New Cancer Disparity Data Show Socioeconomic Impact

Cancer screening disparities continue to draw scrutiny in radiology. A new study in JAMA Network Open takes a closer look at why some people don’t get screened as often as they should – as well as the factors that contribute to cancer prevalence and mortality. 

There’s extensive research backing the lifesaving potential of the major cancer screening exams, and cancer mortality rates have consistently declined thanks to the combination of screening and better treatments. 

  • But the declines are uneven, prompting researchers to investigate reasons for the disparities, such as in a study earlier this month documenting geographic variations in cancer screening rates. 

In the new study, researchers from the ACR’s Harvey L. Neiman Health Policy Institute looked at how 24 measures like lifestyle, socioeconomic status, and environmental background affected breast, prostate, lung, and colorectal cancer, which account for 50% of new cancer cases.

  • In particular, they examined screening completion rates and cancer prevalence and mortality at the county level in a nationally representative sample of 5% of Medicare fee-for-service beneficiaries, of whom 87% were 65 years and older. 

There’s a lot to unpack in the study, but a few highlights are below as they relate to breast and lung cancer, the two cancers for which imaging-based screening is recommended. The top three factors affecting each (in order of importance) are…

  • Breast cancer:
    • Screening rates – Hispanic population share, levels of insufficient sleep, and poverty. 
    • Prevalence – uninsured status, obesity, and housing insecurity.
    • Mortality – non-Hispanic Black race, environmental justice index, and insufficient sleep.
  • Lung cancer:
    • Screening rates – air pollution exposure, lack of access to primary care physicians, and number of poor physical health days.
    • Prevalence – limited access to healthy foods, uninsured status, and severe housing problems.
    • Mortality – smoking, poor physical health days, and environmental justice index. 

While there are some obvious findings in the data (the connection between smoking and lung cancer mortality, for example), the dominance of socioeconomic measures may take some by surprise (or maybe not). 

  • But they do track with previous research finding that socioeconomic factors account for 40-50% of health impacts.

The Takeaway

The new study – as with previous research – reinforces what we know about the strong connection between socioeconomic status and cancer screening disparities. The new data should give clinicians and public health advocates more detail on the specific factors they need to focus on to improve screening compliance and reduce cancer’s burden on society.

Cancer Screening Rates Vary Geographically

Progress has been made in some U.S. regions in boosting adherence rates for cancer screening exams like mammography, but clusters of regional variation remain. That’s according to a new study in JAMA Network Open that offers hope for reducing access disparities in disadvantaged areas.

Disparities in healthcare access remain one of the nagging problems in the U.S. healthcare system. 

  • Previous studies have shown that racial background, socioeconomic status, and geographic location can all affect access to care, and ultimately, patient outcomes.

Nowhere is this more apparent than in cancer screening, where getting patients in for their exams has always been a challenge. 

  • Screening compliance rates (as of 2021) were approximately 76% for breast cancer, 75% for cervical cancer, and 72% for colorectal cancer. 

But how does geography affect screening rates, and has progress been made over time? 

  • To answer these questions, researchers analyzed geographic variations in rates for the three major cancer screening tests (breast, cervical, and colorectal) over a 22-year period. 

Screening data were analyzed at the county level from 1997 to 2019, with screening prevalence estimated over 3-5-year periods. For mammography screening, authors found…

  • Screening rates were highest in the Northeast (Maine, New Hampshire, Vermont, and Massachusetts).
  • Rates were lowest in the Southwest (Texas, New Mexico, and Arizona).
  • Geographic areas that shifted from low to high uptake had lower socioeconomic status and more non-White residents, suggesting the success of efforts to improve screening in disadvantaged areas. 
  • Counties that did not improve had lower socioeconomic status than counties that maintained high screening rates. 
  • Rural areas had persistently low screening rates, reflecting lack of access to facilities as well as transportation. 

The Takeaway

The new study on geographic variation in cancer screening rates offers encouraging news that – at least in some disadvantaged areas – improving screening uptake is possible. But more research is needed to find out why some areas fail to see improvement. 

Doubling Lung Screening Rates with Patient Outreach

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.

How Do Patients Feel about Mammo AI?

As radiology moves (albeit slowly) to adopt clinical AI, how do patients feel about having their images interpreted by a computer? Researchers in a new study in JACR queried patients about their attitudes regarding mammography AI, finding that for the most part the jury is still out. 

Researchers got responses to a 36-question survey from 3.5k patients presenting for breast imaging at eight U.S. practices from 2023-2024, finding …

  • The most common response to four questions on general perceptions of medical AI was “neutral,” with a range of 43-51%. 
  • When asked if using AI for medical tasks was a bad idea, more patients disagreed than agreed (28% vs. 25%). 
  • Regarding confidence that medical AI was safe, patients were more dubious, with higher levels of disagreement (27% vs. 20%).
  • When asked if medical AI was helpful, 43% were neutral but positive attitudes were higher (35% vs. 19%).

The Takeaway

Much like clinicians, patients seem to be taking a wait-and-see attitude toward mammography AI. The new survey does reveal fault lines – like privacy and equitability – that AI developers would do well to address as they work to win broader acceptance for their technology. 

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How to Improve CT Lung Screening Outcomes

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. 

Screening Takes Center Stage at ECR 2025

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.

Opportunistic Screening’s AI Milestone

A new study lays the groundwork for AI-based opportunistic screening – the detection of disease using medical images acquired for other indications. In a paper in AJR, researchers show how their homegrown AI algorithm was able to analyze abdominal CT scans and link body composition measurements to the presence of disease.

Opportunistic screening is a sort of holy grail for radiology, with the potential to help radiologists find pathology from scans ordered for other clinical indications

  • Some researchers specifically are focusing on analysis of body composition characteristics derived from CT scans like muscle, fat, and bone that could be biomarkers for hidden pathology – and AI is key because it can process mountains of patient data without getting tired.

In the new paper, researchers from the NIH and the University of Wisconsin tested the concept of AI-based body composition analysis on a massive database of 118k patients who got abdominal CT scans from 2000 to 2021. 

  • They analyzed the scans with their own internally developed AI tool that measures 13 features of body composition, from volume and attenuation in different organs to area of subcutaneous adipose tissue. 

Their goal was to correlate the AI measurements with actual presence of disease, as well as other factors that could affect body composition like age and sex. They found …

  • AI-based body composition metrics varied by age and sex, confirming previous studies.
  • AI metrics also correlated with the four systemic diseases studied, specifically cancer, cardiovascular disease, diabetes mellitus, and cirrhosis.
  • The predictive power of different metrics varied by disease, from a high of 13 measures for diabetes to a low of nine for cancer. 

What’s the real-world impact of the study? 

  • In addition to validating the concept of AI-based opportunistic screening on a broad scale, the findings could be used to establish a set of normal values for body composition that also take into account the impact of systemic disease on these measurements.

The Takeaway

The new study is a bit technical, but it’s an important milestone on the path to opportunistic screening. It not only demonstrates the concept’s feasibility, but also begins to establish the normal values needed to actually implement screening programs in the real world.

DBT Detects Earlier Cancers in Swedish Tomo Study

A new analysis of a landmark DBT study from Sweden offers more support for the effectiveness of tomosynthesis mammography screening. Published in Radiology, researchers found that DBT screening seems to detect earlier cancers, most likely before they become more aggressive. 

Most U.S. mammography practices have embraced DBT since its approval in 2011, such that 48% of all certified mammography units are DBT and 90% of all facilities have at least one tomosynthesis unit. 

  • But doubts about DBT have persisted, particularly by mammography skeptics who charge that the technology was adopted without conducting randomized controlled trials to prove its value. 

But apart from RCTs, there have been plenty of observational studies in which DBT showed a benefit, one of them being the Malmö Breast Tomosynthesis Screening Trial of almost 15k women in Sweden.

  • First results from MBTST were published in 2018 and showed that single-view DBT screening had a 34% higher cancer detection rate per 1k women than digital mammography (8.7 vs. 6.5), but with a higher recall rate as well (3.6% vs. 2.5%).

In the new study, researchers wanted to see if DBT’s screening benefits persisted over two subsequent screening rounds with conventional digital mammography. 

  • Their assumption was that the cancer detection rate would be lower in subsequent rounds, and there would be fewer slow-growing, less aggressive cancers – a sign of early cancer detection. 

Their analysis found …

  • The cancer detection rate per 1k women was lower in the first (4.6) and second (5.3) rounds compared to the original MBTST
  • Recall rate was 2.1% – also lower 
  • The odds ratio of cancer detection was lower than MBTST in the first (OR=0.46) and second (OR=0.53) follow-up rounds 
  • Invasive cancers were less prevalent in the first round compared to the second round (66% vs. 83%) 

What do the results mean? The implication is that because DBT detected cancers in the initial screening round, there was lower cancer prevalence and less aggressive cancer in follow-up rounds, an effect that wore off as time went on.

The Takeaway

There may never be a randomized controlled trial of DBT due to the ethical problem of denying a live-saving technology to women in a control group. But studies like the MBTST follow-up are important in adding to the body of evidence showing that DBT actually does work.

How to Improve CT Lung Cancer Screening

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:

  • 34% lower risk of lung cancer death by hazard ratio
  • 28% 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.

AI Powers Opportunistic Screening

The growing power of AI is opening up new possibilities for opportunistic screening – the detection of pathology using data acquired for other clinical indications. The potential of CT-based opportunistic screening – and AI’s role in its growth – was explored in a session at RSNA 2023.

What’s so interesting about opportunistic screening with CT? 

  • As one of imaging’s most widely used modalities, CT scans are already being acquired for many clinical indications, collecting body composition data on muscle, fat, and bone that can be biomarkers for hidden pathology. 

What’s more, AI-based tools are replacing many of the onerous manual measurement tasks that previously required radiologist involvement. There are four primary biomarkers for opportunistic screening, which are typically related to several major pathologies, said Perry Pickhardt, MD, of the University of Wisconsin-Madison, who led off the RSNA session:

  • Skeletal muscle density (sarcopenia)
  • Hard calcified plaque, either coronary or aortic (cardiovascular risk)
  • Visceral fat (cardiovascular risk)
  • Bone mineral density (osteoporosis and fractures) 

But what about the economics of opportunistic screening? 

  • A recent study in Abdominal Radiology found that in a hypothetical cohort of 55-year-old men and women, AI-assisted opportunistic screening for cardiovascular disease, osteoporosis, and sarcopenia was more cost-effective compared to both “no-treatment” and “statins for all” strategies – even assuming a $250/scan charge for use of AI.

But there are barriers to opportunistic screening, despite its potential. In a follow-up talk, Arun Krishnaraj, MD, of UVA Health in Virginia said he believes fully automated AI algorithms are needed to avoid putting the burden on radiologists. 

And the regulatory environment for AI tools is complex and must be navigated, said Bernardo Bizzo, MD, PhD, of Mass General Brigham.

Ready to take the plunge? The steps for setting up a screening program using AI were described in another talk by John Garrett, PhD, Pickhardt’s colleague at UW-Madison. This includes: 

  • Normalizing your data for AI tools
  • Identifying the anatomical landmarks you want to focus on
  • Automatically segmenting areas of interest
  • Making the biomarker measurements
  • Plugging your data into AI models to predict outcomes and risk-stratify patients

The Takeaway

Opportunistic screening has the potential to flip the script in the debate over radiology utilization, making imaging exams more cost-effective while detecting additional pathology and paving the way to more personalized medicine. With AI’s help, radiologists have the opportunity to place themselves at the center of modern healthcare. 

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