Tag: medical imaging

Two-for-One CT Screening Hits the Road

Posted on by Brian Casey

A new study takes CT screening on the road in rural Appalachia, showing how a mobile van outfitted with a CT scanner can screen at-risk individuals for both lung cancer and cardiovascular disease in one visit. 

Recent studies have demonstrated the effectiveness of CT lung cancer screening not only among the overall population, but particularly among disadvantaged communities with lower healthcare access. 

  • Such limited access is common in rural areas of Appalachia, which also have some of the highest rates of smoking and cardiovascular disease in the U.S.

Researchers from West Virginia University wanted to tackle two challenges at once with LUCAS, a mobile van outfitted with a CT scanner for lung cancer screening. 

  • They noted that CT lung scans can also be used to acquire data on coronary artery calcium (CAC), a known risk factor for cardiovascular disease. 

LUCAS was launched in September 2021, so WVU researchers analyzed data acquired for 526 low-dose CT screenings of high-risk people conducted through December 2022. 

  • They used the CT lung scans to calculate CAC scores based on Agatson criteria, in which a score of 101-400 indicates moderate risk of cardiovascular disease and >400 is classified as high risk; individuals with scores ≥100 should be referred to aspirin or statin therapy. 

They found that LUCAS scans revealed … 

  • Over 54% of patients had coronary calcification on LDCT scans
  • 31% of patients had CAC scores ≥100 
  • 14% had CAC scores ≥400
  • Elevated CAC scores correlated with lung cancer risk based on Lung-RADS scores as well as smoking history based on pack-years
  • Of the patients with CAC scores ≥1 and who weren’t already on statin or aspirin therapy, 6.2% started statins and 3.3% started aspirin

Despite the firm link between CAC scores and lung cancer risk, the researchers expressed disappointment that so few patients started prevention therapy like statins or aspirin after their exams.

  • Indeed, researchers noted that few patients from the study got additional cardiac testing or follow-up referrals for cardiovascular prevention after their screenings. 

The Takeaway

The new study not only confirms recent research showing that opportunistic screening can enhance the value of CT lung cancer scans, but also the role that lung exams can play in reducing healthcare disparities. On the down side, it shows that all the screening in the world won’t make a difference if patients don’t get appropriate follow-up. 

FDA Keeps Pace on AI Approvals

Posted on by Brian Casey

The FDA has updated its list of AI- and machine learning-enabled medical devices that have received regulatory authorization. The list is a closely watched barometer of the health of the AI sector, and the update shows the FDA is keeping a brisk pace of authorizations.

The FDA has maintained double-digit growth of AI authorizations for the last several years, a pace that reflects the growing number of submissions it’s getting from AI developers. 

  • Indeed, data compiled by regulatory expert Bradley Merrill Thompson show how the number of FDA authorizations has been growing rapidly since the dawn of the medical AI era in around 2016 (see also our article on AI safety below). 

The new FDA numbers show that …

  • The FDA has now authorized 950 AI/ML-enabled devices since it began keeping track
  • Device authorizations are up 15% for the first half of 2024 compared to the same period the year before (107 vs. 93)
  • The pace could grow even faster in late 2024 – in 2023, FDA in the second half authorized 126 devices, up 35% over the first half
  • At that pace, the FDA should hit just over 250 total authorizations in 2024 
  • This would represent 14% growth over 220 authorizations in 2023, and compares to growth of 14% in 2022 and 15% in 2021
  • As with past updates, radiology makes up the lion’s share of AI/ML authorizations, but had a 73% share in the first half, down from 80% for all of 2023
  • Siemens Healthineers led in all H1 2024 clearances with 11, bringing its total to 70 (66 for Siemens and four for Varian). GE HealthCare remains the leader with 80 total clearances after adding three in H1 2024 (GE’s total includes companies it has acquired, like Caption Health and MIM Software). There’s a big drop off after GE and Siemens, including Canon Medical (30), Aidoc (24), and Philips (24).

The FDA’s list includes both software-only algorithms as well as hardware devices like scanners that have built-in AI capabilities, such as a mobile X-ray unit that can alert users to emergent conditions. 

  • Indeed, many of the authorizations on the FDA’s list are for updated versions of already-cleared products rather than brand-new solutions – a trend that tends to inflate radiology’s share of approvals.

The Takeaway

The new FDA numbers on AI/ML regulatory authorizations are significant not only for revealing the growth in approvals, but also because the agency appears to be releasing the updates more frequently – perhaps a sign it is practicing what it preaches when it comes to AI openness and transparency. 

Better Prostate MRI with AI

Posted on by Brian Casey

A homegrown AI algorithm was able to detect clinically significant prostate cancer on MRI scans with the same accuracy as experienced radiologists. In a new study in Radiology, researchers say the algorithm could improve radiologists’ ability to detect prostate cancer on MRI, with fewer false positives.

In past issues of The Imaging Wire, we’ve discussed the need to improve on existing tools like PSA tests to make prostate cancer screening more precise with fewer false positives and less need for patient work-up.

  • Adding MRI to prostate screening protocols is a step forward, but MRI is an expensive technology that requires experienced radiologists to interpret.

Could AI help? In the new study, researchers tested a deep learning algorithm developed at the Mayo Clinic to detect clinically significant prostate cancer on multiparametric (mpMRI) scans.

  • In an interesting wrinkle, the Mayo algorithm does not indicate tumor location, so a second algorithm – called Grad-CAM – was employed to localize tumors.

The Mayo algorithm was trained on a population of 5k patients with a cancer prevalence similar to a screening population, then tested in an external test set of 204 patients, finding …

  • No statistically significant difference in performance between the Mayo algorithm and radiologists based on AUC (0.86 vs. 0.84, p=0.68)
  • The highest AUC was with the combination of AI and radiologists (0.89, p<0.001)
  • The Grad-CAM algorithm was accurate in localizing 56 of 58 true-positive exams

An editorial noted that the study employed the Mayo algorithm on multiparametric MRI exams.

  • Prostate cancer imaging is moving from mpMRI toward biparametric MRI (bpMRI) due to its faster scan times and lack of contrast, and if validated on bpMRI, AI’s impact could be even more dramatic.

The Takeaway
The current study illustrates the exciting developments underway to make prostate imaging more accurate and easier to perform. They also support the technology evolution that could one day make prostate cancer screening a more widely accepted test.

Next-Generation Brain PET

Posted on by Brian Casey

A new paper in JNM includes the first human images acquired with a next-generation dedicated brain PET/CT scanner that could create a new standard for neurological research. United Imaging’s NeuroEXPLORER scanner has sensitivity and spatial resolution “an order of magnitude” better than existing technology. 

In addition to its value as a clinical tool, PET has carved out a research role for investigating some of the most fundamental questions about brain function and pathology. 

  • Commercial whole-body scanners can be used for research, but dedicated brain systems like the High Resolution Research Tomograph (HRRT) offer even higher resolution for imaging tiny structures in the brain. 

NeuroEXPLORER was developed by a consortium that includes United Imaging, UC Davis, and Yale University to adapt for dedicated brain imaging the long-axis PET technology found in United’s uEXPLORER total-body PET/CT system. 

  • NeuroEXPLORER was a highlight at the recent SNMMI 2024 conference, and images acquired with the system won the show’s coveted Image of the Year honors.

In the new study, researchers go into more detail about NeuroEXPLORER’s specifications, which include … 

  • An extended axial field of view (FOV) of 49.5cm for higher sensitivity
  • Transverse spatial resolution ranging from 1.64-2.51mm at full-width half-maximum
  • Average time-of-flight resolution of 236 picoseconds
  • NEMA sensitivities of 46.0 and 47.6 kcps/MBq at center and 10cm offset, and absolute sensitivity of 11.8% at the center of the FOV

Such high sensitivity and spatial resolution enables tasks “previously considered difficult or impossible,” like imaging focal tracer uptake of small subcortical regions or low-density binding sites like cortical dopamine receptors. 

  • What’s more, NeuroEXPLORER’s long axial length enables high-quality imaging of the spinal cord and carotid arteries.

Now for the disclaimer: United Imaging notes that NeuroEXPLORER has not been submitted to the FDA for clearance and at present is only for research use; the company’s uEXPLORER scanner does have clearance and is in operation at several commercial sites. 

The Takeaway

Publication in a journal of the first human images from NeuroEXPLORER are an exciting development and underscore the potential of dedicated brain PET to advance research into neurological function and pathology. Whether the scanner develops into a clinical tool remains to be seen.

DBT Detects Earlier Cancers in Swedish Tomo Study

Posted on by Brian Casey

A new analysis of a landmark DBT study from Sweden offers more support for the effectiveness of tomosynthesis mammography screening. Published in Radiology, researchers found that DBT screening seems to detect earlier cancers, most likely before they become more aggressive. 

Most U.S. mammography practices have embraced DBT since its approval in 2011, such that 48% of all certified mammography units are DBT and 90% of all facilities have at least one tomosynthesis unit. 

  • But doubts about DBT have persisted, particularly by mammography skeptics who charge that the technology was adopted without conducting randomized controlled trials to prove its value. 

But apart from RCTs, there have been plenty of observational studies in which DBT showed a benefit, one of them being the Malmö Breast Tomosynthesis Screening Trial of almost 15k women in Sweden.

  • First results from MBTST were published in 2018 and showed that single-view DBT screening had a 34% higher cancer detection rate per 1k women than digital mammography (8.7 vs. 6.5), but with a higher recall rate as well (3.6% vs. 2.5%).

In the new study, researchers wanted to see if DBT’s screening benefits persisted over two subsequent screening rounds with conventional digital mammography. 

  • Their assumption was that the cancer detection rate would be lower in subsequent rounds, and there would be fewer slow-growing, less aggressive cancers – a sign of early cancer detection. 

Their analysis found …

  • The cancer detection rate per 1k women was lower in the first (4.6) and second (5.3) rounds compared to the original MBTST
  • Recall rate was 2.1% – also lower 
  • The odds ratio of cancer detection was lower than MBTST in the first (OR=0.46) and second (OR=0.53) follow-up rounds 
  • Invasive cancers were less prevalent in the first round compared to the second round (66% vs. 83%) 

What do the results mean? The implication is that because DBT detected cancers in the initial screening round, there was lower cancer prevalence and less aggressive cancer in follow-up rounds, an effect that wore off as time went on.

The Takeaway

There may never be a randomized controlled trial of DBT due to the ethical problem of denying a live-saving technology to women in a control group. But studies like the MBTST follow-up are important in adding to the body of evidence showing that DBT actually does work.

Unpacking 2025 Medicare Changes

Posted on by Brian Casey

Here we go again. CMS has once again proposed cuts in Medicare and Medicaid reimbursement, and the healthcare community is once again rallying to try to stave them off. 

CMS last month released its proposed reimbursement changes for 2025, and there were a few victories for radiology. 

  • CMS finally agreed to pay for CT colonography, and also agreed to unbundle payments for PET radiotracers from the PET scan itself.

But CMS also proposed changes in the Medicare Physician Fee Schedule (MPFS) conversion factor that continue the slow drip of reimbursement reduction for physicians.

  • The agency said the proposal would result in no change for radiology, but a deeper dive reveals that’s not the case. 

For example, the analysts at revenue cycle management firm Healthcare Administrative Partners have reviewed the MPFS changes, calculating that if Congressional adjustments are factored in, the outlook is quite different…

  • Interventional radiology will see a -5.8% reduction in the imaging center global fee and a -1.8% drop in the hospital professional fee, for a combined decline of -4.8%
  • The numbers for radiology and nuclear medicine are -3.8% for imaging centers and -1.8% for hospitals, for combined declines of -2.8%

It may seem like -2.8% isn’t a huge cut, but it continues years of steady declines in Medicare reimbursement (HAP notes that the Medicare physician fee schedule has dropped -10% in the last 10 years).

  • And as anyone in healthcare knows, the costs that healthcare practices face have only gone up over that period.  

There’s always the chance that Congress will come to the rescue, as it did when it passed the Consolidated Appropriations Act of 2024 – indeed, professional medical groups led by the AMA published a letter last week urging lawmakers to reform CMS’ rate-setting system in several ways …

  • Enact an annual payment update tied to inflation
  • Eliminate the requirement that changes in payments be budget-neutral
  • Overhaul the Merit-based Incentive Payment System (MIPS)
  • Make modifications to Alternative Payment Models

The Takeaway

The annual ritual in which CMS proposes sharp cuts in Medicare reimbursement only to have Congress lift them at the last minute is a sort of public policy kabuki dance in which the outcome is practically preordained. Medicare reform is badly needed to end this cycle and put physicians on firmer footing so they can focus on what’s important: caring for patients.

US + Mammo vs. Mammo + AI for Dense Breasts

Posted on by Brian Casey

Artificial intelligence may represent radiology’s future, but for at least one clinical application traditional imaging seems to be the present. In a new study in Radiology, ultrasound was more effective than AI for supplemental imaging of women with dense breast tissue. 

Dense breast tissue has long presented problems for breast imaging specialists. 

  • Women with dense breasts are at higher risk of breast cancer, but traditional screening modalities like X-ray mammography don’t work very well (sensitivity of 30-48%), creating the need for supplemental imaging tools like ultrasound and MRI.

In the new study, researchers from South Korea tested the use of Lunit’s Insight MMG mammography AI algorithm in 5.7k women without symptoms who had breast tissue classified as heterogeneously (63%) or extremely dense (37%). 

  • AI’s performance was compared to both mammography alone as well as to mammography with ultrasound, one of the gold-standard modalities for imaging women with dense breasts. 

All in all, researchers found …

  • Mammography with AI had lower sensitivity than mammography with ultrasound but slightly better than mammography alone (61% vs. 97% vs. 58%)
  • Mammography with AI had a lower cancer detection rate per 1k women but higher than mammography alone (3.5 vs. 5.6 vs. 3.3)
  • Mammography with AI missed 12 cancers detected with mammography with ultrasound
  • Mammography with AI had the highest specificity (95% vs. 78% vs. 94%)
  • And the lowest abnormal interpretation rate (5% vs. 23% vs. 6%)

The results show that while AI can help radiologists interpret screening mammography for most women, at present it can’t compensate for mammography’s low sensitivity in women with dense breast tissue.

In an editorial, breast radiologists Gary Whitman, MD, and Stamatia Destounis, MD, observed that supplemental imaging of women with dense breasts is getting more attention as the FDA prepares to implement breast density notification rules in September. 

  • They recommended follow-up studies with other AI algorithms, more patients, and a longer follow-up period. 

The Takeaway

As with a recent study on AI and teleradiology, the current research is a good step toward real-world evaluation of AI for a specific use case. While AI in this instance didn’t improve mammography’s sensitivity in women with dense breast tissue, it could carve out a role reducing false positives for these women who get mammography and ultrasound.

Teleradiology AI’s Mixed Bag

Posted on by Brian Casey

An AI algorithm that examined teleradiology studies for signs of intracranial hemorrhage had mixed performance in a new study in Radiology: Artificial Intelligence. AI helped detect ICH cases that might have been missed, but false positives slowed radiologists down. 

AI is being touted as a tool that can detect unseen pathology and speed up the workflow of radiologists facing an environment of limited resources and growing image volume.

  • This dynamic is particularly evident at teleradiology practices, which frequently see high volumes during off-hour shifts; indeed, a recent study found that telerad cases had higher rates of patient death and more malpractice claims than cases read by traditional radiology practices.

So teleradiologists could use a bit more help. In the new study, researchers from the VA’s National Teleradiology Program assessed Avicenna.ai’s CINA v1.0 algorithm for detecting ICH on STAT non-contrast head CT studies.

  • AI was used to analyze 58.3k CT exams processed by the teleradiology service from January 2023 to February 2024, with a 2.7% prevalence of ICH.

Results were as follows…

  • AI flagged 5.7k studies as positive for acute ICH and 52.7k as negative
  • Final radiology reports confirmed that 1.2k exams were true positives for a sensitivity of 76% and a positive predictive value of 21%
  • There were 384 false negatives (missed ICH cases), for a specificity of 92% and a negative predictive value of 99.3%
  • The algorithm’s performance at the VA was a bit lower than in previously published literature
  • Cases that the algorithm falsely flagged as positive took over a minute longer to interpret than prior to AI deployment
  • Overall, case interpretation times were slightly lower after AI than before

One issue to note is that the CINA algorithm is not intended for small hemorrhages with volumes < 3 mL; the researchers did not exclude these cases from their analysis, which could have reduced its performance.

  • Also, at 2.7% the VA’s teleradiology program ICH prevalence was lower than the 10% prevalence Avicenna has used to rate its performance.

The Takeaway

The new findings aren’t exactly a slam dunk for AI in the teleradiology setting, but in terms of real-world results they are exactly what’s needed to assess the true value of the technology compared to outcomes in more tightly controlled environments.

6 Solutions to the RT Shortage

Posted on by Brian Casey

Earlier this week, we described the looming shortage of radiologists in the US; this week the focus turns to radiologic technologists. A new report from the ASRT and other groups suggests the shortage of RT positions is severe, but offers some solutions. 

The healthcare industry has suffered in the post-COVID era as the need for medical services has surged due to the aging population while the number of personnel has dropped as staff leave because of retirement, burnout, and other reasons.

  • At the same time, fewer trainees are entering healthcare, a phenomenon that’s particularly problematic with allied health personnel like nurses and technologists. 

The numbers are dire, based on previously collected data …

  • Vacancy rates for all medical imaging and radiation therapy professionals are at the highest levels since the ASRT began tracking staffing in 2003
  • The radiographer vacancy rate nearly tripled in 2023 compared to 2021 (18% vs. 6.2%)
  • The number of people taking the ARRT’s radiography certification exam in 2022 fell 18% compared to 2006 (14.3k vs. 17.5k)

To address the problem, ASRT collaborated with 17 other radiological sciences groups including ARRT and JRCERT to first conduct a survey of 8.7k medical imaging and radiation therapy professionals to assess their work environment. 

  • The groups then convened a two-day meeting in February at ASRT headquarters in Albuquerque, New Mexico. 

They agreed on six major solutions to address the workforce crisis …

  • Raise awareness through campaigns such as via social media to attract new students
  • Articulate clear career pathways so professionals can choose careers in clinical practice, management, or education at different levels and roles. This would include a new entry-level role, imaging medical aide (IMA), that would be offered by high schools and community colleges as a stepping stone to RT status
  • Create a pipeline from educational programs to the workplace, and make AI a mandatory part of the educational curriculum
  • Build a career ladder that defines different clinical titles for professionals in clinical and leadership roles 
  • Expand educational opportunities such as in rural and underserved communities, and create a one-stop-shop portal for educators
  • Improve workplace satisfaction through tools such as awards programs and CE opportunities on workplace satisfaction

The Takeaway

Trying to work against powerful demographic trends can sometimes seem like swimming upstream. But the new report is a good first start toward a more organized and unified response to the radiologic technologist staffing shortage.

Radiologist Shortage Looms

Posted on by Brian Casey

A new report from healthcare staffing firm Medicus Healthcare Solutions paints a gloomy picture of the demographic crush facing radiology as the US population ages and imaging volumes rise, but the number of radiologists remains static. 

Radiology’s demographic dilemma isn’t new to anyone in the field. Radiologists are having to work harder to meet growing demand for imaging by an aging population, while reimbursement falls.

  • Meanwhile, efforts to grow the number of radiologists are hamstrung by the country’s physician training system, which requires a literal act of Congress in order to expand the number of residency slots. 

The new Medicus report mostly draws on established data sources, but it provides insight into the supply and demand challenges facing radiology, presented in an attractive graphical format. Salient points include …

  • There are about 37.7k diagnostic radiologists in the US, with job growth of 4% annually through 2032
  • Since 2020 there have been only 22 new diagnostic radiology residency PGY-1 positions added
  • From 2010 to 2020, the number of diagnostic radiology trainees grew 2.5%, while the number of US adults over 65 rose 34%
  • By 2030, all baby boomers will be aged 65 and older – and will require more medical care
  • The gap between radiology supply and demand is expected to grow through 2034 (see above chart)

What’s more, the vast majority of radiologists reaching retirement age are generalists, while the field’s recent focus on subspecialization means many younger radiologists aren’t comfortable reading scans outside their focus. 

The Medicus report isn’t all doom and gloom. It does offer some possible solutions to the staffing shortage, including teleradiology, AI, and increased use of locums tenens radiologist services (which Medicus provides). 

The Takeaway

The Medicus report provides a snapshot of a medical specialty that – like many others – is facing a demographic crunch between rising demand and fixed supply. Hopefully, technologies like AI will enable radiologists to do more with less in the years to come.

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