Evidence map›Paper›PMID 40078215›Full record

ArticleFrontiers in veterinary science2025

Spatial encoding and growth-related change of sheep lung radiomic features.

David Collie, Chris Cousens, Steven Wright, Ziyuan Chang, James Meehan, Helen Brown, Calum D Gray, Tom J MacGillivray, David J Griffiths, Chad E Eckert and 2 more

Abstract read
In one paragraph

Article in Frontiers in veterinary science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

12 authors.

David CollieThe Roslin Institute, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom.
Chris CousensMoredun Research Institute, Pentlands Science Park, Penicuik, United Kingdom.
Steven WrightThe Roslin Institute, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom.
Ziyuan ChangThe Roslin Institute, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom.
James MeehanThe Roslin Institute, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom.
Helen BrownThe Roslin Institute, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom.
Calum D GrayEdinburgh Imaging Facility, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom.
Tom J MacGillivrayCentre for Clinical Brain Sciences, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom.
David J GriffithsMoredun Research Institute, Pentlands Science Park, Penicuik, United Kingdom.
Chad E EckertInterventional Oncology, Johnson & Johnson Enterprise Innovation, Inc., New Brunswick, NJ, United States.
Nicole StorerInterventional Oncology, Johnson & Johnson Enterprise Innovation, Inc., New Brunswick, NJ, United States.
Mark GrayThe Roslin Institute, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Different regions of the small ruminant lung exhibit variable susceptibility to specific lung pathologies. Such susceptibility may be reflected in regional lung radiomic features extracted from computed tomography (CT) images. In this study, we investigated whether region-specific variation in radiomic features exists in ovine lungs and whether these features remain stable over time. Methods: Thoracic CT image datasets from 30 young adult sheep were subject to an image segmentation protocol directed at partitioning the lung into individual lobar and sub-lobar segments for radiomic feature analysis. After identifying and removing unstable, non-reproducible, and highly correlated features, 22 features remained and were used as input for principal component (PC) analysis. Results: The significance of segment-related influence on PC scores was determined and visualised. For six sheep, successive CT images were acquired at monthly intervals for a period of 9 months in order to assess time-dependent variation in radiomic features. The results indicated that there was a significant difference in radiomic features derived from different lung segments. Visualisation of PC scores highlighted differences between caudodorsal and cranioventral lung, between lobar and sub-lobar segments, and suggested a bias towards one lung or the other. Significant changes in PC scores occurred over time. With few exceptions, largely similar changes occurred across all segments in this regard. Discussion: Overall, our results indicate that although sheep lung radiomic features are influenced by the lung segment of origin, their variation over time is largely consistent throughout the lung. Such influence should be borne in mind when interpreting radiomic features and their changes over time.

Indexed as

computed tomographylobar variationlungradiomic featuressheep

Identifiers

PMID40078215
PMCPMC11897008

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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.