Zhou Taohu, Zhou Xiuxiu, Ni Jiong, Guan Yu, Jiang Xin'ang, Lin Xiaoqing, Li Jie, Xia Yi, Wang Xiang, Wang Yun, Huang Wenjun, Tu Wenting, Dong Peng, Li Zhaobin, Liu Shiyuan, Fan Li
Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, People's Republic of China.
School of Medical Imaging, Shandong Second Medical University, Weifang, Shandong, People's Republic of China.
Int J Chron Obstruct Pulmon Dis. 2024 Dec 11;19:2705-2717. doi: 10.2147/COPD.S483007. eCollection 2024.
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.
To develop and validate a radiomics nomogram based on clinical and whole-lung computed tomography (CT) radiomics features to stratify COPD severity.
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.
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.
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.
慢性阻塞性肺疾病（COPD）是全球主要的健康问题，虽然传统的肺功能测试有效，但最近的放射组学进展通过提供对肺部异质性变化的详细洞察，提供了更全面的评估。
基于临床和全肺计算机断层扫描（CT）放射组学特征开发并验证一种放射组学列线图，以对COPD严重程度进行分层。
从两个机构招募了1099例经肺功能测试确诊的COPD患者（训练集、内部验证集和外部验证集分别为308例、132例和659例）。在全自动分割后获得全肺放射组学特征。此后，构建并验证了一个临床模型、放射组学特征以及包含放射组学特征和独立临床因素的放射组学列线图。此外，使用受试者操作特征（ROC）曲线、ROC曲线下面积（AUC）、决策曲线分析（DCA）和DeLong检验进行性能评估和比较。
与临床模型相比，放射组学特征和放射组学列线图在三组中对COPD严重程度（GOLD I-II和GOLD III-IV）的表现均更好。整合年龄、身高和Radscore的放射组学列线图在三组中的AUC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），是三个模型中最高的（分别为0.857；0.850；0.774），但差异无统计学意义（P>0.05）。决策曲线分析证明了放射组学列线图在临床实用性方面的优越性。
本研究构建并验证了用于识别COPD严重程度的新型诊断放射组学列线图，显示了胸部CT在评估肺部结构和肺功能状态方面的附加价值。
