Chest MRI and CT Predictors of 10-Year All-Cause Mortality in COPD.

IF 2.2 4区医学 Q3 RESPIRATORY SYSTEM

COPD: Journal of Chronic Obstructive Pulmonary Disease Pub Date : 2023-12-01 Epub Date: 2023-09-22 DOI:10.1080/15412555.2023.2259224

Maksym Sharma, Paulina V Wyszkiewicz, Alexander M Matheson, David G McCormack, Grace Parraga

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引用次数: 0

Abstract

Pulmonary imaging measurements using magnetic resonance imaging (MRI) and computed tomography (CT) have the potential to deepen our understanding of chronic obstructive pulmonary disease (COPD) by measuring airway and parenchymal pathologic information that cannot be provided by spirometry. Currently, MRI and CT measurements are not included in mortality risk predictions, diagnosis, or COPD staging. We evaluated baseline pulmonary function, MRI and CT measurements alongside imaging texture-features to predict 10-year all-cause mortality in ex-smokers with (n = 93; 31 females; 70 ± 9years) and without (n = 69; 29 females, 69 ± 9years) COPD. CT airway and vessel measurements, helium-3 (³He) MRI ventilation defect percent (VDP) and apparent diffusion coefficients (ADC) were quantified. MRI and CT texture-features were extracted using PyRadiomics (version2.2.0). Associations between 10-year all-cause mortality and all clinical and imaging measurements were evaluated using multivariable regression model odds-ratios. Machine-learning predictive models for 10-year all-cause mortality were evaluated using area-under-receiver-operator-characteristic-curve (AUC), sensitivity and specificity analyses. DL_CO (%_pred) (HR = 0.955, 95%CI: 0.934-0.976, p < 0.001), MRI ADC (HR = 1.843, 95%CI: 1.260-2.871, p < 0.001), and CT informational-measure-of-correlation (HR = 3.546, 95% CI: 1.660-7.573, p = 0.001) were the strongest predictors of 10-year mortality. A machine-learning model trained on clinical, imaging, and imaging textures was the best predictive model (AUC = 0.82, sensitivity = 83%, specificity = 84%) and outperformed the solely clinical model (AUC = 0.76, sensitivity = 77%, specificity = 79%). In ex-smokers, regardless of COPD status, addition of CT and MR imaging texture measurements to clinical models provided unique prognostic information of mortality risk that can allow for better clinical management.Clinical Trial Registration: www.clinicaltrials.gov NCT02279329.

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COPD患者10年全因死亡率的胸部MRI和CT预测指标。

使用磁共振成像（MRI）和计算机断层扫描（CT）进行的肺部成像测量有可能通过测量肺活量测量无法提供的气道和实质病理信息来加深我们对慢性阻塞性肺病（COPD）的理解。目前，MRI和CT测量不包括在死亡率预测、诊断或COPD分期中。我们评估了基线肺功能、MRI和CT测量以及成像纹理特征，以预测患有（n = 93；女性31例；70 ± 9年）和无（n = 69；29名女性，69名 ± 9岁）COPD。对CT气道和血管测量、氦-3（3He）MRI通气缺陷百分比（VDP）和表观扩散系数（ADC）进行量化。使用PyRadiomics（版本2.2.0）提取MRI和CT纹理特征。使用多变量回归模型优势比评估10年全因死亡率与所有临床和影像学测量之间的相关性。使用受试者特征曲线下面积（AUC）、敏感性和特异性分析评估了10年全因死亡率的机器学习预测模型。DLCO（pred百分比）（HR=0.955，95%CI:0.93-0.976，p p p = 0.001）是10年死亡率的最强预测因素。在临床、成像和成像纹理上训练的机器学习模型是最好的预测模型（AUC=0.82，灵敏度=83%，特异性=84%），并且优于单独的临床模型（AUC=0.76，灵敏度=77%，特异性=79%）。在戒烟者中，无论COPD状态如何，在临床模型中添加CT和MR成像纹理测量提供了独特的死亡风险预后信息，可以更好地进行临床管理。临床试验注册：www.clinicaltrials.gov NCT02279329。

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

COPD: Journal of Chronic Obstructive Pulmonary Disease RESPIRATORY SYSTEM-

CiteScore

4.40

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： From pathophysiology and cell biology to pharmacology and psychosocial impact, COPD: Journal Of Chronic Obstructive Pulmonary Disease publishes a wide range of original research, reviews, case studies, and conference proceedings to promote advances in the pathophysiology, diagnosis, management, and control of lung and airway disease and inflammation - providing a unique forum for the discussion, design, and evaluation of more efficient and effective strategies in patient care.