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Tamoxifen prevents recurrence of breast cancer and is also approved for preventive, risk-reducing, therapy. Tamoxifen alters the breast tissue composition and decreases the mammographic density. We aimed to test if baseline breast tissue composition influences tamoxifen-associated density change. This biopsy-based study included 83 participants randomised to 6 months daily intake of placebo, 20, 10, 5, 2.5, or 1 mg tamoxifen. The study is nested within the double-blinded tamoxifen dose-determination trial Karolinska Mammography Project for Risk Prediction of Breast Cancer Intervention (KARISMA) Study. Ultrasound-guided core-needle breast biopsies were collected at baseline before starting treatment. Biopsies were quantified for epithelial, stromal, and adipose distributions, and epithelial and stromal expression of proliferation marker Ki67, oestrogen receptor (ER) and progesterone receptor (PR). Mammographic density was measured using STRATUS. We found that greater mammographic density at baseline was positively associated with stromal area and inversely associated with adipose area and stromal expression of ER. Premenopausal women had greater mammographic density and epithelial tissue, and expressed more epithelial Ki67, PR, and stromal PR, compared to postmenopausal women. In women treated with tamoxifen (1-20 mg), greater density decrease was associated with higher baseline density, epithelial Ki67, and stromal PR. Women who responded to tamoxifen with a density decrease had on average 17% higher baseline density and a 2.2-fold higher PR expression compared to non-responders. Our results indicate that features in the normal breast tissue before tamoxifen exposure influences the tamoxifen-associated density decrease, and that the age-associated difference in density change may be related to age-dependant differences in expression of Ki67 and PR., (© 2024 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)

Purpose: The number of women living with breast cancer (BC) is increasing, and the efficacy of surveillance programs after BC treatment is essential. Identification of links between mammographic features and recurrence could help design follow up strategies, which may lead to earlier detection of recurrence. The aim of this study was to analyze associations between mammographic features at diagnosis and their potential association with recurrence-free survival (RFS)., Methods: Women with invasive BC in the prospective Malmö Diet and Cancer Study (n = 1116, 1991-2014) were assessed for locoregional and distant recurrences, with a median follow-up of 10.15 years. Of these, 34 women were excluded due to metastatic disease at diagnosis or missing recurrence data. Mammographic features (breast density [BI-RADS and clinical routine], tumor appearance, mode of detection) and tumor characteristics (tumor size, axillary lymph node involvement, histological grade) at diagnosis were registered. Associations were analyzed using Cox regression, yielding hazard ratios (HR) with 95 % confidence intervals (CI)., Results: Of the 1082 women, 265 (24.4 %) had recurrent disease. There was an association between high mammographic breast density at diagnosis and impaired RFS (adjusted HR 1.32 (0.98-1.79). In analyses limited to screen-detected BC, this association was stronger (adjusted HR 2.12 (1.35-3.32). There was no association between mammographic tumor appearance and recurrence., Conclusion: RFS was impaired in women with high breast density compared to those with low density, especially among women with screen-detected BC. This study may lead to insights on mammographic features preceding BC recurrence, which could be used to tailor follow up strategies., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Artificial intelligence (AI) systems are proposed as a replacement of the first reader in double reading within mammography screening. We aimed to assess cancer detection accuracy of an AI system in a Danish screening population., Methods: We retrieved a consecutive screening cohort from the Region of Southern Denmark including all participating women between Aug 4, 2014, and August 15, 2018. Screening mammograms were processed by a commercial AI system and detection accuracy was evaluated in two scenarios, Standalone AI and AI-integrated screening replacing first reader, with first reader and double reading with arbitration (combined reading) as comparators, respectively. Two AI-score cut-off points were applied by matching at mean first reader sensitivity (AI sens ) and specificity (AI spec ). Reference standard was histopathology-proven breast cancer or cancer-free follow-up within 24 months. Coprimary endpoints were sensitivity and specificity, and secondary endpoints were positive predictive value (PPV), negative predictive value (NPV), recall rate, and arbitration rate. Accuracy estimates were calculated using McNemar's test or exact binomial test., Results: Out of 272,008 screening mammograms from 158,732 women, 257,671 (94.7%) with adequate image data were included in the final analyses. Sensitivity and specificity were 63.7% (95% CI 61.6%-65.8%) and 97.8% (97.7-97.8%) for first reader, and 73.9% (72.0-75.8%) and 97.9% (97.9-98.0%) for combined reading, respectively. Standalone AI sens showed a lower specificity (-1.3%) and PPV (-6.1%), and a higher recall rate (+ 1.3%) compared to first reader (p < 0.0001 for all), while Standalone AI spec had a lower sensitivity (-5.1%; p < 0.0001), PPV (-1.3%; p = 0.01) and NPV (-0.04%; p = 0.0002). Compared to combined reading, Integrated AI sens achieved higher sensitivity (+ 2.3%; p = 0.0004), but lower specificity (-0.6%) and PPV (-3.9%) as well as higher recall rate (+ 0.6%) and arbitration rate (+ 2.2%; p < 0.0001 for all). Integrated AI spec showed no significant difference in any outcome measures apart from a slightly higher arbitration rate (p < 0.0001). Subgroup analyses showed higher detection of interval cancers by Standalone AI and Integrated AI at both thresholds (p < 0.0001 for all) with a varying composition of detected cancers across multiple subgroups of tumour characteristics., Conclusions: Replacing first reader in double reading with an AI could be feasible but choosing an appropriate AI threshold is crucial to maintaining cancer detection accuracy and workload., (© 2023. The Author(s).)

Objectives: To evaluate the total number of false-positive recalls, including radiographic appearances and false-positive biopsies, in the Malmö Breast Tomosynthesis Screening Trial (MBTST)., Methods: The 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)., Results: The 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., Conclusions: The 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 Statement: Assessment 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., (© 2023. The Author(s).)

Background Few studies have evaluated the role of artificial intelligence (AI) in prior screening mammography. Purpose To examine AI risk scores assigned to screening mammography in women who were later diagnosed with breast cancer. Materials and Methods Image data and screening information of examinations performed from January 2004 to December 2019 as part of BreastScreen Norway were used in this retrospective study. Prior screening examinations from women who were later diagnosed with cancer were assigned an AI risk score by a commercially available AI system (scores of 1-7, low risk of malignancy; 8-9, intermediate risk; and 10, high risk of malignancy). Mammographic features of the cancers based on the AI score were also assessed. The association between AI score and mammographic features was tested with a bivariate test. Results A total of 2787 prior screening examinations from 1602 women (mean age, 59 years ± 5.1 [SD]) with screen-detected ( n = 1016) or interval ( n = 586) cancers showed an AI risk score of 10 for 389 (38.3%) and 231 (39.4%) cancers, respectively, on the mammograms in the screening round prior to diagnosis. Among the screen-detected cancers with AI scores available two screening rounds (4 years) before diagnosis, 23.0% (122 of 531) had a score of 10. Mammographic features were associated with AI score for invasive screen-detected cancers ( P < .001). Density with calcifications was registered for 13.6% (43 of 317) of screen-detected cases with a score of 10 and 4.6% (15 of 322) for those with a score of 1-7. Conclusion More than one in three cases of screen-detected and interval cancers had the highest AI risk score at prior screening, suggesting that the use of AI in mammography screening may lead to earlier detection of breast cancers. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Mehta in this issue.

Background: Retrospective studies have shown promising results using artificial intelligence (AI) to improve mammography screening accuracy and reduce screen-reading workload; however, to our knowledge, a randomised trial has not yet been conducted. We aimed to assess the clinical safety of an AI-supported screen-reading protocol compared with standard screen reading by radiologists following mammography., Methods: In this randomised, controlled, population-based trial, women aged 40-80 years eligible for mammography screening (including general screening with 1·5-2-year intervals and annual screening for those with moderate hereditary risk of breast cancer or a history of breast cancer) at four screening sites in Sweden were informed about the study as part of the screening invitation. Those who did not opt out were randomly allocated (1:1) to AI-supported screening (intervention group) or standard double reading without AI (control group). Screening examinations were automatically randomised by the Picture Archive and Communications System with a pseudo-random number generator after image acquisition. The participants and the radiographers acquiring the screening examinations, but not the radiologists reading the screening examinations, were masked to study group allocation. The AI system (Transpara version 1.7.0) provided an examination-based malignancy risk score on a 10-level scale that was used to triage screening examinations to single reading (score 1-9) or double reading (score 10), with AI risk scores (for all examinations) and computer-aided detection marks (for examinations with risk score 8-10) available to the radiologists doing the screen reading. Here we report the prespecified clinical safety analysis, to be done after 80 000 women were enrolled, to assess the secondary outcome measures of early screening performance (cancer detection rate, recall rate, false positive rate, positive predictive value [PPV] of recall, and type of cancer detected [invasive or in situ]) and screen-reading workload. Analyses were done in the modified intention-to-treat population (ie, all women randomly assigned to a group with one complete screening examination, excluding women recalled due to enlarged lymph nodes diagnosed with lymphoma). The lowest acceptable limit for safety in the intervention group was a cancer detection rate of more than 3 per 1000 participants screened. The trial is registered with ClinicalTrials.gov, NCT04838756, and is closed to accrual; follow-up is ongoing to assess the primary endpoint of the trial, interval cancer rate., Findings: Between April 12, 2021, and July 28, 2022, 80 033 women were randomly assigned to AI-supported screening (n=40 003) or double reading without AI (n=40 030). 13 women were excluded from the analysis. The median age was 54·0 years (IQR 46·7-63·9). Race and ethnicity data were not collected. AI-supported screening among 39 996 participants resulted in 244 screen-detected cancers, 861 recalls, and a total of 46 345 screen readings. Standard screening among 40 024 participants resulted in 203 screen-detected cancers, 817 recalls, and a total of 83 231 screen readings. Cancer detection rates were 6·1 (95% CI 5·4-6·9) per 1000 screened participants in the intervention group, above the lowest acceptable limit for safety, and 5·1 (4·4-5·8) per 1000 in the control group-a ratio of 1·2 (95% CI 1·0-1·5; p=0·052). Recall rates were 2·2% (95% CI 2·0-2·3) in the intervention group and 2·0% (1·9-2·2) in the control group. The false positive rate was 1·5% (95% CI 1·4-1·7) in both groups. The PPV of recall was 28·3% (95% CI 25·3-31·5) in the intervention group and 24·8% (21·9-28·0) in the control group. In the intervention group, 184 (75%) of 244 cancers detected were invasive and 60 (25%) were in situ; in the control group, 165 (81%) of 203 cancers were invasive and 38 (19%) were in situ. The screen-reading workload was reduced by 44·3% using AI., Interpretation: AI-supported mammography screening resulted in a similar cancer detection rate compared with standard double reading, with a substantially lower screen-reading workload, indicating that the use of AI in mammography screening is safe. The trial was thus not halted and the primary endpoint of interval cancer rate will be assessed in 100 000 enrolled participants after 2-years of follow up., Funding: Swedish Cancer Society, Confederation of Regional Cancer Centres, and the Swedish governmental funding for clinical research (ALF)., Competing Interests: Declaration of interests KL has been an advisory board member for Siemens Healthineers and has received lecture honorarium from AstraZeneca. SH is head of BreastScreen Norway at the Cancer Registry of Norway which has a research agreement with Screenpoint Medical. All other authors declare no competing interests., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Tamoxifen prevents recurrence of breast cancer and is suggested for preventive risk-reducing therapy. Tamoxifen reduces mammographic density, a proxy for therapy response, but little is known about its effects in remodelling normal breast tissue. Our study, a substudy within the double-blinded dose-determination trial KARISMA, investigated tamoxifen-specific changes in breast tissue composition and histological markers in healthy women. We included 83 healthy women randomised to 6 months daily intake of 20, 10, 5, 2.5, 1 mg of tamoxifen or placebo. The groups were combined to "no dose" (0-1 mg), "low-dose" (2.5-5 mg) or "high-dose" (10-20 mg) of tamoxifen. Ultrasound-guided biopsies were collected before and after tamoxifen exposure. In each biopsy, epithelial, stromal and adipose tissues was quantified, and expression of epithelial and stromal Ki67, oestrogen receptor (ER) and progesterone receptor (PR) analysed. Mammographic density using STRATUS was measured at baseline and end-of-tamoxifen-exposure. We found that different doses of tamoxifen reduced mammographic density and glandular-epithelial area in premenopausal women and associated with reduced epithelium and increased adipose tissue. High-dose tamoxifen also decreased epithelial ER and PR expressions in premenopausal women. Premenopausal women with the greatest reduction in proliferation also had the greatest epithelial reduction. In postmenopausal women, high-dose tamoxifen decreased the epithelial area with no measurable density decrease. Tamoxifen at both low and high doses influences breast tissue composition and expression of histological markers in the normal breast. Our findings connect epithelial proliferation with tissue remodelling in premenopausal women and provide novel insights to understanding biological mechanisms of primary prevention with tamoxifen., (© 2023 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)

Purpose: The European Society on Breast Imaging has recommended supplemental magnetic resonance imaging (MRI) every two to four years for women with mammographically dense breasts. This may not be feasible in many screening programs. Also, the European Commission Initiative on Breast Cancer suggests not implementing screening with MRI. By analyzing interval cancers and time from screening to diagnosis by density, we present alternative screening strategies for women with dense breasts., Methods: Our BreastScreen Norway cohort included 508 536 screening examinations, including 3125 screen-detected and 945 interval breast cancers. Time from screening to interval cancer was stratified by density measured by an automated software and classified into Volpara Density Grades (VDGs) 1-4. Examinations with volumetric density ≤3.4% were categorized as VDG1, 3.5%-7.4% as VDG2, 7.5%-15.4% as VDG3, and ≥15.5% as VDG4. Interval cancer rates were also determined by continuous density measures., Results: Median time from screening to interval cancer was 496 (IQR: 391-587) days for VDG1, 500 (IQR: 350-616) for VDG2, 482 (IQR: 309-595) for VDG3 and 427 (IQR: 266-577) for VDG4. A total of 35.9% of the interval cancers among VDG4 were detected within the first year of the biennial screening interval. For VDG2, 26.3% were detected within the first year. The highest annual interval cancer rate (2.7 per 1000 examinations) was observed for VDG4 in the second year of the biennial interval., Conclusions: Annual screening of women with extremely dense breasts may reduce the interval cancer rate and increase program-wide sensitivity, especially in settings where supplemental MRI screening is not feasible., Competing Interests: Declaration of competing interest CIL, SH, KL and ML have no conflicts of interest directly related to this work. CIL has received personal fees from Grail for service on a data and safety monitoring board, personal fees from the ACR for journal editorial board work, and textbook royalties from McGraw-Hill, Oxford University Press, and UpToDate, all outside the submitted work. SH is head of Breast Screen Norway. She has a fixed position at the Cancer Registry of Norway independent of the job as the leader., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objectives: Artificial intelligence (AI) is increasingly tested and integrated into breast cancer screening. Still, there are unresolved issues regarding its possible ethical, social and legal impacts. Furthermore, the perspectives of different actors are lacking. This study investigates the views of breast radiologists on AI-supported mammography screening, with a focus on attitudes, perceived benefits and risks, accountability of AI use, and potential impact on the profession., Methods: We conducted an online survey of Swedish breast radiologists. As early adopter of breast cancer screening, and digital technologies, Sweden is a particularly interesting case to study. The survey had different themes, including: attitudes and responsibilities pertaining to AI, and AI's impact on the profession. Responses were analysed using descriptive statistics and correlation analyses. Free texts and comments were analysed using an inductive approach., Results: Overall, respondents (47/105, response rate 44.8%) were highly experienced in breast imaging and had a mixed knowledge of AI. A majority (n=38, 80.8%) were positive/somewhat positive towards integrating AI in mammography screening. Still, many considered there to be potential risks to a high/somewhat high degree (n=16, 34.1%) or were uncertain (n=16, 34.0%). Several important uncertainties were identified, such as defining liable actor(s) when AI is integrated into medical decision-making., Conclusions: Swedish breast radiologists are largely positive towards integrating AI in mammography screening, but there are significant uncertainties that need to be addressed, especially regarding risks and responsibilities. The results stress the importance of understanding actor-specific and context-specific challenges to responsible implementation of AI in healthcare., Competing Interests: Competing interests: KL is on an advisory board for Siemens Healthineers., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Purpose: To compare the positive predictive values (PPVs) of BI-RADS categories used to assess pure mammographic calcifications in women with and without a previous history of breast cancer (PHBC)., Materials and Methods: In this retrospective study, all consecutive pure mammographic calcifications (n = 320) undergoing a stereotactic biopsy between 2016 and 2018 were identified. Mammograms were evaluated in consensus by two radiologists according to BI-RADS and blinded to patient history and pathology results. Final pathologic results were used as the standard of reference. PPV of BI-RADS categories were compared between the two groups. Data were evaluated using standard statistics, Mann-Whitney U tests and Chi-square tests., Results: Two hundred sixty-eight patients (274 lesions, median age 54 years, inter-quartile range, 50-65 years) with a PHBC (n = 46) and without a PHBC (n = 222) were included. Overall PPVs were the following: BI-RADS 2, 0% (0 of 56); BI-RADS 3, 9.1% (1 of 11); BI-RADS 4a, 16.2% (6 of 37); BI-RADS 4b, 37.5% (48 of 128); BI-RADS 4c, 47.3% (18 of 38) and BI-RADS 5, 100% (4 of 4). The PPV of BI-RADS categories was similar in patients with and without a PHBC (P = .715). Calcifications were more often malignant in patients with a PHBC older than 10 years (47.3%, 9 of 19) compared to 1-2 years (25%, 1 of 4), 2-5 years (20%, 2 of 10) and 5-10 years (0%, of 13) from the first breast cancer (P = .005)., Conclusion: PPV of mammographic calcifications is similar in women with or without PHBC when BI-RADS classification is strictly applied. A higher risk of malignancy was observed in patients with a PHBC longer than 10 years., (© 2023. The Author(s).)

Background: Although digital breast tomosynthesis (DBT) improves breast cancer screen-detection compared to digital mammography (DM), there is less evidence on comparative screening outcomes by age and breast density, and inconsistent evidence on its effect on recall rate., Method: We performed an individual participant data (IPD) meta-analysis from DBT screening studies (identified to November, 30 2019) that contributed to the study protocol. We estimated and compared cancer detection rate (CDR), recall rate, and positive predictive value (PPV) for recall for DBT and DM screening. Two-stage random-effects meta-analyses of detection outcomes adjusted for study and age, and were estimated in age and density subgroups. Screen-detected cancer characteristics were summarized descriptively within studies and screening-groups., Results: Four prospective studies, from European population-based programs, contributed IPD for 66,451 DBT-screened participants and 170,764 DM-screened participants. Age-adjusted pooled CDR difference between DBT and DM was 25.49 of 10,000 (95% CI:6.73-44.25). There was suggestive evidence of a higher CDR for DBT compared to DM in the high-density (35.19 of 10,000; 95% CI:17.82-56.56) compared to low-density (17.4 of 10,000; 95% CI:7.62-27.18) group (P = .08). Pooled CDR difference between DBT and DM did not differ across age-groups (P = .71). Age-adjusted recall rate difference was 0.18% (95% CI:-0.80-1.17), indicating no difference between DBT and DM- this finding did not differ across age-groups (P = .96). Recall PPV was higher for DBT than DM with an estimated rate ratio of 1.31 (95% CI:1.07-1.61)., Discussion: DBT improved CDR compared to DM in all age and density groups. DBT also had higher recall PPV than DM, although further research is needed to explore the heterogeneity in recall rates across studies., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background Artificial intelligence (AI) has shown promising results for cancer detection with mammographic screening. However, evidence related to the use of AI in real screening settings remain sparse. Purpose To compare the performance of a commercially available AI system with routine, independent double reading with consensus as performed in a population-based screening program. Furthermore, the histopathologic characteristics of tumors with different AI scores were explored. Materials and Methods In this retrospective study, 122 969 screening examinations from 47 877 women performed at four screening units in BreastScreen Norway from October 2009 to December 2018 were included. The data set included 752 screen-detected cancers (6.1 per 1000 examinations) and 205 interval cancers (1.7 per 1000 examinations). Each examination had an AI score between 1 and 10, where 1 indicated low risk of breast cancer and 10 indicated high risk. Threshold 1, threshold 2, and threshold 3 were used to assess the performance of the AI system as a binary decision tool (selected vs not selected). Threshold 1 was set at an AI score of 10, threshold 2 was set to yield a selection rate similar to the consensus rate (8.8%), and threshold 3 was set to yield a selection rate similar to an average individual radiologist (5.8%). Descriptive statistics were used to summarize screening outcomes. Results A total of 653 of 752 screen-detected cancers (86.8%) and 92 of 205 interval cancers (44.9%) were given a score of 10 by the AI system (threshold 1). Using threshold 3, 80.1% of the screen-detected cancers (602 of 752) and 30.7% of the interval cancers (63 of 205) were selected. Screen-detected cancer with AI scores not selected using the thresholds had favorable histopathologic characteristics compared to those selected; opposite results were observed for interval cancer. Conclusion The proportion of screen-detected cancers not selected by the artificial intelligence (AI) system at the three evaluated thresholds was less than 20%. The overall performance of the AI system was promising according to cancer detection. © RSNA, 2022.

Objectives: To investigate whether artificial intelligence (AI) can reduce interval cancer in mammography screening., Materials and Methods: Preceding screening mammograms of 429 consecutive women diagnosed with interval cancer in Southern Sweden between 2013 and 2017 were analysed with a deep learning-based AI system. The system assigns a risk score from 1 to 10. Two experienced breast radiologists reviewed and classified the cases in consensus as true negative, minimal signs or false negative and assessed whether the AI system correctly localised the cancer. The potential reduction of interval cancer was calculated at different risk score thresholds corresponding to approximately 10%, 4% and 1% recall rates., Results: A statistically significant correlation between interval cancer classification groups and AI risk score was observed (p < .0001). AI scored one in three (143/429) interval cancer with risk score 10, of which 67% (96/143) were either classified as minimal signs or false negative. Of these, 58% (83/143) were correctly located by AI, and could therefore potentially be detected at screening with the aid of AI, resulting in a 19.3% (95% CI 15.9-23.4) reduction of interval cancer. At 4% and 1% recall thresholds, the reduction of interval cancer was 11.2% (95% CI 8.5-14.5) and 4.7% (95% CI 3.0-7.1). The corresponding reduction of interval cancer with grave outcome (women who died or with stage IV disease) at risk score 10 was 23% (8/35; 95% CI 12-39)., Conclusion: The use of AI in screen reading has the potential to reduce the rate of interval cancer without supplementary screening modalities., Key Points: • Retrospective study showed that AI detected 19% of interval cancer at the preceding screening exam that in addition showed at least minimal signs of malignancy. Importantly, these were correctly localised by AI, thus obviating supplementary screening modalities. • AI could potentially reduce a proportion of particularly aggressive interval cancers. • There was a correlation between AI risk score and interval cancer classified as true negative, minimal signs or false negative., (© 2021. The Author(s).)

Purpose: Tamoxifen prevents breast cancer in high-risk women and reduces mortality in the adjuvant setting. Mammographic density change is a proxy for tamoxifen therapy response. We tested whether lower doses of tamoxifen were noninferior to reduce mammographic density and associated with fewer symptoms., Patients and Methods: Women, 40-74 years of age, participating in the Swedish mammography screening program were invited to the 6-month double-blind six-arm randomized placebo-controlled noninferiority dose-determination KARISMA phase II trial stratified by menopausal status (EudraCT 2016-000882-22). In all, 1,439 women were accrued with 1,230 participants accessible for intention-to-treat analysis. The primary outcome was proportion of women treated with placebo, 1, 2.5, 5, and 10 mg whose mammographic density decreased at least as much as the median reduction in the 20 mg arm. The noninferior margin was 17%. Secondary outcome was reduction of symptoms. Post hoc analyses were performed by menopausal status. Per-protocol population and full population were analyzed in sensitivity analysis., Results: The 1,439 participants, 566 and 873 pre- and postmenopausal women, respectively, were recruited between October 1, 2016, and September 30, 2019. The participants had noninferior mammographic density reduction following 2.5, 5, and 10 mg tamoxifen compared with the median 10.1% decrease observed in the 20 mg group, a reduction confined to premenopausal women. Severe vasomotor symptoms (hot flashes, cold sweats, and night sweats) were reduced by approximately 50% in the 2.5, 5, and 10 mg groups compared with the 20 mg group., Conclusion: Premenopausal women showed noninferior magnitude of breast density decrease at 2.5 mg of tamoxifen, but fewer side effects compared with the standard dose of 20 mg. Future studies should test whether 2.5 mg of tamoxifen reduces the risk of primary breast cancer., Competing Interests: Mikael ErikssonStock and Other Ownership Interests: Swedish FundsPatents, Royalties, Other Intellectual Property: Pending patent on compositions and methods for prevention of breast cancer with an option to license to Atossa Therapeutics. Licensed the algorithm for risk prediction based on analyses of mammographic features to iCAD Martin EklundStock and Other Ownership Interests: A3P BiomedicalConsulting or Advisory Role: QuantumLeap HealthPatents, Royalties, Other Intellectual Property: I am an inventor of five pending patent applications related to prostate cancer diagnostics. I do not receive any royalties Signe BorgquistHonoraria: PfizerTravel, Accommodations, Expenses: Roche Ann RosendahlEmployment: UCBStock and Other Ownership Interests: AstraZeneca, Novo Nordisk, Pfizer Kristina LångHonoraria: AstraZeneca José TapiaTravel, Accommodations, Expenses: Rovi Magnus BäcklundEmployment: Diagnostiskt Centrum Hud i Sverige AB Mattias HammarströmResearch Funding: We are conducting an RCT partly financed by Atossa TherapeuticsPatents, Royalties, Other Intellectual Property: Pending patent on compositions and methods for prevention of breast cancer with an option to license to Atossa Therapeutics. Licensed the algorithm for risk prediction based on analyses of mammographic features to iCAD Kamila CzenePatents, Royalties, Other Intellectual Property: Pending patent on compositions and methods for prevention of breast cancer with an option to license to Atossa Therapeutics. Licensed the algorithm for risk prediction based on analyses of mammographic features to iCAD Per HallHonoraria: iCADResearch Funding: Atossa TherapeuticsPatents, Royalties, Other Intellectual Property: Pending patent on compositions and methods for prevention of breast cancer with an option to license to Atossa Therapeutics. Licensed the algorithm for risk prediction based on analyses of mammographic features to iCADTravel, Accommodations, Expenses: iCADNo other potential conflicts of interest were reported.

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.

Introduction: Breast cancer (BC) screening using digital breast tomosynthesis (DBT) has been shown to increase cancer detection compared with mammography; however, it is unknown whether DBT impacts interval cancer rate (ICR)., Methods: We systematically identified prospective DBT studies reporting data on screen-detected and interval BCs to perform a study-level meta-analysis of the comparative effect of DBT on ICR in population screening. Meta-analysis of cancer detection rate (CDR), ICR, and the differences between DBT and mammography in CDR and ICR pooled estimates, included random-effects. Sensitivity analysis examined whether study methods (imaging used, comparison group design, interval BC ascertainment) affected pooled estimates., Results: Five eligible prospective (non-randomised) studies of DBT population screening reported on 129,969 DBT-screened participants and 227,882 mammography-only screens, including follow-up publications reporting interval BC data. Pooled CDR was 9.03/1000 (95% confidence interval [CI] 8.53-9.56) for DBT, and 5.95/1000 (95% CI 5.65-6.28) for mammography: the pooled difference in CDR was 3.15/1000 (95% CI 2.53-3.77), and was evident for the detection of invasive and in-situ malignancy. Pooled ICR was 1.56/1000 DBT screens (95% CI 1.22-2.00), and 1.75/1000 mammography screens (95% CI 1.46-2.11): the estimated pooled difference in ICR was -0.15/1000 (95% CI -0.59 to 0.29) and was not substantially altered in several sensitivity analyses., Conclusions: Meta-analysis shows consistent evidence that DBT significantly increased CDR compared with mammography screening; however, there was little difference between DBT and mammography in pooled ICR. This could suggest, but does not demonstrate, some over-detection. Meta-analysis using individual participant data, randomised trials and comparative studies quantifying cumulative detection and ICR over repeat DBT screen-rounds would provide valuable evidence to inform screening programs., Competing Interests: Conflict of interest statement The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: N. Houssami receives research support through a National Breast Cancer Foundation (NBCF Australia) Breast Cancer Research Leadership Fellowship; the other authors do not have conflict of interest to declare., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Objectives: To evaluate the potential of artificial intelligence (AI) to identify normal mammograms in a screening population., Methods: In this retrospective study, 9581 double-read mammography screening exams including 68 screen-detected cancers and 187 false positives, a subcohort of the prospective population-based Malmö Breast Tomosynthesis Screening Trial, were analysed with a deep learning-based AI system. The AI system categorises mammograms with a cancer risk score increasing from 1 to 10. The effect on cancer detection and false positives of excluding mammograms below different AI risk thresholds from reading by radiologists was investigated. A panel of three breast radiologists assessed the radiographic appearance, type, and visibility of screen-detected cancers assigned low-risk scores (≤ 5). The reduction of normal exams, cancers, and false positives for the different thresholds was presented with 95% confidence intervals (CI)., Results: If mammograms scored 1 and 2 were excluded from screen-reading, 1829 (19.1%; 95% CI 18.3-19.9) exams could be removed, including 10 (5.3%; 95% CI 2.1-8.6) false positives but no cancers. In total, 5082 (53.0%; 95% CI 52.0-54.0) exams, including 7 (10.3%; 95% CI 3.1-17.5) cancers and 52 (27.8%; 95% CI 21.4-34.2) false positives, had low-risk scores. All, except one, of the seven screen-detected cancers with low-risk scores were judged to be clearly visible., Conclusions: The evaluated AI system can correctly identify a proportion of a screening population as cancer-free and also reduce false positives. Thus, AI has the potential to improve mammography screening efficiency., Key Points: • Retrospective study showed that AI can identify a proportion of mammograms as normal in a screening population. • Excluding normal exams from screening using AI can reduce false positives.

Background Mammography screening reduces breast cancer mortality, but a proportion of breast cancers are missed and are detected at later stages or develop during between-screening intervals. Purpose To develop a risk model based on negative mammograms that identifies women likely to be diagnosed with breast cancer before or at the next screening examination. Materials and Methods This study was based on the prospective screening cohort Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA), 2011-2017. An image-based risk model was developed by using the Stratus method and computer-aided detection mammographic features (density, masses, microcalcifications), differences in the left and right breasts, and age. The lifestyle extended model included menopausal status, family history of breast cancer, body mass index, hormone replacement therapy, and use of tobacco and alcohol. The genetic extended model included a polygenic risk score with 313 single nucleotide polymorphisms. Age-adjusted relative risks and tumor subtype specific risks were estimated by using logistic regression, and absolute risks were calculated. Results Of 70 877 participants in the KARMA cohort, 974 incident cancers were sampled from 9376 healthy women (mean age, 54 years ± 10 [standard deviation]). The area under the receiver operating characteristic curve (AUC) for the image-based model was 0.73 (95% confidence interval [CI]: 0.71, 0.74). The AUCs for the lifestyle and genetic extended models were 0.74 (95% CI: 0.72, 0.75) and 0.77 (95% CI: 0.75, 0.79), respectively. There was a relative eightfold difference in risk between women at high risk and those at general risk. High-risk women were more likely to be diagnosed with stage II cancers and with tumors 20 mm or larger and were less likely to have stage I and estrogen receptor-positive tumors. The image-based model was validated in three external cohorts. Conclusion By combining three mammographic features, differences in the left and right breasts, and optionally lifestyle factors and family history and a polygenic risk score, the model identified women at high likelihood of being diagnosed with breast cancer within 2 years of a negative screening examination and in possible need of supplemental screening. © RSNA, 2020 Online supplemental material is available for this article.

Purpose: In addition to absorption imaging, grating interferometry-based mammography (GIM) is capable of detecting differential-phase and scattering signals. In particular, the scattering signal can enable a quantifiable characterization of breast lesions. The purpose of this study was to determine if suspicious microcalcifications associated with benign or malignant lesions can be discriminated based on their absorption and scattering properties., Materials and Methods: In this prospective, ethically approved study, 62 patients (mean age 60 y, range 39-89) with suspicious microcalcifications, who underwent stereotactic biopsies, were included. Biopsies were measured with an experimental GIM device and the ratios of the scattering and absorption signal (R-value) for microcalcifications were calculated. The mean R-values for benign and malignant lesions associated with microcalcifications were compared with the final histopathological diagnosis using a t-test., Results: Twenty of the 62 participants had microcalcifications associated with malignancy. Comparing the two largest histopathological sub-groups of fibrosis (n = 23) vs. ductal carcinoma in situ (n = 15) resulted in an average R-value of 4.08 for benign and 2.80 for malignant lesions; p = 0.07. All microcalcifications associated with malignancy had an R-value below 4.71. Excluding microcalcifications with an R-value above this threshold would result in an 11 % reduction of false positives., Conclusion: The novel GIM modality has the potential to non-invasively characterize microcalcifications and might aid in the discrimination of benign from malignant lesions in fresh biopsy samples., Competing Interests: Declaration of Competing Interest All authors have approved the manuscript and agree with submission to European Journal of Radiology. The authors have no conflicts of interest to declare., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Objectives: Grating-interferometry-based mammography (GIM) might facilitate breast cancer detection, as several research works have demonstrated in a pre-clinical setting, since it is able to provide attenuation, differential phase contrast, and scattering images simultaneously. In order to translate this technique to the clinics, it has to be adapted to cover a large field-of-view within a clinically acceptable exposure time and radiation dose., Methods: We set up a grating interferometer that fits into a standard mammography system and fulfilled the aforementioned conditions. Here, we present the first mastectomy images acquired with this experimental device., Results and Conclusion: Our system performs at a mean glandular dose of 1.6 mGy for a 5-cm-thick, 18%-dense breast, and a field-of-view of 26 × 21 cm2. It seems to be well-suited as basis for a clinical-environment device. Further, dark-field signals seem to support an improved lesion visualization. Evidently, the effective impact of such indications must be evaluated and quantified within the context of a proper reader study., Key Points: • Grating-interferometry-based mammography (GIM) might facilitate breast cancer detection, since it is sensitive to refraction and scattering and thus provides additional tissue information. • The most straightforward way to do grating-interferometry in the clinics is to modify a standard mammography device. • In a first approximation, the doses given with this technique seem to be similar to those of conventional mammography.

Background Screening that includes digital breast tomosynthesis (DBT) with two-dimensional (2D) synthetic mammography (SM) or standard 2D digital mammography (DM) results in detection of more breast cancers than does screening with DM alone. A decrease in interval breast cancer rates is anticipated but is not reported. Purpose To compare rates and characteristics of (a) interval breast cancer in women screened with DBT and SM versus those screened with DM alone and (b) screen-detected breast cancer at consecutive screenings with DM. Materials and Methods This prospective cohort study from BreastScreen Norway included women screened with DBT and SM (study group) or DM alone (control group) between February 2014 and December 2015 (baseline). All women, except nonattendees, women with breast cancer, and those who exceeded the upper age limit, were consecutively screened with DM after 2 years. Interval breast cancer, sensitivity, and specificity were estimated for women screened at baseline. Recall, screen-detected breast cancer, and positive predictive value were analyzed for consecutively screened women. A χ 2 test, t test ( P < .001 after Bonferroni correction indicated a significant difference), and binomial regression model were used to analyze differences across groups. Results A total of 92 404 women who underwent baseline screening (mean age, 59 years ± 6 [standard deviation]) were evaluated; 34 641 women in the study group (mean age, 59 years ± 6) were screened with DBT and SM and 57 763 women in the control group (mean age, 59 years ± 6) were screened with DM. A total of 26 474 women in the study group (mean age, 60 years ± 5) and 45 543 women in the control group (mean age, 60 years ± 5) were consecutively screened with DM. Rates of interval breast cancer were 2.0 per 1000 screened women in the study group and 1.5 per 1000 screened women in the control group ( P = .12). No differences in histopathologic characteristics of interval breast cancer were observed. In the consecutive screening round, rates of screen-detected breast cancer were 3.9 per 1000 screened women (study group) and 5.6 per 1000 screened women (control group) ( P = .001). Rates of histologic grade 1 invasive cancer were 0.5 per 1000 screened women (study group) and 1.3 per 1000 screened women (control group) ( P = .001). Conclusion No differences in interval breast cancer rates or tumor characteristics were observed in women screened with DBT and SM compared with women screened with DM. Higher rates of low-grade screen-detected tumors were observed in the control group at consecutive screening. © RSNA, 2019 Online supplemental material is available for this article.

Background Screening accuracy can be improved with digital breast tomosynthesis (DBT). To further evaluate DBT in screening, it is important to assess the molecular subtypes of the detected cancers. Purpose To describe tumor characteristics, including molecular subtypes, of cancers detected at DBT compared with those detected at digital mammography (DM) in breast cancer screening. Materials and Methods The Malmö Breast Tomosynthesis Screening Trial is a prospective, population-based screening trial comparing one-view DBT with two-view DM. Tumor characteristics were obtained, and invasive cancers were classified according to St Gallen as follows: luminal A-like, luminal B-like human epidermal growth factor receptor (HER)2-negative/HER2-positive, HER2-positive, and triple-negative cancers. Tumor characteristics were compared by mode of detection: DBT alone or DM (ie, DBT and DM or DM alone). χ 2 test was used for data analysis. Results Between January 2010 and February 2015, 14 848 women were enrolled (mean age, 57 years ± 10; age range, 40-76 years). In total, 139 cancers were detected; 118 cancers were invasive and 21 were ductal carcinomas in situ. Thirty-seven additional invasive cancers (36 cancers with complete subtypes and stage) were detected at DBT alone, and 81 cancers (80 cancers with complete stage) were detected at DM. No differences were seen between DBT and DM in the distribution of tumor size 20 mm or smaller (86% [31 of 36] vs 85% [68 of 80], respectively; P = .88), node-negative status (75% [27 of 36] vs 74% [59 of 80], respectively; P = .89), or luminal A-like subtype (53% [19 of 36] vs 46% [37 of 81], respectively; P = .48). Conclusion The biologic profile of the additional cancers detected at digital breast tomosynthesis in a large prospective population-based screening trial was similar to those detected at digital mammography, and the majority were early-stage luminal A-like cancers. This indicates that digital breast tomosynthesis screening does not alter the predictive and prognostic profile of screening-detected cancers. © RSNA, 2019.

Purpose: To study the feasibility of automatically identifying normal digital mammography (DM) exams with artificial intelligence (AI) to reduce the breast cancer screening reading workload., Methods and Materials: A total of 2652 DM exams (653 cancer) and interpretations by 101 radiologists were gathered from nine previously performed multi-reader multi-case receiver operating characteristic (MRMC ROC) studies. An AI system was used to obtain a score between 1 and 10 for each exam, representing the likelihood of cancer present. Using all AI scores between 1 and 9 as possible thresholds, the exams were divided into groups of low- and high likelihood of cancer present. It was assumed that, under the pre-selection scenario, only the high-likelihood group would be read by radiologists, while all low-likelihood exams would be reported as normal. The area under the reader-averaged ROC curve (AUC) was calculated for the original evaluations and for the pre-selection scenarios and compared using a non-inferiority hypothesis., Results: Setting the low/high-likelihood threshold at an AI score of 5 (high likelihood > 5) results in a trade-off of approximately halving (- 47%) the workload to be read by radiologists while excluding 7% of true-positive exams. Using an AI score of 2 as threshold yields a workload reduction of 17% while only excluding 1% of true-positive exams. Pre-selection did not change the average AUC of radiologists (inferior 95% CI > - 0.05) for any threshold except at the extreme AI score of 9., Conclusion: It is possible to automatically pre-select exams using AI to significantly reduce the breast cancer screening reading workload., Key Points: • There is potential to use artificial intelligence to automatically reduce the breast cancer screening reading workload by excluding exams with a low likelihood of cancer. • The exclusion of exams with the lowest likelihood of cancer in screening might not change radiologists' breast cancer detection performance. • When excluding exams with the lowest likelihood of cancer, the decrease in true-positive recalls would be balanced by a simultaneous reduction in false-positive recalls.

Background: Artificial intelligence (AI) systems performing at radiologist-like levels in the evaluation of digital mammography (DM) would improve breast cancer screening accuracy and efficiency. We aimed to compare the stand-alone performance of an AI system to that of radiologists in detecting breast cancer in DM., Methods: Nine multi-reader, multi-case study datasets previously used for different research purposes in seven countries were collected. Each dataset consisted of DM exams acquired with systems from four different vendors, multiple radiologists' assessments per exam, and ground truth verified by histopathological analysis or follow-up, yielding a total of 2652 exams (653 malignant) and interpretations by 101 radiologists (28 296 independent interpretations). An AI system analyzed these exams yielding a level of suspicion of cancer present between 1 and 10. The detection performance between the radiologists and the AI system was compared using a noninferiority null hypothesis at a margin of 0.05., Results: The performance of the AI system was statistically noninferior to that of the average of the 101 radiologists. The AI system had a 0.840 (95% confidence interval [CI] = 0.820 to 0.860) area under the ROC curve and the average of the radiologists was 0.814 (95% CI = 0.787 to 0.841) (difference 95% CI = -0.003 to 0.055). The AI system had an AUC higher than 61.4% of the radiologists., Conclusions: The evaluated AI system achieved a cancer detection accuracy comparable to an average breast radiologist in this retrospective setting. Although promising, the performance and impact of such a system in a screening setting needs further investigation., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Purpose: To assess the effect on reducing the out-of-plane artifacts from metal objects in breast tomosynthesis (BT) using a novel artifact-reducing reconstruction algorithm in specimen radiography., Methods and Materials: The study was approved by the Regional Ethical Review Board. BT images of 18 partial- and whole mastectomy specimens from women with breast cancer were acquired before and after a needle was inserted close to the lesion. The images were reconstructed using both a standard reconstruction algorithm, and a novel algorithm; the latter uses pre-segmentation to remove highly attenuating artifact-inducing objects from projection images before reconstruction. Images were separately reconstructed with and without segmentation, and combined into an artifact-reduced reconstruction. Standard and artifact-reduced BT-algorithms were compared visually and quantitatively using clinical images of mastectomy specimens and a physical anthropomorphic phantom. Six readers independently assessed the visibility of the lesion with and without artifact-reduction in a side-by-side comparison. A quantitative analysis was performed, comparing the signal-difference to background ratio (SDBR) and artifact spread function (ASF) between the two reconstruction methods., Results: The magnitude of out-of-plane artifacts was clearly reduced with the novel reconstruction compared to BT-images without artifact reduction. Lesion masking by artifacts was largely averted; tumour visibility was comparable to standard BT images without a needle. In 76 ± 8% (standard deviation) of cases overall, readers could confidently state needle location. The same figure was 94 ± 6% for whole mastectomy cases, compared to 62 ± 17% for partial mastectomies. With metal artifact reduction, SDBR increased by 97% in the phantom, and by 69% in the mastectomies. The artifact spread function was substantially narrower., Conclusion: Artifact reduction in BT using a novel reconstruction method enables qualitatively and quantitatively improved clinical use of BT when metal artifacts can be a limiting factor such as in tomosynthesis-guided biopsy., (Copyright © 2019. Published by Elsevier B.V.)

Objectives: To compare software estimates of volumetric breast density (VBD) based on breast tomosynthesis (BT) projections to those based on digital mammography (DM) images in a large screening cohort, the Malmö Breast Tomosynthesis Screening Trial (MBTST)., Methods: DM and BT images of 9909 women (enrolled 2010-2015) were retrospectively analysed with prototype software to estimate VBD. Software calculation is based on a physics model of the image acquisition process and incorporates the effect of masking in DM based on accumulated dense tissue areas. VBD (continuously and categorically) was compared between BT [central projection (mediolateral oblique view (MLO)] and two-view DM, and with radiologists' BI-RADS density 4 th ed. scores. Agreement and correlation were investigated with weighted kappa (κ), Spearman's correlation coefficient (r), and Bland-Altman analysis., Results: There was a high correlation (r = 0.83) between VBD in DM and BT and substantial agreement between the software breast density categories [observed agreement, 61.3% and 84.8%; κ = 0.61 and ĸ = 0.69 for four (a/b/c/d) and two (fat involuted vs. dense) density categories, respectively]. There was moderate agreement between radiologists' BI-RADS scores and software density categories in DM (ĸ = 0.55) and BT (ĸ = 0.47)., Conclusions: In a large public screening setting, we report a substantial agreement between VBD in DM and BT using software with special focus on masking effect. This automated and objective mode of measuring VBD may be of value to radiologists and women when BT is used as the primary breast cancer screening modality., Key Points: • There was a high correlation between continuous volumetric breast density in DM and BT. • There was substantial agreement between software breast density categories (four groups) in DM and BT; with clinically warranted binary software breast density categories, the agreement increased markedly. • There was moderate agreement between radiologists' BI-RADS scores and software breast density categories in DM and BT.

Background: Digital breast tomosynthesis is an advancement of the mammographic technique, with the potential to increase detection of lesions during breast cancer screening. The main aim of the Malmö Breast Tomosynthesis Screening Trial (MBTST) was to investigate the accuracy of one-view digital breast tomosynthesis in population screening compared with standard two-view digital mammography., Methods: In this prospective, population-based screening study, of women aged 40-74 years invited to attend national breast cancer screening at Skåne University Hospital, Malmö, Sweden, a random sample was asked to participate in the trial (every third woman who was invited to attend regular screening was invited to participate). Participants had to be able to speak English or Swedish and were excluded from the study if they were pregnant. Participants underwent screening with two-view digital mammography (ie, craniocaudal and mediolateral oblique views) followed by one-view digital breast tomosynthesis with reduced compression in the mediolateral oblique view (with a wide tomosynthesis angle of 50°) at one screening visit. Images were read with masked double reading and scoring by two separate reading groups, one for each method, made up of seven radiologists. Any cancer detected with a malignancy probability score of three or higher by any reader in either group was discussed in a consensus meeting of at least two readers, from which the decision of whether or not to recall the woman for further investigation was made. The primary outcome measures were sensitivity and specificity of breast cancer detection. Secondary outcome measures were screening performance measures of cancer detection, recall, and interval cancers (cancers clinically detected between screenings), and positive predictive value for screen recalls and negative predictive value of each method. Outcomes were analysed in the per-protocol population. Follow-up of the participants for at least 2 years allowed for identification of interval cancers. This trial is registered with ClinicalTrials.gov, number NCT01091545., Findings: Between Jan 27, 2010, and Feb 13, 2015, of 21 691 women invited, 14 851 (68%) agreed to participate. Three women withdrew consent during follow-up and were excluded from the analyses. 139 breast cancers were detected in 137 (<1%) of 14 848 women. Sensitivity was higher for digital breast tomosynthesis than for digital mammography (81·1%, 95% CI 74·2-86·9, vs 60·4%, 52·3-68·0) and specificity was slightly lower for digital breast tomosynthesis than was for digital mammography (97·2%, 95% CI 97·0-97·5, vs 98·1%, 97·9-98·3). The proportion of cancers detected was significantly higher with digital breast tomosynthesis than with digital mammography (8·7 cancers per 1000 women screened, 95% CI 7·3-10·3 vs 6·5 cancers per 1000 screened, 5·2-7·9; p<0·0001). The proportion of women recalled after discussion was higher among cancers detected by digital breast tomosynthesis than for those detected by digital mammography after consensus (3·6%, 95% CI 3·3-3·9 vs 2·5%, 2·2-2·8; p<0·0001). The positive predictive value for screen recalls was 24·1% (95% CI 20·5-28·0) for digital breast tomosynthesis and 25·9% (21·6-30·7) for digital mammography, and the negative predictive value was 99·8% (99·7-99·9) and 99·6% (99·4-99·7), respectively. The proportion of women who developed interval cancers after trial screening was 1·48 cancers per 1000 women screened (95% CI 0·93-2·24)., Interpretation: Breast cancer screening by use of one-view digital breast tomosynthesis with a reduced compression force has higher sensitivity at a slightly lower specificity for breast cancer detection compared with two-view digital mammography and has the potential to reduce the radiation dose and screen-reading burden required by two-view digital breast tomosynthesis with two-view digital mammography., Funding: The Swedish Cancer Society, The Swedish Research Council, The Breast Cancer Foundation, The Swedish Medical Society, The Crafoord Foundation, The Gunnar Nilsson Cancer Foundation, The Skåne University Hospital Foundation, Governmental funding for clinical research, The South Swedish Health Care Region, The Malmö Hospital Cancer Foundation and The Cancer Foundation at the Department of Oncology, Skåne University Hospital., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Purpose: To compare the performance of one-view digital breast tomosynthesis (1v-DBT) to that of three other protocols combining DBT and mammography (DM) for breast cancer detection., Materials and Methods: Six radiologists, three experienced with 1v-DBT in screening, retrospectively reviewed 181 cases (76 malignant, 50 benign, 55 normal) in two sessions. First, they scored sequentially: 1v-DBT (medio-lateral oblique, MLO), 1v-DBT (MLO) + 1v-DM (cranio-caudal, CC) and two-view DM + DBT (2v-DM+2v-DBT). The second session involved only 2v-DM. Lesions were scored using BI-RADS® and level of suspiciousness (1-10). Sensitivity, specificity, receiver operating characteristic (ROC) and jack-knife alternative free-response ROC (JAFROC) were computed., Results: On average, 1v-DBT was non-inferior to any of the other protocols in terms of JAFROC figure-of-merit, area under ROC curve, sensitivity or specificity (p>0.391). While readers inexperienced with 1v-DBT screening improved their sensitivity when adding more images (69-79 %, p=0.019), experienced readers showed similar sensitivity (76 %) and specificity (70 %) between 1v-DBT and 2v-DM+2v-DBT (p=0.482). Subanalysis by lesion type and breast density showed no difference among modalities., Conclusion: Detection performance with 1v-DBT is not statistically inferior to 2v-DM or to 2v-DM+2v-DBT; its use as a stand-alone modality might be sufficient for readers experienced with this protocol., Key Points: • One-view breast tomosynthesis is not inferior to two-view digital mammography. • One-view DBT is not inferior to 2-view DM plus 2-view DBT. • Training may lead to 1v-DBT being sufficient for screening.

Objectives: Breast Imaging-Reporting and Data System (BI-RADS) mammographic density categories are associated with considerable interobserver variability. Automated methods of measuring volumetric breast density may reduce variability and be valuable in risk and mammographic screening stratification. Our objective was to assess agreement of mammographic density by a volumetric method with the radiologists' classification., Methods: Eight thousand seven hundred and eighty-two examinations from the Malmö Breast Tomosynthesis Screening Trial were classified according to BI-RADS, 4th Edition. Volumetric breast density was assessed using automated software for 8433 examinations. Agreement between volumetric breast density and BI-RADS was descriptively analyzed. Agreement between radiologists and between categorical volumetric density and BI-RADS was calculated, rendering kappa values., Results: The observed agreement between BI-RADS scores of different radiologists was 80.9 % [kappa 0.77 (0.76-0.79)]. A spread of volumetric breast density for each BI-RADS category was seen. The observed agreement between categorical volumetric density and BI-RADS scores was 57.1 % [kappa 0.55 (0.53-0.56)]., Conclusions: There was moderate agreement between volumetric density and BI-RADS scores from European radiologists indicating that radiologists evaluate mammographic density differently than software. The automated method may be a robust and valuable tool; however, differences in interpretation between radiologists and software require further investigation., Key Points: • Agreement between qualitative and software density measurements has not been frequently studied. • There was substantial agreement between different radiologists´ qualitative density assessments. • There was moderate agreement between software and radiologists' density assessments. • Differences in interpretation between software and radiologists require further investigation.

Objectives: To analyse false positives (FPs) in breast cancer screening with tomosynthesis (BT) vs. mammography (DM)., Methods: The Malmö Breast Tomosynthesis Screening Trial (MBTST) is a prospective population-based study comparing one-view BT to DM in screening. This study is based on the first half of the MBTST population (n = 7,500). Differences in FP recall rate, findings leading to recall, work-up and biopsy rate between cases recalled on BT alone, DM alone and BT+DM were analysed., Results: The FP recall rate was 1.7 % for BT alone (n = 131), 0.9 % for DM alone (n = 69) and 1.1 % for BT + DM (n = 81). The FP recall rate for BT alone was halved after the initial phase of the trial, stabilising at 1.5 %. BT doubled the recall of stellate distortions compared to DM (n = 64 vs. n = 33). There were fewer fibroadenomas and cysts, and the biopsy rate was slightly lower for FP recalled on BT alone compared to DM alone (15.3 % vs. 27.6 %: p = 0.037 and 33.8 % vs. 36.2 %; p = 0.641, respectively)., Conclusions: FPs increased with BT screening mainly due to the recall of stellate distortions. The FP recall rate was still well within the European guidelines and showed evidence of a learning curve. Characterisation of rounded lesions was improved with BT., Key Points: • Tomosynthesis screening gave a higher false-positive recall rate than mammography • There was a decline in the false-positive recall rate for tomosynthesis • The recall due to stellate distortions simulating malignancy was doubled with tomosynthesis • Tomosynthesis found more radial and postoperative scar tissue than mammography • Tomosynthesis is better at characterising rounded lesions.

This pictorial review highlights cancers detected only at tomosynthesis screening and screens falsely recalled in the course of breast tomosynthesis screening, illustrating both true-positive (TP) and false-positive (FP) detection attributed to tomosynthesis. Images and descriptive data were used to characterise cases of screen-detection with tomosynthesis, sourced from prospective screening trials that performed standard (2D) digital mammography (DM) and tomosynthesis (3D-mammography) in the same screening participants. Exemplar cases from four trials highlight common themes of relevance to screening practice including: the type of lesions frequently made more conspicuous or perceptible by tomosynthesis (spiculated masses, and architectural distortions); the histologic findings (both TP and FP) of tomosynthesis-only detection; and the need to extend breast work-up protocols (additional imaging including ultrasound and MRI, and tomosynthesis-guided biopsy) if tomosynthesis is adopted for primary screening., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Objective: To assess the performance of one-view digital breast tomosynthesis (DBT) in breast cancer screening., Methods: The Malmö Breast Tomosynthesis Screening Trial is a prospective population-based one-arm study with a planned inclusion of 15000 participants; a random sample of women aged 40-74 years eligible for the screening programme. This is an explorative analysis of the first half of the study population (n = 7500). Participants underwent one-view DBT and two-view digital mammography (DM), with independent double reading and scoring. Primary outcome measures were detection rate, recall rate and positive predictive value (PPV). McNemar's test with 95 % confidence intervals was used., Results: Breast cancer was found in sixty-eight women. Of these, 46 cases were detected by both modalities, 21 by DBT alone and one by DM alone. The detection rate for one-view DBT was 8.9/1000 screens (95 % CI 6.9 to 11.3) and 6.3/1000 screens (4.6 to 8.3) for two-view DM (p < 0.0001). The recall rate after arbitration was 3.8 % (3.3 to 4.2) for DBT and 2.6 % (2.3 to 3.0) for DM (p < 0.0001). The PPV was 24 % for both DBT and DM., Conclusion: Our results suggest that one-view DBT might be feasible as a stand-alone screening modality., Key Points: One-view DBT as a stand-alone breast cancer screening modality has not been investigated. One-view DBT increased the cancer detection rate significantly. The recall rate increased significantly but was still low. Breast cancer screening with one-view DBT as a stand-alone modality seems feasible.

In this study, we investigate which factors affect the false positive fraction (FPF) for digital breast tomosynthesis (DBT) compared to digital mammography (DM) in a screening population by using classification and regression trees (C&RT) and binary marginal generalized linear models. The data was obtained from the Malmö Breast Tomosynthesis Screening Trial, which aimed to compare the performance of DBT to DM in breast cancer screening. By using data from the first half of the study population (7500 women), a tree with the recall probability for different groups was calculated. The effect of age and breast density on the FPF was estimated using a binary marginal generalized linear model. Our results show that breast density and breast cancer were the main factors influencing recall. The FPF is mainly affected by breast density and increases with breast density for DBT and DM. In conclusion, the results obtained with C&RT are easy to interpret and similar to those obtained using binary marginal generalized linear models. The FPF is approximately 40% higher for DBT compared to DM for all breast density categories., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Objectives: To evaluate the efficiency of different methods of reading breast tomosynthesis (BT) image volumes., Methods: All viewing procedures consisted of free scroll volume browsing and three were combined with initial cine loops at three different frame rates (9, 14 and 25 fps). The presentation modes consisted of vertically and horizontally orientated BT image volumes. Fifty-five normal BT image volumes in mediolateral oblique view were collected. In these, simulated lesions were inserted, creating four unique image sets, one for each viewing procedure. Four observers interpreted the cases in a free-response task. Time efficiency, visual attention and search were investigated using eye tracking., Results: Horizontally orientated BT image volumes were read faster than vertically when using free scroll browsing only and when combined with fast cine loop. Cine loops at slow frame rates were ruled out as inefficient., Conclusions: In general, horizontally oriented BT image volumes were read more efficiently. All viewing procedures except for slow frame rates were promising when assuming equivalent detection performance.