Author: Brian Casey

Headlines from SNMMI 2024

Posted on by Brian Casey

SNMMI 2024 wrapped up this week in Toronto, Canada, with the conference once again demonstrating the utility of nuclear medicine and molecular imaging for applications ranging from neurology to oncology to therapeutics. 

An annual SNMMI highlight is always the Image of the Year designation, and this year’s meeting didn’t disappoint. 

  • The honor went to a set of ultra-high-resolution brain PET images acquired with United Imaging’s NeuroEXPLORER (NX) scanner, a PET/CT system that the company developed with Yale and UC Davis and introduced last year for research use (although a clinical introduction could be forthcoming). 

The NX system sports a cylindrical design with a 52.4cm diameter and long axial field-of-view of 49.5cm; in the talk presented at SNMMI, researchers compared it to high-resolution research tomograph images with tracers targeting different dopamine receptors and transporters.

  • Researchers said the NX system had “exceptional” resolution in cortex and subcortical structures, with “low noise and exquisite resolution,” and predicted NX would “dramatically expand the scope of brain PET studies.”

Other important presentations at SNMMI included papers finding … 

  • An AI algorithm developed at Johns Hopkins detected six different types of cancer and automatically quantified tumor burden on whole-body PET/CT scans
  • In a study of 10.5k patients, AI that analyzed SPECT/CT images was able to predict all-cause mortality with an AUC of 0.77 by using CT attenuation correction scans to calculate risk factors like coronary artery calcium
  • Cognitive training is less effective in older adults who have beta-amyloid deposits in the brain on PET scans
  • An ultra-low-dose PET protocol presented by researchers from Bern University Hospital in Switzerland and Siemens Healthineers used deep learning reconstruction for a 50X reduction in PET radiation dose, to 0.15 mSv
  • A gallium-68 FAPI-based PET radiotracer was more accurate than fluorine-18 FDG for systemic staging of newly diagnosed breast cancer
  • A new chelating agent that binds radiometals to the parts of molecules that target cancer reduced off-target toxicity in PSMA radiopharmaceutical therapy
  • A combination of alpha- and beta-radionuclide therapy that combined actinium-225 with lutetium-177 worked well for colorectal cancer in a preclinical study
  • Research sponsored by Novartis on radioligand therapy for prostate cancer with lutetium-177 PSMA-617 (Pluvicto) was chosen as Abstract of the Year

The Takeaway

This year’s SNMMI presentations highlight the exciting advances taking place in nuclear medicine and molecular imaging, with the rise of theranostics giving the field an entirely new wrinkle that places it even closer to the center of precision medicine. Perhaps a new letter – T – will need to be added to the conference before too long.

More Backing for CT Lung Screening

Posted on by Brian Casey

Yet another study is showing support for CT lung cancer screening. In a real-world study in Cancer, researchers tracked screening’s impact on military veterans, finding that it contributed to more early-stage diagnoses as well as lower all-cause mortality. 

It’s no secret that uptake of CT lung screening has been disappointing since the USPSTF in 2013 endorsed the test for high-risk people – mostly those with smoking histories. 

  • Uptake rates have been estimated to be under 10% by some studies, although recent research has shown that targeted interventions can improve that figure.

In the new study, researchers described results from the Veterans Health Administration’s effort to provide low-dose CT lung cancer screening to veterans from 2011 to 2018.

  • The researchers noted that smoking rates are higher among veterans, resulting in lung cancer incidence rates that are 76% higher than the general population. 

Researchers tracked outcomes retrospectively for 2.2k veterans who got screening before a lung cancer diagnosis and compared them to those with lung cancer who weren’t screened, finding that screening led to…

  • Higher rates of stage I diagnosis (52% vs. 27%)
  • Lower rates of stage IV diagnosis (11% vs. 32%)
  • Lower rates of cancer mortality (41% vs. 70%)
  • Lower rates of all-cause mortality (50% vs. 72%)

The sharp reduction in all-cause mortality is particularly striking. 

  • As we’ve discussed in the past, most population-based cancer screening tests have been shown to reduce cancer-specific deaths, but it’s been harder to show a decline in deaths from all causes. 

The study also illustrates the advantage of providing lung screening within a large, integrated healthcare system, where it’s easier to track at-risk individuals and direct them to screening if necessary.

The Takeaway

Of all the positive studies published so far this year on CT lung cancer screening, this one is the most exciting. The findings show that even in an environment of low lung screening uptake, dramatic benefits can be realized with the right approach.

Radiology’s Private-Practice Squeeze

Posted on by Brian Casey

It’s no secret that US radiology’s traditional private-practice model has been slowly fading away, but new numbers published in AJR illustrate the magnitude of the shift. The number of radiologist-affiliated and radiologist-only practices has dropped, even as the total number of US radiologists has gone up. 

Radiology has long prided itself on a cozy business model in which radiologists banded together as owner-operators of small private-practice groups that contracted their services with hospitals. 

  • This model has had many benefits for radiologists, but it’s begun to fray in the face of competitive threats like teleradiology providers, health system consolidation, and large national radiology groups like Radiology Partners.

Many radiologists have chosen to switch rather than fight, selling out to national groups or taking positions as employees within health systems.

  • Meanwhile, some practices that want to stay independent are finding strength in numbers by joining with other like-minded groups or seeking out multi-specialty medical groups. 

In the new study, researchers from the ACR’s Harvey L. Neiman Health Policy Institute analyzed CMS data from 2014 to 2023, tracking not only changes in the number of US radiologists but also their type of employment, finding …

  • The number of radiologists grew 17%, from 30.7k to 36k
  • But the number of radiologist-affiliated practices fell 15%, from 5.1k to 4.3k
  • The number of radiology-only practices fell 32%
  • The number of small radiology practices fell, with the decline varying by practice size: 1-2 radiologists -19%, 3-9 radiologists -34%, and 10-24 radiologists -25%
  • The number of large practices jumped, with the biggest increase – 349% – at very large practices (over 100 radiologists)
  • The mean number of radiologists per practice shot up 84%, from 9.7 to 17.9

Why the shift? The researchers theorized that much of it was driven by federal policy and reimbursement changes that incentivize consolidation, mostly to spread the risk and cost of compliance with various regulations like ACA and MACRA.

The Takeaway

There’s no question that radiology is changing – the question is what impact the changes will have on how radiologists perceive their work. The old guard may choose to rage against the dying of the light, while younger generations embrace the new model and its benefits for both professional careers and patient care. 

AI of Cardiac CT Predicts Risk

Posted on by Brian Casey

In a landmark study of 40k patients from the UK published in The Lancet, an AI-derived score that analyzed coronary arterial inflammation on coronary CT angiography scans was effective in predicting future cardiac risk in people regardless of whether they had obstructive coronary artery disease.

CCTA’s power for predicting heart problems has been demonstrated in multiple studies, and it’s now considered a first-line test for individuals with chest pain. 

  • But the situation is trickier in those without obstructive disease – prompting researchers to ask whether CCTA’s ability to visualize subtle changes in cardiac structure and function could be leveraged – such as with AI – to deliver even more prognostic power. 

The Oxford Risk Factors And Noninvasive imaging (ORFAN) study in the UK is addressing that question by conducting CCTA scans in 40k patients as part of routine clinical care at eight hospitals. 

  • Researchers analyzed outcomes in the entire ORFAN population of 40k patients, then followed a subset of 3.4k higher-risk patients for 7.7 years to study the value of a perivascular fat attenuation index (FAI) score. 

FAI scores measure heart inflammation in coronary arteries and are calculated using Caristo Diagnostics’ CaRi-Heart AI software.

  • The scores are combined with other traditional risk factors to create an AI-Risk classification that predicts the likelihood of an adverse event.  

Researchers found that … 

  • Across the entire 40k cohort, patients without obstructive CAD accounted for 64% of cardiac deaths and 66% of MACE – twice as many as those with obstructive CAD
  • In the smaller higher-risk cohort, patients with an elevated FAI score in all three coronary arteries had a higher risk of cardiac mortality (HR=29.8) or MACE (HR=12.6)
  • Elevated FAI scores in any coronary artery also predicted cardiac mortality
  • AI-Risk scores were associated with cardiac mortality (HR=6.75) and MACE (HR=4.68) when comparing very-high-risk versus low- or medium-risk patients 

The first data point is worth noting, as it illustrates the need to improve risk stratification and management in people without obstructive CAD.

The Takeaway
The ORFAN results are an exciting development for cardiac CT AI (in addition to being a major coup for Caristo, which raised $16.3M last year to commercialize CaRi-Heart globally). Measurements of coronary inflammation could give clinicians another tool – in addition to plaque measurements and calcium scoring – to predict cardiac events.

Radiologist Pay Rebounds

Posted on by Brian Casey

Radiologist pay grew 5.6% and radiology moved up one notch on Doximity’s list of highest-paid US medical specialties for 2023. Physician salaries generally rebounded last year after a decline in 2022.

The Doximity survey of 33k doctors found that overall physician pay grew 5.9% last year, a welcome rebound after a decline of 2.4% in 2022. 

  • In other good news, medicine’s gender pay gap narrowed in the new survey, with women making 23% less than men, down from 26% in 2022 and 28% in 2021.

For radiologists, their average annual compensation was $532k, up from $504k a year ago, and radiology jumped ahead of urology on the top 10 list to occupy the ninth spot. 

  • Still, radiology lagged a number of other specialties in terms of salary growth, ranging from hematology (+12.4%) to psychiatry (+7.2%). 

Other findings in the survey include …

  • Some 81% of physicians reported they are overworked, a number that’s actually down from 86% in 2022
  • 88% of respondents said their clinical practice has been affected by the physician shortage
  • 86% of those surveyed said they are concerned about the US healthcare system’s ability to care for its aging population

The Doximity results roughly track recently released salary data from Medscape, which pegged radiologist salaries at $498k in 2023, up 3.1% and ranking sixth on the list of highest-paid specialties. 

The Takeaway

Say what you want about rising workload and burnout in radiology – radiologists are still among the best-compensated physicians in medicine. And the situation in the US is in sharp contrast to Japan, where radiology is one of the lowest-paid specialties (see our article in The Wire section below).

Lung Screening Narrows Disparities

Posted on by Brian Casey

New research confirms that not only does low-dose CT screening reduce lung cancer mortality, it can also narrow health disparities. Researchers found that screening’s beneficial impact was greater at lower socioeconomic levels in a new study published in Lancet Regional Health – Europe.

As we mentioned in our last issue, CT lung cancer screening is gaining momentum globally; at the same time, researchers have documented greater mortality and morbidity for a variety of diseases among racial minorities and at lower socioeconomic levels.

  • This difference can be especially profound when it comes to lung disease, given higher smoking rates among some minority groups and economically disadvantaged populations.

In the original UK Lung Cancer Screening Trial (UKLS) in 2021, researchers found that a single CT screening round produced a 16% lung cancer mortality reduction. 

  • The new study is a secondary analysis of UKLS to investigate whether CT lung screening’s impact differed by socioeconomic status, which is important given that smoking occurs in England at higher rates in the most deprived neighborhoods compared to wealthier ones (24% vs. 6.8%).

UKLS researchers compared lung cancer mortality rates in 4k individuals in different groups classified by a widely used socioeconomic barometer. They found that … 

  • CT lung screening had the same lung cancer mortality benefit in both low and high socioeconomic groups (-19% vs. -20%)
  • But there was a bigger reduction in death from COPD in lower socioeconomic groups (-34% vs. +4%)
  • And fewer deaths from other lung diseases (-32% vs. +10%)
  • While cardiovascular mortality was also lower (-30% vs. -13%)
  • All-cause mortality was lower in lower socioeconomic groups – a benefit not seen at higher levels

Lung screening’s reduction in all-cause mortality is particularly intriguing, as this is an accomplishment that has eluded most other cancer screening tests – a point that has been repeatedly hammered home by screening skeptics.

The Takeaway

The new findings highlight how – to a greater degree than other major cancer screening tests – CT lung screening has the potential to address ongoing racial and socioeconomic healthcare disparities. It’s yet another reason to press for broader adoption of lung screening.

CT Lung Screening Shows Progress at ATS 2024

Posted on by Brian Casey

Making CT lung cancer screening more effective has been a hot topic at the American Thoracic Society meeting, which convened this weekend in San Diego. Presentations at ATS 2024 have ranged from improving screening compliance rates to eliminating racial disparities in screening attendance.

After years of fits and starts, low-dose CT lung cancer screening appears to be finally making progress. 

  • While the US still struggles with overly restrictive screening criteria and convoluted reimbursement rules, the rest of the world – including Australia, Germany, and Taiwan – is moving ahead with population-based screening programs designed to counter the tobacco epidemic’s deadly scourge.

At ATS 2024, investigators are presenting research to ensure that the benefits of CT lung cancer screening are delivered to those who need it, with the following highlights …

  • Researchers at the University of Minnesota saw a 7.2% completion rate for screening-specific low-dose CT among 91k eligible individuals – an indication of “overall poor uptake of screening” 
  • To improve uptake, another group implemented a centralized nurse coordinator for lung screening, resulting in a 23-day reduction in time from initial consultation to report delivery as well as better adherence to eligibility criteria
  • Patients who self-identify as Black are more likely to miss a scheduled CT screening appointment (OR=2.05), while Hispanic patients also have high miss rates (OR=1.92) as do those with limited English proficiency (OR=1.72). The numbers highlight the need for patient conversations to boost completion rates
  • Incidence rates of lung and bronchus cancer dropped from 2007-2019 compared to 1999-2006, underscoring the importance of smoking cessation and supporting current USPSTF age criteria for lung screening
  • Pulmonary physicians significantly overestimated their patients’ lung screening completion rates, with almost half thinking the rate was higher than 60% when it was actually 17%. Researchers suggested interventions for improving completion rates

The Takeaway

The fact that ATS 2024 has seen so many presentations on CT lung cancer screening – the vast majority presented by US authors – indicates that low screening rates haven’t discouraged American researchers and clinicians. The presentations underscore the progress being made toward making the benefits of lung screening available to Americans who would benefit from it.

Is Radiology’s AI Edge Fading?

Posted on by Brian Casey

Is radiology’s AI edge fading, at least when it comes to its share of AI-enabled medical devices being granted regulatory authorization by the FDA? The latest year-to-date figures from the agency suggest that radiology’s AI dominance could be declining. 

Radiology was one of the first medical specialties to go digital, and software developers have targeted the field for AI applications like image analysis and data reconstruction.

  • Indeed, FDA data from recent years shows that radiology makes up the vast majority of agency authorizations for AI- and machine learning-enabled medical devices, ranging from 86% in 2020 and 2022 to 79% in 2023. 

But in the new data, radiology devices made up only 73% of authorizations from January-March 2024. Other data points indicate that the FDA …

  • Authorized 151 new devices since August 2023
  • Reclassified as AI/ML-enabled 40 devices that were previously authorized 
  • Authorized a total of 882 devices since it began tracking the field 

      In an interesting wrinkle, many of the devices on the updated list are big-iron scanners that the FDA has decided to classify as AI/ML-enabled devices. 

      • These include CT and MRI scanners from Siemens Healthineers, ultrasound scanners from Philips and Canon Medical Systems, an MRI scanner from United Imaging, and the recently launched Butterfly iQ3 POCUS scanner. 

      The additions could be a sign that imaging OEMs increasingly are baking AI functionality into their products at a basic level, blurring the line between hardware and software. 

      The Takeaway

      It should be no cause for panic that radiology’s share of AI/ML authorizations is declining as other medical specialties catch up to the discipline’s head start. The good news is that the FDA’s latest figures show how AI is becoming an integral part of medicine, in ways that clinicians may not even notice.

      Slashing CT Radiation Dose

      Posted on by Brian Casey

      Cutting CT radiation dose should be the goal of every medical imaging facility. A new paper in European Radiology offers a promising technique that slashed CT dose to one-tenth of conventional CT – and just twice that of a standard chest X-ray.

      CT’s wide availability, excellent image quality, and relatively low cost make it an invaluable modality for many clinical applications.

      • CT proved particularly useful during the COVID-19 pandemic for diagnosing lung pathology caused by the virus, and it continues to be used to track cases of long COVID.

      But patient monitoring can involve multiple CT scans, leading to cumulative radiation exposure that can be concerning, especially for younger people.

      • Researchers in Austria wanted to see if they could use commercially available tools to produce ultra-low-dose CT scans, and then assess how they compared to conventional CT for tracking patients with long COVID.

      Using Siemens Healthineers’ Somatom Drive third-generation dual-source CT scanner, they adjusted the parameters on the system’s CAREDose automated exposure control and ADMIRE iterative reconstruction to drive down dose as much as possible.

      • Other ultra-low-dose CT settings versus conventional CT included fixed tube voltage (100 kVp vs. 110 kVp), tin filtration (enabled vs. disabled), and CAREDose tube current modulation (enabled – weak vs. enabled – normal). 

      They then tested the settings in a group of 153 patients with long COVID seen from 2020 to 2021; both ultra-low-dose and conventional CT scans were compared by radiologists, finding … 

      • Mean entrance-dose radiation levels with ultra-low-dose CT were less than one-tenth those of conventional CT in (0.21 mSv vs. 2.24 mSv); a two-view chest X-ray is 0.1 mSv
      • Image quality was rated 40% lower on a five-point scale (3.0 vs. 5.0)
      • But all ultra-low-dose scans were rated as diagnostic quality
      • Intra-reader agreement between the two techniques was “excellent,” at 93%

      The findings led the researchers to conclude that ultra-low-dose CT could be a good option for tracking long COVID, such as in younger patients. 

      The Takeaway

      The study demonstrates that CT radiation dose can be driven down dramatically through existing commercially available tools. While this study covers just one niche clinical application, such tools could be applied to a wider range of uses, ensuring that the benefits of CT will continue to be made available at lower radiation doses than ever.

      Fine-Tuning AI for Breast Screening

      Posted on by Brian Casey

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

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

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

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

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

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

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

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

      The Takeaway

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

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