Tag: medical imaging

Radiology’s Enduring Popularity

Posted on by Brian Casey

Radiology is seeing a resurgence of interest from medical students picking the specialty in the National Resident Matching Program (NRMP). While radiology’s popularity is at historically high levels, the new analysis shows how vulnerable the field is to macro-economic trends in healthcare. 

Radiology’s popularity has always ebbed and flowed. In general the field is seen as one of the more attractive medical specialties due to the perception that it combines high salaries with lifestyle advantages. But there have been times when medical students shunned radiology.

The new paper offers insights into these trends. Published in Radiology by Francis Deng, MD, and Linda Moy, MD, the paper fleshes out an earlier analysis that Deng posted as a Twitter thread after the 2023 Match, showing that diagnostic radiology saw the highest growth in applicants to medical specialties over a three-year period.

Deng and Moy analyze trends in the Match over almost 25 years in the new study, finding…

  • The 2023 Match in radiology was the most competitive since 2001 based on percentage of applicants matching (81.1% vs. 73.3%)
  • 5.9% of seniors in US MD training programs applied to diagnostic radiology in the 2023 Match, the highest level since 2010
  • Fewer radiology residency slots per applicant were available in 2023 compared to the historical average (0.67 vs. 0.81) 

Interest in radiology hit its lowest levels in 1996 and 2015, when the number of applicants fell short of available radiology residency positions in the Match. It’s perhaps no surprise that these lows followed two major seismic healthcare shifts that could have negatively affected job prospects for radiologists: the “Hillarycare” healthcare reform effort in the early 1990s and the emergence of AI for healthcare in the mid-2010s. 

Hillarycare never happened, and Deng and Moy noted that outreach efforts to medical students about AI helped reverse the perspective that the technology would be taking radiologists’ jobs. Another advantage for radiology is its early adoption of teleradiology, which enables remote work and more flexible work options – a major lifestyle perk. 

The Takeaway

The new paper provides fascinating insights that support why radiology remains one of medicine’s most attractive specialties. Radiology’s appeal could even grow, given recent studies showing that work-life balance is a major priority for today’s medical students.

CT Detects Early Lung Cancer

Posted on by Brian Casey

A massive CT lung cancer screening program launched in Taiwan has been effective in detecting early lung cancer. Research presented at this week’s World Conference on Lung Cancer (WCLC) in Singapore offers more support for lung screening, which has seen the lowest uptake of the major population-based screening programs. 

Previous randomized clinical trials like the National Lung Screening Trial and the NELSON study have shown that LDCT lung cancer screening can reduce lung cancer mortality by at least 20%. But screening adherence rates remain low, ranging from the upper single digits to as high as 21% in a recent US study. 

Meanwhile, lung cancer remains the leading cause of cancer death worldwide. To reduce this burden, Taiwan in July 2022 launched the Lung Cancer Early Detection Program, which offers biennial screening nationwide to people at high risk of lung cancer.

The Taiwan program differs from screening programs in the US and South Korea by including family history of lung cancer in the eligibility criteria, rather than just focusing on people who smoke. 

Researchers at WCLC 2023 presented the first preliminary results from the program, covering almost 50k individuals screened from July 2022 to June 2023; 29k had a family history of lung cancer and 19k were people who smoked heavily. Researchers found …

  • 4.4k individuals receive a positive screening result for a positive rate of 9.2%
  • 531 people were diagnosed with lung cancer for a detection rate of 1.1%
  • 85% of cancers were diagnosed at an early stage, either stage 0 or stage 1

This last finding is perhaps the most significant, as part of the reason for lung cancer’s high mortality rate is that it’s often discovered at a late stage, when it’s far more difficult to treat. As such, lung cancer’s five-year survival rate is about 25% – far lower than breast cancer at 91%.

The Takeaway

Taiwan is setting an example to other countries for how to conduct a nationwide LDCT lung cancer screening program, even as some critics take aim at population-based screening. Taiwan’s approach is broader and more proactive than that of the US, for example, which has erected screening barriers like shared decision-making.

Although it’s still early days for the Taiwan program, future results will be examined closely to determine screening’s impact on lung cancer mortality – and respond to screening’s critics.

Tipping Point for Breast AI?

Posted on by Brian Casey

Have we reached a tipping point when it comes to AI for breast screening? This week another study was published – this one in Radiology – demonstrating the value of AI for interpreting screening mammograms. 

Of all the medical imaging exams, breast screening probably could use the most help. Reading mammograms has been compared to looking for a needle in a haystack, with radiologists reviewing thousands of images before finding a single cancer. 

AI could help in multiple ways, either at the radiologist’s side during interpretation or by reviewing mammograms in advance, triaging the ones most likely to be normal while reserving suspicious exams for closer attention by radiologists (indeed, that was the approach used in the MASAI study in Sweden in August).

In the new study, UK researchers in the PERFORMS trial compared the performance of Lunit’s INSIGHT MMG AI algorithm to that of 552 radiologists in 240 test mammogram cases, finding that …

  • AI was comparable to radiologists for sensitivity (91% vs. 90%, P=0.26) and specificity (77% vs. 76%, P=0.85). 
  • There was no statistically significant difference in AUC (0.93 vs. 0.88, P=0.15)
  • AI and radiologists were comparable or no different with other metrics

Like the MASAI trial, the PERFORMS results show that AI could play an important role in breast screening. To that end, a new paper in European Journal of Radiology proposes a roadmap for implementing mammography AI as part of single-reader breast screening programs, offering suggestions on prospective clinical trials that should take place to prove breast AI is ready for widespread use in the NHS – and beyond. 

The Takeaway

It certainly does seem that AI for breast screening has reached a tipping point. Taken together, PERFORMS and MASAI show that mammography AI works well enough that “the days of double reading are numbered,” at least where it is practiced in Europe, as noted in an editorial by Liane Philpotts, MD. 

While double-reading isn’t practiced in the US, the PERFORMS protocol could be used to supplement non-specialized radiologists who don’t see that many mammograms, Philpotts notes. Either way, AI looks poised to make a major impact in breast screening on both sides of the Atlantic.

Screening Foes Strike Back

Posted on by Brian Casey

Opponents of population-based cancer screening aren’t going away anytime soon. Just weeks after publication of a landmark study claiming that cancer screening has saved $7T over 25 years, screening foes published a counterattack in JAMA Internal Medicine casting doubt on whether screening has any value at all. 

Population-based cancer screening has been controversial since the first programs were launched decades ago. 

  • A vocal minority of skeptics continues to raise concerns about screening, despite the fact that mortality rates have dropped and survival rates have increased for the four cancers targeted by population screening.

This week’s JAMA Internal Medicine featured a series of articles that cast doubt on screening. In the main study, researchers performed a meta-analysis of 18 randomized clinical trials (RCTs) covering 2.1M people for six major screening tests, including mammography, CT lung cancer screening, and colon and PSA tests. 

  • The authors, led by Norwegian gastroenterologist Michael Bretthauer, MD, PhD, concluded that only flexible sigmoidoscopy for colon cancer produced a gain in lifetimes. They conclude that RCTs to date haven’t included enough patients who were followed over enough years to show screening has an effect on all-cause mortality.

But a deeper dive into the study produces interesting revelations. For CT lung cancer screening, Bretthauer et al didn’t include the landmark National Lung Screening Trial, an RCT that showed a 20% mortality reduction from screening.

  • With respect to breast imaging, the researchers only included three studies, even though there have been eight major mammography RCTs performed. And one of the three included was the controversial Canadian National Breast Screening Study, originally conducted in the 1980s.

When it comes to colon screening, Bretthauer included his own controversial 2022 NordICC study in his meta-analysis. 

  • The NordICC study found that if a person is invited to colon screening but doesn’t follow through, they don’t experience a mortality benefit. But those who actually got colon screening saw a 50% mortality reduction.  

Other articles in this week’s JAMA Internal Medicine series were penned by researchers well known for their opposition to population-based screening, including Gilbert Welch, MD, and Rita Redberg, MD.

The Takeaway

There’s an old saying in statistics: “If you torture the data long enough, it will confess to anything.” Among major academic journals, JAMA Internal Medicine – which Redberg guided for 14 years as editor until she stepped down in June – has consistently been the most hostile toward screening and new medical technology.

In the end, the arguments being made by screening’s foes would carry more weight if they were coming from researchers and journals that haven’t already demonstrated a longstanding, ingrained bias against population-based cancer screening.

Economic Barriers to AI

Posted on by Brian Casey

A new article in JACR highlights the economic barriers that are limiting wider adoption of AI in healthcare in the US. The study paints a picture of how the complex nature of Medicare reimbursement puts the country at risk of falling behind other nations in the quest to implement healthcare AI on a national scale. 

The success of any new medical technology in the US has always been linked to whether physicians can get reimbursed for using it. But there are a variety of paths to reimbursement in the Medicare system, each one with its own rules and idiosyncrasies. 

The establishment of the NTAP program was thought to be a milestone in paying for AI for inpatients, for example, but the JACR authors note that NTAP payments are time-limited for no more than three years. A variety of other factors are limiting AI reimbursement, including … 

  • All of the AI payments approved under the NTAP program have expired, and as such no AI algorithm is being reimbursed under NTAP 
  • Budget-neutral requirements in the Medicare Physician Fee Schedule mean that AI reimbursement is often a zero-sum game. Payments made for one service (such as AI) must be offset by reductions for something else 
  • Only one imaging AI algorithm has successfully navigated CMS to achieve Category I reimbursement in the Physician Fee Schedule, starting in 2024 for fractional flow reserve (FFR) analysis

Standing in stark contrast to the Medicare system is the NHS in the UK, where regulators see AI as an invaluable tool to address chronic workforce shortages in radiology and are taking aggressive action to promote its adoption. Not only has NHS announced a £21M fund to fuel AI adoption, but it is mulling the implementation of a national platform to enable AI algorithms to be accessed within standard radiology workflow. 

The Takeaway

The JACR article illustrates how Medicare’s Byzantine reimbursement structure puts barriers in the path of wider AI adoption. Although there have been some reimbursement victories such as NTAP, these have been temporary, and the fact that only one radiology AI algorithm has achieved a Category I CPT code must be a sobering thought to AI proponents.

Fine-Tuning Cardiac CT

Posted on by Brian Casey

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

Posted on by Brian Casey

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.

How Vendors Sell AI

Posted on by Brian Casey

Better patient care is the main selling point used by AI vendors when marketing neuroimaging algorithms, followed closely by time savings. Farther down the list of benefits are lower costs and increased revenue for providers. 

So says a new analysis in JACR that takes a close look at how FDA-cleared neuroimaging AI algorithms are marketed by vendors. It also includes several warning signs for both AI developers and clinicians.

AI is the most exciting technology to arrive in healthcare in decades, but questions percolate on whether AI developers are overhyping the technology. In the new analysis, researchers focused on marketing claims made for 59 AI neuroimaging algorithms cleared by the FDA from 2008 to 2022. Researchers analyzed FDA summaries and vendor websites, finding:

  • For 69% of algorithms, vendors highlighted an improvement in quality of patient care, while time savings for clinicians were touted for 44%. Only 16% of algorithms were promoted as lowering costs, while just 11% were positioned as increasing revenue
  • 50% of cleared neuroimaging algorithms were related to detection or quantification of stroke; of these, 41% were for intracranial hemorrhage, 31% for stroke brain perfusion, and 24% for detection of large vessel occlusion 
  • 41% of the algorithms were intended for use with non-contrast CT scans, 36% with MRI, 15% with CT perfusion, 14% with CT angiography, and the rest with MR perfusion and PET
  • 90% of the algorithms studied were cleared in the last five years, and 42% since last year

The researchers further noted two caveats in AI marketing: 

  • There is a lack of publicly available data to support vendor claims about the value of their algorithms. Better transparency is needed to create trust and clinician engagement.
  • The single-use-case nature of many AI algorithms raises questions about their economic viability. Many different algorithms would have to be implemented at a facility to ensure “a reasonable breadth of triage” for critical findings, and the financial burden of such integration is unclear.

The Takeaway

The new study offers intriguing insights into how AI algorithms are marketed by vendors, and how these efforts could be perceived by clinicians. The researchers note that financial pressure on AI developers may cause them to make “unintentional exaggerated claims” to recoup the cost of development; it is incumbent upon vendors to scrutinize their marketing activities to avoid overhyping AI technology.

Grading AI Report Quality

Posted on by Brian Casey

One of the most exciting new use cases for medical AI is in generating radiology reports. But how can you tell whether the quality of a report generated by an AI algorithm is comparable to that of a radiologist?

In a new study in Patterns, researchers propose a technical framework for automatically grading the output of AI-generated radiology reports, with the ultimate goal of producing AI-generated reports that are indistinguishable from those of radiologists. 

Most radiology AI applications so far have focused on developing algorithms to identify individual pathologies on imaging exams. 

  • While this is useful, helping radiologists streamline the production of their main output – the radiology report – could have a far greater impact on their productivity and efficiency. 

But existing tools for measuring the quality of AI-generated narrative reports are limited and don’t match up well with radiologists’ evaluations. 

  • To improve that situation, the researchers applied several existing automated metrics for analyzing report quality and compared them to the scores of radiologists, seeking to better understand AI’s weaknesses. 

Not surprisingly, the automated metrics fell short in several ways, including false prediction of findings, omitting findings, and incorrectly locating and predicting the severity of findings. 

  • These shortcomings point out the need for better scoring systems for gauging AI performance. 

The researchers therefore proposed a new metric for grading AI-generated report quality, called RadGraph F1, and a new methodology, RadCliQ, to predict how well an AI report would measure up to radiologist scrutiny. 

  • RadGraph F1 and RadCliQ could be used in future research on AI-generated radiology reports, and to that end the researchers have made the code for both metrics available as open source.

Ultimately, the researchers see the construction of generalist medical AI models that could perform multiple complex tasks, such as conversing with radiologists and physicians about medical images. 

  • Another use case could be applications that are able to explain imaging findings to patients in everyday language. 

The Takeaway

It’s a complex and detailed paper, but the new study is important because it outlines the metrics that can be used to teach machines how to generate better radiology reports. Given the imperative to improve radiologist productivity in the face of rising imaging volume and workforce shortages, this could be one more step on the quest for the Holy Grail of AI in radiology.

Breast Ultrasound Gets Wearable

Posted on by Brian Casey

Wearable devices are all the rage in personal fitness – could wearable breast ultrasound be next? MIT researchers have developed a patch-sized wearable breast ultrasound device that’s small enough to be incorporated into a bra for early cancer detection. They described their work in a new paper in Science Advances.

This isn’t the first use of wearable ultrasound. In fact, earlier this year UCSD researchers revealed their work on a wearable cardiac ultrasound device that obtains real-time data on cardiac function. 

The MIT team’s concept expands the idea into cancer detection. They took advantage of previous work on conformable piezoelectric ultrasound transducer materials to develop cUSBr-Patch, a one-dimensional phased-array probe integrated into a honeycomb-shaped patch that can be inserted into a soft fabric bra. 

The array covers the entire breast surface and can acquire images from multiple angles and views using 64 elements at a 7MHz frequency. The honeycomb design means that the array can be rotated and moved into different imaging positions, and the bra can even be reversed to acquire images from the other breast. 

The researchers tested cUSBr-Patch on phantoms and a human subject, and compared it to a conventional ultrasound scanner. They found that cUSBr-Patch:

  • Had a field of view up to 100mm wide and an imaging depth up to 80mm
  • Achieved resolution comparable to conventional ultrasound
  • Detected cysts as small as 30mm in the human volunteer, a 71-year-old woman with a history of breast cysts
  • The same cysts were detected with the array in different positions, an important capability for long-term monitoring

The MIT researchers believe that wearable breast ultrasound could detect early-stage breast cancer, in cases such as high-risk people in between routine screening mammograms. 

The researchers ultimately hope to develop a version of the device that’s about the size of a smartphone (right now the array has to be hooked up to a conventional ultrasound scanner to view images). They also want to investigate the use of AI to analyze images.

The Takeaway

It’s still early days for wearable breast ultrasound, but the new results are an exciting development that hints of future advances to come. Wearable breast ultrasound could even have an advantage over other wearable use cases like cardiac monitoring, as it doesn’t require continuous imaging during the user’s activities. Stay tuned.

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