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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: The data strongly suggest that palpable noncalcified solid breast masses with benign morphology at mammography and US can be managed similarly to nonpalpable BI-RADS category 3 lesions, with short-term follow-up (6-month intervals for 2 years).
Abstract: PURPOSE: To determine whether palpable noncalcified solid breast masses with benign morphology at mammography and ultrasonography (US) can be managed similarly to nonpalpable probably benign lesions (Breast Imaging Reporting and Data System [BI-RADS] category 3)—that is, with periodic imaging surveillance—and to determine whether biopsy can be averted in these lesions. MATERIALS AND METHODS: No institutional review board approval or patient consent was required. This retrospective analysis, based on final imaging reports, included 152 patients (age range, 28–77 years; mean age, 48.3 years) with 157 palpable noncalcified solid masses that were classified as probably benign at initial mammography and US. Of 152 patients, 108 underwent follow-up with mammography and US (6-month intervals for 2 years, then 12-month intervals). The remaining 44 patients underwent surgical or needle biopsy after initial imaging. Lesions were analyzed at initial and follow-up examinations. Statistical analysis included Student t...

160 citations

Journal ArticleDOI
TL;DR: Tomosynthesis improves CDR and reduces recall; however, effects are dependent on screening setting, with greater improvement in CDR in European/Scandinavian studies (biennial screening) and reduction in recall in US studies with high baseline recall.
Abstract: Background Tomosynthesis approximates a 3D mammogram of the breast, reducing parenchymal overlap that masks cancers or creates false "lesions" on 2D mammography, and potentially enabling more accurate detection of breast cancer. We compared breast cancer screening detection and recall in asymptomatic women for tomosynthesis vs 2D mammography. Methods A systematic review and random effects meta-analysis were undertaken. Electronic databases (2009-July 2017) were searched for studies comparing tomosynthesis and 2D mammography in asymptomatic women who attended population breast cancer screening and reporting cancer detection rate (CDR) and recall rate. All statistical tests were two-sided. Results Seventeen studies (1 009 790 participants) were included from 413 citations. The pooled incremental CDR for tomosynthesis was 1.6 cancers per 1000 screens (95% confidence interval [CI] = 1.1 to 2.0, P < .001, I2 = 36.9%). Incremental CDR was statistically significantly higher for European/Scandinavian studies, all using a "paired" design where women had both tests (2.4 per 1000 screens, 95% CI = 1.9 to 2.9, P < .001, I2 = 0.0%) compared with US ("unpaired") studies (1.1 per 1000 screens, 95% CI = 0.8 to 1.5, P < .001, I2 = 0.0%; P < .001 between strata). The recall rate for tomosynthesis was statistically significantly lower than for 2D mammography (pooled absolute reduction = -2.2%, 95% CI = -3.0 to -1.4, P < .001, I2 = 98.2%). Stratified analyses showed a decrease in US studies (pooled difference in recall rate = -2.9%, 95% CI = -3.5 to -2.4, P < .001, I2 = 92.9%) but not European/Scandinavian studies (0.5% increase in recall, 95% CI = -0.1 to 1.2, P = .12, I2 = 93.5%; P < .001 between strata). Results were similar in sensitivity analyses excluding studies with overlapping cohorts. Conclusions Tomosynthesis improves CDR and reduces recall; however, effects are dependent on screening setting, with greater improvement in CDR in European/Scandinavian studies (biennial screening) and reduction in recall in US studies with high baseline recall.

159 citations

Journal ArticleDOI
TL;DR: New progress is reported in developing the instrument and software platform of a combined X-ray mammography/diffuse optical breast imaging system and a series of balloon phantom experiments and the optical image analysis of 49 healthy patients, which shows composite features from both tissue structure and pressure distribution.
Abstract: In this paper, we report new progress in developing the instrument and software platform of a combined X-ray mammography/diffuse optical breast imaging system. Particularly, we focus on system validation using a series of balloon phantom experiments and the optical image analysis of 49 healthy patients. Using the finite-element method for forward modeling and a regularized Gauss-Newton method for parameter reconstruction, we recovered the inclusions inside the phantom and the hemoglobin images of the human breasts. An enhanced coupling coefficient estimation scheme was also incorporated to improve the accuracy and robustness of the reconstructions. The recovered average total hemoglobin concentration (HbT) and oxygen saturation (SO2) from 68 breast measurements are 16.2 mum and 71%, respectively, where the HbT presents a linear trend with breast density. The low HbT value compared to literature is likely due to the associated mammographic compression. From the spatially co-registered optical/X-ray images, we can identify the chest-wall muscle, fatty tissue, and fibroglandular regions with an average HbT of 20.1plusmn6.1 nmum for fibroglandular tissue, 15.4plusmn5.0nmum for adipose, and 22.2plusmn7.3nmum for muscle tissue. The differences between fibroglandular tissue and the orresponding adipose tissue are significant ***INVALID TEX*** . At the same time, we recognize that the optical images are influenced, to a certain extent, by mammographical compression. The optical images from a subset of patients show composite features from both tissue structure and pressure distribution. We present mechanical simulations which further confirm this hypothesis.

159 citations

Posted Content
TL;DR: This work presents how Convolutional Neural Networks can be used to directly classify pre-segmented breast masses in mammograms as benign or malignant, using a combination of transfer learning, careful pre-processing and data augmentation to overcome limited training data.
Abstract: Mammography is the most widely used method to screen breast cancer. Because of its mostly manual nature, variability in mass appearance, and low signal-to-noise ratio, a significant number of breast masses are missed or misdiagnosed. In this work, we present how Convolutional Neural Networks can be used to directly classify pre-segmented breast masses in mammograms as benign or malignant, using a combination of transfer learning, careful pre-processing and data augmentation to overcome limited training data. We achieve state-of-the-art results on the DDSM dataset, surpassing human performance, and show interpretability of our model.

159 citations

Journal ArticleDOI
TL;DR: Routine preoperative MR imaging appears to be unnecessary for most patients if a combination of mammography and whole-breast sonography is used, and can be restricted to problematic cases in women with dense breast parenchyma.
Abstract: OBJECTIVE. The purposes of our study were to compare the diagnostic value of whole-breast sonography and MR imaging as adjunctive techniques to mammography and to determine whether MR imaging should be used routinely in the preoperative assessment of patients with suspected breast cancer.SUBJECTS AND METHODS. One hundred four women (age range, 34-84 years; mean age, 60 years) with findings highly suggestive of malignancy in the breast were examined with mammography, sonography, and dynamic MR imaging before undergoing surgery. All visualized suspicious lesions were correlated histologically. The diagnostic relevance of sonographic and MR imaging findings was compared with the diagnostic value of the findings of clinical examination and mammography alone.RESULTS. Twenty-seven tumors showed multifocal or multicentric invasive growth at pathology. Of these 27, 48% were correctly diagnosed via mammography alone; 63%, via the combination of mammography and sonography; and 81%, via MR imaging. Nine of the index...

159 citations


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