arXiv 2301.123402023-12-06eess.IVcs.CV

肺与心外膜脂肪组织影像组学特征在检测COVID-19感染严重程度中的增量价值与可解释性

Incremental Value and Interpretability of Radiomics Features of Both Lung and Epicardial Adipose Tissue for Detecting the Severity of COVID-19 Infection

School of Computer and Communication Engineering, Zhengzhou University of Light Industry（郑州轻工业大学计算机与通信工程学院）；Universidade Federal Fluminense（弗鲁米嫩塞联邦大学）；DASA Complexo Hospitalar de Niterói（达萨尼泰罗伊医院综合体）；Michigan Technological University（密歇根理工大学）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）；Shanghai Institute of Cardiovascular Diseases（上海市心血管病研究所）；National Clinical Research Center for Interventional Medicine（国家介入医学临床研究中心）；Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University（密歇根理工大学生物计算与数字健康中心、计算与网络系统研究所及健康研究所）

Ni Yao, Yanhui Tian, Daniel Gama das Neves, Chen Zhao, Claudio Tinoco Mesquita, Wolney de Andrade Martins, Alair Augusto Sarmet Moreira Damas dos Santos, Yantin… 展开作者

Ni Yao, Yanhui Tian, Daniel Gama das Neves, Chen Zhao, Claudio Tinoco Mesquita, Wolney de Andrade Martins, Alair Augusto Sarmet Moreira Damas dos Santos, Yanting Li, Chuang Han, Fubao Zhu, Neng Dai, Weihua Zhou

备注：: 20 pages, 7 figures

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AI总结：

提出三阶段深度学习提取EAT并融合肺与EAT影像组学特征的混合模型，在515例COVID-19患者中显著提升严重程度检测性能与可解释性。

摘要：

心外膜脂肪组织（EAT）以其促炎特性及其与2019冠状病毒病（COVID-19）严重程度的关联而为人所知。然而，当前的EAT分割方法未考虑位置信息。此外，COVID-19严重程度检测缺乏对EAT影像组学特征的考虑，这限制了可解释性。本研究调查利用来自EAT和肺的影像组学特征来检测COVID-19感染的严重程度。研究对515例COVID-19患者（Cohort1：415例，Cohort2：100例）进行了回顾性分析，采用所提出的三阶段深度学习方法进行EAT提取。肺分割则使用一种已发表的方法实现。研究在推导队列中构建了用于检测COVID-19严重程度的混合模型，并在内部验证队列（125例，来自Cohort1）和外部验证队列（100例，来自Cohort2）中评估其性能和不确定性。在EAT提取方面，两个中心的Dice相似系数（DSC）分别为0.972（±0.011）和0.968（±0.005）。在严重程度检测方面，与仅使用肺影像组学特征的模型相比，同时使用肺和EAT影像组学特征的混合模型在AUC、净重分类改善（NRI）和综合判别改善（IDI）方面均有提升。该混合模型在内部验证队列中分别提高了0.1（p<0.001）、19.3%和18.0%，在外部验证队列中分别提高了0.09（p<0.001）、18.0%和18.0%，同时优于现有检测方法。不确定性量化和影像组学特征分析证实，纳入EAT特征后病例预测具有可解释性。

英文摘要：

Epicardial adipose tissue (EAT) is known for its pro-inflammatory properties and association with Coronavirus Disease 2019 (COVID-19) severity. However, current EAT segmentation methods do not consider positional information. Additionally, the detection of COVID-19 severity lacks consideration for EAT radiomics features, which limits interpretability. This study investigates the use of radiomics features from EAT and lungs to detect the severity of COVID-19 infections. A retrospective analysis of 515 patients with COVID-19 (Cohort1: 415, Cohort2: 100) was conducted using a proposed three-stage deep learning approach for EAT extraction. Lung segmentation was achieved using a published method. A hybrid model for detecting the severity of COVID-19 was built in a derivation cohort, and its performance and uncertainty were evaluated in internal (125, Cohort1) and external (100, Cohort2) validation cohorts. For EAT extraction, the Dice similarity coefficients (DSC) of the two centers were 0.972 (+-0.011) and 0.968 (+-0.005), respectively. For severity detection, the hybrid model with radiomics features of both lungs and EAT showed improvements in AUC, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) compared to the model with only lung radiomics features. The hybrid model exhibited an increase of 0.1 (p<0.001), 19.3%, and 18.0% respectively, in the internal validation cohort and an increase of 0.09 (p<0.001), 18.0%, and 18.0%, respectively, in the external validation cohort while outperforming existing detection methods. Uncertainty quantification and radiomics features analysis confirmed the interpretability of case prediction after inclusion of EAT features.

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