Category: CT Scanners

Predicting Patient Follow-Up for Imaging Exams

Posted on by Brian Casey

There’s nothing more frustrating than patients who don’t comply with follow-up imaging recommendations. But a new study in JACR not only identifies the factors that can lead to patient non-compliance, it also points the way toward IT tools that could predict who will fall short – and help direct targeted outreach efforts.

The new study focuses specifically on incidental pulmonary nodules, a particularly thorny problem in radiology, especially as CT lung cancer screening ramps up around the world.

  • Prevalence of these nodules can range from 24-51% based on different populations, and while most are benign, a missed nodule could develop into a late-stage lung cancer with poor patient survival. 

Researchers from the University of Pennsylvania wanted to test a set of 13 clinical and socioeconomic factors that could predict lack of follow-up in a group of 1.6k patients who got CT scans from 2016 to 2019. 

  • Next, they evaluated how well these factors worked when fed into several different types of homegrown machine learning models – precursors of a tool that could be implemented clinically – finding …
  • Clinical setting had the strongest association in predicting non-adherence, with patients seen in the inpatient or emergency setting far more likely skip follow-up compared to outpatients (OR=7.3 and 8.6)
  • Patients on Medicaid were more likely to skip follow-up compared to those on Medicare (OR=2)
  • On the other hand, patients with high-risk nodules were less likely to skip follow-up compared to those at low risk (OR=0.25) 
  • Comorbidity was the only one of the 13 factors that was not predictive of follow-up 

The authors hypothesized that the strong association between clinical setting and follow-up was due to the different socio-demographic characteristics of patients typically seen in each environment. 

  • Patients in the outpatient setting often have access to more resources like health insurance, transportation, and health literacy, while those without such resources often have to resort to the emergency department or hospital wards when they become sick enough to require care.

In the next step of the study, the data were fed into four types of machine learning algorithms; all turned in good performance for predicting follow-up adherence, with AUCs ranging from 0.76-0.80. 

The Takeaway

It’s not hard to see the findings from this study ultimately making their way into clinical use as part of some sort of commercial machine-learning algorithm that helps clinicians manage incidental findings. Stay tuned.

AI Powers Two-for-One Screening

Posted on by Brian Casey

In our last issue, we described how effective coronary artery calcium scoring is in predicting future major adverse cardiovascular events. This week, we’re highlighting new research in AJR showing how – thanks to AI – CAC scoring can be performed on CT lung cancer screening exams, giving radiologists a two-for-one screening test.

Using data from one screening exam to also look for other diseases – known as opportunistic screening – has become a hot topic as a way to make screening even more clinically and economically effective. 

In the new study, South Korean researchers leveraged the country’s CT lung cancer screening program to also screen for CAC, a known marker for future cardiac events. 

  • They took two commercially available CAC scoring algorithms – Coreline Soft’s Aview CAC and Siemens Healthineers’ syngo Calcium Scoring – to analyze 1k low-dose CT chest images acquired from 2017-2023 as part of the national lung screening program. 

AI results were compared to radiologists’ interpretations of CAC presence and severity, finding … 

  • Substantial agreement between both the AI algorithms and the interpreting radiologists for CAC presence and severity (kappa=0.793 & 0.671)
  • The AI algorithms judged CAC to be more prevalent than radiologists (57-60% vs. 53%)
  • AI was more likely to judge CAC severity as mild (35-40% vs. 28%)
  • But less likely to grade it as severe (6.2%-7.3%  vs. 15%)
  • MACE incidence varied by CAC severity: no CAC (1.1-1.3%), mild (3-5%), moderate (2.9-7.9%), and severe (8.6-11%)

The researchers noted that, as with other studies, MACE incidence increased with CAC severity, underlining the importance of coronary calcium evaluation and supporting the use of CT lung screening for CAC detection. 

The Takeaway

Studies like this highlight the exciting role AI can play in making opportunistic screening a reality. With AI at their side, radiologists will be able to play an even more important role in catching disease early, when it can be treated most effectively.

CT First for Chest Pain

Posted on by Brian Casey

CT should be used first to evaluate patients with stable chest pain who are suspected of having a heart attack. That’s the message of a paper being presented this week at the American College of Cardiology Cardiovascular Summit in Washington, DC.

CT is proving itself useful for a variety of applications in cardiac imaging, from predicting heart disease risk through coronary calcium scores to assessing whether people with chest pain need treatment like invasive angiography – or can be sent home and monitored.

  • But cardiac CT often runs up against decades of clinical practice that relies on tools like stress testing or diagnostic invasive coronary angiography for evaluating patients, with the CT-first strategy reserved for a limited number of people, such as those with unestablished coronary artery disease. 

But the new study suggests that the CT-first approach could be used for the vast majority of patients presenting with stable chest pain. 

  • A research team led by senior author Markus Scherer, MD, of Atrium Health-Sanger Heart & Vascular Institute in Charlotte, North Carolina tested the strategy in 786 patients seen from October 2022 to June 2023 who had no prior diagnosis of coronary artery disease and underwent elective invasive angiography to evaluate suspected angina.

The CT-first strategy compared CT angiography with provisional FFRCT testing to traditional evaluation pathways, which included stress echo, stress myocardial perfusion imaging, stress MRI, or no invasive testing before direct referral to angiography. Revascularization rates by strategy were as follows … 

  • 62% for CT-first
  • 50% for stress MRI
  • 40% for stress echo
  • 34% for no prior test
  • 31% for stress MPI

The Takeaway

The results presented this week offer real-world evidence that support recent clinical studies backing broader use of CT for patients with chest pain. Given CT’s advantages in terms of cost and noninvasiveness, the findings raise the question of whether more can be done to get clinicians to adhere to established guidelines calling for a CT-first protocol. 

CT Lung Screening’s Downstream Costs

Posted on by Brian Casey

The growing momentum of CT lung cancer screening was a major radiology news story in 2023. And while things are looking up as 2024 begins, there are still important issues to be sorted out for CT lung screening to achieve the same level of acceptance as other major cancer screening tests. 

A new study called PROSPR in Annals of Internal Medicine highlights some of these challenges. 

  • Researchers found a higher rate of invasive procedures and complications after CT screening compared to the National Lung Screening Trial, the landmark study that showed that low-dose CT screening reduces lung cancer mortality by 20%. 

The PROSPR researchers studied 9.3k individuals who got baseline LDCT lung screening scans from 2014 to 2018 across five US healthcare systems, finding: 

  • Abnormalities on baseline CT scans for 1.5k individuals (16%)
  • Of these, 9.5% were diagnosed with lung cancer within 12 months 
  • A 32% rate of downstream imaging of screened individuals 
  • A 2.8% rate of invasive procedures such as needle biopsy and bronchoscopy 
  • In those who got invasive procedures, rates were higher than NLST for all complications (31% vs. 18%) and for major complications (21% vs. 9.4%)

What gives with the higher complication rates? 

  • One explanation is that the PROSPR population was older and sicker than in NLST, with more individuals 65 and over (52% vs. 27%) and higher rates of current smoking (55% vs. 48%) and COPD (35% vs. 18%). 

Another reason could be that PROSPR’s population was more racially diverse, with fewer Whites than NLST (73% vs. 91%) and with a higher proportion of women (47% vs. 41%) – a sign of healthcare disparities. 

The PROSPR authors acknowledged that their findings could shift the debate over the benefits and harms of CT lung cancer screening in community practice – a debate that has raged in breast screening for decades.

The Takeaway

The PROSPR findings are something of a wake-up call amid the growing enthusiasm worldwide for CT lung cancer screening. It’s no surprise that real-world results will differ from the highly controlled environment of a clinical study like NLST, but lung screening proponents will need to be prepared with a plan for managing downstream findings and a response to screening skeptics who would use results like PROSPR to question whether lung screening should be performed at all.

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.

Lung Screening’s Long-Term Benefits

Posted on by Brian Casey

CT lung cancer screening produced lung cancer-specific survival over 80% in the most recent data from the landmark I-ELCAP study, a remarkable testament to the effectiveness of screening. 

The findings were published this week in Radiology from I-ELCAP, one of the first large-scale CT lung screening trials, and are the latest in a series of studies pointing to lung screening’s benefits. The findings were originally presented at RSNA 2022. 

The I-ELCAP study is ongoing and has enrolled 89k participants at over 80 sites worldwide from 1992-2022 who have been exposed to tobacco smoke and who received annual low-dose CT (≤ 3mGy) scans. Periodic I-ELCAP follow-up studies have documented the survival rates of those whose cancers were detected with LDCT, and the new numbers offer a 20-year follow-up, finding: 

  • Primary lung cancers were detected on LDCT in 1,257 individuals who had lung cancer-specific survival of 81%, matching the 10-year survival rate of 81%
  • 1,017 patients with clinical stage I lung cancer underwent surgical resection and saw a lung cancer-specific survival rate of 87%
  • The I-ELCAP survival rate is much higher than another landmark screening study, NLST, in which it was 73% for stage I cancer at 10 years
  • Lung cancer-specific survival hit a plateau after 10 years of follow-up, at a cure rate of about 80%

I-ELCAP is unique for a variety of reasons, one of which is that it continues to screen people beyond a baseline scan and 2-3 annual follow-up rounds – perhaps the reason for its higher survival rate relative to NLST. 

  • It also has included people who were exposed to tobacco smoke but who weren’t necessarily smokers – an important distinction in the debate over how broad to expand lung screening criteria.  

The findings come as CT lung cancer screening is generating growing momentum. Studies this year from Germany, Taiwan, and Hungary have demonstrated screening’s value, and several countries are ramping up national population-based screening programs. 

The Takeaway

The 20-year I-ELCAP data show that CT lung cancer screening works if you can get people to do it. But achieving survival rates over 80% also requires work on the part of healthcare providers, in terms of defined protocols for working up findings, data management for screening programs, and patient outreach to ensure adherence to annual screening. Fortunately, I-ELCAP offers a model for how it’s done.

AI Automates Liver Fat Detection

Posted on by Brian Casey

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. 

How COVID Crashed CT Scanners in China

Posted on by Brian Casey

In the early days of the COVID-19 pandemic in China, hospitals were performing so many lung scans of infected patients that CT scanners were crashing. That’s according to an article based on an interview with a Wuhan radiologist that provides a chilling first-hand account of radiology’s role in what’s become the biggest public health crisis of the 21st century.

The interview was originally published in 2022 by the Chinese-language investigative website Caixin and was translated and published this month by U.S. Right to Know, a public health advocacy organization. 

In a sign of the information’s sensitivity, the original publication on Caixin’s website has been deleted, but U.S. Right to Know obtained the document from the US State Department under the Freedom of Information Act. 

Radiologists at a Wuhan hospital noticed how COVID cases began doubling every 3-4 days in early January 2020, the article states, with many patients showing signs of ground-glass opacities on CT lung scans – a telltale sign of COVID infection. But Chinese authorities suppressed news about the rapid spread of the virus, and by January 11 the official estimate was that there were only 41 COVID cases in the entire country.

In reality, COVID cases were growing rapidly. CT machines began crashing in the fourth week of January due to overheating, said the radiologist, who estimated the number of cases in Wuhan at 10,000 by January 21. Hospitals were forced to turn infected patients away, and many people were so sick they were unable to climb onto X-ray tables for exams. Other details included: 

  • Chinese regulatory authorities denied that human-to-human transmission of the SARS CoV-2 virus was occurring even as healthcare workers began falling ill
  • Many workers at Chinese hospitals were discouraged from wearing masks in the pandemic’s early days to maintain the charade that human-to-human contact was not possible – and many ended up contracting the virus
  • Radiologists and other physicians lived in fear of retaliation if they spoke up about the virus’ rapid spread

The Takeaway

The article provides a stunning behind-the-scenes look at the early days of a pandemic that would go on to reshape the world in 2020. What’s more, it demonstrates the vital role of radiology as a front-line service that’s key to the early identification and treatment of disease – even in the face of bureaucratic barriers to delivering quality care.

Is There Hope for CT Lung Screening?

Posted on by Brian Casey

New data on CT lung cancer screening rates offer a good news/bad news story. The bad news is that only 21.2% of eligible individuals in four US states got screened, far lower than other exams like breast or colon screening.

The good news is that, as low as the rate was relative to other tests, 21.2% is still much higher than previous estimates. And the study itself found that the rate of CT lung screening has risen over 8 percentage points in 3 years. 

Compliance has lagged with CT lung screening ever since Medicare approved payments for the exam in 2015. A recent JACR study found that screening rates were low for eligible people for both Medicare and commercial insurance (3.4% and 1.8%).

Why is screening compliance so low? Explanations have ranged from fatalism among people who smoke to reimbursement requirements for “shared decision-making,” which unlike other screening exams require patients and providers to discuss CT lung screening before an exam can be ordered.

In this new study in JAMA Network Open, researchers examined screening rates in four states – Maine, Michigan, New Jersey, and Rhode Island – from January 2021 to January 2022. The study drew data from the National Health Interview Survey and weighted it to reflect the population of the US of individuals eligible for CT lung screening, based on the criteria of ages 55-79, 30-pack-year smoking history, and having smoked or quit within the past 15 years. Major findings included: 

  • The rate for CT lung cancer screening was 21.2%, up from 12.8% in 2019
  • People with a primary health professional (PHP) were nearly 6 times more likely to get screened (OR=5.62)
  • The age sweet spot for screening was 65-77, with lower odds for those 55-64 (OR=0.43) and 78-79 (OR=0.17)
  • Rates varied between states, with Rhode Island having the highest rate (30.3%) and New Jersey the lowest (17.5%).
  • Of those who got screened, 27.7% were in poor health and 4.5% had no health insurance

The Takeaway

The findings offer some hope for CT lung screening, as the compliance rate is among the highest we’ve seen among recent research studies. On the other hand, many of those screened were in such poor health they might not benefit from treatment. The high rate of compliance in people with PHPs indicates that promoting screening with these providers could pay off, especially given the requirement for shared decision-making. 

NeuroLogica’s Photon Counting CT

Posted on by Jake Fishman

Samsung’s NeuroLogica subsidiary announced the FDA 510(k) clearance of its photon counting-based OmniTom Elite PCD, significantly expanding the mobile head/neck CT system’s diagnostic potential and adding to photon counting technology’s recent momentum.

About the OEPCD – The OmniTom Elite with PCD is now available as an optional upgrade (including field upgrades), swapping the standard OmniTom Elite’s energy integrating detector (EID) for a single-source cadmium telluride-based photon counting detector. Beyond the OmniTom Elite with PCD’s imaging advantages (2x higher spatial resolution, spectral CT images at multiple energy levels), all other key features are shared between the two configurations (16 row, 40cm bore, 30cm FoV).

The Photon Counting Race – NeuroLogica’s photon counting CT launch comes about six months after Siemens Healthineers’ NAEOTOM Alpha became the first FDA-cleared PCCT. The OmniTom Elite with PCD’s launch also comes amid major R&D and M&A efforts from essentially all major OEMs, as they compete for photon counting CT leadership. 

The Photon Counting Advantage – Those efforts seem warranted, as PCCTs produce far higher quality images and provide far more imaging data, while potentially allowing lower radiation exposure and contrast dosage. For the OmniTom Elite’s head and neck applications, that could mean improved visualization and segmentation of bones, blood clots, plaques, hemorrhages, and intracranial tumors.

NeuroLogica’s Next Steps – Even if photon counting’s advantages are widely agreed upon, its potential clinical applications are still being explored. Because of that, NeuroLogica’s announcement emphasized ongoing research efforts to evaluate the OmniTom Elite with PCD’s performance with certain patients (e.g. post-trauma and post-surgical patients) and its plans to develop the mobile PCCT’s “full potential.”

The Takeaway

The OmniTom Elite PCD’s head/neck imaging design (vs. whole body) and use of a single-source detector (vs. dual) make it quite different from the other PCCTs being developed, but it’s launch is still a notable milestone for photon counting CT technology. It’s also a testament to Samsung/NeuroLogica’s R&D efforts, coming 4.5 years after showing the detector at RSNA 2017, and reaching the market before most of the biggest CT players released their own PCCTs.

Get every issue of The Imaging Wire, delivered right to your inbox.

You might also like..

Select All

You're signed up!

It's great to have you as a reader. Check your inbox for a welcome email.

-- The Imaging Wire team

You're all set!