CT-Based radiomics nomogram of lung and mediastinal features to identify cardiovascular disease in chronic obstructive pulmonary disease: a multicenter study.

IF 2.8 3区 医学 Q2 RESPIRATORY SYSTEM BMC Pulmonary Medicine Pub Date : 2025-03-15 DOI:10.1186/s12890-025-03568-2
XiaoQing Lin, TaoHu Zhou, Jiong Ni, XiuXiu Zhou, Yu Guan, Xin'ang Jiang, Yi Xia, FangYi Xu, HongJie Hu, Jie Li, Jin Zhang, Shiyuan Liu, Rozemarijn Vliegenthart, Li Fan
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Abstract

Rationale and objectives: To investigate the performance of two diagnostic models based on CT-derived lung and mediastinum radiomics nomograms for identifying cardiovascular disease (CVD) in Chronic Obstructive Pulmonary Disease (COPD) patients.

Materials and methods: Hospitalized participants with COPD were retrospectively recruited between September 2015 and April 2023. Clinical data and visual coronary artery calcium score (CACS) were collected. Radiomics features of lung and mediastinum were extracted. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied for feature selection and radiomic model construction. We constructed 3 radiomics models, based on lung, mediastinum, and combined lung-and-mediastinum. Multivariate logistic regression model was used to establish radiomics nomograms. The performance of radiomics nomograms was evaluated by area under the ROC curve (AUC) and decision curve analysis (DCA).

Results: Of 686 COPD patients, 131 had a history of CVD. Age, neutrophilic granulocyte percentage, hematocrit and GOLD stage were independent clinical factors for CVD. 12 lung, and 6 mediastinum radiomic features were collected to construct the radiomics models. As the lung-and-mediastinum radiomics model included the same 6 features as the mediastinum model, finally 2 radiomics models were studied (lung, mediastinum). The 2 radiomics nomograms showed better discriminatory ability (AUC: 0.79, 95%CI [0.72, 0.86] for lung; 0.86, 95%CI [0.81, 0.92]) for mediastinum) than the clinical factors model (AUC: 0.71, 95%CI [0.64, 0.78]) and visual CACS (AUC: 0.65, 95%CI [0.57, 0.72]). DCA demonstrated the 2 radiomics nomograms outperformed the clinical factors and CACS across the majority of the range of reasonable threshold probabilities.

Conclusion: We developed chest CT-based nomograms to identify CVD in COPD patients, in particular based on mediastinum features, had better discriminatory power than clinical factors and visual CACS.

Trial registration: This retrospective study was approved by the institutional review boards at Second Affiliated Hospital of Naval Medical University, Tongji Hospital of Tongji University and Sir Run Run Shaw Hospital (ChiCTR2300069929 March 29, 2023). Retrospectively registered.

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基于ct的肺和纵隔特征放射组学图识别慢性阻塞性肺疾病的心血管疾病:一项多中心研究
理由和目的:研究基于ct衍生肺和纵隔放射组学图的两种诊断模型在慢性阻塞性肺疾病(COPD)患者心血管疾病(CVD)诊断中的表现。材料和方法:回顾性招募2015年9月至2023年4月期间住院的COPD患者。收集临床资料及冠脉钙化评分(CACS)。提取肺和纵隔放射组学特征。采用最小绝对收缩和选择算子(LASSO)逻辑回归进行特征选择和放射学模型构建。我们构建了基于肺、纵隔和肺-纵隔联合的3种放射组学模型。采用多元logistic回归模型建立放射组学图。通过ROC曲线下面积(area under ROC curve, AUC)和决策曲线分析(decision curve analysis, DCA)来评价放射组学图的性能。结果:686例COPD患者中,131例有心血管疾病史。年龄、中性粒细胞百分比、红细胞压积和GOLD分期是CVD的独立临床因素。收集12个肺和6个纵隔放射学特征,构建放射组学模型。由于肺-纵隔放射组学模型包含与纵隔模型相同的6个特征,因此最终研究了肺、纵隔两种放射组学模型。两组放射组学图对肺的鉴别能力较好(AUC: 0.79, 95%CI [0.72, 0.86];0.86, 95%CI[0.81, 0.92])比临床因素模型(AUC: 0.71, 95%CI[0.64, 0.78])和目视CACS (AUC: 0.65, 95%CI[0.57, 0.72])要好。DCA表明,在合理阈值概率的大部分范围内,2种放射组学图优于临床因素和CACS。结论:我们开发了基于胸部ct的图来识别COPD患者的CVD,特别是基于纵隔特征的图,比临床因素和视觉CACS具有更好的鉴别能力。试验注册:本回顾性研究由海军医科大学第二附属医院、同济大学同济医院和邵逸夫医院机构审查委员会批准(ChiCTR2300069929, 2023年3月29日)。回顾注册。
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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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