ArticlePharmacoEconomics - open2026
Budget Impact of the LungFlag™ Predictive Risk Model for Lung Cancer Screening.
Article in PharmacoEconomics - open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectiveLung cancer screening has historically been associated with false-positive results that can lead to complications and increased costs. LungFlag is a machine-learning risk prediction model using individual-level data to identify people at high risk of developing non-small cell lung cancer (NSCLC), prompting healthcare professionals to consider appropriate actions such as recommended screening with low-dose computed tomography. This study evaluated the budget impact of using the LungFlag risk prediction model to identify candidates for lung cancer screening from a US payer perspective.
methodsThis budget impact model estimated total annual costs with or without LungFlag for a hypothetical 1 million-member US commercial health plan. Incremental costs were evaluated over a 5-year period with a cycle length of 1 year; no discounting was applied. All costs were adjusted to 2021 US dollars. The population included screening-naïve individuals aged 50-80 years with a ≥20-pack-years smoking history who were either current smokers or former smokers who had quit smoking in the previous 15 years (US Preventive Services Task Force 2021 criteria). LungFlag performance was measured 9-12 months before clinical diagnosis. Sensitivity and scenario analyses explored the robustness of base-case assumptions.
resultsThe budget impact model showed that LungFlag was associated with a total cumulative cost savings of US$2.8 million over 5 years, largely attributable to reduced advanced NSCLC treatment costs (-$4.4 million) as more patients were identified with early stage I rather than stage III/IV disease. Using LungFlag, an estimated 17 additional NSCLC diagnoses were identified, 22 fewer NSCLC-related deaths occurred over 5 years, 50 more patients had stage I NSCLC at diagnosis, and 33 fewer patients had stage III or IV NSCLC at diagnosis.
conclusionsUsing LungFlag to identify candidates for lung cancer screening was estimated to save US$2.8 million over 5 years from a US commercial health plan perspective, largely attributable to reduced costs of treating advanced NSCLC.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.