Category: Breast Imaging

Perils of Missed Mammography

Posted on by Brian Casey

Yet another study is illustrating the perils of missing mammography screening. New research in JAMA Network Open found that women diagnosed with breast cancer who missed their previous screening exam had signs of delayed diagnosis and worse clinical outcomes. 

Mammography screening is generally credited – along with improved treatments – with a steady decline in breast cancer death rates since the start of population-based breast screening.

  • But most studies on mammography’s effectiveness tend to compare women who participated regularly in screening with those who never did. 

That’s not really a realistic comparison these days, as mammography’s relatively high compliance rate means that most women are getting screened at least some of the time.

  • But what happens if women miss a screening exam? In a BMJ study published last month, researchers found that women who missed their first screening exam had a 40% higher risk of breast cancer death.

In the current study, researchers took a slightly different tack, looking at 8.6k women in Sweden whose breast cancer was detected on screening exams starting in 2015. 

  • In all, 17% of women missed the screening exam immediately before their cancer diagnosis. 

Compared to women who attended all screening rounds, those who missed their previous exam had higher adjusted odds ratio for…

  • Larger tumors ≥ 20 mm (AOR = 1.55).
  • Lymph node involvement (AOR = 1.28).
  • Distant metastasis (AOR = 4.64).
  • Worse breast cancer-specific survival (AOR = 1.33).
  • Lower 20-year breast cancer-specific survival (86% vs. 89%). 

What’s more, the program’s cancer detection rate per 1k screenings was sharply higher in the second screening round for women who missed the first round (7.35 vs. 5.59). 

  • This is most likely a sign that cancers that could have been detected in the first round instead were detected in the second round – another sign of delayed diagnosis.

Women who had missed their previous screening tended to be younger, unemployed, unmarried, and born outside of Sweden, and also had lower income. 

  • Women with these characteristics could be targeted for more intensive outreach, such as shorter invitation intervals or outreach after a missed appointment. 

The Takeaway

The new study once again highlights the importance of regular mammography screening in detecting breast cancer. Even one missed exam can have serious clinical consequences – highlighting the importance of identifying and contacting women who might be more prone to missed appointments.

Missing Breast Screening Boosts Death Risk

Posted on by Brian Casey

Missing a first breast cancer screening exam can be hazardous to your health. A new study in BMJ found that women who missed their first mammography screening had a 40% higher long-term risk of breast cancer death. 

Mammography screening has been shown to prevent breast cancer deaths by detecting cancer earlier, when it can be treated more effectively.

  • But breast screening adherence rates still aren’t as high as they should be, leaving women’s health advocates to wonder what they can do to spur better compliance.

In the new study, researchers investigated whether mammography compliance itself could be an early warning sign that women might not be taking screening seriously enough.

  • They analyzed data on 433k women invited to the Swedish Mammography Screening Programme from 1991 to 2020 and correlated clinical outcomes over 25 years with whether or not patients completed their first screening exam (32% didn’t).

Compared to women who missed their first mammography appointment, women who followed through with their exam…

  • Had a 40% lower risk of dying from breast cancer. 
  • Had lower breast cancer mortality rates per 1k women (7 vs. 9.9). 
  • Got nearly twice as many breast screenings over the study period (8.7 vs. 4.8 screenings).
  • Had similar breast cancer incidence rates (7.8% vs. 7.6%), a sign that non-participation delayed detection rather than increased incidence. 

What’s more, women who missed their first appointment were 32% more likely to have invasive cancer and had higher odds ratios for stage III and stage IV disease (OR = 1.53 and 3.61, respectively). 

Researchers concluded that women who missed their first mammography appointment were also more likely to miss future ones – putting them at higher risk of breast cancer death.

  • But a missed initial appointment also could serve as a warning to women’s health centers that these patients deserve extra attention, through tools as simple as more provider outreach or automatically scheduled second appointments. 

The Takeaway

The new findings offer – yet again – more support for the effectiveness of population-based breast screening in reducing breast cancer deaths. What’s novel is that they show that non-participation is an early warning sign that could activate a slate of more aggressive outreach measures to bring these women in. 

Mammo Risk Prediction Improves with AI

Posted on by Brian Casey

Artificial intelligence is beginning to show that it can not only detect breast cancer on mammograms, but it can predict a patient’s future risk of cancer. A new study in JAMA Network Open showed that a U.S. university’s homegrown AI algorithm worked well in predicting breast cancer risk across diverse ethnic groups. 

Breast cancer screening traditionally has used a one-size-fits-all model based on age for determining who gets mammography.

  • But screening might be better tailored to a woman’s risk, which can be calculated from various clinical factors like breast density and family history.

At the same time, research into mammography AI has uncovered an interesting phenomenon – AI algorithms can predict whether a woman will develop breast cancer later in life even if her current mammograms are normal. 

The new study involves a risk prediction algorithm developed at Washington University School of Medicine in St. Louis that uses AI to analyze subtle differences and changes in mammograms over time, including texture, calcification, and breast asymmetry.

  • The algorithm then generates a mammogram risk score that can indicate the risk of developing a new tumor.

In clinical trials in British Columbia, the algorithm was used to analyze full-field digital mammograms of 206.9k women aged 40-74, with up to four years of prior mammograms available. Results were as follows …

  • The algorithm had an AUROC of 0.78 for predicting cancer over the next five years.
  • Performance was higher for women older than 50 compared to 40-50 (AUROC of 0.80 vs. 0.76).
  • Performance was consistent across women of different races.
  • 9% of women had a five-year risk higher than 3%. 

The algorithm’s inclusion of multiple mammography screening rounds is a major advantage over algorithms that use a single mammogram as it can capture changes in the breast over time. 

  • The model also showed consistent performance across ethnic groups, a problem that has befallen other risk prediction algorithms trained mostly on data from White women. 

The Takeaway

The new study advances the field of breast cancer risk prediction with a powerful new approach that supports the concept of more tailored screening. This could make mammography even more effective than the one-size-fits-all approach used for decades.

AI Boosts DBT in Detecting More Breast Cancer

Posted on by Brian Casey

A real-world study of AI for DBT screening found that AI-assisted mammogram interpretation nearly doubled the breast cancer detection rate. Radiologists using iCAD’s ProFound AI software saw sharp improvements across multiple metrics. 

Mammography screening has quickly become one of the most promising use cases for AI. 

  • Multiple large-scale studies published in 2024 and 2025 have documented improved radiologist performance when using AI for mammogram interpretation, with the largest studies performed in Europe.

Another new technology changing mammography screening is digital breast tomosynthesis, which is being rapidly adopted in the U.S. 

  • DBT use in Europe is occurring more slowly, so questions have arisen about whether AI’s benefits for 2D mammography would also be found with 3D systems.

To investigate this question, researchers writing in Clinical Breast Cancer tested radiologist performance for DBT screening before and after implementation of iCAD’s ProFound V2.1 AI algorithm in 2020 at Indiana University. 

  • Interestingly, the pre-AI period included use of iCAD’s older PowerLook CAD software. 

Across the 16.7k DBT cases studied, those with AI saw …

  • A sharp improvement in cancer detection rate per 1k exams (6.1 vs. 3.7).
  • A decline in the abnormal interpretation rate (6.5% vs. 8.2%).
  • Higher PPV1 (rate that abnormal mammograms would be positive) (8.8% vs. 4.2%).
  • Higher PPV3 (rate that biopsies would be positive) (57% vs. 32%). 
  • Higher specificity (94% vs. 92%).
  • No statistically significant change in sensitivity.

The findings on sensitivity are curious given AI’s positive impact on other interpretation metrics.

  • Researchers postulated that there was higher breast cancer incidence in the post-AI implementation period, which could have been caused by AI finding cancers that were missed in the period without AI.

The Takeaway

The radiology world has seen multiple positive studies on AI for mammography, but most of these have come from Europe and involved 2D mammography not DBT. The new results suggest that AI’s benefits will also transfer to DBT, the technology that’s becoming the standard of care for breast screening in the U.S.

How Do Patients Feel about Mammo AI?

Posted on by Brian Casey

As radiology moves (albeit slowly) to adopt clinical AI, how do patients feel about having their images interpreted by a computer? Researchers in a new study in JACR queried patients about their attitudes regarding mammography AI, finding that for the most part the jury is still out. 

Researchers got responses to a 36-question survey from 3.5k patients presenting for breast imaging at eight U.S. practices from 2023-2024, finding …

  • The most common response to four questions on general perceptions of medical AI was “neutral,” with a range of 43-51%. 
  • When asked if using AI for medical tasks was a bad idea, more patients disagreed than agreed (28% vs. 25%). 
  • Regarding confidence that medical AI was safe, patients were more dubious, with higher levels of disagreement (27% vs. 20%).
  • When asked if medical AI was helpful, 43% were neutral but positive attitudes were higher (35% vs. 19%).

The Takeaway

Much like clinicians, patients seem to be taking a wait-and-see attitude toward mammography AI. The new survey does reveal fault lines – like privacy and equitability – that AI developers would do well to address as they work to win broader acceptance for their technology. 

We’re testing a new format today – let us know if you prefer two shorter Top Stories or one longer Top Story with this quick survey!

Patients Want Mammo AI, But Mostly As Backup

Posted on by Brian Casey

Patients support the idea of having AI review their screening mammograms – under certain conditions. That’s according to a new study in Radiology: Imaging Cancer that could have implications for breast imagers seeking to integrate AI into their practices.

Mammography screening has been identified as one of the most promising use cases for AI, but clinical adoption has been sluggish for reasons that range from low reimbursement to concerns about data privacy, security, algorithm bias, and transparency. 

  • Vendors and providers are working on solving many of the problems impeding greater AI use, but patient preference is an often overlooked factor – even as some providers are beginning to offer AI review services for which patients pay out of pocket.

To gain more insight into what patients want, researchers from the University of Texas Southwestern Medical Center surveyed 518 women who got screening mammography over eight months in 2023, finding …

  • 71% preferred that AI be used as a second reader along with a radiologist.
  • Only 4.4% accepted standalone AI interpretation of their images.
  • 74% wanted patient consent before AI review.
  • If AI found an abnormality, 89% wanted a radiologist to review their case, versus 51% who wanted AI to review abnormal findings by radiologists.
  • If AI missed a finding, 58% believed “everyone” should be accountable, while 15% said they would hold the AI manufacturer accountable. 

Patient preference for use of AI in collaboration with radiologists tracks with other recent research. 

  • Patients seem to want humans to retain oversight of AI, and seem to value trust, empathy, and accountability in healthcare – values associated with providers, not machines. 

The findings should also be good news for imaging services companies offering out-of-pocket AI review services. 

The Takeaway

The new findings should be encouraging not only for breast imagers and AI developers, but also for the imaging services companies that are banking on patients to shell out their own money for AI review. As insurance reimbursement for AI languishes, this may be the only way to move mammography AI forward in the short term.

High-Risk Breast Clinics: A Smart Move for Imaging Providers

Posted on by Partner Content

High-risk breast cancer clinics are no longer just a good idea – they’re becoming a strategic imperative. These programs, focused on identifying and managing women at elevated risk for breast cancer, are proving their value clinically and financially.

For imaging providers, they present an opportunity both to improve care and grow service lines in a value-based care environment, while also differentiating themselves in increasingly competitive markets. A recently published white paper offers a full explanation of the benefits of high-risk breast clinics.

Treating late-stage breast cancer is extremely costly – $76,000+ in the final year of life alone – and it represents a major portion of oncology spend nationwide. 

  • By identifying high-risk patients early and offering enhanced surveillance with breast MRI, clinics can diagnose more cancers at early stages, when treatment is more effective and less expensive. 

Studies show MRI screening in BRCA1 carriers is cost-effective at ~$50,900 per QALY. 

  • This makes it a smart investment from both a patient and payor perspective.

Historically, preventive programs were considered cost centers. Not so with high-risk breast clinics. 

  • Once a patient is flagged as high risk, the care pathway includes reimbursable   genetic counseling and testing, supplemental imaging (MRI or contrast-enhanced mammography), biopsies, chemoprevention, and even risk-reducing surgeries. Each step creates downstream revenue for imaging centers and affiliated specialists – all while improving patient care.

Integration is key. Embedding risk assessment tools like Tyrer-Cuzick or AI-based models (e.g. Mirai) into the high-risk clinic’s imaging workflow enables automatic triage. 

  • Patients with ≥20% lifetime risk can be directly referred to the high-risk clinic. Some models now use short-term risk from imaging data alone to identify the top 5-10% women most likely to develop cancer within five years – significantly outperforming traditional tools in clinical studies.

Successful clinics rely on multidisciplinary teams. Advanced-practice providers manage most visits. Genetic counselors – in person or via telehealth – manage testing results and family history. Patient navigators coordinate follow-ups and authorizations. 

  • This team-based approach keeps physician time focused and costs under control, ensuring the clinic operates efficiently and sustainably.

The Takeaway

For imaging providers, high-risk breast clinics offer a powerful blend of patient impact and business growth. They reduce expensive late-stage cancers, drive high-value imaging, and create long-term patient relationships. In an era of value-based care, they’re not just a clinical upgrade – they’re a strategic advantage. Forward-thinking imaging leaders are recognizing this model as essential to the future of preventive breast care.

Has Breast Cancer Mortality Bottomed Out?

Posted on by Brian Casey

The decades-long decline in breast cancer mortality has been lauded as a major public health success story. But a new study in Journal of Breast Imaging suggests that the long decline in breast cancer death rates may be coming to an end, at least for some women.

Breast cancer mortality’s drop has been well-documented, with studies estimating the drop to range between 44% to 58% over the last three to four decades – saving at least 500k lives. 

  • Most experts believe the breast cancer mortality decline has been driven by a combination of organized mammography screening and better cancer treatments.

But amid the success are disturbing signs. Cancer incidence rates are increasing for women younger than 40 – the established starting age for screening. 

  • Mammography screening also has seen disparities in care that have resulted in higher incidence and death rates for women of color. 

In the new study, researchers examined U.S. data for breast cancer mortality from 1990 to 2022, finding that over the study period breast cancer mortality …

  • Fell by 44% for women of all ages and ethnicities over the full study period.
  • Decreased by -1.7% to -3.3% annually from 1990 to 2010, but the decline slowed to -1.2% a year from 2010 to 2022. 
  • Declined -2.8% per year for women 20-39 years old from 1990-2010, but showed no decline from 2010-2022.
  • Lowered by -1.3% per year for women older than 75 from 1993-2014, but showed no decline from 2013-2022. 
  • Declined for White and Black women of all ages, but not for Asian, Hispanic, and Native American women.
  • Was 39% higher for Black women compared to White women from 2004-2022.   

The authors acknowledge that much of their data pertain to women who are outside current screening guidelines. 

  • But they see this as an opportunity to revisit whether screening guidelines should be extended – especially to women 75 and older – to realize the benefits of early breast cancer detection. 

The Takeaway

The new findings on breast cancer mortality indicate that even as mammography’s successes are celebrated, more work remains to be done to ensure that breast screening’s benefits are enjoyed by as many women as possible. 

Bridging Quality and Efficiency: Why Radiology Groups Are Adopting AI for Mammography Workflows

Posted on by Partner Content

By Dr. Roger Yang, President, University Radiology Group, and Mo Abdolell, CEO, Densitas

Radiology groups offering mammography services operate under ever-tightening demands, including MQSA EQUIP and ACR accreditation standards. Manual case selection, cumbersome paperwork, and lengthy review cycles often divert radiologists and technologists from what matters most – patient care.

But change is coming. By leveraging AI and mammography workflow automation, private radiology groups are reshaping how they manage quality, reduce administrative overhead, and advance patient care. 

AI-powered platforms can significantly streamline mammography quality management by:

  • Automating case selection for EQUIP reviews.
  • Measuring positioning metrics in near real-time.
  • Centralizing documentation to simplify compliance.

Some practices have reported up to a 90% reduction in EQUIP review time and 80% workload reduction in ACR accreditation using AI. But time savings are only part of the story.

Rather than waiting months for sporadic audits, technologists gain instant insights into positioning accuracy. This rapid feedback loop…

  • Accelerates targeted training.
  • Encourages continuous quality improvement.
  • Empowers technologists to self-monitor performance and identify gaps earlier. 

Today’s vendor-agnostic AI solutions integrate seamlessly with diverse imaging systems across multiple sites. 

  • Standards-based platforms can grow from a single mammography unit to dozens, helping radiology groups expand without adding complexity.

In a crowded marketplace, radiology practices that adopt AI-driven mammography quality management and automation stand out as forward-thinking leaders. Advantages include…

  • Enhancing patient perception: Offering efficient exams and high-quality imaging underscores a commitment to excellence, boosting satisfaction and referrals.
  • Leveraging analytics: Aggregated data on image quality and positioning helps leadership identify trends, optimize workflows, and highlight innovation.
  • Attracting top talent: Skilled technologists and radiologists gravitate toward practices with cutting-edge tools.

By integrating AI early, private practices can differentiate themselves, paving the way for growth and success.

Successful AI adoption and mammography workflow automation relies on more than just software. It requires:

  • Deep mammography expertise from vendors.
  • Robust training programs for staff.
  • Change training programs for staff.
  • Responsive customer support that fosters trust.

Mammography workflow automation cuts administrative burdens, curtails physician burnout, and speeds accreditation. Technologists receive clear, timely feedback, improving morale and performance. 

  • Meanwhile, patients benefit from streamlined workflows and consistent image quality, reinforcing trust in the practice.

The Takeaway

By embracing AI-driven mammography workflow automation and quality management, radiology groups can stay focused on delivering exceptional patient care while meeting regulatory requirements. This strategic investment propels private practices toward sustained growth and innovation, securing a competitive edge in a rapidly evolving healthcare landscape. Learn more.

Mammography Rates Fall for Women in 40s

Posted on by Brian Casey

A new study on mammography screening confirms the worst fears of women’s health advocates: screening rates fell for women ages 40-49 after the USPSTF in 2009 withdrew its recommendation that younger women get biennial screening.

Breast screening has long been the most controversial cancer screening exam, with screening’s opponents claiming that its “harms” – such as breast biopsies and overdiagnosis – don’t justify its benefits.

  • The anti-mammography wave crested in 2009 when the USPSTF withdrew its screening recommendation for women ages 40-49 and older than 75, instead advising them to consult with their physicians. 

The change prompted confusion and anger that persisted until the task force in 2024 rescinded the 2009 guidance and returned to a broad recommendation in favor of biennial screening for women in their 40s (screening still isn’t recommended for women over 74).

  • This left the breast imaging community pondering the impact that 15 years of the more restrictive guidance had on breast screening rates.

Researchers address that question in a new study in JAMA Network Open, in which they analyzed screening records for 1.6M women, finding the probability of getting a biennial mammogram …

  • Fell -1.1 percentage points for all women ages 40-49.
  • Fell -3 percentage points for non-Hispanic Black women 40-49, the biggest decline among younger women.
  • Fell -4.8 percentage points for all women 75 years and older.
  • Fell -6.2 percentage points for Hispanic women over age 75, the biggest decline among all age groups.

The new research confirms other studies finding that the USPSTF 2009 guidance led to a small – but statistically significant – decline in overall breast screening rates. 

  • What’s new is its discovery of demographic variations in the magnitude of the change, an important finding given recent studies showing that Black women have a 39% higher breast cancer mortality rate. 

In fact, rising cancer risk in Black women was cited by the USPSTF as one of its reasons for changing its guidance in 2024. 

  • The USPSTF estimated that lowering screening’s starting age to 40 would avert 1.8 additional deaths per 1k Black women screened every two years

The Takeaway

Hopefully, we’ve seen the end of the “mammography wars” that led to the USPSTF’s 2009 guideline change. A better future is one in which breast screening decisions are made with consideration for factors like cancer risk in addition to just age.

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