Evidence map›Paper›PMID 31420379›Full record

ArticleBMJ open2019

Detection of pulmonary nodules: a clinical study protocol to compare ultra-low dose chest CT and standard low-dose CT using ASIR-V.

Marie Ludwig, Emilie Chipon, Julien Cohen, Emilie Reymond, Maud Medici, Anthony Cole, Alexandre Moreau Gaudry, Gilbert Ferretti

Registry-linked trialOpen access · goldAbstract readComparative Study
In one paragraph

Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03305978 (Detection of Pulmonary Nodules), which is not on this map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.1field-weighted citation impact, top 12% 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.

NCT03305978 nacompletednot on this map

Detection of Pulmonary Nodules: Comparison of Ultra-low-dose Chest CT (Approaching a Two Views Chest X-ray Radiation) and Standard Low Dose CT. A Monocentric, Prospective, Non-randomized, Comparative, Open-label Study With Blind Reading of the Judgment Criteria.

TypeinterventionalSponsorUniversity Hospital, GrenobleRan2017 to 2019Enrolled150ConditionsLung Cancer, Smoking, Radiation ExposureArmsUltra low dose chest CT, Low dose chest CT
3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. [Expert Consensus for Thermal Ablation of Pulmonary Subsolid Nodules (2021 Edition)].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2021
    Article
  5. Review
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 2 institutions in 1 country.

Marie LudwigService de radiologie et imagerie médicale, pôle imagerie, Centre Hospitalier Universitaire Grenoble Alpes, Grenoble, France.
Emilie ChiponCIC 1406, INSERM, Grenoble, France.
Julien CohenService de radiologie et imagerie médicale, pôle imagerie, Centre Hospitalier Universitaire Grenoble Alpes, Grenoble, France.
Emilie ReymondService de radiologie et imagerie médicale, pôle imagerie, Centre Hospitalier Universitaire Grenoble Alpes, Grenoble, France.
Maud MediciCIC 1406, INSERM, Grenoble, France.
Anthony ColeService de radiologie et imagerie médicale, pôle imagerie, Centre Hospitalier Universitaire Grenoble Alpes, Grenoble, France.
Alexandre Moreau GaudryCIC 1406, INSERM, Grenoble, France.
Gilbert FerrettiService de radiologie et imagerie médicale, pôle imagerie, Centre Hospitalier Universitaire Grenoble Alpes, Grenoble, France.
Centre Hospitalier Universitaire de Grenoble · FRInserm · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionLung cancer screening in individuals at risk has been recommended by various scientific institutions. One of the main concerns for CT screening is repeated radiation exposure, with the risk of inducing malignancies in healthy individuals. Therefore, lowering the radiation dose is one of the main objectives for radiologists. The aim of this study is to demonstrate that an ultra-low dose (ULD) chest CT protocol, using recently introduced hybrid iterative reconstruction (ASiR-V, GE medical Healthcare, Milwaukee, Wisconsin, USA), is as performant as a standard 'low dose' (LD) CT to detect non-calcified lung nodules ≥4 mm. METHODS AND ANALYSIS: The total number of patients to include is 150. Those are referred for non-enhanced chest CT for detection or follow-up of lung nodule and will undergo an additional unenhanced ULD CT acquisition, the dose of which is on average 10 times lower than the conventional LD acquisition. Total dose of the entire exam (LD+ULD) is lower than the French diagnostic reference level for a chest CT (6.65 millisievert). ULD CT images will be reconstructed with 50% and 100% ASiR-V and LD CT with 50%. The three sets of images will be read in random order by two pair of radiologists, in a blind test, where patient identification and study outcomes are concealed. Detection rate (sensitivity) is the primary outcome. Secondary outcomes will include concordance of nodule characteristics; interobserver reproducibility; influence of subjects' characteristics, nodule location and nodule size; and concordance of emphysema, coronary calcifications evaluated by visual scoring and bronchial alterations between LD and ULD CT. In case of discordance, a third radiologist will arbitrate. ETHICS AND DISSEMINATION: The study was approved by the relevant ethical committee. Each study participant will sign an informed consent form. TRIAL REGISTRATION NUMBER: NCT03305978; Pre-results.

Indexed as

AlgorithmsAdultEarly Detection of CancerFemaleHumansLung NeoplasmsMaleMiddle AgedMultiple Pulmonary NodulesRadiation DosageReproducibility of ResultsTomography, X-Ray Computediterative reconstructionlow dose computed tomographylung cancer screeningpulmonary noduleultra low dose computed tomography

Identifiers

PMID31420379
PMCPMC6701577
OpenAlexW2972730176

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.