Breast Tomosynthesis Screening Research Articles

3D tomosynthesis evaluation of breast parenchymal density and its association with malignant lesions and menopausal status

BackgroundBreast cancer that has a high mortality rate is now known to decrease due to early detection with the advent of digital breast tomosynthesis (DBT or 3D tomosynthesis) screening, especially in those with dense breasts. The risk of breast cancer related to 'changes' in breast density over time remains controversial as breast density and age have an inverse relationship. Breast density as an independent risk factor for breast cancer is known, but its association with menopausal status, if any, has not been studied thoroughly. MethodAll patients referred for 3D mammography with breast lesions from June 2022 to January 2023 were considered. Patients were categorized as pre-, peri, and post-menopausal, and each category was further sub-classified based on the breast density as either dense or non-dense and the lesion type, whether benign or malignant. The Statistical analysis was performed using a chi-square test to evaluate whether any association exists between malignancy and menopausal status. ResultA total of 60 patients, with 20 in each category of menopausal stage, were imaged and evaluated. 35% of women had non-dense breasts, while 65% had dense breast parenchyma. Breast density and lesion type were associated significantly (p-value = 0.05) where, out of the 23 benign lesions, 48% occurred in dense women, and 52% in non-dense women respectively. In our study, both benign (N = 7) and malignant (N = 13) lesions occurred in equal numbers in the pre-and peri‑ menopausal women, whereas the number of benign and malignant lesions in the post-menopausal women were 9 (45%) and 11 (55%), respectively. Even though no statistically significant association was found between menopausal status and malignancy in our study, out of the 37 malignant lesions, a majority (76%) of lesions occurred in those having dense breasts (N = 28). ConclusionEarlier, the notion was that older women had a higher risk of breast cancer compared to younger, but this study has shown that malignancy and menopausal status have a p-value of 0.754, which is not statistically significant. However, both malignant and benign lesions were found more in women having high breast density, in keeping with previous literature. Hence, precaution and care should be taken during pre-, peri, and post-menopausal phases, especially in those patients with high breast density. Apart from breast density, many other risk factors for breast cancer exist, therefore breast density alone is not sufficient to govern the need for screening in women.

Interval cancer in the Córdoba Breast Tomosynthesis Screening Trial(CBTST): comparison of digital breast tomosynthesis plus digital mammography to digital mammography alone.

This work aims to compare the interval cancer rate and interval cancer characteristics between women screened with digital breast tomosynthesis (DBT) + digital mammography (DM) and those screened with DM alone. The interval cancer rate and interval cancer characteristics of the study population included in the Córdoba Breast Tomosynthesis Screening Trial (CBTST) were compared to a contemporary control population screened with DM. The tumour characteristics of screen-detected and interval cancers were also compared. Contingency tables were used to compare interval cancer rates. The chi-square test and Fisher's exact test were used to compare the qualitative characteristics of the cancers whereas Student's t test and the Mann-Whitney U test were used to analyse quantitative features. A total of 16,068 screening exams with DBT + DM were conducted within the CBTST (mean age 57.59 ± 5.9 [SD]) between January 2015 and December 2016 (study population). In parallel, 23,787 women (mean age 58.89 ± 5.9 standard deviation [SD]) were screened with DM (control population). The interval cancer rate was lower in the study population than in the control population (15 [0.93‰; 95% confidence interval (CI): 0.73, 1.14] vs 43 [1.8‰; 95% CI: 1.58, 2.04] respectively; p = 0.045). The difference in rate was more marked in women with dense breasts (0.95‰ in the study population vs 3.17‰ in the control population; p = 0.031). Interval cancers were smaller in the study population than in the control population (p = 0.031). The interval cancer rate was lower in women screened with DBT + DM compared to those screened with DM alone. These differences were more pronounced in women with dense breasts. Women screened using tomosynthesis and digital mammography had a lower rate of interval cancer than women screened with digital mammography, with the greatest difference in the interval cancer rate observed in women with dense breasts. • The interval cancer rate was lower in the study population (digital breast tomosynthesis [DBT] + digital mammography [DM]) than in the control population (DM). • The difference in interval cancer rates was more pronounced in women with dense breasts. • Interval cancers were smaller in the study population (DBT + DM) than in the control population (DM).

Eye radiation dose from breast cancer screening using full field digital mammography and digital breast tomosynthesis: A phantom study

IntroductionThe eye lens is recently classified as one of the most radiosensitive tissues by the International Commission on Radiological Protection (ICRP), and it has been suggested that the eye lens receives radiation dose during mammography due to scatter radiation. The aim of this study was to investigate the radiation dose received by the lens of the clients’ eye from Full Field Digital Mammography (FFDM) and Digital Breast Tomosynthesis (DBT) screening. MethodsThe eye radiation dose received by ATOM dosimetry phantom was estimated with thermo-luminescent dosemeters (TLDs). One TLD was utilised for each eye. A breast phantom was exposed for four-view screening mammography using 16 FFDM machines and one DBT machine. The breast phantom was exposed three times for each mammographic position and an average TLD dose reading was considered to minimise random error. ResultsFor four-view FFDM screening, the phantom eye radiation dose ranges from 0.013 mGy to 0.029 mGy with a mean±sd of 0.019 ± 0.005 mGy. A higher eye radiation dose of 0.041 mGy was recorded from four-view DBT screening. The statistical analysis demonstrated that the eye lens radiation dose is strongly and significantly correlated to breast organ dose and X-ray tube voltage. ConclusionThe phantom eye lens was exposed to scatter radiation from FFDM and DBT screening. The measured dose via the four-view DBT screening was higher than the four-view FFDM screening, but sits below the internationally acceptable ranges. If the findings of our paper hold true in practice, then the risk to the lens of the eyes for women attending breast screening is acceptable. Implications for practiceThe new lens radiation dose levels recommended by the ICRP necessitate the reevaluation of eye radiation dose from different radiographic examinations, especially those used for screening purpose where healthy individuals involved.

Discordant and false-negative interpretations at digital breast tomosynthesis in the prospective Oslo Tomosynthesis Screening Trial (OTST) using independent double reading.

To analyze discordant and false-negatives of double reading digital breast tomosynthesis (DBT) versus digital mammography (DM) including reading times in the Oslo Tomosynthesis Screening Trial (OTST), and reclassify these in a retrospective reader study as missed, minimal sign, or true-negatives. The prospective OTST comparing double reading DBT vs. DM had paired design with four parallel arms: DM, DM + computer aided detection, DBT + DM, and DBT + synthetic mammography. Eight radiologists interpreted images in batches using a 5-point scale. Reading time was automatically recorded. A retrospective reader study including four radiologists classified screen-detected cancers with at least one false-negative score and screening examinations of interval cancers as negative, non-specific minimal sign, significant minimal sign, and missed; the two latter groups are defined "actionable." Statistics included chi-square, Fisher's exact, McNemar's, and Mann-Whitney U tests. Discordant rate (cancer missed by one reader) for screen-detected cancers was overall comparable (DBT (31% [71/227]) and DM (30% [52/175]), p= .81), significantly lower at DBT for spiculated cancers (DBT, 19% [20/106] vs. DM, 36% [38/106], p= .003), but high (28/49 = 57%, p= 0.001) for DBT-only detected spiculated cancers. Reading time and sensitivity varied among readers. False-negative DBT-only detected spiculated cancers had shorter reading time than true-negatives in 46% (13/28). Retrospective evaluation classified the following DBT exams "actionable": three missed by both readers, 95% (39/41) of discordant cancers detected by both modes, all 30 discordant DBT-only cancers, 25% (13/51) of interval cancers. Discordant rate was overall comparable for DBT and DM, significantly lower at DBT for spiculated cancers, but high for DBT-only detected spiculated lesions. Most false-negative screen-detected DBT were classified as "actionable." Retrospective evaluation of false-negative interpretations from the Oslo Tomosynthesis Screening Trial shows that most discordant and several interval cancers could have been detected at screening. This underlines the potential for modern AI-based reading aids and triage, as high-volume screening is a demanding task. • Digital breast tomosynthesis (DBT) screening is more sensitive and has higher specificity compared to digital mammography screening, but high-volume DBT screening is a demanding task which can result in high discordance rate among readers. • Independent double reading DBT screening had overall comparable discordance rate as digital mammography, lower for spiculated masses seen on both modalities, and higher for small spiculated cancer seen only on DBT. • Almost all discordant digital breast tomosynthesis-detected cancers (72 of 74) and 25% (13 of 51) of the interval cancers in the Oslo Tomosynthesis Screening Trial were retrospectively classified as actionable and could have been detected by the readers.

Impact of breast density on diagnostic accuracy in digital breast tomosynthesis versus digital mammography: results from a European screening trial

BackgroundThe diagnostic accuracy of digital breast tomosynthesis (DBT) and digital mammography (DM) in breast cancer screening may vary per breast density subgroup. The purpose of this study was to evaluate which women, based on automatically assessed breast density subgroups, have the greatest benefit of DBT compared with DM in the prospective Malmö Breast Tomosynthesis Screening Trial.Materials and methodsThe prospective European, Malmö Breast Tomosynthesis Screening Trial (n = 14,848, Jan. 27, 2010–Feb. 13, 2015) compared one-view DBT and two-view DM, with consensus meeting before recall. Breast density was assessed in this secondary analysis with the automatic software Laboratory for Individualized Breast Radiodensity Assessment. DBT and DM’s diagnostic accuracies were compared by breast density quintiles of breast percent density (PD) and absolute dense area (DA) with confidence intervals (CI) and McNemar’s test. The association between breast density and cancer detection was analyzed with logistic regression, adjusted for ages < 55 and ≥ 55 years and previous screening participation.ResultsIn total, 14,730 women (median age: 58 years; inter-quartile range = 16) were included in the analysis. Sensitivity was higher and specificity lower for DBT compared with DM for all density subgroups. The highest breast PD quintile showed the largest difference in sensitivity and specificity at 81.1% (95% CI 65.8–90.5) versus 43.2% (95% CI 28.7–59.1), p < .001 and 95.5% (95% CI 94.7–96.2) versus 97.2% (95% CI 96.6–97.8), p < 0.001, respectively. Breast PD quintile was also positively associated with cancer detected via DBT at odds ratio 1.24 (95% CI 1.09–1.42, p = 0.001).ConclusionWomen with the highest breast density had the greatest benefit from digital breast tomosynthesis compared with digital mammography with increased sensitivity at the cost of slightly lower specificity. These results may influence digital breast tomosynthesis’s use in an individualized screening program stratified by, for instance, breast density.Trial registration. Trial registration at https://www.ClinicalTrials.gov: NCT01091545, registered March 24, 2010.

Personalized breast cancer screening with selective addition of digital breast tomosynthesis through artificial intelligence.

Breast cancer screening is predominantly performed using digital mammography (DM), but digital breast tomosynthesis (DBT) has higher sensitivity. DBT demands more resources than DM, and it might be more feasible to reserve DBT for women with a clear benefit from the technique. We explore if artificial intelligence (AI) can select women who would benefit from DBT imaging. We used data from Malmö Breast Tomosynthesis Screening Trial, where all women prospectively were examined with separately double read DM and DBT. We retrospectively analyzed DM examinations () with a breast cancer detection system and used the provided risk score (1 to 10) for risk stratification. We tested how different score thresholds for adding DBT to an initial DM affects the number of detected cancers, additional DBT examinations needed, detection rate, and false positives. If using a threshold of 9.0, 25 (26%) more cancers would be detected compared to using DM alone. Of the 41 cancers only detected on DBT, 61% would be detected, with only 1797 (12%) of the women examined with both DM and DBT. The detection rate for the added DBT would be 14/1000 women, whereas the false-positive recalls would be increased with 58 (21%). Using DBT only for selected high gain cases could be an alternative to complete DBT screening. AI can analyze initial DM images to identify high gain cases where DBT can be added during the same visit. There might be logistical challenges, and further studies in a prospective setting are necessary.

False-positive recalls in the prospective Malmö Breast Tomosynthesis Screening Trial

ObjectivesTo evaluate the total number of false-positive recalls, including radiographic appearances and false-positive biopsies, in the Malmö Breast Tomosynthesis Screening Trial (MBTST).MethodsThe prospective, population-based MBTST, with 14,848 participating women, was designed to compare one-view digital breast tomosynthesis (DBT) to two-view digital mammography (DM) in breast cancer screening. False-positive recall rates, radiographic appearances, and biopsy rates were analyzed. Comparisons were made between DBT, DM, and DBT + DM, both in total and in trial year 1 compared to trial years 2 to 5, with numbers, percentages, and 95% confidence intervals (CI).ResultsThe false-positive recall rate was higher with DBT, 1.6% (95% CI 1.4; 1.8), compared to screening with DM, 0.8% (95% CI 0.7; 1.0). The proportion of the radiographic appearance of stellate distortion was 37.3% (91/244) with DBT, compared to 24.0% (29/121) with DM. The false-positive recall rate with DBT during trial year 1 was 2.6% (95% CI 1.8; 3.5), then stabilized at 1.5% (95% CI 1.3; 1.8) during trial years 2 to 5. The percentage of stellate distortion with DBT was 50% (19/38) trial year 1 compared to 35.0% (72/206) trial years 2 to 5.ConclusionsThe higher false-positive recall rate with DBT compared to DM was mainly due to an increased detection of stellate findings. The proportion of these findings, as well as the DBT false-positive recall rate, was reduced after the first trial year.Clinical relevance statementAssessment of false-positive recalls gives information on potential benefits and side effects in DBT screening.Key Points• The false-positive recall rate in a prospective digital breast tomosynthesis screening trial was higher compared to digital mammography, but still low compared to other trials.• The higher false-positive recall rate with digital breast tomosynthesis was mainly due to an increased detection of stellate findings; the proportion of these findings was reduced after the first trial year.

National Performance Benchmarks for Screening Digital Breast Tomosynthesis: Update from the Breast Cancer Surveillance Consortium.

Background It is important to establish screening mammography performance benchmarks for quality improvement efforts. Purpose To establish performance benchmarks for digital breast tomosynthesis (DBT) screening and evaluate performance trends over time in U.S. community practice. Materials and Methods In this retrospective study, DBT screening examinations were collected from five Breast Cancer Surveillance Consortium (BCSC) registries between 2011 and 2018. Performance measures included abnormal interpretation rate (AIR), cancer detection rate (CDR), sensitivity, specificity, and false-negative rate (FNR) and were calculated based on the American College of Radiology Breast Imaging Reporting and Data System, fifth edition, and compared with concurrent BCSC DM screening examinations, previously published BCSC and National Mammography Database benchmarks, and expert opinion acceptable performance ranges. Benchmarks were derived from the distribution of performance measures across radiologists (n = 84 or n = 73 depending on metric) and were presented as percentiles. Results A total of 896 101 women undergoing 2 301 766 screening examinations (458 175 DBT examinations [median age, 58 years; age range, 18-111 years] and 1 843 591 DM examinations [median age, 58 years; age range, 18-109 years]) were included in this study. DBT screening performance measures were as follows: AIR, 8.3% (95% CI: 7.5, 9.3); CDR per 1000 screens, 5.8 (95% CI: 5.4, 6.1); sensitivity, 87.4% (95% CI: 85.2, 89.4); specificity, 92.2% (95% CI: 91.3, 93.0); and FNR per 1000 screens, 0.8 (95% CI: 0.7, 1.0). When compared with BCSC DM screening examinations from the same time period and previously published BCSC and National Mammography Database performance benchmarks, all performance measures were higher for DBT except sensitivity and FNR, which were similar to concurrent and prior DM performance measures. The following proportions of radiologists achieved acceptable performance ranges with DBT: 97.6% for CDR, 91.8% for sensitivity, 75.0% for AIR, and 74.0% for specificity. Conclusion In U.S. community practice, large proportions of radiologists met acceptable performance ranges for screening performance metrics with DBT. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Lee and Moy in this issue.

Use of Artificial Intelligence for Digital Breast Tomosynthesis Screening: A Preliminary Real-world Experience.

The purpose of this study is to assess the "real-world" impact of an artificial intelligence (AI) tool designed to detect breast cancer in digital breast tomosynthesis (DBT) screening exams following 12 months of utilization in a subspecialized academic breast center. Following IRB approval, mammography audit reports, as specified in the BI-RADS atlas, were retrospectively generated for five radiologists reading at three locations during a 12-month time frame. One location had the AI tool (iCAD ProFound AI v2.0), and the other two locations did not. The co-primary endpoints were cancer detection rate (CDR) and abnormal interpretation rate (AIR). Secondary endpoints included positive predictive values (PPVs) for cancer among screenings with abnormal interpretations (PPV1) and for biopsies performed (PPV3). Odds ratios (OR) with two-sided 95% confidence intervals (CIs) summarized the impact of AI across radiologists using generalized estimating equations. Nonsignificant differences were observed in CDR, AIR, and PPVs. The CDR was 7.3 with AI and 5.9 without AI (OR 1.3, 95% CI: 0.9-1.7). The AIR was 11.7% with AI and 11.8% without AI (OR 1.0, 95% CI: 0.8-1.3). The PPV1 was 6.2% with AI and 5.0% without AI (OR 1.3, 95% CI: 0.97-1.7). The PPV3 was 33.3% with AI and 32.0% without AI (OR 1.1, 95% CI: 0.8-1.5). Although we are unable to show statistically significant changes in CDR and AIR outcomes in the two groups, the results are consistent with prior reader studies. There is a nonsignificant trend toward improvement in CDR with AI, without significant increases in AIR.

Measuring reader fatigue in the interpretation of screening digital breast tomosynthesis (DBT).

The interpretation of digital breast tomosynthesis (DBT) screening examinations is a complex task for an already overstretched workforce which has the potential to increase pressure on readers leading to fatigue and patient safety issues. Studies in non-medical and medical settings have suggested that changes in blink characteristics can reflect fatigue. The purpose of this study is to investigate the use of blink characteristics as an objective marker of fatigue in readers interpreting DBT breast screening examinations. Twenty-six DBT readers involved in the UK PROSPECTS trial interpreted a test set of 40 DBT cases while being observed by an eye tracking device from November 2019 to February 2021. Raw data from the eye tracker were collected and automated processing software was used to produce eye blinking characteristics data which were analysed using multiple linear regression statistical models. Of the 26 DBT readers recruited, eye tracking data from 23 participants were analysed due to missing data rendering 3 participants' data uninterpretable. The mean reading time per DBT case was 2.81 min. There was a statistically significant increase in blinking duration of 0.38 ms/case as the reading session progressed (p < 0.0001). This was the result of a significant decrease in the number of ultra-short blinks lasting ≤50 ms (p = 0.0005) and a significant increase in longer blinks lasting 51-100 ms (p = 0.008). Changes in blinking characteristics could serve as objective measures of reader fatigue and may prove useful in the development of DBT reading protocols. Blink characteristics can be used as an objective measure of fatigue; however there is limited evidence of their use in radiological settings. Our study suggests that changes in blink duration and frequency could be used to monitor fatigue in DBT reading sessions.

Digital Breast Tomosynthesis Plus Ultrasound Versus Digital Mammography Plus Ultrasound for Screening Breast Cancer in Women With Dense Breasts.

To compare the outcomes of digital breast tomosynthesis (DBT) screening combined with ultrasound (US) with those of digital mammography (DM) combined with US in women with dense breasts. A retrospective database search identified consecutive asymptomatic women with dense breasts who underwent breast cancer screening with DBT or DM and whole-breast US simultaneously between June 2016 and July 2019. Women who underwent DBT + US (DBT cohort) and DM + US (DM cohort) were matched using 1:2 ratio according to mammographic density, age, menopausal status, hormone replacement therapy, and a family history of breast cancer. The cancer detection rate (CDR) per 1000 screening examinations, abnormal interpretation rate (AIR), sensitivity, and specificity were compared. A total of 863 women in the DBT cohort were matched with 1726 women in the DM cohort (median age, 53 years; interquartile range, 40-78 years) and 26 breast cancers (9 in the DBT cohort and 17 in the DM cohort) were identified. The DBT and DM cohorts showed comparable CDR (10.4 [9 of 863; 95% confidence interval {CI}: 4.8-19.7] vs. 9.8 [17 of 1726; 95% CI: 5.7-15.7] per 1000 examinations, respectively; P = 0.889). DBT cohort showed a higher AIR than the DM cohort (31.6% [273 of 863; 95% CI: 28.5%-34.9%] vs. 22.4% [387 of 1726; 95% CI: 20.5%-24.5%]; P < 0.001). The sensitivity for both cohorts was 100%. In women with negative findings on DBT or DM, supplemental US yielded similar CDRs in both DBT and DM cohorts (4.0 vs. 3.3 per 1000 examinations, respectively; P = 0.803) and higher AIR in the DBT cohort (24.8% [188 of 758; 95% CI: 21.8%-28.0%] vs. 16.9% [257 of 1516; 95% CI: 15.1%-18.9%; P < 0.001). DBT screening combined with US showed comparable CDR but lower specificity than DM screening combined with US in women with dense breasts.

The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4 Million Screening and Diagnostic Mammographic Images.

Supplemental material is available for this article. Keywords: Mammography, Breast, Machine Learning © RSNA, 2023.

Breast cancer screening with digital breast tomosynthesis: comparison of different reading strategies implementing artificial intelligence

ObjectivesDigital breast tomosynthesis (DBT) can detect more cancers than the current standard breast screening method, digital mammography (DM); however, it can substantially increase the reading workload and thus hinder implementation in screening. Artificial intelligence (AI) might be a solution. The aim of this study was to retrospectively test different ways of using AI in a screening workflow.MethodsAn AI system was used to analyse 14,772 double-read single-view DBT examinations from a screening trial with paired DM double reading. Three scenarios were studied: if AI can identify normal cases that can be excluded from human reading; if AI can replace the second reader; if AI can replace both readers. The number of detected cancers and false positives was compared with DM or DBT double reading.ResultsBy excluding normal cases and only reading 50.5% (7460/14,772) of all examinations, 95% (121/127) of the DBT double reading detected cancers could be detected. Compared to DM screening, 27% (26/95) more cancers could be detected (p < 0.001) while keeping recall rates at the same level. With AI replacing the second reader, 95% (120/127) of the DBT double reading detected cancers could be detected—26% (25/95) more than DM screening (p < 0.001)—while increasing recall rates by 53%. AI alone with DBT has a sensitivity similar to DM double reading (p = 0.689).ConclusionAI can open up possibilities for implementing DBT screening and detecting more cancers with the total reading workload unchanged. Considering the potential legal and psychological implications, replacing the second reader with AI would probably be most the feasible approach.Key Points• Breast cancer screening with digital breast tomosynthesis and artificial intelligence can detect more cancers than mammography screening without increasing screen-reading workload.• Artificial intelligence can either exclude low-risk cases from double reading or replace the second reader.• Retrospective study based on paired mammography and digital breast tomosynthesis screening data.

Should tomosynthesis replace mammography for breast cancer screening?

A decade has passed since the first prospective trials showed that the mammography technology, digital breast tomosynthesis, increased the rate of cancer detection compared with digital mammography. 1 Ciatto S Houssami N Bernardi D et al. Integration of 3D digital mammography with tomosynthesis for population breast-cancer screening (STORM): a prospective comparison study. Lancet Oncol. 2013; 14: 583-589 Summary Full Text Full Text PDF PubMed Scopus (608) Google Scholar , 2 Skaane P Bandos AI Gullien R et al. Prospective trial comparing full-field digital mammography (FFDM) versus combined FFDM and tomosynthesis in a population-based screening programme using independent double reading with arbitration. Eur Radiol. 2013; 23: 2061-2071 Crossref PubMed Scopus (170) Google Scholar A large body of evidence, comprising prospective non-randomised studies and retrospective studies, 3 Marinovich ML Hunter KE Macaskill P Houssami N Breast cancer screening using tomosynthesis or mammography: a meta-analysis of cancer detection and recall. J Natl Cancer Inst. 2018; 110: 942-949 Crossref PubMed Scopus (108) Google Scholar has since accumulated; however, evidence from randomised controlled trials is scarce. In The Lancet Oncology, Walter Heindel and colleagues report the results of the TOmosynthesis plus SYnthesised MAmmography Study (TOSYMA), 4 Heindel W Weigel W Gerß J et al. Digital breast tomosynthesis plus synthesised mammography versus digital screening mammography for the detection of invasive breast cancer (TOSYMA): a multicentre, open-label, randomised, controlled, superiority trial. Lancet Oncol. 2022; (published online April 12.)https://doi.org/10.1016/S1470-2045(22)00194-2 Summary Full Text Full Text PDF PubMed Scopus (1) Google Scholar a multicentre trial embedded in the German population-wide mammography screening programme. TOSYMA is the largest of three randomised controlled trials providing evidence on breast tomosynthesis versus mammography screening. 4 Heindel W Weigel W Gerß J et al. Digital breast tomosynthesis plus synthesised mammography versus digital screening mammography for the detection of invasive breast cancer (TOSYMA): a multicentre, open-label, randomised, controlled, superiority trial. Lancet Oncol. 2022; (published online April 12.)https://doi.org/10.1016/S1470-2045(22)00194-2 Summary Full Text Full Text PDF PubMed Scopus (1) Google Scholar , 5 Hofvind S Holen AS Aase HS et al. Two-view digital breast tomosynthesis versus digital mammography in a population-based breast cancer screening programme (To-Be): a randomised, controlled trial. Lancet Oncol. 2019; 20: 795-805 Summary Full Text Full Text PDF PubMed Scopus (38) Google Scholar , 6 Pattacini P Nitrosi A Giorgi RP et al. A randomized trial comparing breast cancer incidence and interval cancers after tomosynthesis plus mammography versus mammography alone. Radiology. 2022; (published online Feb 1.)https://doi.org/10.1148/radiol.211132 Crossref PubMed Scopus (3) Google Scholar Heindel and colleagues persevered with TOSYMA despite the COVID-19 pandemic stalling recruitment, resulting in a marginally lower sample size than planned in their adaptive design. 4 Heindel W Weigel W Gerß J et al. Digital breast tomosynthesis plus synthesised mammography versus digital screening mammography for the detection of invasive breast cancer (TOSYMA): a multicentre, open-label, randomised, controlled, superiority trial. Lancet Oncol. 2022; (published online April 12.)https://doi.org/10.1016/S1470-2045(22)00194-2 Summary Full Text Full Text PDF PubMed Scopus (1) Google Scholar Digital breast tomosynthesis plus synthesised mammography versus digital screening mammography for the detection of invasive breast cancer (TOSYMA): a multicentre, open-label, randomised, controlled, superiority trialThe results from this study indicate that the detection rate for invasive breast cancer was significantly higher with digital breast tomosynthesis plus s2D mammography than digital mammography alone. Evaluation of interval cancer rates in the follow-up study will further help to investigate incremental long-term benefits of digital breast tomosynthesis screening. Full-Text PDF

Artificial Intelligence for Reducing Workload in Breast Cancer Screening with Digital Breast Tomosynthesis.

Background Digital breast tomosynthesis (DBT) has higher diagnostic accuracy than digital mammography, but interpretation time is substantially longer. Artificial intelligence (AI) could improve reading efficiency. Purpose To evaluate the use of AI to reduce workload by filtering out normal DBT screens. Materials and Methods The retrospective study included 13 306 DBT examinations from 9919 women performed between June 2013 and November 2018 from two health care networks. The cohort was split into training, validation, and test sets (3948, 1661, and 4310 women, respectively). A workflow was simulated in which the AI model classified cancer-free examinations that could be dismissed from the screening worklist and used the original radiologists' interpretations on the rest of the worklist examinations. The AI system was also evaluated with a reader study of five breast radiologists reading the DBT mammograms of 205 women. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and recall rate were evaluated in both studies. Statistics were computed across 10 000 bootstrap samples to assess 95% CIs, noninferiority, and superiority tests. Results The model was tested on 4310 screened women (mean age, 60 years ± 11 [standard deviation]; 5182 DBT examinations). Compared with the radiologists' performance (417 of 459 detected cancers [90.8%], 477 recalls in 5182 examinations [9.2%]), the use of AI to automatically filter out cases would result in 39.6% less workload, noninferior sensitivity (413 of 459 detected cancers; 90.0%; P = .002), and 25% lower recall rate (358 recalls in 5182 examinations; 6.9%; P = .002). In the reader study, AUC was higher in the standalone AI compared with the mean reader (0.84 vs 0.81; P = .002). Conclusion The artificial intelligence model was able to identify normal digital breast tomosynthesis screening examinations, which decreased the number of examinations that required radiologist interpretation in a simulated clinical workflow. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Philpotts in this issue.

Stand-Alone Use of Artificial Intelligence for Digital Mammography and Digital Breast Tomosynthesis Screening: A Retrospective Evaluation.

Background Use of artificial intelligence (AI) as a stand-alone reader for digital mammography (DM) or digital breast tomosynthesis (DBT) breast screening could ease radiologists' workload while maintaining quality. Purpose To retrospectively evaluate the stand-alone performance of an AI system as an independent reader of DM and DBT screening examinations. Materials and Methods Consecutive screening-paired and independently read DM and DBT images acquired between January 2015 and December 2016 were retrospectively collected from the Tomosynthesis Cordoba Screening Trial. An AI system computed a cancer risk score (range, 1-100) for DM and DBT examinations independently. AI stand-alone performance was measured using the area under the receiver operating characteristic curve (AUC) and sensitivity and recall rate at different operating points selected to have noninferior sensitivity compared with the human readings (noninferiority margin, 5%). The recall rate of AI and the human readings were compared using a McNemar test. Results A total of 15 999 DM and DBT examinations (113 breast cancers, including 98 screen-detected and 15 interval cancers) from 15 998 women (mean age, 58 years ± 6 [standard deviation]) were evaluated. AI achieved an AUC of 0.93 (95% CI: 0.89, 0.96) for DM and 0.94 (95% CI: 0.91, 0.97) for DBT. For DM, AI achieved noninferior sensitivity as a single (58.4%; 66 of 113; 95% CI: 49.2, 67.1) or double (67.3%; 76 of 113; 95% CI: 58.2, 75.2) reader, with a reduction in recall rate (P < .001) of up to 2% (95% CI: -2.4, -1.6). For DBT, AI achieved noninferior sensitivity as a single (77%; 87 of 113; 95% CI: 68.4, 83.8) or double (81.4%; 92 of 113; 95% CI: 73.3, 87.5) reader, but with a higher recall rate (P < .001) of up to 12.3% (95% CI: 11.7, 12.9). Conclusion Artificial intelligence could replace radiologists' readings in breast screening, achieving a noninferior sensitivity, with a lower recall rate for digital mammography but a higher recall rate for digital breast tomosynthesis. Published under a CC BY 4.0 license. See also the editorial by Fuchsjäger and Adelsmayr in this issue.

Artificial Intelligence Detection of Missed Cancers at Digital Mammography That Were Detected at Digital Breast Tomosynthesis

To investigate how an artificial intelligence (AI) system performs at digital mammography (DM) from a screening population with ground truth defined by digital breast tomosynthesis (DBT), and whether AI could detect breast cancers at DM that had originally only been detected at DBT. In this secondary analysis of data from a prospective study, DM examinations from 14 768 women (mean age, 57 years), examined with both DM and DBT with independent double reading in the Malmӧ Breast Tomosynthesis Screening Trial (MBTST) (ClinicalTrials.gov: NCT01091545; data collection, 2010-2015), were analyzed with an AI system. Of 136 screening-detected cancers, 95 cancers were detected at DM and 41 cancers were detected only at DBT. The system identifies suspicious areas in the image, scored 1-100, and provides a risk score of 1 to 10 for the whole examination. A cancer was defined as AI detected if the cancer lesion was correctly localized and scored at least 62 (threshold determined by the AI system developers), therefore resulting in the highest examination risk score of 10. Data were analyzed with descriptive statistics, and detection performance was analyzed with receiver operating characteristics. The highest examination risk score was assigned to 10% (1493 of 14 786) of the examinations. With 90.8% specificity, the AI system detected 75% (71 of 95) of the DM-detected cancers and 44% (18 of 41) of cancers at DM that had originally been detected only at DBT. The majority were invasive cancers (17 of 18). Almost half of the additional DBT-only screening-detected cancers in the MBTST were detected at DM with AI. AI did not reach double reading performance; however, if combined with double reading, AI has the potential to achieve a substantial portion of the benefit of DBT screening.Keywords: Computer-aided Diagnosis, Mammography, Breast, Diagnosis, Classification, Application DomainClinical trial registration no. NCT01091545© RSNA, 2021.

Impact of Race, Ethnicity, and Socioeconomic Status on Digital Breast Tomosynthesis Access and Use.

HomeRadiology: Imaging CancerVol. 3, No. 4 PreviousNext Research HighlightsFree AccessImpact of Race, Ethnicity, and Socioeconomic Status on Digital Breast Tomosynthesis Access and UseKenneth Wimmer, Kimberly RayKenneth Wimmer, Kimberly RayKenneth WimmerKimberly RayPublished Online:Jul 30 2021https://doi.org/10.1148/rycan.2021219013MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In Take-Away Points■ Major Focus: To evaluate sociodemographic disparities regarding access to and use of digital breast tomosynthesis (DBT) for screening.■ Key Result: From 2011 to 2017, DBT access and use became more equitable across sociodemographic groups overall, but disparities were evident during early implementation and persisted throughout the study period for Black and Asian women, as well as women with less than a high school education and those living in rural areas.■ Impact: Efforts are needed to address barriers to DBT access and uptake among underserved women.In 2011, the Food and Drug Administration approved DBT, which has been shown in large, multicenter screening studies to improve cancer detection rates and decrease recall rates when compared to conventional two-dimensional mammography. DBT adoption has been hampered by equipment costs and inconsistent insurance coverage. Lee et al hypothesized that traditionally underserved populations have limited access to DBT and that these individuals would undergo DBT less frequently when available. The authors conducted a cross-sectional study of 2 313 118 screening examinations performed from 2011 to 2017 across 92 geographically diverse centers that are members of the Breast Cancer Surveillance Consortium. Of these examinations, 87.3% were performed in community settings unaffiliated with academic centers.Overall rates of DBT use were lower among Black (37.7%), Hispanic (44%), and Asian (42.8%) women relative to White women (53.1%). Women with lower educational attainment (defined as less than high school vs college level) and those who lived in zip codes with lower median income (defined as lowest vs highest income quartile) also demonstrated relatively lower DBT use rates (40.8% vs 50.6% and 43.9% vs. 51.4%, respectively). Disparities were most pronounced during the initial years of DBT adoption. For example, the relative risk of on-site DBT access for Black relative to White women was 0.05 (95% CI: 0.03, 0.11) in 2012, but increased during the study to peak at 1.28 (95% CI: 1.05, 1.56) in 2016. However, by the end of the study period, the authors observed persistent inequities in DBT access for Asian women, as well as women with less than a high school education and those living in rural areas.Interestingly, despite an improvement in DBT access over time, Black women consistently used DBT less than White women throughout the study period. This latter observation is especially salient as Black women are more likely than White women to present with advanced-stage breast cancer at the time of diagnosis. Thus, decreased DBT use by Black women may exacerbate existing racial disparities in breast cancer outcomes.Overall, the findings of the present study are consistent with prior studies showing that minorities and those of lower socioeconomic status experience delayed and diminished benefits from advanced imaging technologies. The ability to pay out-of-pocket and awareness of the relative benefits and risks of newer screening technologies are likely to influence DBT use. As practices continue to transition to offering DBT, results of this study should inform the efforts of radiology practices and policy makers to address barriers to DBT access and uptake among underserved populations.Highlighted ArticleLee, CI, Zhu, W, Onega, T, et al. Comparative access to and use of digital breast tomosynthesis screening by women's race/ethnicity and socioeconomic status. JAMA Netw Open 2021;4(2):e2037546. doi:https://doi.org/10.1001/jamanetworkopen.2020.37546Highlighted ArticleLee, CI, Zhu, W, Onega, T, et al. Comparative access to and use of digital breast tomosynthesis screening by women’s race/ethnicity and socioeconomic status. JAMA Netw Open 2021;4(2):e2037546. doi:10.1001/jamanetworkopen.2020.37546 Crossref, Medline, Google ScholarArticle HistoryPublished online: July 30 2021 FiguresReferencesRelatedDetailsRecommended Articles Clinical Performance of Synthesized Two-dimensional Mammography Combined with Tomosynthesis in a Large Screening PopulationRadiology2017Volume: 283Issue: 1pp. 70-76Relationship between Race and Access to Newer Mammographic Technology in Women with Medicare InsuranceRadiology2022Volume: 306Issue: 2Addressing Racial Inequities in Access to State-of-the-Art Breast ImagingRadiology2022Volume: 306Issue: 2Does Reader Performance with Digital Breast Tomosynthesis Vary according to Experience with Two-dimensional Mammography?Radiology2017Volume: 283Issue: 2pp. 371-380Racial Disparities in Digital Breast Tomosynthesis ScreeningRadiology: Imaging Cancer2021Volume: 3Issue: 4See More RSNA Education Exhibits Let’s Talk about Next-Generation Breast Cancer Screening Programs: How Should We Do? What Should We Use?Digital Posters2020Encouraging Mammography Screening: Strategies for Designing a Culturally and Linguistically Targeted Breast Cancer Educational Program for a Multicultural PopulationDigital Posters2018Developing a Personalized Breast Screening Program and Centralized Breast Imaging Center in a Community-Based Health Network: A How-To Guide with Clinical OutcomesDigital Posters2018 RSNA Case Collection Invasive Lobular CarcinomaRSNA Case Collection2021Breast edemaRSNA Case Collection2021Deodorant ArtifactRSNA Case Collection2021 Vol. 3, No. 4 Metrics Altmetric Score PDF download

AI-based Strategies to Reduce Workload in Breast Cancer Screening with Mammography and Tomosynthesis: A Retrospective Evaluation.

Background The workflow of breast cancer screening programs could be improved given the high workload and the high number of false-positive and false-negative assessments. Purpose To evaluate if using an artificial intelligence (AI) system could reduce workload without reducing cancer detection in breast cancer screening with digital mammography (DM) or digital breast tomosynthesis (DBT). Materials and Methods Consecutive screening-paired and independently read DM and DBT images acquired from January 2015 to December 2016 were retrospectively collected from the Córdoba Tomosynthesis Screening Trial. The original reading settings were single or double reading of DM or DBT images. An AI system computed a cancer risk score for DM and DBT examinations independently. Each original setting was compared with a simulated autonomous AI triaging strategy (the least suspicious examinations for AI are not human-read; the rest are read in the same setting as the original, and examinations not recalled by radiologists but graded as very suspicious by AI are recalled) in terms of workload, sensitivity, and recall rate. The McNemar test with Bonferroni correction was used for statistical analysis. Results A total of 15 987 DM and DBT examinations (which included 98 screening-detected and 15 interval cancers) from 15 986 women (mean age ± standard deviation, 58 years ± 6) were evaluated. In comparison with double reading of DBT images (568 hours needed, 92 of 113 cancers detected, 706 recalls in 15 987 examinations), AI with DBT would result in 72.5% less workload (P < .001, 156 hours needed), noninferior sensitivity (95 of 113 cancers detected, P = .38), and 16.7% lower recall rate (P < .001, 588 recalls in 15 987 examinations). Similar results were obtained for AI with DM. In comparison with the original double reading of DM images (222 hours needed, 76 of 113 cancers detected, 807 recalls in 15 987 examinations), AI with DBT would result in 29.7% less workload (P < .001), 25.0% higher sensitivity (P < .001), and 27.1% lower recall rate (P < .001). Conclusion Digital mammography and digital breast tomosynthesis screening strategies based on artificial intelligence systems could reduce workload up to 70%. Published under a CC BY 4.0 license.

Interval Breast Cancer Rates and Tumor Characteristics in the Prospective Population-based Malmö Breast Tomosynthesis Screening Trial

Background Interval cancer rates can be used to evaluate whether screening with digital breast tomosynthesis (DBT) contributes to a screening benefit. Purpose To compare interval cancer rates and tumor characteristics in DBT screening to those in a contemporary population screened with digital mammography (DM). Materials and Methods The prospective population-based Malmö Breast Tomosynthesis Screening Trial (MBTST) was designed to compare one-view DBT to two-view DM in breast cancer detection. The interval cancer rates and cancer characteristics in the MBTST were compared with an age-matched contemporary control group, screened with two-view DM at the same center. Conditional logistic regression was used for data analysis. Results There were 14 848 women who were screened with DBT and DM in the MBTST between January 2010 and February 2015. The trial women were matched with two women of the same age and screening occasion at DM screening during the same period. Matches for 13 369 trial women (mean age, 56 years ± 10 [standard deviation]) were found with 26 738 women in the control group (mean age, 56 years ± 10). The interval cancer rate in the MBTST was 1.6 per 1000 screened women (21 of 13 369; 95% CI: 1.0, 2.4) compared with 2.8 per 1000 screened women in the control group (76 of 26 738 [95% CI: 2.2, 3.6]; conditional odds ratio, 0.6 [95% CI: 0.3, 0.9]; P = .02). The invasive interval cancers in the MBTST and in the control group showed in general high Ki-67 (63% [12 of 19] and 75% [54 of 72]), and low proportions of luminal A-like subtype (26% [five of 19] and 17% [12 of 72]), respectively. Conclusion The reduced interval cancer rate after screening with digital breast tomosynthesis compared with a contemporary age-matched control group screened with digital mammography might translate into screening benefits. Interval cancers in the trial generally had nonfavorable characteristics. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Mann in this issue.