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%)
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.
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 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.
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 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.
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%)
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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 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.