Category: Breast Imaging

One Size Doesn’t Fit All for Asian American Cancer Risk

Posted on by Virginia Hunt

Nearly 1 in 8 women in the U.S. will be diagnosed with breast cancer, but those odds aren’t distributed equally across women of different racial backgrounds. And broad ethnic categories like “Asian American” may be masking big differences in breast cancer risk that could help inform better screening practices.

  • Breast cancer is the most commonly diagnosed cancer among women in every Asian American or Native Hawaiian/Pacific Islander (AA/NHPI) ethnic group.
  • But cancer incidence rates vary widely across groups bundled together under one ethnic or racial classification, so the one-size-fits-all approach to determining risk isn’t working.

New data show meaningful differences. In a study published in CANCER, researchers from the American Cancer Society analyzed detailed data from the National Cancer Institute’s Surveillance, Epidemiology, and End Results program from 2000 to 2022…

  • Native Hawaiian women had breast cancer rates of 140 per 100k, 27% higher than white women.
  • Total cancer incidence rates varied twofold among groups who were labeled under the same AA/NHPI term.
  • For example, Native Hawaiian women had breast cancer rates 57% higher than the combined rate for AA/NHPI communities.

Early intervention is crucial as breast cancer rates continue to rise and screening uptake remains low in Asian women…

  • Breast cancer incidence is increasing annually, from 1% in Native Hawaiian and Filipino women to between 3% and 5% in Guamanian/Chamorro, Chinese, Vietnamese, and Korean women.
  • From 2015 to 2018, adherence rates for timely breast screening in women ≥45 years varied from 55% among Asian Indian women to 69% among Filipino women.

“You can’t fix a problem you don’t know is there.” 

  • As risk-based screening programs for breast cancer become more widely adopted, accurate statistics are increasingly important.
  • Breaking out data for Asian women could also improve targeted interventions for other cancers with wide risk ranges, including lung, stomach, and colorectal cancers.

Clearer data could help inform targeted interventions that aim to understand how structural barriers to care differ.

  • For example, NHPI women have a higher likelihood of being uninsured (12%) compared to Korean women (3%) in the U.S. 
  • Thus, study authors emphasize that just as a generic approach to risk determination isn’t cutting it, a one-size-fits-all approach to interventions won’t either.

The Takeaway

Risk awareness is increasingly driving mammography screening in the U.S., but grouping together women across different AA/NHPI ethnicities buries important differences that could help improve screening and early detection.

AI Closes Mammo Gap Between Generalists, Specialists

Posted on by Brian Casey

Mammography screening exams are some of the most challenging medical images to interpret, but due to staffing issues many mammograms are read by general radiologists rather than specialists. A new study in Radiology found that an AI-based workflow helped close the gap, leading to a 25% improvement in the cancer detection rate for general radiologists.  

Most of the big population-based studies on mammography AI have been conducted in Europe, where breast screening is performed under a double-reader paradigm that has two radiologists interpreting exams.

  • In this scenario, studies have shown that AI can eliminate the need for a second reader, cutting workforce requirements with the same or even better cancer detection rates.

But U.S. breast screening programs don’t use double-reading, leaving many to wonder where AI fits into the single-reader paradigm – especially when that reader is a general radiologist with no breast fellowship training. 

  • The new study offers some clarity. Researchers developed an AI-based workflow that integrated DeepHealth’s ProFound Pro 2.x deep-learning application into DBT-based screening programs. 

They set up AI as a “safeguard review.” After a radiologist’s initial interpretation, AI analyzed non-recalled mammograms for suspicion of cancer. Exams that exceeded ProFound’s risk threshold were flagged and routed to an expert reviewer.

  • If the reviewer agreed with AI, the original interpreting radiologist was consulted and had final authority on whether to recall the case. 

The safeguard review concept was tested at 109 breast imaging facilities that saw 578k DBT mammography exams from 2021 to 2022. In particular, researchers focused on the impact the safeguard review had on interpretation accuracy of both specialists and general radiologists, finding…

  • The cancer detection rate of generalists improved 25%, from 3.76 to 4.99 cancers per 1k exams.
  • The CDR of specialists did not change at a statistically significant level (from 4.47 to 4.76, p = 0.33).
  • There was no statistically significant difference between AI-aided generalists and specialists.
  • Generalists became more efficient at cancer detection, as evidenced by 15% improvement in their positive predictive value (from 3.38% to 3.89%). 
  • Although generalists’ recall rates did increase with AI (from 9.06% to 10.4%). 

The Takeaway

The new study offers an intriguing look at how AI can be integrated into U.S. breast screening programs without dramatically disturbing workflow. It also shows how diagnostic performance can be improved in an environment where general radiologists are being asked to fly solo in an area they didn’t train in.

Ultrasound and DBT Screening — Time to Call It Quits?

Posted on by Brian Casey

Ultrasound long ago carved out a role in breast cancer screening as a complementary tool to conventional 2D mammography. But is ultrasound still needed, now that U.S. breast screening programs have largely switched over to 3D digital breast tomosynthesis (DBT)? A new study in Academic Radiology raises questions. 

Conventional 2D mammography has well-known shortcomings, particularly in women with dense breast tissue that can obscure lesions. So alternative modalities like ultrasound, breast MRI, and contrast-enhanced mammography (CEM) are called in to help when needed.

  • Past research has shown that supplemental ultrasound can improve the cancer detection rate (CDR) in a 2D mammography screening program by 3-4 cancers per 1k women. 

But those studies were performed before the switch to DBT, which can often see around overlapping structures thanks to a gantry head that acquires multiple images as it pans across the breast. 

  • So is ultrasound still needed in screening programs using DBT? Researchers from Weill Cornell Medicine at New York-Presbyterian Hospital tested the hypothesis by examining 103k screening exams from 2014 to 2024 in which both DBT and ultrasound were used. 

In the study, researchers found that screening ultrasound after a negative DBT result…

  • Generated 1.9k biopsies, or 19 biopsies for every cancer the modality detected.
  • Produced an additional cancer detection rate of 1.0 per 1k women, compared to an additional CDR of 3-4 cancers for ultrasound in the 2D mammography era. 
  • Had an overall false-positive screening rate of 98.5%, a false-positive biopsy rate of 94.8%, and a positive predictive value of biopsies performed (PPV3) of just 5.2%.
  • Generated $652k false-positive screening ultrasound costs and $1.16M in ultrasound-guided biopsy costs.

The numbers are sobering and indicate that the days of ultrasound as a supplemental screening modality to DBT screening could be coming to a close. 

  • Instead, the researchers recommended that ultrasound screening be replaced by more sensitive modalities like breast MRI or CEM, both of which are fortunately more available now than during the 2D mammography era.

The Takeaway

The new study answers the question – in the negative – of whether supplementary ultrasound is still needed in the era of DBT screening. The positive subtext here is that the research confirms the improved detection performance of 3D compared to 2D mammography.

Mammo Modality Face-Off for Early Breast Cancer

Posted on by Brian Casey

When it comes to early breast cancer detection, which medical imaging modality is best: full-field digital mammography, digital breast tomosynthesis, or breast MRI? A new study in Clinical Radiology picks winners – and brings the receipts. 

Breast imagers are fortunate to have many technologies at their disposal, each with its own strengths and weaknesses. 

  • X-ray-based mammography tools like FFDM and DBT are easily available and relatively low cost, while breast MRI delivers the highest resolution but is expensive, less available, and more time-intensive to perform. 

So when does it make sense to use each modality? Researchers from China tested four techniques – FFDM, DBT, and breast MRI at 1.5T with accelerated and full protocols – in 329 patients with early-stage breast cancer (maximum tumor diameter ≤ 2 cm). 

  • They also analyzed results according to breast tissue density, as dense breast tissue is not only a cancer risk factor but can also obscure lesions on X-ray-based modalities.

Across the study sample, researchers found…

  • There was little difference in sensitivity between the four techniques for women with non-dense breast tissue, with FFDM, DBT, and accelerated breast MRI achieving 91% compared to 94% for full-protocol breast MRI.
  • But breast MRI pulled ahead in sensitivity for women with dense breast tissue, both with accelerated and full protocols (95% and 94%) beating DBT and FFDM (90% and 83%).
  • Accelerated breast MRI had performance comparable to the full protocol regardless of breast density, but at almost half the median scan time (8 vs. 15 minutes).
  • Accelerated and full-protocol breast MRI had the same specificity (94%), ahead of both DBT and FFDM (88% and 83%).

What to make of the results? Researchers said the findings in women with non-dense breast tissue reinforce that X-ray-based modalities are sufficient.

  • For women with dense breast tissue, accelerated breast MRI offers performance close enough to the full protocol that breast imaging practices can feel comfortable offering the faster exam.

The Takeaway

It’s no surprise that breast MRI beat both FFDM and DBT mammography for early breast cancer detection in women with dense breast tissue. But it is intriguing that there wasn’t much difference between breast MRI with either accelerated or full protocols. That’s good news for practices that want to make this powerful modality accessible to more women. 

AI Risk Prediction’s Long-Term Value

Posted on by Brian Casey

AI-based calculations of breast cancer risk derived from screening mammograms can track cancer risk as it evolves over time, giving clinicians a longitudinal tool for following patients who might need additional care. A new study in Radiology adds to the growing body of knowledge on AI-based risk analysis. 

Cancer risk prediction has emerged as a promising new application for AI, as exemplified by a study earlier this month in which three commercial AI models for screening mammograms were also able to predict risk as much as six years before diagnosis. 

  • At least one AI model – Clairity Breast from Clairity – has received FDA clearance for image-based risk prediction, with others under review at the agency. 

But most studies of AI-powered breast cancer risk prediction calculate risk at a single point in time. 

  • While that’s useful, a woman’s breast cancer risk can evolve with factors such as breast tissue density, which is known to change over time – thus changing their risk profile. 

So authors of the current study tracked breast cancer risk longitudinally using the Mirai algorithm, an open-source model that’s been validated in previous studies as more accurate than clinical risk prediction models like Tyrer-Cusick and BCRAT.  

  • They retrospectively applied Mirai to 54k women who got mammograms from 2009 to 2019, and compared changes in risk scores between women who developed cancer and those who didn’t. 

Researchers found… 

  • Median risk scores six years before diagnosis changed from 2.1 to 6.6 in women eventually diagnosed with cancer.
  • Risk scores were essentially stable in women who were cancer-free (1.8 to 2.2).
  • Risk scores rose at a higher annual rate longitudinally in those with cancer versus those without (1.13 vs. 0.09 per year).
  • Women in the group who developed cancer tended to be older and had dense breast tissue or a personal or family history of breast cancer. 

Exactly what is the AI detecting if cancer isn’t visible to radiologists reading the mammograms?

  • Most likely, AI is detecting changes in patterns of breast parenchymal tissue that “may precede radiographic detection.” These changes are basically biomarkers that can be used to develop personalized screening intervals, supplemental modalities, and other preventive strategies. 

The Takeaway

The new study on AI-based breast cancer risk prediction advances our understanding of how risk can be calculated far in advance of a cancer diagnosis. It’s easy to see this knowledge put to use with earlier intervention strategies that exemplify the rise of personalized medicine. 

Mammography Use Falls after USPSTF 2009 Guideline Change

Posted on by Brian Casey

Mammography use fell after the USPSTF rescinded its recommendation in 2009 of routine breast cancer screening for women in their 40s. The findings, in a new study in JAMA Network Open, confirm the fears of many women’s health advocates following the guideline change.

The women’s health world was shocked in 2009 when the USPSTF pulled its guideline advising women aged 40 to 49 to undergo regular breast screening, instead telling them to consult with their physicians.

  • The group reversed course in 2024, stating that women in their 40s should be screened every two years. Driving the decision were rising cancer rates in younger women, as well as higher mortality rates among Black women.

The new study analyzed data from the Behavioral Risk Factor Surveillance System to find changes in mammography use among 2.6M women, divided into various groups based on age, race, and other demographics.

  • Researchers analyzed self-reported mammography use, focusing on the periods 2000-2008 and 2012 and 2022.

The researchers found that, comparing 2002 to 2022, mammography prevalence fell for…

  • Women aged 40 to 49 (from 70% to 59%).
  • Women aged 50 to 74 (from 81% to 77%).
  • Non-Hispanic Black women in their 40s (from 72% to 65%).

The researchers pointed out that for the above categories, the endpoint comparisons were statistically significant. 

  • But the year-to-year changes in intervening years were not, in particular given a change in BRFSS survey methodology in 2011 that appears to have led to a several-point drop in utilization.

But several subgroups of women saw changes in both endpoint and year-to-year mammography prevalence, with use falling among…

  • Non-Hispanic White women in their 40s (from 71% to 60%).
  • Women in their 40s with insurance (from 74% to 62%) and without (from 47% to 33%).
  • Employed women (from 72% to 61%) as well as in women who classified themselves as homemakers (65% to 55%).

The Takeaway

The new study on falling mammography utilization confirms the fears of many women’s health advocates about the impact of the USPSTF’s 2009 guideline change. While the group righted the ship in 2024, it could take many years to see an effect – as suggested by the new study.

Mammo Screening Saves Lives – Even in Late-Stage Cancer

Posted on by Brian Casey

A new study confirms that not only does breast cancer screening save lives, but it also improves survival in women with late-stage disease. Researchers found that women with stage IV breast cancer had a survival rate over three times higher if their disease was detected with screening, thanks largely to its role in driving treatment.

The “Mammography Wars” over breast cancer screening’s effectiveness raged from the 1980s to the 2010s, but eventually were decided in mammography’s favor. 

  • Multiple research studies have demonstrated that the combination of early detection and more effective treatments improve breast cancer survival. The USPSTF’s 2023 shift back to recommending that screening start at 40 settled the issue. 

But pockets of anti-screening resistance remain, with screening skeptics publishing several studies since the USPSTF change questioning the value not only of mammography but also other cancer screening tests.

  • So it’s more important than ever to demonstrate cancer screening’s value.

The new study in the Journal of the National Cancer Institute does just that by analyzing screening’s impact on survival rates in women diagnosed with stage IV disease who had been invited to Denmark’s national breast screening program (not all women completed mammography despite getting invited).

  • In all, 32.8k women with breast cancer were included, of whom 8% presented with stage III or stage IV cancer. 

The researchers found that for women with stage IV breast cancer…

  • Five-year survival was over 2X higher for women with screen-detected cancer versus women who were never screened (75% vs. 32%).
  • Ten-year survival was over 3X higher (62% vs. 17%).
  • Women with later-stage disease detected by screening had survival rates over five years comparable to women with disease one stage lower who were never screened.
  • Survival rates were strongly influenced by treatment type, with surgical treatment showing the longest median survival versus non-surgical treatment and no treatment (6, 2, and 0.1 years, respectively).

The big difference in survival was driven by the fact that women with screen-detected cancers were far more likely to get surgical treatment, and to subsequently have better 10-year survival rates than those treated without surgery (60% vs. 8%).

The Takeaway

The new study once again proves the value of screening mammography, but it goes beyond just showing that screening causes a stage shift to earlier diagnosis. Even in women with late-stage disease, screening is driving more effective treatment that is proving invaluable in saving women’s lives.

Risk-Based Mammography Screening Returns

Posted on by Brian Casey

The idea of risk-based mammography screening is back with the publication of a new study in JAMA Network Open claiming that some risk-based strategies averted more breast cancer deaths with fewer false positives than age-based criteria. But like a previous paper on risk-based screening, the new findings raise concerns.

The idea behind risk-based screening is to focus healthcare resources on the people who need them most while sparing low-risk individuals from unnecessary medical interventions.

  • But risk-based breast cancer screening needs more clinical validation before it can be adopted broadly. This was tried with the WISDOM study, but researchers found no statistically significant difference in biopsy rates and only a modest reduction in mammograms performed.

A slightly different tack was taken with the new study, which compared conventional age-based biennial screening to a package of risk-based approaches based on a patient’s five-year breast cancer risk as calculated by widely accepted techniques like the Gail model and BCSC calculator.

  • Out of 50 risk-based strategies, nine averted more deaths than biennial age-based screening for women aged 40-74 (both were compared to no screening), and resulted in fewer false-positive recalls.

One such strategy highlighted by the authors used no screening for younger low-risk women, biennial screening for average-risk women, and annual screening for intermediate- and high-risk women, with the following results…

  • 6% more breast cancer deaths averted per 1k women versus conventional screening (7.2 vs. 6.8).
  • 8% fewer false-positive recalls (1,257 vs. 1,365).
  • While other risk-based strategies saw death reductions as high as 7.5 deaths per 1k women and false-positive reductions of 8-23%.

One key thing to note with the new study is its use of biennial screening as the control group, in line with current USPSTF recommendations for women aged 40-74. 

  • But many clinical organizations like ACR, ACOG, SBI, and NCCN recommend annual screening, and the new study’s findings may have been very different if compared to an annual model.

The Takeaway

This week’s findings are generally more supportive of risk-based screening than those of last year’s WISDOM study. But the new paper’s marginal improvement in cancer deaths averted might disappear when compared with annual age-based mammography. And like WISDOM, its use of clinical models for risk prediction may soon be obsolete given rapid developments in AI-based risk assessment. 

Breast Density’s Impact on Mammography

Posted on by Brian Casey

Breast density has a well-known effect on the accuracy of mammography screening – and it’s not a positive one. But a new study in Academic Radiology sheds light on density’s impact thanks to a massive patient population and its use of digital breast tomosynthesis, the most current breast screening technology.

Breast density is known to reduce the effectiveness of X-ray mammography by obscuring suspicious areas and making cancers harder to find. 

  • Women with dense breast tissue are typically directed to other imaging modalities for screening, such as ultrasound, breast MRI, and contrast-enhanced mammography.

The problem posed by breast density is significant enough that in 2024 the FDA implemented new MQSA rules requiring women getting screening mammograms to be notified of their density status.

  • It’s particularly important because having dense breast tissue is also a risk factor for breast cancer.

In the new study, MGH researchers aimed to quantify exactly how much breast density affects mammography screening through a large patient population screened with DBT, the state of the art in the U.S.

  • Researchers included 111.1k women who got DBT exams from 2013 to 2019 at their institution. 

They then calculated important metrics like sensitivity and specificity, as well as cancer detection and false-negative rates, across the four categories of dense breast tissue, from entirely fatty (A) to extremely dense (D), finding…

  • Sensitivity was lowest in extremely dense tissue compared to entirely fatty (62% vs. 93%).
  • Specificity was also lower for extremely dense and heterogeneously dense categories compared to entirely fatty (93% for both vs. 97%).
  • The false-negative rate for extremely dense tissue was over 8X that of entirely fatty based on adjusted odds ratio (aOR = 8.35).
  • While the abnormal interpretation rate was 57% higher for extremely dense versus entirely fatty tissue.

The Takeaway

The new findings are some of the most definitive yet on the negative effect breast density has on screening mammography’s accuracy and support the FDA’s 2024 notification requirement. They hopefully will spur development of new technologies to mitigate density’s impact. 

Risk-Based Mammo Screening – Ready for Prime Time?

Posted on by Brian Casey

Is mammography screening based on patient risk ready to take over for age-based screening? Results from the WISDOM study presented at last week’s San Antonio Breast Cancer Symposium and published simultaneously in JAMA suggest that while risk-based screening has its merits, more work may need to be done. 

Cancer screening exams like mammography have reduced disease-specific mortality, but (with the exception of lung cancer screening) all use exclusively age-based criteria to determine who should get screened.

  • Age isn’t a great tool for determining who’s at higher risk of getting cancer, but it’s the best tool we’ve had – up to now.

New cancer risk prediction tools are now becoming available, prompting debate over whether these techniques could make screening more precise by directing it to those most at risk.

  • Higher-risk people could get more frequent screening, while lower-risk individuals might be directed to longer screening intervals.

The WISDOM study presented at SABCS 2025 investigates this question. WISDOM is a randomized clinical trial that compared risk-based breast screening to age-based annual screening in 28.4k women followed for five years. 

  • Risk categorization was performed with genetic testing, polygenic risk scores, and BCSC scores, which incorporate family history and imaging results. 

Women in the risk-based screening group were directed into one of four screening strategies, from alternating mammography and MRI every six months for high-risk women to no screening until age 50 for low-risk women.

  • The study’s primary outcomes were detection rates for breast cancers rated as stage IIB or higher and effectiveness in reducing biopsy rates – a proxy for screening-caused morbidity.

Across the study population, researchers found…

  • The rate of mammograms per 100k person-years was lower in the risk-based cohort compared to age-based screening (43.1k vs. 46.9k). 
  • The rate of stage IIB or higher cancers per 100k person-years was also lower in the risk-based cohort (30 vs. 48).
  • But there was no statistically significant difference in biopsy rates, with a rate difference of 99 per 100k person-years (p = 0.10).

One problem with the WISDOM trial was that the actual screening exams were performed outside the study, and some patients did not comply with screening recommendations, potentially confounding results. 

The Takeaway

The WISDOM authors concluded that a risk-based screening approach is safe, but the lack of a difference in biopsy rates makes one wonder if veering from established age-based criteria is worth it. In any event, the coming arrival of risk stratification based on AI mammogram analysis could make the genetic testing-based approach used in WISDOM obsolete.

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