AI Powers Opportunistic Screening

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. 

CT Detects Early Lung Cancer

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.

Fine-Tuning Cardiac CT

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.

AI Automates Liver Fat Detection

An automated AI algorithm that analyzes CT scans for signs of hepatic steatosis could make it possible to perform opportunistic screening for liver disease. In a study in AJR, researchers described their tool and the optimal CT parameters it needs for highest accuracy. 

Hepatic steatosis (fatty liver) is a common condition that can represent non-alcoholic fatty liver disease (NAFLD), also known as metabolic dysfunction-associated steatotic liver disease (MASLD). Imaging is the only noninvasive tool for detecting steatosis and quantifying liver fat, with CT having an advantage due to its widespread availability. 

Furthermore, abdominal CT data acquired for other clinical indications could be analyzed for signs of fatty liver – the classic definition of opportunistic screening. Patients could then be moved into treatment or intervention.

But who would read all those CT scans? Not who, but what – an AI algorithm trained to identify hepatic steatosis. To that end, researchers from the US, UK, and Israel tested an algorithm from Nanox AI that was trained to detect moderate hepatic steatosis on either non-contrast or post-contrast CT images. (Nanox AI was formed when Israeli X-ray vendor Nanox bought AI developer Zebra Medical Vision in 2021.)

The group’s study population included 2,777 patients with portal venous phase CT images acquired for different indications. AI was used to analyze the scans, and researchers noted the algorithm’s performance for detecting moderate steatosis under a variety of circumstances, such as liver attenuation in Hounsfield units (HU). 

  • The AI algorithm’s performance was higher for post-contrast liver attenuation than post-contrast liver-spleen attenuation difference (AUC=0.938 vs. 0.832)
  • Post-contrast liver attenuation at <80 HU had sensitivity for moderate steatosis of 77.8% and specificity of 93.2%
  • High specificity could be key to opportunistic screening as it enables clinicians to rule out individuals who don’t have disease without requiring diagnostic work-up that might lead to false positives

The authors point out that opportunistic screening would make abdominal CT scans more cost-effective by using them to identify additional pathology at minimal additional cost to the healthcare system. 

The Takeaway

This study represents another step forward in showing how AI can make opportunistic screening a reality. AI algorithms can comb through CT scans acquired for a variety of reasons, identifying at-risk individuals and alerting radiologists that additional work-up is needed. The only question is what’s needed to put opportunistic screening into clinical practice. 

CT Flexes Muscles in Heart

CT continues to flex its muscles as a tool for predicting heart disease risk, in large measure due to its prowess for coronary artery calcium scoring. In JAMA, a new paper found CT-derived CAC scores to be more effective in predicting coronary heart disease than genetic scores when added to traditional risk scoring. 

Traditional risk scoring – based on factors such as cholesterol levels, blood pressure, and smoking status – has done a good job of directing cholesterol-lowering statin therapy to people at risk of future cardiac events. But these scores still provide an imprecise estimate of coronary heart disease risk. 

Two relatively new tools for improving CHD risk prediction are CAC scoring from CT scans and polygenic risk factors, based on genetic variants that could predispose people toward heart disease. But the impact of either of these tools (or both together) when added to traditional risk scoring hasn’t been investigated. 

To answer this question, researchers analyzed the impact of both types of scoring on participants in the Multi-Ethnic Study of Atherosclerosis (1,991 people) and the Rotterdam Study (1,217 people). CHD risk was predicted based on both CAC and PRS and then compared to actual CHD events over the long term. 

They also tracked how accurate both tools were in reclassifying people into different risk categories (higher than 7.5% risk calls for statins). Findings included: 

  • Both CAC scores and PRS were effective in predicting 10-year risk of CHD in the MESA dataset (HR=2.60 for CAC score, HR=1.43 for PRS). Scores were slightly lower but similar in the Rotterdam Study
  • The C statistic was higher for CAC scoring than PRS (0.76 vs. 0.69; 0.7 indicates a “good” model and 0.8 a “strong” model) 
  • The improved accuracy in reclassifying patient risk was statistically significant when CAC was added to traditional factors (half of study participants moved into the high-risk group), but not when PRS was added  

The Takeaway 

This study adds to the growing body of evidence supporting cardiac CT as a prognostic tool for heart disease, and reinforces CT’s prowess in the heart. The findings also support the growing chorus in favor of using CT as a screening tool in cases of intermediate or uncertain risk for future heart disease.

Is CCTA Set for Cardiac Screening?

A new study out of Denmark suggests that coronary CTA could be headed for population-based screening for heart disease. Researchers found that CCTA was remarkably effective in identifying individuals without symptoms who were more likely to experience heart attacks in years to come.

CCTA has proven so effective for cardiac imaging that it’s become a first-line test for stable chest pain, usually for those with symptoms. But researchers have debated whether CCTA’s value could be extended to asymptomatic individuals – which could set the stage for broad-based heart disease screening programs.

To investigate CCTA’s potential in the asymptomatic, researchers in Denmark scanned 9,533 individuals 40 years and older as part of the Copenhagen General Population Study, reporting their results in Annals of Internal Medicine. CCTA scans were conducted with Canon Medical’s 320-detector-row Aquilion One Vision scanner. 

Atherosclerosis was characterized as either obstructive (a luminal stenosis ≥ 50%), extensive (stenoses widely prevalent but not obstructive), or both. Researchers then tracked myocardial events over a median follow-up of 3.5 years. 

They found that 46% of study subjects had evidence of subclinical coronary atherosclerosis, with the type of atherosclerosis impacting risk of myocardial infarction: 

  • Extensive atherosclerosis had eight times higher risk 
  • Obstructive atherosclerosis had nine times higher risk
  • Both extensive and obstructive disease had 12 times higher risk

What’s more, researchers found that 10% of their study population had obstructive disease – which is just 10 percentage points under the 60% atherosclerosis threshold at which therapeutic intervention should be considered for asymptomatic people. 

Participants in the CGPS study did not receive treatment as part of the study, but the researchers have a follow-up study underway – DANE-HEART – in which asymptomatic people will get CCTA scans and some will be directed to preventive treatment if they meet clinical guidelines.

The Takeaway

This study demonstrates not only the widespread incidence of subclinical coronary atherosclerosis, but also CCTA’s ability to detect CAD before symptoms appear. Preventive treatment initiated and directed by CT findings could have a major impact on heart disease morbidity and mortality.

Given CCTA’s prognostic ability and the heavy burden of heart disease on society (more women die of heart disease than breast cancer, for example), how long before calls emerge to add CT-based heart screening to the arsenal of population-based screening programs? DANE-HEART may offer a clue.

RSNA 2021 Reflections

The first in-person RSNA since COVID is officially a wrap. Hope you had a blast if you made it to Chicago and a productive week if you stayed home. We also hope you enjoy The Imaging Wire’s big takeaways from what might have been both the most special and most subdued RSNA ever.

Crowds & Conversations – We were already expecting 50% lower attendance than RSNA 2019, but the exhibit hall and cab lines looked more like 70% below 2019’s crowds (even less on Sunday & Wednesday). That said, most of the stronger companies had steady booth traffic and nearly every exhibitor emphasized that the attendees who did show up were ready to have high-quality conversations.

Focus on Productivity – Just about every product message at RSNA focused on productivity and efficiency, often with greater emphasis than clinical effectiveness. The modality-based efficiency enhancements seemed to be the most impactful, which is good news for technologist bandwidth and patient throughput, but might be bad news for rad burnout unless informatics/AI efficiency can catch up (it doesn’t seem like that happened this year).

Modality Milestones – The major OEMs did a good job making modalities cool again, debuting milestone innovations across both their MR (low-helium, low-field, reconstruction, coils) and CT (photon-counting, spectral, upgradability) lineups. We also saw the latest scanners take big strides in operator efficiency and patient experience. There weren’t many breakthroughs with X-ray or ultrasound, and most point-of-care ultrasound OEMs stayed home (rads aren’t their market anyway), but attendees seemed okay with that.

AI Showcase – The RSNA AI Showcase had solid traffic and high energy (especially on Mon & Tues), helped by continued AI buzz and the fact that RSNA finally let AI vendors out of the basement. The AI Showcase highlighted many of the trends we’ve been seeing all year, including larger vendors transitioning to AI platform strategies, an increased focus on workflow integration and care coordination, and a greater emphasis on radiologist efficiency. There were also far fewer brand-new AI tools than previous years, as many vendors focused on improving their current products and/or expanding their portfolio via partnerships. 

PACS Cloud Focus – PACS vendors continued to place a major emphasis on their respective cloud advantages, and there was a widespread consensus that cloud is on every imaging IT roadmap. The PACS vendors seemed to talk less about multi-ology enterprise imaging than previous years, and expanding EI beyond radiology/cardiology still seemed pretty futuristic for most players. It was also quite clear that most of the PACS players’ AI marketplaces/platforms haven’t been as prioritized as earlier announcements might have suggested.

Best RSNA Since… 2019 – We’ve heard some folks saying this was the “best RSNA ever” because it was easy to get around and it was great to see everyone, but those seem more like pandemic silver linings than “best ever” qualifications. Still, the imaging industry made the most of RSNA 2021, and everyone seemed truly happy to be together again after two long years of working from home. As long as COVID cooperates, we should be set up for an excellent RSNA 2022.

GE’s Productive RSNA

GE Healthcare had another busy RSNA, highlighted by several major modality launches and an overarching focus on helping imaging teams be more productive. 

Return to MR Hardware – After focusing on AIR Recon DL during the last two RSNAs, GE Healthcare’s MR team made sure to roll-out new hardware at this year’s show. 

  • GE’s MR section was headlined by its new SIGNA Hero 3T MR, which brings a wide range of improvements (image quality, workflows, productivity, comfort, reconstruction, helium & energy), and a major focus on operator efficiency.  
  • GE also unveiled the SIGNA Artist Evo, which allows health systems / imaging centers to upgrade their existing 1.5T 60cm-bore MRs with 70cm bore systems (w/ AIR Recon DL & AIR Coils), without the construction and downtime typically required when upgrading to a net new MR system.

GE’s Scalable CT Platform – GE unveiled the unique Revolution Apex platform, which offers the modularity and scalability to cover a wide range of current and future needs, and represents GE’s biggest CT launch since 2014. 

  • The FDA-cleared Revolution Apex CT is available with multiple detector coverage configurations (40mm, 80mm, 160mm, upgradable w/o replacing gantry) and is offered with GE’s new Smart Subscription service (allows software upgrades/downgrades, plus auto updates). 
  • True to GE’s productivity focus, the Revolution Apex also includes a range of features to improve technologist efficiency and/or expand clinical applications (e.g. “Effortless Workflow,” patient positioning camera, TrueFidelity DLIR, motion correction for cardiac).

Much More – GE Healthcare has been busy throughout 2021, so although the other products in its RSNA booth were still quite new, they’ve already been detailed in previous Imaging Wire issues. Some of these other highlights include its in-development Photon Counting CT, it’s now FDA-approved Endotracheal Tube X-ray AI tool, its StarGuide SPECT/CT scanner, and its recent alliance with Optellum.

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