AI performance by mammographic density in a retrospective cohort study of 99,489 participants in BreastScreen Norway

OBJECTIVE

To explore the ability of artificial intelligence (AI) to classify breast cancer by mammographic density in an organized screening program.

MATERIALS AND METHOD

We included information about 99,489 examinations from 74,941 women who participated in BreastScreen Norway, 2013-2019. All examinations were analyzed with an AI system that assigned a malignancy risk score (AI score) from 1 (lowest) to 10 (highest) for each examination. Mammographic density was classified into Volpara density grade (VDG), VDG1-4; VDG1 indicated fatty and VDG4 extremely dense breasts. Screen-detected and interval cancers with an AI score of 1-10 were stratified by VDG.

RESULTS

We found 10,406 (10.5% of the total) examinations to have an AI risk score of 10, of which 6.7% (704/10,406) was breast cancer. The cancers represented 89.7% (617/688) of the screen-detected and 44.6% (87/195) of the interval cancers. 20.3% (20,178/99,489) of the examinations were classified as VDG1 and 6.1% (6047/99,489) as VDG4. For screen-detected cancers, 84.0% (68/81, 95% CI, 74.1-91.2) had an AI score of 10 for VDG1, 88.9% (328/369, 95% CI, 85.2-91.9) for VDG2, 92.5% (185/200, 95% CI, 87.9-95.7) for VDG3, and 94.7% (36/38, 95% CI, 82.3-99.4) for VDG4. For interval cancers, the percentages with an AI score of 10 were 33.3% (3/9, 95% CI, 7.5-70.1) for VDG1 and 48.0% (12/25, 95% CI, 27.8-68.7) for VDG4.

CONCLUSION

The tested AI system performed well according to cancer detection across all density categories, especially for extremely dense breasts. The highest proportion of screen-detected cancers with an AI score of 10 was observed for women classified as VDG4.

CLINICAL RELEVANCE STATEMENT

Our study demonstrates that AI can correctly classify the majority of screen-detected and about half of the interval breast cancers, regardless of breast density.

KEY POINTS

• Mammographic density is important to consider in the evaluation of artificial intelligence in mammographic screening. • Given a threshold representing about 10% of those with the highest malignancy risk score by an AI system, we found an increasing percentage of cancers with increasing mammographic density. • Artificial intelligence risk score and mammographic density combined may help triage examinations to reduce workload for radiologists.

Association Between False-Positive Results and Return to Screening Mammography in the Breast Cancer Surveillance Consortium Cohort

BACKGROUND

False-positive results on screening mammography may affect women's willingness to return for future screening.

OBJECTIVE

To evaluate the association between screening mammography results and the probability of subsequent screening.

DESIGN

Cohort study.

SETTING

177 facilities participating in the Breast Cancer Surveillance Consortium (BCSC).

PATIENTS

3 529 825 screening mammograms (3 184 482 true negatives and 345 343 false positives) performed from 2005 to 2017 among 1 053 672 women aged 40 to 73 years without a breast cancer diagnosis.

MEASUREMENTS

Mammography results (true-negative result or false-positive recall with a recommendation for immediate additional imaging only, short-interval follow-up, or biopsy) from 1 or 2 screening mammograms. Absolute differences in the probability of returning for screening within 9 to 30 months of false-positive versus true-negative screening results were estimated, adjusting for race, ethnicity, age, time since last mammogram, BCSC registry, and clustering within women and facilities.

RESULTS

Women were more likely to return after a true-negative result (76.9% [95% CI, 75.1% to 78.6%]) than after a false-positive recall for additional imaging only (adjusted absolute difference, -1.9 percentage points [CI, -3.1 to -0.7 percentage points]), short-interval follow-up (-15.9 percentage points [CI, -19.7 to -12.0 percentage points]), or biopsy (-10.0 percentage points [CI, -14.2 to -5.9 percentage points]). Asian and Hispanic/Latinx women had the largest decreases in the probability of returning after a false positive with a recommendation for short-interval follow-up (-20 to -25 percentage points) or biopsy (-13 to -14 percentage points) versus a true negative. Among women with 2 screening mammograms within 5 years, a false-positive result on the second was associated with a decreased probability of returning for a third regardless of the first screening result.

LIMITATION

Women could receive care at non-BCSC facilities.

CONCLUSION

Women were less likely to return to screening after false-positive mammography results, especially with recommendations for short-interval follow-up or biopsy, raising concerns about continued participation in routine screening among these women at increased breast cancer risk.

PRIMARY FUNDING SOURCE

National Cancer Institute.

Breast Cancer Screening Using Mammography, Digital Breast Tomosynthesis, and Magnetic Resonance Imaging by Breast Density

Importance

Information on long-term benefits and harms of screening with digital breast tomosynthesis (DBT) with or without supplemental breast magnetic resonance imaging (MRI) is needed for clinical and policy discussions, particularly for patients with dense breasts.

Objective

To project long-term population-based outcomes for breast cancer mammography screening strategies (DBT or digital mammography) with or without supplemental MRI by breast density.

Design, Setting, and Participants

Collaborative modeling using 3 Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer simulation models informed by US Breast Cancer Surveillance Consortium data. Simulated women born in 1980 with average breast cancer risk were included. Modeling analyses were conducted from January 2020 to December 2023.

Intervention

Annual or biennial mammography screening with or without supplemental MRI by breast density starting at ages 40, 45, or 50 years through age 74 years.

Main outcomes and Measures

Lifetime breast cancer deaths averted, false-positive recall and false-positive biopsy recommendations per 1000 simulated women followed-up from age 40 years to death summarized as means and ranges across models.

Results

Biennial DBT screening for all simulated women started at age 50 vs 40 years averted 7.4 vs 8.5 breast cancer deaths, respectively, and led to 884 vs 1392 false-positive recalls and 151 vs 221 false-positive biopsy recommendations, respectively. Biennial digital mammography had similar deaths averted and slightly more false-positive test results than DBT screening. Adding MRI for women with extremely dense breasts to biennial DBT screening for women aged 50 to 74 years increased deaths averted (7.6 vs 7.4), false-positive recalls (919 vs 884), and false-positive biopsy recommendations (180 vs 151). Extending supplemental MRI to women with heterogeneously or extremely dense breasts further increased deaths averted (8.0 vs 7.4), false-positive recalls (1088 vs 884), and false-positive biopsy recommendations (343 vs 151). The same strategy for women aged 40 to 74 years averted 9.5 deaths but led to 1850 false-positive recalls and 628 false-positive biopsy recommendations. Annual screening modestly increased estimated deaths averted but markedly increased estimated false-positive results.

Conclusions and relevance

In this model-based comparative effectiveness analysis, supplemental MRI for women with dense breasts added to DBT screening led to greater benefits and increased harms. The balance of this trade-off for supplemental MRI use was more favorable when MRI was targeted to women with extremely dense breasts who comprise approximately 10% of the population.

Relative Timing of Mammography and MRI for Breast Cancer Screening: Impact on Performance Evaluation

OBJECTIVE

Mammography and MRI screening typically occur in combination or in alternating sequence. We compared multimodality screening performance accounting for the relative timing of mammography and MRI and overlapping follow-up periods.

METHODS

We identified 8,260 screening mammograms performed 2005 to 2017 in the Breast Cancer Surveillance Consortium, paired with screening MRIs within ±90 days (combined screening) or 91 to 270 days (alternating screening). Performance for combined screening (cancer detection rate [CDR] per 1,000 examinations and sensitivity) was calculated with 1-year follow-up for each modality, and with a single follow-up period treating the two tests as a single test. Alternating screening performance was calculated with 1-year follow-up for each modality and also with follow-up ending at the next screen if within 1 year (truncated follow-up).

RESULTS

For 3,810 combined screening pairs, CDR per 1,000 screens was 6.8 (95% confidence interval [CI]: 4.6-10.0) for mammography and 12.3 (95% CI: 9.3-16.4) for MRI as separate tests compared with 13.1 (95% CI: 10.0-17.3) as a single combined test. Sensitivity of each test was 48.1% (35.0%-61.5%) for mammography and 79.7% (95% CI: 67.7%-88.0%) for MRI compared with 96.2% (95% CI: 85.9%-99.0%) for combined screening. For 4,450 alternating screening pairs, mammography CDR per 1,000 screens changed from 3.6 (95% CI: 2.2-5.9) to zero with truncated follow-up; sensitivity was incalculable (denominator = 0). MRI CDR per 1,000 screens changed from 12.1 (95% CI 9.3-15.8) to 11.7 (95% CI: 8.9-15.3) with truncated follow-up; sensitivity changed from 75.0% (95% CI 63.8%-83.6%) to 86.7% (95% CI 75.5%-93.2%).

DISCUSSION

Updating auditing approaches to account for combined and alternating screening sequencing and to address outcome attribution issues arising from overlapping follow-up periods can improve the accuracy of multimodality screening performance evaluation.

Establishing a Validation Infrastructure for Imaging-Based Artificial Intelligence Algorithms Before Clinical Implementation

With promising artificial intelligence (AI) algorithms receiving FDA clearance, the potential impact of these models on clinical outcomes must be evaluated locally before their integration into routine workflows. Robust validation infrastructures are pivotal to inspecting the accuracy and generalizability of these deep learning algorithms to ensure both patient safety and health equity. Protected health information concerns, intellectual property rights, and diverse requirements of models impede the development of rigorous external validation infrastructures. The authors propose various suggestions for addressing the challenges associated with the development of efficient, customizable, and cost-effective infrastructures for the external validation of AI models at large medical centers and institutions. The authors present comprehensive steps to establish an AI inferencing infrastructure outside clinical systems to examine the local performance of AI algorithms before health practice or systemwide implementation and promote an evidence-based approach for adopting AI models that can enhance radiology workflows and improve patient outcomes.

Performance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway

Purpose To explore the stand-alone breast cancer detection performance, at different risk score thresholds, of a commercially available artificial intelligence (AI) system. Materials and Methods This retrospective study included information from 661 695 digital mammographic examinations performed among 242 629 female individuals screened as a part of BreastScreen Norway, 2004-2018. The study sample included 3807 screen-detected cancers and 1110 interval breast cancers. A continuous examination-level risk score by the AI system was used to measure performance as the area under the receiver operating characteristic curve (AUC) with 95% CIs and cancer detection at different AI risk score thresholds. Results The AUC of the AI system was 0.93 (95% CI: 0.92, 0.93) for screen-detected cancers and interval breast cancers combined and 0.97 (95% CI: 0.97, 0.97) for screen-detected cancers. In a setting where 10% of the examinations with the highest AI risk scores were defined as positive and 90% with the lowest scores as negative, 92.0% (3502 of 3807) of the screen-detected cancers and 44.6% (495 of 1110) of the interval breast cancers were identified with AI. In this scenario, 68.5% (10 987 of 16 040) of false-positive screening results (negative recall assessment) were considered negative by AI. When 50% was used as the cutoff, 99.3% (3781 of 3807) of the screen-detected cancers and 85.2% (946 of 1110) of the interval breast cancers were identified as positive by AI, whereas 17.0% (2725 of 16 040) of the false-positive results were considered negative. Conclusion The AI system showed high performance in detecting breast cancers within 2 years of screening mammography and a potential for use to triage low-risk mammograms to reduce radiologist workload. Keywords: Mammography, Breast, Screening, Convolutional Neural Network (CNN), Deep Learning Algorithms Supplemental material is available for this article. © RSNA, 2024 See also commentary by Bahl and Do in this issue.

Impact of a Categorical AI System for Digital Breast Tomosynthesis on Breast Cancer Interpretation by Both General Radiologists and Breast Imaging Specialists

Purpose To evaluate performance improvements of general radiologists and breast imaging specialists when interpreting a set of diverse digital breast tomosynthesis (DBT) examinations with the aid of a custom-built categorical artificial intelligence (AI) system. Materials and Methods A fully balanced multireader, multicase reader study was conducted to compare the performance of 18 radiologists (nine general radiologists and nine breast imaging specialists) reading 240 retrospectively collected screening DBT mammograms (mean patient age, 59.8 years ± 11.3 [SD]; 100% women), acquired between August 2016 and March 2019, with and without the aid of a custom-built categorical AI system. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity across general radiologists and breast imaging specialists reading with versus without AI were assessed. Reader performance was also analyzed as a function of breast cancer characteristics and patient subgroups. Results Every radiologist demonstrated improved interpretation performance when reading with versus without AI, with an average AUC of 0.93 versus 0.87, demonstrating a difference in AUC of 0.06 (95% CI: 0.04, 0.08; P < .001). Improvement in AUC was observed for both general radiologists (difference of 0.08; P < .001) and breast imaging specialists (difference of 0.04; P < .001) and across all cancer characteristics (lesion type, lesion size, and pathology) and patient subgroups (race and ethnicity, age, and breast density) examined. Conclusion A categorical AI system helped improve overall radiologist interpretation performance of DBT screening mammograms for both general radiologists and breast imaging specialists and across various patient subgroups and breast cancer characteristics. Keywords: Computer-aided Diagnosis, Screening Mammography, Digital Breast Tomosynthesis, Breast Cancer, Screening, Convolutional Neural Network (CNN), Artificial Intelligence Supplemental material is available for this article. © RSNA, 2024.

Digital breast tomosynthesis in mammographic screening: false negative cancer cases in the To-Be 1 trial

OBJECTIVES

The randomized controlled trial comparing digital breast tomosynthesis and synthetic 2D mammograms (DBT + SM) versus digital mammography (DM) (the To-Be 1 trial), 2016-2017, did not result in higher cancer detection for DBT + SM. We aimed to determine if negative cases prior to interval and consecutive screen-detected cancers from DBT + SM were due to interpretive error.

METHODS

Five external breast radiologists performed the individual blinded review of 239 screening examinations (90 true negative, 39 false positive, 19 prior to interval cancer, and 91 prior to consecutive screen-detected cancer) and the informed consensus review of examinations prior to interval and screen-detected cancers (n = 110). The reviewers marked suspicious findings with a score of 1-5 (probability of malignancy). A case was false negative if ≥ 2 radiologists assigned the cancer site with a score of ≥ 2 in the blinded review and if the case was assigned as false negative by a consensus in the informed review.

RESULTS

In the informed review, 5.3% of examinations prior to interval cancer and 18.7% prior to consecutive round screen-detected cancer were considered false negative. In the blinded review, 10.6% of examinations prior to interval cancer and 42.9% prior to consecutive round screen-detected cancer were scored ≥ 2. A score of ≥ 2 was assigned to 47.8% of negative and 89.7% of false positive examinations.

CONCLUSIONS

The false negative rates were consistent with those of prior DM reviews, indicating that the lack of higher cancer detection for DBT + SM versus DM in the To-Be 1 trial is complex and not due to interpretive error alone.

CRITICAL RELEVANCE STATEMENT

The randomized controlled trial on digital breast tomosynthesis and synthetic 2D mammograms (DBT) and digital mammography (DM), 2016-2017, showed no difference in cancer detection for the two techniques. The rates of false negative screening examinations prior to interval and consecutive screen-detected cancer for DBT were consistent with the rates in prior DM reviews, indicating that the non-superior DBT performance in the trial might not be due to interpretive error alone.

KEY POINTS

• Screening with digital breast tomosynthesis (DBT) did not result in a higher breast cancer detection rate compared to screening with digital mammography (DM) in the To-Be 1 trial. • The false negative rates for examinations prior to interval and consecutive screen-detected cancer for DBT were determined in the trial to test if the lack of differences was due to interpretive error. • The false negative rates were consistent with those of prior DM reviews, indicating that the lack of higher cancer detection for DBT versus DM was complex and not due to interpretive error alone.

Population Attributable Risk of Advanced-Stage Breast Cancer by Race and Ethnicity

Importance

Advanced-stage breast cancer rates vary by race and ethnicity, with Black women having a 2-fold higher rate than White women among regular screeners. Clinical risk factors that explain a large proportion of advanced breast cancers by race and ethnicity are unknown.

Objective

To evaluate the population attributable risk proportions (PARPs) for advanced-stage breast cancer (prognostic pathologic stage IIA or higher) associated with clinical risk factors among routinely screened premenopausal and postmenopausal women by race and ethnicity.

Design, Setting, and Participants

This cohort study used data collected prospectively from Breast Cancer Surveillance Consortium community-based breast imaging facilities from January 2005 to June 2018. Participants were women aged 40 to 74 years undergoing 3 331 740 annual (prior screening within 11-18 months) or biennial (prior screening within 19-30 months) screening mammograms associated with 1815 advanced breast cancers diagnosed within 2 years of screening examinations. Data analysis was performed from September 2022 to August 2023.

Exposures

Heterogeneously or extremely dense breasts, first-degree family history of breast cancer, overweight/obesity (body mass index >25.0), history of benign breast biopsy, and screening interval (biennial vs annual) stratified by menopausal status and race and ethnicity (Asian or Pacific Islander, Black, Hispanic/Latinx, White, other/multiracial).

Main Outcomes and Measures

PARPs for advanced breast cancer.

Results

Among 904 615 women, median (IQR) age was 57 (50-64) years. Of the 3 331 740 annual or biennial screening mammograms, 10.8% were for Asian or Pacific Islander women; 9.5% were for Black women; 5.3% were for Hispanic/Latinx women; 72.0% were for White women; and 2.0% were for women of other races and ethnicities, including those who were Alaska Native, American Indian, 2 or more reported races, or other. Body mass index PARPs were larger for postmenopausal vs premenopausal women (30% vs 22%) and highest for postmenopausal Black (38.6%; 95% CI, 32.0%-44.8%) and Hispanic/Latinx women (31.8%; 95% CI, 25.3%-38.0%) and premenopausal Black women (30.3%; 95% CI, 17.7%-42.0%), with overall prevalence of having overweight/obesity highest in premenopausal Black (84.4%) and postmenopausal Black (85.1%) and Hispanic/Latinx women (72.4%). Breast density PARPs were larger for premenopausal vs postmenopausal women (37% vs 24%, respectively) and highest among premenopausal Asian or Pacific Islander (46.6%; 95% CI, 37.9%-54.4%) and White women (39.8%; 95% CI, 31.7%-47.3%) whose prevalence of dense breasts was high (62%-79%). For premenopausal and postmenopausal women, PARPs were small for family history of breast cancer (5%-8%), history of breast biopsy (7%-12%), and screening interval (2.1%-2.3%).

Conclusions and Relevance

In this cohort study among routinely screened women, the proportion of advanced breast cancers attributed to biennial vs annual screening was small. To reduce the number of advanced breast cancer diagnoses, primary prevention should focus on interventions that shift patients with overweight and obesity to normal weight.

Artificial Intelligence-Driven Mammography-Based Future Breast Cancer Risk Prediction: A Systematic Review

PURPOSE

To summarize the literature regarding the performance of mammography-image based artificial intelligence (AI) algorithms, with and without additional clinical data, for future breast cancer risk prediction.

MATERIALS AND METHODS

A systematic literature review was performed using six databases (medRixiv, bioRxiv, Embase, Engineer Village, IEEE Xplore, and PubMed) from 2012 through September 30, 2022. Studies were included if they used real-world screening mammography examinations to validate AI algorithms for future risk prediction based on images alone or in combination with clinical risk factors. The quality of studies was assessed, and predictive accuracy was recorded as the area under the receiver operating characteristic curve (AUC).

RESULTS

Sixteen studies met inclusion and exclusion criteria, of which 14 studies provided AUC values. The median AUC performance of AI image-only models was 0.72 (range 0.62-0.90) compared with 0.61 for breast density or clinical risk factor-based tools (range 0.54-0.69). Of the seven studies that compared AI image-only performance directly to combined image + clinical risk factor performance, six demonstrated no significant improvement, and one study demonstrated increased improvement.

CONCLUSIONS

Early efforts for predicting future breast cancer risk based on mammography images alone demonstrate comparable or better accuracy to traditional risk tools with little or no improvement when adding clinical risk factor data. Transitioning from clinical risk factor-based to AI image-based risk models may lead to more accurate, personalized risk-based screening approaches.

Risk of cancer versus risk of cancer diagnosis? Accounting for diagnostic bias in predictions of breast cancer risk by race and ethnicity

OBJECTIVES

Cancer risk prediction may be subject to detection bias if utilization of screening is related to cancer risk factors. We examine detection bias when predicting breast cancer risk by race/ethnicity.

METHODS

We used screening and diagnosis histories from the Breast Cancer Surveillance Consortium to estimate risk of breast cancer onset and calculated relative risk of onset and diagnosis for each racial/ethnic group compared with non-Hispanic White women.

RESULTS

Of 104,073 women aged 40-54 receiving their first screening mammogram at a Breast Cancer Surveillance Consortium facility between 2000 and 2018, 10.2% (n = 10,634) identified as Asian, 10.9% (n = 11,292) as Hispanic, and 8.4% (n = 8719) as non-Hispanic Black. Hispanic and non-Hispanic Black women had slightly lower screening frequencies but biopsy rates following a positive mammogram were similar across groups. Risk of cancer diagnosis was similar for non-Hispanic Black and White women (relative risk vs non-Hispanic White = 0.90, 95% CI 0.65 to 1.14) but was lower for Asian (relative risk = 0.70, 95% CI 0.56 to 0.97) and Hispanic women (relative risk = 0.82, 95% CI 0.62 to 1.08). Relative risks of disease onset were 0.78 (95% CI 0.68 to 0.88), 0.70 (95% CI 0.59 to 0.83), and 0.95 (95% CI 0.84 to 1.09) for Asian, Hispanic, and non-Hispanic Black women, respectively.

CONCLUSIONS

Racial/ethnic differences in mammography and biopsy utilization did not induce substantial detection bias; relative risks of disease onset were similar to or modestly different than relative risks of diagnosis. Asian and Hispanic women have lower risks of developing breast cancer than non-Hispanic Black and White women, who have similar risks.

Digital mammography and digital breast tomosynthesis for detecting invasive lobular and ductal carcinoma

PURPOSE

Invasive lobular carcinoma (ILC) is a distinct histological subtype of breast cancer that can make early detection with mammography challenging. We compared imaging performance of digital breast tomosynthesis (DBT) to digital mammography (DM) for diagnoses of ILC, invasive ductal carcinoma (IDC), and invasive mixed carcinoma (IMC) in a screening population.

METHODS

We included screening exams (DM; n = 1,715,249 or DBT; n = 414,793) from 2011 to 2018 among 839,801 women in the Breast Cancer Surveillance Consortium. Examinations were followed for one year to ascertain incident ILC, IDC, or IMC. We measured cancer detection rate (CDR) and interval invasive cancer rate/1000 screening examinations for each histological subtype and stratified by breast density and modality. We calculated relative risk (RR) for DM vs. DBT using log-binomial models to adjust for the propensity of receiving DBT vs. DM.

RESULTS

Unadjusted CDR per 1000 mammograms of ILC overall was 0.33 (95%CI: 0.30-0.36) for DM; 0.45 (95%CI: 0.39-0.52) for DBT, and for women with dense breasts- 0.33 (95%CI: 0.29-0.37) for DM and 0.54 (95%CI: 0.43-0.66) for DBT. Similar results were noted for IDC and IMC. Adjusted models showed a significantly increased RR for cancer detection with DBT compared to DM among women with dense breasts for all three histologies (RR; 95%CI: ILC 1.53; 1.09-2.14, IDC 1.21; 1.02-1.44, IMC 1.76; 1.30-2.38), but no significant increase among women with non-dense breasts.

CONCLUSION

DBT was associated with higher CDR for ILC, IDC, and IMC for women with dense breasts. Early detection of ILC with DBT may improve outcomes for this distinct clinical entity.

Racial and Ethnic Variation in Diagnostic Mammography Performance among Women Reporting a Breast Lump

BACKGROUND

We evaluated diagnostic mammography among women with a breast lump to determine whether performance varied across racial and ethnic groups.

METHODS

This study included 51,014 diagnostic mammograms performed between 2005 and 2018 in the Breast Cancer Surveillance Consortium among Asian/Pacific Islander (12%), Black (7%), Hispanic/Latina (6%), and White (75%) women reporting a lump. Breast cancers occurring within 1 year were ascertained from cancer registry linkages. Multivariable regression was used to adjust performance statistic comparisons for breast cancer risk factors, mammogram modality, demographics, additional imaging, and imaging facility.

RESULTS

Cancer detection rates were highest among Asian/Pacific Islander [per 1,000 exams, 84.2 (95% confidence interval (CI): 72.0-98.2)] and Black women [81.4 (95% CI: 69.4-95.2)] and lowest among Hispanic/Latina women [42.9 (95% CI: 34.2-53.6)]. Positive predictive values (PPV) were higher among Black [37.0% (95% CI: 31.2-43.3)] and White [37.0% (95% CI: 30.0-44.6)] women and lowest among Hispanic/Latina women [22.0% (95% CI: 17.2-27.7)]. False-positive results were most common among Asian/Pacific Islander women [per 1,000 exams, 183.9 (95% CI: 126.7-259.2)] and lowest among White women [112.4 (95% CI: 86.1-145.5)]. After adjustment, false-positive and cancer detection rates remained higher for Asian/Pacific Islander and Black women (vs. Hispanic/Latina and White). Adjusted PPV was highest among Asian/Pacific Islander women.

CONCLUSIONS

Among women with a lump, Asian/Pacific Islander and Black women were more likely to have cancer detected and more likely to receive a false-positive result compared with White and Hispanic/Latina women.

IMPACT

Strategies for optimizing diagnostic mammography among women with a lump may vary by racial/ethnic group, but additional factors that influence performance differences need to be identified. See related In the Spotlight, p. 1479.

Early screening outcomes before, during, and after a randomized controlled trial with digital breast tomosynthesis

PURPOSE

To describe and compare early screening outcomes before, during and after a randomized controlled trial with digital breast tomosynthesis (DBT) including synthetic 2D mammography versus standard digital mammography (DM) (To-Be 1) and a follow-up cohort study using DBT (To-Be 2).

METHODS

Retrospective results of 125,020 screening examinations from four consecutive screening rounds performed in 2014-2021 were described and compared for pre-To-Be 1 (DM), To-Be 1 (DM or DBT), To-Be 2 (DBT), and post-To-Be 2 (DM) cohorts. Descriptive analyses of rates of recall, biopsy, screen-detected and interval cancer, distribution of histopathologic tumor characteristics and time spent on image interpretation and consensus were presented for the four rounds including five cohorts, one cohort in each screening round except for the To-Be 1 trail, which included a DBT and a DM cohort. Odds ratios (OR) with 95% CIs was calculated for recall and cancer detection rates.

RESULTS

Rate of screen-detected cancer was 0.90% for women screened with DBT in To-Be 2 and 0.64% for DM in pre-To-Be 1. The rates did not differ for the To-Be 1 DM (0.61%), To-Be 1 DBT (0.66%) and post-To-Be 2 DM (0.67%) cohorts. The interval cancer rates ranged between 0.13% and 0.20%. The distribution of histopathologic tumor characteristics did not differ between the cohorts.

CONCLUSIONS

Screening all women with DBT following a randomized controlled trial in an organized, population-based screening program showed a temporary increase in the rate of screen-detected cancer.