Accuracy of Digital Breast Tomosynthesis for Depicting Breast Cancer Subgroups in a UK Retrospective Reading Study (TOMMY Trial)

Survey on Current Utilization and Perception of Synthesized Mammography

OBJECTIVE

To assess utilization and perceptions of 2D synthesized mammography (SM) for digital breast tomosynthesis (DBT) among practicing U.S. breast radiologists.

METHODS

An IRB-exempt 23-question anonymized survey was developed by the Society of Breast Imaging (SBI) Patient Care and Delivery Committee and emailed to practicing U.S. radiologist SBI members on October 9, 2023. Questions assessed respondents' demographics, current mammographic screening protocol, confidence interpreting SM for mammographic findings, and perceived advantages and disadvantages of SM.

RESULTS

Response rate was 13.4% (371/2771). Of 371 respondents, 208 were currently screening with DBT/SM (56.1%), 98 with DBT/SM/digital mammography (DM) (26.4%), 61 with DBT/DM (16.4%), and 4 with DM (1.1%). Most respondents felt confident using DBT/SM to evaluate masses (254/319, 79.6%), asymmetries (247/319, 77.4%), and distortions (265/318, 83.3%); however, confidence was mixed for calcifications (agreement 130/320, 40.6%; disagreement 156/320, 48.8%; neutral 34/320, 10.6%). The most frequently cited disadvantage and advantage of SM were reconstruction algorithm false-positive results (199/347, 57.4%) and lower radiation dose (281/346, 81.2%), respectively. Higher confidence and fewer disadvantages were reported by radiologists who had more SM experience, screened with DBT/SM, or exclusively used Hologic vendor (all P <.05).

CONCLUSION

For most survey respondents (56.1%), SM has replaced DM in DBT screening. Radiologists currently screening with DBT/SM or with more SM experience reported greater confidence in SM with fewer perceived disadvantages.

Digital breast tomosynthesis for breast cancer diagnosis in women with dense breasts and additional breast cancer risk factors: A systematic review

INTRODUCTION

Digital breast tomosynthesis (DBT) may improve sensitivity in population screening. However, evidence is currently limited on the performance of DBT in patients at a higher risk of breast cancer. This systematic review compares the clinical effectiveness and cost-effectiveness of DBT, digital mammography (DM), and ultrasound, for breast cancer detection in women with dense breasts and additional risk factors.

METHODS

Medline, Embase, and Evidence-Based Medicine Reviews via OvidSP were searched to identify literature from 2010 to August 21, 2023. Selection of studies, data extraction, and quality assessment (using QUADAS-2 and CHEERS) were completed in duplicate. Findings were summarised descriptively and narratively.

RESULTS

Twenty-six studies met pre-specified inclusion criteria. In women with breast symptoms or recalled for investigation of screen-detected findings (19 studies), DBT may be more accurate than DM. For example, in symptomatic women, the sensitivity of DBT + DM ranged from 82.8 % to 92.5 % versus 56.8 %-81.3 % for mammography (DM/synthesised images). However, most studies had a high risk of bias due to participant selection. Evidence regarding DBT in women with a personal or family history of breast cancer, for DBT versus ultrasound alone, and cost-effectiveness of DBT was limited.

CONCLUSIONS

In women with dense breasts and additional risk factors for breast cancer, evidence is limited about the accuracy of DBT compared to other imaging modalities, particularly in those with personal or family history of breast cancer. Future research in this population should consider head-to-head comparisons of imaging modalities to determine the relative effectiveness of these imaging tests.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration number CRD42021236470.

Breast Cancers Detected during a Decade of Screening with Digital Breast Tomosynthesis: Comparison with Digital Mammography

Background Digital breast tomosynthesis (DBT) has been shown to help increase cancer detection compared with two-dimensional digital mammography (DM). However, it is unclear whether additional tumor detection will improve outcomes or lead to overdiagnosis of breast cancer. Purpose This study aimed to compare cancer types and stages over 3 years of DM screening and 10 years of DBT screening to determine the effect of DBT. Materials and Methods A retrospective search identified breast cancers detected by using screening mammography from August 2008 through July 2021. Data collected included demographic, imaging, and pathologic information. Invasive cancers 2 cm or larger, human epidermal growth factor 2-positive or triple-negative tumors greater than 10 mm, axillary nodes positive for cancer, and distant organ spread were considered advanced cancers. The DBT and DM cohorts were compared and further analyzed by prevalent versus incident examinations. False-negative findings were also assessed. Results A total of 1407 breast cancers were analyzed (142 with DM, 1265 with DBT). DBT showed a higher rate of cancer depiction than DM (5.3 vs four cancers per 1000, respectively; P = .001), with a similar ratio of invasive cancers to ductal carcinomas in situ (76.5%:23.5% [968 and 297 of 1265, respectively] vs 71.1%:28.9% [101 and 41 of 142, respectively]). Mean invasive cancer size did not differ between DM and DBT (1.44 cm ± 0.93 [SD] vs 1.36 cm ± 1.14, respectively; P = .49), but incident DBT cases were smaller than prevalent cases (1.2 cm ± 1.0 vs 1.6 cm ± 1.4, respectively; P < .001). DBT and DM had similar rates of invasive cancer subtypes: low grade (26.5% [243 of 912] vs 29% [28 of 96], respectively), moderate grade (57.2% [522 of 912] vs 51% [49 of 96], respectively), and high grade (16.1% [147 of 912] vs 20% [19 of 96], respectively) (P = .65). The proportion of advanced cancers was lower with DBT than DM (32.6% [316 of 968] vs 43.6% [44 of 101], respectively; P = .04) and between DBT prevalent and incident screening (39.1% [133 of 340] vs 29.1% [183 of 628], respectively; P = .003). There was no difference in interval cancer rates (0.14 per 1000 with DM and 0.2 per 1000 with DBT; P = .42) for both groups. Conclusion DBT helped to increase breast cancer detection rate and depicted invasive cancers with a lower rate of advanced cancers compared with DM, with further improvement observed at incident rounds of screening. © RSNA, 2024 See also the editorial by Kim and Woo in this issue.

Imaging the dense breast

The sensitivity of mammography reduces as breast density increases, which impacts breast screening and locoregional staging in breast cancer. Supplementary imaging with other modalities can offer improved cancer detection, but this often comes at the cost of more false positives. Magnetic resonance imaging and contrast-enhanced mammography, which assess tumour enhancement following contrast administration, are more sensitive than digital breast tomosynthesis and ultrasound, which predominantly rely on the assessment of tumour morphology.

Architectural distortion on digital breast tomosynthesis mammograms in symptomatic breast clinics: what are the result outcomes?

OBJECTIVES

In April 2020, standard two-dimensional (2D) full-field digital mammograms were replaced with digital breast tomosynthesis (DBT) and synthesised 2D views for symptomatic breast clinics. This study aimed to evaluate the positive predictive value (PPV) for malignancy in DBT-detected Architectural distortion (AD).

METHODS

All mammogram reports with the word "distortion" were assessed between April 2020 and October 2022. There were 458 mammograms with the word "distortion." After excluding mammograms with no distortion (n = 128), post-surgical distortion (n = 173), distortion with mass (n = 33), and unchanged distortion (n = 14), there were 111 patients with pure distortion. Correlation with histopathology was obtained where possible. All patients were followed for a minimum of 2 years.

RESULTS

Forty-two out of 111 patients (37.84%) with AD had a normal ultrasound (US) and were discharged. Fifty-five (49.5%) patients had sonographic correlation corresponding to the distortion, leading to US-guided biopsy. Thirteen (23.6%) had tomosynthesis-guided biopsy, and one had a skin biopsy. The PPV for malignancy was 42.34%. Malignancy diagnoses were higher with US-guided biopsies than tomosynthesis-guided biopsies, 78.1% and 30%, respectively.

CONCLUSION

With a total malignancy rate of 42.34%, DBT-detected AD has a high enough PPV for malignancy to justify selective tissue sampling if a sonographic correlate is present or with suspicious mammograms. The chances of malignancy are higher when a sonographic correlate corresponding to AD is present.

ADVANCES IN KNOWLEDGE

AD on DBT/synthesized mammograms views in symptomatic breast clinic patients justifies selective sampling.

Revolutionizing Breast Cancer Detection With Artificial Intelligence (AI) in Radiology and Radiation Oncology: A Systematic Review

The number one cause of cancer in women worldwide is breast cancer. Over the last three decades, the use of traditional screen-film mammography has increased, but in recent years, digital mammography and 3D tomosynthesis have become standard procedures for breast cancer screening. With the advancement of technology, the interpretation of images using automated algorithms has become a subject of interest. Initially, computer-aided detection (CAD) was introduced; however, it did not show any long-term benefit in clinical practice. With recent advances in artificial intelligence (AI) methods, these technologies are showing promising potential for more accurate and efficient automated breast cancer detection and treatment. While AI promises widespread integration in breast cancer detection and treatment, challenges such as data quality, regulatory, ethical implications, and algorithm validation are crucial. Addressing these is essential for fully realizing AI's potential in enhancing early diagnosis and improving patient outcomes in breast cancer management. In this review article, we aim to provide an overview of the latest developments and applications of AI in breast cancer screening and treatment. While the existing literature primarily consists of retrospective studies, ongoing and future prospective research is poised to offer deeper insights. Artificial intelligence is on the verge of widespread integration into breast cancer detection and treatment, holding the potential to enhance early diagnosis and improve patient outcomes.

Impact of super-resolution and image acquisition on the detection of calcifications in digital breast tomosynthesis

OBJECTIVES

A virtual clinical trial (VCT) method is proposed to determine the limit of calcification detection in tomosynthesis.

METHODS

Breast anatomy, focal findings, image acquisition, and interpretation (n = 14 readers) were simulated using screening data (n = 660 patients). Calcifications (0.2-0.4 mm3) were inserted into virtual breast phantoms. Digital breast tomosynthesis (DBT) acquisitions were simulated assuming various acquisition geometries: source motion (continuous and step-and-shoot), detector element size (140 and 70 µm), and reconstructed voxel size (35-140 µm). VCT results were estimated using multiple-reader multiple-case analyses and d' statistics. Signal-to-noise (SNR) analyses were also performed using BR3D phantoms.

RESULTS

Source motion and reconstructed voxel size demonstrated significant changes in the performance of imaging systems. Acquisition geometries that use 70 µm reconstruction voxel size and step-and-shoot motion significantly improved calcification detection. Comparing 70 with 100 µm reconstructed voxel size for step-and-shoot, the ΔAUC was 0.0558 (0.0647) and d' ratio was 1.27 (1.29) for 140 µm (70 µm) detector element size. Comparing step-and-shoot with a continuous motion for a 70 µm reconstructed voxel size, the ΔAUC was 0.0863 (0.0434) and the d' ratio was 1.40 (1.19) for 140 µm (70 µm) detector element. Small detector element sizes (e.g., 70 µm) did not significantly improve detection. The SNR results with the BR3D phantom show that calcification detection is dependent upon reconstructed voxel size and detector element size, supporting VCT results with comparable agreement (ratios: d' = 1.16 ± 0.11, SNR = 1.34 ± 0.13).

CONCLUSION

DBT acquisition geometries that use super-resolution (smaller reconstructed voxels than the detector element size) combined with step-and-shoot motion have the potential to improve the detection of calcifications.

CLINICAL RELEVANCE

Calcifications may not always be discernable in tomosynthesis because of differences in acquisition and reconstruction methods. VCTs can identify strategies to optimize acquisition and reconstruction parameters for calcification detection in tomosynthesis, most notably through super-resolution in the reconstruction.

KEY POINTS

• Super-resolution improves calcification detection and SNR in tomosynthesis; specifically, with the use of smaller reconstruction voxels. • Calcification detection using step-and-shoot motion is superior to that using continuous tube motion. • A detector element size of 70 µm does not provide better detection than 140 µm for small calcifications at the threshold of detectability.

Deep learning denoising of digital breast tomosynthesis: Observer performance study of the effect on detection of microcalcifications in breast phantom images

BACKGROUND

The noise in digital breast tomosynthesis (DBT) includes x-ray quantum noise and detector readout noise. The total radiation dose of a DBT scan is kept at about the level of a digital mammogram but the detector noise is increased due to acquisition of multiple projections. The high noise can degrade the detectability of subtle lesions, specifically microcalcifications (MCs).

PURPOSE

We previously developed a deep-learning-based denoiser to improve the image quality of DBT. In the current study, we conducted an observer performance study with breast radiologists to investigate the feasibility of using deep-learning-based denoising to improve the detection of MCs in DBT.

METHODS

We have a modular breast phantom set containing seven 1-cm-thick heterogeneous 50% adipose/50% fibroglandular slabs custom-made by CIRS, Inc. (Norfolk, VA). We made six 5-cm-thick breast phantoms embedded with 144 simulated MC clusters of four nominal speck sizes (0.125-0.150, 0.150-0.180, 0.180-0.212, 0.212-0.250 mm) at random locations. The phantoms were imaged with a GE Pristina DBT system using the automatic standard (STD) mode. The phantoms were also imaged with the STD+ mode that increased the average glandular dose by 54% to be used as a reference condition for comparison of radiologists' reading. Our previously trained and validated denoiser was deployed to the STD images to obtain a denoised DBT set (dnSTD). Seven breast radiologists participated as readers to detect the MCs in the DBT volumes of the six phantoms under the three conditions (STD, STD+, dnSTD), totaling 18 DBT volumes. Each radiologist read all the 18 DBT volumes sequentially, which were arranged in a different order for each reader in a counter-balanced manner to minimize any potential reading order effects. They marked the location of each detected MC cluster and provided a conspicuity rating and their confidence level for the perceived cluster. The visual grading characteristics (VGC) analysis was used to compare the conspicuity ratings and the confidence levels of the radiologists for the detection of MCs.

RESULTS

The average sensitivities over all MC speck sizes were 65.3%, 73.2%, and 72.3%, respectively, for the radiologists reading the STD, dnSTD, and STD+ volumes. The sensitivity for dnSTD was significantly higher than that for STD (p < 0.005, two-tailed Wilcoxon signed rank test) and comparable to that for STD+. The average false positive rates were 3.9 ± 4.6, 2.8 ± 3.7, and 2.7 ± 3.9 marks per DBT volume, respectively, for reading the STD, dnSTD, and STD+ images but the difference between dnSTD and STD or STD+ did not reach statistical significance. The overall conspicuity ratings and confidence levels by VGC analysis for dnSTD were significantly higher than those for both STD and STD+ (p ≤ 0.001). The critical alpha value for significance was adjusted to be 0.025 with Bonferroni correction.

CONCLUSIONS

This observer study using breast phantom images showed that deep-learning-based denoising has the potential to improve the detection of MCs in noisy DBT images and increase radiologists' confidence in differentiating noise from MCs without increasing radiation dose. Further studies are needed to evaluate the generalizability of these results to the wide range of DBTs from human subjects and patient populations in clinical settings.

Synthetic Mammography: Benefits, Drawbacks, and Pitfalls

Digital breast tomosynthesis (DBT) allows three-dimensional assessment of breast tissue; however, DBT requires a two-dimensional (2D) image for comparison with prior mammograms and accurate interpretation of calcifications. Traditionally, full-field digital mammography (FFDM) has been performed after the DBT image acquisition. Synthetic mammography (SM), the 2D reconstruction of the tomosynthesis slice dataset, has been designed to replace FFDM. Advantages of SM include decreased image acquisition time and decreased radiation exposure, with maintained or improved screening performance metrics. Because SM algorithms give extra weight to lesion-like characteristics (eg, calcifications and architectural distortions), they may enable increased visibility of these characteristics relative to that at FFDM. Although SM algorithms were designed to improve lesion identification, they have led to varied outcomes in studies reported in the literature. Compared with FFDM, SM has been reported to be associated with a higher false-positive rate for calcifications, decreased conspicuity of asymmetries, lower breast density assessments, and imaging artifacts (eg, metallic artifact, bright-band artifact, blurring of the axilla, and truncation artifact). The authors review the literature on SM, including its implementation, benefits, and artifacts. ©RSNA, 2023 Quiz questions for this article are available through the Online Learning Center.

New Approaches and Recommendations for Risk-Adapted Breast Cancer Screening

Population-based breast cancer screening using mammography as the gold standard imaging modality has been in clinical practice for over 40 years. However, the limitations of mammography in terms of sensitivity and high false-positive rates, particularly in high-risk women, challenge the indiscriminate nature of population-based screening. Additionally, in light of expanding research on new breast cancer risk factors, there is a growing consensus that breast cancer screening should move toward a risk-adapted approach. Recent advancements in breast imaging technology, including contrast material-enhanced mammography (CEM), ultrasound (US) (automated-breast US, Doppler, elastography US), and especially magnetic resonance imaging (MRI) (abbreviated, ultrafast, and contrast-agent free), may provide new opportunities for risk-adapted personalized screening strategies. Moreover, the integration of artificial intelligence and radiomics techniques has the potential to enhance the performance of risk-adapted screening. This review article summarizes the current evidence and challenges in breast cancer screening and highlights potential future perspectives for various imaging techniques in a risk-adapted breast cancer screening approach. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 5.

Comparing the performance of full-field digital mammography and digital breast tomosynthesis in the post-treatment surveillance of patients with a history of breast cancer: A retrospective study

INTRODUCTION

The purpose of our study was to compare the performance of 2D (FFDM) against 3D (FFDM plus DBT) examinations in the post-treatment surveillance of asymptomatic breast cancer survivors.

METHODS

A list of women with a history of breast cancer who underwent screening mammography (2D or 3D) from 5/2017 to 5/2020 was retrieved. A total of 20,210 examinations were identified and performance metrics were compared.

RESULTS

There were no statistically significant difference in cancer detection rate (CDR) (p = 0.38), recall rate (RR) (p = 0.087), or positive predictive value (PPV) (p = 0.74) between 2D vs. 3D examinations. Stratification by breast tissue identified no statistically significant difference in CDR (p = 0.581 and p = 0.428), RR (p = 0.230 and p = 0.205), or PPV (p = 0.908 and p = 0.721) between fatty/scattered and heterogeneous/extremely dense breast tissue when comparing 2D vs 3D examinations. Stratification by age did not identify a significant difference in RR or PPV between the two groups. CDR was statistically increased with 2D vs. 3D examinations in the 60-69 years group (p = 0.021). Stratification by race did not identify a significant difference in RR or PPV between the two groups. CDR was statistically increased with 3D vs. 2D examinations in white women (p = 0.036). Stratification by laterality (bilateral vs. unilateral post mastectomy) did not identify a significant difference in RR or PPV between the two groups. CDR was statistically increased in 2D vs. 3D examinations in unilateral studies (p = 0.009).

CONCLUSION

For asymptomatic women with a history of breast cancer, there is no evidence that the addition of DBT to FFDM improves CDR, RR, or PPV.

IMPLICATIONS FOR PRACTICE

More studies are needed concerning screening methodologies supplementing FFDM in the screening regimens of breast cancer survivors.

Digital Breast Tomosynthesis: Towards Dose Reduction through Image Quality Improvement

Currently, breast cancer is the most commonly diagnosed type of cancer worldwide. Digital Breast Tomosynthesis (DBT) has been widely accepted as a stand-alone modality to replace Digital Mammography, particularly in denser breasts. However, the image quality improvement provided by DBT is accompanied by an increase in the radiation dose for the patient. Here, a method based on 2D Total Variation (2D TV) minimization to improve image quality without the need to increase the dose was proposed. Two phantoms were used to acquire data at different dose ranges (0.88-2.19 mGy for Gammex 156 and 0.65-1.71 mGy for our phantom). A 2D TV minimization filter was applied to the data, and the image quality was assessed through contrast-to-noise ratio (CNR) and the detectability index of lesions before and after filtering. The results showed a decrease in 2D TV values after filtering, with variations of up to 31%, increasing image quality. The increase in CNR values after filtering showed that it is possible to use lower doses (-26%, on average) without compromising on image quality. The detectability index had substantial increases (up to 14%), especially in smaller lesions. So, not only did the proposed approach allow for the enhancement of image quality without increasing the dose, but it also improved the chances of detecting small lesions that could be overlooked.

A Survey of Convolutional Neural Network in Breast Cancer

Problems

For people all over the world, cancer is one of the most feared diseases. Cancer is one of the major obstacles to improving life expectancy in countries around the world and one of the biggest causes of death before the age of 70 in 112 countries. Among all kinds of cancers, breast cancer is the most common cancer for women. The data showed that female breast cancer had become one of the most common cancers.

Aims

A large number of clinical trials have proved that if breast cancer is diagnosed at an early stage, it could give patients more treatment options and improve the treatment effect and survival ability. Based on this situation, there are many diagnostic methods for breast cancer, such as computer-aided diagnosis (CAD).

Methods

We complete a comprehensive review of the diagnosis of breast cancer based on the convolutional neural network (CNN) after reviewing a sea of recent papers. Firstly, we introduce several different imaging modalities. The structure of CNN is given in the second part. After that, we introduce some public breast cancer data sets. Then, we divide the diagnosis of breast cancer into three different tasks: 1. classification; 2. detection; 3. segmentation.

Conclusion

Although this diagnosis with CNN has achieved great success, there are still some limitations. (i) There are too few good data sets. A good public breast cancer dataset needs to involve many aspects, such as professional medical knowledge, privacy issues, financial issues, dataset size, and so on. (ii) When the data set is too large, the CNN-based model needs a sea of computation and time to complete the diagnosis. (iii) It is easy to cause overfitting when using small data sets.

Multi-scale cascaded networks for synthesis of mammogram to decrease intensity distortion and increase model-based perceptual similarity

PURPOSE

Synthetic digital mammogram (SDM) is a 2D image generated from digital breast tomosynthesis (DBT) and used as a substitute for a full-field digital mammogram (FFDM) to reduce the radiation dose for breast cancer screening. The previous deep learning-based method used FFDM images as the ground truth, and trained a single neural network to directly generate SDM images with similar appearances (e.g., intensity distribution, textures) to the FFDM images. However, the FFDM image has a different texture pattern from DBT. The difference in texture pattern might make the training of the neural network unstable and result in high-intensity distortion, which makes it hard to decrease intensity distortion and increase perceptual similarity (e.g., generate similar textures) at the same time. Clinically, radiologists want to have a 2D synthesized image that feels like an FFDM image in vision and preserves local structures such as both mass and microcalcifications (MCs) in DBT because radiologists have been trained on reading FFDM images for a long time, while local structures are important for diagnosis. In this study, we proposed to use a deep convolutional neural network to learn the transformation to generate SDM from DBT.

METHOD

To decrease intensity distortion and increase perceptual similarity, a multi-scale cascaded network (MSCN) is proposed to generate low-frequency structures (e.g., intensity distribution) and high-frequency structures (e.g., textures) separately. The MSCN consist of two cascaded sub-networks: the first sub-network is used to predict the low-frequency part of the FFDM image; the second sub-network is used to generate a full SDM image with textures similar to the FFDM image based on the prediction of the first sub-network. The mean-squared error (MSE) objective function is used to train the first sub-network, termed low-frequency network, to generate a low-frequency SDM image. The gradient-guided generative adversarial network's objective function is to train the second sub-network, termed high-frequency network, to generate a full SDM image with textures similar to the FFDM image.

RESULTS

1646 cases with FFDM and DBT were retrospectively collected from the Hologic Selenia system for training and validation dataset, and 145 cases with masses or MC clusters were independently collected from the Hologic Selenia system for testing dataset. For comparison, the baseline network has the same architecture as the high-frequency network and directly generates a full SDM image. Compared to the baseline method, the proposed MSCN improves the peak-to-noise ratio from 25.3 to 27.9 dB and improves the structural similarity from 0.703 to 0.724, and significantly increases the perceptual similarity.

CONCLUSIONS

The proposed method can stabilize the training and generate SDM images with lower intensity distortion and higher perceptual similarity.

Our experience using synthesized mammography vs full field digital mammography in population-based screening

Background

Synthesized Mammogram (SM) from Digital Breast Tomosynthesis (DBT) images is introduced to replace the routine Full Field Digital Mammography (FFDM) to reduce radiation dose.

Purpose

to compare the conspicuity of cancer related findings between SM and FFDM and combination of these methods with DBT.

Methods

The study was conducted in a tertiary breast imaging center, where 200 women referred for screening were enrolled in the study sequentially. Patients underwent FFDM and DBT simultaneously and a two-year follow-up was done. Data was evaluated for Breast Imaging Reporting and Data System (BI-RADS) score, breast density, mass lesions, calcification, and focal asymmetry by two expert breast radiologists. Comparison between different methods was made by Cohen Kappa test.

Results

22 patients with likely malignant findings went under biopsy. Taking histopathologic findings and two-year follow up as reference, the overall sensitivity and specificity for FFDM+DBT (86.1 and 88.9 respectively) and SM+DBT (86.1 and 88.2) didn't show a meaningful difference. Comparing SM and FFDM, calcification in 20 subjects were overlooked on SM, but later detected when combined with DBT. Considering breast composition and BI-RADS categorization, an excellent agreement existed between the readers.

Conclusion

Screening with SM+DBT shows comparable results with FFDM+DBT considering BI-RADS categorization of the patients. Although SM showed slightly inferior sensitivity compared to FFDM, after combining DBT with SM no malignant appearing calcification or mass lesion was missed.

Clinical performance of digital breast tomosynthesis-guided vacuum-assisted biopsy: a single-institution experience in Japan

BACKGROUND

The purpose of this study was to evaluate the clinical performance of Digital Breast Tomosynthesis guided vacuum-assisted biopsy (DBT-VAB) for microcalcifications in the breast.

METHODS

Retrospective review of 131 mammography-guided VABs at our institution were performed. All of the targets were calcification lesion suspicious for cancer. 45 consecutive stereotactic vacuum-assisted biopsies (ST-VABs) and 86 consecutive DBT-VABs were compared. Written informed consent was obtained. Tissue sampling methods and materials were the same with both systems. Student's t-test was used to compare procedure time and the Fisher's exact test was used to compare success rate, complications, and histopathologic findings for the 2 methods.

RESULTS

The tissue sampling success rate was 95.6% for ST-VAB (43/45) and 97.7% (84/86) for DBT-VAB. Time for positioning (10.6 ± 6.4 vs. 6.7 ± 5.3 min), time for biopsy (33.4 ± 13.1 vs. 22.5 ± 13.1 min), and overall procedure time (66.6 ± 16.6 min vs. 54.5 ± 13.0 min) were substantially shorter with DBT-VAB (P < 0.0001). There were no differences in the distribution of pathological findings between the 2 groups.

CONCLUSION

Depth information and stable visibility of the target provided by DBT images led to quick decisions about target coordinates and improved the clinical performance of microcalcification biopsies.

Comparisons between artificial intelligence computer-aided detection synthesized mammograms and digital mammograms when used alone and in combination with tomosynthesis images in a virtual screening setting

PURPOSE

To compare the reader performance of artificial intelligence computer-aided detection synthesized mammograms (AI CAD SM) with that of digital mammograms (DM) when used alone or in combination with digital breast tomosynthesis (DBT) images.

MATERIALS AND METHODS

This retrospective multireader (n = 4) study compared the reader performances in 388 cases (84 cancer, 83 benign, and 221 normal or benign cases). The overall accuracy of the breast-based assessment was determined by four radiologists using two sequential reading modes: DM followed by DM + DBT; and AI CAD SM followed by AI CAD SM + DBT. Each breast was rated by each reader using five-category ratings, where 3 or higher was considered positive. The area under the receiver-operating characteristic curve (AUC) and reading time were evaluated.

RESULTS

The mean AUC values for DM, AI CAD SM, DM + DBT, and AI CAD SM + DBT were 0.863, 0.895, 0.886, and 0.902, respectively. The mean AUC of AI CAD SM was significantly higher (P < 0.0001) than that of DM. The mean AUC of AI CAD SM + DBT was higher than that of DM + DBT (P = 0.094). A significant reduction in the reading time was observed after using AI CAD SM + DBT when compared with that after using DM + DBT (P < 0.001).

CONCLUSION

AI CAD SM + DBT might prove more effective than DM + DBT in a screening setting because of its lower radiation dose, noninferiority, and shorter reading time compared to DM + DBT.

Performance evaluation of digital mammography, digital breast tomosynthesis and ultrasound in the detection of breast cancer using pathology as gold standard: an institutional experience

Background

Mammography is the primary imaging modality for diagnosing breast cancer in women more than 40 years of age. Digital breast tomosynthesis (DBT), when supplemented with digital mammography (DM), is useful for increasing the sensitivity and improving BIRADS characterization by removing the overlapping effect. Ultrasonography (US), when combined with the above combination, further increases the sensitivity and diagnostic confidence. Since most of the research regarding tomosynthesis has been in screening settings, we wanted to quantify its role in diagnostic mammography. The purpose of this study was to assess the performance of DM alone vs. DM combined with DBT vs. DM plus DBT and ultrasound in diagnosing malignant breast neoplasms with the gold standard being histopathology or cytology.

Results

A prospective study of 1228 breasts undergoing diagnostic or screening mammograms was undertaken at our institute. Patients underwent 2 views DM, single view DBT and US. BIRADS category was updated after each step. Final categorization was made with all three modalities combined and pathological correlation was done for those cases in which suspicious findings were detected, i.e. 256 cases. Diagnosis based on pathology was done for 256 cases out of which 193 (75.4%) were malignant and the rest 63 (24.6%) were benign. The diagnostic accuracy of DM alone was 81.1%. Sensitivity, Specificity, PPV and NPV were 87.8%, 60%, 81.3% and 61.1%, respectively. With DM + DBT the diagnostic accuracy was 84.8%. Sensitivity, Specificity, PPV and NPV were 92%, 56.5%, 89% and 65%, respectively. The diagnostic accuracy of DM + DBT + US was found to be 85.1% and Sensitivity, Specificity, PPV and NPV were 96.3%, 50.7%, 85.7% and 82%, respectively.

Conclusion

The combination of DBT to DM led to higher diagnostic accuracy, sensitivity and PPV. The addition of US to DM and DBT further increased the sensitivity and diagnostic accuracy and significantly increased the NPV even in diagnostic mammograms and should be introduced in routine practice for characterizing breast neoplasms.

Accuracy of cone-beam computed tomography, digital mammography and digital breast tomosynthesis for microcalcifications and margins to microcalcifications in breast specimens

Accurate determination of resection margins in breast specimens is important as complete removal of malignancy is a prerequisite for patients' outcome. Mammography (DM) as 2D-technique provides only limited value in margin assessment. Therefore, we investigated whether cone-beam computed tomography (CBCT) or digital breast tomosynthesis (DBT) has incremental value in assessing margins to microcalcifications. Three independent readers investigated breast specimens for presence of microcalcifications and the smallest distance to margins. Histopathology served as gold standard. Microcalcifications were detected in 15 out of 21 included specimens (71%). Pooled sensitivity for DM, DBT and CBCT for microcalcifications compared to preoperative DM was 0.98 (CI 0.94-0.99), 0.83 (CI 0.73-0.94) and 0.94 (CI 0.87-0.99), pooled specificity was 0.99 (CI 0.99-0.99), 0.73 (CI 0.51-0.96) and 0.60 (CI 0.35-0.85). Mean measurement error for margin determination for DM, DBT and CBCT was 10 mm, 14 mm and 6 mm (p = 0.002) with significant difference between CBCT and the other devices (p < 0.03). Mean reading time required by the readers to analyze DM, DBT and CBCT, was 36, 43 and 54 s (p < 0.001). Although DM allows reliable detection of microcalcifications, measurement of resection margin was significantly more accurate with CBCT. Thus, a combination of methods or improved CBCT might provide a more accurate determination of disease-free margins in breast specimens.

Automatic Classification of Simulated Breast Tomosynthesis Whole Images for the Presence of Microcalcification Clusters Using Deep CNNs

Microcalcification clusters (MCs) are among the most important biomarkers for breast cancer, especially in cases of nonpalpable lesions. The vast majority of deep learning studies on digital breast tomosynthesis (DBT) are focused on detecting and classifying lesions, especially soft-tissue lesions, in small regions of interest previously selected. Only about 25% of the studies are specific to MCs, and all of them are based on the classification of small preselected regions. Classifying the whole image according to the presence or absence of MCs is a difficult task due to the size of MCs and all the information present in an entire image. A completely automatic and direct classification, which receives the entire image, without prior identification of any regions, is crucial for the usefulness of these techniques in a real clinical and screening environment. The main purpose of this work is to implement and evaluate the performance of convolutional neural networks (CNNs) regarding an automatic classification of a complete DBT image for the presence or absence of MCs (without any prior identification of regions). In this work, four popular deep CNNs are trained and compared with a new architecture proposed by us. The main task of these trainings was the classification of DBT cases by absence or presence of MCs. A public database of realistic simulated data was used, and the whole DBT image was taken into account as input. DBT data were considered without and with preprocessing (to study the impact of noise reduction and contrast enhancement methods on the evaluation of MCs with CNNs). The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance. Very promising results were achieved with a maximum AUC of 94.19% for the GoogLeNet. The second-best AUC value was obtained with a new implemented network, CNN-a, with 91.17%. This CNN had the particularity of also being the fastest, thus becoming a very interesting model to be considered in other studies. With this work, encouraging outcomes were achieved in this regard, obtaining similar results to other studies for the detection of larger lesions such as masses. Moreover, given the difficulty of visualizing the MCs, which are often spread over several slices, this work may have an important impact on the clinical analysis of DBT images.

Reducing Unnecessary Biopsies Using Digital Breast Tomosynthesis and Ultrasound in Dense and Nondense Breasts

Aim: To compare digital breast tomosynthesis (DBT) and ultrasound in women recalled for assessment after a positive screening mammogram and assess the potential for each of these tools to reduce unnecessary biopsies. Methods: This data linkage study included 538 women recalled for assessment from January 2017 to December 2019. The association between the recalled mammographic abnormalities and breast density was analysed using the chi-square independence test. Relative risks and the number of recalled cases requiring DBT and ultrasound assessment to prevent one unnecessary biopsy were compared using the McNemar test. Results: Breast density significantly influenced recall decisions (p < 0.001). Ultrasound showed greater potential to decrease unnecessary biopsies than DBT: in entirely fatty (21% vs. 5%; p = 0.04); scattered fibroglandular (23% vs. 10%; p = 0.003); heterogeneously dense (34% vs. 7%; p < 0.001) and extremely dense (39% vs. 9%; p < 0.001) breasts. The number of benign cases needing assessment to prevent one unnecessary biopsy was significantly lower with ultrasound than DBT in heterogeneously dense (1.8 vs. 7; p < 0.001) and extremely dense (1.9 vs. 5.1; p = 0.03) breasts. Conclusion: Women with dense breasts are more likely to be recalled for assessment and have a false-positive biopsy. Women with dense breasts benefit more from ultrasound assessment than from DBT.

The Benefits of Early Detection: Evidence From Modern International Mammography Service Screening Programs

Research from randomized controlled trials initiated up to 60 years ago consistently confirms that regular screening with mammography significantly reduces breast cancer mortality. Despite this success, there is ongoing debate regarding the efficacy of screening, which is confounded by technologic advances and concerns about cost, overdiagnosis, overtreatment, and equitable care of diverse patient populations. More recent screening research, designed to quell the debates, derives data from variable study designs, each with unique strengths and weaknesses. This article reviews observational population-based screening research that has followed the early initial long-term randomized controlled trials that are no longer practical or ethical to perform. The advantages and disadvantages of observational data and study design are outlined, including the three subtypes of population-based observational studies: cohort/case-control, trend, and incidence-based mortality/staging. The most recent research, typically performed in countries that administer screening mammography to women through centralized health service programs and directly track patient-specific outcomes and detection data, is summarized. These data are essential to understand and inform construction of effective new databases that facilitate continuous assessment of optimal screening techniques in the current era of rapidly developing medical technology, combined with a focus on health care that is both personal and equitable.

Prospective Comparison of Synthesized Mammography with DBT and Full-Field Digital Mammography with DBT Uncovers Recall Disagreements That may Impact Cancer Detection

RATIONALE AND OBJECTIVES

Synthesized mammography with digital breast tomosynthesis (SM+DBT) and full-field digital mammography with DBT were prospectively evaluated for recall rate (RR), cancer detection rate (CDR), positive predictive value 1 (PPV1), lesion recall differences, and disagreements in recall for additional imaging.

MATERIALS AND METHODS

From December 15, 2015 to January 15, 2017, after informed consent was obtained for this Health Insurance Portability and Accountability Act compliant study, each enrolled patient's SM+DBT and FFDM+DBT were interpreted sequentially by one of eight radiologists. RR, CDR, PPV1, and imaging findings (asymmetry, focal asymmetry, mass, architectural distortion, and calcifications) recalled were reviewed.

RESULTS

For SM+DBT and FFDM+DBT in 1022 patients, RR was 7.3% and 7.9% (SM+DBT vs. FFDM+DBT: diff= -0.6%; 90% CI= -1.4%, 0.1%); CDR was 6.8 and 7.8 per 1000 (SM+DBT vs. FFDM+DBT: diff= -1.0, 95% CI= -5.5, 2.8, p = 0.317); PPV1 was 9.3% and 9.9% (relative positive predictive value for SM+DBT vs. FFDM+DBT: 0.95, 95% CI: 0.73-1.22, p = 0.669). FFDM+DBT detected eight cancers; SM+DBT detected seven (missed 1 cancer with calcifications). SM+DBT and FFDM+DBT disagreed on patient recall for additional imaging in 19 patients, with majority (68%, 13/19 patients) in the recall of patients for calcifications. For calcifications, SM+DBT recalled six patients that FFDM+DBT did not recall, and FFDM+DBT recalled seven patients that SM+DBT did not recall, even though the total number of calcifications finding recalled was similar overall for both SM+DBT and FFDM+DBT.

CONCLUSION

Disagreement in recall of patients for calcifications may impact cancer detection by SM+DBT, warranting further investigation.

Comparison of breast density assessments between synthesized C-View™ & intelligent 2D™ mammography

OBJECTIVE

To compare breast density assessments between C-View™ and Intelligent 2D™, different generations of synthesized mammography (SM) from Hologic.

METHODS

In this retrospective study, we identified a subset of females between March 2017 and December 2019 who underwent screening digital breast tomosynthesis (DBT) with C-View followed by DBT with Intelligent 2D. Clinical Breast Imaging Reporting and Database System breast density was obtained along with volumetric breast density measures (including density grade, breast volume, percentage volumetric density, dense volume) using VolparaTM. Differences in density measures by type of synthesized image were calculated using the pairwise t-test or McNemar's test, as appropriate.

RESULTS

67 patients (avg age 62.7; range 40-84) were included with an average of 13.3 months between the two exams. No difference was found in Breast Imaging Reporting and Database System density between the SM reconstructions (p = 0.74). Similarly, there was no difference in VolparaTM mean density grade (p = 0.71), mean breast volume (p = 0.48), mean dense volume (p = 0.43) or mean percent volumetric density (p = 0.12) between the exams.

CONCLUSION

We found no significant differences in clinical and automated breast density assessments between these two versions of SM.

ADVANCES IN KNOWLEDGE

Lack of differences in density estimates between the two SM reconstructions is important as density assignment impacts risk stratification and adjunct screening recommendations.

Synthetic Mammography: Review of Benefits and Drawbacks in Clinical Use

Digital breast tomosynthesis (DBT) has been widely adopted as a breast cancer screening tool, demonstrating decreased recall rates and other improved screening performance metrics when compared to digital mammography (DM) alone. Drawbacks of DBT when added to 2D DM include the increased radiation dose and longer examination time. Synthetic mammography (SM), a 2D reconstruction from the tomosynthesis slices, has been introduced to eliminate the need for a separate acquisition of 2D DM. Data show that the replacement of 2D DM by SM, when used with DBT, maintains the benefits of DBT, such as decreased recall rates, improved cancer detection rates, and similar positive predictive values. Key differences between SM and 2D DM include how the image is acquired, assessment of breast density, and visualization of mammographic findings, such as calcifications. Although SM is approved by the Food and Drug Administration and has been shown to be non-inferior when used with DBT, concerns surrounding SM include image quality and artifacts. The purpose of this review article is to review the benefits, drawbacks, and screening performance metrics of SM versus DBT.

Breast Radiation Exposure of 3D Digital Breast Tomosynthesis Compared to Full-Field Digital Mammography in a Clinical Follow-Up Setting

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) (CC_FFDM+MLO_DBT) (a) left FFDM_CC 1.40 mGy (0.36 mGy, 1.13–1.59 mGy), left DBT_MLO 1.62 mGy (0.51 mGy, 1.27–1.82 mGy); (b) right FFDM_CC 1.36 mGy (0.34 mGy, 1.14–1.51 mGy), right DBT_MLO 1.59 mGy (0.52 mGy, 1.27–1.62 mGy). (II) (CC_FFDM+MLO_FFDM) (a) left FFDM_CC 1.35 mGy (0.35 mGy, 1.10–1.60 mGy), left FFDM_MLO 1.40 mGy (0.39 mGy, 1.12–1.59 mGy), (b) right FFDM_CC 1.35 mGy (0.33 mGy, 1.12–1.48 mGy), right FFDM_MLO 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.

Virtual clinical trial to compare cancer detection using combinations of 2D mammography, digital breast tomosynthesis and synthetic 2D imaging

OBJECTIVES

This study was designed to compare the detection of subtle lesions (calcification clusters or masses) when using the combination of digital breast tomosynthesis (DBT) and synthetic mammography (SM) with digital mammography (DM) alone or combined with DBT.

METHODS

A set of 166 cases without cancer was acquired on a DBT mammography system. Realistic subtle calcification clusters and masses in the DM images and DBT planes were digitally inserted into 104 of the acquired cases. Three study arms were created: DM alone, DM with DBT and SM with DBT. Five mammographic readers located the centre of any lesion within the images that should be recalled for further investigation and graded their suspiciousness. A JAFROC figure of merit (FoM) and lesion detection fraction (LDF) were calculated for each study arm. The visibility of the lesions in the DBT images was compared with SM and DM images.

RESULTS

For calcification clusters, there were no significant differences (p > 0.075) in FoM or LDF. For masses, the FoM and LDF were significantly improved in the arms using DBT compared to DM alone (p < 0.001). On average, both calcification clusters and masses were more visible on DBT than on DM and SM images.

CONCLUSIONS

This study demonstrated that masses were detected better with DBT than with DM alone and there was no significant difference (p = 0.075) in LDF between DM&DBT and SM&DBT for calcifications clusters. Our results support previous studies that it may be acceptable to not acquire digital mammography alongside tomosynthesis for subtle calcification clusters and ill-defined masses.

KEY POINTS

• The detection of masses was significantly better using DBT than with digital mammography alone. • The detection of calcification clusters was not significantly different between digital mammography and synthetic 2D images combined with tomosynthesis. • Our results support previous studies that it may be acceptable to not acquire digital mammography alongside tomosynthesis for subtle calcification clusters and ill-defined masses for the imaging technology used.

Digital Breast Tomosynthesis Complements Two-Dimensional Synthetic Mammography for Secondary Examination of Breast Cancer

Objective:

To compare the performance of two-dimensional synthetic mammography (SM) combined with digital breast tomosynthesis (DBT) (SM/DBT) and full-field digital mammography (FFDM) including women with DBT (FFDM/DBT) undergoing secondary examination for breast cancer.

Material and Methods:

Out of 186 breasts, including 52 with breast cancers; FFDM/DBT and SM/DBT findings were interpreted by four expert clinicians. Radiation doses of FFDM, SM/DBT, and FFDM/DBT were determined. Inter-rater reliabilities were analyzed between readers and between FFDM/DBT and SM/DBT by Cohen’s Kappa coefficients. Diagnostic accuracy was compared between SM/DBT and FFDM/DBT by Fisher’s exact tests. Two representative cancer cases were examined for differences in the interpretation between FFDM and SM.

Results:

A higher radiation dose was required in FFDM/DBT than in SM/DBT (median: 1.50 mGy vs. 2.95 mGy). Inter-rater reliabilities were similar between both readers and modalities. Both sensitivity and specificity were equivalent in FFDM/DBT and SM/DBT (p = 0.874–1.00). Compared with FFDM, SM did not clearly show abnormalities with subtle margins in the two representative cancer cases.

Conclusion:

SM/DBT had a similar performance to FFDM/DBT in detecting breast abnormalities but requires less radiation. DBT complements SM to improve accuracy to a level equivalent to that of FFDM. Taken together, SM/DBT may be a good substitute for FFDM/DBT for the secondary examination of breast cancer.

Effect of glandularity on the detection of simulated cancers in planar, tomosynthesis, and synthetic 2D imaging of the breast using a hybrid virtual clinical trial

PURPOSE

The purpose of this study was to measure the threshold diameter of calcifications and masses for 2D imaging, digital breast tomosynthesis (DBT), and synthetic 2D images, for a range of breast glandularities. This study shows the limits of detection for each of the technologies and the strengths and weaknesses of each in terms of visualizing the radiological features of small cancers.

METHODS

Mathematical voxel breast phantoms with glandularities by volume of 9%, 18%, and 30% with a thickness of 53 mm were created. Simulated ill-defined masses and calcification clusters with a range of diameters were inserted into some of these breast models. The imaging characteristics of a Siemens Inspiration X-ray system were measured for a 29 kV, tungsten/rhodium anode/filter combination. Ray tracing through the breast models was undertaken to create simulated 2D and DBT projection images. These were then modified to adjust the image sharpness, and to add scatter and noise. The mean glandular doses for the images were 1.43, 1.47, and 1.47 mGy for 2D and 1.92, 1.97, and 1.98 mGy for DBT for the three glandularities. The resultant images were processed to create 2D, DBT planes and synthetic 2D images. Patches of the images with or without a simulated lesion were extracted, and used in a four-alternative forced choice study to measure the threshold diameters for each imaging mode, lesion type, and glandularity. The study was undertaken by six physicists.

RESULTS

The threshold diameters of the lesions were 6.2, 4.9, and 6.7 mm (masses) and 225, 370, and 399 μm, (calcifications) for 2D, DBT, and synthetic 2D, respectively, for a breast glandularity of 18%. The threshold diameter of ill-defined masses is significantly smaller for DBT than for both 2D (p≤0.006) and synthetic 2D (p≤0.012) for all glandularities. Glandularity has a significant effect on the threshold diameter of masses, even for DBT where there is reduced background structure in the images. The calcification threshold diameters for 2D images were significantly smaller than for DBT and synthetic 2D for all glandularities. There were few significant differences for the threshold diameter of calcifications between glandularities, indicating that the background structure has little effect on the detection of calcifications. We measured larger but nonsignificant differences in the threshold diameters for synthetic 2D imaging than for 2D imaging for masses in the 9% (p = 0.059) and 18% (p = 0.19) glandularities. The threshold diameters for synthetic 2D imaging were larger than for 2D imaging for calcifications (p < 0.001) for all glandularities.

CONCLUSIONS

We have shown that glandularity has only a small effect on the detection of calcifications, but the threshold diameter of masses was significantly larger for higher glandularity for all of the modalities tested. We measured nonsignificantly larger threshold diameters for synthetic 2D imaging than for 2D imaging for masses at the 9% (p = 0.059) and 18% (p = 0.19) glandularities and significantly larger diameters for calcifications (p < 0.001) for all glandularities. The lesions simulated were very subtle and further work is required to examine the clinical effect of not seeing the smallest calcifications in clusters.

ACR Appropriateness Criteria® Supplemental Breast Cancer Screening Based on Breast Density

Mammography remains the only validated screening tool for breast cancer, however, there are limitations to mammography. One of the limitations of mammography is the variable sensitivity based on breast density. Supplemental screening may be considered based on the patient's risk level and breast density. For average-risk women with nondense breasts, the sensitivity of digital breast tomosynthesis (DBT) screening is high; additional supplemental screening is not warranted in this population. For average-risk women with dense breasts, given the decreased sensitivity of mammography/DBT, this population may benefit from additional supplemental screening with contrast-enhanced mammography, screening ultrasound (US), breast MRI, or abbreviated breast MRI. In intermediate-risk women, there is emerging evidence suggesting that women in this population may benefit from breast MRI or abbreviated breast MRI. In intermediate-risk women with dense breasts, given the decreased sensitivity of mammography/DBT, this population may benefit from additional supplemental screening with contrast-enhancedmammography or screening US. There is strong evidence supporting screening high-risk women with breast MRI regardless of breast density. Contrast-enhanced mammography, whole breast screening US, or abbreviated breast MRI may be also considered. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Opportunities in cancer imaging: risk-adapted breast imaging in screening

In the UK, women between 50-70 years are invited for 3-yearly mammography screening irrespective of their likelihood of developing breast cancer. The only risk adaption is for women with >30% lifetime risk who are offered annual magnetic resonance imaging (MRI) and mammography, and annual mammography for some moderate-risk women. Using questionnaires, breast density, and polygenic risk scores, it is possible to stratify the population into the lowest 20% risk, who will develop <4% of cancers and the top 4%, who will develop 18% of cancers. Mammography is a good screening test but has low sensitivity of 60% in the 9% of women with the highest category of breast density (BIRADS D) who have a 2.5- to fourfold breast cancer risk. There is evidence that adding ultrasound to the screening mammogram can increase the cancer detection rate and reduce advanced stage interval and next round cancers. Similarly, alternative tests such as contrast-enhanced mammography (CESM) or abbreviated MRI (ABB-MRI) are much more effective in detecting cancer in women with dense breasts. Scintimammography has been shown to be a viable alternative for dense breasts or for follow-up in those with a personal history of breast cancer and scarring as result of treatment. For supplemental screening to be worthwhile in these women, new technologies need to reduce the number of stage II cancers and be cost effective when tested in large scale trials. This article reviews the evidence for supplemental imaging and examines whether a risk-stratified approach is feasible.

Comparison of diagnostic performances in the evaluation of breast microcalcifications: synthetic mammography versus full-field digital mammography

BACKGROUND

Although several studies proved that SM could substitute for FFDM, the efficacy of SM in microcalcification evaluation remains controversial.

AIMS

To investigate the diagnostic performance of synthetic mammography (SM) in the evaluation of microcalcifications in comparison with full-field digital mammography (FFDM).

METHODS

In this retrospective study, 76 mammograms of 76 patients who underwent FFDM and digital breast tomosynthesis (DBT) acquisitions concomitantly between 2018 and 2019 and whose final mammography interpretation revealed microcalcifications (28 malignant microcalcifications and 48 benign microcalcifications) were included. All mammograms were reviewed independently by three radiologists with different levels of breast imaging experience. Readers were blinded to patient outcomes and interpreted each case in two separate reading sessions (first FFDM, second SM + DBT), according to the BI-RADS lexicon. The area under the receiver operating characteristic (ROC) curve (AUC) was calculated using ROC analysis in all cases for FFDM and SM + DBT sessions. The readers also assigned conspicuity scores to mammograms. The interobserver agreement was calculated using intraclass correlation coefficients (ICC).

RESULTS

The overall AUCs for malignant microcalcifications were 0.80 (95% CI: 0.75-0.85) in FFDM and 0.85 (95% CI: 0.80-0.89) in SM, and no significant difference was found between the groups (p = 0.0603). The sensitivity of the readers increased slightly with experience. The ICC values of BI-RADS categorization between readers were 0.93 (95% CI: 0.90-0.95) and 0.94 (95% CI: 0.91-0.96) for FFDM and SM, respectively.

CONCLUSIONS

SM had similar diagnostic performance in the evaluation of breast microcalcifications in comparison with FFDM, regardless of reader experience levels.

The impact on lesion detection via a multi-vendor study: A phantom-based comparison of digital mammography, digital breast tomosynthesis, and synthetic mammography

PURPOSE

The aim of this study is to perform a test object-based comparison of the imaging performance of digital mammography (DM), digital breast tomosynthesis (DBT), and synthetic mammography (SM).

METHODS

Two test objects were used, the CDMAM and the L1-structured phantom. Small-detail detectability was assessed using CDMAM and the microcalcification simulating specks in the L1-structured background. Detection of spiculated and non-spiculated mass-like objects was assessed using the L1 phantom. Six different systems were included: Amulet Innovality (Fujifilm), Senographe Pristina (GEHC), 3Dimensions (Hologic), Giotto Class (IMS), Clarity 2D/3D (Planmed), and Mammomat Revelation (Siemens). Images were acquired under automatic exposure control (AEC) and at adjusted levels of AEC/2 and 2 × AEC level. Threshold gold thickness (T_tr ) was established for the 0.13-mm-diameter CDMAM discs. Threshold diameters for the calcifications (d_{tr_c} ), the spiculated masses (d_{tr_sm} ), and for the non-spiculated masses (d_{tr_nsm} ) were established. The threshold condition was defined as the thickness or diameter for a 62.5% correct score.

RESULTS

T_tr for DM was generally superior to DBT, which in turn was superior to SM, but for most systems, these differences between modes were not significant. For L1, no significant differences in d_{tr_c} were found between DM and DBT. The increase in d_{tr_c} from DM to SM at AEC dose was 1%, 19%, 11%, 14%, 46%, and 27% for the Fujifilm, GEHC, Hologic, IMS, Planmed, and Siemens, respectively, indicating significantly poorer performance for all vendors except for Fujifilm, Hologic, and IMS. For both mass types, DBT performed better than SM, while SM showed no significant difference with DM (except for Fujifilm spiculated masses). The dose had an impact on small-detail detectability for both phantoms but did not influence the detection of either mass type.

CONCLUSIONS

Both phantoms indicated potentially reduced small-detail detectability for SM versus DM and DBT and should therefore not be used in stand-alone mode. The L1 phantom demonstrated no significant difference in microcalcification detection between DM and DBT and also demonstrated the superiority of DBT, compared to DM for mass detection, for all six systems.

Mammographic Surveillance After Breast Conserving Therapy: Impact of Digital Breast Tomosynthesis and Artificial Intelligence-Based Computer-Aided Detection

Background: Postoperative mammograms present interpretive challenges due to postoperative distortion and hematomas. The application of digital breast tomosynthesis (DBT) and artificial intelligence-based computer-aided detection (AI-CAD) after breast concerving therapy (BCT) has not been widely investigated. Objective: To assess the impact of additional DBT or AI-CAD on recall rate and diagnostic performance in women undergoing mammographic surveillance after BCT. Methods: This retrospective study included 314 women (mean age 53.2±10.6 years; 4 with bilateral breast cancer) who underwent BCT followed by DBT (mean interval from surgery to DBT of 15.2±15.4 months). Three breast radiologists independently reviewed images in three sessions: digital mammography (DM), DM with DBT (DM+DBT), and DM with AI-CAD (DM+AI-CAD). Recall rates and diagnostic performance were compared between DM, DM+DBT, and DM+AI-CAD, using readers' mean results. Results: Of the 314 women, 6 breast recurrences (3 ipsilateral, 3 contralateral) developed at the time of surveillance mammography. Ipsilateral breast recall rate was lower for DM+AI-CAD (1.9%) than for DM (11.2%) or DM+DBT (4.1%) (p<.001). Contralateral breast recall rate was lower for DM+AI-CAD (1.5%, p<.001) than for DM (6.6%) but not DM+DBT (2.7%, p=.08). In ipsilateral breast, accuracy was higher for DM+AI-CAD (97.0%) than for DM (88.5%) or DM+DBT (94.8%) (p<.05); specificity was higher for DM+AICAD (98.3%) than for DM (89.3%) or DM+DBT (96.1%) (p<.05); sensitivity was lower for DM+AI-CAD (22.2%) than for DM (66.7%, p=.03) but not DM+DBT (22.2%, p>.99). In contralateral breast, accuracy was higher for DM+AI-CAD (97.1%) than for DM (92.5%, p<.001) but not DM+DBT (96.1%, p=.25); specificity was higher for DM+AI-CAD (98.6%) than for DM (93.7%, p<.001) but not DM+DBT (97.5%) (p=.09); sensitivity was not different between DM (33.3%), DM+DBT (22.2%), and DM+AI-CAD (11.1%) (p>.05). Conclusion: After BCT, adjunct DBT or AI-CAD reduced recall rates and improved accuracy in the ipsilateral and contralateral breasts compared with DM. In the ipsilateral breast, addition of AI-CAD resulted in lower recall rate and higher accuracy than addition of DBT. Clinical Impact: AI-CAD may help address the challenges of post-BCT surveillance mammograms.

Synthesis of Mammogram From Digital Breast Tomosynthesis Using Deep Convolutional Neural Network With Gradient Guided cGANs

Synthetic digital mammography (SDM), a 2D image generated from digital breast tomosynthesis (DBT), is used as a potential substitute for full-field digital mammography (FFDM) in clinic to reduce the radiation dose for breast cancer screening. Previous studies exploited projection geometry and fused projection data and DBT volume, with different post-processing techniques applied on re-projection data which may generate different image appearance compared to FFDM. To alleviate this issue, one possible solution to generate an SDM image is using a learning-based method to model the transformation from the DBT volume to the FFDM image using current DBT/FFDM combo images. In this study, we proposed to use a deep convolutional neural network (DCNN) to learn the transformation to generate SDM using current DBT/FFDM combo images. Gradient guided conditional generative adversarial networks (GGGAN) objective function was designed to preserve subtle MCs and the perceptual loss was exploited to improve the performance of the proposed DCNN on perceptual quality. We used various image quality criteria for evaluation, including preserving masses and MCs which are important in mammogram. Experiment results demonstrated progressive performance improvement of network using different objective functions in terms of those image quality criteria. The methodology we exploited in the SDM generation task to analyze and progressively improve image quality by designing objective functions may be helpful to other image generation tasks.

Breast cancer screening using synthesized two-dimensional mammography: A systematic review and meta-analysis

Purpose

We conducted a systematic review and meta-analysis to compare the screening performance of synthesized mammography (SM) plus digital breast tomosynthesis (DBT) with digital mammography (DM) plus DBT or DM alone.

Methods

Medline, Embase, Web of Science, and the Cochrane Library databases were searched from January 2010 to January 2021. Eligible population-based studies on breast cancer screening comparing SM/DBT with DM/DBT or DM in asymptomatic women were included. A random-effect model was used in this meta-analysis. Data were summarized as risk differences (RDs), with 95 % confidence intervals (CIs).

Results

Thirteen studies involving 1,370,670 participants were included. Compared with DM/DBT, screening using SM/DBT had similar breast cancer detection rate (CDR) (RD = −0.1/1000 screens, 95 % CI = −0.4 to 0.2, p = 0.557, I² = 0 %), but lower recall rate (RD = −0.56 %, 95 % CI = −1.03 to −0.08, p = 0.022, I² = 90 %) and lower biopsy rate (RD = −0.33 %, 95 % CI = −0.56 to −0.10, p = 0.005, I² = 78 %). Compared with DM, SM/DBT improved CDR (RD = 2.0/1000 screens, 95 % CI = 1.4 to 2.6, p < 0.001, I² = 63 %) and reduced recall rate (RD = −0.95 %, 95 % CI = −1.91 to −0.002, p = 0.049, I² = 99 %). However, SM/DBT and DM had similar interval cancer rate (ICR) (RD = 0.1/1000 screens, 95 % CI = −0.6 to 0.8, p = 0.836, I² = 71 %) and biopsy rate (RD = −0.05 %, 95 % CI = −0.35 to 0.24, p = 0.727, I² = 93 %).

Conclusions

Screening using SM/DBT has similar breast cancer detection but reduces recall and biopsy when compared with DM/DBT. SM/DBT improves CDR when compared with DM, but they have little difference in ICR. SM/DBT could replace DM/DBT in breast cancer screening to reduce radiation dose.

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Screening using SM/DBT has similar breast cancer detection but reduces recall and biopsy when compared with DM/DBT.

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Screening using SM/DBT improves cancer detection rate when compared with DM/DBT alone.

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There was no significant difference in interval cancer rate between SM/DBT and DM.

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SM/DBT could replace DM/DBT in breast cancer screening to reduce radiation dose.

Accuracy of Digital Breast Tomosynthesis for Detecting Breast Cancer in the Diagnostic Setting: A Systematic Review and Meta-Analysis

Objective

To compare the accuracy for detecting breast cancer in the diagnostic setting between the use of digital breast tomosynthesis (DBT), defined as DBT alone or combined DBT and digital mammography (DM), and the use of DM alone through a systematic review and meta-analysis.

Materials and Methods

Ovid-MEDLINE, Ovid-Embase, Cochrane Library and five Korean local databases were searched for articles published until March 25, 2020. We selected studies that reported diagnostic accuracy in women who were recalled after screening or symptomatic. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A bivariate random effects model was used to estimate pooled sensitivity and specificity. We compared the diagnostic accuracy between DBT and DM alone using meta-regression and subgroup analyses by modality of intervention, country, existence of calcifications, breast density, Breast Imaging Reporting and Data System category threshold, study design, protocol for participant sampling, sample size, reason for diagnostic examination, and number of readers who interpreted the studies.

Results

Twenty studies (n = 44513) that compared DBT and DM alone were included. The pooled sensitivity and specificity were 0.90 (95% confidence interval [CI] 0.86–0.93) and 0.90 (95% CI 0.84–0.94), respectively, for DBT, which were higher than 0.76 (95% CI 0.68–0.83) and 0.83 (95% CI 0.73–0.89), respectively, for DM alone (p < 0.001). The area under the summary receiver operating characteristics curve was 0.95 (95% CI 0.93–0.97) for DBT and 0.86 (95% CI 0.82–0.88) for DM alone. The higher sensitivity and specificity of DBT than DM alone were consistently noted in most subgroup and meta-regression analyses.

Conclusion

Use of DBT was more accurate than DM alone for the diagnosis of breast cancer. Women with clinical symptoms or abnormal screening findings could be more effectively evaluated for breast cancer using DBT, which has a superior diagnostic performance compared to DM alone.

Deep Learning-Based Artificial Intelligence for Mammography

During the past decade, researchers have investigated the use of computer-aided mammography interpretation. With the application of deep learning technology, artificial intelligence (AI)-based algorithms for mammography have shown promising results in the quantitative assessment of parenchymal density, detection and diagnosis of breast cancer, and prediction of breast cancer risk, enabling more precise patient management. AI-based algorithms may also enhance the efficiency of the interpretation workflow by reducing both the workload and interpretation time. However, more in-depth investigation is required to conclusively prove the effectiveness of AI-based algorithms. This review article discusses how AI algorithms can be applied to mammography interpretation as well as the current challenges in its implementation in real-world practice.

Diagnostic Performance of Adjunctive Imaging Modalities Compared to Mammography Alone in Women with Non-Dense and Dense Breasts: A Systematic Review and Meta-Analysis

PURPOSE

To compare the diagnostic performance of mammography (MG) alone versus MG combined with adjunctive imaging modalities, including handheld ultrasound (HHUS), automated breast ultrasound (ABUS), digital breast tomosynthesis (DBT), contrast-enhanced mammography (CEM), and magnetic resonance imaging (MRI) in women with non-dense and dense breasts.

PATIENTS AND METHODS

Medline, Embase, PubMed, CINAHL, Scopus, and the Web of Science databases were searched up to October 2019. Quality assessment was performed using QUADAS-2. RevMan 5.3 was used to conduct a meta-analysis of the studies.

RESULTS

In dense breasts, adding adjunctive modalities significantly increased cancer detection rates (CDRs): HHUS (relative risk [RR] = 1.49; 95% confidence interval [CI], 1.19-1.86; P = .0005); ABUS (RR = 1.44; 95% CI, 1.16-1.78; P = .0008); DBT (RR = 1.38; 95% CI, 1.14-1.67; P = .001); CEM (RR = 1.37; 95% CI, 1.12-1.69; P = .003); and MRI (RR = 2.16; 95% CI, 1.81-2.58; P < .00001). The recall rate was significantly increased by HHUS (RR = 2.03; 95% CI, 1.89-2.17; P < .00001), ABUS (RR = 1.90; 95% CI, 1.81-1.99; P < .00001), and MRI (RR = 2.71; 95% CI, 1.73-4.25; P < .0001), but not by DBT (RR = 1.14; 95% CI, 0.95-1.36; P = .15). In non-dense breasts, HHUS and MRI showed significant increases in CDRs but not DBT: HHUS (RR = 1.14; 95% CI, 1.01-1.29; P = .04); MRI (RR = 1.78; 95% CI, 1.14-2.77; P = .01); and DBT (RR = 1.09; 95% CI, 1.13-1.75; P = .08). The recall rate was also significantly increased by HHUS (RR = 1.43; 95% CI, 1.28-1.59; P < .00001) and MRI (RR = 3.01; 95% CI, 1.68-5.39; P = .0002), whereas DBT showed a non-significant reduction (RR = 0.83; 95% CI, 0.65-1.05; P = .12).

CONCLUSION

Adding adjunctive modalities to MG increases CDRs in women with dense and non-dense breasts. Ultrasound and MRI increase recall rates across all breast densities; however, MRI results in higher values for both CDRs and recall rates.

Application of artificial intelligence-based computer-assisted diagnosis on synthetic mammograms from breast tomosynthesis: comparison with digital mammograms

OBJECTIVE

To compare the diagnostic agreement and performances of synthetic and conventional mammograms when artificial intelligence-based computer-assisted diagnosis (AI-CAD) is applied.

MATERIAL AND METHOD

From January 2017 to April 2017, 192 patients (mean age 53.7 ± 11.7 years) diagnosed with 203 breast cancers were enrolled in this retrospective study. All patients underwent digital breast tomosynthesis (DBT) with digital mammograms (DM) simultaneously. Commercial AI-CAD was applied to the reconstructed synthetic mammograms (SM) from DBT and DM respectively and abnormality scores were calculated. We compared the median abnormality scores between DM and SM with the Wilcoxon signed-rank test and used the Bland-Altman analysis to evaluate agreements between the two mammograms and to investigate clinicopathological factors which might affect agreement. Diagnostic performances were compared using an area under the receiver operating characteristic curve (AUC).

RESULT

The abnormality scores showed a mean difference (bias) of - 3.26 (95% limits of agreement: - 32.69, 26.18) between the two mammograms by the Bland-Altman analysis. The concordance correlation coefficient was 0.934 (95% CI: 0.92, 0.946), suggesting high reproducibility. SM showed higher abnormality scores in cancer with distortion and occult findings, T1 and N0 cancer, and luminal type cancer than DM (all p ≤ 0.001). Diagnostic performance did not differ between the mammograms (AUC 0.945 for conventional mammograms, 0.938 for synthetic mammograms, p = 0.499).

CONCLUSION

AI-CAD can also work well on synthetic mammograms, showing good agreement and comparable diagnostic performance compared to its application to DM.

KEY POINTS

• AI-CAD which was developed based on imaging findings of digital mammograms can also be applied to synthetic mammograms. • AI-CAD showed good agreement and similar diagnostic performance when applied to both synthetic and digital mammograms. • With AI-CAD, synthetic mammograms showed relatively higher abnormality scores in cancer with distortion and occult findings, T1 and N0 cancer, and luminal type cancer than digital mammograms.

Digital breast tomosynthesis compared to diagnostic mammographic projections (including magnification) among women recalled at screening mammography: a systematic review for the European Commission Initiative on Breast Cancer (ECIBC)

Background

Diagnostic mammography projections (DxMM) have been traditionally used in the assessment of women recalled after a suspicious screening mammogram. Digital breast tomosynthesis (DBT) reduces the tissue overlap effect, thus improving image assessment. Some studies have suggested DBT might replace DxMM with at least equivalent performance.

Objective

To evaluate the replacement of DxMM with DBT in women recalled at screening.

Methods

We searched PubMed, EMBASE, and the Cochrane Library databases to identify diagnostic paired cohort studies or RCTs comparing DBT vs DxMM, published in English that: reported accuracy outcomes, recruited women recalled for assessment at mammography screening, and included a reference standard. Subgroup analysis was performed over lesion characteristics. We provided pooled accuracy estimates and differences between tests using a quadrivariate model. We assessed the certainty of the evidence using the GRADE approach.

Results

We included ten studies that reported specificity and sensitivity. One study included 7060 women while the remaining included between 52 and 738 women. DBT compared with DxMM showed a pooled difference for the sensitivity of 2% (95% CI 1%–3%) and a pooled difference for the specificity of 6% (95%CI 2%–11%). Restricting the analysis to the six studies that included women with microcalcification lesions gave similar results. In the context of a prevalence of 21% of breast cancer (BC) in recalled women, DBT probably detects 4 (95% CI 2–6) more BC cases and has 47 (95%CI 16–87) fewer false‐positive results per 1000 assessments. The certainty of the evidence was moderate due to risk of bias.

Conclusion

The evidence in the assessment of screen‐recalled findings with DBT is sparse and of moderate certainty. DBT probably has higher sensitivity and specificity than DxMM. Women, health care providers and policymakers might value as relevant the reduction of false‐positive results and related fewer invasive diagnostic procedures with DBT, without missing BC cases.

Digital Breast Tomosynthesis: Update on Technology, Evidence, and Clinical Practice

Digital breast tomosynthesis (DBT) has been widely adopted in breast imaging in both screening and diagnostic settings. The benefits of DBT are well established. Compared with two-dimensional digital mammography (DM), DBT preferentially increases detection of invasive cancers without increased detection of in-situ cancers, maximizing identification of biologically significant disease, while mitigating overdiagnosis. The higher sensitivity of DBT for architectural distortion allows increased diagnosis of invasive cancers overall and particularly improves the visibility of invasive lobular cancers. Implementation of DBT has decreased the number of recalls for false-positive findings at screening, contributing to improved specificity at diagnostic evaluation. Integration of DBT in diagnostic examinations has also resulted in an increased percentage of biopsies with positive results, improving diagnostic confidence. Although individual DBT examinations have a longer interpretation time compared with that for DM, DBT has streamlined the diagnostic workflow and minimized the need for short-term follow-up examinations, redistributing much-needed time resources to screening. Yet DBT has limitations. Although improvements in cancer detection and recall rates are seen for patients in a large spectrum of age groups and breast density categories, these benefits are minimal in women with extremely dense breast tissue, and the extent of these benefits may vary by practice environment and by geographic location. Although DBT allows detection of more invasive cancers than does DM, its incremental yield is lower than that of US and MRI. Current understanding of the biologic profile of DBT-detected cancers is limited. Whether DBT improves breast cancer-specific mortality remains a key question that requires further investigation. ^©RSNA, 2021.

Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology

The NCCN Guidelines for Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic focus primarily on assessment of pathogenic or likely pathogenic variants associated with increased risk of breast, ovarian, and pancreatic cancer and recommended approaches to genetic testing/counseling and management strategies in individuals with these pathogenic or likely pathogenic variants. This manuscript focuses on cancer risk and risk management for BRCA-related breast/ovarian cancer syndrome and Li-Fraumeni syndrome. Carriers of a BRCA1/2 pathogenic or likely pathogenic variant have an excessive risk for both breast and ovarian cancer that warrants consideration of more intensive screening and preventive strategies. There is also evidence that risks of prostate cancer and pancreatic cancer are elevated in these carriers. Li-Fraumeni syndrome is a highly penetrant cancer syndrome associated with a high lifetime risk for cancer, including soft tissue sarcomas, osteosarcomas, premenopausal breast cancer, colon cancer, gastric cancer, adrenocortical carcinoma, and brain tumors.

Diagnostic accuracy of digital breast tomosynthesis in combination with 2D mammography for the characterisation of mammographic abnormalities

This study aims to assess the diagnostic accuracy of digital breast tomosynthesis in combination with full field digital mammography (DBT + FFDM) in the charaterisation of Breast Imaging-reporting and Data System (BI-RADS) category 3, 4 and 5 lesions. Retrospective cross-sectional study of 390 patients with BI-RADS 3, 4 and 5 mammography with available histopathology examination results were recruited from in a single center of a multi-ethnic Asian population. 2 readers independently reported the FFDM and DBT images and classified lesions detected (mass, calcifications, asymmetric density and architectural distortion) based on American College of Radiology-BI-RADS lexicon. Of the 390 patients recruited, 182 malignancies were reported. Positive predictive value (PPV) of cancer was 46.7%. The PPV in BI-RADS 4a, 4b, 4c and 5 were 6.0%, 38.3%, 68.9%, and 93.1%, respectively. Among all the cancers, 76% presented as masses, 4% as calcifications and 20% as asymmetry. An additional of 4% of cancers were detected on ultrasound. The sensitivity, specificity, PPV and NPV of mass lesions detected on DBT + FFDM were 93.8%, 85.1%, 88.8% and 91.5%, respectively. The PPV for calcification is 61.6% and asymmetry is 60.7%. 81.6% of cancer detected were invasive and 13.3% were in-situ type. Our study showed that DBT is proven to be an effective tool in the diagnosis and characterization of breast lesions and supports the current body of literature that states that integrating DBT to FFDM allows good characterization of breast lesions and accurate diagnosis of cancer.

Pros and cons for breast cancer screening with tomosynthesis - a review of the literature

Breast cancer screening programs using mammography proved their value in detecting breast cancer at early stages and, consequently, reducing the mortality from this disease.

Due to the technological progress, the screening programs have shifted from screen-film mammography to digital mammography and nowadays digital breast tomosynthesis became the focus of breast imaging research. Using tomosynthesis in screening increases cancer detection rates and decreases recall and false-positive rates, thus improving the effectiveness of breast cancer screening programs, with positive consequences on health care costs and on patient psychology.

More long-term follow-up data must be collected for assessing absolute sensitivity and specificity of digital breast tomosynthesis, together with efforts for addressing the limitations of the method.

Digital Breast Tomosynthesis: Technique and Common Artifacts

Image optimization at digital breast tomosynthesis (DBT) involves a series of trade-offs between multiple variables. Wider sweep angles provide better separation of overlapping tissues, but they result in decreased in-plane resolution as well as increased scan times that may be prone to patient motion. Techniques to reduce scan time, such as continuous tube motion and pixel binning during detector readout, reduce the chances of patient motion but may degrade the in-plane resolution. Image artifacts are inherent to DBT because of the limited angular range of the acquisition. Iterative reconstruction algorithms have been shown to reduce various DBT artifacts.

Multicenter Evaluation of Breast Cancer Screening with Digital Breast Tomosynthesis in Combination with Synthetic versus Digital Mammography

BackgroundDigital breast tomosynthesis (DBT) combined with digital mammography (DM) is increasingly used in the United States instead of DM alone for breast cancer screening. Early screening outcomes incorporating synthetic mammography (SM) with DBT have suggested that SM is an acceptable non-radiation dose alternative to DM.PurposeTo compare multicenter outcomes from breast cancer screening with SM/DBT versus DM/DBT.Materials and MethodsThis was a retrospective study of consecutive screening mammograms obtained at two institutions. Eligible studies consisted of 34 106 DM/DBT examinations between October 3, 2011, and October 31, 2014, and 34 180 SM/DBT examinations between January 7, 2015, and February 2, 2018, at the University of Pennsylvania and 51 148 DM/DBT examinations between January 1, 2012, and May 31, 2016, and 31 929 SM/DBT examinations between June 1, 2016, and March 30, 2018, at the University of Vermont. Demographics of women who attended screening and results from screening were recorded. Recall rate, biopsy rate, false-negative rate, cancer detection rate, positive predictive value, sensitivity, and specificity were calculated according to modality and institution. Descriptive statistics, χ² tests, and logistic regression were used in analysis.ResultsThe study included 151 363 screening examinations among 151 363 women (mean age, 58.1 years ± 10.9 [standard deviation]). The unadjusted recall rate was lower with SM/DBT than with DM/DBT (7.0% [4630 of 66 109 examinations] for SM/DBT vs 7.9% [6742 of 85 254 examinations] for DM/DBT; P < .01). However, after multivariable adjustment, SM/DBT was associated with a slightly higher recall rate compared with DM/DBT (adjusted odds ratio [OR], 1.06; adjusted 95% CI: 1.01, 1.11; P = .02). Similarly, after multivariable adjustment, SM/DBT was associated with slightly lower specificity compared with DM/DBT (adjusted OR, 0.95; adjusted 95% CI: 0.90, 0.99; P = .02). There was no statistically significant difference in biopsy rate (P = .54), false-negative rate (P = .38), cancer detection rate (P = .55), invasive or in situ cancer detection rate (P = .52 and P = .98, respectively), positive predictive value (P = .78), or sensitivity (P = .33) for SM/DBT versus DM/DBT overall or within either institution (P > .05 for all).ConclusionBreast cancer screening performance is maintained within benchmarks when synthetic mammography replaces digital mammography in digital breast tomosynthesis imaging.© RSNA, 2020Online supplemental material is available for this article.See also the editorial by Lång in this issue.