Tag: lung screening

Better Lung Screening Strategies

Posted on by Brian Casey

In our last issue, we discussed how CT lung cancer screening is falling short, mostly because so many eligible people still aren’t getting screened. In this issue, we offer strategies for fixing the problem, based on studies published last week in JACR. 

A lung cancer diagnosis was once thought of as a death sentence, as it was usually diagnosed at a late stage.

  • But that dynamic has changed with low-dose CT screening, which detects cancers small enough to be effectively treated – as was the case with Pope Leo XIV earlier this month. 

But uptake of LDCT screening has been slow, with U.S. screening rates below 20% for eligible people – compared to nearly 80% for mammography. 

  • The new JACR articles include three studies that cover a range of relevant topics in screening, from boosting participation rates to ensuring quality in screening programs. 

In the first study, researchers described their LungCheck program for boosting LDCT rates. 

  • LungCheck focuses on outreach to primary care providers with educational materials and meetings that explain screening eligibility criteria, as well as patient navigators for follow-up and scheduling and EHR modifications to capture eligible people.

Across 5.9k people, LungCheck led to almost 3X higher odds of an eligible person getting LDCT screening at academic centers (OR = 2.67), and almost 5X at federally qualified health centers (OR = 4.53).

The second JACR study specifically addresses outreach to Hispanics, a traditionally underserved population for LDCT screening. 

  • Researchers analyzed the effectiveness of their Empower LCS program, which included a culturally adapted decision aid, provider notifications, and patient text reminders. 

While the sample size of 60 patients was small, there were significantly higher ordering rates for patients in Empower LCS at four months than usual care (50% vs. 13%), as well as more lung screening discussions (40% vs. 17%). 

  • There was a trend toward greater completion of LDCT screening exams, but the difference wasn’t statistically significant, probably due to the small sample size (30% vs. 13%, p = 0.117). 

In the third paper, researchers assessed how well the ACR’s Lung Cancer Screening Registry mapped to IASLC quality measures.

  • While 69% of IASLC measures mapped to registry data, 31% didn’t line up, especially data on patient entry, treatment, and wait times. 

The Takeaway

The new JACR studies show the promising advances being made to boost CT lung cancer screening participation, while also helping imaging facilities improve the quality of their service. All that’s left to do is to put these promising ideas into broader practice.

One-Stop CT Chest Screening

Posted on by Brian Casey

Could opportunistic screening be driving the evolution of low-dose CT from a screening test just for lung cancer into a broader one-stop exam for multiple chest pathologies? A new study in JACR suggests as much – and provides a blueprint for how to do it. 

There’s no question LDCT lung cancer screening is gaining traction around the world. But as screening programs are starting, clinicians are finding that CT scans can detect far more than just lung cancer. 

  • Emphysema, commonly associated with COPD, is one such condition, as it goes hand-in-hand with lung cancer in people with long smoking histories. Another is coronary artery calcium (CAC), a known risk factor for cardiovascular disease. 

In the new study, researchers wanted to investigate the prevalence of COPD and CAC in their LDCT screening program covering three hospitals in the NewYork-Presbyterian system. 

  • In addition to documenting prevalence rates, they wanted to connect clinical outcomes to social determinants of health, which have been demonstrated to affect patient care. 

They tracked results from 2.4k people who got baseline LDCT scans from 2015 to 2022, finding…

  • Emphysema was detected in 77% of patients, and CAC in 65%.
  • 87% of patients with emphysema had no previous diagnosis.
  • The proportion of patients getting pulmonary care after their LDCT scan increased (from 24% to 46%), as did the rate of those getting cardiovascular care (from 43% to 63%).  
  • Lower household income was associated with 57% higher adjusted odds of emphysema, and food insecurity with 52% higher odds. 
  • No social determinants of health were associated with CAC burden.

It’s worth noting that the study did not use any IT tools to classify COPD and CAC, like AI image analysis or longitudinal tracking and reporting. 

  • It’s entirely possible that the results could be even more promising given the plethora of algorithms that have become available for opportunistic screening applications. 

The Takeaway

The new study on incidental CAC and emphysema findings on LDCT exams shows that lung screening has the potential for opportunistic disease detection even if you don’t have fancy AI algorithms at your disposal. All you need are radiologists and clinical staff dedicated to providing better patient care. 

Lung Cancer Mystery in Non-Smokers

Posted on by Brian Casey

One of the emerging mysteries around lung cancer is why it occurs so often in people who don’t smoke – particularly in Asia. A new paper in JAMA Network Open investigates the phenomenon by presenting results from South Korea indicating that established screening criteria would have missed nearly two-thirds of lung cancer patients.

From its earliest days, CT lung screening has been targeted at heavy smokers who have the greatest chance of developing lung cancer.

  • Risk-based criteria determine who should be screened, usually based on some metric of smoking history (such as the USPSTF’s 20-pack-year threshold).

But many countries that have had national lung screening programs in place for a while – mostly in Asia – are learning that lung cancer also occurs in people who have never smoked at all. 

  • Whether and how to screen these individuals has become the subject of debate. 

In the current study, researchers from South Korea examined 89.9k patients who were diagnosed with lung cancer from 2013 to 2018, just before the country started its national lung screening program in 2019. 

  • Researchers focused on whether patients would have been eligible for screening under established criteria, such as from the USPSTF, ACS, and NCCN.

Among people diagnosed with lung cancer…

  • 65% would not have been eligible for lung screening under international guidelines. 
  • 44% had no smoking history. 
  • Screening eligibility differed sharply by sex, with men eligible at far higher rates than women (57% vs. 2.3%).
  • Smokers who weren’t eligible for screening had 5% lower rates of all-cause mortality and 4% lower lung cancer-specific mortality than smokers who were.
  • People who had never smoked had 14% lower rates of both all-cause and lung cancer-specific mortality. 

The data point out an interesting dilemma inherent in CT lung cancer screening, which from the outset was set up to be risk-based rather than population-based like mammography to avoid many of screening’s downsides, like overdiagnosis.

  • But that approach is missing many people who would otherwise benefit from screening, especially as we learn more about the elevated risk among many non-smoking populations, such as East Asian women.

The Takeaway

The new study shows momentum building toward broader CT lung cancer screening criteria that go beyond smoking status. The question is how quickly such criteria are adopted, and whether they gain traction outside of Asia, where recognition of lung cancer among non-smokers is growing.

Two Views on Lung Screening’s Progress

Posted on by Brian Casey

Two new research studies published this week offer slightly conflicting views on the progress of CT lung cancer screening. One study saw the screening adherence rate rising to nearly 25%, while another revealed that screening might not be as effective in the real world as it was in the landmark National Lung Screening Trial. 

Low-dose CT lung cancer screening was approved for Medicare reimbursement in 2015 following promising NLST results, but the exam was slow to catch on.

  • That’s begun to change as U.S. physicians identify how to get eligible high-risk people into screening, while other countries are launching organized population-based screening programs, Germany being the most recent.

In the first study this week, published in JAMA Internal Medicine, researchers calculated the most recent uptake rate for LDCT lung cancer screening.

  • Data from 26.1k people from the BRFSS survey in 2024 were analyzed and compared to 2022 data.

The rate of up-to-date LDCT screening prevalence in 2024…

  • Rose six percentage points (24% vs. 18%).
  • Differed by just 0.5 percentage points between men and women.
  • Was highest and rose the most for people ages 65-69 (33%, increasing by 9 percentage points).
  • But still lagged screening rates for other cancers like breast (80%), cervical (75%), and colorectal (67%).

This week’s second study was published in JAMA Network Open and offers additional context, finding that real-world lung screening may not reduce all-cause mortality by as much as what was seen in the NLST.

  • Researchers analyzed screening’s effectiveness in a program run by the U.S. Veterans Health Administration, which has one of the more successful lung screening programs in the country.

They compared five-year all-cause mortality rates in the VA program for 732 people who were screened, finding that, compared to NLST…

  • Mortality rates were 2.5X higher in the VA population (24% vs. 9.7%).
  • VA participants had 3X the mortality risk (HR = 2.98).

VA researchers noted that while their population met the same screening eligibility criteria as NLST, the VA patients were sicker and probably less likely to see as much long-term benefit from LDCT screening.

The Takeaway

This week’s studies are a window into the complex changes underway in CT lung cancer screening. While screening’s rising participation rate is good news, the mortality findings show that estimating screening’s effectiveness is more complex than simply overlaying NLST results on real-world populations. 

The Danger of Incidental Findings in CT Lung Screening

Posted on by Brian Casey

CT lung cancer screening is gaining momentum around the world, but one of the challenges providers face is how to manage incidental findings. It’s especially important given that a new study in JAMA Network Open suggests that incidental findings on screening exams are associated with a higher risk of cancer occurring outside the lung. 

Incidental findings are suspicious areas discovered outside the target region being imaged, and are especially a concern with cancer screening exams.

  • Incidental findings turn out to be normal most of the time, but pathology occurs often enough that most clinicians agree they’re worth investigating. 

The problem is that many providers don’t have a robust system in place for alerting referring physicians to incidental findings and ensuring that patients get the follow-up exams they need.

The new study addresses incidental findings within the context of CT lung cancer screening, specifically in the National Lung Screening Trial, the landmark study that established low-dose CT’s lifesaving benefit.

  • It’s an important question, because chasing down a large number of benign incidental findings would be a resource-intensive task that could alter the cost-benefit ratio of lung screening.

Researchers analyzed significant incidental findings unrelated to lung cancer in 26.4k people across three rounds of LDCT screening who were followed for a year, revealing…

  • Cancer findings outside the lung occurred in 6.8% of people, and 13% of them had multiple cancers. 
  • Patients with significant incidental findings had a higher absolute risk of being diagnosed with extrapulmonary cancer within a year (16 per 1k participants). 
  • Study participants with incidental findings tended to be slightly older (62 vs. 61 years) and more likely to have a history of smoking-related disease (69% vs. 66%).

The findings confirm that having a plan to manage incidental findings should be an important part of any CT lung cancer screening program, especially given previous research showing that 23% of deaths in NLST were due to cancers outside the lung. 

  • In fact, an effective incidental finding program could enhance LDCT screening’s value, especially given that people eligible for screening have heavy smoking histories.

The Takeaway

The new study shows that incidental findings on CT lung screening exams are common and serious enough to warrant further investigation. Screening programs that are able to do so effectively will deliver even more value to their patients than lung screening alone.

Lung Cancer in Non-Smokers Creates Questions

Posted on by Brian Casey

Behind the growing enthusiasm for CT lung cancer screening is a nagging question – should we be screening people who have never smoked too? It’s a dilemma that’s addressed in a new paper in Radiology that offers some insight.

CT lung screening is the only major cancer screening test that’s exclusively targeted at high-risk individuals, essentially people who have smoked long enough to meet inclusion criteria.

  • Other cancer screening exams – for breast, colorectal, and cervical cancer– are offered to broader segments of the population, with age typically the only qualifying factor.

But lung cancer still occurs in people who have never smoked, who account for 10-25% of lung cancer cases, the fifth most common cause of cancer mortality globally.

  • For example, East Asian women, even those who have never smoked, seem to have higher lung cancer incidence rates, indicating a genetic risk factor that’s still not fully understood. 

The new Radiology paper reviews the state of knowledge regarding lung cancer in people who have never smoked, and examines whether the phenomenon’s prevalence calls for a rethinking of how CT lung cancer screening is offered. 

The authors explain that lung cancer in non-smokers…

  • Can be caused by environmental factors like workplace exposure, air pollution, genetic susceptibility, and exposure to second-hand smoke (20-26% higher risk for spousal exposure).  
  • Has a different carcinogenesis mechanism than lung cancer in smokers, and tends to be more slow-growing.
  • Has different characteristics than cancer in smokers, being overwhelmingly dominated by adenocarcinoma (90%). 

So with this knowledge in hand, should current U.S. and European lung cancer screening guidelines be changed? 

  • Japan is already conducting mass lung screening regardless of smoking history, while China’s guidelines include people who have never smoked but have other risk factors like occupational exposure.

But broader screening could lead to higher rates of overdiagnosis and overtreatment, and early studies from Asia have found screening had little effect on overall mortality in non-smokers. 

  • That led the Radiology authors to conclude that, at present, it’s probably not advisable to begin screening people who have never smoked until more research is conducted.

The Takeaway

The new paper on CT lung cancer screening of people who have never smoked is more than just an interesting thought experiment. It surfaces an issue that’s been percolating as risk-based lung screening gains momentum, and that ultimately may require a completely different approach to lung screening from what’s been used to date.

CT Lung Screening Leads RSNA’s First Day

Posted on by Brian Casey

Day 1 of RSNA 2025 is in the books, and new research into CT lung cancer screening dominated the scientific sessions at Chicago’s McCormick Place.

Lung cancer screening is drawing attention as screening programs go into effect internationally.

  • In the U.S., lung screening is hampered by low completion rates (18-19%), but providers are finding that participation can be improved with aggressive identification and outreach to eligible patients.

Some highlights from Sunday (with handy session numbers to help you follow along) include…

  • The ScreenLungNet AI model predicted three-year lung cancer risk from CT lung screening scans with AUCs from 0.93-0.94 (S4-SSCH02-1).
  • In a study of 2.6k patients with lung cancer, only 36% met 2021 USPSTF lung cancer screening criteria, and just 5% actually got screened. Only 23% had data on their smoking history in the EMR (S4-SSCH02-2).
  • Risk assessment scores were used to perform CT lung screening of lower-risk people every two years rather than annually, reducing screening’s harms without missing many cancers (S4-SSCH02-4).
  • Compared to the landmark NLST study, a real-world CT lung screening program had fewer benign surgeries (12% vs. 18%), lower complication rates (24% vs. 32%), and better recurrence-free survival (HR = 0.60) (S4-SSCH02-5).
  • CT radiation dose was reduced 51% and contrast iodine use 61% through a triple-optimized protocol that included 80-kVp scanning, GE HealthCare’s TrueFidelity deep learning reconstruction, and low-iodine adaptive contrast injection (S2-SSCA01-1).
  • Using AI for automated patient positioning and scan range in CT exams cut positioning time 41% with 10-13% lower radiation dose and no discernible impact on image quality (S4-SSIN01-1).
  • Measures of adiposity acquired opportunistically from coronary artery calcium CT scans using HeartLung Technologies’ AI-CVD algorithm predicted adults at risk of diabetes in a study of 2.9k people (S5-SSCA02-6). 
  • The Promedius AI algorithm for osteoporosis assessment of chest radiographs had an AUC of 0.84 in a study of 1k adults from three countries (M3-SSCH03-1).
  • A real-world study of 2.1k patients found that DeepTek’s chest X-ray AI algorithm had an AUROC of 0.95 for detecting any of 13 clinically significant findings (M3-SSCH03-2).
  • Researchers presented a feasibility study of a compression-free spectral DBT mammography system, finding spatial resolution close to state-of-the-art systems (S4-SSPH02-6).
  • Researchers presented their protocol for MRI scanning of patients with cardiac implanted electronic devices. Over 10 years they scanned 7.3k patients with no major adverse events (S5-SSCA02-1).
  • Adding MRI data to a multimodal transformer AI model improved its ability to predict five-year breast cancer risk in intermediate- and high-risk women (S2-SSBR01-6).

The Takeaway

RSNA 2025 is off to a great start. Be sure to check back with Thursday’s newsletter for more radiology news from Chicago, and follow along on our social media channels for ongoing video updates. 

Doubling Lung Screening Rates with Patient Outreach

Posted on by Brian Casey

Low CT lung cancer screening rates have disappointed medical imaging professionals and public health advocates alike since the test received USPSTF recommendation over 10 years ago. But a new study shows how one health system doubled its lung cancer screening rates – to levels approaching those of more established cancer screening exams. 

USPSTF recommended low-dose CT lung cancer screening in 2013, but 10 years later patient screening rates languished in the mid-teens, compared to rates of around 75% for breast and cervical cancer and above 72% for colorectal cancer. 

  • That means many lung cancer patients are showing up with late-stage disease, when it’s more difficult to cure. Perhaps as a result, lung cancer is expected to cause almost 125k deaths in the U.S. in 2025.

Breaking that cycle was the goal of researchers at the University of Rochester Medical Center in New York, who wrote about their experiences in a study published in NEJM Catalyst. 

  • They wanted to boost lung cancer screening adherence across their network of 42 locations in western New York. 

So how did they do it? Success came through a combination of IT innovation and old-fashioned legwork in patient outreach. Clinicians…

  • Provided evidence on lung cancer screening to primary care providers.
  • Updated their EHR software to identify patients eligible for lung screening based on the daily schedule to provide screening prompts during patient visits.
  • Created dashboards to guide outreach to patients due or overdue for screening exams.
  • Developed an extensive follow-up program with patient navigators to facilitate recall for annual exams.
  • Created a centralized pulmonary team to provide referrals for smoking cessation, conduct shared decision making for screening exams, and manage pulmonary nodules.

The program produced immediate results. In an analysis comparing screening rates in March 2022 to June 2025, researchers found…

  • Lung screening rates doubled (from 33% to 72%).
  • On-time completion of annual LDCT screening exceeded 94%.
  • 78% of lung cancer cases in 2023 and 2024 were diagnosed at an early stage.
  • There were no statistically significant differences in screening rates by patient race.

The Takeaway
The new results match up with recent findings – such as those presented at WCLC 2025 in September – underscoring the importance of reaching out to potential lung cancer screening candidates to bring them into the fold. Despite CT lung screening’s halting history, these studies show that it can be done.

Does BMI Affect AI Accuracy?

Posted on by Brian Casey

High body mass index is known to create problems for various medical imaging modalities, from CT to ultrasound. Could it also affect the accuracy of artificial intelligence algorithms? Researchers asked this question as it pertains to lung nodule detection in a new study in European Journal of Radiology. 

X-ray photons attenuate as they pass through body tissue, which can decrease image quality and produce more noise.

  • This is particularly a challenge for CT exams that don’t use a lot of radiation, like low-dose CT lung screening. 

At the same time, AI algorithms are being developed to make LDCT screening more efficient, such as by identifying and classifying lung nodules.

  • But if high BMI makes CT images noisier, will that affect AI’s performance? Researchers from the Netherlands tested the idea in 352 patients who got LDCT screening as part of the Lifelines study.

Researchers compared patients at both the high end of the BMI spectrum (mean 39.8) and low end (mean 18.7). 

  • Lung nodule detection by both Siemens Healthineers’ AI-Rad Companion Chest CT algorithm and a human radiologist was performed and compared. 

Across the study population, researchers found…

  • There was no statistically significant difference in AI’s sensitivity between high and low BMI groups (0.75 vs. 0.80, p = 0.37). 
  • Nor was there any difference in the human radiologist’s sensitivity (0.76 vs. 0.84, p = 0.17).
  • AI had fewer false positives per scan in the high BMI group than low BMI (0.30 vs. 0.55), a difference that was statistically significant (p = 0.05). 
  • While the difference in false positives with the human radiologist was not statistically significant (0.05 vs. 0.16, p = 0.09).

The study authors attributed AI’s lower performance to more noise in the high BMI scans.

  • They recommended that AI developers include people with both high and low BMI in datasets used for training algorithms.

The Takeaway

The results offer some comfort that patient BMI probably doesn’t have a huge effect on AI performance for nodule detection in lung screening, but it suggests a possible effect that might have achieved statistical significance with a larger sample size. More study in the area is definitely needed given the rising importance of AI for CT lung cancer screening. 

AI Spots Lung Nodules

Posted on by Brian Casey

A new study in Radiology on an AI algorithm for analyzing lung nodules on CT lung cancer screening exams shows that radiologists may be able to have their cake and eat it too: better identification of malignant nodules with lower false-positive rates. 

The rising utilization of low-dose CT screening is great news for clinicians (and eligible patients), but managing suspicious nodules remains a major challenge, as false-positive findings expose patients to unnecessary biopsies and costs.

  • False-positive rates have come down somewhat from the high rates seen in the big lung cancer screening clinical trials like NLST and NELSON, but there is still room for improvement.

Dutch researchers applied AI to the problem, developing a deep learning algorithm trained on 16.1k NLST nodules that produces a score from 0% to 100% based on a nodule’s likelihood of malignancy. 

  • They then tested the algorithm with baseline screening rounds of 4.1k patients from three datasets drawn from different lung cancer screening trials: NELSON, DLSCT in Denmark, and MILD in Italy.

The algorithm’s performance was compared to the Pan-Canadian Early Detection of Lung Cancer model, a widely used clinical guideline that uses patient characteristics like age and family history and nodule characteristics size and location to estimate risk.

Compared to PanCan, the deep learning algorithm…

  • Reduced false-positive findings sharply by classifying more benign cases as low risk (68% vs. 47%) when set at 100% sensitivity for cancers diagnosed within one year.
  • For all nodules, achieved comparable AUCs at one year (0.98 vs. 0.98), two years (0.96 vs. 0.94), and throughout screening (0.94 vs. 0.93).
  • For indeterminate nodules 5-15 mm, significantly outperformed PanCan at one year (0.95 vs. 0.91), two years (0.94 vs. 0.88), and throughout screening (0.91 vs. 0.86).

The model’s performance for indeterminate nodules is particularly intriguing, as these are challenging to manage due to their small size and can lead to unnecessary follow-up procedures.

The Takeaway

Using AI to differentiate malignant from benign nodules promises to make CT lung cancer screening more accurate and easier to perform than manual nodule classification methods – and should add to the exam’s growing momentum.

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