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. 

Theranostics Grabs SNMMI Spotlight

The emerging field of theranostics – in which two radiopharmaceuticals work in tandem for diagnostic and therapeutic purposes – is one of the most exciting new areas of medicine. Nowhere is this more evident than at this week’s SNMMI 2023 meeting in Chicago

Theranostics involves the use first of a highly targeted diagnostic radiotracer to detect pathology with a technology like PET, then sending in another tracer to deliver a stronger radioactive payload to the site of disease – almost the definition of precision medicine. Some estimates are that theranostics could soon develop into a market worth $30B.

In addition to talks on theranostics, SNMMI 2023 highlights so far have included presentations covering the following:

  • An ultra-high-resolution brain PET scanner that can visualize and quantify nuclei in the brainstem for the first time, opening up new inquiries into neurological disorders like Alzheimer’s disease
  • The discovery of the optimal tracer kinetic model for quantifying myocardial uptake of 18F-flutemetamol in patients with transthyretin (ATTR) cardiac amyloidosis, a buildup of amyloid plaque in the heart
  • A technique called augmented whole-body scanning via magnifying PET (AWSM-PET) that uses two high-resolution add-on detectors as an “outsert” to improve image resolution and reduce noise
  • Imaging of rheumatoid arthritis with 68Ga-FAPI PET/CT, which showed a greater number and degree of affected joints than FDG-PET/CT
  • A PET radiotracer called 18F-Cholestify has the potential to improve neuroimaging by visualizing metabolic cholesterol degradation in the brain.

The commercial side of SNMMI 2023 is active as well. Siemens Healthineers, GE HealthCare, and United Imaging Healthcare are launching new hybrid scanners, and other vendor news includes the following: 

  • Blue Earth Diagnostics is touting its recent shipments of Posluma, a PET radiotracer targeting PSMA in prostate cancer patients
  • GE HealthCare is migrating AIR technologies found on its MRI scanners to its new Signa PET/MRI AIR system (see below) 
  • Isotopia is discussing its plans for a US radioisotope manufacturing facility
  • Lantheus researchers are presenting talks on AI-enabled PSMA-PET reporting using its Pylarify AI software
  • Mediso received FDA clearance for its InterView Fusion and InterView XP multimodality image processing and reporting software
  • Siemens Healthineers has launched a new PET/CT scanner, Biograph Vision.X, sporting a 20% improvement in time of flight (see below)
  • Subtle Medical is demonstrating its SubtlePET solution, which uses AI to remove noise for low-count PET images, enabling up to 75% faster PET scans
  • Telix Pharmaceuticals is highlighting clinical results of several agents: the Illuccix gallium-based prostate cancer imaging agent; ProstACT lutetium-based antibody-directed prostate cancer therapy; and TLX250-CDx, a zircon-89-based tracer for diagnosing clear cell renal cell carcinoma.
  • United Imaging Healthcare is launching uMI Panorama, a new wide-bore PET/CT scanner (see below).  

The Takeaway

This week’s proceedings in Chicago illustrate the new energy that theranostics is bringing to nuclear medicine and molecular imaging, one of radiology’s most venerable modalities. Stay tuned for the announcement of SNMMI’s Henry N. Wagner, Jr. Image of the Year award, always a conference highlight.  

Is There Hope for CT Lung Screening?

New data on CT lung cancer screening rates offer a good news/bad news story. The bad news is that only 21.2% of eligible individuals in four US states got screened, far lower than other exams like breast or colon screening.

The good news is that, as low as the rate was relative to other tests, 21.2% is still much higher than previous estimates. And the study itself found that the rate of CT lung screening has risen over 8 percentage points in 3 years. 

Compliance has lagged with CT lung screening ever since Medicare approved payments for the exam in 2015. A recent JACR study found that screening rates were low for eligible people for both Medicare and commercial insurance (3.4% and 1.8%).

Why is screening compliance so low? Explanations have ranged from fatalism among people who smoke to reimbursement requirements for “shared decision-making,” which unlike other screening exams require patients and providers to discuss CT lung screening before an exam can be ordered.

In this new study in JAMA Network Open, researchers examined screening rates in four states – Maine, Michigan, New Jersey, and Rhode Island – from January 2021 to January 2022. The study drew data from the National Health Interview Survey and weighted it to reflect the population of the US of individuals eligible for CT lung screening, based on the criteria of ages 55-79, 30-pack-year smoking history, and having smoked or quit within the past 15 years. Major findings included: 

  • The rate for CT lung cancer screening was 21.2%, up from 12.8% in 2019
  • People with a primary health professional (PHP) were nearly 6 times more likely to get screened (OR=5.62)
  • The age sweet spot for screening was 65-77, with lower odds for those 55-64 (OR=0.43) and 78-79 (OR=0.17)
  • Rates varied between states, with Rhode Island having the highest rate (30.3%) and New Jersey the lowest (17.5%).
  • Of those who got screened, 27.7% were in poor health and 4.5% had no health insurance

The Takeaway

The findings offer some hope for CT lung screening, as the compliance rate is among the highest we’ve seen among recent research studies. On the other hand, many of those screened were in such poor health they might not benefit from treatment. The high rate of compliance in people with PHPs indicates that promoting screening with these providers could pay off, especially given the requirement for shared decision-making. 

Better Together at SIIM

Humans have a deep-seated need for interpersonal contact, and understanding that need should guide not only how we structure our work relationships in the post-COVID era, but also our development and deployment of new technologies like AI in radiology. 

That’s according to James Whitfill, MD, who gave Thursday’s opening address at SIIM 2023. Whitfill’s talk – which was followed by a raucous audience participation exercise – was a ringing demonstration that in-person meetings like SIIM still have relevance despite the proliferation of Zoom calls and remote work. 

Whitfill, chief transformation officer at HonorHealth in Arizona and an internist at the University of Arizona, was chair of the SIIM board in 2020 when the society made the difficult decision to move its annual meeting to be fully online during the pandemic.

The experience led Whitfill to ponder whether technology designed to help us work and collaborate virtually was an adequate substitute for in-person interaction. Unfortunately, the research suggests otherwise: 

  • Numerous studies have demonstrated the negative effect that the isolation of the COVID pandemic has had on adolescent mental health and academic performance 
  • Loneliness can also have a negative effect on physical well-being, with a recent U.S. Surgeon General’s report finding that prolonged isolation is the health equivalent of smoking 15 cigarettes a day
  • Peer-reviewed studies have shown that people working in in-person collaborative environments are about 10% more productive and creative than those working virtually. 

Whitfill’s talk was especially on-point given recent research indicating that workers across different industries who used AI were more lonely than those who didn’t, a phenomenon that shouldn’t be ignored by those planning radiology’s AI-based future. 

That said, virtual technologies can still play a role in making access to information more equitable. Whitfill noted that some 160 people were following the SIIM proceedings entirely online, and they otherwise would not have been able to benefit from the meeting’s content.

To drive the point home, Whitfill then had audience members participate in a team-based Rochambeau competition that sent peals of laughter ringing through Austin Convention Center.  

The Takeaway
Whitfill’s point was underscored repeatedly by SIIM 2023 attendees, who reiterated the value of interpersonal connections and networking at the conference. It’s ironic that a meeting devoted at least in part to intelligence that’s artificial has made us better appreciate relationships that are real.

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. 

Mayo’s AI Model

SAN DIEGO – What’s behind the slow clinical adoption of artificial intelligence? That question permeated the discussion at this week’s AIMed Global Summit, an up-and-coming conference dedicated to AI in healthcare.

Running June 4-7, this week’s meeting saw hundreds of healthcare professionals gather in San Diego. Radiology figured prominently as the medical specialty with a lion’s share of the over 500 FDA-cleared AI algorithms available for clinical use.

But being available for use and actually being used are two different things. A common refrain at AIMed 2023 was slow clinical uptake of AI, a problem widely attributed to difficulties in deploying and implementing the technology. One speaker noted that less than 5% of practices are using AI today.

One way to spur AI adoption is the platform approach, in which AI apps are vetted by a single entity for inclusion in a marketplace from which clinicians can pick and choose what they want. 

The platform approach is gaining steam in radiology, but Mayo Clinic is rolling the platform concept out across its entire healthcare enterprise. First launched in 2019, Mayo Clinic Platform aims to help clinicians enjoy the benefits of AI without the implementation headache, according to Halim Abbas, senior director of AI at Mayo, who discussed Mayo’s progress on the platform at AIMed. 

The Mayo Clinic Platform has several main features:

  • Each medical specialty maintains its own internal AI R&D team with access to its own AI applications 
  • At the same time, Mayo operates a centralized AI operation that provides tools and services accessible across departments, such as data de-identification and harmonization, augmented data curation, and validation benchmarks
  • Clinical data is made available outside the -ologies, but the data is anonymized and secured, an approach Mayo calls “data behind glass”

Mayo Clinic Platform gives different -ologies some ownership of AI, but centralizes key functions and services to improve AI efficiency and smooth implementation. 

The Takeaway 

Mayo Clinic Platform offers an intriguing model for AI deployment. By removing AI’s implementation pain points, Mayo hopes to ramp up clinical utilization, and Mayo has the organizational heft and technical expertise to make it work (see below for news on Mayo’s new generative AI deal with Google Cloud). 

But can Mayo’s AI model be duplicated at smaller health systems and community providers that don’t have its IT resources? Maybe we’ll find out at AIMed 2024.

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.

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