Evidence map›Paper›PMID 34995141›Full record

ArticleThe British journal of radiology2022

European lung cancer screening: valuable trial evidence for optimal practice implementation.

Mario Silva, Gianluca Milanese, Roberta E Ledda, Sundeep M Nayak, Ugo Pastorino, Nicola Sverzellati

Registry-linked trialAbstract read
In one paragraph

Article in The British journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05766046 (Early Diagnosis of Lung Cancer of the Italian Pulmonary Screening Network), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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.

NCT05766046 naactive not recruitingnot on this map

Early Diagnosis of Lung Cancer of the Italian Pulmonary Screening Network (RISP): Comparative Analysis for the Use of Low Dose Computed Tomography and Promotion of Primary Prevention Interventions in Subjects at High Risk for Lung Cancer

TypeinterventionalSponsorUgo PastorinoRan2022 to 2026Enrolled7,324ConditionsLung Cancer, COPD (Chronic Obstructive Pulmonary Disease) With Acute Lower Respiratory Infection, Cardiovascular DiseasesArmsearly lung cancer detection, blood test
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Mario SilvaScienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy.ORCID 0000-0002-2538-7032
Gianluca MilaneseScienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy.
Roberta E LeddaScienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy.
Sundeep M NayakDepartment of Radiology, Kaiser Permanente Northern California, San Leandro, California, USA.
Ugo PastorinoSection of Thoracic Surgery, IRCCS Istituto Nazionale Tumori, Milano, Italy.
Nicola SverzellatiScienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer screening (LCS) by low-dose computed tomography is a strategy for secondary prevention of lung cancer. In the last two decades, LCS trials showed several options to practice secondary prevention in association with primary prevention, however, the translation from trial to practice is everything but simple. In 2020, the European Society of Radiology and European Respiratory Society published their joint statement paper on LCS. This commentary aims to provide the readership with detailed description about hurdles and potential solutions that could be encountered in the practice of LCS.

Indexed as

Lung NeoplasmsRadiologyEarly Detection of CancerHumansMass ScreeningTomography, X-Ray Computed

Identifiers

PMID34995141
PMCPMC10993986

What OpenQuestion holds

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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.