MASAI Gets Even Better at ECR 2024

One of the biggest radiology stories of 2023 was the release of impressive interim results from the MASAI study, a large-scale trial of AI for breast screening in Sweden. At ECR 2024, MASAI researchers put an emphatic cap on the conference by presenting final data indicating that AI could have an even bigger impact on mammography screening than we thought. 

If you remember, MASAI’s interim results were published in August in Lancet Oncology and showed that ScreenPoint Medical’s Transpara AI algorithm was able to reduce radiologist workload by 44% when used as part of the kind of double-reading screening program that’s common in Europe.

  • Another MASAI finding was that AI-aided screening had a 20% higher cancer detection rate than conventional double-reading with human radiologists, but the difference was not statistically significant. 

That’s all changed with the final MASAI results, presented at ECR on March 2 by senior author Kristina Lång, MD, of Lund University.

  • Lång presented data from 106k participants who were randomized to either screening with Transpara V. 1.7 or conventional double reading without AI.

Transpara triaged mammograms by giving them a risk score of 1-10, and only those classified as high risk received double reading; lower-risk mammograms got a single human reader. In the final analysis, AI-aided screening … 

  • Had a 28% higher cancer detection rate per 1k women (6.4 vs. 5.0), a difference that was statistically significant (p=0.002)
  • Detected more cancers 10-20 mm (122 vs. 79)
  • Detected more cancers of non-specific histologic type (204 vs. 155)
  • Detected 20 more non-luminal A invasive cancers and 12 more DCIS grade 3 lesions

The Takeaway

When combined with the Lancet Oncology data, the new MASAI results indicate that AI could enable breast radiologists to have their cake and eat it too: a lower workload with higher cancer detection rates. 

When Access to Screening Isn’t Enough

A new study published this week in JAMA Network Open indicates that – even when women have access to breast screening – other factors can limit mammography’s life-saving impact. Researchers found that women with more unmet social needs had lower breast screening rates and higher rates of advanced disease – even if they had access to a mammography center. 

Research into social determinants of health – the racial, demographic, and environmental factors that can affect the quality of a person’s health – have gained steam in the last several years.

In the new study, researchers noted that unmet social needs can include housing instability, social isolation, food insecurity, and transportation challenges, and these needs can occur even in high-income areas with access to screening mammography. 

  • They studied the issue in Miami-Dade County, Florida, where all women 200% below the poverty line have access to no-cost screening mammography at safety net hospitals – in theory removing cost as a barrier to breast screening.

Researchers studied 336 women who filled out a survey on social needs; of these, 62% self-identified as Hispanic, 19% as Black, and 19% as White, and 76% had screening mammograms. Researchers found a lower odds ratio for getting a mammogram due to …

  • An increasing number of unmet social needs (OR=0.74)
  • Increasing age at diagnosis (OR=0.92)

Patients were also more likely to present with late-stage disease if they …

  • Had two or more unmet social needs (33% vs. 18%)
  • Had problems with their home utilities (17% vs. 5%) or childcare access (12% vs. 3%)
  • Were presenting to a safety net hospital (31% vs. 18%)

The authors noted that although no single unmet social need was found to have a statistically significant impact on screening mammography rates, multiple needs piling up could “overwhelm” patients so they can’t find the time to schedule preventive health check-ups. 

The Takeaway

The new findings offer a more complex view of breast screening disparities beyond just access to mammograms. Public health authorities and hospitals providing women’s health services may need to offer screening of at-risk patients and a broader range of services in order to make sure that the life-saving benefits of mammography are enjoyed on a wider – and more equitable – scale.

A New Breast Imaging Option?

When it comes to mammography screening for women with dense breast tissue, radiologists have long looked for alternatives to established modalities like MRI and ultrasound. In a paper in Radiology: Imaging Cancer, researchers put a new twist on an older technology, positron emission mammography (PEM). 

Molecular imaging technologies like PEM have been investigated for years as potential adjuncts to conventional mammography due to the challenges X-ray imaging has with dense breast tissue. 

  • These technologies have carried different names – PEM, breast-specific gamma imaging, molecular breast imaging – but in the end all have fallen short due to the higher radiation dose they deliver compared to mammography. 

But Canadian startup Radialis has developed a new version of PEM with its Radialis PET Imager that drastically cuts radiation dose by targeting specific organs, enabling clinicians to use far lower doses of radiopharmaceuticals. The company received clearance for the system in 2022. 

  • Radialis touts its system as having high spatial resolution and a small field of view thanks to digital detectors with thousands of silicon sensors that can be placed next to the target organ; this makes it well-suited for imaging specific organs like the breast.

In the new paper, Canadian researchers tested the Radialis system as an adjunct to X-ray mammography in a pilot study of 25 women recently diagnosed with breast cancer. 

  • They wanted to see if PEM performed as well as breast MRI, but with fewer false positives and a radiation dose closer to screening mammography.  

Women underwent PEM at three FDG dose levels – 37, 74, or 185 MBq (for comparison, standard whole-body PET uses 370 MBq, a level that translates to a radiation exposure of 6.2-7.1 mSv). Researchers found …

  • PEM had sensitivity of 87% across all FDG dose levels (MRI was 100%)
    • The sample size was too small to detect statistically significant differences in sensitivity between dose levels
  • PEM had specificity of 95%
  • PEM detected 96% of known index malignant lesions (24 of 25), with the one miss occurring in a patient at the 37MBq level
  • PEM’s radiation dose ranged from 0.62-1.42 mSv, versus 0.44 mSv for a two-view screening digital mammogram

The Takeaway

The findings show that PEM with the Radialis system is a feasible adjunctive breast imaging modality at a radiation dose that’s mostly acceptable relative to X-ray-based mammography. But (as always) additional studies with larger patient populations are needed.

Breast Cancer in Younger Women Rises

Breast cancer rates have been rising in younger women – many of whom aren’t yet eligible for screening – and a new study in JAMA Network Open offers a perspective. 

Breast cancer mortality has dropped consistently over the last several decades, with a recent study in JAMA attributing the decline to the combination of screening and treatment. 

The problem is that even the most liberal breast screening guidelines recommend that average-risk women don’t start getting screened until age 40. 

  • This leaves younger women at risk of developing cancers that may present as more advanced disease.

The new study delves into this phenomenon, with researchers examining data from 218k women ages 20-49 who were diagnosed with invasive breast cancer from 2000-2019. Researchers found that cancer incidence …

  • Increased 0.79% annually across all women
  • Accelerated “dramatically” starting in 2016 
  • Rates per 100k women were similar for non-Hispanic Black and White women (71 & 70) across all age groups
  • But were sharply lower for Hispanic women (53)
  • Rates for Black women 20-29 and 30-39 were the highest among race and age cohorts (8 and 51)
  • Rates varied by hormone receptor status

The lower incidence rate for Hispanic women was an intriguing finding that researchers attributed to younger age at the birth of their first child, higher maternal parity, and longer periods of breastfeeding – all factors that may be changing with lower fertility rates.

  • The higher incidence rates for younger Black women are particularly problematic as these women also are more likely to present with advanced disease, which leads to higher mortality rates.

The Takeaway

The new study provides background to what’s become one of the more disturbing trends in public health. While incidence rates in younger women are still much lower than in older women, the rise raises the question of whether health interventions such as risk assessment and targeted screening – such as for younger Black women – are necessary.

Why Has Breast Cancer Mortality Fallen?

There’s no question that breast cancer mortality has fallen dramatically over the last several decades. The question is why. 

Proponents of cancer screening believe that early detection has played a major role by finding cancer and enabling treatment to start before it spreads. 

  • But that position is disputed by a vocal minority of skeptics who believe that better cancer treatments deserve most of the credit. 

A case in point was the Bretthauer et al study published in 2023, which claimed that there was no evidence to support screening’s beneficial impact on all-cause mortality. 

  • This despite a demonstrated long-term decline in mortality for the cancers targeted by the four major population-based screening programs: breast, cervical, prostate, and lung. 

A new study in JAMA offers clarity in the debate by placing a numeric value on the tools that have contributed to lower breast cancer mortality. Researchers led by Jennifer Caswell-Jin, MD, of Stanford University used simulation models based on CISNET data to analyze breast cancer mortality from 1975 to 2019, drawing the following conclusions:

  • Screening and treatment together produced a 58% decline in breast cancer mortality, from a death rate of 48/100,000 women to 27/100,000
  • 47% of the reduction was due to treatment of stage I to III cancer 
  • 29% was due to treatment for metastatic breast cancer 
  • 25% was associated with mammography screening 

The authors also discovered that the biggest improvement in breast cancer survival after metastatic recurrence (3.2 vs. 1.9 years) happened between 2000-2019. 

The Takeaway

The new results in Caswell-Jin et al should be seen as another victory for the screening community. In addition to setting a numeric figure for screening’s value, they also demonstrate the synergistic effect when screening and treatment work together to target breast cancer before it has a chance to spread. Efforts to separate the two are quixotic at best and dangerous to women at worst. 

AI’s Impact on Breast Screening

One of the most exciting radiology use cases for AI is in breast screening. At last week’s RSNA 2023 show, a paper highlighted the technology’s potential for helping breast imagers focus on cases more likely to have cancer.

Looking for cancers on screening mammography has been compared to finding a needle in a haystack, and as such it’s considered to be one of the areas where AI can best help. 

  • One of the earliest use cases was in identifying suspicious breast lesions during radiologist interpretation (remember computer-aided detection?), but more recently researchers have focused on using AI as a triage tool, by identifying cases most likely to be normal that could be removed from the radiologist’s urgent worklist. Studies have found that 30-40% of breast screening cases could be read by AI alone or triaged to a low-suspicion list.

But what impact would AI-based breast screening triage have on radiologist metrics such as recall rate? 

  • To answer this question, researchers from NYU Langone Health prospectively tested their homegrown AI algorithm for analyzing DBT screening cases.

The algorithm was trained to identify extremely low-risk cases that could be triaged from the worklist while more complex cases where the AI was uncertain were sent to radiologists, who knew in advance the cases they were reading were more complicated. In 11.7k screening mammograms, researchers examined recall rates over two periods, one before AI triage and one after, finding: 

  • The overall recall rate went from 13% before the triage period to 15% after 
  • Recall rates for complex cases went from 17% to 20%
  • Recall rates for extremely low-risk studies went from 6% to 5%
  • There were no statistically significant differences in any of the comparisons
  • No change in median self-reported perceived difficulty of reading from the triage lists compared to non-triage list, regardless of years of experience

In future work, the NYU Langone researchers will continue their study to look at AI’s impact on cancer detection rate, biopsy rate, positive predictive value, and other metrics.

The Takeaway

The NYU Langone study puts a US spin on research like MASAI from Sweden, in which AI was able to reduce radiologists’ breast screening workload by 44%. Given the differences in screening protocols between the US and Europe, it’s important to assess how AI affects workload between the regions.

Further work is needed in this ongoing study, but early results indicate that AI can triage complex cases without having an undue impact on recall rate or self-perceived difficulty in interpreting exams – a surrogate measure for burnout.

AI Dominates at RSNA 2023

Take a deep breath. You survived another RSNA conference.

While a few hardy souls are still enjoying educational sessions in the cozy confines of McCormick Place, the final day of the exhibit floor yesterday marks the end of RSNA 2023 for most attendees. And what a show it was. 

Predictions were that AI would dominate the scientific sessions at RSNA 2023, a forecast that largely panned out. A November 28 session was a case in point, in which a series of top-quality papers were presented on one of the most promising use cases of AI, for breast screening:

  • A homegrown AI algorithm that analyzed screening breast ultrasound exams in addition to FFDM and DBT mammograms boosted sensitivity for detecting cancer in 12.5k patients, with better sensitivity for women with dense breasts (71% vs. 60%) and non-dense breasts (79% vs. 63%)
  • AI did a good job of detecting breast arterial calcification (BAC) when used prospectively to analyze screening mammograms in 16k women across 15 sites.  It found 15% of women had BAC, a possible marker for atherosclerotic disease
  • Swedish researchers used their VAI-B validation platform to compare three AI algorithms (Therapixel, Lunit, and Vara) in 34k women, finding that using AI with a single radiologist boosted sensitivity 10-30% compared to double reading, with a slight loss in specificity (2-7%). VAI-B could be used to validate AI implementation and guide purchasing decisions
  • Why does AI miss some breast cancers? South Korean researchers addressed this question by analyzing 1.1k patients with invasive cancers in which AI had a miss rate of 14%. Luminal cancers were missed most often
  • Adding AI analysis of prior images to current studies with FFDM and DBT boosted sensitivity for cancer detection in 30k patients, with sensitivity the highest for two years of priors compared to no priors (74% vs. 70%)

The Takeaway

This week’s research points to an exciting near-term future in which AI will help make mammography screening more accurate while helping breast radiologists perform their jobs more efficiently. Landmark studies toward this end were published in 2023 – this week’s RSNA conference shows that we can expect the momentum to continue in 2024. 

Uneven Success Against Breast Cancer

The decline in breast cancer mortality has been one of public health’s major success stories. But when you look at it from a global perspective, it’s the best of times and the worst of times. 

That’s because success in fighting breast cancer has been uneven around the world. While countries in North America, Western Europe, and Oceania have seen dramatic declines in breast cancer mortality and advanced-stage disease, other regions continue to be plagued by what really is becoming a survivable disease for most women. 

A new study in JAMA Oncology points out these disparities, documenting major differences in rates of advanced breast disease between countries in what researchers said was the most comprehensive review to date of global differences in breast cancer stage at diagnosis. 

  • Researchers conducted a meta-analysis of 133 studies covering 2.4M women across 81 nations over the past two decades, documenting differences in rates of advanced breast disease at diagnosis both over time and between countries. 

While most high-income nations have seen declines in rates of distant metastatic disease over the past 20 years, advanced-stage disease remains stubbornly common in lower middle-income countries. Researchers found: 

  • Rates of distant metastatic disease varied across countries by region, with sub-Saharan Africa the highest and North America the lowest (6-31% vs. 0-6%)
  • Lower socioeconomic status was tied to more advanced disease when women in the most disadvantaged group were compared to least disadvantaged (3-11% vs. 2-8%)
  • There were pronounced disparities even in high-resource countries with established screening programs, as rates of metastatic disease were twice as high in women of low socioeconomic status (SES) compared to high SES women, such as in the US (8% vs. 4%) 
  • Older women had a much higher prevalence of advanced disease across different countries compared to younger women (range of 4-34% vs. 2-16%), a phenomenon that could be because most screening programs stop at age 75
  • 40% of countries did not meet the Global Breast Cancer Initiative goal of having 60% or more of patients diagnosed at stage I or II

The Takeaway

The new findings indicate that it’s too soon to take a victory lap in the battle against breast cancer. While progress at higher socioeconomic levels in high-income countries has been impressive, breast cancer remains a scourge among more disadvantaged women and across wide regions of the world.

Canada’s Breast Screening Push

As Canada examines revisions to its breast cancer screening guidelines, a new study adds support to the proposal of lowering its screening age to 40 – a move made in the US earlier this year. 

When to start breast screening has long been one of the most controversial aspects of mammography. 

  • In the US, a firestorm erupted in 2009 when the USPSTF withdrew its recommendation that women start in their 40s … a policy that wasn’t rescinded until May. 

In Canada, the Canadian Task Force on Preventive Health Care is reviewing its 2018 screening guidelines, which currently advise women to wait until 50 to start routine breast screening, and then be screened every 2-3 years after that. 

  • The Canadian task force’s 2018 guidelines also don’t mention dense breast tissue, a known risk factor for breast cancer (the FDA earlier this year said it would begin requiring breast density reporting). 

Canadian breast specialists have been pushing for the task force to lower the screening age, and their efforts got a boost with a new study that found starting breast screening at age 40 and continuing with it annually saved the greatest number of lives.

Researchers in MDPI used the OncoSim-Breast microsimulation model to simulate various screening regimens in a cohort of 1.5M Canadian women born in 1975. They assessed the earlier screening strategy by various metrics, including impact on breast cancer mortality, number needed to be screened to avert one breast cancer death, and stage at diagnosis, finding …

  • Annual screening starting at age 40 had the biggest mortality reduction compared to no screening, at 7.9 fewer deaths per 1,000 women, compared to biennial 40-74 (5.9) and biennial 50-74 (4.6) 
  • Annual screening from 40-74 had the lowest number of women who must be screened to avert one death (127) compared to biennial 40-74 (169) and biennial 50-74 (220)
  • Earlier annual screening would produce the greatest stage shift to more early invasive (stage 1 and stage 2a) cancers detected compared to other regimens 

The Takeaway

The Canadian task force is expected to complete its review by the end of the year – where it will land on the issue is anyone’s guess. It’s hoped that the new study – as well as other research on mammography’s effectiveness in Canada published in the last couple years – will spur the group to lower the screening age. But breast imaging experts we spoke with are skeptical given the task force’s preference for randomized clinical trials, which haven’t been performed in Canada on breast screening in decades.

Can AI Direct Breast MRI?

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. 

Get every issue of The Imaging Wire, delivered right to your inbox.

You might also like..

Select All

You're signed up!

It's great to have you as a reader. Check your inbox for a welcome email.

-- The Imaging Wire team

You're all set!