Tag: LDCT

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.

Screening Takes Center Stage at ECR 2025

Posted on by Brian Casey

New advances in cancer screening were among the major trends at last week’s ECR 2025 conference in Vienna. From traditional screening exams like mammography to up-and-coming tests like CT lung cancer exams, radiologists are emerging at the forefront of efforts to improve population health through early detection.

CT lung cancer screening is gaining momentum in Europe, and a Friday afternoon session explored the experiences of multiple sites…

  • U.K. researchers used DeepHealth’s Lung Nodules AI solution for automated triage of lung nodules found on non-screening CT chest exams, finding the approach could save £25k-£37k annually.
  • A German team documented technical lung CT acquisition parameters for screening centers in the SOLACE consortium across 10 countries, finding some room for improvement. 
  • Preliminary results from an Italian lung screening project were reported, with 2k people scanned with a 1.5% cancer detection rate (77% stage I-II) and 17% recall rate. Smoking cessation advice was also given.
  • Early results from a pilot screening project in Poland were given, with a 1.9% cancer detection rate in 3.1k people screened. They recommend screening be implemented nationwide. 
  • In a secondary analysis of 23.4k people in the NLST study, CT-derived body composition metrics predicted mortality beyond traditional risk factors.

Meanwhile, new ECR cancer screening research builds on the landmark accomplishments from 2024 in AI for breast screening. A Saturday afternoon session explored the progress being made…

  • German breast screening programs that deployed ScreenPoint Medical’s Transpara AI algorithm for 119k women saw their cancer detection rate grow (6 vs. 4.8 cancers per 1k) while the recall rate remained stable at around 2.5%. 
  • AI-supported double-reading in Italy for 120k women led to more breast cancers detected on baseline exams compared to subsequent screening rounds, as well as a 42% lower recall rate.
  • Patients found an AI chatbot based on GPT-4 generated responses to their questions that were more empathetic and readable than those of radiologists.
  • Another Italian study found that using AI for double-reading mammograms of 266k women led to a 21% increase in cancer detection rate and 15% drop in recall rate.
  • A secondary analysis of the MASAI trial suggested that double-reading with two radiologists continue to be used for high-risk women. Single reading of 3.8k high-risk exams resulted in 8.9% fewer detected cancers and 5.9% fewer recalls.

The Takeaway

Last week’s research on cancer screening at ECR 2025 shows that imaging experts see screening as a way to not only improve population health on a broad scale, but also to give radiologists the opportunity to raise their profile with patients and take a more direct role in patient care. The question is whether it’s an opportunity radiologists are ready to take.

Are CT Lung Screening Patients Sicker?

Posted on by Brian Casey

Amid the rush of enthusiasm for CT lung cancer screening, a new study published in JAMA Health Forum offers a cautionary note. Researchers found that in the real world, people eligible for lung screening were sicker than those in research studies, and thus may not enjoy screening’s benefits to the same extent. 

Support for CT lung cancer screening is based on randomized controlled trials published in 2011 (NLST) and NELSON (2020) that showed screening reduced lung cancer mortality among high-risk individuals who typically had long smoking histories. 

  • The studies have spurred momentum for large-scale CT lung cancer screening programs, with a number of European and Asian countries starting national initiatives. 

But how generalizable are these results? Researchers noted that people who participated in the NLST study tended to be younger and healthier than individuals who qualify for screening in the real world. 

  • Co-morbidities like COPD, diabetes, and heart disease, as well as age and racial background, can have an impact on survival after treatment for lung cancer, and thus could reduce screening’s risk/benefit calculation. 

In the new Personalized Lung Cancer Screening study, researchers analyzed the comorbidity profiles of 31.8k people who got screened between 2016 and 2021 in California, Florida, and South Carolina. 

  • They noted that their PLuS study cohort was more diverse in terms of age, race, and ethnicity than that used in NLST, and potentially had more comorbid conditions. 

In analyzing their population, PLuS researchers found that compared to NLST participants, people screened in their real-world programs had …

  • Higher rates of COPD (33% vs. 18%).
  • Higher rates of diabetes (25% vs. 9.7%).
  • Higher rates of heart disease (16% vs. 13%).
  • Were more likely to be aged 70 and over (25% vs. 8.8%).
  • Had high scores on various metrics of comorbidity and frailty. 

Older, sicker patients are less likely to have good health outcomes after lung cancer surgery, and might also succumb to conditions like COPD, diabetes, and heart disease before lung cancer, which could also reduce lung screening’s benefits.

The Takeaway

While the new findings aren’t likely to seriously dampen CT lung cancer screening’s growing momentum, they do illustrate a point that should always be kept in mind when looking at research results: in the real world, your mileage may vary. 

CT Lung Screening’s Weak Link

Posted on by Brian Casey

CT lung cancer screening rates in the U.S. remain abysmally low, over a decade after the exam was recommended. Is part of lung screening’s problem its reliance on provider referrals? A new research letter in JAMA Network Open examines this question. 

Unlike breast screening, in which eligible women are able to self-refer themselves for exams, CT lung screening revolves around provider referrals to start the process. 

  • CMS requires a shared decision-making session that results in a written order from a practitioner for a CT lung screening exam in order to pay for screening through Medicare and Medicaid. 

When CMS created the rules in 2015, provider referrals and shared decision-making were seen as ways to get patients involved in their own care by making choices in coordination with their caregivers.

  • But many are starting to see the requirements as a barrier, especially given low CT lung screening rates in the U.S.

In the new article, researchers investigated how easy it would be for an eligible individual to secure a CT lung screening appointment by just calling hospitals – without a provider referral. 

  • They note that one-third of Americans don’t have primary care clinicians, and are often told to call hospitals directly to set up appointments.

So they did just that, placing phone calls to 527 hospitals asking to arrange CT lung screening appointments, finding …

  • 317 calls (60%) failed because the caller did not have a primary care provider’s order.
  • Only 51 hospitals (9.7%) were able to connect callers to any component of a lung cancer screening process. 

The study authors note that the provider referral requirement isn’t the only thing holding CT lung cancer screening back, as even patients with primary care providers aren’t getting screened, and managing nodule follow-up can also be challenging. 

  • But Medicare’s cumbersome reimbursement rules certainly don’t help bring new people into the fold.

The Takeaway

Given CT lung cancer screening’s undisputed life-saving value, there’s no reason to put unnecessary barriers in its way. The provider referral and shared decision-making requirements are lung screening’s weak link to securing greater adoption, and CMS should rescind them to put CT lung cancer screening on the path to greater adoption.

Two-for-One CT Screening Hits the Road

Posted on by Brian Casey

A new study takes CT screening on the road in rural Appalachia, showing how a mobile van outfitted with a CT scanner can screen at-risk individuals for both lung cancer and cardiovascular disease in one visit. 

Recent studies have demonstrated the effectiveness of CT lung cancer screening not only among the overall population, but particularly among disadvantaged communities with lower healthcare access. 

  • Such limited access is common in rural areas of Appalachia, which also have some of the highest rates of smoking and cardiovascular disease in the U.S.

Researchers from West Virginia University wanted to tackle two challenges at once with LUCAS, a mobile van outfitted with a CT scanner for lung cancer screening. 

  • They noted that CT lung scans can also be used to acquire data on coronary artery calcium (CAC), a known risk factor for cardiovascular disease. 

LUCAS was launched in September 2021, so WVU researchers analyzed data acquired for 526 low-dose CT screenings of high-risk people conducted through December 2022. 

  • They used the CT lung scans to calculate CAC scores based on Agatson criteria, in which a score of 101-400 indicates moderate risk of cardiovascular disease and >400 is classified as high risk; individuals with scores ≥100 should be referred to aspirin or statin therapy. 

They found that LUCAS scans revealed … 

  • Over 54% of patients had coronary calcification on LDCT scans
  • 31% of patients had CAC scores ≥100 
  • 14% had CAC scores ≥400
  • Elevated CAC scores correlated with lung cancer risk based on Lung-RADS scores as well as smoking history based on pack-years
  • Of the patients with CAC scores ≥1 and who weren’t already on statin or aspirin therapy, 6.2% started statins and 3.3% started aspirin

Despite the firm link between CAC scores and lung cancer risk, the researchers expressed disappointment that so few patients started prevention therapy like statins or aspirin after their exams.

  • Indeed, researchers noted that few patients from the study got additional cardiac testing or follow-up referrals for cardiovascular prevention after their screenings. 

The Takeaway

The new study not only confirms recent research showing that opportunistic screening can enhance the value of CT lung cancer scans, but also the role that lung exams can play in reducing healthcare disparities. On the down side, it shows that all the screening in the world won’t make a difference if patients don’t get appropriate follow-up. 

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