Tag: medical imaging

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:

  • 49% lower risk of lung cancer death by hazard ratio
  • 46% 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.

AI Powers Opportunistic Screening

Posted on by Brian Casey

The growing power of AI is opening up new possibilities for opportunistic screening – the detection of pathology using data acquired for other clinical indications. The potential of CT-based opportunistic screening – and AI’s role in its growth – was explored in a session at RSNA 2023.

What’s so interesting about opportunistic screening with CT? 

  • As one of imaging’s most widely used modalities, CT scans are already being acquired for many clinical indications, collecting body composition data on muscle, fat, and bone that can be biomarkers for hidden pathology. 

What’s more, AI-based tools are replacing many of the onerous manual measurement tasks that previously required radiologist involvement. There are four primary biomarkers for opportunistic screening, which are typically related to several major pathologies, said Perry Pickhardt, MD, of the University of Wisconsin-Madison, who led off the RSNA session:

  • Skeletal muscle density (sarcopenia)
  • Hard calcified plaque, either coronary or aortic (cardiovascular risk)
  • Visceral fat (cardiovascular risk)
  • Bone mineral density (osteoporosis and fractures) 

But what about the economics of opportunistic screening? 

  • A recent study in Abdominal Radiology found that in a hypothetical cohort of 55-year-old men and women, AI-assisted opportunistic screening for cardiovascular disease, osteoporosis, and sarcopenia was more cost-effective compared to both “no-treatment” and “statins for all” strategies – even assuming a $250/scan charge for use of AI.

But there are barriers to opportunistic screening, despite its potential. In a follow-up talk, Arun Krishnaraj, MD, of UVA Health in Virginia said he believes fully automated AI algorithms are needed to avoid putting the burden on radiologists. 

And the regulatory environment for AI tools is complex and must be navigated, said Bernardo Bizzo, MD, PhD, of Mass General Brigham.

Ready to take the plunge? The steps for setting up a screening program using AI were described in another talk by John Garrett, PhD, Pickhardt’s colleague at UW-Madison. This includes: 

  • Normalizing your data for AI tools
  • Identifying the anatomical landmarks you want to focus on
  • Automatically segmenting areas of interest
  • Making the biomarker measurements
  • Plugging your data into AI models to predict outcomes and risk-stratify patients

The Takeaway

Opportunistic screening has the potential to flip the script in the debate over radiology utilization, making imaging exams more cost-effective while detecting additional pathology and paving the way to more personalized medicine. With AI’s help, radiologists have the opportunity to place themselves at the center of modern healthcare. 

AI’s Impact on Breast Screening

Posted on by Brian Casey

One of the most exciting radiology use cases for AI is in breast screening. At last week’s RSNA 2023 show, a paper highlighted the technology’s potential for helping breast imagers focus on cases more likely to have cancer.

Looking for cancers on screening mammography has been compared to finding a needle in a haystack, and as such it’s considered to be one of the areas where AI can best help. 

  • One of the earliest use cases was in identifying suspicious breast lesions during radiologist interpretation (remember computer-aided detection?), but more recently researchers have focused on using AI as a triage tool, by identifying cases most likely to be normal that could be removed from the radiologist’s urgent worklist. Studies have found that 30-40% of breast screening cases could be read by AI alone or triaged to a low-suspicion list.

But what impact would AI-based breast screening triage have on radiologist metrics such as recall rate? 

  • To answer this question, researchers from NYU Langone Health prospectively tested their homegrown AI algorithm for analyzing DBT screening cases.

The algorithm was trained to identify extremely low-risk cases that could be triaged from the worklist while more complex cases where the AI was uncertain were sent to radiologists, who knew in advance the cases they were reading were more complicated. In 11.7k screening mammograms, researchers examined recall rates over two periods, one before AI triage and one after, finding: 

  • The overall recall rate went from 13% before the triage period to 15% after 
  • Recall rates for complex cases went from 17% to 20%
  • Recall rates for extremely low-risk studies went from 6% to 5%
  • There were no statistically significant differences in any of the comparisons
  • No change in median self-reported perceived difficulty of reading from the triage lists compared to non-triage list, regardless of years of experience

In future work, the NYU Langone researchers will continue their study to look at AI’s impact on cancer detection rate, biopsy rate, positive predictive value, and other metrics.

The Takeaway

The NYU Langone study puts a US spin on research like MASAI from Sweden, in which AI was able to reduce radiologists’ breast screening workload by 44%. Given the differences in screening protocols between the US and Europe, it’s important to assess how AI affects workload between the regions.

Further work is needed in this ongoing study, but early results indicate that AI can triage complex cases without having an undue impact on recall rate or self-perceived difficulty in interpreting exams – a surrogate measure for burnout.

RSNA 2023 Video Highlights

Posted on by Brian Casey

That’s a wrap! 

RSNA 2023 just concluded, and by most accounts it was a successful conference. Preliminary figures indicate that attendance was up 11% over 2022. While short of the glory days of RSNA, the numbers indicate that the meeting’s recovery from the COVID-19 pandemic will be slow but steady.

As expected, AI was a dominant theme at McCormick Place, and that’s reflected in our video coverage of the technical exhibit floor. AI busted out of the AI Showcase to permeate both exhibit halls, a sign of the technology’s growing influence on radiology.

We profiled many of the most intriguing companies that were exhibiting at RSNA 2023 – some of them dominant players in the field while others are new entries looking to secure a foothold. 

We hope you enjoy watching our coverage as much as we enjoyed producing it! Check out the links below or visit the Shows page on our website.

AI’s Incremental Revolution

Posted on by Brian Casey

So AI dominated the discussion at last week’s RSNA 2023 meeting. But does that mean it’s finally on the path to widespread clinical use? 

Maybe not so much. For a technology that’s supposed to have a revolutionary impact on medicine, AI is taking a frustratingly long time to arrive. 

Indeed, there was plenty of skepticism about AI in the halls of McCormick Place last week. (For two interesting looks at AI at RSNA 2023, also see Hugh Harvey, MD’s list of takeaways in a post on X/Twitter and Herman Oosterwijk’s post on LinkedIn.) 

But as one executive we talked to pointed out, AI’s advance to routine clinical use in radiology is likely to be more incremental than all at once. 

  • And from that perspective, last week’s RSNA meeting was undoubtedly positive for AI. Scientific sessions were full of talks on practical clinical applications of AI, from breast AI to CT lung screening. 

Researchers also discussed the use of AI apart from image interpretation, with generative AI and large language models taking on tasks from answering patient questions about their reports to helping radiologists with dictation.

It’s fine to be a skeptic (especially when it comes to things you hear at RSNA), but for perspective look at many of the past arguments casting doubt on AI: 

  • AI algorithms don’t have FDA clearance (the FDA authorized 171 algorithms in just the past year)
  • You can’t get paid for using AI clinically (16 algorithms have CPT codes, with more on the way) 
  • There isn’t enough clinical evidence backing the use of AI (tell that to the authors of MASAI, PERFORMS, and a number of other recent studies with positive findings)
  • The AI market is overcrowded with companies and ripe for consolidation (what exciting new growth market isn’t?)

The Takeaway

Sure, it’s taking longer than expected for AI to take hold in radiology. But last week’s conference showed that AI’s incremental revolution is not only advancing but expanding in ways no one expected when IBM Watson was unveiled to an RSNA audience a mere 6-7 years ago. One can only imagine what the field will look like at RSNA 2030.

Looking for more coverage of RSNA 2023? Be sure to check out our videos from the technical exhibit floor, which you can find on our new Shows page.

AI Dominates at RSNA 2023

Posted on by Brian Casey

Take a deep breath. You survived another RSNA conference.

While a few hardy souls are still enjoying educational sessions in the cozy confines of McCormick Place, the final day of the exhibit floor yesterday marks the end of RSNA 2023 for most attendees. And what a show it was. 

Predictions were that AI would dominate the scientific sessions at RSNA 2023, a forecast that largely panned out. A November 28 session was a case in point, in which a series of top-quality papers were presented on one of the most promising use cases of AI, for breast screening:

  • A homegrown AI algorithm that analyzed screening breast ultrasound exams in addition to FFDM and DBT mammograms boosted sensitivity for detecting cancer in 12.5k patients, with better sensitivity for women with dense breasts (71% vs. 60%) and non-dense breasts (79% vs. 63%)
  • AI did a good job of detecting breast arterial calcification (BAC) when used prospectively to analyze screening mammograms in 16k women across 15 sites.  It found 15% of women had BAC, a possible marker for atherosclerotic disease
  • Swedish researchers used their VAI-B validation platform to compare three AI algorithms (Therapixel, Lunit, and Vara) in 34k women, finding that using AI with a single radiologist boosted sensitivity 10-30% compared to double reading, with a slight loss in specificity (2-7%). VAI-B could be used to validate AI implementation and guide purchasing decisions
  • Why does AI miss some breast cancers? South Korean researchers addressed this question by analyzing 1.1k patients with invasive cancers in which AI had a miss rate of 14%. Luminal cancers were missed most often
  • Adding AI analysis of prior images to current studies with FFDM and DBT boosted sensitivity for cancer detection in 30k patients, with sensitivity the highest for two years of priors compared to no priors (74% vs. 70%)

The Takeaway

This week’s research points to an exciting near-term future in which AI will help make mammography screening more accurate while helping breast radiologists perform their jobs more efficiently. Landmark studies toward this end were published in 2023 – this week’s RSNA conference shows that we can expect the momentum to continue in 2024. 

Welcome to RSNA 2023

Posted on by Brian Casey

It’s off to the races at RSNA 2023 as radiology’s showcase conference kicked off on Sunday. 

“Leading Through Change” is the theme of this year’s meeting, and it’s an appropriate slogan for a specialty that seems on the cusp of disruption with the growing use of AI, deep learning, and other tools. 

  • AI is being featured prominently in scientific presentations and vendor exhibits in McCormick Place, with a particular focus on whether large language models like ChatGPT can find practical application in radiology. Early research is promising but still inconclusive.

Another major focus at RSNA 2023 has been lung cancer screening, with Sunday afternoon sessions investigating how screening can be expanded: 

  • Researchers mined a database of 32k women who got screening mammography to find eligible candidates for lung screening, finding 5% who met screening criteria. 
  • Using the USPTSF’s 2021 guideline revision to find screening candidates led to shorter smoking histories (42 vs. 29 pack-years) and slightly more women being eligible (48% vs. 46%). 
  • ChatGPT gave more correct answers than Google Bard to non-expert questions on lung screening (71% vs. 52%).
  • ChatGPT, GPT-4, and Bard needed multiple iterations to produce reports readable by patients. 

AI is also proving its value for selecting screening candidates and identifying lung pathology: 

  • An AI algorithm analyzed chest X-rays to determine whether an individual would benefit from CT lung cancer screening – even if they don’t smoke. In 17.4k patients, the model classified 28% as high risk, 2.9% of whom were later diagnosed with lung cancer, a higher level than the 1.3% six-year threshold at which guidelines recommend CT lung screening.
  • A deep learning algorithm analyzed chest X-rays in a cohort of 10k patients to predict who would develop type 2 diabetes, turning in better accuracy than a model that only looked at clinical factors like age, BMI and HbA1c levels (AUCs:  0.84 vs. 0.79). 

Looking for more coverage of RSNA 2023? Be sure to check out our videos from the technical exhibit floor, which you can find on our new Shows page. 

The Takeaway
The RSNA has always been known as the Super Bowl of radiology, and this year’s meeting is off to a great start. Be sure to check back on our Twitter/X, LinkedIn, and YouTube pages for more coverage of this week’s events in Chicago.

Vendors Enter RSNA on Q3 Roll

Posted on by Brian Casey

As RSNA 2023 approaches, medical imaging vendors appear to be on a roll when it comes to financial results. In the weeks leading up to the meeting, companies have posted numbers that for the most part are strongly positive and appear to be leaving the bad old days of the COVID-19 pandemic behind.

Agfa – Between Agfa’s two imaging divisions, healthcare IT continues to outperform the radiology solutions business. Healthcare IT saw growth in revenue (3.3% to $67M) and EBITDA (44.3% to $6.4M), but revenue declined at radiology solutions (-5.7% to $127M) as did EBITDA (-21% to $10M). 

Canon – Canon Medical Systems saw firm revenues in Japan and Europe, which propelled the business unit to higher revenues (5% to $913M) while income before taxes edged up (0.3% to $46M). 

Fujifilm – Revenues tapered off slightly in Fujifilm’s healthcare business at constant currency rates (-1.9% to $1.66B) as a 12.4% decline in its contract manufacturing business offset 1.7% growth in medical systems. Operating income in healthcare slipped due to a one-time benefit in the year-ago quarter (-6.5% to $217M).

GE HealthCare – Revenue growth in its molecular imaging and CT businesses helped propel GE HealthCare’s revenue growth (5.4% to $4.82B), assisted by 13% growth in pharmaceutical diagnostics and a 9% increase in patient care solutions. Net income was lower (-23% to $375M). 

Guerbet – Strong revenues for the third quarter in Asia (+15%) and stability in the EMEA region (0.6%) helped counter a decline in the Americas (-5.2%), enabling Guerbet to turn in overall quarterly revenue growth at constant exchange rates (2.3% to $212M). The company expects sales of its Elucirem MRI contrast agent to ramp up in the fourth quarter. 

Hologic – The semiconductor shortage that had impacted Hologic in previous quarters eased, leading to a sharp jump in revenues in the company’s breast health business (27% to $353M). The rebound didn’t extend to Hologic’s overall net income as its net margin narrowed (-24% to $91M). 

Konica Minolta – A decline in sales of X-ray systems to hospitals in its core market of Japan and a slower US hospital market produced lower revenues in Konica Minolta’s healthcare division (-5% to $238M), and the business posted an operating loss (-$5.5M).

Philips – Philips rebounded in the most recent quarter, with revenues in its diagnosis and treatment division rising sharply after currency conversion thanks to double-digit growth in all businesses (14% to $2.39B). Operating income doubled (to $272M). 

RadNet – RadNet saw a double-digit jump in revenues (15% to $402M) while net income leaped ($17.5M vs. $668k). Revenue jumped 221% in the company’s AI segment, which made progress narrowing its EBITDA loss (-$2.5M vs. -$4.5M) on higher consumer adoption of its Enhanced Breast Cancer Detection offering.  

Siemens Healthineers – Siemens Healthineers closed its financial year with “outstanding” 8.3% revenue growth at constant exchange rates, including double-digit growth in its imaging business (11% to $3.62B) while adjusted EBIT edged up (2% to $812M). Its Varian radiation therapy business saw a strong recovery in revenue (30% to $1.1B) and adjusted EBIT (90% to $207M).

Varex – Growth in Varex’s industrial X-ray imaging business propelled the company to higher overall revenues even as revenues in its medical business fell (-9.8% to $164M). The medical division’s gross profit also slipped (-7% to $53M).

The Takeaway

Not every company was a winner in this last round of quarterly earnings, but at least the macroeconomic headwinds of the COVID-19 pandemic are fading. The fourth calendar quarter is typically radiology’s strongest period due to the impact of the RSNA conference on equipment purchasing, so let’s hope the momentum continues.

Reimbursement Drives AI Adoption

Posted on by Brian Casey

It’s no secret that insurance reimbursement drives adoption of new medical technology. But a new analysis in NEJM AI shows exactly how reimbursement is affecting the diffusion into clinical practice of perhaps the newest medical technology – artificial intelligence. 

Researchers analyzed a database of over 11B CPT claims from January 2018 to June 2023 to find out how often reimbursement claims are being submitted for the use of the over 500 AI devices that had been approved by the FDA at the time the paper was finalized. 

  • The authors chose to focus on CPT claims rather than claims under the NTAP program for new technologies because CPT codes are used by both public and private payors in inpatient and outpatient settings, while NTAP only applies to Medicare inpatient payments. 

They found 16 medical AI procedures billable under CPT codes; of these, 15 codes were created since 2021 and the median age of a CPT code was about 374 days, indicating the novelty of medical AI.

  • Also, only four of the 16 had more than 1k claims submitted, leading the authors to state “overall utilization of medical AI products is still limited and focused on a few leading procedures,” such as coronary artery disease and diabetic retinopathy.

The top 10 AI products and number of CPT claims submitted are as follows:

  1. HeartFlow Analysis for coronary artery disease (67,306)
  2. LumineticsCore for diabetic retinopathy (15,097)
  3. Cleerly for coronary atherosclerosis (4,459)
  4. Perspectum LiverMultiScan for liver MRI (2,428)
  5. Perspectum CoverScan for multiorgan MRI (591)
  6. Koios DS for breast ultrasound (552)
  7. Anumana for ECG cardiac dysfunction (435)
  8. CADScor for cardiac acoustic waveform recording (331)
  9. Perspectum MRCP for quantitative MR cholangiopancreatography (237)
  10. CompuFlo for epidural infusion (67)

While radiology may rule in terms of the sheer number of FDA-approved AI products (79% in a recent analysis), the list shows that cardiology is king when it comes to paying the bills. 

The Takeaway

Amid the breathless hype around medical AI, the NEJM AI study comes as a bit of a wake-up call, showing how the cold reality of healthcare economics can limit technology diffusion – a finding also indicated in other studies of economic barriers to AI. 

On the positive side, it shows that a rosy future lies ahead for those AI algorithms – like HeartFlow Analysis – that can make the leap.

Uneven Success Against Breast Cancer

Posted on by Brian Casey

The decline in breast cancer mortality has been one of public health’s major success stories. But when you look at it from a global perspective, it’s the best of times and the worst of times. 

That’s because success in fighting breast cancer has been uneven around the world. While countries in North America, Western Europe, and Oceania have seen dramatic declines in breast cancer mortality and advanced-stage disease, other regions continue to be plagued by what really is becoming a survivable disease for most women. 

A new study in JAMA Oncology points out these disparities, documenting major differences in rates of advanced breast disease between countries in what researchers said was the most comprehensive review to date of global differences in breast cancer stage at diagnosis. 

  • Researchers conducted a meta-analysis of 133 studies covering 2.4M women across 81 nations over the past two decades, documenting differences in rates of advanced breast disease at diagnosis both over time and between countries. 

While most high-income nations have seen declines in rates of distant metastatic disease over the past 20 years, advanced-stage disease remains stubbornly common in lower middle-income countries. Researchers found: 

  • Rates of distant metastatic disease varied across countries by region, with sub-Saharan Africa the highest and North America the lowest (6-31% vs. 0-6%)
  • Lower socioeconomic status was tied to more advanced disease when women in the most disadvantaged group were compared to least disadvantaged (3-11% vs. 2-8%)
  • There were pronounced disparities even in high-resource countries with established screening programs, as rates of metastatic disease were twice as high in women of low socioeconomic status (SES) compared to high SES women, such as in the US (8% vs. 4%) 
  • Older women had a much higher prevalence of advanced disease across different countries compared to younger women (range of 4-34% vs. 2-16%), a phenomenon that could be because most screening programs stop at age 75
  • 40% of countries did not meet the Global Breast Cancer Initiative goal of having 60% or more of patients diagnosed at stage I or II

The Takeaway

The new findings indicate that it’s too soon to take a victory lap in the battle against breast cancer. While progress at higher socioeconomic levels in high-income countries has been impressive, breast cancer remains a scourge among more disadvantaged women and across wide regions of the world.

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