Tag: mammography

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.

New Mammography AI Insights

Posted on by Brian Casey

Breast screening is becoming one of the most promising use cases for AI, but there’s still a lot we’re learning about it. A new study in Radiology: Artificial Intelligence revealed new insights into how well mammography AI performs in a screening environment. 

As we’ve reported in the past, mammography is one of radiology’s most challenging cancer screening exams, with radiologists sorting through large volumes of normal images before encountering a case that might be cancer.

In the new study, researchers applied Lunit’s Insight MMG algorithm to mammograms in a retrospective study of 136.7k women screened in British Columbia from 2019 to 2020. 

  • Canada uses single reading for mammography, unlike the double-reading protocols employed in the U.K. and Europe. 

AI’s performance was compared to single-reading radiologists using various metrics and follow-up periods, finding … 

  • At one-year follow-up, AI had slightly lower sensitivity (89% vs. 93%) and specificity (79% vs. 92%) compared to radiologists.
  • At two-year follow-up, there was no statistically significant difference in sensitivity between the two (83.5% vs. 84.3%, p=0.69). 
  • AI’s overall AUC at one year was 0.93, but this varied based on mammographic and demographic features, with AI performing better in cases with fatty versus dense breasts (0.96 vs. 0.84) and cases with architectural distortion (0.96 vs. 0.92) but worse in cases with calcifications (0.87 vs. 0.92).

The researchers then constructed hypothetical scenarios in which AI might be used to assist radiologists, finding …

  • If radiologists only read cases ruled abnormal by AI, it would reduce workload by 78%, but at a price of reduced sensitivity (86% vs. 93%) and 59 missed cancers across the cohort.

It’s worth noting that Insight MMG is designed to analyze 2D digital mammography exams.

The Takeaway

While the new findings aren’t a slam dunk for mammography AI, they do provide valuable insight into its performance that can inform future research, especially into areas where AI could use improvement. 

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. 

Screening Takes Center Stage at ECR 2025

Posted on by Brian Casey

New advances in cancer screening were among the major trends at last week’s ECR 2025 conference in Vienna. From traditional screening exams like mammography to up-and-coming tests like CT lung cancer exams, radiologists are emerging at the forefront of efforts to improve population health through early detection.

CT lung cancer screening is gaining momentum in Europe, and a Friday afternoon session explored the experiences of multiple sites…

  • U.K. researchers used DeepHealth’s Lung Nodules AI solution for automated triage of lung nodules found on non-screening CT chest exams, finding the approach could save £25k-£37k annually.
  • A German team documented technical lung CT acquisition parameters for screening centers in the SOLACE consortium across 10 countries, finding some room for improvement. 
  • Preliminary results from an Italian lung screening project were reported, with 2k people scanned with a 1.5% cancer detection rate (77% stage I-II) and 17% recall rate. Smoking cessation advice was also given.
  • Early results from a pilot screening project in Poland were given, with a 1.9% cancer detection rate in 3.1k people screened. They recommend screening be implemented nationwide. 
  • In a secondary analysis of 23.4k people in the NLST study, CT-derived body composition metrics predicted mortality beyond traditional risk factors.

Meanwhile, new ECR cancer screening research builds on the landmark accomplishments from 2024 in AI for breast screening. A Saturday afternoon session explored the progress being made…

  • German breast screening programs that deployed ScreenPoint Medical’s Transpara AI algorithm for 119k women saw their cancer detection rate grow (6 vs. 4.8 cancers per 1k) while the recall rate remained stable at around 2.5%. 
  • AI-supported double-reading in Italy for 120k women led to more breast cancers detected on baseline exams compared to subsequent screening rounds, as well as a 42% lower recall rate.
  • Patients found an AI chatbot based on GPT-4 generated responses to their questions that were more empathetic and readable than those of radiologists.
  • Another Italian study found that using AI for double-reading mammograms of 266k women led to a 21% increase in cancer detection rate and 15% drop in recall rate.
  • A secondary analysis of the MASAI trial suggested that double-reading with two radiologists continue to be used for high-risk women. Single reading of 3.8k high-risk exams resulted in 8.9% fewer detected cancers and 5.9% fewer recalls.

The Takeaway

Last week’s research on cancer screening at ECR 2025 shows that imaging experts see screening as a way to not only improve population health on a broad scale, but also to give radiologists the opportunity to raise their profile with patients and take a more direct role in patient care. The question is whether it’s an opportunity radiologists are ready to take.

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.

Hospital Slashes Mammography Backlog

Posted on by Brian Casey

A Michigan hospital was able to reduce its backlog of screening mammograms and speed up report turnaround time through a series of steps that included batched workflow and elimination of paper forms. Researchers describe their work in a new paper in Current Problems in Diagnostic Radiology. 

Mammography screening has always been a big challenge for breast radiologists, who typically read hundreds of normal mammograms before encountering an actual breast cancer. 

  • These challenges have only gotten worse with rising exam volumes and the well-documented shortage of radiologists, a combination that can lead to growing backlogs and longer report turnaround times. 

At the University of Michigan Health System, turnaround times for mammography reports had ballooned to 8.3 days, prompting researchers to investigate ways to make the breast imaging service more efficient. 

Study authors identified three main areas that slowed mammography TAT …

  • Interruptions during radiologist reading shifts.
  • Paper-based workflow. 
  • Cumbersome report dictation workflow.

So they developed a program called “Uninterrupted with Assistant” that eliminated the facility’s traditional reading model – eight-hour reading shifts in which radiologists were also responsible for other tasks like breast MRI and interventional procedures. 

  • Instead, they implemented four-hour shifts where radiologists batch-read mammograms without interruption. They were also aided by a clerical staff member as a “live transcriptionist” who reviewed charts and drafted pre-dictated reports in real time. 

The mammography service also ditched its paper workflow in favor of having patients complete intake forms on tablets, while technologists entered data on computers.

  • Finally, they updated their reporting to a standard template with pre-populated fields, based on FDA- and MQSA-approved verbiage. 

They then tested the Uninterrupted with Assistant program over 32 weeks in 2021, finding that during the program … 

  • Mean report turnaround time fell 39% (51 vs. 83 hours).
  • The institution’s TAT goal of less than 72 hours was achieved more often (93% vs. 35%).
  • Radiologists experienced fewer distractions (2.0 vs. 5.6 on a 10-point scale). 

The Takeaway

Batch reading isn’t new (neither is mammography worklist software), but combining the two with a ride-along assistant in the reading room creates a powerful productivity package. The Michigan model is an experience that can be emulated by other mammography centers struggling to improve efficiency and clear their backlog. 

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