Category: Cardiac Imaging

CT Flexes Muscles in Heart

Posted on by Brian Casey

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.

Cardiac Imaging in 2040

Posted on by Brian Casey

What will cardiac imaging look like in 2040? It will be more automated and preventive, and CT will continue to play a major – and growing – role.

That’s according to an April 11 article in Radiology in which Dr. David Bluemke and Dr. João Lima look into the future and offer a top 10 list of major developments in cardiovascular imaging in 2040.

Cardiovascular disease carries a massive medical burden, with over 800,000 myocardial infarctions occurring annually in the US alone. By 2030 almost one-third of deaths worldwide are expected to be due to cardiovascular disease.

Multiple different imaging modalities are adept at identifying both ischemic and nonischemic heart disease, but CT has risen to the top for ischemic imaging, making “quantum” advances in the last decade thanks to its growing prowess in the coronary arteries.

CT’s advances have been so great that the modality occupies seven of the top 10 spots on Bluemke and Lima’s list. In brief, they see: 

  • Coronary CTA becoming totally automated, a development that will no doubt benefit AI developers like HeartFlow (see below).
  • CCTA becoming a preventive tool rather than a gatekeeper to interventional cardiology (also hinted at in a recent study from Denmark). For example, CCTA will be used to track the effectiveness of statin therapy
  • Photon-counting CT flexing its muscles for coronary artery evaluation and routine plaque characterization and quantification
  • Next-generation cardiac CT becoming more like MRI
  • Next-generation cardiac MRI becoming more like CT
Table of Top 10 Cardiovascular Imaging Developments by 2040

They also see a major growing role for software-assisted cardiac CT with AI and other tools. Software-based automation has simplified the “postprocessing nightmares” once common with coronary CT, making it “wonderfully ordinary” to perform. 

The Takeaway

Bluemke and Lima offer a fascinating glimpse of cardiac imaging’s future. But one area they don’t touch on is whether CT’s rising prominence means radiologists will start taking back turf in heart imaging once ceded to cardiologists. Heart specialists haven’t taken over cardiac CT in the same way that they monopolized echocardiography and nuclear cardiology. Could we be seeing a renaissance of radiology in the heart?

Is CCTA Set for Cardiac Screening?

Posted on by Brian Casey

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.

Creating A Novice Echo Screening Pathway

Posted on by Jake Fishman

We hear a lot about AI’s potential to expand ultrasound to far more users and clinical settings, and a new study out of Singapore suggests that ultrasound’s AI-driven expansion might go far beyond what many of us had in mind.

The PANES-HF trial set up a home-based echo heart failure screening program that equipped a team of complete novices (no experience with echo, or in healthcare) with EchoNous’s AI-guided handheld ultrasound system and Us2.ai’s AI-automated echo analysis and reporting solution.

After just two weeks of training, the novices performed at-home echocardiography exams on 100 patients with suspected heart failure, completing the studies in an average of 11.5 minutes per patient.

When compared to the same 100 patients’ NT-proBNP blood test results and reference standard echo exams (expert sonographers, cart-based echo systems, and cardiologist interpretations), the novice echo AI pathway…

  • Yielded interpretable results in 96 patients 
  • Improved risk prediction accuracy versus NT-proBNP by 30% 
  • Detected abnormal LVEF <50% scans with an 0.880 AUC (vs. NT-proBNP’s 0.651-0.690 AUCs)
  • Achieved good agreement with expert clinicians for LVEF<50% detection (k=0.742)

These findings were strong enough for the authors to suggest that emerging ultrasound and AI technologies will enable healthcare organizations to create completely new heart failure pathways. That might start with task-shifting from cardiologists to primary care, but could extend to novice-performed exams and home-based care.

The Takeaway

Considering the rising prevalence of heart failure, the recent advances in HF treatments, and the continued sonographer shortage, there’s clearly a need for more accessible and efficient echo pathways — and this study is arguably the strongest evidence that AI might be at the center of those new pathways.

Echo AI Coronary Artery Calcium Scoring

Posted on by Jake Fishman

A Cedars-Sinai-led team developed an echocardiography AI model that was able to accurately assess coronary artery calcium buildup, potentially revealing a safer, more economical, and more accessible approach to CAC scoring.

The researchers used 1,635 Cedars-Sinai patients’ transthoracic echocardiogram (TTE) videos paired with their CT-based Agatston CAC scores to train an AI model to predict patients’ CAC scores based on their PLAX view TTE videos. 

When tested against Cedars-Sinai TTEs that weren’t used for AI training, the TTE CAC AI model detected…

  • Zero CAC patients with “high discriminatory abilities” (AUC: 0.81)
  • Intermediate patients “modestly well” (≥200 scores; AUC: 0.75)
  • High CAC patients “modestly well” (≥400 scores; AUC: 0.74)

When validated against 92 TTEs from an external Stanford dataset, the AI model similarly predicted which patients had zero and high CAC scores (AUCs: 0.75 & 0.85).

More importantly, the TTE AI CAC scores accurately predicted the patients’ future risks. TTE CAC scores predicted one-year mortality similarly to CT CAC scores, and they even improved overall prediction of low-risk patients by downgrading patients who had high CT CAC scores and zero TTE CAC scores.

The Takeaway

CT-based CAC scoring is widely accepted, but it isn’t accessible to many patients, and concerns about its safety and value (cost, radiation, incidentals) have kept the USPSTF from formally recommending it for coronary artery disease surveillance. We’d need a lot more research and AI development efforts, but if TTE CAC AI solutions like this prove to be reliable, it could make CAC scoring far more accessible and potentially even more accepted.

CCTA AI Predicts Ischemia and MBF

Posted on by Jake Fishman

A Cedars-Sinai and Amsterdam UMC-led team developed a machine learning system that analyzes quantitative plaque in coronary CTA exams to identify patients with ischemia and impaired myocardial blood flow (MBF), potentially creating an alternative to current methods.

The researchers trained the ML model using invasive FFR data from 254 patients (484 FFR vessels) to predict ischemia and impaired MBF by analyzing plaque data in CCTA exams. 

They then tested it with CCTAs from 208 patients (581 vessels) who also underwent invasive FFR and H2O PET exams, finding that the CCTA ML scores:

  • Predicted FFR-defined ischemia far more accurately than standard CCTA stenosis evaluations, while rivaling FFRCT assessments (AUCs: 0.92 vs. 0.84 & 0.93)
  • Predicted PET-based impaired MBF more accurately than standard CCTA stenosis evaluations and FFRCT assessments (AUCs: 0.80 vs. 0.74 & 0.77)

Because the ML scoring system operates locally, the authors highlighted its potential to quickly assess high-risk patients before invasive coronary angiography (avoiding off-site processing delays) or to assess low-risk patients at earlier stages, helping to improve ICA efficiency and accuracy.

The researchers plan to continue to develop their CCTA plaque AI solution, including adding more plaque features and CCTA metrics, and potentially seeking regulatory approval depending on the results of future validation studies.

The Takeaway

CCTA plaque AI is already one of the hottest segments on the commercial side of imaging AI, and this study highlights similar advances in academic centers, while showing that CCTA plaque AI can quickly and accurately predict both ischemia and lower MBF.

Echo AI Detects More Aortic Stenosis

Posted on by Jake Fishman

A team of Australian researchers developed an echo AI solution that accurately assesses patients’ aortic stenosis (AS) severity levels, including many patients with severe AS who might go undetected using current methods.

The researchers trained their AI-Decision Support Algorithm (AI-DSA) using the Australian Echo Database, which features more than 1M echo exams from over 630k patients, and includes the patients’ 5-year mortality outcomes.

Using 179k echo exams from the same Australian Echo Database, the researchers found that AI-DSA detected…

  • Moderate-to-severe AS in 2,606 patients, who had a 56.2% five-year mortality rate
  • Severe AS in 4,622 patients, who had a 67.9% five-year mortality rate

Those mortality rates are far higher than the study’s remaining 171,826 patients (22.9% 5yr rate), giving the individuals that AI-DSA classified with moderate-to-severe or severe AS significantly higher odds of dying within five years (Adjusted odds ratios: 1.82 & 2.80).

AI-DSA also served as a valuable complement to current methods, as 33% of the patients that AI-DSA identified with severe AS would not have been detected using the current echo assessment guidelines. However, severe AS patients who were only flagged by the AI-DSA algorithm had similar 5-year mortality rates as patients who were flagged by both AI-DSA and the current guidelines (64.4% vs. 69.1%).

Takeaway

There’s been a lot of promising echo AI research lately, but most studies have highlighted the technology’s performance in comparison to sonographers. This new study suggests that echo AI might also help identify high-risk AS patients who wouldn’t be detected by sonographers (at least if they are using current methods), potentially steering more patients towards life-saving aortic valve replacement procedures.

Chest Pain Implications

Posted on by Jake Fishman

The major cardiac imaging societies weighed-in on the AHA/ACC’s new Chest Pain Guidelines, highlighting the notable shifts coming to cardiac imaging, and the adjustments they could require.

The cardiac CT and MRI societies took a victory lap, highlighting CCTA and CMR’s now-greater role in chest pain diagnosis, while forecasting that the new guideline will bring:

  • Increased demand for cardiac CT & MR exams and scanners
  • A need for more cardiac CT & MR staff, training, and infrastructure
  • Requests for more cardiac CT & MR funding and reimbursements
  • More collaborations across radiology, cardiology, and emergency medicine

The angiography and nuclear cardiology societies were less celebratory. Rather than warning providers to start buying more scanners and training more techs (like CT & MR), they focused on defending their roles in chest pain diagnosis, reiterating their advantages, and pointing out how the new guidelines might incorrectly steer patients to unnecessary or insufficient tests.

FFR-CT’s new role as a key post-CT diagnostic step made headlines when the guidelines came out, but the cardiac imaging societies don’t seem to be ready to welcome the AI approach. The nuclear cardiology and radiology societies called out FFR-CT’s low adoption and limited supporting evidence, while the SCCT didn’t even mention FFR-CT in its statement (and they’re the cardiac CT society!).

Echocardiography maintained its core role in chest pain diagnosis, but the echo society clearly wanted more specific guidelines around who can perform echo and how well they’re trained to perform those exams. That reaction is understandable given the sonographer workforce challenges and the expansion of cardiac POCUS to new clinical roles (w/ less echo training), although some might argue that echo AI tools might help address these problems.

The Takeaway

Imaging and shared decision-making play a prominent role in the new chest pain guidelines, which seems like good news for patient-specific care (and imaging department/vendor revenues), but it also leaves room for debate within the clinic and across clinical societies. 

The JACC seems to understand that it needs to clear up many of these gray areas in future versions of the chest pain guidelines. Until then, it will be up to providers to create decision-making and care pathways that work best for them, and evolve their teams and technologies accordingly.

Chest CT’s Untapped Potential

Posted on by Jake Fishman

A new AJR study out of Toronto General Hospital highlighted the largely-untapped potential of non-gated chest CT CAC scoring, and the significant impact it could have with widespread adoption.

Current guidelines recommend visual CAC evaluations with all non-gated non-contrast chest CTs. However, these guidelines aren’t consistently followed and they exclude contrast-enhanced chest CTs.

The researchers challenged these practices, performing visual CAC assessments on 260 patients’ non-gated chest CT exams (116 contrast-enhanced, 144 non-contrast) and comparing them to the same patients’ cardiac CT CAC scores (performed within 12-months) and ~6-year cardiac event outcomes.

As you might expect, visual contrast-enhanced and non-contrast chest CT CAC scoring:  

  • Detected CAC with high sensitivity (83% & 90%) and specificity (both 100%)
  • Accurately predicted major cardiac events (Hazard ratios: 4.5 & 3.4)
  • Had relatively benign false negatives (0 of 26 had cardiac events)
  • Achieved high inter-observer agreement (κ=0.89 & 0.95)

The Takeaway

Considering that CAC scores were only noted in 37% of the patients’ original non-contrast chest CT reports and 23% of their contrast-enhanced chest CT reports, this study adds solid evidence in favor of more widespread CAC score reporting in non-gated CT exams.

That might also prove to be good news for the folks working on opportunistic CAC AI solutions, noting that AI has (so far) seen the greatest adoption when it supports processes that most radiologists are actually doing.

Cleerly’s Downstream Effect

Posted on by Jake Fishman

A new AJR study showed that Cleerly’s coronary CTA AI solution detects obstructive coronary artery disease (CAD) more accurately than myocardial perfusion imaging (MPI), and could substantially reduce unnecessary invasive angiographies. 

The researchers used Cleerly to analyze Coronary CTAs from 301 patients with stable myocardial ischemia symptoms who also received stress MPI exams. They then compared these Cleerly CCTA and MPI results with the patients’ invasive angiography exams, and quantitative coronary angiography (QCA) and fractional flow reserve (FFR) measurements. 

The Cleerly-based coronary CTA results significantly outperformed MPI for predicting stenosis and caught cases that MPI-based ischemia results didn’t flag:

  • Cleerly AI detected more patients with obstructive stenosis (≥50%; 0.88 vs. 0.66 AUCs)
  • Cleerly AI identified more patients with severe stenosis (≥70%; 0.92 vs. 0.81 AUCs)
  • Cleerly AI detected far more patients with signs of ischemia in FFR (<0.80; 0.90 vs. 0.71 AUCs) 
  • Out of 102 patients with negative MPI ischemia results, Cleerly identified 55 patients with obstructive stenosis and 20 with severe stenosis (54% & 20%)
  • Out of 199 patients with positive MPI ischemia results, Cleerly identified 46 patients with non-obstructive stenosis (23%)

MPI and Cleerly-based CCTA analysis also worked well together. The combination of ≥50% stenosis via Cleerly and ischemia in MPI achieved 95% sensitivity and 63% specificity for detecting serious stenosis (vs. 74% & 43% using QCA measurements).

Based on those results, pathways that use a Cleerly AI-based CCTA benchmark of ≥70% stenosis to approve patients for invasive angiography would reduce invasive angiography utilization by 39%. Meanwhile, workflows requiring a positive MPI ischemia result and CCTA Cleerly AI benchmark of ≥70% would reduce invasive angiography utilization by 49%.

The Takeaway
We’re seeing strong research and policy momentum towards using coronary CTA as the primary CAD diagnosis method and reducing reliance on invasive angiography. This and other recent studies suggest that CCTA AI solutions like Cleerly could play a major role in that CCTA-first shift.

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