Mass Detection and Segmentation in Digital Breast Tomosynthesis Using 3D-Mask Region-Based Convolutional Neural Network: A Comparative Analysis.

The detection of cancerous mass lesions using digital breast tomosynthesis (DBT) has been shown to be limited in patients with dense breasts. Detection may potentially be improved by increasing the DBT angular range (AR), which reduces breast structural noise and increases object contrast in the reconstructed slice. We investigate the impact of DBT AR on the detection of masses in a simulation study using a cascaded linear system model (CLSM) for DBT. We compare the mass conspicuity between wide- and narrow-AR DBT system in a clinical pilot study. The simulation results show reduced in-plane breast structural noise and increased in-plane detectability of masses with increasing AR. The clinical results show that masses are more conspicuous in wide-AR DBT than narrow-AR DBT. Our study indicates that the detection of mass lesions in dense breasts can be improved by increasing DBT AR.

AimsTo assess the diagnostic performance of digital breast tomosynthesis (DBT) in older women across varying breast densities and to compare its effectiveness for cancer detection with 2D mammography and ultrasound (U/S) for different breast density categories. Furthermore, our study aimed to predict the potential reduction in unnecessary additional examinations among older women due to DBT.MethodsThis study encompassed a cohort of 224 older women. Each participant underwent both 2D mammography and digital breast tomosynthesis examinations. Supplementary views were conducted when necessary, including spot compression and magnification, ultrasound, and recommended biopsies. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) were calculated for 2D mammography, DBT, and ultrasound. The impact of DBT on diminishing the need for supplementary imaging procedures was predicted through binary logistic regression.ResultsIn dense breast tissue, DBT exhibited notably heightened sensitivity and NPV for lesion detection compared to non-dense breasts (61.9% vs. 49.3%, p < 0.001) and (72.9% vs. 67.9%, p < 0.001), respectively. However, the AUC value of DBT in dense breasts was lower compared with non-dense breasts (0.425 vs. 0.670). Regarding the ability to detect calcifications, DBT demonstrated significantly improved sensitivity and NPV in dense breasts compared to non-dense breasts (100% vs. 99.2%, p < 0.001) and (100% vs. 94.7%, p < 0.001), respectively. On the other hand, the AUC value of DBT was slightly lower in dense breasts compared with non-dense (0.682 vs. 0.711). Regarding lesion detection for all cases between imaging examinations, the highest sensitivity was observed in 2D mammography (91.7%, p < 0.001), followed by DBT (83.7%, p < 0.001), and then ultrasound (60.6%, p < 0.001). In dense breasts, sensitivity for lesion detection was highest in 2D mammography (92.9%, p < 0.001), followed by ultrasound (76.2%, p < 0.001), and the last one was DBT. In non-dense breasts, sensitivities were 91% (p < 0.001) for 2D mammography, 50.7% (p < 0.001) for ultrasound, and 49.3% (p < 0.001) for DBT. In terms of calcification detection, DBT displayed significantly superior sensitivity compared to 2D mammography in both dense and non-dense breasts (100% vs. 91.4%, p < 0.001) and (99.2% vs. 78.5%, p < 0.001), respectively. However, the logistic regression model did not identify any statistically significant relationship (p > 0.05) between DBT and the four dependent variables.ConclusionOur findings indicate that among older women, DBT does not significantly decrease the requirement for further medical examinations.

Trio of mammography studies demonstrate enhanced screening effectiveness: Recent research on the use of adjunctive technologies and expanded-age screening provides promising results for women with dense breasts.

Detecting masses in dense breast using independent component analysis.

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.

Digital breast tomosynthesis (DBT) is a breast cancer screening modality that has gained popularity in recent years. Although insurance coverage for DBT is not mandated under the Patient Protection and Affordable Care Act, several states have required coverage without cost sharing for private insurers. To evaluate the association between state-level insurance coverage mandates for DBT and changes in DBT use, price, and out-of-pocket payments. This cohort study used an event-study design with repeated cross-sectional observations of US states. Data were obtained from the Blue Cross Blue Shield Axis database for commercially insured women aged 40 to 64 years who underwent screening mammography between January 1, 2015, and June 30, 2019. Data were analyzed between January 14, 2021, and January 20, 2022. Passage of state-level legislation requiring insurance coverage of DBT. Change in DBT use among women screened for breast cancer, overall DBT price, and out-of-pocket payments for DBT in states with mandates for coverage of DBT compared with states that did not pass legislation. This study included 9 604 084 screening mammograms from 5 754 123 women (mean [SD] age, of 53 [6.7] years). During the study period, 15 states enacted DBT coverage mandates and 34 states did not. In states with coverage mandates, DBT use increased by 9.0 percentage points (95% CI, 1.8-16.3 percentage points; P = .02) 2 years after the mandate compared with states without coverage mandates. Coverage mandates were also associated with a net $38.7 (95% CI, $13.4-$63.9; P = .003) decrease in the mean price of DBT compared with no coverage mandates. There was no association between coverage mandates and out-of-pocket payments 2 years after mandate passage ($-2.1; 95% CI, $-5.3 to $1.0; P = .18). In this cohort study, DBT coverage mandates were associated with an increase in DBT use but not with any change in out-of-pocket payments. The findings suggest that coverage mandates for DBT may have been associated with broader use but were not associated with changes in direct costs to patients.

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.

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.

Invasive lobular carcinoma (ILC) is a distinct histological subtype of breast cancer that can make early detection with mammography challenging. We compared imaging performance of digital breast tomosynthesis (DBT) to digital mammography (DM) for diagnoses of ILC, invasive ductal carcinoma (IDC), and invasive mixed carcinoma (IMC) in a screening population. We included screening exams (DM; n = 1,715,249 or DBT; n = 414,793) from 2011 to 2018 among 839,801 women in the Breast Cancer Surveillance Consortium. Examinations were followed for one year to ascertain incident ILC, IDC, or IMC. We measured cancer detection rate (CDR) and interval invasive cancer rate/1000 screening examinations for each histological subtype and stratified by breast density and modality. We calculated relative risk (RR) for DM vs. DBT using log-binomial models to adjust for the propensity of receiving DBT vs. DM. Unadjusted CDR per 1000 mammograms of ILC overall was 0.33 (95%CI: 0.30-0.36) for DM; 0.45 (95%CI: 0.39-0.52) for DBT, and for women with dense breasts- 0.33 (95%CI: 0.29-0.37) for DM and 0.54 (95%CI: 0.43-0.66) for DBT. Similar results were noted for IDC and IMC. Adjusted models showed a significantly increased RR for cancer detection with DBT compared to DM among women with dense breasts for all three histologies (RR; 95%CI: ILC 1.53; 1.09-2.14, IDC 1.21; 1.02-1.44, IMC 1.76; 1.30-2.38), but no significant increase among women with non-dense breasts. DBT was associated with higher CDR for ILC, IDC, and IMC for women with dense breasts. Early detection of ILC with DBT may improve outcomes for this distinct clinical entity.

Breast cancer detection: Comparison of digital mammography and digital breast tomosynthesis across non-dense and dense breasts

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.

Digital mammography (DM) is the cornerstone of breast cancer detection. Digital breast tomosynthesis (DBT) is an advanced imaging technique used for diagnosing and screening breast lesions, particularly in dense breasts. This study aimed to evaluate the impact of combining DBT with DM on the BI-RADS categorization of equivocal breast lesions. We prospectively evaluated 148 females with equivocal BI-RADS breast lesions (BI-RADS 0, 3, and 4) with DM. All patients underwent DBT. Two experienced radiologists analyzed the lesions. They then assigned a BI-RADS category for each lesion according to the BI-RADS 2013 lexicon, using DM, DBT, and integrated DM and DBT. We compared the results based on major radiological characteristics, BI-RADS classification, and diagnostic accuracy, using the histopathological examination of the lesions as a reference standard. The total number of lesions was 178 on DBT and 159 on DM. Nineteen lesions were discovered using DBT and were missed by DM. The final diagnoses of 178 lesions were malignant (41.6%) and benign (58.4%). Compared to DM, DBT produced 34.8% downgrading and 32% upgrading of breast lesions. Compared with DM, DBT decreased the number of BI-RADS 4 and 3. All the upgraded BI-RADS 4 lesions were confirmed to be malignant. The combination of DM and DBT improves the diagnostic accuracy of BI-RADS for evaluating and characterizing mammographic equivocal breast lesions and allows for proper BI-RADS categorization.

Evidence on screening outcomes with digital breast tomosynthesis (DBT) vs digital mammography (DM) in women with a family history of breast cancer is limited. To compare the performance of DBT and DM screening in women with a family history of breast cancer overall and subdivided by breast cancer family history category, breast density, age group, screening interval, and screening round, and to describe characteristics of cancers detected on screening vs interval cancers. In this comparative cohort study at imaging facilities affiliated with the Breast Cancer Surveillance Consortium, adult women 18 years and older with a self-reported family history of breast cancer who underwent DBT or DM from 2011 to 2018 were included, with a 1-year follow-up for breast carcinoma. Data analysis was performed between November 2023 and August 2024. DBT or DM. The main outcomes were absolute risk difference (ARD) between DBT and DM for recall rate, cancer detection rate, interval cancer rate, advanced cancer rate, biopsy rate, positive predictive values, sensitivity, and specificity, with inverse probability of treatment weighting. A total of 208 945 women with a family history of breast cancer undergoing 502 357 screening examinations were included in the sample. Median (IQR) age was 58 (50-66) and 57 (49-66) years for the DBT and DM groups, respectively. Adjusted ARDs (DBT vs DM) were significant for recall rate (-1.51%; 95% CI, -2.42% to -0.59%) and specificity (1.56%; 95% CI, 0.65%-2.46%) in the overall cohort of 121 698 DBT and 380 561 DM examinations and among women with 1 first-degree relative (recall rate ARD, -1.72%; 95% CI, -2.70% to -0.74%; specificity ARD, 1.75%; 95% CI, 0.81%-2.69%). Among those with only second-degree relatives, the biopsy rate for DBT was significantly higher (ARD, 0.39%; 95% CI, 0.18%-0.61%). Significant ARDs were observed for the ductal carcinoma in situ detection rate (-0.71 per 1000 examinations; 95% CI, -1.03 to -0.38 per 1000 examinations) in women with almost entirely fatty breasts; recall rate (-1.90%; 95% CI, -2.88% to -0.92%) and specificity (1.93%; 95% CI, 0.97%-2.89%) in women with scattered fibroglandular densities. Significant ARDs were also observed for the positive predictive value for recall (1.75%; 95% CI, 0.84%-2.67%) in heterogeneously dense breasts, as well as the biopsy rate (0.48%; 95% CI, 0.16%-0.80%) and advanced cancer rate (-0.61 per 1000 examinations; 95% CI, -1.02 to -0.20 per 1000 examinations) in extremely dense breasts. DBT screening had a higher proportion than DM of screen-detected early-stage, invasive cancers with favorable prognostic characteristics. In this cohort study of women with a family history of breast cancer, DBT screening reduced recall rates and increased specificity compared to DM, particularly in women with 1 first-degree relative with breast cancer and those with scattered fibroglandular breast density, and reduced advanced cancer rates in women with extremely dense breasts.

To evaluate the recall rates of digital mammography (DM) and synthetic images after adding digital breast tomosynthesis (DBT) in patients with breast-conserving surgery. From November 2015 to April 2017, 229 women with breast-conserving surgery due to breast cancer who underwent DBT after surgery were included (mean interval, 12.9 ± 1.4 months). All women underwent combo-mode DBT examinations including full-field DM, tomosynthesis, and reconstructed synthetic 2D images. Three board-certified breast radiologists reviewed the images sequentially: synthetic 2D+DBT and, 1 month later, DM and then DM+DBT. Recall rates and the abnormality type causing the recall were calculated and compared for each mammographic modality and breast density. Of the 229 patients included, 230 mammography images were reviewed. One patient (0.4%) developed locoregional recurrences during follow-up (mean duration, 25.8 ± 4.5 months). Recall rates for synthetic 2D+DBT were significantly lower than for DM alone (4.1% (2.6-6.2) vs. 11.6% (9.2-14.5), respectively; p < 0.001). Recall rates did not differ between synthetic 2D+DBT and DM+DBT (4.1% (2.6-6.2) vs. 2.9% (1.9-4.5), respectively; p = 0.234). Recall rates of synthetic 2D+DBT and DM+DBT were significantly lower than those of DM alone, regardless of mammographic breast density (all p < 0.05, respectively). Adding DBT to synthetic 2D images or DM shows significant reduction in recall rates compared with DM alone for women who undergo breast-conserving surgery for breast cancer, regardless of mammographic density. • Recall rates for synthetic 2D+DBT were significantly lower than those of DM alone (4.1% (2.6-6.2) vs. 11.6% (9.2-14.5), respectively; p < 0.001). • No significant differences were seen in recall rates between synthetic 2D+DBT and DM+DBT (4.1 (2.6-6.2) vs. 2.9 (1.9-4.5), respectively; p = 0.234). • Reader-averaged recall rates after adding DBT to synthetic 2D or DM were significantly lower than those of DM alone, regardless of mammographic breast density (all p < 0.05, respectively).

Background Breast cancer screening with digital breast tomosynthesis (DBT) plus synthesized mammography (SM) increases invasive tumor detection compared with digital mammography (DM). However, it is not known how the prognostic characteristics of the cancers detected with the two screening approaches differ. Purpose To compare invasive breast cancers detected with DBT plus SM (test arm) versus DM (control arm) screening with regard to tumor stage, histologic grade, patient age, and breast density. Materials and Methods This exploratory subanalysis of the Tomosynthesis plus Synthesized Mammography (TOSYMA) study, which is a multicenter randomized controlled trial embedded in the German mammography screening program, recruited women aged 50-70 years from July 2018 to December 2020. It compared invasive cancer detection rates (iCDRs), rate differences, and odds ratios (ORs) between the arms stratified by Union for International Cancer Control (UICC) stage (I vs II-IV), histologic grade (1 vs 2 or 3), age group (50-59 vs 60-70 years), and Breast Imaging Reporting and Data System categories of breast density (A or B vs C or D). Results In total, 49 462 (median age, 57 years [IQR, 53-62 years]) and 49 669 (median age, 57 years [IQR, 53-62 years]) participants were allocated to DBT plus SM and DM screening, respectively. The iCDR of stage I tumors with DBT plus SM was 51.6 per 10 000 women (255 of 49 462) and with DM it was 30.0 per 10 000 women (149 of 49 669). DBT plus SM depicted more stage I tumors with grade 2 or 3 (166 of 49 462, 33.7 per 10 000 women) than DM (106 of 49 669, 21.3 per 10 000 women; rate difference, +12.3 per 10 000 women [95% CI: 0.3, 24.9]; OR, 1.6 [95% CI: 0.9, 2.7]). DBT plus SM achieved the highest iCDR of stage I tumors with grade 2 or 3 among women aged 60-70 years with dense breasts (41 of 7364, 55.4 per 10 000 women; rate difference, +21.6 per 10 000 women [95% CI: -21.1, 64.3]; OR, 1.6 [95% CI: 0.6, 4.5]). Conclusion DBT plus SM screening appears to lead to higher detection of early-stage invasive breast cancers of grade 2 or 3 than DM screening, with the highest rate among women aged 60-70 years with dense breasts. Clinical trial registration no. NCT03377036 © RSNA, 2023 See also the editorial by Ha and Chang in this issue.