Tag: breast screening

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.

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.

US + Mammo vs. Mammo + AI for Dense Breasts

Posted on by Brian Casey

Artificial intelligence may represent radiology’s future, but for at least one clinical application traditional imaging seems to be the present. In a new study in Radiology, ultrasound was more effective than AI for supplemental imaging of women with dense breast tissue. 

Dense breast tissue has long presented problems for breast imaging specialists. 

  • Women with dense breasts are at higher risk of breast cancer, but traditional screening modalities like X-ray mammography don’t work very well (sensitivity of 30-48%), creating the need for supplemental imaging tools like ultrasound and MRI.

In the new study, researchers from South Korea tested the use of Lunit’s Insight MMG mammography AI algorithm in 5.7k women without symptoms who had breast tissue classified as heterogeneously (63%) or extremely dense (37%). 

  • AI’s performance was compared to both mammography alone as well as to mammography with ultrasound, one of the gold-standard modalities for imaging women with dense breasts. 

All in all, researchers found …

  • Mammography with AI had lower sensitivity than mammography with ultrasound but slightly better than mammography alone (61% vs. 97% vs. 58%)
  • Mammography with AI had a lower cancer detection rate per 1k women but higher than mammography alone (3.5 vs. 5.6 vs. 3.3)
  • Mammography with AI missed 12 cancers detected with mammography with ultrasound
  • Mammography with AI had the highest specificity (95% vs. 78% vs. 94%)
  • And the lowest abnormal interpretation rate (5% vs. 23% vs. 6%)

The results show that while AI can help radiologists interpret screening mammography for most women, at present it can’t compensate for mammography’s low sensitivity in women with dense breast tissue.

In an editorial, breast radiologists Gary Whitman, MD, and Stamatia Destounis, MD, observed that supplemental imaging of women with dense breasts is getting more attention as the FDA prepares to implement breast density notification rules in September. 

  • They recommended follow-up studies with other AI algorithms, more patients, and a longer follow-up period. 

The Takeaway

As with a recent study on AI and teleradiology, the current research is a good step toward real-world evaluation of AI for a specific use case. While AI in this instance didn’t improve mammography’s sensitivity in women with dense breast tissue, it could carve out a role reducing false positives for these women who get mammography and ultrasound.

US Tomo for Dense Breasts

Posted on by Brian Casey

What’s the best way to provide supplemental imaging when screening women with dense breasts? A new study this week in Radiology offers support for a newer method, whole-breast ultrasound tomography. 

It’s well-known by now that dense breast tissue presents challenges to traditional X-ray-based mammography.

  • In fact, mammography screening’s mortality reduction is far lower in women with dense breasts compared to nondense breasts (13% vs. 41%). 

A variety of alternative technologies have been developed to provide supplemental imaging for women with dense breasts, from handheld ultrasound to breast MRI to molecular breast imaging. 

  • One supplemental technology is whole-breast tomography, developed by Delphinus Medical Technologies; the firm’s SoftVue 3D system was approved by the FDA in 2021 as an adjunct to full-field digital mammography for screening women with dense breast tissue. 

With SoftVue, women lie prone on a table with the breast stabilized in a water-filled chamber that provides coupling of sound energy between the breast and a ring transducer that scans the entire breast in 2-4 minutes.

  • Unlike handheld ultrasound, the scanner provides volumetric coronal images that provide a better view of the fat-glandular interface, where many cancers are located.

SoftVue’s performance was analyzed by researchers from USC and the University of Chicago in a retrospective study funded by Delphinus. 

  • They performed SoftVue scans along with digital mammography on 140 women with dense breast tissue from 2017 to 2019; 36 of the women were eventually diagnosed with cancer. 

In all, 32 readers interpreted the scans, comparing the performance of FFDM with ultrasound tomography to FFDM alone, finding … 

  • Better performance with FFDM + ultrasound tomography (AUC=0.60 vs. 0.54)
  • An increase in sensitivity in women with mammograms graded as BI-RADS 4 (suspicious), (37% vs. 30%) 
  • No statistically significant difference in sensitivity in BI-RADS 3 cases (probably benign), (40% vs. 33%, p=0.08)
  • A mean of 3.3 more true-positive and 0.9 false-negative findings per reader with ultrasound tomography, a net gain of 2.4

The Takeaway

The findings indicate that ultrasound tomography could become a new supplementary tool for imaging women with dense breasts. They are also a shot in the arm for Delphinus, which as a smaller vendor has the challenge of competing with large multinational OEMs that also offer technologies for supplemental breast screening. 

Fine-Tuning AI for Breast Screening

Posted on by Brian Casey

AI has shown in research studies it can help radiologists interpret breast screening exams, but for routine clinical use many questions remain about the optimal AI parameters to catch the most cancers while generating the fewest callbacks. Fortunately, a massive new study out of Norway in Radiology: Artificial Intelligence provides some guidance. 

Recent research such as the MASAI trial has already demonstrated that AI can help reduce the number of screening mammograms radiologists have to review, and for many low-risk cases eliminate the need for double-reading, which is commonplace in Europe. 

  • But growing interest in breast screening AI is tempered by the field’s experience with computer-aided detection, which was introduced over 20 years ago but generated many false alarms that slowed radiologists down. 

Fast forward to 2024. The new generation of breast AI algorithms seems to have addressed CAD’s shortcomings, but it’s still not clear exactly how they can best be used. 

  • Researchers from Norway’s national breast screening program tested one mammography AI tool – Lunit’s Insight MMG – in a study with data obtained from 662k women screened with 2D mammography from 2004 to 2018. 

Researchers tested AI with a variety of specificity and sensitivity settings based on AI risk scores; in one scenario, 50% of the highest risk scores were classified as positive for cancer, while in another that threshold was set to 10%. The group found …

  • At the 50% cutoff, AI would correctly identify 99% of screen-detected cancers and 85% of interval cancers. 
  • At the 10% cutoff, AI would detect 92% of screen-detected cancers and 45% of interval cancers 
  • AI understandably performed better in identifying false-positive cases as negative at the 10% threshold than 50% (69% vs. 17%)
  • AI had a higher AUC than double-reading for screen-detected cancers (0.97 vs. 0.88)

How generalizable is the study? It’s worth noting that the research relied on AI of 2D mammography, which is prevalent in Europe (most mammography in the US employs DBT). In fact, Lunit is targeting the US with its recently cleared Insight DBT algorithm rather than Insight MMG. 

The Takeaway

As with MASAI, the new study offers an exciting look at AI’s potential for breast screening. Ultimately, it may turn out that there’s no single sensitivity and specificity threshold at which mammography AI should be set; instead, each breast imaging facility might choose the parameters they feel best suit the characteristics of their radiologists and patient population. 

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