Category: Breast Imaging

Can AI Direct Breast MRI?

Posted on by Brian Casey

A deep learning algorithm trained to analyze mammography images did a better job than traditional risk models in predicting breast cancer risk. The study shows the AI model could direct the use of supplemental screening breast MRI for women who need it most. 

Breast MRI has emerged (along with ultrasound) as one of the most effective imaging modalities to supplement conventional X-ray-based mammography. Breast MRI performs well regardless of breast tissue density, and can even be used for screening younger high-risk women for whom radiation is a concern. 

But there are also disadvantages to breast MRI. It’s expensive and time-consuming, and clinicians aren’t always sure which women should get it. As a result, breast MRI is used too often in women at average risk and not often enough in those at high risk. 

In the current study in Radiology, researchers from MGH compared the Mirai deep learning algorithm to conventional risk-prediction models. Mirai was developed at MIT to predict five-year breast cancer risk, and the first papers on the model emerged in 2019; previous studies have already demonstrated the algorithm’s prowess for risk prediction. 

Mirai was used to analyze mammograms and develop risk scores for 2.2k women who also received 4.2k screening breast MRI exams from 2017-2020 at four facilities. Researchers then compared the performance of the algorithm to traditional risk tools like Tyrer-Cuzick and NCI’s Breast Cancer Risk Assessment (BCRAT), finding that … 

  • In women Mirai identified as high risk, the cancer detection rate per 1k on breast MRI was far higher compared to those classified as high risk by Tyrer-Cuzick and BCRAT (20.6 vs. 6.0 & 6.8)
  • Mirai had a higher PPV for predicting abnormal findings on breast MRI screening (14.6% vs. 5.0% & 5.5%)
  • Mirai scored higher in PPV of biopsies recommended (32.4% vs. 12.7% & 11.1%) and PPV for biopsies performed (36.4% vs. 13.5% & 12.5%)

The Takeaway
Breast imaging has become one of the AI use cases with the most potential, based on recent studies like PERFORMS and MASAI, and the new study shows Mirai could be useful in directing women to breast MRI screening. Like the previous studies, the current research is pointing to a near-term future in which AI and deep learning can make breast screening more accurate and cost-effective than it’s ever been before. 

Tipping Point for Breast AI?

Posted on by Brian Casey

Have we reached a tipping point when it comes to AI for breast screening? This week another study was published – this one in Radiology – demonstrating the value of AI for interpreting screening mammograms. 

Of all the medical imaging exams, breast screening probably could use the most help. Reading mammograms has been compared to looking for a needle in a haystack, with radiologists reviewing thousands of images before finding a single cancer. 

AI could help in multiple ways, either at the radiologist’s side during interpretation or by reviewing mammograms in advance, triaging the ones most likely to be normal while reserving suspicious exams for closer attention by radiologists (indeed, that was the approach used in the MASAI study in Sweden in August).

In the new study, UK researchers in the PERFORMS trial compared the performance of Lunit’s INSIGHT MMG AI algorithm to that of 552 radiologists in 240 test mammogram cases, finding that …

  • AI was comparable to radiologists for sensitivity (91% vs. 90%, P=0.26) and specificity (77% vs. 76%, P=0.85). 
  • There was no statistically significant difference in AUC (0.93 vs. 0.88, P=0.15)
  • AI and radiologists were comparable or no different with other metrics

Like the MASAI trial, the PERFORMS results show that AI could play an important role in breast screening. To that end, a new paper in European Journal of Radiology proposes a roadmap for implementing mammography AI as part of single-reader breast screening programs, offering suggestions on prospective clinical trials that should take place to prove breast AI is ready for widespread use in the NHS – and beyond. 

The Takeaway

It certainly does seem that AI for breast screening has reached a tipping point. Taken together, PERFORMS and MASAI show that mammography AI works well enough that “the days of double reading are numbered,” at least where it is practiced in Europe, as noted in an editorial by Liane Philpotts, MD. 

While double-reading isn’t practiced in the US, the PERFORMS protocol could be used to supplement non-specialized radiologists who don’t see that many mammograms, Philpotts notes. Either way, AI looks poised to make a major impact in breast screening on both sides of the Atlantic.

Mammography AI’s Leap Forward

Posted on by Brian Casey

A new study out of Sweden offers a resounding vote of confidence in the use of AI for analyzing screening mammograms. Published in The Lancet Oncology, researchers found that AI cut radiologist workload almost by half without affecting cancer detection or recall rates.

AI has been promoted as the technology that could save radiology from rising imaging volumes, growing burnout, and pressure to perform at a higher level with fewer resources. But many radiology professionals remember similar promises made in the 1990s around computer-aided detection (CAD), which failed to live up to the hype.

Breast screening presents a particular challenge in Europe, where clinical guidelines call for all screening exams to be double-read by two radiologists – leading to better sensitivity but also imposing a higher workload. AI could help by working as a triage tool, enabling radiologists to only double-read those cases most likely to have cancer.

In the MASAI study, researchers are assessing AI for breast screening in 100k women in a population-based screening program in Sweden, with mammograms being analyzed by ScreenPoint’s Transpara version 1.7.0 software. In an in-progress analysis, researchers looked at results for 80k mammography-eligible women ages 40-80. 

The Transpara software applies a 10-point score to mammograms; in MASAI those scored 1-9 are read by a single radiologist, while those scored 10 are read by two breast radiologists. This technique was compared to double-reading, finding that:

  • AI reduced the mammography reading workload by almost 37k screening mammograms, or 44%
  • AI had a higher cancer detection rate per 1k screened participants (6.1 vs. 5.1) although the difference was not statistically significant (P=0.052)
  • Recall rates were comparable (2.2% vs. 2.0%)

The results demonstrate the safety of using AI as a triage tool, and the MASAI researchers plan to continue the study until it reaches 100k participants so they can measure the impact of AI on detection of interval cancers – cancers that appear between screening rounds.

The Takeaway

It’s hard to overestimate the MASAI study’s significance. The findings strongly support what AI proponents have been saying all along – that AI can save radiologists time while maintaining diagnostic performance. The question is the extent to which the MASAI results will apply outside of the double-reading environment, or to other clinical use cases.

Breast Ultrasound Gets Wearable

Posted on by Brian Casey

Wearable devices are all the rage in personal fitness – could wearable breast ultrasound be next? MIT researchers have developed a patch-sized wearable breast ultrasound device that’s small enough to be incorporated into a bra for early cancer detection. They described their work in a new paper in Science Advances.

This isn’t the first use of wearable ultrasound. In fact, earlier this year UCSD researchers revealed their work on a wearable cardiac ultrasound device that obtains real-time data on cardiac function. 

The MIT team’s concept expands the idea into cancer detection. They took advantage of previous work on conformable piezoelectric ultrasound transducer materials to develop cUSBr-Patch, a one-dimensional phased-array probe integrated into a honeycomb-shaped patch that can be inserted into a soft fabric bra. 

The array covers the entire breast surface and can acquire images from multiple angles and views using 64 elements at a 7MHz frequency. The honeycomb design means that the array can be rotated and moved into different imaging positions, and the bra can even be reversed to acquire images from the other breast. 

The researchers tested cUSBr-Patch on phantoms and a human subject, and compared it to a conventional ultrasound scanner. They found that cUSBr-Patch:

  • Had a field of view up to 100mm wide and an imaging depth up to 80mm
  • Achieved resolution comparable to conventional ultrasound
  • Detected cysts as small as 30mm in the human volunteer, a 71-year-old woman with a history of breast cysts
  • The same cysts were detected with the array in different positions, an important capability for long-term monitoring

The MIT researchers believe that wearable breast ultrasound could detect early-stage breast cancer, in cases such as high-risk people in between routine screening mammograms. 

The researchers ultimately hope to develop a version of the device that’s about the size of a smartphone (right now the array has to be hooked up to a conventional ultrasound scanner to view images). They also want to investigate the use of AI to analyze images.

The Takeaway

It’s still early days for wearable breast ultrasound, but the new results are an exciting development that hints of future advances to come. Wearable breast ultrasound could even have an advantage over other wearable use cases like cardiac monitoring, as it doesn’t require continuous imaging during the user’s activities. Stay tuned.

Taking Ultrasound Beyond Breast Density

Posted on by Brian Casey

When should breast ultrasound be used as part of mammography screening? It’s often used in cases of dense breast tissue, but other factors should also come into play, say researchers in a new study in Cancer. 

Conventional X-ray mammography has difficulties when used for screening women with dense breast tissue, so supplemental modalities like ultrasound and MRI are called into play. But focusing too much on breast density alone could mean that many women who are at high risk of breast cancer don’t get the additional imaging they need.

To study this issue, researchers analyzed the risk of mammography screening failures (defined as interval invasive cancer or advanced cancer) in ~825k screening mammograms in ~377k women, and more than ~38k screening ultrasound studies in ~29k women. All exams were acquired from 2014 to 2020 at 32 healthcare facilities across the US.

Researchers then compared the mammography failure rate in women who got ultrasound and mammography to those who got mammography alone. Their findings included: 

  • Ultrasound was appropriately targeted at women with heterogeneously or extremely dense breasts, with 95.3% getting scans
  • However, based on their complete risk factor profile, women with dense breasts who got ultrasound had only a modestly higher risk of interval breast cancer compared to women who only got mammography (23.7% vs. 18.5%) 
  • More than half of women undergoing ultrasound screening had low or average risk of an interval breast cancer based on their risk factor profile, despite having dense breasts
  • The risk of advanced cancer was very close between the two groups (32.0% vs. 30.5%), suggesting that a large fraction of women at risk of advanced cancer are getting only mammography screening with no supplemental imaging

The Takeaway 

On the positive side, ultrasound is being widely used in women with dense breast tissue, indicating success in identifying these women and getting them the supplemental imaging they need. But the high rate of advanced cancer in women who only received mammography indicates that consideration of other risk factors – such as family history of breast cancer and body mass index – is necessary beyond just breast tissue density to identify women in need of supplemental imaging. 

A New Day for Breast Screening

Posted on by Brian Casey

In a breathtaking about-face, the USPSTF said it would reverse 14 years of guidance in breast screening and lower its recommended starting age for routine mammography to 40.

In a proposed guidance, USPSTF said it would recommend screening for women every other year starting at age 40 and continuing through 74. The task force called for research into additional screening with breast ultrasound or MRI for women with dense breasts, and on screening in women older than 75.

The move will reverse a policy USPSTF put in place in 2009, when it withdrew its recommendation that all women start screening at 40, instead advising women in their 40s to consult with their physicians about starting screening. Routine mammography was advised starting at age 50. The move drew widespread condemnation from women’s health advocates, but the USPSTF stuck to the policy even through a 2016 revision.

The task force remained steadfast even as studies showed that the 2009 policy change led to confusion and lower breast screening attendance. The change also gave fuel to anti-mammography extremists who questioned whether any breast screening was a good idea.

That all changes now. In its announcement of the 2023 guidance, USPSTF said it based the new policy on its review of the 2016 update. No new RCTs on breast screening have been conducted for decades (it’s considered unethical to deny screening to women in a control group), so the task force commissioned collaborative modeling studies from CISNET.

USPSTF said the following findings factored into its decision to change the guidance: 

  • Biennial screening from 40-74 would avert 1.3 additional breast cancer deaths per 1,000 women screened compared to biennial screening of women 50-74.
  • The benefits of screening at 40 would be even greater for Black women, at 1.8 deaths averted. 
  • The incidence rate of invasive breast cancer for women 40-49 has increased 2.0% annually from 2015-2019, a higher rate than in previous years. 
  • Biennial screening results in greater incremental life-years gained and mortality reduction per mammogram and better balance of benefits to harms compared to annual screening.

The Takeaway 

As with the FDA’s recent decision to require density reporting nationwide, the USPSTF’s proposal to move the starting age for mammography screening to 40 was long overdue. The question now is how long it will take to repair 14 years of lost momentum and eliminate confusion about breast screening.

Learning Curve in DBT Screening

Posted on by Brian Casey

Digital breast tomosynthesis continues to evolve. First introduced initially as a problem-solving tool in breast imaging, DBT is becoming the workhorse modality for breast screening as well. 

But DBT still requires some adjustment when used for screening. In a study of nearly 15k women in European Radiology, Swedish researchers describe how the false-positive recall rate for DBT cancer screening started higher but then fell over time as radiologists got used to the appearance of lesions on DBT exams.

The Malmö Breast Tomosynthesis Screening Trial was set up to compare one-view DBT to two-view digital mammography for breast screening. Unlike some DBT screening trials, the study did not use synthesized 2D DBT images. DBT images were acquired 2010-2015 with Siemens Healthineers’ Mammomat Inspiration system. 

Findings in the study included: 

  • DBT had a sharply higher false-positive recall rate in year 1 of the study compared to DM (2.6% vs. 0.5%)
  • DBT’s recall rate fell over the five-year course of the study, stabilizing at 1.5% 
  • Recall rates for DM varied between 0.5% and 1% over five years
  • Most of the DBT recalls (37.3%) were for stellate lesions, in which spicules radiate out from a central point or mass. With DM, only 24.0% of recalls were for stellate lesions
  • The number of stellate distortions being recalled with DBT declined over time, a trend the authors attributed to a learning curve in reading DBT images

The authors said that the DBT false-positive recall rate in their study was “in general low” compared to other European trials. They claimed that MBTST is among the first studies to analyze recall rates by lesion appearance, an important point because radiologists may see a different distribution of lesion types on screening DBT compared to what they’re used to with DM.

The Takeaway 

The Malmö Breast Tomosynthesis Screening Trial was one of the first to investigate DBT for breast screening, and previous MBTST research showed that DBT can also reduce interval cancers, which occur between screening rounds. 

The new findings offer further support for DBT breast screening and give hope that whatever shortcomings the technology might have early on in a screening role can be addressed through training and experience. It also confirms recent research indicating that DBT has become the new gold standard for breast screening.

ABUS Boosts Breast Screening

Posted on by Brian Casey

Automated breast ultrasound led to sharp increases in cancer detection rates and sensitivity when it was performed as a supplement to screening digital mammography in a study of Asian women. 

In Radiology, researchers from South Korea explain the shortcomings of X-ray-based mammography, which has limited sensitivity in women with dense breast tissue. Handheld ultrasound can be used as a screening supplement, but it has drawbacks of its own, such as longer exam time and operator variability. 

ABUS has been proposed as an alternative, acquiring 3D volumes of the entire breast in an automated mode that’s more structured and standardized. ABUS also provides coronal-plane images that can help differentiate malignant from benign lesions.

But most of the studies validating ABUS have been conducted on Western women, and Asian women tend to have mammographically denser breasts.

So researchers decided to test ABUS as a supplement to digital mammography with 2,301 South Korean women who were screened from 2018 to 2019. Women were first screened with digital mammography (either Hologic’s Selenia Dimensions or Siemens Healthineers’ Mammomat Revelation), then received ABUS scans with GE HealthCare’s Invenia ABUS system. 

For women with dense breasts, screening with ABUS and DM turned in better performance than DM alone in multiple categories, including:

  • Higher cancer detection rate per 1,000 screening exams (9.3 vs. 6.5)
  • Better sensitivity (90.9% vs. 63.6%)
  • Higher AUC (0.89 vs. 0.79)
  • Detection of smaller cancers, with a mean size of 1.2 cm vs. 2.3 cm

On the down side, ABUS + DM in women with dense breasts had lower specificity (86.8% vs. 94.6%), driving higher biopsy rates (3.3% vs. 1.9%) and false-positive biopsy rates (2.4% vs. 1.3%).

The Takeaway

In a time when breast cancer inequities are under the microscope, the new study provides encouraging news that imaging technology can help compensate for the shortcomings of the traditional “one size fits all” paradigm of breast screening. 

The results are also a shot in the arm for ABUS as it seeks to cement a role as a complement to X-ray-based screening mammography, although work remains to be done in improving specificity and recall rates.

Health Inequity & Breast Cancer

Posted on by Brian Casey

The last several years have seen growing awareness of how structural inequities can impact individual health outcomes. Two powerful new JAMA Network Open studies reinforced what we know about structural inequity, particularly as it relates to breast cancer. 

In the first study on April 19 addressing racial differences in breast cancer mortality, researchers looked at over 415k women from 2011 to 2020, finding:

  • Black women between 40 and 49 years old had the highest breast cancer mortality rates per 100,000 person years, at 27 deaths. This compares to 15 deaths for White women, and 11 deaths for other ethnicities.
  • If breast screening were tailored based on risk at age 50, Black women should start screening eight years earlier than White women, at 42 years of age versus 51. 
  • Biennial mammography screening of Black women starting at age 40 would reduce the gap in breast cancer mortality compared to White women by 57%. 

In the second study on April 21, researchers drilled even deeper into structural inequity, focusing on breast cancer outcomes in disadvantaged neighborhoods in a large, racially diverse region in southern Florida that’s home to 6.2M people. 

In all, their study covered 5,027 women with breast cancer, and they categorized neighborhoods into three levels based on socioeconomic status. Findings included:

  • Patients living in the second most disadvantaged neighborhoods were 36% more likely to die of breast cancer (HR=1.36).  
  • Women living in the most disadvantaged neighborhoods were 77% more likely to die (HR=1.77).

The researchers pointed out that their results went beyond merely linking race to health outcomes, as they adjusted for race and ethnicity “as a proxy for structural racism.” They suggested that there could be “unaccounted,” biologic mechanisms related to neighborhood disadvantage that lead to shorter breast cancer survival. The findings echo other studies that have linked patient location to access to imaging.

The Takeaway

Over the past several decades, breast cancer’s dropping mortality rate has been a health policy success story. But the new studies indicate that progress has been uneven, and more attention is needed to ensure that the benefits of improved breast cancer diagnosis and treatment are distributed more equitably.

Breast Screening’s New Gold Standard?

Posted on by Brian Casey

A new study in Radiology on the use of digital breast tomosynthesis for breast screening makes the case that DBT has so many advantages over conventional 2D digital mammography that it should be considered the gold standard for breast screening. 

Unlike 2D mammography, DBT systems scan around the breast in an arc, acquiring multiple breast images that are combined into 3D volumes. The technique is believed to be more effective in revealing pathology that might be obscured on 2D projections.

Previous research already demonstrated the effectiveness of DBT for certain uses, but the new study is notable for its large patient population, as well as its focus on general screening rather than subgroups like women with cancer risk factors such as dense breast tissue.

Researchers led by Dr. Emily Conant of the University of Pennsylvania reviewed DBT’s performance in five large U.S. healthcare systems, with a total study population of over 1 million women. 

The advantages of DBT were notable:

  • Higher cancer detection rate: 5.5 vs. 4.5 per 1k women screened
  • Lower recall rate:  8.9% vs. 10.3%
  • Higher recall PPV: 5.9% vs. 4.3%.

On the negative side, DBT had higher biopsy rates, of 17.6 biopsies per 1,000 women versus 14.5 biopsies for 2D digital mammography. But PPV of biopsy for both techniques was largely the same. 

Researchers note that breast cancer mortality rates have fallen 41% since 1989, a development attributed to earlier diagnosis and better treatment. DBT could help accelerate this trend as it finds more cancers relative to 2D digital mammography.

The Takeaway

This study reinforces the idea that DBT is now the gold standard for breast screening. While mammography vendors have already seen high market penetration for DBT systems, the new study is likely to convince any remaining holdouts that 3D mammography is a necessary technology for any breast imaging facility. 

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