Tag: breast cancer

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. 

AI for Breast Cancer Risk

Posted on by Jake Fishman

Artificial intelligence may be capable of identifying subtle mammographic signs of breast cancer years before conventional diagnosis, according to a new study published in Radiology. Researchers from Sweden found that three commercially available AI algorithms for mammography screening generated elevated cancer scores as early as 10 years before diagnosis, with detection signals strengthening as diagnosis approached.

Predicting breast cancer risk offers the prospect not only of detecting cancer earlier, but also of tailoring mammography screening to women most likely to benefit from it.

  • Clinical risk calculators like Tyrer-Cuzick and breast density analysis are available, but AI-based algorithms are showing promise by predicting risk from screening mammograms.

In the new study, researchers analyzed 89k mammograms from 31.4k women collected over a 10-year period, drawn from Sweden’s national screening program, where women aged 40-74 undergo biennial mammography interpreted by two radiologists.  

  • During the study period, 12.1k women (39%) were ultimately diagnosed with breast cancer. Three commercially available AI algorithms were used to generate risk scores (Vara AI from Vara, Lunit Insight MMG from Lunit, and MammoScreen from Therapixel). (It’s worth noting all three were originally designed for cancer detection rather than risk prediction.) 

AI scores increased progressively over time in women who later developed cancer, while remaining relatively stable among cancer-free participants…

  • At 90% specificity, AI systems flagged 19%-20% of future breast cancer cases six years before diagnosis.
  • Detection increased to 23%-25% at four years before diagnosis.
  • Performance rose further to 35%-39% at two years before diagnosis.
  • Even 10 years before diagnosis, the systems identified 13%-17% of future cancers.
  • Across all pre-diagnostic examinations, AI achieved AUC values of 0.63-0.67, outperforming mammographic density alone (AUC = 0.57).

The findings suggest that AI tools developed for cancer detection may also have value as early-alert systems for identifying women who could benefit from closer surveillance or supplemental imaging.

  • While prospective validation is still needed, sequential AI scoring may ultimately help identify women who would benefit from supplemental imaging, closer surveillance, or earlier intervention.

The Takeaway

The study adds to growing evidence that mammography AI can extend beyond cancer detection to long-term risk stratification. By identifying subtle imaging patterns years before diagnosis, AI-derived detection scores could provide an additional layer of longitudinal risk monitoring and help guide more personalized screening strategies.

AI Reduces Mammography Workload

Posted on by Brian Casey

Using AI to triage low-risk breast screening exams that don’t need extra review could remove more than three-quarters of mammography cases from radiologists’ workload and allow them to spend more time on high-risk cases. That’s according to a new study in Radiology: Artificial Intelligence that confirms other recent studies. 

Much of recent mammography AI research has focused on its ability to triage low-risk cases to avoid additional radiologist review – saving precious personnel resources.

  • This is particularly valuable in Europe, which uses a double-reading paradigm in which two radiologists review all mammography cases (the U.S. employs single readers but tends to screen women annually rather than every two years). 

The new study comes from France, which employs a slightly different paradigm from the rest of Europe. Double reading is conducted only for lower-risk BI-RADS 1 and 2 cases, while BI-RADS 3-5 go directly to diagnostic workup. 

  • As such, double reading occurs with cases that have low cancer prevalence, which can make it more difficult for radiologists to detect cancers that don’t occur very often.

But what if you offloaded low-risk double reading to AI? 

  • In the new paper, researchers tried that with Therapixel’s MammoScreen AI algorithm, which was employed retrospectively to analyze mammograms from 42.4k women acquired from 2015 to 2019.

AI results were compared to standard radiologist double reading, with the following findings…

  • AI classified 77% of cases as low-risk, meaning these could be safely triaged from the double-reading paradigm.
  • AI missed only one cancer in the low-risk group, a rate the researchers characterized as “small but measurable.” 
  • Eleven cancers were found in the group AI classified as non-low-risk, which would have undergone double reading anyway in the AI triage paradigm.
  • Rates of interval cancer (cancer that occurs between screening rounds) were 5X higher in the cases AI classified as non-low-risk compared to low-risk (2.16 vs. 0.47 cancers per 1k exams). 

Using AI to classify and remove low-risk cases from double reading could therefore save significant resources from the French mammography screening program, with a “small but non-zero risk” of missed cancers.

The Takeaway
The new results track with findings from other recent studies that apply AI to mammography screening, particularly in Europe. While the French reading paradigm is unique, it’s instructive to see that AI maintains its ability to reduce radiologist workload across different types of breast cancer screening programs.

Mammography AI Improves Breast Screening

Posted on by Brian Casey

Radiologists using a commercially available mammography AI algorithm saw improved diagnostic performance in breast cancer screening, mainly due to better specificity. The study adds to a growing body of research supporting mammography AI.

Mammography screening has been one of the most promising use cases for AI, and recent randomized controlled trials have demonstrated that AI can both improve diagnostic accuracy and speed up workflows. 

  • But RCTs are usually performed under highly controlled conditions in high-income Western countries, and the results might not be generalizable to other countries around the world. 

In the new study in Academic Radiology, researchers in Singapore tested Lunit’s Insight MMG algorithm in a retrospective review of a dataset of 302 digital mammograms that was enriched with 89 breast cancers.

  • Researchers noted that many countries have a high breast cancer incidence-to-mortality ratio due to limitations in population-based screening programs, and AI potentially could help. 

The authors focused on AI’s ability to improve the diagnostic performance of nine breast radiologists from four countries in Asia and North Africa who interpreted the mammograms, finding that AI assistance…

  • Improved radiologist accuracy as measured by AUC (from 0.799 to 0.851).
  • Generated a big jump in specificity (from 77% to 88%). 
  • And significantly reduced per-case image interpretation times (from 122 to 83 seconds per case).
  • Without changing sensitivity at a statistically significant level (83% vs. 82%, p = 0.73).

There were some subtle differences in the current study’s findings relative to previous research, some of which were the result of using a cancer-enriched dataset rather than a screening population as would be the case in an RCT.

  • The specificity improvement with AI would reduce unnecessary recalls in a population-based screening program and make mammography more cost-effective – an important consideration in countries with constrained public health budgets.

The Takeaway

The new study doesn’t have the statistical heft of a large, randomized controlled trial, but it still adds to the body of knowledge supporting AI for mammography, especially at facilities that haven’t been party to the large-scale RCTs.

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 AI Momentum Builds

Posted on by Brian Casey

Momentum is building toward routine clinical use of AI for breast cancer screening. Several new studies offer even more support for mammography AI, including research published today in Nature Medicine in which AI reduced radiologist workload by over 60% by excluding low-risk studies from human review.

Breast screening has become one of the most promising use cases for AI, with the potential to reduce radiologists’ workload while improving their ability to detect cancer. 

  • For example, the recent MASAI study found that ScreenPoint Medical’s Transpara AI algorithm could replace the second human reader in a double-reading protocol, reducing workload by 44% and improving cancer detection rates by 28%.

The new research in Nature Medicine also used Transpara, as part of the AITIC study in Spain with the goal of seeing if AI could triage low-risk studies so they don’t require review by human radiologists. 

  • AITIC had a prospective design, involving 31k women with screening exams split between 2D mammography (17k) and digital breast tomosynthesis (14k). 

Women in the control arm of the study got conventional double reading by two radiologists – the standard mammography paradigm in Europe.

  • The intervention arm used a partially autonomous AI approach: cases that AI interpreted as low risk were classified as normal and were not reviewed by radiologists, while all other cases were double-read by radiologists using AI support.

In analyzing the results, researchers found…

  • Workload in the AI arm was 64% lower than conventional double reading.
  • AI’s workload reduction was similar between DBT and conventional digital mammography (-66% and -62%, respectively).
  • The AI arm’s cancer detection rate per 1k women was 15% higher (7.3 vs. 6.3 cancers).
  • But the recall rate was also 15% higher.

It’s worth noting that the AITIC study differed from MASAI in its inclusion of DBT screening exams, whereas MASAI only included 2D digital mammography. 

  • While 2D mammography is the norm in Europe, much of the U.S. has switched to DBT for breast screening, so the AITIC results offer good news for U.S. breast imaging practices considering AI adoption.

The Takeaway

The AITIC study’s new results are powerful confirmation of findings from the recent MASAI trial and support broader clinical deployment of mammography AI. Taken together with positive findings from last week’s Nature Cancer articles (see The Wire section in this newsletter), they paint a picture of a technology that’s ready for prime time.

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.

More Positive News on Mammo AI from MASAI

Posted on by Brian Casey

The latest results from the landmark MASAI study of AI for mammography screening show a favorable trend toward reducing the rate of interval cancers, or breast cancers that appear between screening rounds. The new findings – published Friday in The Lancet – also confirm mammography AI’s sharp workload reduction and trend toward higher sensitivity. 

MASAI is a large randomized controlled trial conducted in Sweden that examined the impact of ScreenPoint Medical’s Transpara AI algorithm on breast screening.

  • It’s an important issue, because mammography is one of the radiology segments where AI can provide the most help by reducing radiologist workload while improving cancer detection.

Previous MASAI studies demonstrated that AI can reduce radiologist workload by 44% and improve cancer detection rates by 28%.

  • The findings suggest that AI could eliminate the need for double-reading of most mammograms, a practice that’s common in European screening programs.

The new findings focus specifically on interval cancers, cancers that are missed in one screening round, only to be found later. 

  • Like other MASAI studies, the patient population consisted of 106k women screened with mammography and Transpara AI in Sweden’s national program in 2021 and 2022. 

Results indicated that AI-aided mammography…

  • Cut interval cancer rates by 12% per 1k women (1.55 vs. 1.76).
  • Reduced invasive interval cancers by 16% (75 vs. 89) with 27% fewer cancers of aggressive subtypes (43 vs. 59).
  • Detected 9% more cancers at screening (81% vs. 74%) with comparable specificity (99% for both) and recall rates (1.5% vs. 1.4%).

The researchers acknowledged that the study was not powered to show a statistically significant difference in the interval cancer rate. 

  • But its positive trend indicates that AI can be used to replace double-reading without negative consequences for patients – resulting in a sharp workload reduction for radiologists. 

The Takeaway

Results from the MASAI study on mammography AI just keep on getting better. Last week’s findings indicate that there’s really no reason for European breast screening programs to not dive in and replace their second readers with AI for the majority of exams.

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