Category: Breast Imaging

Mobile Mammography’s Value

Posted on by Brian Casey

Despite the proven value of breast screening, compliance rates still aren’t as high as they should be. A new study in Clinical Breast Cancer shows how mobile mammography can improve screening adherence – especially among groups traditionally underserved in the healthcare system.

Estimates of mammography compliance vary – the American Cancer Society estimates that the overall U.S. breast screening rate held steady at 64-66% from 2000 to 2018. 

  • But a variety of factors can influence screening rates, from race to income to location.

Mobile mammography is an obvious solution that brings the imaging test to women rather than requiring them to travel. 

  • But some questions have persisted about mobile screening, such as whether it might cannibalize facility-based mammography programs, which have higher fixed costs. 

In the new study, researchers from the Harvey L. Neiman Health Policy Institute reviewed CMS claims data for 2.6M eligible women from 2004 to 2021. 

Researchers found …  

  • 50% of women had received a screening mammogram.
  • Only 0.4% used mobile mammography, but rates were higher in rural areas (1%) compared to large cities (0.3%) and small towns (0.4%).
  • American Indian or Alaska Native race was the factor most predictive for receiving mobile mammography (OR=5.5).
  • Other predictive factors included residence in a rural geography (OR=3.3), as well as in a community with lower income (OR=1.4).
  • Mobile mammography did not cannibalize facility-based mammography, based on data from heat maps showing utilization of both types of service.

Researchers concluded that mobile mammography can reduce health disparities by bringing imaging technology to underserved communities that might not otherwise have access to it. 

  • The findings echo a study earlier this year in which mobile mammography was also found to benefit the environment by reducing greenhouse gas emissions that occur when patients have to travel to medical facilities for screening.

The Takeaway

It may seem like a no-brainer to bring imaging to the people who need it, but the new study provides valuable evidence that the practice works on a national scale. Increased use of mobile imaging is an important tool for addressing persistent disparities in access to care. 

Mammo AI Kicks Off RSNA 2024

Posted on by Brian Casey

Welcome to RSNA 2024! This year’s meeting is starting with a bang, with two important sessions highlighting the key role AI can play in breast screening. 

Sunday’s presentations cap a year that’s seen the publication of several large studies demonstrating that AI can improve breast cancer screening while potentially reducing radiologist workload. 

  • That momentum is continuing at RSNA 2024, with morning and afternoon sessions on Sunday dedicated to mammography AI. 

Some findings from yesterday’s morning session include … 

  • Two AI algorithms were better than one when supporting radiologists in breast screening, with cancer detection ratios relative to historic performance rising from 0.97 to 1.08 with one AI to 1.09 to 1.14 with two algorithms.
  • ScreenPoint Medical’s Transpara algorithm was able to prioritize the worklist for 57% of breast screening exams by assigning risk scores to mammograms, helping reduce report turnaround times. 
  • iCAD’s ProFound AI software helped radiologists detect 7.8% more breast cancers on DBT exams, and cancers were detected at an earlier stage. 
  • Applying AI for breast screening to a racially diverse population yielded evenly distributed performance improvements.

Meanwhile, the Sunday afternoon session also included significant mammography AI presentations, such as …

  • A hybrid screening strategy – with suspicious breast cancer cases only recalled if the AI exhibits high certainty – reduced workload 50%. 
  • Lunit’s Insight DBT AI showed potential to reduce interval cancer rates in DBT screening by identifying 27% of false-negative and 36% of interval cancers.
  • In the ScreenTrustCAD trial in Sweden, using Lunit’s Insight MMG algorithm to replace a double-reading radiologist reduced workload 50% with comparable cancer detection rates.
  • A German screening program found that ScreenPoint Medical’s Transpara AI boosted the cancer detection rate by 8.7% (from 0.68% to 0.74%), with 8.8% of cancers solely detected by AI.
  • Researchers took a look back at abnormality scores from three commercially available AI algorithms after cancer diagnosis, finding evidence that cancers could be detected earlier. 

The Takeaway

Breast screening seems to be the clinical use case where radiologists need the most help, and Sunday’s sessions show the progress AI is making toward achieving that reality. 

Be sure to check back on our X, LinkedIn, and YouTube pages for more coverage of this week’s events in Chicago. And if you see us on the floor of McCormick Place, stop and say hello!

Studies Support Breast Ultrasound for Screening

Posted on by Brian Casey

A pair of new research studies offers guidance on when and where to use ultrasound for breast screening. The publications highlight the important advances being made in one of radiology’s most versatile modalities. 

Ultrasound is used in developed countries for supplementary breast cancer screening in women who may not be suitable for X-ray-based mammography due to issues like dense breast tissue.

  • Ultrasound is also being examined as a primary screening tool in developing regions like China and Africa, where access to mammography may be limited.

But despite growing use, there are still many questions about exactly when and where ultrasound is best employed in a breast screening role – and this week’s studies shed some light. 

First up is a study in Academic Radiology in which researchers compared second-look ultrasound to mammography in women with suspicious lesions found on breast MRI. 

  • Their goal was to find the best clinical path for working up MRI-detected lesions without performing too many unnecessary biopsies. 

In a group of 221 women, second-look ultrasound was largely superior to mammography with… 

  • Higher detection rates for mass lesions (56% vs. 17%).
  • A much higher detection rate for malignant mass lesions > 10 mm (89%).
  • But worse performance with malignant non-mass lesions (22% vs. 38%).

They concluded second-look ultrasound is a great tool for assessment and biopsy of MRI-detected lesions > 10 mm without calcifications. 

  • It’s not so great for suspicious non-mass lesions, which might be better sent to mammography for further workup. 

Breast ultrasound of non-mass lesions was also the focus of a second study, this one published in Radiology. 

  • Non-mass lesions are becoming more frequent as more women with dense breast tissue get supplemental screening, but incidence and malignancy rates are low. 

So how should they be managed? In a study of 993 women with non-mass lesions found on whole-breast handheld screening ultrasound, researchers classified by odds ratios the factors indicating malignancy…

  • Associated calcifications (OR=21.6).
  • Posterior shadowing (OR=6.9).
  • Segmental distribution (OR=6.2).
  • Mixed echogenicity (OR=5.0).
  • Larger size (2.6 vs. 1.9 mm).
  • Negative mammography (2.8% vs. 29%).

The Takeaway

Ultrasound’s value comes from its high prevalence, low cost, and ease of use, but in many ways clinicians are still exploring its optimal role in breast cancer screening. This week’s research studies should help.

ABUS Flies Solo for Breast Screening

Posted on by Brian Casey

Is breast ultrasound ready for use as a primary breast screening modality – without mammography? Maybe not in developed countries, but researchers in China gave automated breast ultrasound a try, with results that are worth checking out in a new study in AJR. 

Mammography is unquestionably the primary imaging modality for first-line breast screening, with other technologies like ultrasound and MRI taking a supplemental role, such as for working up questionable cases or for women with dense breast tissue.

  • But the standard mammography-dominated paradigm might not be suitable for some resource-challenged countries that have yet to build an installed base of X-ray-based mammography systems. 

One of these countries is China, which not only has fewer mammography systems in rural areas but also has a population of women who have denser breast tissue, which can cause problems with conventional mammography. 

  • As a result, the Chinese National Breast Cancer Screening Program has adopted ultrasound as its primary screening modality, with women ages 35-69 eligible for screening breast ultrasound every 2-3 years. Mammography is reserved for additional workup. 

But handheld ultrasound has challenges of its own. It’s operator-dependent, and image interpretation requires experienced radiologists – also in short supply in some Chinese regions.

  • So the AJR researchers performed a study of 6k women who were screened with GE HealthCare’s Invenia ABUS 2.0 scanner, which uses ultrasound to scan women lying in the supine position. Images were sent via teleradiology to expert radiologists at a remote institution.

How did ABUS perform as a primary screening modality? The researchers found that after a single round of screening …

  • ABUS had a cancer detection rate of 4.0 cancers per 1k women (4.4 for women 40-69).
  • Sensitivity was 92% and specificity was 88%.
  • Abnormal interpretation rate was 12%.
  • 96% of detected cancers were invasive, and 74% were node-negative.
  • Two interval cancers were detected (rate of 0.33 per 1k).

How do the numbers compare to mammography? 

  • The cancer detection rate in the Breast Cancer Surveillance Consortium study was 5.1 cancers per 1k women, so not far off. 

The Takeaway

The results offer an interesting look at an alternative to the mammography-first breast screening paradigm used in developed countries, where ABUS is mostly used as a supplemental technology. For resource-challenged areas around the world, ABUS with teleradiology could solve multiple problems at once.

Mammography AI Predicts Cancer Before It’s Detected

Posted on by Brian Casey

A new study highlights the predictive power of AI for mammography screening – before cancers are even detected. Researchers in a study JAMA Network Open found that risk scores generated by Lunit’s Insight MMG algorithm predicted which women would develop breast cancer – years before radiologists found it on mammograms. 

Mammography image analysis has always been one of the most promising use cases for AI – even dating back to the days of computer-aided detection in the early 2000s. 

  • Most mammography AI developers have focused on helping radiologists identify suspicious lesions on mammograms, or triage low-risk studies so they don’t require extra review.

But a funny thing has happened during clinical use of these algorithms – radiologists found that AI-generated risk scores appeared to predict future breast cancers before they could be seen on mammograms. 

  • Insight MMG marks areas of concern and generates a risk score of 0-100 for the presence of breast cancer (higher numbers are worse). 

Researchers decided to investigate the risk scores’ predictive power by applying Insight MMG to screening mammography exams acquired in the BreastScreen Norway program over three biennial rounds of screening from 2004 to 2018. 

  • They then correlated AI risk scores to clinical outcomes in exams for 116k women for up to six years after the initial screening round.

Major findings of the study included … 

  • AI risk scores were higher for women who later developed cancer, 4-6 years before the cancer was detected.
  • The difference in risk scores increased over three screening rounds, from 21 points in the first round to 79 points in the third round.
  • Risk scores had very high accuracy by the third round (AUC=0.93).
  • AI scores were more accurate than existing risk tools like the Tyrer-Cuzick model.

How could AI risk scores be used in clinical practice? 

  • Women without detectable cancer but with high scores could be directed to shorter screening intervals or screening with supplemental modalities like ultrasound or MRI.

The Takeaway
It’s hard to overstate the significance of the new results. While AI for direct mammography image interpretation still seems to be having trouble catching on (just like CAD did), risk prediction is a use case that could direct more effective breast screening. The study is also a major coup for Lunit, continuing a string of impressive clinical results with the company’s technology.

Breast Cancer Mortality Falls Again

Posted on by Brian Casey

New data from the American Cancer Society highlight the remarkable strides that have been made against breast cancer, with the U.S. death rate falling 44% over the last 33 years – saving over half a million lives. But the statistics also underscore the work that remains to be done, particularly with minority women. 

The fight against breast cancer has been one of public health’s major success stories.

  • High mammography screening uptake has led to early detection of cancers that can then be treated with revolutionary new therapies. 

Much of the credit for this success goes to the women’s health movement, which has conducted effective advocacy campaigns that have led to …

But breast cancer remains the third most common killer of women after heart disease and lung cancer, and there have been disturbing trends even as the overall death rate falls. 

  • Breast cancer incidence has been rising especially in younger women, and major disparities continue to be seen, particularly with survival in Black women.

The American Cancer Society’s new report represents the group’s biennial review of breast cancer statistics, finding … 

  • In 2024 there will be 311k new cases of invasive breast cancer, 56.5k cases of DCIS, and 42.3k deaths. 
  • The breast cancer mortality rate has fallen 44% from 1989 to 2022, from 33 deaths per 100k women to 19 deaths.
  • Some 518k breast cancer deaths have been averted.
  • The mortality rate ranges from 39% higher than average for Black women to 38% lower for Asian American Pacific Islander women. 
  • The mortality rate is slightly higher than average (0.5%) for White women.
  • The average breast cancer incidence rate is 132 per 100k women, but ranges from 5% higher for White women to 21% lower for Hispanic women.
  • Women 50 years and older will account for most invasive cases (84%) and deaths (91%).

The Takeaway

As Breast Cancer Awareness Month begins, women’s health advocates should be heartened by the progress that’s been made overall. But battles remain, from eliminating patient out-of-pocket payments for follow-up studies to addressing race-based disparities in breast cancer mortality. In many ways, the fight is just beginning. 

The Cost of Extra Cancer Detection

Posted on by Brian Casey

It’s well known that using additional screening modalities beyond traditional 2D mammography can detect more cancers in women with dense breast tissue. But at what cost? A new study in Clinical Breast Cancer documents both the clinical value and the economic cost of supplemental breast imaging technologies. 

2D mammography is the basis for any breast cancer screening program, but the modality’s shortcomings are well known, especially in women with dense breasts. 

  • In fact, the FDA earlier this month began requiring breast imaging providers to notify women of their density status and explain how higher density is a breast cancer risk factor. 

Imaging vendors and clinicians have developed a range of technologies to supplement 2D mammography when needed, ranging from DBT to molecular breast imaging to breast MRI.

  • Each has its own advantages and disadvantages, which can leave many breast imaging providers confused about the best technology to use.

To shed some light, Matthew Covington, MD, of the University of Utah compared detection rates for various supplemental imaging modalities; he then estimated costs for each if it was the only modality used for supplemental imaging with 2D mammography in a U.S. population with 469k detectable breast cancers. 

  • The study assumed that 2D mammography would detect only 41% of cancers – leaving the majority undetected. 

Adding a supplemental modality boosted cancer detection rates, but also screening’s cost …

  • DBT detected 47% of all cancers at a cost of $933M
  • Ultrasound detected 51% at a cost of $1.84B
  • MBI detected 71% at a cost of $4.16B
  • Contrast-enhanced mammography detected 80% at a cost of $3.87B
  • MRI detected 100% at a cost of $6.36B

As the data indicate, MRI is clearly the most effective supplemental modality, but at a cost that’s almost 7X that of DBT. 

The Takeaway

The new data are a fascinating – if sobering – look at the intersection of clinical value and economic cost. They also highlight healthcare’s inconvenient truth: The resources needed to provide the highest-quality care are finite, regardless of whether you’re in a single-payor or fee-for-service system.

Why the FDA’s Density Rule Matters

Posted on by Brian Casey

The FDA’s new rules on reporting breast density to women getting mammograms went into effect on September 10. The implementation has been expected for some time, but this week’s rollout generated a wave of positive press coverage that highlights the importance both of breast density awareness and of breast screening.

The FDA in March 2023 said it would implement a national standard requiring providers to inform women of their breast density, which can obscure lesions on conventional X-ray mammography. 

  • Breast density is also a risk factor for cancer, and patient advocacy groups had been pressuring the FDA to set a standard to replace what has become a patchwork of state-by-state notification rules. 

The FDA’s rules have been incorporated into the Mammography Quality Standards Act, and require that … 

  • Mammography reports include a plain-language patient summary with “an overall assessment of breast density.” 
  • The summary must include specific language that defines breast density, explains its ramifications for detection and cancer risk, and suggests the need for additional imaging tests.

A novel aspect of the new rules is that they were mostly driven by patients – women like JoAnn Pushkin and the late Nancy Cappello who as patients discovered first-hand the shortcomings of X-ray-based mammography for women with dense breast tissue. 

What’s next? Density-awareness proponents are now turning their attention to reimbursement, which for supplemental imaging is inconsistent across the U.S.

  • A fix for the problem – the Find It Early Act – is working its way through Congress, and women’s health advocates lobbied on Capitol Hill this week to try to push the legislation through before the end of the current Congressional session. 

The new reporting landscape also creates opportunities for better software tools to detect and manage breast density and better predict risk in patients with dense breast tissue. 

  • Clinicians already realize that women with dense breasts not only need different screening modalities like MRI and ultrasound, but that they might also require more frequent screening due to their heightened cancer risk. 

The Takeaway

The FDA’s new breast density rules matter for a variety of reasons, from showing the power of patients to change their imaging experience to outlining a future in which risk plays a more prominent role in breast screening. While more work remains to be done, this is a good time to savor the triumph.

AI Detects Interval Cancer on Mammograms

Posted on by Brian Casey

In yet another demonstration of AI’s potential to improve mammography screening, a new study in Radiology shows that Lunit’s Insight MMG algorithm detected nearly a quarter of interval cancers missed by radiologists on regular breast screening exams. 

Breast screening is one of healthcare’s most challenging cancer screening exams, and for decades has been under attack by skeptics who question its life-saving benefit relative to “harms” like false-positive biopsies.  

  • But AI has the potential to change the cost-benefit equation by detecting a higher percentage of early-stage cancers and improving breast cancer survival rates. 

Indeed, 2024 has been a watershed year for mammography AI. 

U.K. researchers used Insight MMG (also used in the BreastScreen Norway trial) to analyze 2.1k screening mammograms, of which 25% were interval cancers (cancers occurring between screening rounds) and the rest normal. 

  • The AI algorithm generates risk scores from 0-100, with higher scores indicating likelihood of malignancy, and this study was set at a 96% specificity threshold, equivalent to the average 4% recall rate in the U.K. national breast screening program.

In analyzing the results, researchers found … 

  • AI flagged 24% of the interval cancers and correctly localized 77%.
  • AI localized a higher proportion of node-positive than node-negative cancers (24% vs. 16%).
  • Invasive tumors had higher median risk scores than noninvasive (62 vs. 33), with median scores of 26 for normal mammograms.

Researchers also tested AI at a lower specificity threshold of 90%. 

  • AI detected more interval cancers at this level, but in real-world practice this would bump up recall rates.  

It’s also worth noting that Insight MMG is designed for the analysis of 2D digital mammography, which is more common in Europe than DBT. 

  • For the U.S., Lunit is emphasizing its recently cleared Insight DBT algorithm, which may perform differently.  

The Takeaway

As with the MASAI and BreastScreen Norway results, the new study points to an exciting role for AI in making mammography screening more accurate with less drain on radiologist resources. But as with those studies, the new results must be interpreted against Europe’s double-reading paradigm, which differs from the single-reading protocol used in the U.S. 

DBT Detects Earlier Cancers in Swedish Tomo Study

Posted on by Brian Casey

A new analysis of a landmark DBT study from Sweden offers more support for the effectiveness of tomosynthesis mammography screening. Published in Radiology, researchers found that DBT screening seems to detect earlier cancers, most likely before they become more aggressive. 

Most U.S. mammography practices have embraced DBT since its approval in 2011, such that 48% of all certified mammography units are DBT and 90% of all facilities have at least one tomosynthesis unit. 

  • But doubts about DBT have persisted, particularly by mammography skeptics who charge that the technology was adopted without conducting randomized controlled trials to prove its value. 

But apart from RCTs, there have been plenty of observational studies in which DBT showed a benefit, one of them being the Malmö Breast Tomosynthesis Screening Trial of almost 15k women in Sweden.

  • First results from MBTST were published in 2018 and showed that single-view DBT screening had a 34% higher cancer detection rate per 1k women than digital mammography (8.7 vs. 6.5), but with a higher recall rate as well (3.6% vs. 2.5%).

In the new study, researchers wanted to see if DBT’s screening benefits persisted over two subsequent screening rounds with conventional digital mammography. 

  • Their assumption was that the cancer detection rate would be lower in subsequent rounds, and there would be fewer slow-growing, less aggressive cancers – a sign of early cancer detection. 

Their analysis found …

  • The cancer detection rate per 1k women was lower in the first (4.6) and second (5.3) rounds compared to the original MBTST
  • Recall rate was 2.1% – also lower 
  • The odds ratio of cancer detection was lower than MBTST in the first (OR=0.46) and second (OR=0.53) follow-up rounds 
  • Invasive cancers were less prevalent in the first round compared to the second round (66% vs. 83%) 

What do the results mean? The implication is that because DBT detected cancers in the initial screening round, there was lower cancer prevalence and less aggressive cancer in follow-up rounds, an effect that wore off as time went on.

The Takeaway

There may never be a randomized controlled trial of DBT due to the ethical problem of denying a live-saving technology to women in a control group. But studies like the MBTST follow-up are important in adding to the body of evidence showing that DBT actually does work.

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