Baseline breast tissue characteristics determine the effect of tamoxifen on mammographic density change.

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.

Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study.

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).

Effects of tamoxifen on normal breast tissue histological composition: Results from a randomised six-arm placebo-controlled trial in healthy women.

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.

AI Risk Score on Screening Mammograms Preceding Breast Cancer Diagnosis.

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.

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

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.

Mammographic calcifications undergoing percutaneous biopsy: outcome in women with and without a personal history of breast cancer.

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.

Artificial Intelligence Evaluation of 122 969 Mammography Examinations from a Population-based Screening Program.

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.

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.

Methanogenesis in the Digestive Tracts of the Tropical Millipedes Archispirostreptus gigas (Diplopoda, Spirostreptidae) and Epibolus pulchripes (Diplopoda, Pachybolidae).

Methanogens represent the final decomposition step in anaerobic degradation of organic matter, occurring in the digestive tracts of various invertebrates. However, factors determining their community structure and activity in distinct gut sections are still debated. In this study, we focused on the tropical millipede species Archispirostreptus gigas (Diplopoda, Spirostreptidae) and Epibolus pulchripes (Diplopoda, Pachybolidae), which release considerable amounts of methane. We aimed to characterize relationships between physicochemical parameters, methane production rates, and methanogen community structure in the two major gut sections, midgut and hindgut. Microsensor measurements revealed that both sections were strictly anoxic, with reducing conditions prevailing in both millipedes. Hydrogen concentration peaked in the anterior hindgut of E. pulchripes. In both species, the intestinal pH was significantly higher in the hindgut than in the midgut. An accumulation of acetate and formate in the gut indicated bacterial fermentation activities in the digestive tracts of both species. Phylogenetic analysis of 16S rRNA genes showed a prevalence of Methanobrevibacter spp. (Methanobacteriales), accompanied by a small fraction of so-far-unclassified "Methanomethylophilaceae" (Methanomassiliicoccales), in both species, which suggests that methanogenesis is mostly hydrogenotrophic. We conclude that anoxic conditions, negative redox potential, and bacterial production of hydrogen and formate promote gut colonization by methanogens. The higher activities of methanogens in the hindgut are explained by the higher pH of this compartment and their association with ciliates, which are restricted to this compartment and present an additional source of methanogenic substrates. IMPORTANCE Methane (CH4) is the second most important atmospheric greenhouse gas after CO2 and is believed to account for 17% of global warming. Methanogens are a diverse group of archaea and can be found in various anoxic habitats, including digestive tracts of plant-feeding animals. Termites, cockroaches, the larvae of scarab beetles, and millipedes are the only arthropods known to host methanogens and emit large amounts of methane. Millipedes are ranked as the third most important detritivores after termites and earthworms, and they are considered keystone species in many terrestrial ecosystems. Both methane-producing and non-methane-emitting species of millipedes have been observed, but what limits their methanogenic potential is not known. In the present study, we show that physicochemical gut conditions and the distribution of symbiotic ciliates are important factors determining CH4 emission in millipedes. We also found close similarities to other methane-emitting arthropods, which might be associated with their similar plant-feeding habits.

Can artificial intelligence reduce the interval cancer rate in mammography screening?

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.

Identifying normal mammograms in a large screening population using artificial intelligence.

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.

Identification of Women at High Risk of Breast Cancer Who Need Supplemental Screening.

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.

Interval and Consecutive Round Breast Cancer after Digital Breast Tomosynthesis and Synthetic 2D Mammography versus Standard 2D Digital Mammography in BreastScreen Norway.

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 χ2 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.

Correction to: Towards clinical grating-interferometry mammography.

The article Towards clinical grating-interferometry mammography, written by Carolina Arboleda, Zhentian Wang, Konstantins Jefimovs, Thomas Koehler, Udo Van Stevendaal, Norbert Kuhn, Bernd David, Sven Prevrhal, Kristina Lång, Serafino Forte, Rahel Antonia Kubik-Huch, Cornelia Leo.

Towards clinical grating-interferometry mammography.

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.

Tumor Characteristics and Molecular Subtypes in Breast Cancer Screening with Digital Breast Tomosynthesis: The Malmö Breast Tomosynthesis Screening Trial.

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). χ2 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.

Comparison between software volumetric breast density estimates in breast tomosynthesis and digital mammography images in a large public screening cohort.

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 4th 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.

Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study.

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.

Simulated Dose Reduction for Abdominal CT With Filtered Back Projection Technique: Effect on Liver Lesion Detection and Characterization.

OBJECTIVE: Previous studies have shown the possibility to reduce radiation dose in abdominal CT by 25-50% without negatively affecting detection of liver lesions. How radiation dose reduction affects characterization of liver metastases is not as well known. The objective of this study was to investigate how different levels of simulated dose reduction affect the detection and characterization of liver lesions, primarily hypovascular metastases. A secondary objective was to analyze the relationship between the lesion size and contrast-to-noise ratio (CNR) and the detection rate. MATERIALS AND METHODS: Thirty-nine patients (19 with metastases and 20 without) were retrospectively selected. The following radiation dose levels (DLs) were simulated: 100% (reference level), 75%, 50%, and 25%. Five readers were asked to mark liver lesions and rate the probability of malignancy on a 5-grade Likert scale. Noninferiority analysis using the jackknife free-response ROC (JAFROC) method was performed as well as direct comparison of detection rates and grades. RESULTS: JAFROC analysis showed noninferior detection and characterization of metastases at DL75 as compared with DL100. However, the number of benign lesions and false-positive localizations rated as "suspected malignancy" was significantly higher at DL75. CONCLUSION: Radiation dose can be reduced by 25% without negatively affecting diagnosis of hypovascular liver metastases. Characterization of benign lesions, however, is impaired at DL75, which may lead to unnecessary follow-up examinations. Finally, increased image noise seems to affect the detection of small lesions to a degree that cannot be explained solely by the reduction in CNR.

One-view breast tomosynthesis versus two-view mammography in the Malmö Breast Tomosynthesis Screening Trial (MBTST): a prospective, population-based, diagnostic accuracy study.

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.