Showing papers on "Digital mammography published in 2016"

Mammographic density and the risk and detection of breast cancer

Abstract: With the increase in breast cancer risk over the years, there are many factors estimated that lead to it. However, till date which factor is majorly involved in development of breast cancer or which factor accounts more is not clearly evident. Mammography technique accounting for 80-90% of cancer being detected is believed to be the best method of detection. While mammographic density is manifested by increased proliferation of fat, stoma, epithelium and connective tissue, it is considered to be a risk factor for development of breast cancer. The current study was thus conducted to find out whether the mammographic density is actually a risk factor for development of breast cancer and to find out the better detection tool available. For this, the methodology adopted was review of various journals and studies already published with respect to mammographic density and its risk on development of breast cancer. The conclusion of the current study as well as from another comparable study was that the frequency of screening might be dependent on breast density and in such cases diagnostic techniques such as “digital mammography, ultra sonography and magnetic resonance imaging” may prove to be better detection tools. Moreover, recent studies have also suggested that mammographic density as a marker for risk of developing breast cancer holds true however, this fact needs to be evaluated further. Article DOI: https://dx.doi.org/10.20319/lijhls.2016.22.4854 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

Breast cancer screening with tomosynthesis (3D mammography) with acquired or synthetic 2D mammography compared with 2D mammography alone (STORM-2): a population-based prospective study

Abstract: Summary Background Breast tomosynthesis (pseudo-3D mammography) improves breast cancer detection when added to 2D mammography. In this study, we examined whether integrating 3D mammography with either standard 2D mammography acquisitions or with synthetic 2D images (reconstructed from 3D mammography) would detect more cases of breast cancer than 2D mammography alone, to potentially reduce the radiation burden from the combination of 2D plus 3D acquisitions. Methods The Screening with Tomosynthesis Or standard Mammography-2 (STORM-2) study was a prospective population-based screening study comparing integrated 3D mammography (dual-acquisition 2D–3D mammography or 2D synthetic–3D mammography) with 2D mammography alone. Asymptomatic women aged 49 years or older who attended population-based screening in Trento, Italy were recruited for the study. All participants underwent digital mammography with 2D and 3D mammography acquisitions, with the use of software that allowed synthetic 2D mammographic images to be reconstructed from 3D acquisitions. Mammography screen-reading was done in two parallel double-readings conducted sequentially for 2D acquisitions followed by integrated acquisitions. Recall based on a positive mammography result was defined as recall at any screen read. Primary outcome measures were a comparison between integrated (2D–3D or 2D synthetic–3D) mammography and 2D mammography alone of the number of cases of screen-detected breast cancer, the cancer detection rate per 1000 screens, the incremental cancer detection rate, and the number and percentage of false-positive recalls. Findings Between May 31, 2013, and May 29, 2015, 10 255 women were invited to participate, of whom 9672 agreed to participate and were screened. In these 9672 participants (median age 58 years [IQR 53–63]), screening detected 90 cases of breast cancer, including 74 invasive breast cancers, in 85 women (five women had bilateral breast cancer). To account for these bilateral cancers in cancer detection rate estimates, the number of screens used for analysis was 9677. Both 2D–3D mammography (cancer detection rate 8·5 per 1000 screens [82 cancers detected in 9677 screens]; 95% CI 6·7–10·5) and 2D synthetic–3D mammography (8·8 per 1000 [85 in 9677]; 7·0–10·8) had significantly higher rates of breast cancer detection than 2D mammography alone (6·3 per 1000 [61 in 9677], 4·8–8·1; p Interpretation Integration of 3D mammography (2D–3D or 2D synthetic–3D) detected more cases of breast cancer than 2D mammography alone, but increased the percentage of false-positive recalls in sequential screen-reading. These results should be considered in the context of the trade-off between benefits and harms inherent in population breast cancer screening, including that significantly increased breast cancer detection from integrating 3D mammography into screening has the potential to augment screening benefit and also possibly contribute to overdiagnosis. Funding None.

Effectiveness of Digital Breast Tomosynthesis Compared With Digital Mammography: Outcomes Analysis From 3 Years of Breast Cancer Screening

Abstract: Importance Breast cancer screening with digital breast tomosynthesis (DBT) combined with digital mammography (DM) decreases false-positive examinations and increases cancer detection compared with screening with DM alone. However, the longitudinal performance of DBT screening is unknown. Objectives To determine whether the improved outcomes observed after initial implementation of DBT screening are sustainable over time at a population level and to evaluate the effect of more than 1 DBT screening at the individual level. Design, Setting, and Participants Retrospective analysis of screening mammography metrics was performed for all patients presenting for screening mammography in an urban, academic breast center during 4 consecutive years (DM, year 0; DBT, years, 1-3). The study was conducted from September 1, 2010, to September 30, 2014 (excluding September 2011, which was the transition period from DM to DBT), for a total of 44 468 screening events attributable to a total of 23 958 unique women. Differences in screening outcomes between each DBT year and the DM year, as well as between groups of women with only 1, 2, or 3 DBT screenings, were assessed, and the odds of recall adjusted for age, race/ethnicity, breast density, and prior mammograms were estimated. Data analysis was performed between February 16 and October 26, 2015. Exposure Digital mammography screening supplemented with DBT. Main Outcomes and Measures Recall rates, cancer cases per recalled patients, and biopsy and interval cancer rates were determined. Results Screening outcome metrics were evaluated for a total of 44 468 examinations attributable to 23 958 unique women (mean [SD] age, 56.8 [11.0] years) over a 4-year period: year 0 cohort (DM0), 10 728 women; year 1 cohort (DBT1), 11 007; year 2 cohort (DBT2), 11 157; and year 3 cohort (DBT3), 11 576. Recall rates rose slightly for years 1 to 3 of DBT (88, 90, and 92 per 1000 screened, respectively) but remained significantly reduced compared with the DM0 rate of 104 per 1000 screened. Reported as odds ratios (95% CIs), the findings were DM vs DBT1, 0.83 (0.76-0.91, P P P = .003). The cancer cases per recalled patients continued to rise from DM0 rate of 4.4% to 6.2% ( P = .06), 6.5% ( P = .03), and 6.7% ( P = .02) for years 1 to 3 of DBT, respectively. Outcomes assessed for the most recent screening for individual women undergoing only 1, 2, or 3 DBT screenings during the study period demonstrated decreasing recall rates of 130, 78, and 59 per 1000 screened, respectively ( P Conclusions and Relevance Digital breast tomosynthesis screening outcomes are sustainable, with significant recall reduction, increasing cancer cases per recalled patients, and a decline in interval cancers.

Breast cancer screening using tomosynthesis in combination with digital mammography compared to digital mammography alone: A cohort study within the PROSPR consortium

Abstract: Digital breast tomosynthesis (DBT) is emerging as the new standard of care for breast cancer screening based on improved cancer detection coupled with reductions in recall compared to screening with digital mammography (DM) alone. However, many prior studies lack follow-up data to assess false negatives examinations. The purpose of this study is to assess if DBT is associated with improved screening outcomes based on follow-up data from tumor registries or pathology. Retrospective analysis of prospective cohort data from three research centers performing DBT screening in the PROSPR consortium from 2011 to 2014 was performed. Recall and biopsy rates were assessed from 198,881 women age 40-74 years undergoing screening (142,883 DM and 55,998 DBT examinations). Cancer, cancer detection, and false negative rates and positive predictive values were assessed on examinations with one year of follow-up. Logistic regression was used to compare DBT to DM adjusting for research center, age, prior breast imaging, and breast density. There was a reduction in recall with DBT compared to DM (8.7 vs. 10.4 %, p < 0.0001), with adjusted OR = 0.68 (95 % CI = 0.65-0.71). DBT demonstrated a statistically significant increase in cancer detection over DM (5.9 vs. 4.4/1000 screened, adjusted OR = 1.45, 95 % CI = 1.12-1.88), an improvement in PPV1 (6.4 % for DBT vs. 4.1 % for DM, adjusted OR = 2.02, 95 % CI = 1.54-2.65), and no significant difference in false negative rates for DBT compared to DM (0.46 vs. 0.60/1000 screened, p = 0.347). Our data support implementation of DBT screening based on increased cancer detection, reduced recall, and no difference in false negative screening examinations.

Factors Associated With Rates of False-Positive and False-Negative Results From Digital Mammography Screening: An Analysis of Registry Data

Abstract: This analysis of registry data, conducted to inform the new recommendation on breast cancer screening from the USPSTF, describes rates of false-positive and false-negative results among U.S. women ...

Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment

Abstract: The assessment of a woman’s risk for developing breast cancer has become increasingly important for establishing personalized screening recommendations and forming preventive strategies. Studies have consistently shown a strong relationship between breast cancer risk and mammographic parenchymal patterns, typically assessed by percent mammographic density. This paper will review the advancing role of mammographic texture analysis as a potential novel approach to characterize the breast parenchymal tissue to augment conventional density assessment in breast cancer risk estimation. The analysis of mammographic texture provides refined, localized descriptors of parenchymal tissue complexity. Currently, there is growing evidence in support of textural features having the potential to augment the typically dichotomized descriptors (dense or not dense) of area or volumetric measures of breast density in breast cancer risk assessment. Therefore, a substantial research effort has been devoted to automate mammographic texture analysis, with the aim of ultimately incorporating such quantitative measures into breast cancer risk assessment models. In this paper, we review current and emerging approaches in this field, summarizing key methodological details and related studies using novel computerized approaches. We also discuss research challenges for advancing the role of parenchymal texture analysis in breast cancer risk stratification and accelerating its clinical translation. The objective is to provide a comprehensive reference for researchers in the field of parenchymal pattern analysis in breast cancer risk assessment, while indicating key directions for future research.

Implementation of Synthesized Two-dimensional Mammography in a Population-based Digital Breast Tomosynthesis Screening Program.

Abstract: Purpose To evaluate the early implementation of synthesized two-dimensional (s2D) mammography in a population screened entirely with s2D and digital breast tomosynthesis (DBT) (referred to as s2D/DBT) and compare recall rates and cancer detection rates to historic outcomes of digital mammography combined with DBT (referred to as digital mammography/DBT) screening. Materials and Methods This was an institutional review board-approved and HIPAA-compliant retrospective interpretation of prospectively acquired data with waiver of informed consent. Compared were recall rates, biopsy rates, cancer detection rates, and radiation dose for 15 571 women screened with digital mammography/DBT from October 1, 2011, to February 28, 2013, and 5366 women screened with s2D/DBT from January 7, 2015, to June 30, 2015. Two-sample z tests of equal proportions were used to determine statistical significance. Results Recall rate for s2D/DBT versus digital mammography/DBT was 7.1% versus 8.8%, respectively (P < .001). Biopsy rate for s2D/DBT versus digital mammography/DBT decreased (1.3% vs 2.0%, respectively; P = .001). There was no significant difference in cancer detection rate for s2D/DBT versus digital mammography/DBT (5.03 of 1000 vs 5.45 of 1000, respectively; P = .72). The average glandular dose was 39% lower in s2D/DBT versus digital mammography/DBT (4.88 mGy vs 7.97 mGy, respectively; P < .001). Conclusion Screening with s2D/DBT in a large urban practice resulted in similar outcomes compared with digital mammography/DBT imaging. Screening with s2D/DBT allowed for the benefits of DBT with a decrease in radiation dose compared with digital mammography/DBT. © RSNA, 2016 An earlier incorrect version of this article appeared online. This article was corrected on August 11, 2016.

Radiation-Induced Breast Cancer Incidence and Mortality From Digital Mammography Screening: A Modeling Study

Abstract: textBackground: Estimates of risk for radiation-induced breast cancer from mammography screening have not considered variation in dose exposure or diagnostic work-up after abnormal screening results. Objective: To estimate distributions of radiation-induced breast cancer incidence and mortality from digital mammography screening while considering exposure from screening and diagnostic mammography and dose variation among women. Design: 2 simulation-modeling approaches. Setting: U.S. population. Patients: Women aged 40 to 74 years. Intervention: Annual or biennial digital mammography screening from age 40, 45, or 50 years until age 74 years. Measurements: Lifetime breast cancer deaths averted (bene-fits) and radiation-induced breast cancer incidence and mortality (harms) per 100 000 women screened. Results: Annual screening of 100 000 women aged 40 to 74 years was projected to induce 125 breast cancer cases (95% CI, 88 to 178) leading to 16 deaths (CI, 11 to 23), relative to 968 breast cancer deaths averted by early detection from screening. Women exposed at the 95th percentile were projected to develop 246 cases of radiation-induced breast cancer leading to 32 deaths per 100 000 women. Women with large breasts requiring extra views for complete examination (8% of population) were projected to have greater radiation-induced breast cancer risk (266 cancer cases and 35 deaths per 100 000 women) than other women (113 cancer cases and 15 deaths per 100 000 women). Biennial screening starting at age 50 years reduced risk for radiation-induced cancer 5-fold. Limitation: Life-years lost from radiation-induced breast cancer could not be estimated. Conclusion: Radiation-induced breast cancer incidence and mortality from digital mammography screening are affected by dose variability from screening, resultant diagnostic work-up, initiation age, and screening frequency. Women with large breasts may have a greater risk for radiation-induced breast cancer. Primary Funding Source: Agency for Healthcare Research and Quality, U.S. Preventive Services Task Force, National Cancer Institute.

Tailoring breast cancer screening intervals by breast density and risk for women aged 50 years or older: Collaborative modeling of screening outcomes

Abstract: Although biennial digital mammography screening is generally recommended for average-risk women aged 50 to 74 years, more tailored screening strategies may provide greater benefits. This collaborat...

Tomosynthesis-detected Architectural Distortion: Management Algorithm with Radiologic-Pathologic Correlation

Abstract: As use of digital breast tomosynthesis becomes increasingly widespread, new management challenges are inevitable because tomosynthesis may reveal suspicious lesions not visible at conventional two-dimensional (2D) full-field digital mammography. Architectural distortion is a mammographic finding associated with a high positive predictive value for malignancy. It is detected more frequently at tomosynthesis than at 2D digital mammography and may even be occult at conventional 2D imaging. Few studies have focused on tomosynthesis-detected architectural distortions to date, and optimal management of these distortions has yet to be well defined. Since implementing tomosynthesis at our institution in 2011, we have learned some practical ways to assess architectural distortion. Because distortions may be subtle, tomosynthesis localization tools plus improved visualization of adjacent landmarks are crucial elements in guiding mammographic identification of elusive distortions. These same tools can guide more focused ultrasonography (US) of the breast, which facilitates detection and permits US-guided tissue sampling. Some distortions may be sonographically occult, in which case magnetic resonance imaging may be a reasonable option, both to increase diagnostic confidence and to provide a means for image-guided biopsy. As an alternative, tomosynthesis-guided biopsy, conventional stereotactic biopsy (when possible), or tomosynthesis-guided needle localization may be used to achieve tissue diagnosis. Practical uses for tomosynthesis in evaluation of architectural distortion are highlighted, potential complications are identified, and a working algorithm for management of tomosynthesis-detected architectural distortion is proposed.

Automated Breast Ultrasound in Breast Cancer Screening of Women With Dense Breasts: Reader Study of Mammography-Negative and Mammography-Positive Cancers.

Abstract: OBJECTIVE. The objective of our study was to assess and compare, in a reader study, radiologists' performance in the detection of breast cancer using full-field digital mammography (FFDM) alone and using FFDM with 3D automated breast ultrasound (ABUS). MATERIALS AND METHODS. In this multireader, multicase, sequential-design reader study, 17 Mammography Quality Standards Act–qualified radiologists interpreted a cancer-enriched set of FFDM and ABUS examinations. All imaging studies were of asymptomatic women with BI-RADS C or D breast density. Readers first interpreted FFDM alone and subsequently interpreted FFDM combined with ABUS. The analysis included 185 cases: 133 noncancers and 52 biopsy-proven cancers. Of the 52 cancer cases, the screening FFDM images were interpreted as showing BI-RADS 1 or 2 findings in 31 cases and BI-RADS 0 findings in 21 cases. For the cases interpreted as BI-RADS 0, a forced BI-RADS score was also given. Reader performance was compared in terms of AUC under the ROC curve, sensi...

Screening for Breast Cancer: A Systematic Review to Update the 2009 U.S. Preventive Services Task Force Recommendation

Abstract: Background In 2009, the US Preventive Services Task Force (USPSTF) recommended biennial screening mammography for women age 50 to 74 years, and based decisions for earlier screening on individual patient context and values Evidence was insufficient to recommend screening beyond age 75 Purpose To systematically update the 2009 USPSTF review on screening for breast cancer in average risk women age 40 years and older Data Sources The Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews (through December 2014), Ovid MEDLINE (through December 2014), and reference lists were searched for relevant studies Additional data were obtained from investigators of randomized trials and from the Breast Cancer Surveillance Consortium Study Selection Randomized controlled trials and observational studies of breast cancer screening in asymptomatic women age 40 and older reporting breast cancer mortality, all-cause mortality, advanced breast cancer, treatment morbidity, and the harms of screening Data Extraction One investigator abstracted data and a second investigator confirmed accuracy Investigators independently dual-rated study quality and applicability using established criteria Discrepancies were resolved through a consensus process Data Synthesis A meta-analysis of screening trials with updated data from the Canadian (CNBSS-1 and CNBSS-2), Swedish Two-County Study, and Age trials indicated breast cancer mortality reductions for age 39 to 49 years (relative risk [RR] 088; 95% confidence interval [CI], 073 to 1003; 9 trials; 4 deaths prevented/10,000 over 10 years); 50 to 59 years (RR 086 [95% CI, 068 to 097]; 7 trials; 8/10,000); 60 to 69 years (RR 067 [95% CI, 054 to 083]; 21/10,000); and 70 to 74 years (RR 080 [95% CI, 051 to 128]; 3 trials; 13/10,000) Risk reduction was 25 to 31 percent for women age 50 to 69 years across several observational studies, with similar reductions for women age 40 to 49 in two studies Trials indicated no statistically significant reductions in all-cause mortality with screening Risk for higher-stage breast cancer was reduced for age 50 years and older (RR 062 [95% CI, 046 to 083]; 3 trials), but not for age 39 to 49 years (RR 098 [95% CI, 074 to 137]; 4 trials) The majority of cases from screening were ductal carcinoma in situ and early stage, and screening resulted in more mastectomies (RR 120 [95% CI, 111 to 130]; 5 trials) and radiation (RR 132 [95% CI, 116 to 150]; 2 trials) Younger women and those with risk factors had more false-positive results and recommendations for additional imaging and biopsies Cumulative rates for false-positive mammography results over 10 years were 61 percent for annual and 42 percent for biennial screening; rates for biopsy were 7 to 9 percent for annual and 5 to 6 percent for biennial screening Estimates of overdiagnosis ranged from 11 to 22 percent in trials; and 1 to 10 percent in observational studies Some women with false-positive results or pain experienced distress and were less likely to return for their next mammogram Tomosynthesis with mammography reduced recalls (16/1000), but increased biopsies (13/1000) and cancer detection (12/1,000) The number of deaths due to radiation induced cancer from screening with digital mammography was estimated through modeling as between 2 to 11 per 100,000 depending on age at onset and screening intervals Limitations Limited to English-language articles; the number, quality, and applicability of studies varied widely Trials of mammography screening reflect imaging technologies and cancer treatment therapies that are not currently in use Studies are lacking on screening effectiveness based on risk factors, intervals, and modalities; and on screening modalities relevant to women who are not high-risk Conclusions Breast cancer mortality is reduced with mammography screening, although estimates are of borderline statistical significance, the magnitudes of effect are small for younger ages, and results vary depending on how cases were accrued in trials Higher stage tumors are also reduced with screening for age 50 years and older False-positive results are common in all age groups, and are higher for younger women and those with risk factors Approximately 11 to 22 percent of cases may be overdiagnosed Observational studies indicate that tomosynthesis with mammography reduces recalls, but increases biopsies and cancer detection Mammography screening at any age is a tradeoff of a continuum of benefits and harms

Smart detection on abnormal breasts in digital mammography based on contrast-limited adaptive histogram equalization and chaotic adaptive real-coded biogeography-based optimization

Abstract: In this study, we proposed a smart detection method for abnormal breasts in digital mammography. Firstly, preprocessing was carried out to deaden noises, enhance images, and remove background and pectoral muscles. Secondly, fractional Fourier entropy was employed to extract global features. Thirdly, the Welch’s t-test was utilized to select important features. Fourthly, the multi-layer perceptron was used as the classifier. Finally, we proposed a novel chaotic adaptive real-coded biogeography-based optimization to train the classifier. We implemented 10-fold cross-validation for statistical analysis. The experimental results showed our method selected in total 23 distinguishing features, and yielded a sensitivity of 92.54%, a specificity of 92.50%, a precision of 92.50%, and an accuracy of 92.52%. This proposed system performs better than five state-of-the-art methods. It is effective in abnormal breast detection.

Computer-aided diagnosis of abnormal breasts in mammogram images by weighted-type fractional Fourier transform:

Abstract: Abnormal breast can be diagnosed using the digital mammography. Traditional manual interpretation method cannot yield high accuracy. In this study, we proposed a novel computer-aided diagnosis system for detecting abnormal breasts. Our dataset contains 200 mammogram images with size of 1024 × 1024. First, we segmented the region of interest from mammogram images. Second, the fractional Fourier transform was employed to obtain the unified time–frequency spectrum. Third, spectrum coefficients were reduced by principal component analysis. Finally, both support vector machine and k-nearest neighbors were used and compared. The proposed “weighted-type fractional Fourier transform+principal component analysis+support vector machine” achieved sensitivity of 92.22% ± 4.16%, specificity of 92.10% ± 2.75%, and accuracy of 92.16% ± 3.60%. It is better than both the proposed “weighted-type fractional Fourier transform+principal component analysis+k-nearest neighbors” and other five state-of-the-art approaches in term...

Multiple-Instance Learning for Anomaly Detection in Digital Mammography

Abstract: This paper describes a computer-aided detection and diagnosis system for breast cancer, the most common form of cancer among women, using mammography. The system relies on the Multiple-Instance Learning (MIL) paradigm, which has proven useful for medical decision support in previous works from our team. In the proposed framework, breasts are first partitioned adaptively into regions. Then, features derived from the detection of lesions (masses and microcalcifications) as well as textural features, are extracted from each region and combined in order to classify mammography examinations as “normal” or “abnormal”. Whenever an abnormal examination record is detected, the regions that induced that automated diagnosis can be highlighted. Two strategies are evaluated to define this anomaly detector. In a first scenario, manual segmentations of lesions are used to train an SVM that assigns an anomaly index to each region; local anomaly indices are then combined into a global anomaly index. In a second scenario, the local and global anomaly detectors are trained simultaneously, without manual segmentations, using various MIL algorithms (DD, APR, mi-SVM, MI-SVM and MILBoost). Experiments on the DDSM dataset show that the second approach, which is only weakly-supervised, surprisingly outperforms the first approach, even though it is strongly-supervised. This suggests that anomaly detectors can be advantageously trained on large medical image archives, without the need for manual segmentation.

How does c-view image quality compare with conventional 2D FFDM?

Abstract: Purpose: The FDA approved the use of digital breast tomosynthesis (DBT) in 2011 as an adjunct to 2D full field digital mammography (FFDM) with the constraint that all DBT acquisitions must be paired with a 2D image to assure adequate interpretative information is provided. Recently manufacturers have developed methods to provide a synthesized 2D image generated from the DBT data with the hope of sparing patients the radiation exposure from the FFDM acquisition. While this much needed alternative effectively reduces the total radiation burden, differences in image quality must also be considered. The goal of this study was to compare the intrinsic image quality of synthesized 2D c-view and 2D FFDM images in terms of resolution, contrast, and noise. Methods: Two phantoms were utilized in this study: the American College of Radiology mammography accreditation phantom (ACR phantom) and a novel 3D printed anthropomorphic breast phantom. Both phantoms were imaged using a Hologic Selenia Dimensions 3D system. Analysis of the ACR phantom includes both visual inspection and objective automated analysis using in-house software.Analysis of the 3D anthropomorphic phantom includes visual assessment of resolution and Fourier analysis of the noise. Results: Using ACR-defined scoring criteria for the ACR phantom, the FFDM images scored statistically higher than c-view according to both the average observer and automated scores. In addition, between 50% and 70% of c-viewimages failed to meet the nominal minimum ACR accreditation requirements—primarily due to fiber breaks. Softwareanalysis demonstrated that c-view provided enhanced visualization of medium and large microcalcification objects; however, the benefits diminished for smaller high contrast objects and all low contrast objects. Visual analysis of the anthropomorphic phantom showed a measureable loss of resolution in the c-viewimage (11 lp/mm FFDM, 5 lp/mm c-view) and loss in detection of small microcalcification objects. Spectral analysis of the anthropomorphic phantom showed higher total noise magnitude in the FFDM image compared with c-view. Whereas the FFDM image contained approximately white noise texture, the c-viewimage exhibited marked noise reduction at midfrequency and high frequency with far less noise suppression at low frequencies resulting in a mottled noise appearance. Conclusions: Their analysis demonstrates many instances where the c-viewimage quality differs from FFDM. Compared to FFDM, c-view offers a better depiction of objects of certain size and contrast, but provides poorer overall resolution and noise properties. Based on these findings, the utilization of c-viewimages in the clinical setting requires careful consideration, especially if considering the discontinuation of FFDM imaging. Not explicitly explored in this study is how the combination of DBT + c-view performs relative to DBT + FFDM or FFDM alone.

Radiation-Induced Breast Cancer Incidence and Mortality From Digital Mammography Screening

Abstract: This modeling study, conducted to inform the new recommendation on breast cancer screening from the U.S. Preventive Services Task Force (USPSTF), examines radiation-induced breast cancer incidence ...

Breast Cancer Screening, Mammography, and Other Modalities.

Abstract: This article is an overview of the modalities available for breast cancer screening. The modalities discussed include digital mammography, digital breast tomosynthesis, breast ultrasonography, magnetic resonance imaging, and clinical breast examination. There is a review of pertinent randomized controlled trials, studies and meta-analyses which contributed to the evolution of screening guidelines. Ultimately, 5 major medical organizations formulated the current screening guidelines in the United States. The lack of consensus in these guidelines represents an ongoing controversy about the optimal timing and method for breast cancer screening in women. For mammography screening, the Breast Imaging Reporting and Data System lexicon is explained which corresponds with recommended clinical management. The presentation and discussion of the data in this article are designed to help the clinician individualize breast cancer screening for each patient.

Comparison between two-dimensional synthetic mammography reconstructed from digital breast tomosynthesis and full-field digital mammography for the detection of T1 breast cancer

Abstract: Objective To evaluate the interpretative performance of two-dimensional (2D) synthetic mammography (SM) reconstructed from digital breast tomosynthesis (DBT) in the detection of T1-stage invasive breast cancers, compared to 2D full-field digital mammography (FFDM).

Association Between Changes in Mammographic Image Features and Risk for Near-Term Breast Cancer Development

Abstract: The purpose of this study is to develop and test a new computerized model for predicting near-term breast cancer risk based on quantitative assessment of bilateral mammographic image feature variations in a series of negative full-field digital mammography (FFDM) images. The retrospective dataset included series of four sequential FFDM examinations of 335 women. The last examination in each series ("current") and the three most recent "prior" examinations were obtained. All "prior" examinations were interpreted as negative during the original clinical image reading, while in the "current" examinations 159 cancers were detected and pathologically verified and 176 cases remained cancer-free. From each image, we initially computed 158 mammographic density, structural similarity, and texture based image features. The absolute subtraction value between the left and right breasts was selected to represent each feature. We then built three support vector machine (SVM) based risk models, which were trained and tested using a leave-one-case-out based cross-validation method. The actual features used in each SVM model were selected using a nested stepwise regression analysis method. The computed areas under receiver operating characteristic curves monotonically increased from 0.666±0.029 to 0.730±0.027 as the time-lag between the "prior" (3 to 1) and "current" examinations decreases. The maximum adjusted odds ratios were 5.63, 7.43, and 11.1 for the three "prior" (3 to 1) sets of examinations, respectively. This study demonstrated a positive association between the risk scores generated by a bilateral mammographic feature difference based risk model and an increasing trend of the near-term risk for having mammography-detected breast cancer.

Automated Volumetric Breast Density Measurements in the Era of the BI-RADS Fifth Edition: A Comparison With Visual Assessment.

Abstract: OBJECTIVE. The purpose of this study is to evaluate automated volumetric measurements in comparison with visual assessment of mammographic breast density by use of the fifth edition of BI-RADS. MATERIALS AND METHODS. A total of 1185 full-field digital mammography examinations with standard views were retrospectively analyzed. All images were visually assessed by two blinded radiologists according to breast density category in the fifth edition of the BI-RADS lexicon. Automated volumetric breast density assessment was performed using two different software programs, Quantra and Volpara. A weighted kappa value was calculated to assess the degree of agreement among the visual and volumetric assessments of the density category. The volumes of fibroglandular tissue or total breast and the percentage breast density provided by the two software programs were compared. RESULTS. Compared with a visual assessment, the agreement of density category ranged from moderate to substantial in Quantra (κ = 0.54–0.61) and f...

Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches

Abstract: Computer-aided detection (CAD) has been used in screening mammography for many years and is likely to be utilized for digital breast tomosynthesis (DBT). Higher detection performance is desirable as it may have an impact on radiologist's decisions and clinical outcomes. Recently the algorithms based on deep convolutional architectures have been shown to achieve state of the art performance in object classification and detection. Similarly, we trained a deep convolutional neural network directly on patches sampled from two-dimensional mammography and reconstructed DBT volumes and compared its performance to a conventional CAD algorithm that is based on computation and classification of hand-engineered features. The detection performance was evaluated on the independent test set of 344 DBT reconstructions (GE SenoClaire 3D, iterative reconstruction algorithm) containing 328 suspicious and 115 malignant soft tissue densities including masses and architectural distortions. Detection sensitivity was measured on a region of interest (ROI) basis at the rate of five detection marks per volume. Moving from conventional to deep learning approach resulted in increase of ROI sensitivity from 0:832 ± 0:040 to 0:893 ± 0:033 for suspicious ROIs; and from 0:852 ± 0:065 to 0:930 ± 0:046 for malignant ROIs. These results indicate the high utility of deep feature learning in the analysis of DBT data and high potential of the method for broader medical image analysis tasks.

Fully Automated Quantitative Estimation of Volumetric Breast Density from Digital Breast Tomosynthesis Images: Preliminary Results and Comparison with Digital Mammography and MR Imaging.

Abstract: This study introduces a fully automated method for the computation of quantitative volumetric breast density from digital breast tomosynthesis (DBT) images and demonstrates a comparison with volume-based measures from full-field mammography, DBT, and MR imaging.

Added Value of Contrast-Enhanced Spectral Mammography in Postscreening Assessment.

Abstract: To assess the value on diagnostic and treatment management of contrast-enhanced spectral mammography (CESM), as adjunct to mammography (MG) and ultrasound (US) in postscreening in a breast cancer unit for patients with newly diagnosed breast cancer or with suspicious findings on conventional imaging. Retrospective review of routine use of bilateral CESM performed between September 2012 and September 2013 in 195 women with suspicious or undetermined findings on MG and/or US. CESM images were blindly reviewed by two radiologists for BI-RADS(®) assessment and probability of malignancy. Each lesion was definitely confirmed either with histopathology or follow-up. Two hundred and ninety-nine lesions were detected (221 malignant). CESM sensitivity, specificity, positive-predictive value and negative-predictive value were 94% (CI: 89-96%), 74% (CI: 63-83%), 91% (CI: 86-94%) and 81% (CI: 70-89%), respectively, with 18 false positive and 14 false negative. CESM changed diagnostic and treatment strategy in 41 (21%) patients either after detection of additional malignant lesions in 38 patients (19%)-with a more extensive surgery (n = 21) or neo-adjuvant chemotherapy (n = 1)-or avoiding further biopsy for 20 patients with negative CESM. CESM can be performed easily in a clinical assessment after positive breast cancer screening and may change significantly the diagnostic and treatment strategy through breast cancer staging.

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

Abstract: 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.