The research, led by Adoma Manful of Vanderbilt University Medical Center together with senior authors Jennifer Richmond of Wake Forest University School of Medicine and Meng-Yun Lin, also of Wake Forest, set out to quantify how the guideline change reshapes the pool of people who qualify for annual screening. The team used data from the National Health Interview Survey, a large, publicly available household survey administered by the National Center for Health Statistics at the Centers for Disease Control and Prevention. Because the survey is designed to represent the civilian, noninstitutionalized population of the United States, it allows researchers to translate eligibility criteria written on paper into estimates of how many real people, and which kinds of people, would now be directed toward screening.

To understand why the change matters, it helps to trace the short history of lung cancer screening policy. The landmark National Lung Screening Trial, published in the New England Journal of Medicine in 2011, demonstrated that annual LDCT screening reduced lung cancer mortality by 20 percent compared with chest radiography among people at high risk. That evidence gave rise to the first national screening recommendations, which used a combination of age, smoking intensity measured in pack-years, and years since quitting to define eligibility. The US Preventive Services Task Force later broadened its criteria in 2021, lowering the starting age to 50 and the smoking threshold to 20 pack-years, but it retained a cutoff that excluded anyone who had quit smoking more than 15 years earlier.

The American Cancer Society’s 2023 guideline update broke with that tradition. Drawing on modeling analyses, including work by Landy and colleagues showing that absolute lung cancer risk continues to rise among former smokers with more than 15 quit-years, the Society removed the years-since-quitting restriction entirely. Under the updated recommendation, adults aged 50 to 80 who currently smoke or formerly smoked and have at least a 20 pack-year smoking history are eligible for annual LDCT screening, regardless of how long ago they stopped. The rationale was straightforward: a person’s cumulative exposure does not vanish the moment they extinguish their last cigarette, and former smokers who quit long ago may still carry substantial residual risk.

The new analysis asked a deceptively simple question about that expanded eligibility: among the people swept into the screening pool by the removal of the quit-years limit, what is their predicted risk of lung cancer? This question matters because screening is not free of downsides. LDCT scans detect a large number of incidental findings, including nodules that turn out to be benign, and every follow-up scan, biopsy, or surgery carries its own burden of anxiety, cost, and occasional harm. Screening programs work best when they concentrate on people whose underlying probability of disease is high enough that the expected benefit of early detection outweighs these risks. If the expanded criteria bring in large numbers of people at low predicted risk, the efficiency of the entire enterprise could be diluted.

Using the National Health Interview Survey data, the investigators classified respondents according to the old and new eligibility criteria and estimated the predicted lung cancer risk of each group. The study, which was funded by the National Institute on Minority Health and Health Disparities under award R21MD020158, was reviewed and deemed exempt by the Wake Forest University School of Medicine Institutional Review Board, with all survey participants having provided informed consent. The authors report no conflicts of interest, and the funding agencies had no role in the design, analysis, or decision to publish the findings.

The technical heart of the study lies in how smoking exposure is translated into risk. Pack-years, the standard metric, multiply the number of packs smoked per day by the number of years of smoking, yielding a cumulative dose measure. Risk prediction models developed for screening selection, including the widely used criteria from Tammemägi and colleagues published in 2013, incorporate pack-years alongside age, sex, and other characteristics to estimate an individual’s probability of developing or dying from lung cancer within a defined period. By applying such risk stratification to a nationally representative sample, the researchers could compare the risk profiles of people eligible under the previous rules with those eligible only under the expanded 2023 criteria, providing an evidence-based picture of who the guideline change actually adds.

The findings carry practical weight for clinicians and health systems preparing to implement the expanded guidelines. Primary care practices, which serve as the main gateway to screening, now face a larger eligible population and must decide how to prioritize outreach, shared decision-making visits, and imaging capacity. The study’s risk-based framing offers a way to think about that challenge: eligibility rules define the outer boundary of who may benefit, but predicted risk within the eligible pool varies considerably, and understanding that variation can help clinicians communicate more honestly with patients about the likely balance of benefits and harms. It also informs the ongoing debate about whether screening selection should move away from simple smoking-history thresholds toward more refined, multivariable risk models.

There is an equity dimension as well. Lung cancer screening uptake in the United States has remained strikingly low, with only a small fraction of eligible adults receiving annual scans, and disparities in access and completion persist across racial, geographic, and socioeconomic lines. The National Institute on Minority Health and Health Disparities funding of this work reflects growing recognition that guideline expansions only save lives if the people they cover actually get screened. The authors, who include thoracic surgeon Brian Mitzman of the University of Utah Health and Huntsman Cancer Institute, health economist Richard Nelson of the VA Salt Lake City Healthcare System, and pharmacoepidemiologist Nathorn Chaiyakunapruk, bring a deliberately multidisciplinary perspective to a problem that spans oncology, implementation science, and health policy.

The broader lesson of the study is that screening guidelines are living instruments that must be recalibrated as evidence accumulates. The removal of the quit-years limit was a principled correction grounded in the biology of tobacco-related carcinogenesis, where risk declines slowly, if at all, after cessation. By measuring the predicted risk of the newly eligible population in a nationally representative frame, this research gives policymakers and clinicians a clearer view of the trade-offs embedded in the expanded criteria. As screening programs scale up to meet the larger eligible population, the findings underscore a central tenet of modern cancer prevention: the goal is not simply to scan more people, but to scan the right people, at the right time, with a clear-eyed understanding of who stands to benefit most.

Subject of Research: Predicted lung cancer risk among adults newly eligible for low-dose CT screening under the expanded 2023 American Cancer Society guidelines

Article Title: Expanded American Cancer Society Lung Cancer Screening Eligibility and Predicted Risk

Article References: Expanded American Cancer Society Lung Cancer Screening Eligibility and Predicted Risk. (n.d.). https://doi.org/10.1007/s11606-026-10907-4

Image Credits: AI Generated

DOI: 10.1007/s11606-026-10907-4

Keywords: lung cancer, cancer screening, low-dose CT, American Cancer Society, smoking, pack-years, risk prediction, National Health Interview Survey, screening guidelines, health disparities, preventive medicine, public health

The research, published in JNCI, the Journal of the National Cancer Institute, is a meta-analysis that brings together data from all eight randomised controlled trials of mammography screening ever conducted: the New York Health Insurance Plan trial, the Malmö trial, the Two-County trial, the Edinburgh trial, the Canadian National Breast Screening Study, the Stockholm trial, the Gothenburg trial and the UK Age trial. Rather than treating each trial as an isolated source of truth, the team reanalysed the pooled evidence against a real-world benchmark, using two Danish regional screening programmes as a reference population. The comparison included both invasive breast cancer and ductal carcinoma in situ, the non-invasive condition often picked up through calcifications on mammograms.

The central finding is striking in its simplicity. When the researchers accounted for the temporal dynamics of screening, the excess number of breast cancer cases detected in the randomised trials closely matched what would be expected in a Danish screening population, where overdiagnosis is estimated to be below five percent. In other words, the apparent surplus of cancers in the trials, which has long been cited as proof that overdiagnosis is a substantial problem, looks far more like the ordinary lead-time effect of finding cancers earlier than they otherwise would have been found.

Understanding why earlier estimates were so high requires a technical detour into how screening changes the timing of diagnosis. When a screening programme is introduced, the number of breast cancer diagnoses initially rises sharply, because cancers are detected earlier than they would have been without screening. Over time, this initial surge should be followed by a compensatory drop, as some of the cancers counted in the early period would otherwise have been diagnosed later in the absence of screening. If researchers fail to take this pattern into account, the temporary increase can be mistaken for overdiagnosis, inflating the estimate considerably.

Professor Elsebeth Lynge, professor emerita at the Department of Public Health at the University of Copenhagen, explained the mechanics behind the distortion. She noted that the pattern can also be affected if women in either the screening or control groups continue to undergo screening after the trials had ended, which was common in practice. Contamination of this kind blurs the boundary between the compared groups and can make the control group’s later diagnoses look like a shortfall rather than a delay. The Danish experience offered a natural laboratory for disentangling these effects, because organised screening was introduced in some Danish regions 17 years before others, allowing the researchers to observe how breast cancer diagnoses changed both when screening was introduced and over the longer term.

By comparing breast cancer incidence at identical timepoints in the trials and in Danish routine screening data, the team could examine how similar the observed patterns were and what that similarity implies for overdiagnosis estimates. The alignment between trial data and routine programme data suggests that the trials, when interpreted in their full temporal context, are consistent with overdiagnosis of less than five percent rather than with estimates approaching 50 percent. Matejka Rebolj, senior epidemiologist at Queen Mary University of London, said that some previous high estimates, which influenced screening guidelines and patient communication, were based on evidence gathered before trial data had fully matured.

The analysis also corrected for three structural differences between the trials that can skew overdiagnosis calculations. The first was whether women in the control groups were themselves screened, either after the trial ended or following the introduction of routine programmes, which erodes the contrast between screened and unscreened populations. The second was the number of screening rounds offered in each group, since more rounds provide more opportunities to detect cancers early and shift diagnoses forward in time. The third was the length of follow-up, because a longer observation period allows time for cancers detected early through screening to be diagnosed later in the control group, which is exactly what the lead-time model predicts. After accounting for these differences in screening intensity and follow-up between the groups, the results point to overdiagnosis being substantially less common than previously believed.

The definition of overdiagnosis itself deserves attention, because it is broader than many people assume. It occurs when screening detects a cancer that would never have become life-threatening or caused symptoms during a woman’s lifetime, meaning that without screening she would never have known she had it. It can also include women who die from other causes shortly after a breast cancer diagnosis, since poor health or limited life expectancy means these women were unlikely to benefit from early detection in the first place, as treating the cancer had little chance of improving their health or prolonging their lives. Distinguishing genuine overdiagnosis from cancers that were simply found earlier is therefore one of the hardest measurement problems in screening research, and it is precisely the problem the Danish reference data helped solve.

For Sisse Helle Njor, professor at the University of Southern Denmark and Lillebælt Hospital, the motivation was to bring together all the randomised evidence for a clearer picture. She observed that randomised trials have often been cited as evidence that overdiagnosis is a substantial problem, but that this interpretation is not as straightforward as it may seem. Her conclusion for women is direct: most women will not develop breast cancer, but the study offers reassurance that the benefits of detecting breast cancer early and preventing premature death will outweigh the small risk of unnecessary treatment. Accurate information about how often benefits and harms occur is essential for women weighing whether to attend screening, and the new figures suggest that the balance tilts more favourably than earlier communications implied.

The implications extend beyond the clinic into public health policy. Overdiagnosis estimates have shaped screening guidelines, invitation letters and the international debate about population-based mammography for decades, and figures as high as one in two screen-detected cancers have fuelled scepticism about organised programmes. If the matured trial data support estimates below five percent, the framework for communicating risk to invited women may need revision, and the researchers hope their analysis will provide a basis for a more realistic interpretation of the evidence. The work was supported by the Novo Nordisk Foundation and Cancer Research UK, and its publication in a leading oncology journal signals that the long-running controversy over mammography’s harms may be entering a more settled phase, one in which the fear of unnecessary treatment no longer overshadows the demonstrated capacity of screening to prevent deaths from breast cancer.

Subject of Research: Overdiagnosis in mammography screening estimated through a meta-analysis of randomised trials benchmarked against Danish screening data

Article Title: New analysis points to low overdiagnosis in breast cancer screening

Article References: New analysis points to low overdiagnosis in breast cancer screening. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: breast cancer, mammography screening, overdiagnosis, meta-analysis, randomised controlled trials, lead-time bias, Danish screening programmes, ductal carcinoma in situ, cancer epidemiology, screening guidelines, JNCI, public health