Evidence map›Paper›PMID 31748855›Full record

ArticleEuropean radiology2020

Cost-effectiveness of lung MRI in lung cancer screening.

Bradley D Allen, Mark L Schiebler, Gregor Sommer, Hans-Ulrich Kauczor, Juergen Biederer, Timothy J Kruser, James C Carr, Gordon Hazen

Abstract read
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In one paragraph

Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 5 pooled it
2.6field-weighted citation impact, top 9% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 5 syntheses or guidelines pooled it, 36 citations in OpenAlex.

  1. Pooled it
  2. Guideline
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Article
  7. Review
  8. MRI for Lung Cancer Management: Any Closer to Clinical Application?Journal of magnetic resonance imaging : JMRI · 2026
    Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Natural history models for lung Cancer: A scoping review.Lung cancer (Amsterdam, Netherlands) · 2025
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 4 countries.

Bradley D AllenDepartment of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA. bdallen@northwestern.edu.ORCID http://orcid.org/0000-0002-0667-137X
Mark L SchieblerDepartment of Radiology, UW-Madison School of Medicine and Public Health, Madison, WI, USA.
Gregor SommerDepartment of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland.
Hans-Ulrich KauczorDepartment of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany.
Juergen BiedererDepartment of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany.
Timothy J KruserDepartment of Radiation Oncology, Feinberg School of Medicine, Northwestern University , Chicago, IL, USA.
James C CarrDepartment of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Gordon HazenDepartment of Industrial Engineering and Management Sciences, Northwestern University, Chicago, IL, USA.
Northwestern University · USHeidelberg University · DEUniversity of Basel · CHUniversity of Wisconsin–Madison · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesRecent studies with lung MRI (MRI) have shown high sensitivity (Sn) and specificity (Sp) for lung nodule detection and characterization relative to low-dose CT (LDCT). Using this background data, we sought to compare the potential screening performance of MRI vs. LDCT using a Markov model of lung cancer screening.

methodsWe created a Markov cohort model of lung cancer screening which incorporated lung cancer incidence, progression, and mortality based on gender, age, and smoking burden. Sensitivity (Sn) and Sp for LDCT were taken from the MISCAN Lung Microsimulation and Sn/Sp for MRI was estimated from a published substudy of the German Lung Cancer Screening and Intervention Trial. Screening, work-up, and treatment costs were estimated from published data. Screening with MRI and LDCT was simulated for a cohort of male and female smokers (2 packs per day; 36 pack/years of smoking history) starting at age 60. We calculated the screening performance and cost-effectiveness of MRI screening and performed a sensitivity analysis on MRI Sn/Sp and cost.

resultsThere was no difference in life expectancy between MRI and LDCT screening (males 13.28 vs. 13.29 life-years; females 14.22 vs. 14.22 life-years). MRI had a favorable cost-effectiveness ratio of $258,169 in men and $403,888 in women driven by fewer false-positive screens. On sensitivity analysis, MRI remained cost effective at screening costs < $396 dollars and Sp > 81%.

conclusionsIn this Markov model of lung cancer screening, MRI has a near-equivalent life expectancy benefit and has superior cost-effectiveness relative to LDCT. KEY POINTS: • In this Markov model of lung cancer screening, there is no difference in mortality between yearly screening with MRI and low-dose CT. • Compared to low-dose CT, screening with MRI led to a reduction in false-positive studies from 26 to 2.8% in men and 26 to 2.6% in women. • Due to similar life-expectancy and reduced false-positive rate, we found a favorable cost-effectiveness ratio of $258,169 in men and $403,888 in women of MRI relative to low-dose CT.

Indexed as

AgedCost-Benefit AnalysisEarly Detection of CancerFemaleHumansLungLung NeoplasmsMagnetic Resonance ImagingMaleMass ScreeningMiddle AgedTomography, X-Ray ComputedCost-benefit analysisHealth care costsLung cancerMagnetic resonance imagingScreening

Identifiers

PMID31748855
OpenAlexW2989960506

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.