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

Published: 2025

Authors:

  • David Collie

  • Chris Cousens

  • Steven Wright

  • Ziyuan Chang

  • James Meehan

  • Helen Brown

  • Calum D. Gray

  • Tom J. MacGillivray

  • David J. Griffiths

  • Chad E. Eckert

  • Nicole Storer

  • Mark Gray

Abstract:

IntroductionDifferent 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.MethodsThoracic 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.ResultsThe 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.DiscussionOverall, 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.

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