Fibrotic and vascular abnormalities quantified by an AI-based model are associated with outcomes in patients with idiopathic pulmonary fibrosis.

IF 2.8 3区 医学 Q2 RESPIRATORY SYSTEM
Julien Guiot, Jonne Engelberts, Monique Henket, Benoit Ernst, Quentin Maloir, Renaud Louis, David A Lynch, Stephen M Humphries, Jean-Paul Charbonnier
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引用次数: 0

Abstract

Background: Idiopathic pulmonary fibrosis (IPF) is a progressive fibrosing interstitial lung disease associated with high morbidity and mortality despite specific anti-fibrotic therapies. Management of IPF is complex and relies on pulmonary function tests (PFT) to evaluate severity and monitor progression. CT provides non-invasive morphologic assessment and emerging software techniques enable quantitative analysis.

Methods: We included 319 individuals with IPF from the OSIC dataset. A cross-sectional analysis was made for all patients, with a longitudinal evaluation for 143 of them. We used LungQ software (Thirona, The Netherlands) to quantify lung and pulmonary vessel volumes, as well as the extent of interstitial lung disease and to assess correlation with PFT and mortality.

Results: Quantitative extent of fibrotic abnormalities was correlated with baseline FVC and DLCO (r -0.47, p < 0.0001 and r -0.55, p < 0.0001 respectively) and longitudinal modifications over time (r -0.48, p < 0.0001 and r-0.43 p < 0.0001, respectively). Median baseline extent of ILD, expressed as a percentage of lung volume, was 16.5% (10.8-25.5) and increased to 17.3% (11.6-29) on follow-up (p < 0.001). The median ILD progression was of 9.8% (-9.5-40.0). Vascular enlargement quantification as well as ILD quantification were predictive marker of death (p < 0.0001). However, vascular abnormalities' independent predictive value could not be assessed in multivariate models due to multicollinearity with other variables.

Conclusions: LungQ allows to quantify interstitial and vascular lung features and their changes over time in a large cohort of patients with IPF. Imaging markers were negatively correlated with PFT at baseline and follow-ups were predictive of mortality confirming their potential as disease quantifiers. Further clinical validation is needed to specify the potential clinical use.

基于人工智能的模型量化的纤维化和血管异常与特发性肺纤维化患者的预后相关。
背景:特发性肺纤维化(IPF)是一种进行性纤维化间质性肺疾病,尽管有特异性抗纤维化治疗,但发病率和死亡率都很高。IPF的治疗是复杂的,依赖于肺功能试验(PFT)来评估严重程度和监测进展。CT提供了无创的形态学评估,新兴的软件技术使定量分析成为可能。方法:我们从OSIC数据集中纳入了319名IPF患者。对所有患者进行横断面分析,并对其中143例进行纵向评估。我们使用LungQ软件(Thirona, The Netherlands)来量化肺和肺血管体积,以及间质性肺疾病的程度,并评估与PFT和死亡率的相关性。结果:纤维化异常的定量程度与基线FVC和DLCO相关(r -0.47, p)。结论:LungQ可以量化IPF患者间质和血管肺特征及其随时间的变化。影像学标志物与基线PFT呈负相关,随访可预测死亡率,证实其作为疾病量化指标的潜力。需要进一步的临床验证来明确潜在的临床应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Pulmonary Medicine
BMC Pulmonary Medicine RESPIRATORY SYSTEM-
CiteScore
4.40
自引率
3.20%
发文量
423
审稿时长
6-12 weeks
期刊介绍: BMC Pulmonary Medicine is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of pulmonary and associated disorders, as well as related molecular genetics, pathophysiology, and epidemiology.
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