Fine-Tuning Cardiac CT

CT has established itself as an excellent cardiac imaging modality. But there can still be some fine-tuning in terms of exactly how and when to use it, especially for assessing people presenting with chest pain. 

Two studies in JAMA Cardiology tackle this head-on, presenting new evidence that supports a more conservative – and precise – approach to determining which patients get follow-up testing. The studies also address concerns that using coronary CT angiography (CCTA) as an initial test before invasive catheterization could lead to unnecessary testing.

In the PRECISE study, researchers analyzed 2.1k patients from 2018 to 2021 who had stable symptoms of suspected coronary artery disease (CAD). Patients were randomized to a usual testing strategy (such as cardiac SPECT or stress echo), or a precision strategy that employed CCTA with selected fractional flow reserve CT (FFR-CT). 

The precision strategy group was further subdivided into a subgroup of those at minimal risk of cardiac events (20%) for whom testing was deferred to see if utilization could be reduced even further. In the precision strategy group….

  • Rates of invasive catheterization without coronary obstruction were lower (4% vs. 11%)
  • Testing was lower versus the usual testing group (84% vs. 94%)
  • Positive tests were more common (18% vs. 13%)
  • 64% of the deferred-testing subgroup got no testing at all
  • Adverse events were higher, but the difference was not statistically significant

To expand on the analysis, JAMA Cardiology published a related study that further investigated the safety of the deferred-testing strategy at one-year follow-up. Researchers compared adverse events in the deferred testing group to those who got the usual testing strategy, finding that the deferred testing group had…

  • A lower incidence rate of adverse events (0.9 vs. 5.9)
  • A lower rate of invasive cardiac cath without obstructive CAD per 100 patient years (1.0 vs. 6.5)

The results from both studies show that a strategy of deferring testing for low-risk CAD patients while sending higher-risk patients to CCTA and FFR-CT is clinically effective with no adverse impact on patient safety.

The Takeaway
The new findings don’t take any of the luster off cardiac CT; they simply add to the body of knowledge demonstrating when to use – and not to use – this incredibly powerful tool for directing patient care. And in the emerging era of precision medicine, that’s what it’s all about.

Radiation and Cancer Risk

New research on the cancer risk of low-dose ionizing radiation could have disturbing implications for those who are exposed to radiation on the job – including medical professionals. In a new study in BMJ, researchers found that nuclear workers exposed to occupational levels of radiation had a cancer mortality risk that was higher than previously estimated.

The link between low-dose radiation and cancer has long been controversial. Most studies on the radiation-cancer connection are based on Japanese atomic bomb survivors, many of whom were exposed to far higher levels of radiation than most people receive over their lifetimes – even those who work with ionizing radiation. 

The question is whether that data can be extrapolated to people exposed to much lower levels of radiation, such as nuclear workers, medical professionals, or even patients. To that end, researchers in the International Nuclear Workers Study (INWORKS) have been tracking low-dose radiation exposure and its connection to mortality in nearly 310k people in France, the UK, and the US who worked in the nuclear industry from 1944 to 2016.

INWORKS researchers previously published studies showing low-dose radiation exposure to be carcinogenic, but the new findings in BMJ offer an even stronger link. For the study, researchers tracked radiation exposure based on dosimetry badges worn by the workers and then rates of cancer mortality, and calculated rates of death from solid cancer based on their exposure levels, finding: 

  • Mortality risk was higher for solid cancers, at 52% per 1 Gy of exposure
  • Individuals who received the occupational radiation limit of 20 mSv per year would have a 5.2% increased solid cancer mortality rate over five years
  • There was a linear association between low-dose radiation exposure and cancer mortality, meaning that cancer mortality risk was also found at lower levels of exposure 
  • The dose-response association seen the study was even higher than in studies of atomic bomb survivors (52% vs. 32%)

The Takeaway

Even though the INWORKS study was conducted on nuclear workers rather than medical professionals, the findings could have implications for those who might be exposed to medical radiation, such as interventional radiologists and radiologic technologists. The study will undoubtedly be examined by radiation protection organizations and government regulators; the question is whether it leads to any changes in rules on occupational radiation exposure.

Mammography AI’s Leap Forward

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.

H1 Radiology Recap

That’s a wrap for the first half of 2023. Below are the top stories in radiology for the past 6 months, as well as some tips on what to look for in the second half of the year.

  • Radiology Bounces Back – After several crushing years in the wake of the COVID-19 pandemic, the first half brought welcome news to radiology on several fronts. The 2023 Match wrapped up with diagnostic radiology on top as the most popular medical specialty for medical students over the past 3 years. Radiology was one of the highest-compensated specialties in surveys from Medscape and Doximity, and even vendors got into the act, reporting higher revenue and earnings as supply chain delays cleared up. Will the momentum continue in the second half? 
  • Burnout Looms Large – Even as salaries grow, healthcare is grappling with increased physician burnout. Realization is growing that burnout is a systemic problem – tied to rising healthcare volumes – that defies self-care solutions. Congressional legislation would boost residency slots 5% a year for 7 years, but is even this enough? Alternatively, could IT tools like AI help offload medicine’s more mundane tasks and alleviate workloads? Both questions will be debated in the back half of 2023. 
  • In-Person Shows Are Back – The pandemic took a wrecking ball to the trade show calendar, but things began to return to normal in the first half of 2023. Both ECR and HIMSS held meetings that saw respectable attendance, following up on a successful RSNA 2022. By the time SIIM 2023 rolled around in early June, the pandemic was a distant memory as radiology focused on the value of being together

The Takeaway

As the second half of 2023 begins, all eyes will be on ChatGPT and whether a technology that’s mostly a curious novelty now can evolve into a useful clinical tool in the future. 

AI Reinvigorates SIIM 2023

AUSTIN – Before AI came along, the Society for Imaging Informatics in Medicine (SIIM) seemed to be a conference in search of itself. SIIM (and before it, SCAR) built its reputation on education and training for radiology’s shift to digital image management. 

But what happens when the dog catches the truck? Radiology eventually fully adopted digital imaging, and that meant less need to teach people about technology they were already using every day.

Fast forward to the AI era, and SIIM seems to have found its new mission. Once again, radiology is faced with a transformative IT technology that few understand and even fewer know how to put into clinical practice. With its emphasis on education and networking, SIIM is a great forum to learn how to do both. 

That’s exemplified by the SIIM keynote address on Wednesday, by Ziad Obermeyer, MD, a physician and researcher in machine learning at UC Berkeley who has published important research on bias in machine learning. 

While not a radiologist, Obermeyer served up a fascinating talk on how AI should be designed and adopted to have maximum impact. His advice included:

  • Don’t design AI to perform the same tasks humans do already. Train algorithms to perform in ways that make up for the shortcomings of humans.
  • Training algorithms on medical knowledge from decades ago is likely to produce bias when today’s patient populations don’t match those of the past.
  • Access to high-quality data is key to algorithm development. Data should be considered a public good, but there is too much friction in getting it. 

To solve some of these challenges, Obermeyer is involved in two projects, Nightingale Open Science to connect researchers with health systems, and Dandelion Health, designed to help AI developers access clinical data they need to test their algorithms. 

The Takeaway 

The rise of AI – particularly generative AI models like ChatGPT –  has given SIIM a shot in the arm from a content perspective, and the return of in-person meetings plays to the conference’s strength as an intimate get-together where the networking and relationship-building is almost as important as the content. Please follow along with the proceedings of SIIM 2023 on our Twitter and LinkedIn pages. 

Taking Ultrasound Beyond Breast Density

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. 

When AI Goes Wrong

What impact do incorrect AI results have on radiologist performance? That question was the focus of a new study in European Radiology in which radiologists who received incorrect AI results were more likely to make wrong decisions on patient follow-up – even though they would have been correct without AI’s help.

The accuracy of AI has become a major concern as deep learning models like ChatGPT become more powerful and come closer to routine use. There’s even a term – the “hallucination effect” – for when AI models veer off script to produce text that sounds plausible but in fact is incorrect.

While AI hallucinations may not be an issue in healthcare – yet – there is still concern about the impact that AI algorithms are having on clinicians, both in terms of diagnostic performance and workflow. 

To see what happens when AI goes wrong, researchers from Brown University sent 90 chest radiographs with “sham” AI results to six radiologists, with 50% of the studies positive for lung cancer. They employed different strategies for AI use, ranging from keeping the AI recommendations in the patient’s record to deleting them after the interpretation was made. Findings included:

  • When AI falsely called a true-pathology case “normal,” radiologists’ false-negative rates rose compared to when they didn’t use AI (20.7-33.0% depending on AI use strategy vs. 2.7%)
  • AI calling a negative case “abnormal” boosted radiologists’ false-positive rates compared to without AI (80.5-86.0% vs. 51.4%)
  • Not surprisingly, when AI calls were correct, radiologists were more accurate with AI than without, with increases in both true-positive rates (94.7-97.8% vs. 88.3%) and true-negative rates (89.7-90.7% vs. 77.3%)

Fortunately, the researchers offered suggestions on how to mitigate the impact of incorrect AI. Radiologists had fewer false negatives when AI provided a box around the region of suspicion, a phenomenon the researchers said could be related to AI helping radiologists focus. 

Also, radiologists’ false positives were higher when AI results were retained in the patient record versus when they were deleted. Researchers said this was evidence that radiologists were less likely to disagree with AI if there was a record of the disagreement occurring. 

The Takeaway 
As AI becomes more widespread clinically, studies like this will become increasingly important in shaping how the technology is used in the real world, and add to previous research on AI’s impact. Awareness that AI is imperfect – and strategies that take that awareness into account – will become key to any AI implementation.

The Perils of Worklist Cherry-Picking

If you’re a radiologist, chances are at some point in your career you’ve cherry-picked the worklist. But picking easy, high-RVU imaging studies to read before your colleagues isn’t just rude – it’s bad for patients and bad for healthcare.

That’s according to a new study in Journal of Operations Management that analyzes radiology cherry-picking in the context of operational workflow and efficiency. 

Based on previous research, researchers hypothesized that radiologists who are free to pick from an open worklist would choose the easier studies with the highest compensation – the classic definition of cherry-picking.

To test their theory, they analyzed a dataset of 2.2M studies acquired at 62 hospitals from 2014 to 2017 that were read by 115 different radiologists. They developed a statistical metric called “bang for the buck,” or BFB, to classify the value of an imaging study in terms of interpretation time relative to RVU level. 

They then assessed the impact of BFB on turnaround time (TAT) for different types of imaging exams based on priority, classified as Stat, Expedited, and Routine. Findings included:

  • High-priority Stat studies were reported quickly regardless of BFB, indicating little cherry-picking impact
  • For Routine studies, those with higher BFB had much lower reductions in turnaround — a sign of cherry-picking
  • Adding one high-BFB Routine study to a radiologist’s worklist resulted in a much longer increase in TAT for Expedited exams compared to low-BFB studies (increase of 17.7 minutes vs. 2 minutes)
  • The above delays could result in longer patient lengths of stay that translate to $2.1M-$4.2M in extra costs across the 62 hospitals in the study. 

The findings suggest that radiologists in the study prioritized high-BFB Routine studies over Expedited exams – undermining the exam prioritization system and impacting care for priority cases.

Fortunately, the researchers offer suggestions for countering the cherry-picking effect, such as through intelligent scheduling or even hiding certain studies – like high-BFB Routine exams – from radiologists when there are Expedited studies that need to be read. 

The Takeaway 

The study concludes that radiology’s standard workflow of an open worklist that any radiologist can access can become an “imbalanced compensation scheme” that can lead to poorer service for high-priority tasks. On the positive side, the solutions proposed by the researchers seem tailor-made for IT-based interventions, especially ones that are rooted in AI. 

A New Day for Breast Screening

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

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.

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