Showing papers on "Digital mammography published in 2015"

The TOMMY trial: a comparison of TOMosynthesis with digital MammographY in the UK NHS Breast Screening Programme - a multicentre retrospective reading study comparing the diagnostic performance of digital breast tomosynthesis and digital mammography with digital mammography alone.

Abstract: This project was funded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 19, No. 4. See the HTA programme website for further project information.

Comparative effectiveness of combined digital mammography and tomosynthesis screening for women with dense breasts.

Abstract: Our analysis suggests that adding tomosynthesis to biennial digital mammography screening for women aged 50–74 years with dense breasts is likely to improve health outcomes at a reasonable cost relative to biennial mammography screening alone.

Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment

Abstract: Purpose: Mammographic percent density (PD%) is known to be a strong risk factor for breast cancer. Recent studies also suggest that parenchymal texture features, which are more granular descriptors of the parenchymal pattern, can provide additional information about breast cancer risk. To date, most studies have measured mammographic texture within selected regions of interest (ROIs) in the breast, which cannot adequately capture the complexity of the parenchymal pattern throughout the whole breast. To better characterize patterns of the parenchymal tissue, the authors have developed a fully automated software pipeline based on a novel lattice-based strategy to extract a range of parenchymal texture features from the entire breast region. Methods: Digital mammograms from 106 cases with 318 age-matched controls were retrospectively analyzed. The lattice-based approach is based on a regular grid virtually overlaid on each mammographic image. Texture features are computed from the intersection (i.e., lattice) points of the grid lines within the breast, using a local window centered at each lattice point. Using this strategy, a range of statistical (gray-level histogram, co-occurrence, and run-length) and structural (edge-enhancing, local binary pattern, and fractal dimension) features are extracted. To cover the entire breast, the size of the local window for feature extraction is set equal to the lattice grid spacing and optimized experimentally by evaluating different windows sizes. The association between their lattice-based texture features and breast cancer was evaluated using logistic regression with leave-one-out cross validation and further compared to that of breast PD% and commonly used single-ROI texture features extracted from the retroareolar or the central breast region. Classification performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC). DeLong’s test was used to compare the different ROCs in terms of AUC performance. Results: The average univariate performance of the lattice-based features is higher when extracted from smaller than larger window sizes. While not every individual texture feature is superior to breast PD% (AUC: 0.59, STD: 0.03), their combination in multivariate analysis has significantly better performance (AUC: 0.85, STD: 0.02, p 0.05). Conclusions: The proposed lattice-based strategy for mammographic texture analysis enables to characterize the parenchymal pattern over the entire breast. As such, these features provide richer information compared to currently used descriptors and may ultimately improve breast cancer risk assessment. Larger studies are warranted to validate these findings and also compare to standard demographic and reproductive risk factors.

Characterisation of microcalcification clusters on 2D digital mammography (FFDM) and digital breast tomosynthesis (DBT): does DBT underestimate microcalcification clusters? Results of a multicentre study.

Abstract: Objectives To compare DBT and FFDM in the classification of microcalcification clusters (MCs) using BI-RADS.

Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: comparison of fully automated area and volumetric density measures in a case–control study with digital mammography

Abstract: Breast density, commonly quantified as the percentage of mammographically dense tissue area, is a strong breast cancer risk factor. We investigated associations between breast cancer and fully automated measures of breast density made by a new publicly available software tool, the Laboratory for Individualized Breast Radiodensity Assessment (LIBRA). Digital mammograms from 106 invasive breast cancer cases and 318 age-matched controls were retrospectively analyzed. Density estimates acquired by LIBRA were compared with commercially available software and standard Breast Imaging-Reporting and Data System (BI-RADS) density estimates. Associations between the different density measures and breast cancer were evaluated by using logistic regression after adjustment for Gail risk factors and body mass index (BMI). Area under the curve (AUC) of the receiver operating characteristic (ROC) was used to assess discriminatory capacity, and odds ratios (ORs) for each density measure are provided. All automated density measures had a significant association with breast cancer (OR = 1.47–2.23, AUC = 0.59–0.71, P < 0.01) which was strengthened after adjustment for Gail risk factors and BMI (OR = 1.96–2.64, AUC = 0.82–0.85, P < 0.001). In multivariable analysis, absolute dense area (OR = 1.84, P < 0.001) and absolute dense volume (OR = 1.67, P = 0.003) were jointly associated with breast cancer (AUC = 0.77, P < 0.01), having a larger discriminatory capacity than models considering the Gail risk factors alone (AUC = 0.64, P < 0.001) or the Gail risk factors plus standard area percent density (AUC = 0.68, P = 0.01). After BMI was further adjusted for, absolute dense area retained significance (OR = 2.18, P < 0.001) and volume percent density approached significance (OR = 1.47, P = 0.06). This combined area-volume density model also had a significantly (P < 0.001) improved discriminatory capacity (AUC = 0.86) relative to a model considering the Gail risk factors plus BMI (AUC = 0.80). Our study suggests that new automated density measures may ultimately augment the current standard breast cancer risk factors. In addition, the ability to fully automate density estimation with digital mammography, particularly through the use of publically available breast density estimation software, could accelerate the translation of density reporting in routine breast cancer screening and surveillance protocols and facilitate broader research into the use of breast density as a risk factor for breast cancer.

Slit-scanning differential x-ray phase-contrast mammography: Proof-of-concept experimental studies

Abstract: Purpose: The purpose of this work is to investigate the feasibility of grating-based, differential phase-contrast, full-field digital mammography (FFDM) in terms of the requirements for field-of-view (FOV), mechanical stability, and scan time. Methods: A rigid, actuator-free Talbot interferometric unit was designed and integrated into a state-of-the-art x-ray slit-scanning mammography system, namely, the Philips MicroDose L30 FFDM system. A dedicated phase-acquisition and phase retrieval method was developed and implemented that exploits the redundancy of the data acquisition inherent to the slit-scanning approach to image generation of the system. No modifications to the scan arm motion control were implemented. Results: The authors achieve a FOV of 160 × 196 mm consisting of two disjoint areas measuring 77 × 196 mm with a gap of 6 mm between them. Typical scanning times vary between 10 and 15 s and dose levels are lower than typical FFDM doses for conventional scans with identical acquisition parameters due to the presence of the source-grating G 0. Only minor to moderate artifacts are observed in the three reconstructed images, indicating that mechanical vibrations induced by other system components do not prevent the use of the platform for phase contrast imaging. Conclusions: To the best of our knowledge, this is the first attempt to integrate x-ray gratings hardware into a clinical mammography unit. The results demonstrate that a scanning differential phase contrast FFDM system that meets the requirements of FOV, stability, scan time, and dose can be build.

Suspicious Findings at Digital Breast Tomosynthesis Occult to Conventional Digital Mammography: Imaging Features and Pathology Findings

Abstract: To review the imaging and pathologic features of a series of lesions detected at digital breast tomosynthesis (DBT), which are occult to conventional digital mammography (DM). We retrospectively reviewed 268 consecutive breast imaging reporting and data system 4 and 5 lesions imaged with both DM and DBT at our facility from July 2012 through April 2013. For each lesion, we recorded the mammographic finding, breast density, mode of biopsy, and pathology results. A total of 19 lesions were identified at DBT that were occult to DM. Sixty three percent (12/19) of these lesions were identified in dense breasts. Architectural distortion was seen in 74% (14/19) of cases and spiculated masses accounted for the remaining 26% (5/19). The positive predictive value of biopsy was 53% (10/19). Seven cases were infiltrating ductal carcinomas and three were infiltrating lobular carcinomas. High-risk lesions accounted for 47% (9/19) of the lesions, the majority of which were radial scars 67% (6/9). Eighty four percent (16/19) of the lesions underwent ultrasound guided core biopsy while the remainder underwent magnetic resonance imaging guided core biopsy. DBT may demonstrate suspicious lesions that are occult to DM, particularly in women with dense breasts. Such lesions have a high likelihood of malignancy and warrant biopsy.

A computational model to generate simulated three-dimensional breast masses.

Abstract: Purpose: To develop algorithms for creating realistic three-dimensional (3D) simulated breast masses and embedding them within actual clinical mammograms. The proposed techniques yield high-resolution simulated breast masses having randomized shapes, with user-defined mass type, size, location, and shape characteristics. Methods: The authors describe a method of producing 3D digital simulations of breast masses and a technique for embedding these simulated masses within actual digitized mammograms. Simulated 3D breast masses were generated by using a modified stochastic Gaussian random sphere model to generate a central tumor mass, and an iterative fractal branching algorithm to add complex spicule structures. The simulated masses were embedded within actual digitized mammograms. The authors evaluated the realism of the resulting hybrid phantoms by generating corresponding left- and right-breast image pairs, consisting of one breast image containing a real mass, and the opposite breast image of the same patient containing a similar simulated mass. The authors then used computer-aided diagnosis (CAD) methods and expert radiologist readers to determine whether significant differences can be observed between the real and hybrid images. Results: The authors found no statistically significant difference between the CAD features obtained from the real and simulated images of masses with either spiculated or nonspiculated margins. Likewise, the authors found that expert human readers performed very poorly in discriminating their hybrid images from real mammograms. Conclusions: The authors’ proposed method permits the realistic simulation of 3D breast masses having user-defined characteristics, enabling the creation of a large set of hybrid breast images containing a well-characterized mass, embedded within real breast background. The computational nature of the model makes it suitable for detectability studies, evaluation of computer aided diagnosis algorithms, and teaching purposes.

Breast MRI, digital mammography and breast tomosynthesis: comparison of three methods for early detection of breast cancer.

Abstract: Breast cancer is the most common malignancy in women and early detection is important for its successful treatment. The aim of this study was to investigate the sensitivity and specificity of three methods for early detection of breast cancer: breast magnetic resonance imaging (MRI), digital mammography, and breast tomosynthesis in comparison to histopathology, as well as to investigate the intraindividual variability between these modalities. We included 57 breast lesions, each detected by three diagnostic modalities: digital mammography, breast MRI, and breast tomosynthesis, and subsequently confirmed by histopathology. Breast Imaging-Reporting and Data System (BI-RADS) was used for characterizing the lesions. One experienced radiologist interpreted all three diagnostic modalities. Twenty-nine of the breast lesions were malignant while 28 were benign. The sensitivity for digital mammography, breast MRI, and breast tomosynthesis, was 72.4%, 93.1%, and 100%, respectively; while the specificity was 46.4%, 60.7%, and 75%, respectively. Receiver operating characteristics (ROC) curve analysis showed an overall diagnostic advantage of breast tomosynthesis over both breast MRI and digital mammography. The difference in performance between breast tomosynthesis and digital mammography was significant (p <0.001), while the difference between breast tomosynthesis and breast MRI was not significant (p=0.20).

Comparison of mammographic density estimation by Volpara software with radiologists' visual assessment: analysis of clinical–radiologic factors affecting discrepancy between them:

Abstract: BackgroundVolumetric breast density analysis is useful for quantitative mammographic assessment. However, there are few studies about clinical–radiologic factors contributing to discrepancies in the visual assessment by radiologists.PurposeTo compare automated volumetric breast density measurement with BI-RADS breast density category by radiologists' visual assessments and to evaluate the clinical–radiologic factors affecting disagreement between two estimations.Material and MethodsFrom February 2011 to September 2012, 860 patients (mean age, 54.7 ± 10.2 years) who had undergone digital mammography including fully automated volumetric breast density analysis, were enrolled. The agreement in breast density assessments between two radiologists, and between an experienced radiologist and the automated software were evaluated using a weighted kappa (k) value. Clinical–radiologic factors contributing to disagreement between the results obtained by a radiologist and the automated software were evaluated using u...

Comparative evaluation of average glandular dose and breast cancer detection between single-view digital breast tomosynthesis (DBT) plus single-view digital mammography (DM) and two-view DM: correlation with breast thickness and density

Abstract: Objectives To compare the average glandular dose (AGD) and diagnostic performance of mediolateral oblique (MLO) digital breast tomosynthesis (DBT) plus cranio-caudal (CC) digital mammography (DM) with two-view DM, and to evaluate the correlation of AGD with breast thickness and density.

Baseline Screening Mammography: Performance of Full-Field Digital Mammography Versus Digital Breast Tomosynthesis.

Abstract: OBJECTIVE. Baseline mammography studies have significantly higher recall rates than mammography studies with available comparison examinations. Digital breast tomosynthesis reduces recalls when compared with digital mammographic screening alone, but many sites operate in a hybrid environment. To maximize the effect of screening digital breast tomosynthesis with limited resources, choosing which patient populations will benefit most is critical. This study evaluates digital breast tomosynthesis in the baseline screening population. MATERIALS AND METHODS. Outcomes were compared for 10,728 women who underwent digital mammography screening, including 1204 (11.2%) baseline studies, and 15,571 women who underwent digital breast tomosynthesis screening, including 1859 (11.9%) baseline studies. Recall rates, cancer detection rates, and positive predictive values were calculated. Logistic regression estimated the odds ratios of recall for digital mammography versus digital breast tomosynthesis for patients undergo...

Assessment of a Four-View Mammographic Image Feature Based Fusion Model to Predict Near-Term Breast Cancer Risk

Abstract: The purpose of this study was to develop and assess a new quantitative four-view mammographic image feature based fusion model to predict the near-term breast cancer risk of the individual women after a negative screening mammography examination of interest. The dataset included fully-anonymized mammograms acquired on 870 women with two sequential full-field digital mammography examinations. For each woman, the first “prior” examination in the series was interpreted as negative (not recalled) during the original image reading. In the second “current” examination, 430 women were diagnosed with pathology verified cancers and 440 remained negative (“cancer-free”). For each of four bilateral craniocaudal and mediolateral oblique view images of left and right breasts, we computed and analyzed eight groups of global mammographic texture and tissue density image features. A risk prediction model based on three artificial neural networks was developed to fuse image features computed from two bilateral views of four images. The risk model performance was tested using a ten-fold cross-validation method and a number of performance evaluation indices including the area under the receiver operating characteristic curve (AUC) and odds ratio (OR). The highest AUC = 0.725 ± 0.026 was obtained when the model was trained by gray-level run length statistics texture features computed on dense breast regions, which was significantly higher than the AUC values achieved using the model trained by only two bilateral one-view images (p < 0.02). The adjustable OR values monotonically increased from 1.0 to 11.8 as model-generated risk score increased. The regression analysis of OR values also showed a significant increase trend in slope (p < 0.01). As a result, this preliminary study demonstrated that a new four-view mammographic image feature based risk model could provide useful and supplementary image information to help predict the near-term breast cancer risk.

Average glandular dose in digital mammography and digital breast tomosynthesis: comparison of phantom and patient data.

Abstract: For the evaluation of the average glandular dose (AGD) in digital mammography (DM) and digital breast tomosynthesis (DBT) phantoms simulating standard model breasts are used. These phantoms consist of slabs of polymethyl methacrylate (PMMA) or a combination of PMMA and polyethylene (PE). In the last decades the automatic exposure control (AEC) increased in complexity and became more sensitive to (local) differences in breast composition. The question is how well the AGD estimated using these simple dosimetry phantoms agrees with the average patient AGD. In this study the AGDs for both dosimetry phantoms and for patients have been evaluated for 5 different x-ray systems in DM and DBT modes. It was found that the ratios between patient and phantom AGD did not differ considerably using both dosimetry phantoms. These ratios averaged over all breast thicknesses were 1.14 and 1.15 for the PMMA and PMMA-PE dosimetry phantoms respectively in DM mode and 1.00 and 1.02 in the DBT mode. These ratios were deemed to be sufficiently close to unity to be suitable for dosimetry evaluation in quality control procedures. However care should be taken when comparing systems for DM and DBT since depending on the AEC operation, ratios for particular breast thicknesses may differ substantially (0.83-1.96). Although the predictions of both phantoms are similar we advise the use of PMMA + PE slabs for both DM and DBT to harmonize dosimetry protocols and avoid any potential issues with the use of spacers with the PMMA phantoms.

Toward Clinically Compatible Phase-Contrast Mammography

Abstract: Phase-contrast mammography using laboratory X-ray sources is a promising approach to overcome the relatively low sensitivity and specificity of clinical, absorption-based screening. Current research is mostly centered on identifying potential diagnostic benefits arising from phase-contrast and dark-field mammography and benchmarking the latter with conventional state-of-the-art imaging methods. So far, little effort has been made to adjust this novel imaging technique to clinical needs. In this article, we address the key points for a successful implementation to a clinical routine in the near future and present the very first dose-compatible and rapid scan-time phase-contrast mammograms of both a freshly dissected, cancer-bearing mastectomy specimen and a mammographic accreditation phantom.

The Divide Between Breast Density Notification Laws and Evidence-Based Guidelines for Breast Cancer Screening: Legislating Practice

Abstract: In 2003, Dr Nancy Cappello was stunned by her diagnosis of breast cancer just 6 weeks after normal findings on mammography.1 She subsequently discovered medical literature documenting the lower sensitivity of mammography in women with dense breast tissue, as well as the association between greater breast density and increased cancer risk. Frustrated by the reluctance of her physicians to discuss the potential role of breast density in her diagnosis, Cappello pursued legislation in Connecticut, where she lived. The legislation was enacted, requiring insurers to cover breast ultrasonography as an adjunctive screening test to mammography for women with dense breast tissue and to notify women of their breast density results and the potential effect on the sensitivity of screening. As of April 2015, breast density notification laws had been enacted in 22 states and are advancing in an additional 13 states, and federal legislation has been introduced.2 Modeling suggests that at least 28 million women in the United States between the ages of 40 and 74 years (43%of women in this age group) could be affected by such legislation.3 Breast density notification laws have the well-intentioned goals of improving individual decision making and the quality of breast cancer screening. However, there are few data that the laws actually improve the understanding of breast cancer risk, the limitations of mammography as a screening test, and the diagnosis or patient outcomes. Moreover, the laws create the unsubstantiated anticipation that additional testing is better for women. A recent survey found that Connecticut women were more likely than women from other states to have discussed their breast density with a clinician (67% vs 43%) despite similar knowledge of the effect of breast density on cancer risk.4 Women with extremely dense breast tissue have 2 times the relative risk of developing breast cancer than women with average breast density. However, the difference in absolute risk is small. The absolute 5-year risk of breast cancer for a 45-year-old woman with average breast density, no family history of breast cancer, and no history of prior breast biopsy is 0.7% and that of a similar woman with extremely dense breast tissue is 1.3%.5 Such laws also do not address other important risk factors for breast cancer such as age, family history of breast or ovarian cancer, BRCA (OMIM 113705 and 600185) and other genetic mutations, or prior breast biopsy. Breast density notification laws may limit a broader understanding and discussion of personal risk, as well as the benefits and harms of different screening approaches. For some women, other risk factors may be more relevant but not highlighted. The legislation brings the probability of greater clinical uncertainty and increased liability for radiologists and primary care physicians, for whom failure to diagnose breast cancer is an important source of malpractice liability. For women, there are the likelihoods of false-positive results, unnecessary biopsies, and overdiagnosis that lead to the morbidity of unneeded treatment. Some states also require that a woman must be told that she may benefit from additional screening tests (eg, breast ultrasonography), and a few states (eg, Connecticut) mandate that insurance companies must cover additional testing for women with dense breast tissue. Such legislation goes beyond the evidence that supports additional testing for women who are at high risk for breast cancer on the basis of other established risk factors.6 Modeling suggests that supplemental ultrasonography after normal findings on screening mammography for women between the ages of 50 and 74 years with heterogeneously or extremely dense breast tissue may avert only 0.4 breast cancer deaths but result in 354 additional biopsy recommendations per 1000 women screened compared with biennial screening mammography alone, with a cost-effectiveness ratio of $325 000 per quality-adjusted life-year gained.7 Women with lower breast density but with other risk factors for breast cancer may be falsely reassured that their low breast density confers some protection and may defer or postpone screening. The laws raise additional issues. For example, interpretation of breast density is subjective. Wary of the additional complexity introduced by breast density legislation, radiologists may downgrade their assessment of density to avoid requirements for reporting, or they may upgrade their reporting so that supplemental screening can be ordered to minimize liability, thus limiting the validity of a breast density assessment. When pressed for time during office visits, primary care clinicians may reflexively order supplemental screening without assessing the benefits and harms or reaching informed decisions with their patients. The focus on breast cancer screening may divert attention from discussion of other health risks, such as risk factors for heart disease, which is the leading cause of death for women. Women who live in states where notification laws do not mandate insurance coverage may be responsible for paying for supplemental screening. Finally, a legislated approach to medical care is cumbersome as new evidence becomes available. Digital mammography and digital breast tomosynthesis, which creates a series of reconstructed images with a 3-dimensional format that minimizes the effect of overlapping and obscuring breast structures, are being broadly disseminated into clinical practice. Both technologies may be superior to the film mammography that was commonly used a decade ago, when the advocacy began for breast density notification laws.8Overtime, advances in imaging, new screening modalities, and improved understanding of breast cancer risk related to density and other factors are likely to decrease the salience of this legislation. The weaknesses of breast density notification laws are accentuated by the guidance from the US Preventive Services Task Force (USPSTF), which in April 2015 released for public comment a draft of updated recommendations for breast cancer screening.9 Specifically addressing the situation of women identified with dense breast tissue on an otherwise negative screening mammogram, the draft recommendations conclude that current evidence is insufficient to assess the balance of the benefits and harms of adjunctive screening with ultrasonography or magnetic resonance imaging. This draft further solidifies the task force's 2009 recommendations, particularly that the decision to start screening mammography in women before age 50 years should be individualized (grade C recommendation, which reflects an assessment that there is at least moderate certainty that the net benefit is small). The draft also reaffirms that the frequency of screening for average-risk women between the ages of 50 and 74 years should be biennial and not annual (grade B recommendation, which reflects an assessment that there is high certainty that the net benefit is moderate or there is moderate certainty that the net benefit is moderate to substantial).10 The Patient Care and Affordable Care Act requires that Medicare and other health plans must cover screening and preventive services in those with a USPSTF grade A or B recommendation, without patient cost sharing. Because of the controversy that surrounded the release of the task force's 2009 guidelines, the Patient Care and Affordable Care Act specified that the most current USPSTF recommendations for breast cancer screening other than those issued in 2009 would be considered the most current, therefore allowing insurance coverage of mammography for women between the ages of 40 and 49 years. When the final 2015 USPSTF recommendations are issued, the rules for coverage of breast cancer screening and other screening and preventive services may finally be the same. Between 2009 and 2015, little has changed in the USPSTF recommendations, reflecting the unfortunate reality that meaningful medical evidence often takes years to develop. It is not surprising that patients and advocates are using legislation to address the limits of our knowledge about breast cancer screening. However, breast density notification laws are unlikely to improve our understanding of breast cancer risk, screening, and diagnosis or to save lives. Instead, the laws may result in substantial personal harms and societal costs. The 2015 update of the USPSTF recommendations provides an opportunity for states and the federal government to reconsider a legislative approach to breast cancer screening and to instead endorse care that is based on evidence.

Computer-aided classification of breast masses in mammogram images based on spherical wavelet transform and support vector machines

Abstract: Breast cancer can be effectively detected and diagnosed using the technology of digital mammography. However, although this technology has been rapidly developing recently, suspicious regions cannot be detected in some cases by radiologists, because of the noise or inappropriate mammogram contrast. This study presents a classification of segmented region of interests ROIs as either benign or malignant to serve as a second eye of the radiologists. Our study consists of three steps. In the first step, spherical wavelet transform SWT is applied to the original ROIs. In the second step, shape, boundary and grey level based features of wavelet detail and scaling approximation coefficients are extracted. Finally, in the third step, malignant/benign classification of the masses is implemented by giving the feature matrices to a support vector machine system. The proposed system achieves 91.4% and 90.1% classification accuracy using the dataset acquired from the hospital of Istanbul University in Turkey and the free Mammographic Image Analysis Society, respectively. Furthermore, discrete wavelet transform, which produces 83.3% classification accuracy, is applied to the coefficients to make a comparison with the SWT method.

Radiologist-performed hand-held ultrasound screening at average risk of breast cancer: results from a single health screening center.

Abstract: BackgroundUltrasound (US) screening is not currently recommended as a routine screening modality in the general population of average risk. The cancer detection yield and positive predictive value in an average risk general population who undergo breast screening by experienced radiologists is unclear.PurposeTo determine the performance of screening breast US in women at an average risk for breast cancer undergoing breast screening by experienced radiologists.Material and MethodsThis study received institutional review board approval, and informed consent was waived. A retrospective review of our database revealed 1526 women who underwent prevalence screening US at a single health screening center and had negative findings on digital mammography (MG). The Breast Imaging and Reporting Data System (BI-RADS) final assessments of the breast US were analyzed retrospectively, with the reference standard defined as a combination of pathology and a 12-month follow-up. The cancer detection rate and positive predic...

Accuracy of GE digital breast tomosynthesis versus supplementary mammographic views for diagnosis of screen-detected soft tissue breast lesions

Abstract: Objective: To compare the accuracy of standard supplementary views and GE digital breast tomosynthesis (DBT) for assessment of soft-tissue mammographic abnormalities. Methods: Women recalled for further assessment of soft-tissue abnormalities were recruited and received standard supplementary views (typically spot compression views) and two-view GE DBT. The added value of DBT in the assessment process was determined by analysing data collected prospectively by radiologists working up the cases. Following anonymization of cases, there was also a retrospective multireader review. The readers first read bilateral standard two-view digital mammography (DM) together with the supplementary mammographic views and gave a combined score for suspicion of malignancy on a five-point scale. The same readers then read bilateral standard two-view DM together with two-view DBT. Pathology data were obtained. Differences were assessed using receiver operating characteristic analysis. Results: The study population was 342 lesions in 322 patients. The final diagnosis was malignant in 113 cases (33%) and benign/normal in 229 cases (67%). In the prospective analysis, the performance of two-view DM plus DBT was at least equivalent to the performance of two-view DM and standard mammographic supplementary views—the area under the curve (AUC) was 0.946 and 0.922, respectively, which did not reach statistical significance. Similar results were obtained for the retrospective review—AUC was 0.900 (DBT) and 0.873 (supplementary views), which did not reach statistical significance. Conclusion: The accuracy of GE DBT in the assessment of screen detected soft-tissue abnormalities is equivalent to the use of standard supplementary mammographic views. Advances in knowledge: The vast majority of evidence relating to the use of DBT has been gathered from research using Hologic equipment. This study provides evidence for the use of the commercially available GE DBT system demonstrating that it is at least equivalent to supplementary mammographic views in the assessment of soft-tissue screen-detected abnormalities.

Using multiscale texture and density features for near-term breast cancer risk analysis.

Abstract: Purpose: To help improve efficacy of screening mammography by eventually establishing a new optimal personalized screening paradigm, the authors investigated the potential of using the quantitative multiscale texture and density feature analysis of digital mammograms to predict near-term breast cancer risk. Methods: The authors’ dataset includes digital mammograms acquired from 340 women. Among them, 141 were positive and 199 were negative/benign cases. The negative digital mammograms acquired from the “prior” screening examinations were used in the study. Based on the intensity value distributions, five subregions at different scales were extracted from each mammogram. Five groups of features, including density and texture features, were developed and calculated on every one of the subregions. Sequential forward floating selection was used to search for the effective combinations. Using the selected features, a support vector machine (SVM) was optimized using a tenfold validation method to predict the risk of each woman having image-detectable cancer in the next sequential mammography screening. The area under the receiver operating characteristic curve (AUC) was used as the performance assessment index. Results: From a total number of 765 features computed from multiscale subregions, an optimal feature set of 12 features was selected. Applying this feature set, a SVM classifier yielded performance of AUC = 0.729 ± 0.021. The positive predictive value was 0.657 (92 of 140) and the negative predictive value was 0.755 (151 of 200). Conclusions: The study results demonstrated a moderately high positive association between risk prediction scores generated by the quantitative multiscale mammographic image feature analysis and the actual risk of a woman having an image-detectable breast cancer in the next subsequent examinations.

Population of 224 realistic human subject-based computational breast phantoms.

Abstract: Purpose: To create a database of highly realistic and anatomically variable 3D virtual breast phantoms based on dedicated breast computed tomography (bCT) data. Methods: A tissue classification and segmentation algorithm was used to create realistic and detailed 3D computational breast phantoms based on 230 + dedicated bCT datasets from normal human subjects. The breast volume was identified using a coarse three-class fuzzy C-means segmentation algorithm which accounted for and removed motion blur at the breast periphery. Noise in the bCT data was reduced through application of a postreconstruction 3D bilateral filter. A 3D adipose nonuniformity (bias field) correction was then applied followed by glandular segmentation using a 3D bias-corrected fuzzy C-means algorithm. Multiple tissue classes were defined including skin, adipose, and several fractional glandular densities. Following segmentation, a skin mask was produced which preserved the interdigitated skin, adipose, and glandular boundaries of the skin interior. Finally, surface modeling was used to produce digital phantoms with methods complementary to the XCAT suite of digital human phantoms. Results: After rejecting some datasets due to artifacts, 224 virtual breast phantoms were created which emulate the complex breast parenchyma of actual human subjects. The volume breast density (with skin) ranged from 5.5% to 66.3% with a mean value of 25.3% ± 13.2%. Breast volumes ranged from 25.0 to 2099.6 ml with a mean value of 716.3 ± 386.5 ml. Three breast phantoms were selected for imaging with digital compression (using finite element modeling) and simple ray-tracing, and the results show promise in their potential to produce realistic simulated mammograms. Conclusions: This work provides a new population of 224 breast phantoms based on in vivo bCT data for imaging research. Compared to previous studies based on only a few prototype cases, this dataset provides a rich source of new cases spanning a wide range of breast types, volumes, densities, and parenchymal patterns.

Characterization of Breast Lesions: Comparison of Digital Breast Tomosynthesis and Ultrasonography

Abstract: OBJECTIVE: To compare the diagnostic performance of digital breast tomosynthesis (DBT) and conventional breast ultrasound (US) to characterize breast lesions as benign or malignant. MATERIALS AND METHODS: A total of 332 women, presenting for screening examinations or for breast biopsy between March and June 2012 were recruited to undergo digital mammography (DM), DBT, and breast US examination. Among them, 113 patients with 119 breast lesions depicted on DM were finally included. Three blinded radiologists performed an enriched reader study and reviewed the DBT and US images. Each reader analyzed the lesions in random order, assigned Breast Imaging Reporting and Data System (BI-RADS) descriptors, rated the images for the likelihood of malignancy (%) and made a BI-RADS final assessment. Diagnostic accuracy, as assessed by the area under the receiver operating characteristic curve, sensitivity, and specificity of DBT and US were compared. RESULTS: Among the 119 breast lesions depicted on DM, 75 were malignant and the remaining 44 were benign. The average diagnostic performance for characterizing breast lesions as benign or malignant in terms of area under the curve was 0.899 for DBT and 0.914 for US (p = 0.394). Mean sensitivity (97.3% vs. 98.7%, p = 0.508) and specificity (44.7% vs. 39.4%, p = 0.360) were also not significantly different. CONCLUSION: Digital breast tomosynthesis may provide similar reader lesion characterization performance to that of US for breast lesions depicted on DM.

A new approach to develop computer-aided detection schemes of digital mammograms.

Abstract: The purpose of this study is to develop a new global mammographic image feature analysis based computer-aided detection (CAD) scheme and evaluate its performance in detecting positive screening mammography examinations. A dataset that includes images acquired from 1896 full-field digital mammography (FFDM) screening examinations was used in this study. Among them, 812 cases were positive for cancer and 1084 were negative or benign. After segmenting the breast area, a computerized scheme was applied to compute 92 global mammographic tissue density based features on each of four mammograms of the craniocaudal (CC) and mediolateral oblique (MLO) views. After adding three existing popular risk factors (woman's age, subjectively rated mammographic density, and family breast cancer history) into the initial feature pool, we applied a sequential forward floating selection feature selection algorithm to select relevant features from the bilateral CC and MLO view images separately. The selected CC and MLO view image features were used to train two artificial neural networks (ANNs). The results were then fused by a third ANN to build a two-stage classifier to predict the likelihood of the FFDM screening examination being positive. CAD performance was tested using a ten-fold cross-validation method. The computed area under the receiver operating characteristic curve was AUC = 0.779 ± 0.025 and the odds ratio monotonically increased from 1 to 31.55 as CAD-generated detection scores increased. The study demonstrated that this new global image feature based CAD scheme had a relatively higher discriminatory power to cue the FFDM examinations with high risk of being positive, which may provide a new CAD-cueing method to assist radiologists in reading and interpreting screening mammograms.

Computer-aided detection system for clustered microcalcifications in digital breast tomosynthesis using joint information from volumetric and planar projection images.

Abstract: We propose a novel approach for the detection of microcalcification clusters (MCs) using joint information from digital breast tomosynthesis (DBT) volume and planar projection (PPJ) image. A data set of 307 DBT views was collected with IRB approval using a prototype DBT system. The system acquires 21 projection views (PVs) from a wide tomographic angle of 60° (60°-21PV) at about twice the dose of a digital mammography (DM) system, which allows us the flexibility of simulating other DBT acquisition geometries using a subset of the PVs. In this study, we simulated a 30° DBT geometry using the central 11 PVs (30°-11PV). The narrower tomographic angle is closer to DBT geometries commercially available or under development and the dose is matched approximately to that of a DM. We developed a new joint-CAD system for detection of clustered microcalcifications. The DBT volume was reconstructed with a multiscale bilateral filtering regularized method and a PPJ image was generated from the reconstructed volume. Task-specific detection strategies were designed to combine information from the DBT volume and the PPJ image. The data set was divided into a training set (127 views with MCs) and an independent test set (104 views with MCs and 76 views without MCs). The joint-CAD system outperformed the individual CAD systems for DBT volume or PPJ image alone; the differences in the test performances were statistically significant (p < 0.05) using JAFROC analysis.

Breast Cancer Characteristics Associated With Digital Versus Film-Screen Mammography for Screen-Detected and Interval Cancers.

Abstract: OBJECTIVE. The purpose of this study was to determine whether pathologic findings of screen-detected and interval cancers differ for digital versus film mammography. MATERIALS AND METHODS. Breast Cancer Surveillance Consortium data from 2003–2011 on 3,021,515 screening mammograms (40.3% digital, 59.7% film) of women 40–89 years old were reviewed. Cancers were considered screen detected if diagnosed within 12 months of an examination with positive findings and interval if diagnosed within 12 months of an examination with negative findings. Tumor characteristics for screen-detected and interval cancers were compared for digital versus film mammography by use of logistic regression models to estimate the odds ratio and 95% CI with adjustment for age, race and ethnicity, hormone therapy use, screening interval, examination year, and registry. Generalized estimating equations were used to account for correlation within facilities. RESULTS. Among 15,729 breast cancers, 85.3% were screen detected and 14.7% were ...