Evidence map›Paper›PMID 40848139›Full record

GuidelineEuropean radiology2026

ESR Essentials: lung cancer screening with low-dose CT-practice recommendations by the European Society of Thoracic Imaging.

Marie-Pierre Revel, Jurgen Biederer, Arjun Nair, Mario Silva, Colin Jacobs, Annemiek Snoeckx, Mathias Prokop, Helmut Prosch, Anagha P Parkar, Thomas Frauenfelder and 1 more

Abstract readPractice GuidelineReview
In one paragraph

Guideline in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
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

9 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
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  9. Article
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

11 authors.

Marie-Pierre RevelDepartment of Radiology, Cochin Hospital, Université Paris Cité, Paris, France. Marie-pierre.revel@aphp.fr.ORCID http://orcid.org/0000-0001-7906-6499
Jurgen BiedererDepartment of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany.
Arjun NairDepartment of Radiology, University College London Hospital, London, UK.
Mario SilvaScienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy.
Colin JacobsDepartment of Medical Imaging, Radboud University Center, Nijmegen, The Netherlands.
Annemiek SnoeckxDepartment of Radiology, Antwerp University Hospital, Edegem, Belgium.
Mathias ProkopDepartment of Medical Imaging, Radboud University Center, Nijmegen, The Netherlands.
Helmut ProschDepartment of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna General Hospital, Vienna, Austria.
Anagha P ParkarDepartment of Radiology, Haraldsplass Deaconess Hospital, Bergen, Norway.
Thomas FrauenfelderInstitute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Anna Rita LariciDepartment of Radiological and Hematological Sciences, Catholic University of the Sacred Heart, Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Low-dose CT screening for lung cancer reduces the risk of death from lung cancer by at least 21% in high-risk participants and should be offered to people aged between 50 and 75 with at least 20 pack-years of smoking. Iterative reconstruction or deep learning algorithms should be used to keep the effective dose below 1 mSv. Deep learning algorithms are required to facilitate the detection of nodules and the measurement of their volumetric growth. Only large solid nodules larger than 500 mm

Indexed as

Early Detection of CancerLung NeoplasmsTomography, X-Ray ComputedAgedEuropeFemaleHumansMaleMass ScreeningMiddle AgedRadiation DosageSocieties, MedicalArtificial intelligenceLung cancerLung neoplasmsMultidetector computed tomographyScreening programs (Diagnostic)

Identifiers

PMID40848139
PMCPMC12963080

What OpenQuestion holds

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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.