Calcifications in the Breast and Digital Breast Tomosynthesis

Time for Change in Digital Breast Tomosynthesis Research.

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.

Objectives: This study aimed to evaluate the impact of digital breast tomosynthesis (DBT) on BI-RADS categorization compared to conventional mammography (MMG), across different mammographic breast density types. Methods: In this retrospective study, 520 female patients aged 35–85 years who underwent both MMG and DBT between 2023 and 2025 were included. Standard craniocaudal (CC) views were acquired for MMG and mediolateral oblique (MLO) views for DBT. Synthetic MLO (sMLO) images were automatically generated by the device from DBT data. Patients were categorized according to the breast density (Type A–D), and all images were reviewed in consensus by two experienced radiologists. Breast Imaging Reporting and Data System (BI-RADS) categories, lesion types, were recorded. BI-RADS categorizations from MMG and DBT were compared overall and within density subgroups. Results: The mean age was 53.0 ± 9.9 years. Breast density distribution was: Type A (3.8%, n=20), B (28.5%, n= 148), C (43.5%, n=226), and D (24.2%, n=126). BI-RADS classifications differed significantly between MMG and DBT, especially in dense breasts. Of the BI-RADS 2 cases on MMG, 21.6% (n=64) were reclassified as BI-RADS 3 or 4 with DBT, while 6.3% (n=7) of BI-RADS 3 cases were downgraded. BI-RADS 5 categorization showed complete agreement between modalities, though 33% (n=5) of BI-RADS 4 cases were downgraded. At least one pathological finding was observed in 91.3% (n=475) of patients, most commonly nodular opacities (57.1%, n=297), vascular wall calcifications (13.3%, n=69), and coarse calcifications (5.4%, n=28). McNemar analysis revealed significant reclassification between BI-RADS 2 and 3 in Types C and D (P&amp;lt;0.001). Conclusions: DBT significantly alters BI-RADS categorization in dense breasts. These findings support the potential role of DBT as a complementary tool in screening protocols, particularly for patients with heterogeneously dense or extremely dense breast tissue.

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.

According to a position paper of the European Commission Initiative on Breast Cancer (ECIBC), DBT is close to being introduced in European breast cancer screening programmes. Our study aimed to examine radiation dose delivered by digital breast tomosynthesis (DBT) and digital mammography (FFDM) in comparison to sole FFDM in a clinical follow-up setting and in an identical patient cohort. Retrospectively, 768 breast examinations of 96 patients were included. Patients received both DBT and FFDM between May 2015 and July 2019: (I) FFDM in cranio-caudal (CC) and DBT in mediolateral oblique (MLO) view, as well as a (II) follow-up examination with FFDM in CC and MLO view. The mean glandular dose (MGD) was determined by the mammography system according to Dance’s model. The MGD (standard deviation (SD), interquartile range (IQR)) was distributed as follows: (I) (CCFFDM+MLODBT) (a) left FFDMCC 1.40 mGy (0.36 mGy, 1.13–1.59 mGy), left DBTMLO 1.62 mGy (0.51 mGy, 1.27–1.82 mGy); (b) right FFDMCC 1.36 mGy (0.34 mGy, 1.14–1.51 mGy), right DBTMLO 1.59 mGy (0.52 mGy, 1.27–1.62 mGy). (II) (CCFFDM+MLOFFDM) (a) left FFDMCC 1.35 mGy (0.35 mGy, 1.10–1.60 mGy), left FFDMMLO 1.40 mGy (0.39 mGy, 1.12–1.59 mGy), (b) right FFDMCC 1.35 mGy (0.33 mGy, 1.12–1.48 mGy), right FFDMMLO 1.40 mGy (0.36 mGy, 1.14–1.58 mGy). MGD was significantly higher for DBT mlo views compared to FFDM (p < 0.001). Radiation dose was significantly higher for DBT in MLO views compared to FFDM. However, the MGD of DBT MLO lies below the national diagnostic reference level of 2 mGy for an FFDM view. Hence, our results support the use of either DBT or FFDM as suggested in the ECIBC’s Guidelines.

Digital breast tomosynthesis (DBT) emerges as a new 3D modality for breast cancer screening and diagnosis. Like in conventional 2D mammography the breast is scanned in a compressed state. For orientation during surgical planning, e.g., during presurgical ultrasound-guided anchor-wire marking, as well as for improving communication between radiologists and surgeons it is desirable to estimate an uncompressed model of the acquired breast along with a spatial mapping that allows localizing lesions marked in DBT in the uncompressed model. We therefore propose a method for 3D breast decompression and associated lesion mapping from 3D DBT data. The method is entirely data-driven and employs machine learning methods to predict the shape of the uncompressed breast from a DBT input volume. For this purpose a shape space has been constructed from manually annotated uncompressed breast surfaces and shape parameters are predicted by multiple multi-variate Random Forest regression. By exploiting point correspondences between the compressed and uncompressed breasts, lesions identified in DBT can be mapped to approximately corresponding locations in the uncompressed breast model. To this end, a thin-plate spline mapping is employed. Our method features a novel completely data-driven approach to breast shape prediction that does not necessitate prior knowledge about biomechanical properties and parameters of the breast tissue. Instead, a particular deformation behavior (decompression) is learned from annotated shape pairs, compressed and uncompressed, which are obtained from DBT and magnetic resonance image volumes, respectively. On average, shape prediction takes 26s and achieves a surface distance of 15.80 +/- 4.70 mm. The mean localization error for lesion mapping is 22.48 +/- 8.67 mm.

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.

Screening Sensitivity According to Breast Cancer Location

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

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.

Pathological outcome of sonographically occult architectural distortions (AD) visible only on digital breast tomosynthesis, and comparison with AD visible also on 2D mammography

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.

Digital breast tomosynthesis (DBT) mammography is a promising imaging technique for detecting early-stage breast cancers. However, DBT imaging usually contains truncation artifacts around the chest wall area, where many recurring breast tumors or masses are located. Extrapolation or interpolation and weighting techniques are used to suppress artifacts in existing methods, but these are inappropriate for DBT due to its high spatial resolution requirement. To solve this problem, we propose a practical truncation correction method, which can realize simultaneous truncation artifact reduction and image reconstruction (STARIR) for DBT. In contrast to extrapolation or interpolation estimations and weighting strategy, this integrated method renews each truncated voxel by considering the information of all measured projections rather than that of one projection at each reconstruction iteration. Qualitative and quantitative studies were performed on simulated and realistic DBT data to validate the proposed method. Results showed that STARIR reduces truncation artifacts and preserves tissue details in reconstructed images more effectively than do the current methods. The proposed method can be used for simultaneous and accurate truncation artifact reduction and image reconstruction in DBT.

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.

P.698 Relationship between psychosocial functioning and quality of life of schizophrenic patients treated with second generation long acting injectable antipsychotics