Tag: lung screening

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.

CT Lung Screening News from WCLC 2025

Posted on by Brian Casey

The World Conference on Lung Cancer wrapped up this week in Barcelona, and CT lung cancer screening was a highlighted topic, as it was at WCLC 2024 in San Diego.

The last year has seen significant global progress toward new population-based lung screening programs, and sessions at WCLC 2025 highlighted the advances being made… 

  • A screening program serving Kentucky and Indiana since 2013 has seen a 30-percentage-point decline in late-stage lung cancer diagnoses – over 3.5X faster than national trends – with far higher uptake than national averages (52% vs. 16%).
  • In the European 4-IN-THE-LUNG-RUN trial, AI had a negative predictive value similar to radiologists (98% vs. 97%) in analyzing 2.2k CT lung screen exams, indicating its potential as a first reader.
  • Another 4-IN-THE-LUNG-RUN study of 2.6k individuals revealed that AI had a 2.5% incidental findings rate, with none having acute consequences after a year.
  • The USPSTF’s 2021 guideline expansion may have reduced the number of at-risk individuals eligible for screening. A California analysis of 11.7k lung cancer patients found 8.8% fewer patients were eligible.
  • Researchers from Illinois found that basing screening eligibility on a 20-year smoking history rather than USPSTF 2021’s 20-pack-year threshold would capture more eligible individuals (70% vs. 65%), especially racial minorities.
  • A screening program at a VA healthcare system in Northern California achieved a 94% adherence rate for 3.9k military veterans, with 67% of cancers diagnosed at early stages.
  • U.S. military veterans had much higher screening rates (50% vs. 29%) in an analysis of 413.6k cancer survivors. Among women, 71% were up to date on mammography screening but only 25% were current for lung screens. 
  • Researchers used Qure.ai’s algorithm to detect malignant pulmonary nodules on 198k routine chest X-rays in a tuberculosis screening program.
  • Asian American women are at higher risk of lung cancer – even if they don’t smoke – and a session explored whether they should be screened.
  • A Stanford University program using electronic alerts to primary care physicians boosted screening compliance after one year (16% vs. 8.9%).
  • Attending lung screening didn’t make people feel they had a “license to smoke” in a U.K. study of 87.8k people.
  • Italian researchers tested Coreline Soft’s AVIEW AI solution as a first reader for screening.

The Takeaway

Findings from this week’s WCLC 2025 conference show both the challenges and opportunities in CT lung cancer screening. Researchers around the world are demonstrating that with hard work, dedication, and persistence, lung screening can become an effective, life-saving exam.

AI-Driven Lung Cancer Screening and Improving Patient Outcomes

Posted on by Partner Content

AI is reshaping clinical decision-making, optimizing resource allocation, and enhancing both patient outcomes and experience in CT lung cancer screening. Radiology providers are successfully integrating new AI software tools into hospital operations – supporting diagnostic accuracy and improving patient outcomes.

At the center of this trend is Coreline Soft’s FDA-cleared AVIEW LCS Plus, a 3-in-1 solution capable of detecting lung nodules, quantifying emphysema, and analyzing coronary artery calcification – all from a single low-dose CT scan. 

  • AVIEW LCS Plus is in use at Temple Health, a nationally recognized institution in the U.S. Northeast, where it has allowed providers to streamline clinical workflows from detection to follow-up, delivering measurable improvements in care and ROI.

Coreline Soft will co-host a strategic webinar with the Temple Lung Center on August 1 at 1:30 PM ET, focused on AI-powered lung cancer screening and the evolving paradigm of early detection for chest diseases.

The webinar will offer firsthand insight into how Temple Health is drawing attention as a model for integrating AI beyond diagnosis – transforming it into a scalable, patient-centered care strategy.

The discussion will focus on two main areas…

  • Real-world outcomes: How AI improved diagnostic efficiency, early detection, and comorbidity detection.
  • A deep dive into the precision technology of the AVIEW LCS Plus platform.

AI like Coreline’s is not replacing clinical judgment, but reinforcing it, enhancing radiologists’ ability to detect, triage, and treat lung disease earlier and more efficiently, Criner believes. 

  • The webinar is open to pulmonologists, radiologists, cardiologists, respiratory-adjacent professionals, hospital stakeholders and administrators, and primary care providers across the U.S. and Canada. Interested participants can register for free in advance via the official registration link. 

The Takeaway

AI solutions like Coreline Soft’s AVIEW LCS Plus platform are having a real-world impact on healthcare providers as they roll out CT lung cancer screening programs. Sign up to learn more on August 1.

CT Lung Screening Chats Pay Off

Posted on by Brian Casey

Patients who talked about CT lung cancer screening with their doctors were more likely to actually follow through on getting scanned. That’s according to a study this week in CHEST that offers support for shared decision making – a process that some screening proponents have criticized.

The U.S. continues to see disappointing compliance rates for CT lung cancer screening, over 10 years after the USPSTF recommended the exam.

  • Some lung screening proponents suggest that one barrier to screening is a CMS rule requiring a shared decision-making session between patients and doctors before the first scan is performed – a requirement that’s not in place for any of the other major cancer screening tests.

But the new study indicates that shared decision making could actually work to boost compliance. 

  • Researchers from the Harvey L. Neiman Health Policy Institute led by first author YoonKyung Chung, PhD, examined lung screening compliance rates for 22.6k people who had their first CT exam between 2016 and 2019.

Researchers looked at differences in annual follow-up lung screening rates between people who got shared decision-making sessions and those who didn’t, finding… 

  • Only 11% of study participants had a session before their first scan.
  • One year after the initial scan, those who participated in sessions were 27% more likely to get a follow-up exam.
  • Four years later, the compliance rate rose to 33%. 

If CMS requires shared decision-making sessions for reimbursement, why are they occurring so infrequently? 

  • The authors called this phenomenon “puzzling,” and suggested it’s because CMS is not enforcing the mandate through tools like claims denial. CMS could also boost utilization by providing higher reimbursement for the discussions.

The Takeaway

The new findings suggest that shared decision making should be viewed as an opportunity rather than a barrier to convincing patients of CT lung cancer screening’s value. The results track with other studies showing that a high-touch approach with tools like patient navigators can work.

AI-Driven Diagnostics Detects Multiple Chest Diseases from Single CT Scan

Posted on by Partner Content

A new generation of AI solutions is enabling clinicians to detect multiple chest pathologies from a single CT scan. Lung cancer, cardiovascular disease, and chronic obstructive pulmonary disease (COPD) can all be detected in just one imaging session, ushering in a new era of more efficient imaging that benefits both providers and patients. 

Advances in CT lung cancer screening have been generating headlines as new research highlights the improved clinical outcomes possible when lung cancer is detected early. 

  • But lung cancer is just one of a “big three” of thoracic comorbidities – the others being cardiovascular disease and COPD – that can result from long-term exposure to toxic substances like tobacco smoke. 

These co-morbidities will be encountered more often as health systems ramp up lung cancer screening efforts, creating challenges for radiologists in managing the many incidental findings discovered with chest CT scans.

  • And it’s common knowledge that radiologists already have their hands full in an era of personnel shortages and rising imaging volumes. 

Fortunately, new AI technologies offer a solution. One of these is Coreline Soft’s AVIEW LCS Plus, an integrated three-in-one solution that enables simultaneous detection of lung cancer, cardiovascular disease, and COPD from a single chest CT scan. 

  • AVIEW LCS Plus is the only solution adopted for national lung cancer screening projects across key countries, including Korea (K-LUCAS), Germany (HANSE), Italy (RISP), and the pan-European consortium (4ITLR). 

Coreline’s solution is widely recognized as a pioneering AI platform for an era where unexpected findings can save lives, gaining increasing attention in academic journals and health policy reports alike.

  • In the U.S., AVIEW LCS Plus has been adopted by Temple Health, and the Pennsylvania system’s use of the solution in their Temple Healthy Chest initiative has been recognized as an innovative healthcare model within the Philadelphia region. 

Temple Health clinicians are finding that AI contributes to early detection of incidental findings, improved survival rates, and more proactive care planning.

  • AVIEW LCS Plus is streamlining lung cancer screening, helping identify chest conditions at earlier stages, when treatment is most effective. It is finding not only lung nodules but also undetected comorbidities that were often missed with conventional CT workflow. 

Coreline Soft will be presenting AVIEW LCS Plus in collaboration with Temple Health at the upcoming American Thoracic Society (ATS 2025) international conference in San Francisco from May 16-21. 

  • Attendees will be able to learn how AI in medical imaging can establish new standards, not just in diagnostics, but across policy, patient care, and hospital strategy. 

How to Improve CT Lung Screening Outcomes

Posted on by Brian Casey

Getting patients to attend cancer screening exams is one of the biggest challenges in healthcare. But a new study in JAMA Network Open should provide motivation, showing that people who showed up for annual CT lung cancer screening exams had better clinical outcomes than those who didn’t. 

Low cancer screening adherence frustrates clinicians and healthcare policy experts alike, but nowhere is the situation as dire as in CT lung cancer screening.

  • U.S. lung screening adherence rates languished in the single digits for years after the exam was approved by the USPSTF, and while there has been some recent improvement, screening rates are nowhere near those of more established exams like mammography. 

At the same time, statistical modeling studies (and common sense) suggest that complying with screening would reduce lung cancer mortality. 

  • So researchers from multiple institutions in the U.S. and Canada decided to track adherence to a real-world CT lung screening program consisting of a baseline scan and then two follow-up scans roughly a year apart. 

In all, 10.2k eligible adults were screened from 2015 to 2018, with researchers finding …

  • Screening adherence rates fell from the first follow-up round to the second (61% to 51%).
  • People who attended the first follow-up round were more likely to attend the second (67% vs. 25%). 
  • Patients who completed both screening rounds had higher lung cancer diagnosis rates (1% vs. 0.2%).
  • Patients who attended the second round and got a lung cancer diagnosis were more likely to have early-stage disease (73% vs. 25%) and less likely to have late-stage disease (21% vs. 58%). 

In analyzing the results, researchers said the drop-off in adherence rates between the first and second follow-up screening rounds represented an opportunity to reach out to people who missed the first round and get them to the second.

  • This position dovetails with other recent research underscoring the importance of patient navigators in guiding eligible people to lung cancer screening. 

The Takeaway

So as radiology and other disciplines look to build on the momentum behind CT lung cancer screening, what’s the key to success in improving patient outcomes? Sometimes, it’s just getting people to show up. 

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