999Co-Authors: Marcia L Burman, Stephen H Taplin, Douglas F Herta, Joann G ElmoreAbstract:Among women with no history of breast cancer, having a false-positive Mammogram did not adversely affect screening behavior in the next recommended interval. Women with false-positive Mammograms, e...
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a test of an expanded theory of reasoned action to predict mammography participation
Social Science & Medicine, 1991Co-Authors: Daniel E Montano, Stephen H TaplinAbstract:This paper presents the results of a prospective study testing an expanded theory of reasoned action (TRA) to predict mammography participation. A questionnaire was developed to measure each of the expanded TRA model components. A sample was identified of 946 women age 40 and above who were invited to obtain a Mammogram at the Group Health Cooperative of Puget Sound Breast Cancer Screening Program (BCSP). The sample was stratified by risk category as determined by the screening program. The study questionnaire was administered to all woman in the sample within 2 weeks after they were sent the invitation to obtain a Mammogram. Mammography participation was obtained from the BCSP data base 6 months after the invitation. Regression analyses attitude, affect, subjective norm, and facilitating conditions to all be significantly associated with participation. The expanded TRA model explained 39% of the variance in women's intentions and 20% of the variance in participation behavior. A stepwise hierarchical regression found that no other psychosocial measures were able to improve the model predictions of behavior. An interaction between habit and intention was found such that women with larger numbers of previous Mammograms were less likely to carry out their intentions than women with fewer previous Mammograms. Contrary to expectations, some demographic characteristics did significantly improve prediction. The need for further work investigating the roles of fear and experience is discussed.
Nicholas Petrick - One of the best experts on this subject based on the ideXlab platform.
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breast cancer detection evaluation of a mass detection algorithm for computer aided diagnosis experience in 263 patients
Radiology, 2002Co-Authors: Nicholas Petrick, Heang Ping Chan, Berkman Sahiner, Mark A Helvie, Sophie Paquerault, Lubomir M HadjiiskiAbstract:PURPOSE: To evaluate the performance of a computer-aided diagnosis (CAD) mass-detection algorithm in marking preoperative masses. MATERIALS AND METHODS: Digitized Mammograms were processed with an adaptive enhancement filter followed by a local border refinement stage. Features were then extracted from each detected structure and used to identify potential masses. The performance of the algorithm was evaluated in independent cases obtained from 263 patients from two institutions. Each case contained one or more pathologically proved breast masses. Contralateral Mammograms obtained in the same patients that did not contain a visible lesion were used to estimate the CAD marker rate for the algorithm. The tradeoff between detection sensitivity and the number of CAD marks was analyzed in this study. RESULTS: Malignant masses were detected with the computer in 87% (135 of 156), 83% (130 of 156), and 77% (120 of 156) of the malignant cases at CAD marker rates of 1.5, 1.0, and 0.5 marks per Mammogram, respective...
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automated registration of breast lesions in temporal pairs of Mammograms for interval change analysis local affine transformation for improved localization
Medical Physics, 2001Co-Authors: Lubomir M Hadjiiski, Heang Ping Chan, Berkman Sahiner, Nicholas Petrick, Mark A HelvieAbstract:Analysis of interval change is important for mammographic interpretation. The aim of this study is to evaluate the use of an automated registration technique for computer-aided interval change analysis in mammography. Previously we developed a regional registration technique for identifying masses on temporal pairs of Mammograms. In the current study, we improved lesion registration by including a local alignment step. Initially, the lesion position on the prior Mammogram was estimated based on the breast geometry. An initial fan-shaped search region was then defined on the prior Mammogram. In the second stage, the location of the fan-shaped region on the prior Mammogram was refined by warping, based on an affine transformation and simplex optimization in a local region. In the third stage, a search for the best match between the lesion template from the current Mammogram and a structure on the prior Mammogram was carried out within the search region. This technique was evaluated on 124 temporal pairs of Mammograms containing biopsy-proven masses. Eighty-seven percent of the estimated lesion locations resulted in an area overlap of at least 50% with the true lesion locations and an average distance of 2.4±2.1 mm between their centroids. The average distance between the estimated and the true centroid of the lesions on the prior Mammogram over all 124 temporal pairs was 4.2±5.7 mm. The registration accuracy was improved in comparison with our previous study that used a data set of 74 temporal pairs of Mammograms. This improvement in accuracy resulted from the improved geometry estimation and the local affine transformation.
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an adaptive density weighted contrast enhancement filter for mammographic breast mass detection
IEEE Transactions on Medical Imaging, 1996Co-Authors: Nicholas Petrick, Heang Ping Chan, Berkman Sahiner, Datong WeiAbstract:Presents a novel approach for segmentation of suspicious mass regions in digitized Mammograms using a new adaptive density-weighted contrast enhancement (DWCE) filter in conjunction with Laplacian-Gaussian (LG) edge detection. The DWCE enhances structures within the digitized Mammogram so that a simple edge detection algorithm can be used to define the boundaries of the objects. Once the object boundaries are known, morphological features are extracted and used by a classification algorithm to differentiate regions within the image. This paper introduces the DWCE algorithm and presents results of a preliminary study based on 25 digitized Mammograms with biopsy proven masses. It also compares morphological feature classification based on sequential thresholding, linear discriminant analysis, and neural network classifiers for reduction of false-positive detections.
Lubomir M Hadjiiski - One of the best experts on this subject based on the ideXlab platform.
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breast cancer detection evaluation of a mass detection algorithm for computer aided diagnosis experience in 263 patients
Radiology, 2002Co-Authors: Nicholas Petrick, Heang Ping Chan, Berkman Sahiner, Mark A Helvie, Sophie Paquerault, Lubomir M HadjiiskiAbstract:PURPOSE: To evaluate the performance of a computer-aided diagnosis (CAD) mass-detection algorithm in marking preoperative masses. MATERIALS AND METHODS: Digitized Mammograms were processed with an adaptive enhancement filter followed by a local border refinement stage. Features were then extracted from each detected structure and used to identify potential masses. The performance of the algorithm was evaluated in independent cases obtained from 263 patients from two institutions. Each case contained one or more pathologically proved breast masses. Contralateral Mammograms obtained in the same patients that did not contain a visible lesion were used to estimate the CAD marker rate for the algorithm. The tradeoff between detection sensitivity and the number of CAD marks was analyzed in this study. RESULTS: Malignant masses were detected with the computer in 87% (135 of 156), 83% (130 of 156), and 77% (120 of 156) of the malignant cases at CAD marker rates of 1.5, 1.0, and 0.5 marks per Mammogram, respective...
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automated registration of breast lesions in temporal pairs of Mammograms for interval change analysis local affine transformation for improved localization
Medical Physics, 2001Co-Authors: Lubomir M Hadjiiski, Heang Ping Chan, Berkman Sahiner, Nicholas Petrick, Mark A HelvieAbstract:Analysis of interval change is important for mammographic interpretation. The aim of this study is to evaluate the use of an automated registration technique for computer-aided interval change analysis in mammography. Previously we developed a regional registration technique for identifying masses on temporal pairs of Mammograms. In the current study, we improved lesion registration by including a local alignment step. Initially, the lesion position on the prior Mammogram was estimated based on the breast geometry. An initial fan-shaped search region was then defined on the prior Mammogram. In the second stage, the location of the fan-shaped region on the prior Mammogram was refined by warping, based on an affine transformation and simplex optimization in a local region. In the third stage, a search for the best match between the lesion template from the current Mammogram and a structure on the prior Mammogram was carried out within the search region. This technique was evaluated on 124 temporal pairs of Mammograms containing biopsy-proven masses. Eighty-seven percent of the estimated lesion locations resulted in an area overlap of at least 50% with the true lesion locations and an average distance of 2.4±2.1 mm between their centroids. The average distance between the estimated and the true centroid of the lesions on the prior Mammogram over all 124 temporal pairs was 4.2±5.7 mm. The registration accuracy was improved in comparison with our previous study that used a data set of 74 temporal pairs of Mammograms. This improvement in accuracy resulted from the improved geometry estimation and the local affine transformation.
