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比较多纹理纤维化分析与基于二元不透明度的异常检测用于特发性肺纤维化的定量评估。

Comparing multi-texture fibrosis analysis versus binary opacity-based abnormality detection for quantitative assessment of idiopathic pulmonary fibrosis.

作者信息

Nowak Sebastian, Creuzberg Dominik, Theis Maike, Pizarro Carmen, Isaak Alexander, Pieper Claus C, Luetkens Julian A, Skowasch Dirk, Sprinkart Alois M, Kütting Daniel

机构信息

Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.

Department of Internal Medicine II, Cardiology/Pneumology, University Hospital Bonn, Bonn, Germany.

出版信息

Sci Rep. 2025 Jan 9;15(1):1479. doi: 10.1038/s41598-025-85135-7.

Abstract

Automated tools for quantification of idiopathic pulmonary fibrosis (IPF) can aid in ensuring reproducibility, however their complexity and costs can differ substantially. In this retrospective study, two automated tools were compared in 45 patients with biopsy proven (12/45) and imaging-based (33/45) IPF diagnosis (mean age 74 ± 9 years, 37 male) for quantification of pulmonary fibrosis in CT. First, a tool that identifies multiple characteristic lung texture features was applied to measure multi-texture fibrotic lung (MTFL) by combining the amount of ground glass, reticulation, and honeycombing. Opacity-based fibrotic lung (OFL) was measured by a second tool that performs a simpler binary classification of tissue into either normal or opacified lung and was originally developed for quantifying pneumonia. Differences in quantification of MTFL and OFL were assessed by Mann-Whitney U-test and Pearson correlation (r). Also, correlation with spirometry parameters (percent predicted total lung capacity (TLC), percent predicted vital capacity (VC), percent predicted forced expiratory volume in 1 s (FEV), diffusing capacity of the lungs for carbon monoxide (DL), partial pressure of oxygen (P) and carbon dioxide (P)) were assessed by r. The prognostic values for 3-year patient survival of OFL, LSS and MTFL were investigated by multivariable Cox-proportional-hazards (CPH) models including sex, age and TLC and including sex, age and VC. Also, Kaplan-Meier analysis with log rank test between subgroups separated by median OFL and MTFL were conducted. No significant difference between OFL and MTFL was observed (median and interquartile range: OFL = 29% [20-38%], MTFL = 31% [19-45%]; P = 0.44). For OFL significant correlation was observed to MTFL (r = 0.93, P < 0.01) and VC (r=-0.50, P = 0.03). For MTFL no significant correlation to spirometry parameters was found. The total time for one analysis was lower for the automated MTFL (MTFL: 313 ± 25s vs. OFL: 612 ± 61s, P < 0.001). Both analyses were significant predictors in the multivariable CPH analysis including TLC (hazard-ratios: MTFL 1.03 [1.01-1.06], P = 0.02; OFL 1.03 [1.00-1.06], P = 0.03). No parameter was a significant predictor in the CPH models including VC (hazard-ratios: MTFL 1.01 [0.98-1.04], P = 1; OFL 1.01 [0.97-1.05], P = 1). OFL showed significance in Kaplan-Meier analysis (MTFL: P = 0.17; OFL: P = 0.03). Using a simple opacity-based quantification of pulmonary fibrosis in IPF patients displayed similar results and prognostic value compared to a more complex multi-texture based analysis.

摘要

用于量化特发性肺纤维化(IPF)的自动化工具有助于确保可重复性,但其复杂性和成本可能有很大差异。在这项回顾性研究中,对45例经活检证实(12/45)和基于影像学诊断(33/45)为IPF的患者(平均年龄74±9岁,男性37例)使用两种自动化工具对CT中的肺纤维化进行量化。首先,应用一种识别多种特征性肺纹理特征的工具,通过结合磨玻璃影、网状影和蜂窝状影的数量来测量多纹理纤维化肺(MTFL)。基于密度的纤维化肺(OFL)通过第二种工具测量,该工具对组织进行更简单的二分类,分为正常肺或混浊肺,最初是为量化肺炎而开发的。通过曼-惠特尼U检验和皮尔逊相关性(r)评估MTFL和OFL量化的差异。此外,通过r评估与肺功能参数(预测总肺容量百分比(TLC)、预测肺活量百分比(VC)、预测1秒用力呼气量百分比(FEV)、肺一氧化碳弥散量(DL)、氧分压(P)和二氧化碳分压(P))的相关性。通过多变量Cox比例风险(CPH)模型研究OFL、LSS和MTFL对患者3年生存率的预后价值,模型包括性别、年龄和TLC以及性别、年龄和VC。此外,对按OFL和MTFL中位数分开的亚组进行了Kaplan-Meier分析和对数秩检验。未观察到OFL和MTFL之间存在显著差异(中位数和四分位间距:OFL = 29% [20 - 38%],MTFL = 31% [19 - 45%];P = 0.44)。观察到OFL与MTFL显著相关(r = 0.93,P < 0.01)以及与VC显著相关(r = -0.50,P = 0.03)。对于MTFL,未发现与肺功能参数有显著相关性。自动化MTFL单次分析的总时间更短(MTFL:313±25秒 vs. OFL:612±61秒,P < 0.001)。在包括TLC的多变量CPH分析中,两种分析都是显著的预测指标(风险比:MTFL 1.03 [1.01 - 1.06],P = 0.02;OFL 1.03 [1.00 - 1.06],P = 0.03)。在包括VC的CPH模型中,没有参数是显著的预测指标(风险比:MTFL 1.01 [0.98 - 1.04],P = 1;OFL 1.01 [0.97 - 1.05],P = 1)。OFL在Kaplan-Meier分析中显示出显著性(MTFL:P = 0.17;OFL:P = 0.03)。与更复杂的基于多纹理的分析相比,在IPF患者中使用简单的基于密度的肺纤维化量化显示出相似的结果和预后价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/61a4/11718064/9839de1b2151/41598_2025_85135_Fig1_HTML.jpg

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