A CT-Based Lung Radiomics Nomogram for Classifying the Severity of Chronic Obstructive Pulmonary Disease.

IF 2.7 3区 医学 Q2 RESPIRATORY SYSTEM
Taohu Zhou, Xiuxiu Zhou, Jiong Ni, Yu Guan, Xin'ang Jiang, Xiaoqing Lin, Jie Li, Yi Xia, Xiang Wang, Yun Wang, Wenjun Huang, Wenting Tu, Peng Dong, Zhaobin Li, Shiyuan Liu, Li Fan
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Abstract

Background: Chronic obstructive pulmonary disease (COPD) is a major global health concern, and while traditional pulmonary function tests are effective, recent radiomics advancements offer enhanced evaluation by providing detailed insights into the heterogeneous lung changes.

Purpose: To develop and validate a radiomics nomogram based on clinical and whole-lung computed tomography (CT) radiomics features to stratify COPD severity.

Patients and methods: One thousand ninety-nine patients with COPD (including 308, 132, and 659 in the training, internal and external validation sets, respectively), confirmed by pulmonary function test, were enrolled from two institutions. The whole-lung radiomics features were obtained after a fully automated segmentation. Thereafter, a clinical model, radiomics signature, and radiomics nomogram incorporating radiomics signature as well as independent clinical factors were constructed and validated. Additionally, receiver-operating characteristic (ROC) curve, area under the ROC curve (AUC), decision curve analysis (DCA), and the DeLong test were used for performance assessment and comparison.

Results: In comparison with clinical model, both radiomics signature and radiomics nomogram outperformed better on COPD severity (GOLD I-II and GOLD III-IV) in three sets. The AUC of radiomics nomogram integrating age, height and Radscore, was 0.865 (95% CI, 0.818-0.913), 0.851 (95% CI, 0.778-0.923), and 0.781 (95% CI, 0.740-0.823) in three sets, which was the highest among three models (0.857; 0.850; 0.774, respectively) but not significantly different (P > 0.05). Decision curve analysis demonstrated the superiority of the radiomics nomogram in terms of clinical usefulness.

Conclusion: The present work constructed and verified the novel, diagnostic radiomics nomogram for identifying the severity of COPD, showing the added value of chest CT to evaluate not only the pulmonary structure but also the lung function status.

基于ct的肺放射组学图对慢性阻塞性肺疾病严重程度的分级。
背景:慢性阻塞性肺疾病(COPD)是一个主要的全球健康问题,虽然传统的肺功能测试是有效的,但最近放射组学的进展通过提供对异质性肺变化的详细见解,提供了增强的评估。目的:开发并验证基于临床和全肺计算机断层扫描(CT)放射组学特征的放射组学图,以分层COPD严重程度。患者和方法:从两个机构纳入经肺功能检查确认的慢性阻塞性肺病患者1199例(分别包括308例、132例和659例在培训组、内部验证组和外部验证组)。在全自动分割后获得全肺放射组学特征。随后,构建并验证了结合放射组学特征和独立临床因素的临床模型、放射组学特征和放射组学nomogram。采用受试者工作特征(ROC)曲线、ROC曲线下面积(AUC)、决策曲线分析(DCA)和DeLong检验进行绩效评价和比较。结果:与临床模型相比,三组放射组学特征和放射组学nomogram在COPD严重程度(GOLD I-II和GOLD III-IV)上表现更好。综合年龄、身高和Radscore的放射组学nomogram AUC在三组中分别为0.865 (95% CI, 0.818-0.913)、0.851 (95% CI, 0.778-0.923)和0.781 (95% CI, 0.740-0.823),在三组模型中均最高(0.857;0.850;0.774),差异无统计学意义(P < 0.05)。决策曲线分析显示放射组学图在临床应用方面的优越性。结论:本工作构建并验证了一种新的诊断性COPD的放射组学影像学图,显示了胸部CT在评估肺结构和肺功能状态方面的附加价值。
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来源期刊
CiteScore
4.80
自引率
10.70%
发文量
372
审稿时长
16 weeks
期刊介绍: An international, peer-reviewed journal of therapeutics and pharmacology focusing on concise rapid reporting of clinical studies and reviews in COPD. Special focus will be given to the pathophysiological processes underlying the disease, intervention programs, patient focused education, and self management protocols. This journal is directed at specialists and healthcare professionals
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