Differential detection by breast density for digital breast tomosynthesis versus digital mammography population screening: a systematic review and meta-analysis

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.

Prevalent vs Incident Screen: Why Does It Matter?

There are important differences in the performance and outcomes of breast cancer screening in the prevalent compared to the incident screening rounds. The prevalent screen is the first screening examination using a particular imaging technique and identifies pre-existing, undiagnosed cancers in the population. The incident screen is any subsequent screening examination using that technique. It is expected to identify fewer cancers than the prevalent screen because it captures only those cancers that have become detectable since the prior screening examination. The higher cancer detection rate at prevalent relative to incident screening should be taken into account when analyzing the medical audit and effectiveness of new screening technologies.

Effectiveness of hybrid digital breast tomosynthesis/digital mammography compared to digital mammography in women presenting for routine screening at Maroondah BreastScreen: Study protocol for a co-designed, non-randomised prospective trial

BACKGROUND

Digital breast tomosynthesis (DBT) for breast cancer screening has been shown in international trials to increase cancer detection compared with mammography; however, results have varied across screening settings, and currently there is limited and conflicting evidence on interval cancer rates (a surrogate for screening effectiveness). Australian pilot data also indicated substantially longer screen-reading time for DBT posing a barrier for adoption. There is a critical need for evidence on DBT to inform its role in Australia, including evaluation of potentially more feasible models of implementation, and quantification of screening outcomes by breast density which has global relevance.

METHODS

This study is a prospective trial embedded in population-based Australian screening services (Maroondah BreastScreen, Eastern Health, Victoria) comparing hybrid screening comprising DBT (mediolateral oblique view) and digital mammography (cranio-caudal view) with standard mammography screening in a concurrent group attending another screening site. All eligible women aged ≥40 years attending the Maroondah service for routine screening will be enrolled (unless they do not provide verbal consent and opt-out of hybrid screening; are unable to provide consent; or where a 'pushback' image on hybrid DBT cannot be obtained). Each arm will enrol 20,000 women. The primary outcomes are cancer detection rate (per 1000 screens) and recall rate (percentage). Secondary outcomes include 'opt-out' rate; cohort characteristics; cancer characteristics; assessment outcomes; screen-reading time; and interval cancer rate at 24-month follow-up. Automated volumetric breast density will be measured to allow stratification of outcomes by mammographic density. Stratification by age and screening round will also be undertaken. An interim analysis will be undertaken after the first 5000 screens in the intervention group.

DISCUSSION

This is the first Australian prospective trial comparing hybrid DBT/mammography with standard mammography screening that is powered to show differences in cancer detection. Findings will inform future implementation of DBT in screening programs world-wide and provide evidence on whether DBT should be adopted in the broader BreastScreen program in Australia or in subgroups of screening participants.

TRIAL REGISTRATION

The trial is registered with the Australian New Zealand Clinical Trials Registry (ANZCTR, ACTRN12623001144606, https://www.anzctr.org.au/). Registration will be updated to reflect trial progress and protocol amendments.

Atypical architectural distortion detection in digital breast tomosynthesis: a multi-view computer-aided detection model with ipsilateral learning

Objective.Breast architectural distortion (AD), a common imaging symptom of breast cancer, is associated with a particularly high rate of missed clinical detection. In clinical practice, atypical ADs that lack an obvious radiating appearance constitute most cases, and detection models based on single-view images often exhibit poor performance in detecting such ADs. Existing multi-view deep learning methods have overlooked the correspondence between anatomical structures across different views.Approach.To develop a computer-aided detection (CADe) model for AD detection that effectively utilizes the craniocaudal (CC) and mediolateral oblique (MLO) views of digital breast tomosynthesis (DBT) images, we proposed an anatomic-structure-based multi-view information fusion approach by leveraging the related anatomical structure information between these ipsilateral views. To obtain a representation that can effectively capture the similarity between ADs in images from ipsilateral views, our approach utilizes a Siamese network architecture to extract and compare information from both views. Additionally, we employed a triplet module that utilizes the anatomical structural relationship between the ipsilateral views as supervision information.Main results.Our method achieved a mean true positive fraction (MTPF) of 0.05-2.0, false positives (FPs) per volume of 64.40%, and a number of FPs at 80% sensitivity (FPs@0.8) of 3.5754; this indicates a 6% improvement in MPTF and 16% reduction in FPs@0.8 compared to the state-of-the-art baseline model.Significance.From our experimental results, it can be observed that the anatomic-structure-based fusion of ipsilateral view information contributes significantly to the improvement of CADe model performance for atypical AD detection based on DBT. The proposed approach has the potential to lead to earlier diagnosis and better patient outcomes.

Breast Digital Tomosynthesis versus Contrast-Enhanced Mammography: Comparison of Diagnostic Application and Radiation Dose in a Screening Setting

This study aims to evaluate the Average Glandular Dose (AGD) and diagnostic performance of CEM versus Digital Mammography (DM) as well as versus DM plus one-view Digital Breast Tomosynthesis (DBT), which were performed in the same patients at short intervals of time. A preventive screening examination in high-risk asymptomatic patients between 2020 and 2022 was performed with two-view Digital Mammography (DM) projections (Cranio Caudal and Medio Lateral) plus one Digital Breast Tomosynthesis (DBT) projection (mediolateral oblique, MLO) in a single session examination. For all patients in whom we found a suspicious lesion by using DM + DBT, we performed (within two weeks) a CEM examination. AGD and compression force were compared between the diagnostic methods. All lesions identified by DM + DBT were biopsied; then, we assessed whether lesions found by DBT were also highlighted by DM alone and/or by CEM. We enrolled 49 patients with 49 lesions in the study. The median AGD was lower for DM alone than for CEM (3.41 mGy vs. 4.24 mGy, p = 0.015). The AGD for CEM was significantly lower than for the DM plus one single projection DBT protocol (4.24 mGy vs. 5.55 mGy, p < 0.001). We did not find a statistically significant difference in the median compression force between the CEM and DM + DBT. DM + DBT allows the identification of one more invasive neoplasm one in situ lesion and two high-risk lesions, compared to DM alone. The CEM, compared to DM + DBT, failed to identify only one of the high-risk lesions. According to these results, CEM could be used in the screening of asymptomatic high-risk patients.

Artificial intelligence for digital breast tomosynthesis: Impact on diagnostic performance, reading times, and workload in the era of personalized screening

The ultimate goals of the application of artificial intelligence (AI) to digital breast tomosynthesis (DBT) are the reduction of reading times, the increase of diagnostic performance, and the reduction of interval cancer rates. In this review, after outlining the journey from computer-aided detection/diagnosis systems to AI applied to digital mammography (DM), we summarize the results of studies where AI was applied to DBT, noting that long-term advantages of DBT screening and its crucial ability to decrease the interval cancer rate are still under scrutiny. AI has shown the capability to overcome some shortcomings of DBT in the screening setting by improving diagnostic performance and by reducing recall rates (from -2 % to -27 %) and reading times (up to -53 %, with an average 20 % reduction), but the ability of AI to reduce interval cancer rates has not yet been clearly investigated. Prospective validation is needed to assess the cost-effectiveness and real-world impact of AI models assisting DBT interpretation, especially in large-scale studies with low breast cancer prevalence. Finally, we focus on the incoming era of personalized and risk-stratified screening that will first see the application of contrast-enhanced breast imaging to screen women with extremely dense breasts. As the diagnostic advantage of DBT over DM was concentrated in this category, we try to understand if the application of AI to DM in the remaining cohorts of women with heterogeneously dense or non-dense breast could close the gap in diagnostic performance between DM and DBT, thus neutralizing the usefulness of AI application to DBT.