ArticleInsights into imaging2026
CT acquisition protocols in lung cancer screening: implications for guideline development from a worldwide survey.
Article in Insights into imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Associations between CT characteristics of lung cancer lesions and histologic subtypes in lung cancer screening.European radiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo assess the currently applied CT image acquisition protocols in lung cancer screening (LCS) and thereby fill a knowledge gap to support guideline development. MATERIALS AND
methodsThrough worldwide distribution of an online survey, data on institutional and technical factors regarding CT acquisition protocols in LCS were collected between 06/2024 and 09/2025 on behalf of the SOLACE (Strengthening the screening of lung cancer in Europe) consortium.
resultsGlobal responses were received from 71 LCS institutions across 29 countries (all continents). Responsibility for CT protocol establishment and modification varied among professions (radiologists, radiographers, medical physicists, and manufacturer personnel). Protocol establishment was dominated by radiologists (64 of 115), with only one-third of institutions involving multiple professions. Technical questions were partially answered. Automatic exposure control was implemented in 88% of centers (43 of 49). Reconstructed slice thickness ranged from 0.625 to 1.5 mm, with 1.0 mm being most common (43 of 67). Increment ranged between 0.5 and 1.25 mm. Software support for LCS was used by 90% of respondents (35 of 39), primarily for nodule detection (92%), volumetry (89%), and calculation of volume doubling time (71%). Image reconstruction was dominated by iterative reconstruction with statistical modeling (30) or deep learning support (7), while filtered-back projection was marginally used (4).
conclusionsLung cancer screening often pushes current device limits, which warrants a multiprofessional establishment of CT protocols. Variability in reconstruction calls for further study on the effects on volumetry. Optimizing protocols remains crucial to balance radiation dose reduction and diagnostic accuracy in guideline development. CRITICAL RELEVANCE STATEMENT: This international study evaluates current CT image acquisition protocols in lung cancer screening and implications for guidelines, highlighting insufficient multiprofessional engagement for protocol definition and pronounced variability in technical parameters, both of which demand harmonization to inform robust guideline development. KEY POINTS: Variability of CT acquisition protocols impacts lung cancer screening. International survey results shed light on currently applied protocols. The narrowed knowledge gap supports guideline recommendations and standardization.
Indexed as
Identifiers
What OpenQuestion holds
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.