Evidence map›Paper›PMID 36828367›Full record

ReviewTomography (Ann Arbor, Mich.)2023

Radiation Exposure to Low-Dose Computed Tomography for Lung Cancer Screening: Should We Be Concerned?

Chiara Pozzessere, Christophe von Garnier, Catherine Beigelman-Aubry

Open access · goldAbstract readReview
In one paragraph

Review in Tomography (Ann Arbor, Mich.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
5.3field-weighted citation impact, top 4% 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

16 citing papers in PubMed, 23 citations in OpenAlex.

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  16. New developments in the imaging of lung cancer.Breathe (Sheffield, England) · 2024
    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

3 authors at 1 institution in 1 country.

Chiara PozzessereDepartment of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV), 1011 Lausanne, Switzerland.ORCID 0000-0003-1379-5888
Christophe von GarnierFaculty of Biology and Medicine, University of Lausanne (UNIL), 1011 Lausanne, Switzerland.
Catherine Beigelman-AubryDepartment of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV), 1011 Lausanne, Switzerland.
University of Lausanne · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer screening (LCS) programs through low-dose Computed Tomography (LDCT) are being implemented in several countries worldwide. Radiation exposure of healthy individuals due to prolonged CT screening rounds and, eventually, the additional examinations required in case of suspicious findings may represent a concern, thus eventually reducing the participation in an LCS program. Therefore, the present review aims to assess the potential radiation risk from LDCT in this setting, providing estimates of cumulative dose and radiation-related risk in LCS in order to improve awareness for an informed and complete attendance to the program. After summarizing the results of the international trials on LCS to introduce the benefits coming from the implementation of a dedicated program, the screening-related and participant-related factors determining the radiation risk will be introduced and their burden assessed. Finally, future directions for a personalized screening program as well as technical improvements to reduce the delivered dose will be presented.

Indexed as

Lung NeoplasmsRadiation ExposureEarly Detection of CancerHumansRadiation DosageTomography, X-Ray Computedcomputed tomographydiagnostic screening programsearly detectionlungneoplasmradiation dose-response relationshipradiation exposure

Identifiers

PMID36828367
PMCPMC9964027
OpenAlexW4317934320

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.