Fully automatic quantification of pulmonary fat attenuation volume by CT: an exploratory pilot study.

IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING European Radiology Experimental Pub Date : 2024-12-05 DOI:10.1186/s41747-024-00536-z
Luca Salhöfer, Mathias Holtkamp, Francesco Bonella, Lale Umutlu, Johannes Wienker, Dirk Westhölter, Matthias Welsner, Christian Taube, Kaid Darwiche, Judith Kohnke, Jannis Straus, Nikolas Beck, Marko Frings, Sebastian Zensen, Rene Hosch, Giulia Baldini, Felix Nensa, Marcel Opitz, Johannes Haubold
{"title":"Fully automatic quantification of pulmonary fat attenuation volume by CT: an exploratory pilot study.","authors":"Luca Salhöfer, Mathias Holtkamp, Francesco Bonella, Lale Umutlu, Johannes Wienker, Dirk Westhölter, Matthias Welsner, Christian Taube, Kaid Darwiche, Judith Kohnke, Jannis Straus, Nikolas Beck, Marko Frings, Sebastian Zensen, Rene Hosch, Giulia Baldini, Felix Nensa, Marcel Opitz, Johannes Haubold","doi":"10.1186/s41747-024-00536-z","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Non-malignant chronic diseases remain a major public health concern. Given the alterations in lipid metabolism and deposition in the lung and its association with fibrotic interstitial lung disease (fILD) and chronic obstructive pulmonary disease (COPD), this study aimed to detect those alterations using computed tomography (CT)-based analysis of pulmonary fat attenuation volume (CTpfav).</p><p><strong>Methods: </strong>This observational retrospective single-center study involved 716 chest CT scans from three subcohorts: control (n = 279), COPD (n = 283), and fILD (n = 154). Fully automated quantification of CTpfav based on lung segmentation and HU-thresholding. The pulmonary fat index (PFI) was derived by normalizing CTpfav to the CT lung volume. Statistical analyses were conducted using Kruskal-Wallis with Dunn's post hoc tests.</p><p><strong>Results: </strong>Patients with fILDs demonstrated a significant increase in CTpfav (median 71.0 mL, interquartile range [IQR] 59.7 mL, p < 0.001) and PFI (median 1.9%, IQR 2.4%, p < 0.001) when compared to the control group (CTpfav median 43.6 mL, IQR 16.94 mL; PFI median 0.9%, IQR 0.5%). In contrast, individuals with COPD exhibited significantly reduced CTpfav (median 36.2 mL, IQR 11.4 mL, p < 0.001) and PFI (median 0.5%, IQR 0.2%, p < 0.001).</p><p><strong>Conclusion: </strong>The study underscores the potential of CTpfav and PFI as imaging biomarkers for detecting changes in lung lipid metabolism and deposition and demonstrates a possibility of tracking these alterations in patients with COPD and ILDs. Further research is needed to validate these findings and explore the clinical relevance of CTpfav and PFI in lung disease management.</p><p><strong>Relevance statement: </strong>This study introduces a fully automated method for quantifying CTpfav, potentially establishing it as a new imaging biomarker for chronic lung diseases.</p><p><strong>Key points: </strong>This retrospective observational study employed an open-source, automated algorithm for the quantification of CT pulmonary fat attenuation volume (CTpfav). Patients with fibrotic interstitial lung disease (fILD) showed a significantly higher CTpfav and pulmonary fat index (PFI), i.e., CTpfav/CT lung volume, compared to a control group. Patients with chronic obstructive pulmonary disease (COPD) showed significantly lower CTpfav and PFI compared to the control group. CTpfav and PFI may each serve as imaging biomarkers for various lung diseases and warrant further investigation.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"139"},"PeriodicalIF":3.7000,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11621257/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Radiology Experimental","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s41747-024-00536-z","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Non-malignant chronic diseases remain a major public health concern. Given the alterations in lipid metabolism and deposition in the lung and its association with fibrotic interstitial lung disease (fILD) and chronic obstructive pulmonary disease (COPD), this study aimed to detect those alterations using computed tomography (CT)-based analysis of pulmonary fat attenuation volume (CTpfav).

Methods: This observational retrospective single-center study involved 716 chest CT scans from three subcohorts: control (n = 279), COPD (n = 283), and fILD (n = 154). Fully automated quantification of CTpfav based on lung segmentation and HU-thresholding. The pulmonary fat index (PFI) was derived by normalizing CTpfav to the CT lung volume. Statistical analyses were conducted using Kruskal-Wallis with Dunn's post hoc tests.

Results: Patients with fILDs demonstrated a significant increase in CTpfav (median 71.0 mL, interquartile range [IQR] 59.7 mL, p < 0.001) and PFI (median 1.9%, IQR 2.4%, p < 0.001) when compared to the control group (CTpfav median 43.6 mL, IQR 16.94 mL; PFI median 0.9%, IQR 0.5%). In contrast, individuals with COPD exhibited significantly reduced CTpfav (median 36.2 mL, IQR 11.4 mL, p < 0.001) and PFI (median 0.5%, IQR 0.2%, p < 0.001).

Conclusion: The study underscores the potential of CTpfav and PFI as imaging biomarkers for detecting changes in lung lipid metabolism and deposition and demonstrates a possibility of tracking these alterations in patients with COPD and ILDs. Further research is needed to validate these findings and explore the clinical relevance of CTpfav and PFI in lung disease management.

Relevance statement: This study introduces a fully automated method for quantifying CTpfav, potentially establishing it as a new imaging biomarker for chronic lung diseases.

Key points: This retrospective observational study employed an open-source, automated algorithm for the quantification of CT pulmonary fat attenuation volume (CTpfav). Patients with fibrotic interstitial lung disease (fILD) showed a significantly higher CTpfav and pulmonary fat index (PFI), i.e., CTpfav/CT lung volume, compared to a control group. Patients with chronic obstructive pulmonary disease (COPD) showed significantly lower CTpfav and PFI compared to the control group. CTpfav and PFI may each serve as imaging biomarkers for various lung diseases and warrant further investigation.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
CT全自动定量肺脂肪衰减体积:一项探索性的初步研究。
背景:非恶性慢性疾病仍然是一个主要的公共卫生问题。鉴于肺中脂质代谢和沉积的改变及其与纤维化间质性肺疾病(fILD)和慢性阻塞性肺疾病(COPD)的关联,本研究旨在通过基于计算机断层扫描(CT)的肺脂肪衰减体积(CTpfav)分析来检测这些改变。方法:这项观察性回顾性单中心研究包括来自三个亚队列的716例胸部CT扫描:对照组(n = 279), COPD (n = 283)和field (n = 154)。基于肺分割和hu阈值的CTpfav全自动定量。肺脂肪指数(PFI)通过将CTpfav与CT肺体积归一化而得到。采用Kruskal-Wallis和Dunn’s事后检验进行统计分析。结果:慢性阻塞性肺病患者CTpfav显著升高(中位值71.0 mL,四分位间距[IQR] 59.7 mL)。结论:该研究强调了CTpfav和PFI作为检测肺脂质代谢和沉积变化的成像生物标志物的潜力,并证明了在慢性阻塞性肺病和慢性阻塞性肺病患者中追踪这些变化的可能性。需要进一步的研究来验证这些发现,并探索CTpfav和PFI在肺部疾病管理中的临床相关性。相关声明:本研究引入了一种全自动量化CTpfav的方法,有可能将其作为慢性肺部疾病的一种新的成像生物标志物。重点:这项回顾性观察研究采用了一种开源的自动算法来量化CT肺脂肪衰减体积(CTpfav)。与对照组相比,纤维化间质性肺病(fILD)患者CTpfav和肺脂肪指数(PFI),即CTpfav/CT肺体积显著升高。慢性阻塞性肺疾病(COPD)患者的CTpfav和PFI明显低于对照组。CTpfav和PFI都可以作为各种肺部疾病的成像生物标志物,值得进一步研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
European Radiology Experimental
European Radiology Experimental Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
6.70
自引率
2.60%
发文量
56
审稿时长
18 weeks
期刊最新文献
Flexible and wireless metasurface coils for knee and elbow MRI. Photon-counting detector CTA to assess intracranial stents and flow diverters: an in vivo study with ultrahigh-resolution spectral reconstructions. CompositIA: an open-source automated quantification tool for body composition scores from thoraco-abdominal CT scans. Feasibility of generating sagittal radiographs from coronal views using GAN-based deep learning framework in adolescent idiopathic scoliosis. Can ChatGPT4-vision identify radiologic progression of multiple sclerosis on brain MRI?
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1