Suppr超能文献

绵羊肺腺癌的影像组学特征

Radiomic Feature Characteristics of Ovine Pulmonary Adenocarcinoma.

作者信息

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

机构信息

The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Edinburgh EH25 9RG, UK.

Moredun Research Institute, Pentlands Science Park, Bush Loan, Penicuik EH26 0PZ, UK.

出版信息

Vet Sci. 2025 Apr 23;12(5):400. doi: 10.3390/vetsci12050400.

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.

摘要

计算机断层扫描(CT)图像的放射组学特征(RF)分析可能有助于绵羊肺腺癌(OPA)的诊断和分期。我们评估了感染JSRV的绵羊中OPA肿瘤与非肿瘤肺组织相比的RF特征,检查了它们随时间的稳定性,并分析了新生肿瘤区域(NTF)和新生肿瘤边缘区域(NTmF)的RF变化。在每月的CT扫描中,通过密度自动分割肺组织,手动分割肺肿瘤。为每个成像会话计算RF,根据稳定性和可重复性进行选择,并在适当情况下针对体积依赖性进行调整。通过基准配准和空间变换促进绵羊内部扫描之间的比较。最初,9/36个RF与密度相似的非肿瘤肺组织有显著差异。主要的RF变化包括ngtdm_复杂性、glrlm_游程长度均匀性_VN和gldm_小双同调梯度长度熵。肺肿瘤段中的RF显示出随时间的变化,而密度相似的非肿瘤肺组织则保持一致。OPA肺肿瘤的RF特征与密度相似的其他肺组织不同,并且随着肿瘤的发展而演变。这些特征表明,放射组学分析为JSRV相关肺肿瘤的早期检测和管理提供了潜力。这项研究增强了对OPA成像的理解,可能为自然发生感染的更好诊断和控制措施提供信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bd8/12115574/6669af8e741f/vetsci-12-00400-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验