Radiomic Feature Characteristics of Ovine Pulmonary Adenocarcinoma

Radiomic feature (RF) analysis of computed tomography (CT) images may aid the diagnosis and staging of ovine pulmonary adenocarcinoma (OPA). We assessed the RF characteristics of OPA tumours in JSRV-infected sheep compared to non-tumour lung tissues, examined their stability over time, and analysed RF variations in the nascent tumour field (NTF) and nascent tumour margin field (NTmF). In monthly CT scans, lung tissues were automatically segmented by density, and lung tumours were manually segmented. RFs were calculated for each imaging session, selected according to stability and reproducibility, and adjusted for volume dependence where appropriate. Comparisons between scans within sheep were facilitated through fiducial registration and spatial transformations. Initially, 9/36 RFs differed significantly from non-tumour lung tissue of similar density. Predominant RF changes included ngtdm_Complexity, glrlm_RunLNUnif_VN, and gldm_SmDHGLE. RFs in lung tumour segments showed time-dependent changes, whereas non-tumour lung tissue of similar density remained consistent. OPA lung tumour RF characteristics are distinct from those of other lung tissues of similar density and evolve as the tumour develops. Such characteristics suggest that radiomic analysis offers potential for the early detection and management of JSRV-related lung tumours. This research enhances the understanding of OPA imaging, potentially informing better diagnosis and control measures for naturally occurring infections.

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Publication Details

Journal
Veterinary Sciences
Published
2025-04-23
DOI
https://doi.org/10.3390/vetsci12050400
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
1.36

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article

Radiomic Feature Characteristics of Ovine Pulmonary Adenocarcinoma

Mark Gray, Ziyuan Chang, Calum Gray, S. Jo Moore et al.
1 citations
Veterinary Sciences
Radiomics and Machine Learning in Medical Imaging
1.36
article

Radiomic Feature Characteristics of Ovine Pulmonary Adenocarcinoma

Mark Gray, Ziyuan Chang, Calum Gray, S. Jo Moore, David J. Griffiths, Chris Cousens, Chad E. Eckert, James Meehan, David Collie, Tom MacGillivray, Helen Brown, Nicole Storer, Jennifer Savage, Steven H. Wright, Helen Todd
article en
1 citations

Abstract

Radiomic feature (RF) analysis of computed tomography (CT) images may aid the diagnosis and staging of ovine pulmonary adenocarcinoma (OPA). We assessed the RF characteristics of OPA tumours in JSRV-infected sheep compared to non-tumour lung tissues, examined their stability over time, and analysed RF variations in the nascent tumour field (NTF) and nascent tumour margin field (NTmF). In monthly CT scans, lung tissues were automatically segmented by density, and lung tumours were manually segmented. RFs were calculated for each imaging session, selected according to stability and reproducibility, and adjusted for volume dependence where appropriate. Comparisons between scans within sheep were facilitated through fiducial registration and spatial transformations. Initially, 9/36 RFs differed significantly from non-tumour lung tissue of similar density. Predominant RF changes included ngtdm_Complexity, glrlm_RunLNUnif_VN, and gldm_SmDHGLE. RFs in lung tumour segments showed time-dependent changes, whereas non-tumour lung tissue of similar density remained consistent. OPA lung tumour RF characteristics are distinct from those of other lung tissues of similar density and evolve as the tumour develops. Such characteristics suggest that radiomic analysis offers potential for the early detection and management of JSRV-related lung tumours. This research enhances the understanding of OPA imaging, potentially informing better diagnosis and control measures for naturally occurring infections.

Veterinary SciencesVol. 12(5)
Johnson & Johnson (United States) (US), Roslin Institute (GB), Moredun Research Institute (GB), The Queen's Medical Research Institute (GB), University of Edinburgh (GB)
University of Edinburgh, Biotechnology and Biological Sciences Research Council
Good health and well-being
Openalex Percentile: Top 21%
Radiomics and Machine Learning in Medical Imaging
1.36
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