Edward Hendrick

Bio: Edward Hendrick is an academic researcher from Northwestern University. The author has contributed to research in topics: Breast cancer screening & Breast cancer. The author has an hindex of 4, co-authored 5 publications receiving 3127 citations.

Diagnostic Performance of Digital versus Film Mammography for Breast-Cancer Screening

Abstract: background Film mammography has limited sensitivity for the detection of breast cancer in women with radiographically dense breasts. We assessed whether the use of digital mammography would avoid some of these limitations. methods A total of 49,528 asymptomatic women presenting for screening mammography at 33 sites in the United States and Canada underwent both digital and film mammography. All relevant information was available for 42,760 of these women (86.3 percent). Mammograms were interpreted independently by two radiologists. Breast-cancer status was ascertained on the basis of a breast biopsy done within 15 months after study entry or a follow-up mammogram obtained at least 10 months after study entry. Receiver-operating-characteristic (ROC) analysis was used to evaluate the results. results In the entire population, the diagnostic accuracy of digital and film mammography was similar (difference between methods in the area under the ROC curve, 0.03; 95 percent confidence interval, i0.02 to 0.08; P=0.18). However, the accuracy of digital mammography was significantly higher than that of film mammography among women under the age of 50 years (difference in the area under the curve, 0.15; 95 percent confidence interval, 0.05 to 0.25; P=0.002), women with heterogeneously dense or extremely dense breasts on mammography (difference, 0.11; 95 percent confidence interval, 0.04 to 0.18; P=0.003), and premenopausal or perimenopausal women (difference, 0.15; 95 percent confidence interval, 0.05 to 0.24; P=0.002). conclusions The overall diagnostic accuracy of digital and film mammography as a means of screening for breast cancer is similar, but digital mammography is more accurate in women under the age of 50 years, women with radiographically dense breasts, and premenopausal or perimenopausal women. (clinicaltrials.gov number, NCT00008346.)

Diagnostic Performance of Digital versus Film Mammography for Breast-Cancer Screening

Abstract: Previous trials, limited in many respects, have not found digital mammography to be significantly more accurate than the standard film method. A total of 42,760 asymptomatic women seen at 33 sites in the United States and Canada requested screening mammography and underwent both film and digital examinations. Two radiologists independently interpreted the film and digital mammograms. All participants either had breast biopsy within 15 months after evaluation or had a follow-up mammogram 10 months or longer after entry to the study. The results were assessed by receiver operating characteristic analysis. Both digital and film mammograms were positive in 0.5% of women. Another 2.2% had only a positive digital study, whereas 1.9% had only a positive film study. In the remaining women, approximately 95% of the total, both imaging studies were negative. Of 335 breast cancers diagnosed within 455 days after entry to the study, approximately three fourths were found within a year after evaluation. There were no substantial differences between the digital and film findings with respect to histology or stage of disease. The area under the curve was similar for the 2 studies and was not influenced by race or the risk of breast cancer. Digital mammography did, however, perform significantly better than the film method in women less than 50 years of age, in those having heterogeneously dense or very dense breasts, and premenopausal or perimenopausal women. The digital and film methods performed equally well in women age 50 years and older, those with fatty breasts or scattered fibroglandular densities, and those who were postmenopausal.

American Cancer Society Guidelines for Breast Cancer Screening: Update 2003

Abstract: In 2003, the American Cancer Society updated its guidelines for early detection of breast cancer based on recommendations from a formal review of evidence and a recent workshop. The new screening recommendations address screening mammography, physical examination, screening older women and women with comorbid conditions, screening women at high risk, and new screening technologies. (CA Cancer J Clin 2003;54:141-169.) © American Cancer Society, 2003.

Optimizing feature selection across a multimodality database in computerized classification of breast lesions

Abstract: Linear step-wise feature selection is performed for computerized analysis methods on a set of mammography features using a database of mammography cases, a set of ultrasound features using a database of ultrasound cases, and a set of mammography and sonography features using a multi- modality database of lesions with both mammograms and sonograms. The large mammography and sonography databases were randomly split 20 times into three subdatabases for feature selection, classifier training and independent validation. The average validation Az value over the 20 random splits for the mammography database was 0.82 +/- 0.04 and for the sonography database was 0.85 +/- 0.03. The average consistency feature selection A z value for the mammography and sonography databases were 0.87 +/- 0.02 and 0.88 +/- 0.02, respectively. For the multi-modality database, the consistency feature selection A z value was 0.93.

Computer-aided diagnosis of lesions on multimodality images of the breast

Abstract: We have developed computerized methods for the analysis of lesions that combine results from different imaging modalities, in this case digitized mammograms and sonograms of the breast, for distinguishing between malignant and benign lesions. The computerized classification method -- applied here to mass lesions seen on both digitized mammograms and sonograms, includes: (1) automatic lesion extraction, (2) automated feature extraction, and (3) automatic classification. The results for both modalities are then merged into an estimate of the likelihood of malignancy. For the mammograms, computer-extracted lesion features include degree of spiculation, margin sharpness, lesion density, and lesion texture. For the ultrasound images, lesion features include margin definition, texture, shape, and posterior acoustic attenuation. Malignant and benign lesions are better distinguished when features from both mammograms and ultrasound images are combined.

American Cancer Society Guidelines for Breast Screening with MRI as an Adjunct to Mammography

Abstract: New evidence on breast Magnetic Resonance Imaging (MRI) screening has become available since the American Cancer Society (ACS) last issued guidelines for the early detection of breast cancer in 2003. A guideline panel has reviewed this evidence and developed new recommendations for women at different defined levels of risk. Screening MRI is recommended for women with an approximately 20-25% or greater lifetime risk of breast cancer, including women with a strong family history of breast or ovarian cancer and women who were treated for Hodgkin disease. There are several risk subgroups for which the available data are insufficient to recommend for or against screening, including women with a personal history of breast cancer, carcinoma in situ, atypical hyperplasia, and extremely dense breasts on mammography. Diagnostic uses of MRI were not considered to be within the scope of this review.

Effect of screening and adjuvant therapy on mortality from breast cancer

Abstract: BACKGROUND We used modeling techniques to assess the relative and absolute contributions of screening mammography and adjuvant treatment to the reduction in breast-cancer mortality in the United States from 1975 to 2000. METHODS A consortium of investigators developed seven independent statistical models of breast-cancer incidence and mortality. All seven groups used the same sources to obtain data on the use of screening mammography, adjuvant treatment, and benefits of treatment with respect to the rate of death from breast cancer. RESULTS The proportion of the total reduction in the rate of death from breast cancer attributed to screening varied in the seven models from 28 to 65 percent (median, 46 percent), with adjuvant treatment contributing the rest. The variability across models in the absolute contribution of screening was larger than it was for treatment, reflecting the greater uncertainty associated with estimating the benefit of screening. CONCLUSIONS Seven statistical models showed that both screening mammography and treatment have helped reduce the rate of death from breast cancer in the United States.

Mammographic Density and the Risk and Detection of Breast Cancer

Abstract: Methods We carried out three nested case–control studies in screened populations with 1112 matched case–control pairs. We examined the association of the measured percentage of density in the baseline mammogram with risk of breast cancer, according to method of cancer detection, time since the initiation of screening, and age. Results As compared with women with density in less than 10% of the mammogram, women with density in 75% or more had an increased risk of breast cancer (odds ratio, 4.7; 95% confidence interval [CI], 3.0 to 7.4), whether detected by screening (odds ratio, 3.5; 95% CI, 2.0 to 6.2) or less than 12 months after a negative screening examination (odds ratio, 17.8; 95% CI, 4.8 to 65.9). Increased risk of breast cancer, whether detected by screening or other means, persisted for at least 8 years after study entry and was greater in younger than in older women. For women younger than the median age of 56 years, 26% of all breast cancers and 50% of cancers detected less than 12 months after a negative screening test were attributable to density in 50% or more of the mammogram. Conclusions Extensive mammographic density is strongly associated with the risk of breast cancer detected by screening or between screening tests. A substantial fraction of breast cancers can be attributed to this risk factor.

International evaluation of an AI system for breast cancer screening.

Abstract: Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful1. Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false positives and false negatives2. Here we present an artificial intelligence (AI) system that is capable of surpassing human experts in breast cancer prediction. To assess its performance in the clinical setting, we curated a large representative dataset from the UK and a large enriched dataset from the USA. We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. This robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening. An artificial intelligence (AI) system performs as well as or better than radiologists at detecting breast cancer from mammograms, and using a combination of AI and human inputs could help to improve screening efficiency.