A Practical Truncation Correction Method for Digital Breast Tomosynthesis

To compare the diagnostic performance of two-dimensional (2D) mammography, 2D mammography plus digital breast tomosynthesis (DBT), and synthetic 2D mammography plus DBT in depicting malignant radiographic features. In this multicenter, multireader, retrospective reading study (the TOMMY trial), after written informed consent was obtained, 8869 women (age range, 29-85 years; mean, 56 years) were recruited from July 2011 to March 2013 in an ethically approved study. From these women, a reading dataset of 7060 cases was randomly allocated for independent blinded review of (a) 2D mammography images, (b) 2D mammography plus DBT images, and (c) synthetic 2D mammography plus DBT images. Reviewers had no access to results of previous examinations. Overall sensitivities and specificities were calculated for younger women and those with dense breasts. Overall sensitivity was 87% for 2D mammography, 89% for 2D mammography plus DBT, and 88% for synthetic 2D mammography plus DBT. The addition of DBT was associated with a 34% increase in the odds of depicting cancer (odds ratio [OR] = 1.34, P = .06); however, this level did not achieve significance. For patients aged 50-59 years old, sensitivity was significantly higher (P = .01) for 2D mammography plus DBT than it was for 2D mammography. For those with breast density of 50% or more, sensitivity was 86% for 2D mammography compared with 93% for 2D mammography plus DBT (P = .03). Specificity was 57% for 2D mammography, 70% for 2D mammography plus DBT, and 72% for synthetic 2D mammography plusmDBT. Specificity was significantly higher than 2D mammography (P < .001in both cases) and was observed for all subgroups (P < .001 for all cases). The addition of DBT increased the sensitivity of 2D mammography in patients with dense breasts and the specificity of 2D mammography for all subgroups. The use of synthetic 2D DBT demonstrated performance similar to that of standard 2D mammography with DBT. DBT is of potential benefit to screening programs, particularly in younger women with dense breasts. (©) RSNA, 2015.

Purpose To examine the outcomes of a breast cancer screening program based on digital breast tomosynthesis (DBT) plus synthesized two-dimensional (2D) mammography compared with those after full-field digital mammography (FFDM). Materials and Methods This prospective study included 16 666 asymptomatic women aged 50-69 years who were recruited in April 2015 through March 2016 for DBT plus synthetic 2D screening in the Verona screening program. A comparison cohort of women screened with FFDM (n = 14 423) in the previous year was included. Screening detection measures for the two groups were compared by calculating the proportions associated with each outcome, and the relative rates (RRs) were estimated with multivariate logistic regression. Results Cancer detection rate (CDR) for DBT plus synthetic 2D imaging was 9.30 per 1000 screening examinations versus 5.41 per 1000 screening examinations with FFDM (RR, 1.72; 95% confidence interval [CI]: 1.30, 2.29). CDR was significantly higher in patients screened with DBT plus synthetic 2D imaging than in those screened with FFDM among women classified as having low breast density (RR, 1.53; 95% CI: 1.13, 2.10) or high breast density (RR, 2.86; 95% CI: 1.42, 6.25). The positive predictive value (PPV) for recall was almost doubled with DBT plus synthetic 2D imaging: 23.3% versus 12.9% of recalled patients who were screened with FFDM (RR, 1.81; 95% CI: 1.34, 2.47). The recall rate was similar between groups (RR, 0.95; 95% CI: 0.84, 1.06), whereas the recall rate with invasive assessment was higher for DBT plus synthetic 2D imaging than for FFDM (RR, 1.93; 95% CI: 1.31, 2.03). The mean number of screening studies interpreted per hour was significantly lower for screening examinations performed with DBT plus synthetic 2D imaging (38.5 screens per hour) than with FFDM (60 screens per hour) (P < .001). Conclusion DBT plus synthetic 2D imaging increases CDRs with recall rates comparable to those of FFDM. DBT plus synthetic 2D imaging increased image reading time and the time needed for invasive assessments. © RSNA, 2017.

Time for Change in Digital Breast Tomosynthesis Research.

As a special case of tomography, digital breast tomosynthesis (DBT) can realize quasi-3D image reconstruction for breast lesion detection from few-view and limited-angle projection data. For DBT image reconstruction, iterative algorithms are needed to suppress artifacts due to undersampling, and adaptive regularizations are necessary for preserving the edges of masses and calcifications. This paper presents a novel reconstruction method by regularizing projection onto convex sets (POCS) with multiscale Tikhonov-total variation (MTTV). The regularization, known as adaptive multiscale anisotropic diffusion, is able to preserve edges to a considerable extent and selectively suppress noise without introducing artifacts. The proposed method is referred to as MTTV-POCS and is evaluated quantitatively using 3D numerical breast and Shepp-Logan phantoms as well as two clinical volume images acquired from an advanced DBT machine. Experimental results show that the proposed method has better performance in metrics of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) over two existing methods: adaptive-steepest-descent-POCS (ASD-POCS) and selective-diffusion regularized simultaneous algebraic reconstruction technique (SD-SART). As indicated by the results, the proposed method is applicable to DBT for high-quality image reconstruction.

Mammographic breast density is a known breast cancer risk factor. Studies have shown the potential to automate breast density estimation by using computerized texture-based segmentation of the dense tissue in mammograms. Digital breast tomosynthesis (DBT) is a tomographic x-ray breast imaging modality that could allow volumetric breast density estimation. We evaluated the feasibility of distinguishing between dense and fatty breast regions in DBT using computer-extracted texture features. Our long-term hypothesis is that DBT texture analysis can be used to develop 3D dense tissue segmentation algorithms for estimating volumetric breast density. DBT images from 40 women were analyzed. The dense tissue area was delineated within each central source projection (CSP) image using a thresholding technique (Cumulus, Univ. Toronto). Two (2.5cm) 2 ROIs were manually selected: one within the dense tissue region and another within the fatty region. Corresponding (2.5cm) 3 ROIs were placed within the reconstructed DBT images. Texture features, previously used for mammographic dense tissue segmentation, were computed. Receiver operating characteristic (ROC) curve analysis was performed to evaluate feature classification performance. Different texture features appeared to perform best in the 3D reconstructed DBT compared to the 2D CSP images. Fractal dimension was superior in DBT (AUC=0.90), while contrast was best in CSP images (AUC=0.92). We attribute these differences to the effects of tissue superimposition in CSP and the volumetric visualization of the breast tissue in DBT. Our results suggest that novel approaches, different than those conventionally used in projection mammography, need to be investigated in order to develop DBT dense tissue segmentation algorithms for estimating volumetric breast density.

Our study was to compare the clarity with which calcifications are seen on conventional mammography (CM) with the same calcifications on digital breast tomosynthesis (DBT). We define clarity as the sharpness, contrast, and diagnostic quality by which the calcifications were depicted. In a HIPPA compliant Institutional Review Board approved study, 3,000 women volunteered to have both a screening mammogram and a DBT study. A total of 119 sequential cases with relevant calcifications (not clearly benign) were reviewed. Two board certified, dedicated, breast imaging radiologists reviewed the CM and DBT images in an unblinded paired comparison. Only the mediolateral oblique (MLO) projection was available for the DBT studies. The MLO and craniocaudal projections were reviewed using the 2D images. Window and leveling, and electronic zoom were permitted. Unlimited time was allowed to provide a subjective assessment as too how well the calcifications were seen, from a diagnostic perspective, when the two studies were evaluated side-by-side. In 41.6% of the cases, the readers felt that calcifications were seen with superior clarity on DBT. In 50.4% of the cases, the visibility of calcifications was the same for DBT and CM, and in 8% of the cases, calcifications were seen with greater clarity on CM than DBT. In 92% of the cases, the clarity with which calcifications were seen on DBT was equal to or better than for CM and in almost half, the clarity on DBT was judged to be better than for CM. Our analysis shows that calcifications can be demonstrated with equal or greater clarity on DBT as on CM, thus allowing for comparable, and, perhaps, improved interpretive analysis of detected calcifications.

In many western countries, there is good evidence documenting the performance of radiologists reading digital breast tomosynthesis (DBT) images. However, the diagnostic efficiency of Chinese radiologists using DBT, particularly type of errors being made and type of cancers being missed, is understudied. This study aims to investigate the pattern of diagnostic errors across different lesion types produced by Chinese radiologists diagnosing from DBT images. Australian radiologists will be used as a benchmark. Twelve Chinese radiologists read a DBT test set and located each perceived cancer lesion. True positives, false positives (FP), true negatives and false negatives (FN) were generated. The same test set was also read by 14 Australian radiologists. Z-scores and Pearson correlations were used to compare interpretation of lesions and identification of normal appearances between two groups of radiologists. Architectural distortions (p<.001) and stellate masses (p=.02) were more difficult for Chinese radiologists to correctly diagnose compared to their Australian counterparts. Chinese readers categorised more FPs as discrete masses (p<.001) and fewer FPs as architectural distortions (p<.001) comparing with Australian radiologists. The percentages of FN for each cancer case were not correlated (r=0.37, p=.18) but the percentages of FP for each normal case were moderately correlated (r=0.52, p=.02) between two groups of readers. Architectural distortions and stellate masses were challenging to Chinese radiologists when reading DBT. Our findings proposed the need of development of training and education programs focussing on imaging cases tailored for specific groups of readers with certain interpretation patterns.

Digital breast tomosynthesis (DBT) has superior detection performance than mammography (DM) for population-based breast cancer screening, but the higher number of images that must be reviewed poses a challenge for its implementation. This may be ameliorated by creating a twodimensional synthetic mammographic image (SM) from the DBT volume, containing the most relevant information. When creating a SM, it is of utmost importance to have an accurate lesion localization detection algorithm, while segmenting fibroglandular tissue could also be beneficial. These tasks encounter an extra challenge when working with images in the medio-lateral oblique view, due to the presence of the pectoral muscle, which has similar radiographic density. In this work, we present an automatic pectoral muscle segmentation model based on a u-net deep learning architecture, trained with 136 DBT images acquired with a single system (different BIRADS ® densities and pathological findings). The model was tested on 36 DBT images from that same system resulting in a dice similarity coefficient (DSC) of 0.977 (0.967-0.984). In addition, the model was tested on 125 images from two different systems and three different modalities (DBT, SM, DM), obtaining DSCs between 0.947 and 0.970, a range determined visually to provide adequate segmentations. For reference, a resident radiologist independently annotated a mix of 25 cases obtaining a DSC of 0.971. The results suggest the possibility of using this model for inter-manufacturer DBT, DM and SM tasks that benefit from the segmentation of the pectoral muscle, such as SM generation, computer aided detection systems, or patient dosimetry algorithms.

Recall Rate Reduction with Tomosynthesis During Baseline Screening Examinations: An Assessment From a Prospective Trial

Background Multiple studies showed digital breast tomosynthesis (DBT) has a higher cancer detection rate and a lower recall rate than full-field digital mammography (FFDM). However, there is a paucity of studies on whether the benign biopsy rate will be lower with DBT. Purpose To evaluate the benign biopsy rate of DBT versus that of FFDM in women recalled after breast screening within the National Health Service Breast Screening Programme. Materials and Methods This prospective single institution study included women who underwent screening FFDM or screening breast MRI between November 13, 2015, and July 29, 2016, and were recalled. Women who were recalled underwent a breast examination, additional imaging (mammography, US), and biopsy, if required. The number of interventions and the outcome of assessment were recorded. FFDM was performed at the screening appointment, and DBT images were acquired when participants attended a screening assessment within 3 weeks after screening FFDM. Two of four readers blinded to biopsy results analyzed DBT images in isolation and then in conjunction with FFDM images and any additional two-dimensional mammographic or US images. Two-tailed McNemar tests were used to test differences in sensitivity and specificity of the two conditions at 5% significance level. Results During the study period, 30 933 women underwent FFDM. A total of 1470 women were recalled (recall rate, 4.8%), and 827 were included after exclusion criteria were applied. Their mean age was 56.7 years ± 7.7 (standard deviation). A total of 145 breast cancers were detected (142 with FFDM, two with DBT only, one with surveillance MRI). Triple assessment without DBT resulted in 571 breast biopsies and enabled detection of 142 cancers. The addition of DBT would have resulted in 298 biopsies and detection of 142 cancers, reducing the number of biopsies from 571 of 827 (69.0%) to 298 of 827 (36.0%). Conclusion When compared with full-field digital mammography, digital breast tomosynthesis can reduce the benign biopsy rate while maintaining the cancer detection rate. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Skaane in this issue.

BACKGROUND. Prior studies comparing cancer screening by digital breast tomosynthesis (DBT) and digital mammography (DM) have largely entailed prospective trials and investigations at tertiary academic centers, which may encounter high-risk patient populations and lack heterogeneity among interpreting radiologists. Thus, results may not generalize across real-world community settings in the United States. OBJECTIVE. The purpose of this study was to compare DBT and DM in terms of screening performance and tumor characteristics of screen-detected cancers in a community practice setting. METHODS. Data were retrospectively retrieved for all screening mammography examinations performed by DBT or DM at four outpatient private practice facilities from January 1, 2012, to July 10, 2019. Examinations were interpreted by one of 26 radiologists (21 breast radiologists, five general radiologists). Further detailed information was recorded from radiology and pathology reports for all screen-detected cancers. Statistical comparisons were performed between DBT and DM in terms of screening performance and tumor characteristics of screen-detected cancers. RESULTS. A total of 310 cancers were detected in 47,096 screening DBT examinations and 83,200 screening DM examinations. Cancer detection rate was higher (p < .001) for DBT (3.4 per 1000 women) than for DM (1.8 per 1000 women). PPV1 was higher (p < .001) for DBT (3.5% [161/4641]) than for DM (2.1% [149/7116]). Patients with DBT-detected cancer were younger than those with DM-detected cancer (mean age [range], 61 years [40-87 years] vs 64 years [37-88 years]; p = .02). A greater percentage of DBT-detected than DM-detected cancers were invasive (85.1% [137/161] vs 72.5% [108/149]; p = .006), grade 1 when invasive (27.9% [38/136] vs 17.8% [19/107]; p = .04), and node negative (92.2% [71/77] vs 78.4% [58/74]; p = .02). Cancers detected by DBT and DM were not significantly different in histologic subtype, molecular subtype, or mean size (all p > .05). CONCLUSION. DBT showed a higher cancer detection rate and PPV1 than DM, and patients were younger at cancer diagnosis with DBT. Cancers detected on DBT were more often invasive, grade 1, and node negative. CLINICAL IMPACT. The findings support the generalizability of insights into DBT-based screening, which previously have been investigated primarily in academic settings.

Purpose To determine whether the rates and tumor characteristics of screening-detected and interval cancers differ for two-dimensional digital mammography (DM) versus digital breast tomosynthesis (DBT) mammography. Materials and Methods Consecutive screening mammograms from January 2009 to February 2011 (DM group, before DBT integration) and from January 2013 to February 2015 (DBT group, after complete DBT integration) were reviewed. Cancers were considered screening detected if diagnosed within 365 days of a positive screening examination and interval if diagnosed within 365 days of a negative screening examination. Z tests were used to compare cancers on DM versus DBT examinations. Results A total of 948 breast cancers were diagnosed after 78 385 DM and 76 896 DBT examinations. Although the overall rate of screening-detected cancers was similar with DM and DBT (5.0 vs 5.0 per 1000 examinations, P = .98), a higher proportion of screening-detected cancers were invasive rather than in situ with DBT (74.2% [287 of 387] vs 66.0% [260 of 394], P = .01). There were no significant differences in tumor characteristics, including size at pathologic examination, grade, hormone receptor status, and nodal status, between the screening-detected invasive cancers on DM versus DBT (P = .09-.99). The rate of interval cancers was similar with DM and DBT (1.1 vs 1.1 per 1000 examinations, P = .84). Compared with symptomatic interval cancers, magnetic resonance imaging-detected interval cancers were more likely to be minimal cancers. Conclusion The overall rates of screening-detected and interval cancers are similar with DM and DBT, but a higher proportion of screening-detected cancers are invasive rather than in situ with DBT. © RSNA, 2017.

The purpose of this study was to analyze the clinicopathologic and immunohistochemical features of invasive breast cancers detected only with digital breast tomosynthesis (DBT), compared with those of cancers detected with both DBT and full-field digital mammography (FFDM). The medical records of 261 women (108 without and 153 with symptoms) with invasive breast cancers who underwent FFDM and DBT between April 2015 and June 2016 were retrospectively reviewed. To assess detectability, all DBT and FFDM images were reviewed independently by three radiologists blinded to clinicopathologic information. The reference standard was established by an unblinded consensus review of all images. Clinicopathologic and immunohistochemical features were analyzed according to the detectability status. Of the 261 cancers, 223 (85.4%) were detected with both DBT and FFDM (both-detected group). Twenty-four cancers (9.2%) not detected with FFDM (DBT-only group) were classified by DBT as a mass (58.3%), architectural distortion (33.3%), or asymmetry (8.3%). The remaining 14 cancers (5.4%) were not detected with either DBT or FFDM (both-occult group). On multivariate analysis, a dense breast parenchyma (p = 0.007), small tumor size (≤ 2 cm; p = 0.027), and luminal A-like subtype (estrogen receptor positive or progesterone receptor positive or both, human epidermal growth factor receptor 2 negative, and Ki-67 expression < 14%; p = 0.008) were significantly associated with the DBT-only group. For 108 screening-detected cancers, a dense breast parenchyma (p = 0.007) and luminal A-like subtype (p = 0.008) also maintained significance. The addition of DBT to FFDM in screening would aid in the detection of less-aggressive subtypes of invasive breast cancers in women with dense breasts.

Architectural distortion (AD) is one of the breast abnormal signs in digital breast tomosynthesis (DBT) and digital mammography (DM). It is hard to be detected because of its subtle appearance and similar intensity with surrounding tissue. Since DBT is a three-dimensional imaging, it can address the problem of tissue superimposition in DM, so as to reduce false positives and false negatives. Several clinical studies have confirmed that radiologists can detect more ADs in DBT than in DM. These conclusions are based on subjective experience. To explore whether the engineering model and the experience of radiologists are consistent in AD detection tasks, this study compared the AD detection performance of a deep-learning-based computer-aided detection (CADe) model in DBT and DM images of the same group of cases. 394 DBT volumes and their corresponding DM images were collected retrospectively from 99 breast cancer screening cases. Among them, 203 DBT volumes and DM images contained ADs and the remaining 191 ones were negative group without any AD. Ten-fold cross-validation was used to train and evaluate the models and mean true positive fraction (MTPF) was used as figure-of-merit. The results showed that the CADe model achieved significantly better detection performance in DBT than DM (MTPF: 0.7026&plusmn;0.0394 for DBT vs. 0.5870&plusmn;0.0407 for DM, p=0.002). Qualitative analysis illustrated that DBT indeed had the ability to overcome tissue superimposition and showed more details of breast tissue. It helped the CADe model detect more ADs, which was consistent with clinical experience.

We propose computer-aided detection (CADe) algorithm for microcalcification (MC) clusters in reconstructed digital breast tomosynthesis (DBT) images. The algorithm consists of prescreening, MC detection, clustering, and false-positive (FP) reduction steps. The DBT images containing the MC-like objects were enhanced by a multiscale Hessian-based three-dimensional (3D) objectness response function and a connected-component segmentation method was applied to extract the cluster seed objects as potential clustering centers of MCs. Secondly, a signal-to-noise ratio (SNR) enhanced image was also generated to detect the individual MC candidates and prescreen the MC-like objects. Each cluster seed candidate was prescreened by counting neighboring individual MC candidates nearby the cluster seed object according to several microcalcification clustering criteria. As a second step, we introduced bounding boxes for the accepted seed candidate, clustered all the overlapping cubes, and examined. After the FP reduction step, the average number of FPs per case was estimated to be 2.47 per DBT volume with a sensitivity of 83.3%.