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肺 F-FDG PET/HRCT 与基于计算机的 CT 分析协同应用于传统严重程度测量,以完善特发性肺纤维化(IPF)当前的风险分层。

Synergistic application of pulmonary F-FDG PET/HRCT and computer-based CT analysis with conventional severity measures to refine current risk stratification in idiopathic pulmonary fibrosis (IPF).

作者信息

Fraioli Francesco, Lyasheva Maria, Porter Joanna C, Bomanji Jamshed, Shortman Robert I, Endozo Raymond, Wan Simon, Bertoletti Linda, Machado Maria, Ganeshan Balaji, Win Thida, Groves Ashley M

机构信息

Institute of Nuclear Medicine, UCL(H) and University College London Hospital, 235 Euston Rd, London, NW1 2BU, UK.

Department of Oncology, School of Clinical Medicine, University of Cambridge, Cambridge, UK.

出版信息

Eur J Nucl Med Mol Imaging. 2019 Sep;46(10):2023-2031. doi: 10.1007/s00259-019-04386-5. Epub 2019 Jul 8.

DOI:10.1007/s00259-019-04386-5
PMID:31286201
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6667685/
Abstract

INTRODUCTION

To investigate the combined performance of quantitative CT (qCT) following a computer algorithm analysis (IMBIO) and F-FDG PET/CT to assess survival in patients with idiopathic pulmonary fibrosis (IPF).

METHODS

A total of 113 IPF patients (age 70 ± 9 years) prospectively and consecutively underwent F-FDG PET/CT and high-resolution CT (HRCT) at our institution. During a mean follow-up of 29.6 ± 26 months, 44 (48%) patients died. As part of the qCT analysis, pattern evaluation of HRCT (using IMBIO software) included the total extent (percentage) of the following features: normal-appearing lung, hyperlucent lung, parenchymal damage (comprising ground-glass opacification, reticular pattern and honeycombing), and the pulmonary vessels. The maximum (SUV) and minimum (SUV) standardized uptake value (SUV) for F-FDG uptake in the lungs, and the target-to-background (SUV/SUV) ratio (TBR) were quantified using routine region-of-interest (ROI) analysis. Pulmonary functional tests (PFTs) were acquired within 14 days of the PET/CT/HRCT scan. Kaplan-Meier (KM) survival analysis was used to identify associations with mortality.

RESULTS

Data from 91 patients were available for comparative analysis. The average ± SD GAP [gender, age, physiology] score was 4.2 ± 1.7 (range 0-8). The average ± SD SUV, SUV, and TBR were 3.4 ± 1.4, 0.7 ± 0.2, and 5.6 ± 2.8, respectively. In all patients, qCT analysis demonstrated a predominantly reticular lung pattern (14.9 ± 12.4%). KM analysis showed that TBR (p = 0.018) and parenchymal damage assessed by qCT (p = 0.0002) were the best predictors of survival. Adding TBR and qCT to the GAP score significantly increased the ability to differentiate between high and low risk (p < 0.0001).

CONCLUSION

F-FDG PET and qCT are independent and synergistic in predicting mortality in patients with IPF.

摘要

引言

研究定量CT(qCT)结合计算机算法分析(IMBIO)和F-FDG PET/CT评估特发性肺纤维化(IPF)患者生存率的联合性能。

方法

共有113例IPF患者(年龄70±9岁)在我院前瞻性、连续地接受了F-FDG PET/CT和高分辨率CT(HRCT)检查。在平均29.6±26个月的随访期间,44例(48%)患者死亡。作为qCT分析的一部分,HRCT的模式评估(使用IMBIO软件)包括以下特征的总范围(百分比):外观正常的肺、肺透亮度增加、实质损害(包括磨玻璃影、网状影和蜂窝状影)以及肺血管。使用常规感兴趣区(ROI)分析对肺内F-FDG摄取的最大(SUV)和最小(SUV)标准化摄取值(SUV)以及靶本比(SUV/SUV)(TBR)进行量化。在PET/CT/HRCT扫描后14天内进行肺功能测试(PFT)。采用Kaplan-Meier(KM)生存分析来确定与死亡率的关联。

结果

91例患者的数据可用于比较分析。平均±标准差GAP[性别、年龄、生理学]评分为4.2±1.7(范围0-8)。平均±标准差SUV、SUV和TBR分别为3.4±1.4、0.7±0.2和5.6±2.8。在所有患者中,qCT分析显示主要为网状肺模式(14.9±12.4%)。KM分析表明,TBR(p=0.018)和qCT评估的实质损害(p=0.0002)是生存的最佳预测指标。将TBR和qCT添加到GAP评分中显著提高了区分高风险和低风险的能力(p<0.0001)。

结论

F-FDG PET和qCT在预测IPF患者死亡率方面具有独立性和协同性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88a4/6667685/8540384a5afe/259_2019_4386_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88a4/6667685/66cd45730a66/259_2019_4386_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88a4/6667685/513e7daec172/259_2019_4386_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88a4/6667685/9f5fabc20a1b/259_2019_4386_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88a4/6667685/8540384a5afe/259_2019_4386_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88a4/6667685/66cd45730a66/259_2019_4386_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88a4/6667685/513e7daec172/259_2019_4386_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88a4/6667685/9f5fabc20a1b/259_2019_4386_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88a4/6667685/8540384a5afe/259_2019_4386_Fig4_HTML.jpg

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