Tag: radiologist

Is Head CT Overused in the ED?

Posted on by Brian Casey

A new study suggests that head CT could be overused in the emergency department for patients presenting with conditions like headache and dizziness. Writing in a paper in Internal and Emergency Medicine, researchers looking at CT angiography use at a large medical center found a big increase in CTA utilization – even as the rate of positive findings dropped. 

CTA is a powerful tool that can quickly and efficiently give clinicians information to guide treatment of acute neurovascular conditions like aneurysm and stroke. 

  • As such, many emergency departments have been installing their own CT scanners to enable them to scan emergent patients without transporting them to the radiology department. 

But with great power comes great responsibility, and there is always the temptation to scan first and ask questions later. 

  • To better understand changing CTA use in the emergency setting, researchers from the Harvey L. Neiman Health Policy Institute analyzed CTA exams at a level 1 trauma center that sees about 110k emergency patients a year.

Researchers analyzed 25k ED visits from 2017 to 2021 and correlated them to head and neck CTA exams for headache and/or dizziness, finding …

  • The rate of CTA exams rose 64%, from 7.9% of ED visits to 13%
  • Symptomatic patients were 15% more likely to have a CTA in 2021 versus 2017
  • The rate of positive CTA findings fell 38%, from 17% to 10%
  • Patients with private insurance were more likely to have CTA (OR=1.44)
  • Black patients were less likely to be scanned (OR=0.69)

The researchers said the findings indicate the need for better clinical decision support tools, which they believe can help emergency physicians provide an accurate diagnosis without exposing patients to unnecessary radiation and incurring additional cost. 

The Takeaway

This study further confirms widespread accounts that head and neck CTA is overused and on the rise. As the US government backs off on its attempt to force clinical decision support on referring physicians, it may be up to health systems and providers themselves to ensure more appropriate utilization – in a way that doesn’t rely on heavy-handed tools like prior authorization. 

Out-of-Network Radiology Claims Fall

Posted on by Brian Casey

Is out-of-network billing – when a patient receives care outside their insurance network – still a problem in radiology? A new study in JACR shows that out-of-network commercial claims have dropped dramatically since 2007.

Out-of-network healthcare has been the focus of a number of legislative efforts in recent years as lawmakers try to protect patients from the financial sting of getting a big bill for services rendered outside their provider’s network.

  • Probably the centerpiece of this effort is the federal No Surprises Act, which went into effect in January 2022; not only did it cap the amount that patients can be billed for out-of-network services, but it created an independent dispute resolution mechanism for adjudicating disagreement between providers and payors over how much they should be paid.

The IDR mechanism has been the focus of legal wrangling in recent months, but the new study in JACR indicates that it might not be getting much use after all, at least in radiology.

Researchers from the ACR’s Harvey L. Neiman Health Policy Institute analyzed 80M commercial claims for radiology services from 2007 to 2021, finding…

  • Out-of-network radiology claims fell dramatically (to 1.1% vs. 13%)
  • Out-of-network claims fell for inpatient stays (to 1.4% vs. 10%)
  • Claims also fell for emergency visits (to 0.4% vs. 3.9%)
  • By modality, most claims were for X-ray (57%), followed by ultrasound and CT (15% each) 
  • By 2021, radiologists practiced almost exclusively in-network

What’s the reason for the dramatic decline? The study authors credit good-faith negotiations between radiology practices and commercial payors, as well as the impact of state surprise billing laws (the study period occurred before the federal No Surprises Act went into effect).

  • Other possible factors include consolidation among practices, hospitals, and payors; expansion of academic centers into communities; and the COVID-19 pandemic.   

The Takeaway

The JACR study is welcome news for both patients and radiology practices. Patients are less likely to be hit with surprise medical charges, while practices are less likely to have to fight through the IDR process to resolve claims. In the end, everybody wins – even insurance companies.

AI Models Go Head-to-Head in Project AIR Study

Posted on by Brian Casey

One of the biggest challenges in assessing the performance of different AI algorithms is the varying conditions under which AI research studies are conducted. A new study from the Netherlands published this week in Radiology aims to correct that by testing a variety of AI algorithms head-to-head under similar conditions. 

There are over 200 AI algorithms on the European market (and even more in the US), many of which address the same clinical condition. 

  • Therefore, hospitals looking to acquire AI can find it difficult to assess the diagnostic performance of different models. 

The Project AIR initiative was launched to fill the gap in accurate assessment of AI algorithms by creating a Consumer Reports-style testing environment that’s consistent and transparent.

  • Project AIR researchers have assembled a validated database of medical images for different clinical applications, against which multiple AI algorithms can be tested; to ensure generalizability, images have come from different institutions and were acquired on equipment from different vendors. 

In the first test of the Project AIR concept, a team led by Kicky van Leeuwen of Radboud University Medical Centre in the Netherlands invited AI developers to participate, with nine products from eight vendors validated from June 2022 to January 2023: two models for bone age prediction and seven algorithms for lung nodule assessment (one vendor participated in both tests). Results included:

  • For bone age analysis, both of the tested algorithms (Visiana and Vuno) showed “excellent correlation” with the reference standard, with an r correlation coefficient of 0.987-0.989 (1 = perfect agreement)
  • For lung nodule analysis, there was a wider spread in AUC between the algorithms and human readers, with humans posting a mean AUC of 0.81
  • Researchers found superior performance for Annalise.ai (0.90), Lunit (0.93), Milvue (0.86), and Oxipit (0.88)

What’s next on Project AIR’s testing agenda? Van Leeuwen told The Imaging Wire that the next study will involve fracture detection. Meanwhile, interested parties can follow along on leaderboards for both bone age and lung nodule use cases. 

The Takeaway

Head-to-head studies like the one conducted by Project AIR may make many AI developers squirm (several that were invited declined to participate), but they are a necessary step toward building clinician confidence in the performance of AI algorithms that needs to take place to support the widespread adoption of AI. 

Top 12 Radiology Trends for 2024

Posted on by Brian Casey

What will be the top radiology trends for 2024? We talked to key opinion leaders across the medical imaging spectrum to get their opinions on the technologies, clinical applications, and regulatory developments that will shape the specialty for the next 12 months.

AI – Generative AI to Reduce Radiology’s Workload: “New generative AI methods will summarize complex medical records, draft radiology reports from images, and explain radiology reports to patients using language they understand. These innovative systems will reduce our workload and will provide more time for us to connect with our colleagues and our patients.” — Curtis Langlotz, MD, PhD, Stanford University and president, RSNA 2024

AI – Generative AI Will Get Multimodal: “In 2024, we can expect continued innovations in generative AI with a greater emphasis on integrating GenAI into existing and new radiology and patient-facing applications with growing interests in retrieval-augmented generation, fine-tuning, smaller models, multi-model routing, and AI assistants. Medicine being multimodal, the term ‘multimodal’ will become more ubiquitous.” — Woojin Kim, MD, CMIO at Rad AI

AI – Will AI Really Reduce Radiology Burnout? “Burnout will continue to be a huge issue in radiology with no solution in sight. AI vendors will offer algorithms as solutions to burnout with catchy slogans such as ‘buy our lung nodule detector and become the radiologist your parents wanted you to be.’ Their enthusiasm will cause even more burnout.” — Saurabh Jha, MBBS, AKA RogueRad, Hospital of the University of Pennsylvania

Breast Imaging – Prepare Now for Density Reporting: “The FDA ‘dense breast’ reporting standard to patients becomes effective on September 10, 2024, and breast imaging centers should be prepared for new patient questions and conversations. A plan for a consistent approach to recommending supplemental screening and facilitating ordering of additional imaging from referring providers should be put into action.” — JoAnn Pushkin, executive director, DenseBreast-info.org

Breast Imaging – Density Reporting to Spur Earlier Detection: “In March 2023, FDA issued a national requirement for reporting breast density to patients and referring providers after mammography. Facilities performing mammograms must meet the September 2024 deadline incorporating breast density type and associated breast cancer risk in their reporting. This change can lead to earlier breast cancer detection as these patients will be informed of supplemental screening as it relates to their breast density and [will] choose to pursue it.” — Stamatia Destounis, MD, Elizabeth Wende Breast Care and chair, ACR Breast Imaging Commission

CT – Lung Cancer Screening to Build Momentum: “Uptake of LDCT screening for lung cancer will increase in the US and worldwide. AI-enabled cardiac evaluation, even on non-gated scans, will allow for prediction of illnesses such as AFib and heart failure.  Quantifying measurement error across platforms will become an important aspect of nodule management.” — David Yankelevitz, MD, Icahn School of Medicine at Mount Sinai Health System

CT – Photon-Counting CT to Expand: “In 2024, we will continue to see many papers published on photon-counting CT, strengthening the body of scientific evidence as to its many strengths. Results from clinical trials involving multiple manufacturers’ systems will also increase in number, perhaps leading to more commercial systems entering the market.” — Cynthia McCollough, PhD, director, CT Clinical Innovation Center, Mayo Clinic

Enterprise Imaging – Time is Ripe for Cloud and AI: “Healthcare has an opportunity for change in 2024, and imaging is ripe for disruption, with burnout, staffing challenges, and new technology needs. Many organizations are expanding their enterprise imaging strategy and are asking how and where they can take the plunge into cloud and AI. Vendors have got the message; now it’s time to push the gas and deliver.” — Monique Rasband, VP of strategy & research, imaging/oncology at KLAS

Imaging IT – Data Brokerage to Go Mainstream: “A new market will hit the mainstream in 2024 – radiology data brokerage. As data-hungry LLMs scale up and the use of companion diagnostics in lifesciences proliferates, health systems will look to cash in on curated radiology data. This will also be an even bigger driver for migration to cloud-based imaging IT.” — Steve Holloway, managing director, Signify Research     

MRI – Prostate MRI to Reduce Biopsies: “Prostate MRI in conjunction with PSMA PET will explode in 2024 and reduce the number of unnecessary biopsies for patients.” — Stephen Pomeranz, MD, CEO of ProScan Imaging and chair, Naples Florida Community Hospital Network 

Theranostics – New Radiotracers to Drive Diagnosis & Treatment: “Through 2024, nuclear medicine theranostics will increasingly be integrated into standard global practice. With many new radiopharmaceuticals in development, theranostics promise early diagnosis and precision treatment for a broadening range of cancers, expanding options for patients resistant to traditional therapies. Treatments will be enhanced by personalized dosimetry, artificial intelligence, and combination therapies.” — Helen Nadel, MD, Stanford University and president, SNMMI 2023-2024

Radiology Operations – Reimbursement Challenges Continue: “In 2024, we will continue to experience recruitment challenges coupled with decreases in reimbursement. Now, more than ever, every radiologist needs to be diligent in advocating for the specialty, focus on business plan diversification, and ensure all services rendered are optimally documented and billed.” — Rebecca Farrington, chief revenue officer, Healthcare Administrative Partners 

The Takeaway
To paraphrase Robert F. Kennedy, radiology is indeed living in interesting times – times of “danger and uncertainty,” but also times of unprecedented creativity and innovation. In 2024, radiology will get a much better glimpse of where these trends are taking us.

How to Improve CT Lung Cancer Screening

Posted on by Brian Casey

As the US grapples with low CT lung cancer screening rates, researchers and clinicians around the world are pressing ahead with ways to make the exam more effective – especially in countries with high smoking rates. Two new studies published this week show the progress that’s being made.

In Brazil, researchers in JAMA Network Open found that using broader criteria to determine who should get CT lung screening not only expanded the eligible population, but it also reduced racial disparities in screening’s effectiveness. 

Researchers compared three strategies for determining screening eligibility: two based on 2013 and 2021 USPSTF criteria, and one in which all ever-smokers ages 50-80 were screened, finding: 

  • Screening all ever-smokers generated the largest possible screening population (27.3M people) compared to USPSTF criteria for 2013 (5.1M) and 2021 (8.4M)
  • Number of life-years gained if lung cancer is averted due to screening was highest with all-screening (23 vs. 19 & 21)
  • But the all-screening strategy also had the highest number needed to screen to prevent one lung cancer death (472 vs 177 & 242)
  • The USPSTF 2021 criteria reduced (but did not eliminate) racial disparities; the USPSTF 2013 criteria produced the greatest disparity 

The authors said the results showed that CT lung cancer screening in Brazil could identify 57% of preventable lung cancer deaths if 22% of ever-smokers are screened. Their study should help the country decide which screening strategy to adopt. 

In a second paper in the same journal, researchers from China described how they performed CT lung cancer screening via opportunistic screening, offering low-dose CT scans to patients visiting their doctor for other reasons, such as a routine checkup or a health problem other than a pulmonary issue. Among 5.2k patients, researchers found that people who got opportunistic LDCT screening had:

  • 34% lower risk of lung cancer death by hazard ratio
  • 28% lower risk of all-cause mortality
  • 43% received their lung cancer diagnosis through opportunistic screening

The Takeaway

This week’s studies continue the positive progress toward CT lung cancer screening that’s being made around the world. Both offer different strategies for making screening even more effective, and add to the growing weight of evidence in favor of population-based lung screening.

More Support for CT Lung Cancer Screening

Posted on by Brian Casey

Yet another study supporting CT lung cancer screening has been published, adding to a growing body of evidence that population-based CT screening programs will be effective in reducing lung cancer deaths. 

The new study comes from European Radiology, where researchers from Hungary describe findings from HUNCHEST-II, a population-based program that screened 4.2k high-risk people at 18 institutions. 

  • Screening criteria were largely similar to other studies: people between the ages of 50 and 75 who were current or former smokers with at least 25 pack-year histories. Former smokers had quit within the last 15 years. 

Recruitment for HUNCHEST-II took place from September 2019 to January 2022. Participants received a baseline low-dose CT (LDCT) scan, with the study protocol calling for annual follow-up scans (more on this later). Researchers found: 

  • The prevalence of baseline screening exams positive for lung cancer was 4.1%, comparable to the NELSON trial (2.3%) but much lower than the NLST (27%)
  • 1.8% of participants were diagnosed with lung cancer throughout screening rounds
  • 1.5% of participants had their cancer found with the baseline exam
  • Positive predictive value was 58%, at the high end of population-based lung screening programs
  • 79% of screen-detected cancers were early stage, making them well-suited for treatment
  • False-positive rate was 42%, a figure the authors said was “concerning”

Taking a deeper dive into the data produces interesting revelations. Overdiagnosis is a major concern with any screening test; it was a particular problem with NLST but was lower with HUNCHEST-II. 

  • Researchers said they used a volume-based nodule evaluation protocol, which reduced the false-positive rate compared to the nodule diameter-based approach in NLST.

Also, a high attrition rate occurred between the baseline scan and annual screening rounds, with only 12% of individuals with negative baseline LDCT results going on to follow-up screening (although the COVID-19 pandemic may have affected these results). 

The Takeaway

The HUNCHEST-II results add to the growing momentum in favor of national population-based CT lung screening programs. Germany is planning to implement a program in early 2024, and Taiwan is moving in the same direction. The question is, does the US need to step up its game as screening compliance rates remain low?

Unpacking the Biden Administration’s New AI Order

Posted on by Brian Casey

It seems like watershed moments in AI are happening on a weekly basis now. This time, the big news is the Biden Administration’s sweeping executive order that directs federal regulation of AI across multiple industries – including healthcare. 

The order comes as AI is becoming a clinical reality for many applications. 

  • The number of AI algorithms cleared by the FDA has been surging, and clinicians – particularly radiologists – are getting access to new tools on an almost daily basis.

But AI’s rapid growth – and in particular the rise of generative AI technologies like ChatGPT – have raised questions about its future impact on patient care and whether the FDA’s existing regulatory structure is suitable for such a new technology. 

The executive order appears to be an effort to get ahead of these trends. When it comes to healthcare, its major elements are summarized in a succinct analysis of the plan by Health Law Advisor. In short, the order: 

  • Calls on HHS to work with the VA and Department of Defense to create an HHS task force on AI within 90 days
  • Requires the task force to develop a strategic plan within a year that could include regulatory action regarding the deployment and use of AI for applications such as healthcare delivery, research, and drug and device safety
  • Orders HHS to develop a strategy within 180 days to determine if AI-enabled technologies in healthcare “maintain appropriate levels of quality” – basically, a review of the FDA’s authorization process
  • Requires HHS to set up an AI safety program within a year, in conjunction with patient safety organizations
  • Tells HHS to develop a strategy for regulating AI in drug development

Most analysts are viewing the executive order as the Biden Administration’s attempt to manage both risk and opportunity. 

  • The risk is that AI developers lose control of the technology, with consequences such as patients potentially harmed by inaccurate AI. The opportunity is for the US to become a leader in AI development by developing a long-term AI strategy. 

The Takeaway

The question is whether an industry that’s as fast-moving as AI – with headlines changing by the week – will lend itself to the sort of centralized long-term planning envisioned in the Biden Administration’s executive order. Time will tell.

AI Tug of War Continues

Posted on by Brian Casey

The ongoing tug of war over AI’s value to radiology continues. This time the rope has moved in AI’s favor with publication of a new study in JAMA Network Open that shows the potential of a new type of AI language model for creating radiology reports.

  • Headlines about AI have ping-ponged in recent weeks, from positive studies like MASAI and PERFORMS to more equivocal trials like a chest X-ray study in Radiology and news from the UK that healthcare authorities may not be ready for chest X-ray AI’s full clinical roll-out. 

In the new paper, Northwestern University researchers tested a chest X-ray AI algorithm they developed with a transformer technique, a type of generative AI language model that can both analyze images and generate radiology text as output. 

  • Transformer language models show promise due to their ability to combine both image and non-image data, as researchers showed in a paper last week.

The Northwestern researchers tested their transformer model in 500 chest radiographs of patients evaluated overnight in the emergency department from January 2022 to January 2023. 

Reports generated by AI were then compared to reports from a teleradiologist as well as the final report by an in-house radiologist, which was set as the gold standard. The researchers found that AI-generated reports …

  • Had sensitivity a bit lower than teleradiology reports (85% vs. 92%)
  • Had specificity a bit higher (99% vs. 97%)
  • In some cases improved on the in-house radiology report by detecting subtle abnormalities missed by the radiologist

Generative AI language models like the Northwestern algorithm could perform better than algorithms that rely on a classification approach to predicting the presence of pathology. Such models limit medical diagnoses to yes/no predictions that may omit context that’s relevant to clinical care, the researchers believe. 

In real-world clinical use, the Northwestern team thinks their model could assist emergency physicians in circumstances where in-house radiologists or teleradiologists aren’t immediately available, helping triage emergent cases.

The Takeaway

After the negative headlines of the last few weeks, it’s good to see positive news about AI again. Although the current study is relatively small and much larger trials are needed, the Northwestern research has promising implications for the future of transformer-based AI language models in radiology.

CT Lung Screening Saves Women

Posted on by Brian Casey

October may be Breast Cancer Awareness Month, but a new study has great news for women when it comes to another life-threatening disease: lung cancer. 

Italian researchers in Lung Cancer found that CT lung cancer screening delivered survival benefits that were particularly dramatic for women – and could address cardiovascular disease as well. 

  • They found that in addition to much higher survival rates, women who got CT lung screening after 12 years of follow-up had lower all-cause mortality than men. 

Of all the cancer screening tests, lung screening is the new kid on the block.

  • Although randomized clinical trials have shown it to deliver lung cancer mortality benefits of 20% and higher, uptake of lung screening has been relatively slow compared to other tests.

In the current study, researchers from the Fondazione IRCCS Istituto Nazionale dei Tumori in Milan analyzed data from 6.5k heavy smokers in the MILD and BioMILD trials who got low-dose CT screening from 2005 to 2016. 

In addition to cancer incidence and mortality, they also used Coreline Soft’s AVIEW software to calculate coronary artery calcium (CAC) scores acquired with the screening exams to see if they predicted lung cancer mortality. Researchers found that after 12 years of follow-up …

  • There was no statistically significant difference in lung cancer incidence between women and men (4.4% vs. 4.7%)
  • But women had lower lung cancer mortality than men (1% vs. 1.9%) as well as lower all-cause mortality (4.1% vs. 7.7%), both statistically significant
  • Women had higher lung cancer survival than men (72% vs. 52%)
  • 15% of participants had CAC scores between 101-400, and all-cause mortality increased with higher scores
  • Women had lower CAC scores, which could play a role in lower all-cause mortality due to less cardiovascular disease

The Takeaway

This is a fascinating study on several levels. First, it shows that lung cancer screening produces a statistically significant decline in all-cause mortality for women compared to men.

Second, it shows that CT lung cancer screening can also serve as a screening test for cardiovascular disease, helping direct those with high CAC scores to treatment such as statin therapy. This type of opportunistic screening could change the cost-benefit dynamic when it comes to analyzing lung screening’s value – especially for women.

More Work Ahead for Chest X-Ray AI?

Posted on by Brian Casey

In another blow to radiology AI, the UK’s national technology assessment agency issued an equivocal report on AI for chest X-ray, stating that more research is needed before the technology can enter routine clinical use.

The report came from the National Institute for Health and Care Excellence (NICE), which assesses new health technologies that have the potential to address unmet NHS needs. 

The NHS sees AI as a potential solution to its challenge of meeting rising demand for imaging services, a dynamic that’s leading to long wait times for exams. 

But at least some corners of the UK health establishment have concerns about whether AI for chest X-ray is ready for prime time. 

  • The NICE report states that – despite the unmet need for quicker chest X-ray reporting – there is insufficient evidence to support the technology, and as such it’s not possible to assess its clinical and cost benefits. And it said there is “no evidence” on the accuracy of AI-assisted clinician review compared to clinicians working alone.

As such, the use of AI for chest X-ray in the NHS should be limited to research, with the following additional recommendations …

  • Centers already using AI software to review chest X-rays may continue to do so, but only as part of an evaluation framework and alongside clinician review
  • Purchase of chest X-ray AI software should be made through corporate, research, or non-core NHS funding
  • More research is needed on AI’s impact on a number of outcomes, such as CT referrals, healthcare costs and resource use, review and reporting time, and diagnostic accuracy when used alongside clinician review

The NICE report listed 14 commercially available chest X-ray algorithms that need more research, and it recommended prospective studies to address gaps in evidence. AI developers will be responsible for performing these studies.

The Takeaway

Taken with last week’s disappointing news on AI for radiology, the NICE report is a wakeup call for what had been one of the most promising clinical use cases for AI. The NHS had been seen as a leader in spearheading clinical adoption of AI; for chest X-ray, clinicians in the UK may have to wait just a bit longer.

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