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Background 

The accuracy of mammography depends in part on the experience and expertise of the interpreting radiologist. In the U.S.A., most screening mammograms are interpreted by general radiologists rather than fellowship-trained breast imaging specialists. Artificial intelligence (AI) may help support both generalist radiologists and specialists to improve cancer detection, as summarized in DBI’s Mammography AI Screening Tools resource. 

Recent Study 

A recent prospective multicenter study evaluated an AI-supported workflow for screening digital breast tomosynthesis (3D mammography).¹ Mammograms all had initial ProFound Pro computer-assisted detection (v. 2.x, DeepHealth) and radiologist review.  Those examinations initially considered negative by the radiologist then underwent “Safeguard Review” by AI and a preset threshold (up to 12.5% of examinations) was used to check for suspicious findings.  If there was suspicion of missed cancer on AI, the examination was routed for additional review by a breast imaging specialist, defined by formal fellowship in breast or women’s imaging (with other definitions used and results robust). Researchers compared cancer detection, recall rates, and the positive predictive value of recalls before and after implementation of the AI workflow. DeepHealth provided all financial and material support for the research. 

Key Findings 

• The AI-supported workflow significantly increased cancer detection among general radiologists.² 

• With AI, cancer detection by general radiologists was comparable to that of breast imaging specialists.² 

• Cancer detection among breast imaging specialists did not significantly change with AI.³ 

• Among general radiologists, the positive predictive value of recalls improved, though recall rates also increased.⁴ 

• Negative mammograms in women with dense breasts were more likely to undergo AI-triggered expert review than those in women with non-dense breasts.⁵ 

These findings suggest that an AI-supported workflow can improve cancer detection by general radiologists and help narrow the performance gap between generalists and breast imaging specialists. This could be particularly useful in community settings where access to breast imaging specialists is limited. 

The findings are also of interest for women with dense breasts, since dense tissue can mask cancers on mammography. However, the study did not show that AI specifically improved cancer detection in women with dense breasts; rather, negative mammograms in women with dense breasts were more likely to undergo additional expert review. AI does not eliminate the masking effect of dense tissue or replace supplemental screening. 

The study was observational rather than randomized and did not evaluate interval cancers or false-negative rates. As Cozzi and Schiaffino note in an accompanying editorial, these gains may also come with increased downstream diagnostic workload and demands on breast imaging specialists. Further research is needed to determine whether improved screening performance translates into fewer cancers missed at screening and to better understand the impact of AI specifically in women with dense breasts. 

Detailed Results 

¹ The prospective study included 577,742 bilateral DBT screening examinations interpreted by 95 radiologists (60 generalists and 35 fellowship-trained breast imaging specialists) at 109 U.S.A. imaging facilities.  

² Among general radiologists, adjusted cancer detection increased 33%, from 3.76 cancers per 1,000 examinations (95% CI, 3.46–4.08) to 4.99 per 1,000 (95% CI, 4.51–5.53; P < .001). With AI, cancer detection did not significantly differ between generalists and specialists (P = .53). 

³ Among breast imaging specialists, adjusted cancer detection was 4.47 cancers per 1,000 examinations (95% CI, 3.94–5.07) before AI and 4.76 per 1,000 (95% CI, 4.28–5.30) with AI (P = .33). 

⁴ Among general radiologists, adjusted PPV1 (positive predictive value of recalls) increased from 3.38% to 3.89% (P = .02), while adjusted recall rate increased from 9.06% to 10.40% (P = .007). With AI, PPV1 did not significantly differ between generalists and specialists (P = .64). 

⁵ Among examinations interpreted by general radiologists, 8.96% of negative examinations in women with dense breasts underwent additional expert review compared with 6.35% in women with non-dense breasts (P < .001). Among specialists, the corresponding rates were 10.34% and 7.57%, respectively (P < .001). 

Larsen and colleagues examined two important issues in the implementation of AI (Transpara, v. 2.1, ScreenPoint Medical BV) in a retrospective study of 200,000 mammograms from BreastScreen Norway. The first issue was automatic breast density assessment into one of four categories: volumetric breast density (VBD) 1-4, roughly corresponding to fatty, scattered, heterogeneously dense, and extremely dense, at the breast level.  Up front, they defined 15% of examinations as VBD 1, 40% as VBD2, 40% as VBD 3, and 5% as VBD 4.  The second issue was AI breast cancer detection (based on risk score from 1-10) stratified by breast density categories; temporal comparison was not included.

Key Findings:

VBD was slightly inconsistent across vendors, though these were different mammograms.¹

VBD was substantively inconsistent between breasts.²

AUC using raw AI scores decreased with increasing breast density, but a very high percentage of screen-detected cancers were given the highest risk score of 10 by the AI software.³

Interval cancer rates (which represent false negative cases on screening) increased with increasing breast density.  From 33 to 41% of mammograms in women with interval cancers diagnosed between screens had the highest AI risk score of 10, indicating they could have been detected earlier by use of AI software.

Comment:

Variability in human breast density assessment has hampered consistent application of supplemental screening guidelines and risk assessment.  Unfortunately, variability across vendors and between breasts was observed using AI to assess density: appropriately standardizing recommendations requires further refinement.  The risk score performed much better than the density score at potentially triaging mammograms to supplemental screening. While AI performed well across all breast density categories, performance was reduced in dense breasts.  Interval cancer rates could potentially be reduced by at least 33-41%, but not eliminated, by use of current AI.  This study was entirely retrospective: it is not clear that potential benefits of AI would be fully realized in practice, and further studies are needed.  Importantly, Transpara AI is FDA cleared and CE-marked for use on 2D and tomosynthesis mammograms, but it was not stated if all mammograms in this analysis were 2D or how many were tomosynthesis.

¹ Half the mammograms were performed on Hologic and half on Siemens. Median exam level breast density was higher for mammograms performed on Hologic equipment, at 7.8% (IQR 5.8-11.6) vs. 6.7% (IQR 5.4-9.4) for Siemens, and 3.3% were classified as VBD category 4 on Siemens vs. 6.7% on Hologic.

² Comparing right and left breasts, 18.5% were classified into different VBD groups (20.2% for Siemens and 16.7% for Hologic).

³ AUC for VBD 1 was 0.955 and decreased with increasing density to 0.857 for VBD 4.  Differences between categories were significant except for VBD 3 vs. 4 (where P=.094).  Using area-based AI density, AUC was also significantly lower for heterogeneously dense vs. scattered density, for each vendor.  The percentage of screen-detected cancer with the highest AI risk score of 10 varied by breast density from 86.7% for VBD 1, 92.7% for VBD 2, 94.6% for VBD 3, to 93.3% for VBD 4.

Background

Supplemental US is commonly recommended for women with dense breasts who cannot undergo breast MRI or who need additional screening beyond mammography. Although annual HHUS improves cancer detection by an average of 2-3 cancers per 1000 women screened, it is highly operator dependent and frequently results in BI-RADS 3 assessments that require short-interval follow-up.

Automated breast ultrasound (ABUS) was developed to provide standardized whole-breast imaging with improved reproducibility. Until recently, however, relatively few large-scale U.S. analyses have compared the performance of ABUS with HHUS in routine clinical practice.

Recent Study

In a recent study using Breast Cancer Surveillance Consortium (BCSC) data, investigators evaluated 25,328 supplemental screening US examinations performed in 21,446 women with dense breasts between 2014 and 2020.¹ The examinations were interpreted by 98 radiologists across 27 U.S. imaging facilities. Researchers compared the performance of ABUS and HHUS after adjusting for patient breast cancer risk and facility characteristics.

Key Findings

• ABUS was associated with a significantly lower abnormal interpretation and false-positive rates compared to HHUS.²
• The rate of short-interval follow-up recommendations was substantially lower for ABUS than for HHUS.³
• Cancer detection rates (CDR) were comparable between the two modalities⁴.
• There were no significant differences in biopsy recommendation rates or actual biopsies performed between ABUS and HHUS⁵.

These findings suggest that ABUS is an effective alternative to HHUS for supplemental screening in women with dense breasts. With ABUS, it is easier to recognize multiplicity and bilaterality of benign-appearing masses and to assess such cases as BI-RADS 2, benign, rather than BI-RADS 3 with unnecessary six-month follow-up examinations, patient anxiety, and associated healthcare costs. Importantly, these improvements were achieved without compromising cancer detection, as ABUS detected cancers at rates comparable to HHUS while generating fewer false-positive screening assessments. By providing standardized, reproducible whole-breast imaging that is less dependent on operator technique, ABUS may improve the consistency and efficiency of supplemental screening programs for women with dense breasts.

Limitations

ABUS generates over 1000 images for review and interpretation time was not considered.  Unlike HHUS, there is no Doppler or elastography with ABUS, sometimes necessitating recall for more complete characterization of findings. False-positive rates decrease with incidence screening and this study did not distinguish prevalence from incidence screens, with 70.5% of the examinations reported having no prior screening US.  The BCSC registries may not fully represent all U.S. screening practices, and investigators could not determine whether HHUS examinations were performed by radiologists or technologists. Cancer outcomes were limited to 90-day follow-up, potentially underestimating cancers initially classified as BI-RADS 3. Incomplete one-year follow-up prevented formal estimates of sensitivity and specificity.

Detailed Results

¹ The study included 21,446 women with dense breasts who underwent 25,328 supplemental screening US examinations, including 4,218 ABUS and 21,110 HHUS examinations. Most examinations were performed in women with heterogeneously dense breasts (21,913; 87%), while 3,415 (13%) were performed in women with extremely dense breasts.  The vast majority of women had tomosynthesis (22,383; 89% overall).

² The adjusted abnormal interpretation rate was 14.9% (95% CI, 13.8%–15.9%) for ABUS compared with 19.2% (95% CI, 18.7%–19.8%) for HHUS (P<.0001). ⁶ Adjusted false-positive initial assessment rates were 14.6% (95% CI, 13.6%–15.7%) for ABUS compared with 19.1% (95% CI, 18.5%–19.6%) for HHUS (P<.0001).

³ The adjusted short-interval follow-up recommendation rate was 3.0% (95% CI, 2.5%–3.5%) for ABUS versus 14.6% (95% CI, 14.1%–15.0%) for HHUS (P<.0001).

⁴ Adjusted cancer detection rates were statistically similar between ABUS and HHUS at 2.5 cancers per 1,000 examinations (95% CI, 1.0–4.0) versus 1.7 cancers per 1,000 examinations (95% CI, 1.1–2.2), respectively (P=.33).

⁵ Adjusted biopsy rates were similar for ABUS (2.7%; 95% CI, 2.2%–3.2%) and HHUS (2.6%; 95% CI, 2.4%–2.8%) (P=.75) and PPV3 of biopsies remained low at 9.5% (95% CI 3.8%-15.3%) for ABUS and 6.4% (95% CI 4.3%-8.5%) for HHUS (P=.33).

Background

Screening mammography remains the cornerstone of breast cancer screening, but cancers are often masked in women with dense breasts, increasing the risk of interval cancers and delaying diagnosis. Dense breasts also increase the risk of developing breast cancer (see how breast density compares to other risk factors).  Because of these challenges, supplemental screening breast MRI is recommended [or ultrasound (US) if MRI is not an option] for women with extremely dense breasts, women with heterogeneously dense breasts who have other risk factors, and for high-risk women with any breast density.

To improve patient awareness and access to supplemental screening, many U.S. states have enacted dense breast legislation. These laws typically fall into two categories: inform (also called “notification”), or insurance coverage. Inform mandates require radiologists to include information about breast density in the mammogram results letter, with a national standard mandated by the FDA as of 9/10/2024.  Insurance mandates require insurers to cover supplemental screening (though coinsurance and deductibles often apply and there are many exemptions), with a patchwork of state laws.  Federal HRSA guidelines effective 1/1/2026 have set a federal standard for expanded insurance coverage for most private plans, but Medicare, Tricare, and VHA are exempt, and Medicaid coverage varies.  The Find It Early Act would address gaps in coverage, and reaching out to senators and representatives in support of this act is important.

Recent Study

In this retrospective quasi-experimental cohort study using the USA national MarketScan Commercial Claims and Encounter Database from 2008 through 2019, researchers evaluated which legislative frameworks and specific messaging features were most effective at driving the clinical uptake of supplemental imaging. The study included 12,555,082 commercially insured women aged 40–64 years who underwent screening mammography and maintained continuous insurance coverage for at least six months afterward. Using a staggered-adoption difference-in-differences design, investigators evaluated how different state dense breast laws influenced the use of screening US and MRI.

Key Findings

• Insurance coverage mandates were the strongest legislative driver of screening US use¹.
• Dense breast inform mandates independently increased the use of both screening US and MRI².
• Inform laws that explicitly mentioned the potential benefit of supplemental screening were associated with substantially greater US utilization³.
• Targeted notifications directed specifically to women with dense breasts were more effective than universal notifications at increasing use of supplemental screening⁴.
• Despite these legislative changes, the overall use of supplemental screening remained low across the study population⁵.

The findings suggest that insurance coverage is the most important factor in translating patient awareness into clinical action. While inform laws increased awareness, insurance mandates had a much greater impact on supplemental US use (13 vs. 47 additional examinations per 1,000 women), highlighting the importance of reducing financial barriers. Laws that notified only women with dense breasts and included information about supplemental screening were more effective than universal notification of all women. Although insurance coverage increased screening US use, MRI utilization remained low, suggesting that factors beyond insurance, such as access, availability, referral patterns, and patient preference, continue to limit its use.

Limitations

Claims data could not reliably distinguish screening from diagnostic imaging, which may have overestimated supplemental screening rates. The study included only commercially insured women, and do not generalize to Medicare, VHA, Tricare, Medicaid, or uninsured populations.  Claims data lacked patient level characteristics, including breast density: uptake rates were determined across all women and are expected to be substantially higher in women with dense breasts. The authors did not distinguish state laws that require informing all woman of their specific density category (e.g. PA) vs. those that sent general information to all women about breast density (e.g. CT). These data preceded the 2024 national FDA standard. Further studies integrating claims data with cancer registries and electronic health records are needed to determine whether current legislative changes ultimately improve breast cancer outcomes.

Detailed Results

¹ Insurance coverage mandates increased supplemental US use by an absolute 4.7 percentage points (47 additional examinations per 1,000 women) and this represented significantly higher odds of US utilization (OR, 1.88; 95% CI, 1.86–1.90). MRI insurance coverage was not associated with increased MRI use (OR, 1.01; 95% CI, 0.96–1.05).

² Notification mandates were associated with modest but significant increases in use of screening US (OR, 1.09; 95% CI, 1.08–1.10) and breast MRI (OR, 1.09; 95% CI, 1.06–1.12). These findings corresponded to approximately 13 additional screening US examinations and 1 additional screening MRI examination per 1,000 women.

³ Notifications that included explicit language about supplemental screening tests were associated with substantially greater US use (OR, 2.84; 95% CI, 2.81–2.86).

⁴ Targeted notifications (sent only to women with dense breasts) were associated with higher odds of use of screening US (OR, 1.32; 95% CI, 1.31–1.33) and breast MRI (OR, 1.23; 95% CI, 1.19–1.27) compared with universal notification laws.

⁵ Across the entire study population of 12,555,082 women, only 9.0% underwent supplemental US and 0.6% underwent breast MRI within six months of screening mammography, demonstrating that overall supplemental screening remained relatively uncommon despite legislative changes. The study could not detail results by patient breast density or risk factors and was conducted at the state population level. The authors did report that 0.5% of the population had a personal history of breast cancer.

Artificial intelligence (AI) is rapidly transforming breast imaging—from helping detect cancers on mammograms to improving workflow efficiency and identifying women who may benefit from supplemental screening based on their individual risk.

As interest in these technologies continues to grow, so do questions from both patients and healthcare providers. To help separate fact from hype, our DenseBreast-info.org experts have developed a comprehensive new resource, Mammography AI Screening Tools, that explains the current role of AI in breast imaging in clear, practical terms.

The resource answers common questions, reviews how AI is being used today, explores its potential and limitations, and includes easy-to-reference tables designed to help breast imaging professionals better understand the available tools and their clinical applications.

True to our mission, we are delighted to share this new educational tool.

Standard mammography reduces breast cancer mortality but is less sensitive in women with dense breasts. Contrast-enhanced mammography (CEM) combines mammography with intravenous iodinated contrast. CEM thereby depicts tumor-associated blood flow and provides functional information similar to breast MRI.

Most studies have evaluated CEM during an initial (prevalence) screening examination and have shown very promising results. Less is known about its performance during subsequent (incidence) screening rounds. In a recent retrospective study from Memorial Sloan Kettering Cancer Center, investigators evaluated CEM examinations performed between 2015 and 2021. Most women were at increased risk for breast cancer and had dense breast tissue¹. The authors compared cancer detection rate, sensitivity, specificity, and cancer characteristics from first screens vs. those with a prior comparison CEM or MRI.

Key Findings

• CEM maintained high sensitivity and cancer detection rates during both prevalence and incidence screening rounds.²
• Specificity and overall accuracy improved significantly during incidence screening compared with the initial examination.³
• More than half of cancers were seen only because of contrast enhancement.⁴
• CEM continued to detect predominantly invasive, node-negative cancers during subsequent screening rounds.⁵

These findings suggest that CEM remains an effective supplemental screening tool over repeated examinations. The improved specificity seen during incidence screening may reduce unnecessary recalls while maintaining high cancer detection.

There were limitations to this study. It was a retrospective, single-center analysis involving primarily women with a personal history of breast cancer, which may limit generalizability. Larger prospective studies, including the ongoing CMIST trial, are needed to further define the role of CEM in risk-based screening. CEM is currently only FDA approved for diagnostic use, not screening.  Contrast-enhanced biopsy capability is necessary for centers performing CEM.  In this study, biopsies were performed using MRI guidance, but not all women having CEM can tolerate MRI: direct CEM-guided biopsy should be available.

In an accompanying editorial, Freitas et al. noted that these findings support CEM as a practical screening option, particularly for women who cannot undergo breast MRI or in settings where MRI access is limited.  Because low-energy images mimic standard mammography, CEM can be performed instead of standard mammography rather than as a supplement.

Detailed Results

¹ Most women had dense breast tissue. Dense breasts (BI-RADS C or D) were present in 83.1% of women undergoing prevalence screening and 88.2% of women undergoing incidence screening.  Of 1575 prevalence screens, 941 (59.7%) were in women with a personal history of breast cancer, as were 2894/5336 (54.2%) of incidence screens.

² Sensitivity remained stable at 86.1% (30/35) during prevalence screening and 87.1% (62/71) during incidence screening (P=.60). Cancer detection rates (CDR) were 19.0 per 1,000 examinations during prevalence screening and 11.6 per 1,000 examinations during incidence screening (P=.65). 

³ Specificity increased from 83.4% (95% CI, 81.6–85.4%) during prevalence screening to 91.5% (95% CI, 90.7–92.2%) during incidence screening (P=.008). Overall accuracy increased from 83.5% to 91.4% (P=.01).

⁴ Contrast enhancement alone showed 60% (18/30) of cancers during prevalence screening and 58% (36/62) during incidence screening.  Incremental CDR due to contrast was 11.4/1000 for prevalence screening and 6.7/1000 for incidence screening.

⁵ There was no significant difference in tumor subtype between screening rounds (P=.50). Most cancers were invasive (70.0% during prevalence screening and 61.2% during incidence screening), and lymph node positivity remained low (5.9% vs. 6.1%). Mean invasive tumor size was 8 mm (range 6-11 mm) for prevalence screens and 7 mm (range 4-10 mm) for incidence screens. The interval cancer rate was 5/1292 (3.9/1000) for prevalence screens and 9/4828 (1.9%) for incidence screens.

World Dense Breast Day is just around the corner! On September 30, we will proudly celebrate the 5th anniversary of this remarkable global educational event. As the Official Sponsor of World Dense Breast Day, DenseBreast-info is honored to once again work together across the globe with organizations, healthcare professionals, advocates, and individuals who share our commitment to ensuring that every woman has access to potentially life-saving information about breast density.

What began as a single day of awareness has grown into an international movement—and it remains the only day of its kind recognized on the National Day Calendar, dedicated exclusively to breast density education rather than fundraising.

In the weeks ahead, we’ll provide everything you need to participate, including ready-to-share social media posts, artwork for emails, and the 24-hour event schedule. Together, we’ll create a powerful, around-the-world educational effort that helps women better understand their breast density, their breast cancer risk, and the screening options available to them.

Thank you for being part of this important mission. We can’t wait to celebrate this milestone—and to once again make September 30 a day when the world comes together to educate, empower, and save lives.

Medical society and organization guidelines differ on mammography, dense breast, and high/increased risk screening recommendations. DBI has developed helpful comparative tables for each:

• Mammography Guideline Comparison
• Dense Breast Screening Guidelines
• High/Increased-Risk Screening Guidelines

Breast density is highest in younger women and can decrease around menopause as glandular tissue is replaced by fat, a process known as lobular involution. However, little is known about how breast density changes over time in women over age 60 or which factors influence these changes.

In a retrospective Korean study, investigators evaluated 674,993 women aged 60 years and older who were followed over a 10-year period (January 2009–December 2020) through six biennial screening cycles. Visual breast density was assessed on a mix of film, computed radiography, and digital 2D mammograms. The authors identified distinct breast density trajectories over time and examined associations between these patterns and known breast cancer risk factors.

Key Findings

• Five distinct breast density trajectories were identified. Most women had relatively stable breast density over time (grouped as those that were fatty, scattered density, or dense), but one group experienced a notable increase and another a notable decrease in density over the 10-year study period.¹
• Overall, 17.2% of women demonstrated increasing breast density over time, despite the expectation that density generally declines with aging.¹
• Older age, higher baseline body mass index (BMI), and weight gain during follow-up were associated with a lower likelihood of increasing breast density.²
• Family history of breast cancer, hormone replacement therapy use, later age at menopause, and prior hysterectomy were associated with a greater likelihood of persistently dense or increasing breast density.³
• Having more children and a longer duration of breastfeeding were associated with decreasing breast density over time.⁴

These findings suggest that breast density remains dynamic even in later life and may reflect cumulative hormonal, reproductive, genetic, and lifestyle influences. Monitoring changes in density over time may provide additional information about breast tissue aging beyond chronological age alone.

There were limitations to this work.  Importantly, this study did not evaluate whether the identified density trajectories were directly associated with future breast cancer risk. Visual breast density assessment is subject to inter-reader variability and potential misclassification, particularly when comparing film and digital mammograms.  Because the study population consisted of Korean women, the findings may not be fully generalizable to Western populations. Further research is needed to determine whether density trajectories are associated with subsequent breast cancer risk and whether they can help guide individualized screening and prevention strategies.

Detailed Results

¹ Five trajectory groups were identified among 674,993 women aged 60 years and older: Persistently Fatty (20.5%), Increasing Density (17.2%), Decreasing Density (10.8%), Persistently Scattered Density (36.1%), and Persistently Dense (15.4%).

² Older age was associated with a lower likelihood of increasing density [adjusted odds ratio (aOR) 0.98; P<.001]. Higher baseline BMI was similarly associated with lower odds of increasing density (aOR 0.96; P<.001). Women with higher BMI and those who gained weight during follow-up were less likely to exhibit increasing density trajectories.

³ Family history of breast cancer was associated with increased odds of increasing density (aOR 1.21; P<.001). Hormone replacement therapy use for more than 5 years (aOR 1.23; P<.001), prior hysterectomy (aOR 1.22; P<.001), and later age at menopause were also associated with increasing density or persistently dense trajectories.

⁴ Greater parity and a longer duration of breastfeeding were associated with decreasing breast density over time.

Excess weight, especially after menopause, is a major modifiable risk factor for breast cancer. Glucagon-like peptide-1 (GLP-1) drugs are very effective for weight loss and improving metabolic health, and therefore may reduce risk for breast cancer.

In a retrospective study from the University of Pennsylvania Health System, investigators evaluated electronic health records from women aged 45–80 years who were overweight (BMI 25-<30) or obese (BMI ≥30). The authors compared breast cancer incidence among 15,264 women with prior GLP-1 exposure¹ to the same number of women without exposure, with matching for age, BMI, breast density, race, ethnicity, and history of diabetes.

Key Findings

• Breast cancer incidence was lower in women who had used GLP-1 drugs than in women who had not.²
• After matching women for age, BMI, breast density, race, ethnicity, and diabetes history, GLP-1 use remained associated with reduced rate of breast cancer. Women using GLP-1 medications had an approximately 30% reduced risk for breast cancer.³
• The association between GLP-1 use and lower breast cancer incidence was independent of breast density.³
• Reduced risk for breast cancer was similar in women both with and without diabetes and among both Black and White women.⁴

This study did not examine duration of GLP-1 use, dosage, nor time from starting GLP-1 use to the breast imaging. The authors did not require any specific follow-up period to ascertain breast cancer diagnosis nor linkage to a tumor registry. Women in the control group may have been using non-prescription GLP-1s (which would have diluted the observed effect).

There is need for long-term prospective trials of GLP-1 drugs to confirm efficacy in breast cancer
prevention. With weight loss, breast density can appear to increase, and this could mask detection of breast cancer on mammography and may also prompt additional false alarms for findings that are not cancer. This study did not evaluate serial mammograms over time for changes in breast density associated with GLP-1 use and did not quantify weight loss.

Detailed Results

¹From January 1, 2022, to June 30, 2025, GLP-1 exposure was defined as first prescription prior to the breast imaging examination with documented outcome from the imaging; In the total study population of 111,646 women, 15,264 women had GLP-1 exposure and 96,382 did not. The analysis was based on 15,264 women with and 15,264 matched women without GLP-1 exposure. Overall, 4059 (13.3%) women had breast ultrasound or MRI and not mammography for this analysis.

²In the full study population, breast cancer was diagnosed in 1.62% (247/15,264) of women with GLP-1 exposure compared with 2.47% (2,381/96,382) of women without exposure, corresponding to an odds ratio (OR) of 0.649 (95% CI, 0.569–0.741; P<0.0001).

³After propensity-score matching for age, BMI, breast density, race, ethnicity, and diabetes history (n=30,528), breast cancer occurred in 1.62% (247/15,264) of GLP-1 users over the study period of 3.5 years, compared with 2.31% (353/15,264) of matched non-users. This corresponded to an OR of 0.695 (95% CI, 0.590 to 0.819; P<;0.0001), with an absolute risk reduction of 6.9 per 1000 (95% CI, 3.8 to 10.1%).

⁴Among women without diabetes, the OR was 0.653 (95% CI, 0.502–0.849; P=0.0014). Among women with diabetes, the OR was 0.723 (95% CI, 0.586–0.893; P=0.0026). Among Black women, the OR was 0.767 (P=0.0384), and among White women, the OR was 0.674 (P=0.0007).

Neoadjuvant chemotherapy (NAT) is commonly used before surgery in women with triple-negative and HER2-positive breast cancers.

Multiple studies have established that tamoxifen therapy consistently reduces mammographic breast density [1], serving as a powerful predictor of clinical benefit. Research demonstrates that women experiencing at least a 10% decrease in density had a 63% reduction in primary breast cancer risk [2], while those with pronounced reductions saw a 50% to 56% lower risk of breast cancer-specific mortality [3,4], a 55% lower risk of a contralateral primary [5] and a significantly reduced risk of recurrence [4,6]. While breast density is known to be influenced by hormonal factors, less is known about how chemotherapy affects mammographic density or whether density changes correlate with treatment
response.

In a new retrospective study [7], investigators in Italy evaluated paired pre- and post-treatment mammograms from 135 women with stage T1-2N0M0 triple-negative and/or HER2-positive invasive ductal breast cancer treated with NAT between 2018 and 2023. Mammograms included digital mammography and digital breast tomosynthesis obtained before and after treatment. The authors assessed changes in BI-RADS breast density based on an expert reader (and validated by in-house AI software) and examined whether density reduction was associated with pathological response to therapy.

Key findings:

• Breast density decreased significantly after NAT, with the proportion of women classified as having dense breasts (BI-RADS C or D) falling from 46.7% before treatment to 24.4% after treatment. ^a
• Women with greater reductions in breast density were more likely to have a favorable pathological response to chemotherapy. ^b
• The greatest reductions in density were seen in younger, premenopausal women and in women who had the densest breasts before treatment.
• An AI tool demonstrated good agreement with an expert breast radiologist in assessing density changes and performed more consistently than less experienced readers. ^c

Reduced density after treatment may improve visualization of residual or recurrent disease on mammography and may facilitate surgical planning. These findings suggest that mammographic density may be a dynamic imaging feature that reflects treatment-related changes in breast tissue. Although density reduction cannot yet be considered a validated biomarker of treatment response, the results support further investigation into whether changes in density could provide additional information about the effectiveness of NAT.

^a Dense breast categories (BI-RADS C/D) decreased from 46.7% (63/135) before NAT to 24.4% (33/135) after NAT (P&lt;0.001). BI-RADS C (heterogeneously dense) decreased from 31.9% (43/135) to 17.8% (24/135), while BI-RADS D (extremely dense) decreased from 14.8% (20/135) to 6.7% (9/135).

^b Greater density reduction was associated with a better pathological response to chemotherapy (Spearman rho = 0.53). Younger age (OR 0.74) and premenopausal status (OR 1.89) were independent predictors of larger density reductions.

^c Agreement between the AI tool and the expert breast radiologist was good (ICC = 0.78) and exceeded agreement with a moderately experienced radiologist (ICC = 0.68), a radiology resident (ICC = 0.55), and a non-radiologist reader (ICC = 0.39).

References
1. Ekpo EU, Brennan PC, Mello-Thoms C, McEntee MF. Relationship between breast density and selective estrogen-receptor modulators, aromatase inhibitors, physical activity, and diet: a systematic review. Integr Cancer Ther. 2016;15(2):127-144. doi:10.1177/1534735416628343.

2. Cuzick J, Warwick J, Pinney E, et al. Tamoxifen-induced reduction in mammographic density and breast cancer risk reduction: a nested case-control study. J Natl Cancer Inst. 2011;103(9):744- 752. doi:10.1093/jnci/djr079.

3. Li J, Humphreys K, Eriksson L, Edgren G, Czene K, Hall P. Mammographic density reduction is a prognostic marker of response to adjuvant tamoxifen therapy in postmenopausal patients with breast cancer. J Clin Oncol. 2013;31(18):2249-2256. doi:10.1200/JCO.2012.44.5015.

4. Shawky MS, Martin H, Hugo HJ, et al. Mammographic density: a potential monitoring biomarker for adjuvant and preventative breast cancer endocrine therapies. Oncotarget. 2017;8(3):5578-5591.

5. Sandberg MEC, Li J, Hall P, et al. Change of mammographic density predicts the risk of contralateral breast cancer: a case-control study. Breast Cancer Res. 2013;15(4):R57. doi:10.1186/bcr3451.

6. Kim JY, Cho N, Jeyanth JX, et al. Smaller reduction in 3D breast density associated with subsequent cancer recurrence in patients with breast cancer receiving adjuvant tamoxifen therapy. AJR Am J Roentgenol. 2014;202(4):912-921. doi:10.2214/AJR.13.11109.

7. Pesapane F, Sorce A, Di Millo B, et al. Impact of neoadjuvant chemotherapy on breast tissue density and its correlation with pathologic response: a retrospective AI-enhanced radiological study. Br J Radiol. 2026;99. doi:10.1093/bjr/tqag114.

A variety of thresholds and time frames have been suggested for estimating risk of developing breast cancer as a guide to interventions including supplemental screening and risk-reducing medications:

• A lifetime risk threshold of ≥20% was recommended by the American Cancer Society in 2007 to guide decisions for supplemental MRI.
• A five-year Gail risk of ≥1.7% has been used to guide use of tamoxifen or other risk-reducing medications and recently was proposed by NCCN to “consider” MRI, but see DBI’s prior discussion that such a suggestion is misguided as nearly all women over age 58 would qualify.
• Tabár had previously shown that it takes 9-10 years to see any reduction in breast cancer deaths due to screening, and such a horizon (10-year life expectancy) is also suggested for making decisions about continuing to screen for a variety of cancers (including breast).  As such, a ten-year risk estimate could be the “Goldilocks” for decisions about supplemental screening.

In a recent paper, Eriksson et al described the development and validation of an AI-based breast cancer risk model using only mammograms and a 10-year threshold. The study included a total of 17,913 individuals across three primary datasets used for developing and validating the AI risk model ranging in age from 31 to 94 years with a median follow-up of 10 years.¹

Key Findings

• Discriminatory Performance: The model outperformed Tyrer-Cuzick-v8 and BCSC-v3, as well as the 5-year image-based Mirai tool.² The AI model achieved a 10-year area under the curve (AUC) of 0.70 to 0.73 across the validation cohorts..
• High-Risk Identification: The percent of women identified with future cancers was higher than with other models.³ In the top 10% of individuals identified as high-risk by the AI tool (i.e. 8.5% 10-year absolute risk), 30-33% of future cancers developed.
• Diversity and Consistency: Performance remained consistent across various age groups, tumor characteristics (ER status), and between white and Black participants, an important finding given longstanding concerns about bias in AI tools.⁴

In this data set, it is not clear how many of the women who developed cancer had their cancer detected on screening mammography vs. due to symptoms, or at early vs. later stage.  As such, it is uncertain at this time how best to use this information to guide supplemental screening recommendations.

¹ Model development: 7,603 women from the Swedish KARMA screening cohort were used to train the AI model. Validation cohorts: 8,696 women underwent 10-year validation, including 2,959 women in the KARMA internal validation cohort and 5,737 women in the Olmsted County (Minnesota, USA) external validation cohort. Additional external validation: the EMBED hospital-based cohort (Atlanta, USA) included 1,614 women (269 breast cancer cases and 1,345 matched controls) with shorter-term follow-up.

²The AI model achieved 10-year AUCs of 0.70–0.73 across validation cohorts. Invasive breast cancer AUC was 0.72 in both KARMA and Olmsted; overall breast cancer AUC was 0.73 in KARMA and 0.70 in Olmsted. Performance remained consistent across age, ER status, and race with time-dependent 10-year AUCs of ≥0.7 across cohorts. In KARMA, invasive cancer AUC was 0.72 (95% CI: 0.68–0.76) versus 0.64 (0.60–0.69) for Tyrer-Cuzick-v8 and 0.66 (0.61–0.70) for BCSC-v3 (both P<0.01). Mirai AUC was 0.66 (0.63–0.70) in KARMA and 0.64 (0.62–0.67) in Olmsted; AI performance was significantly higher across KARMA, Olmsted, and EMBED cohorts (P<0.01 in KARMA/Olmsted; P<0.05 in EMBED).

³In the top 10% highest-risk group, the AI model identified 33% of future breast cancers in KARMA and 30% in Olmsted versus 23% for Tyrer-Cuzick-v8, 24%/23% for Mirai (KARMA/Olmsted), and 20% for BCSC-v3.

⁴ Age-stratified 10-year AUCs were 0.67 (40–49), 0.71 (50–69), and 0.75 (70–74) in KARMA, and 0.68 (35–49), 0.72 (50–69), and 0.66 (70–94) in Olmsted. ER-positive AUC was 0.73 in both cohorts; ER-negative AUC was 0.70 in both cohorts. For in situ cancers, AUC was 0.76 in KARMA and 0.66 in Olmsted. Race-stratified AUCs in Olmsted were 0.70 for white and 0.67 for non-white participants; in the diverse EMBED cohort (42–47% Black participants), AUC was 0.7 for both white and non-white participants.

We are so proud of our Chief Scientific Advisor, Dr. Wendie Berg, who was awarded the SBI Gold Medal at this year’s Society of Breast Imaging Annual Meeting — the organization’s highest honor. We are honored to share her remarks in full and congratulate her on an incredible career.

Receiving the SBI Gold Medal is both very gratifying and very humbling, and the words “gratifying” and “humbling” really sum up a career in breast imaging.  We are blessed by being able to truly make a difference in the lives of our patients every day, and mostly for the better.  I have been super fortunate to have had some impact on the field more broadly, and that gives me a deep sense of meaning.  But we all know that there is much still to be done.  I want to share some thoughts about a good friend of mine.  Susanne was a very successful research geneticist, very involved in her community, public health, and also in supporting political candidates who were interested in public health, communities, and scientific research.  She had dense breasts and knew to get screening MRI starting in 2007.  In 2009, the MRI showed what proved to be a 9-mm invasive lobular carcinoma.  Her nodes were negative and she had bilateral mastectomy.  She took aromatase inhibitors for ten years.  Still, despite all the favorable prognostic factors, in 2023, she was having abdominal discomfort, and was found to have carcinomatosis, and, soon thereafter, orbital metastases.  She signed up for many clinical trials and offered her tissue to researchers, recognizing that she was unlikely to personally benefit, but she felt it might help others in the future.  Sadly, Susanne passed almost exactly a year ago.  We have so much more to learn, and that sentiment is our shared legacy.

Today is an opportunity for me to publicly thank the research organizations, including NIH, the former Avon Foundation, the Breast Cancer Research Foundation, the Pennsylvania Breast Cancer Coalition, Komen Foundation, the Shear Family Foundation, and the many companies who have supported my research and other efforts.  I particularly want to thank the many, many national and international individuals who have helped me and supported me in this endeavor, and you know who you are, but especially Etta Pisano, Ellen Mendelson, Phil Evans, Daniel Lehrer, the many other investigators in ACRIN 6666 (which I just heard referred to as the “demon study”), and those behind the scenes (including Carl D’Orsi and the late Steve Feig).  Thank you to all those involved in DenseBreast-info.org, especially advocate and honorary SBI Fellow JoAnn Pushkin, and to the investigators in the trials comparing positron emission mammography and MRI, screening ultrasound and tomosynthesis, ultrasound AI, and my colleagues at the University of Pittsburgh now helping in studies of CEM.  I especially want to recognize the many women who have given their time and trust to participate in the many research studies I have led in the hopes that they will help others in the future. And today, I can say with confidence, that they have.  

Finally, I want to close with a bit more about Susanne.  She was a devoted and accomplished gardener, and one of her favorite sayings was “Friends are the flowers in the garden of life.”  I am blessed to have had many blooms in a rich garden of friends at the SBI.  We have accomplished so much together, and we have much more growing to do.  Thank you all.

– Dr. Wendie Berg, DenseBreast-info.org Chief Scientific Advisor

New Survey: Supplemental Screening ABUS Better Tolerated than MRI or CEM

DBI European Educator for Albania, and attending radiologist at the University of Cambridge and Cambridge University Hospitals, Dr. Iris Allajbeu, and colleagues, published a survey of 159 BRAID trial participants assessing patient acceptance, experience, and feedback for different methods of supplemental screening in women with dense breasts and negative mammography. Each of automated breast ultrasound (ABUS), abbreviated MRI (AB-MRI) and contrast-enhanced mammography (CEM) were generally well accepted by patients.

• When compared to standard mammography, nearly all participants rated ABUS as the same or better; nearly 1 in 6 women rated AB-MRI as worse and 1 in 10 rated CEM as worse.
• Mild or severe discomfort was common with ABUS and CEM.¹
• Anxiety before or during the imaging was least common with ABUS.
• AB-MRI was well-received, with several women highlighting benefits, such as reduced pain due to less breast compression and improved detection accuracy. However, AB-MRI was associated with a higher proportion of negative feedback, suggesting that for some patients, its benefits may be outweighed by procedural drawbacks.

• Withdrawal from the study was due to adverse patient experience for 151 women overall and was much more common with contrast-enhanced methods than with ABUS,² making ABUS the most well tolerated imaging method.

Previously, we summarized the greater cancer detection rates from CEM and AB-MRI compared to ABUS in the first round of screening in the BRAID trial.³  Cancer detection rates were similar at 7-9.5/1000 across all three modalities in the second round of screening.³  False alarm rates were higher for CEM and AB-MRI in the first round and comparable across modalities in the second round.⁴

¹ 40/44 (90.1%) of ABUS participants reported discomfort as did 38/48 (79.2%) of CEM participants, compared to only 14/38 (36.8%) of MRI participants.

² Of 151 withdrawals due to adverse patient experience, 69 were in women having CEM, 66 in women having AB-MRI, and only 12 in women having ABUS.

³ Cancer detection rates were 15/1572 (9.5/1000) for ABUS+MG; 9/1051 (8.6/1000) for AB-MRI+MG; and 6/870 (7.0/1000) for CEM in the second round. (By comparison, cancer detection rates in the first round were 4.2/1000 for ABUS; 17.4/1000 for AB-MRI; and 19.2/1000 for CEM after a negative mammogram.)

⁴ Recall rates in the second round were 4.3% for ABUS; 3.5% for AB-MRI; and 4.7% for CEM.  In the first round, recall rates were 4.0% for ABUS; 9.7% for AB-MRI; and 9.7% for CEM.

Updated Overview: International Atomic Energy Agency Panel Endorses Molecular Breast Imaging

An IAEA international expert panel has provided an updated overview of MBI using dual head detectors and ^99mTc-sestamibi as a complement to mammography, especially in women with dense breasts or inconclusive conventional imaging. They conclude that “it remains a valuable modality in breast imaging, offering high sensitivity for the detection of small, node negative disease and detection of large, advanced cancers that are undetected by mammographic screening. MBI is performed at a safe radiation dose, is well-tolerated by patients, and has exceedingly low risk of adverse events.”

The ACR 2021 position is that MBI is “usually not appropriate” for screening in dense breasts due to whole body radiation exposure, though the radiation dose has decreased and additional promising prospective multicenter results have come out since that time.

New Insurance Laws

Alabama and Wisconsin are the latest U.S. states to enact insurance laws for expanded no-cost breast imaging coverage. Based on DBI legislative analysis, there are now 38 states  (plus DC) that have enacted laws for expanded coverage (Minnesota’s is for diagnostic imaging only). The laws vary from state to state, and some still include out-of-pocket costs. Visit DBI’s legislation MAP for details by state.

Efforts to expand insurance coverage for extra testing are active at state and federal levels. To learn more about how state insurance laws intersect with federal initiatives like the new HRSA guidelines and the Find It Early Act, read our new content on the legislation MAP page (located below the map).