基于心脏磁共振的影像组学模型对COVID-19相关心肌损伤的分类研究

doi:10.3969/j.issn.1674-8115.2026.10.007

摘要/Abstract

摘要：

目的·比较基于心脏磁共振（cardiac magnetic resonance，CMR）的影像组学模型与细胞外容积分数（extracellular volume fraction，ECV）模型对3类人群［即新型冠状病毒感染（coronavirus disease 2019，COVID-19）伴肌钙蛋白升高者、COVID-19伴肌钙蛋白正常者及健康对照者］的诊断效能，为临床提供可精准分层的影像学生物标志物。方法·采用单中心前瞻性队列研究（2022年1月—2024年12月），纳入70例于上海交通大学医学院附属第六人民医院就诊的COVID-19患者，分为肌钙蛋白升高组（n=24）与肌钙蛋白正常组（n=46）；同时，纳入70名健康对照者作为健康对照组。按7∶3将以上140例受试者随机划分为训练集（n=98）与测试集（n=42）。所有受试者均完成3.0 T CMR扫描，于短轴晚期钆增强（late gadolinium enhancement，LGE）图像提取影像组学特征（包括一阶特征、形状特征及纹理特征），经特征降维后筛选出最具判别力的核心特征用于构建CMR影像组学模型，并采用心肌ECV构建ECV对照模型，于测试集验证该2个模型的诊断效能。采用受试者操作特征曲线的曲线下面积（area under the curve，AUC）、准确率、精确率、灵敏度、特异度及F1分数综合评估模型性能，运用DeLong检验比较模型的AUC差异。结果·在训练集和测试集上，CMR影像组学模型的性能（AUC）均显著优于ECV对照模型。具体表现为：训练集中CMR影像组学模型与ECV对照模型的AUC对比依次为，肌钙蛋白升高组0.944 vs 0.741、肌钙蛋白正常组0.903 vs 0.552、健康对照组0.929 vs 0.684；测试集中2组模型的AUC对比依次为，肌钙蛋白升高组0.865 vs 0.708、肌钙蛋白正常组0.691 vs 0.557、健康对照组0.873 vs 0.667。结论·CMR影像组学模型能有效识别COVID-19相关心肌损伤，可为此类心肌损伤的早期精准评估提供影像学依据。

关键词: 心脏磁共振, 新型冠状病毒感染, 心肌损伤, 影像组学

Abstract:

Objective ·To compare the diagnostic performance of the cardiac magnetic resonance (CMR)-based radiomic model and the extracellular volume fraction (ECV) model in three groups, patients with coronavirus disease 2019 (COVID-19) accompanied by elevated troponin, patients with COVID-19 accompanied by normal troponin, and healthy controls, thereby providing imaging biomarkers for precise clinical stratification. Methods ·This single-center prospective cohort study, conducted from January 2022 to December 2024, enrolled 70 patients with COVID-19 from Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine. These patients were stratified into the elevated troponin group (n=24) and the normal troponin group (n=46). Meanwhile, 70 healthy participants were recruited as the healthy control group. The 140 participants were randomly divided into a training set (n=98) and a test set (n=42) at a 7∶3 ratio. All participants underwent 3.0 T CMR scanning. Radiomic features, including first-order, shape, and texture features, were extracted from short-axis late gadolinium enhancement (LGE) images. After feature dimensionality reduction, the most discriminative core features were selected to construct the CMR radiomic model. A control model based on myocardial ECV was also constructed. The diagnostic performance of the two models was validated in the test set. Model performance was comprehensively evaluated using the area under the receiver operator characteristic curve (AUC), accuracy, precision, sensitivity, specificity, and F1 score. The DeLong test was used to compare the AUCs between the two models. Results ·The CMR radiomic model demonstrated significantly superior performance, as measured by AUC, to the ECV control model in both the training and test sets. Specifically, in the training set, the AUC values for the CMR radiomic model versus the ECV control model were 0.944 vs. 0.741 for the elevated troponin group, 0.903 vs. 0.552 for the normal troponin group, and 0.929 vs. 0.684 for the healthy control group. In the test set, the corresponding values were 0.865 vs. 0.708, 0.691 vs. 0.557, and 0.873 vs. 0.667, respectively. Conclusion ·The CMR-based radiomic model can effectively identify COVID-19-associated myocardial injury and provide imaging evidence for the early and precise assessment of such myocardial injury.

Key words: cardiac magnetic resonance (CMR), coronavirus disease 2019 (COVID-19), myocardial injury, radiomics

中图分类号:

R542.2

肖活源, 旦增曲央, 张清晨, 冯锐, 刘雨婷, 康桑, 潘静薇. 基于心脏磁共振的影像组学模型对COVID-19相关心肌损伤的分类研究[J]. 上海交通大学学报（医学版）, 2026, 46(10): 1375-1382.

Xiao Huoyuan, Quyang Danzeng, Zhang Qingchen, Feng Rui, Liu Yuting, Kang Sang, Pan Jingwei. A radiomic model based on cardiac magnetic resonance for the classification of COVID-19-associated myocardial injury[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2026,(10): 1375-1382.

图/表 7

参考文献 17

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Item	Elevated troponin group (n=24)	Normal troponin group (n=46)	Healthy control group (n=70)	P value^①	P value^②	P value^③
Age/year	38.0 (29.0, 52.0)	35.5 (26.8, 41.0)	38.0 (28.0, 56.8)	0.294	0.766	0.159
Hematocrit/%	41.3±5.4	42.0±4.9	43.1±5.2	0.599	0.203	0.323
hs-cTnI/(ng·L^-1)	1 366.0 (114.0, 4 707.7)	2.0 (2.0, 3.0)	2.0 (1.9, 2.0)	<0.001	<0.001	0.001
NT-proBNP/(pg·mL^-1)	639.5 (112.7, 2 194.1)	54.8 (36.8, 98.2)	22.9 (22.9, 39.1)	<0.001	<0.001	<0.001
LVEF/%	61.7 (55.0, 63.7)	57.6 (55.0, 63.2)	61.0 (56.1, 65.0)	0.444	0.402	0.121
T1/ms	1 279 (1 225, 1 319)	1 246 (1 219, 1 268)	1 198 (1 159, 1 234)	0.017	<0.001	<0.001
T2/ms	43 (41, 45)	44 (41, 47)	41 (40, 43)	0.774	0.034	0.007
ECV/%	35±4	32±4	30±4	0.035	<0.001	0.011

Item	Elevated troponin group (n=24)	Normal troponin group (n=46)	Healthy control group (n=70)	P value^①	P value^②	P value^③
Age/year	38.0 (29.0, 52.0)	35.5 (26.8, 41.0)	38.0 (28.0, 56.8)	0.294	0.766	0.159
Hematocrit/%	41.3±5.4	42.0±4.9	43.1±5.2	0.599	0.203	0.323
hs-cTnI/(ng·L^-1)	1 366.0 (114.0, 4 707.7)	2.0 (2.0, 3.0)	2.0 (1.9, 2.0)	<0.001	<0.001	0.001
NT-proBNP/(pg·mL^-1)	639.5 (112.7, 2 194.1)	54.8 (36.8, 98.2)	22.9 (22.9, 39.1)	<0.001	<0.001	<0.001
LVEF/%	61.7 (55.0, 63.7)	57.6 (55.0, 63.2)	61.0 (56.1, 65.0)	0.444	0.402	0.121
T1/ms	1 279 (1 225, 1 319)	1 246 (1 219, 1 268)	1 198 (1 159, 1 234)	0.017	<0.001	<0.001
T2/ms	43 (41, 45)	44 (41, 47)	41 (40, 43)	0.774	0.034	0.007
ECV/%	35±4	32±4	30±4	0.035	<0.001	0.011

Item	Training set (n=98)	Test set (n=42)	P value
Age/year	38.0 (28.0, 51.0)	36.5 (26.3, 49.3)	0.896
Hematocrit/%	41.9±5.3	43.2±4.7	0.202
hs-cTnI/(ng·L^-1)	2.0 (2.0, 3.7)	2.0 (1.9, 3.0)	0.567
NT-proBNP/(pg·mL^-1)	39.9 (22.9, 70.5)	39.4 (22.9, 83.4)	0.891
LVEF/%	60.3 (56.0, 64.0)	61.0 (54.0, 64.1)	0.842
T1/ms	1 224 (1 173, 1 268)	1 228 (1 209, 1 275)	0.260
T2/ms	42 (40, 45)	41 (39, 45)	0.180
ECV/%	32±5	32±4	0.894

Item	Training set (n=98)	Test set (n=42)	P value
Age/year	38.0 (28.0, 51.0)	36.5 (26.3, 49.3)	0.896
Hematocrit/%	41.9±5.3	43.2±4.7	0.202
hs-cTnI/(ng·L^-1)	2.0 (2.0, 3.7)	2.0 (1.9, 3.0)	0.567
NT-proBNP/(pg·mL^-1)	39.9 (22.9, 70.5)	39.4 (22.9, 83.4)	0.891
LVEF/%	60.3 (56.0, 64.0)	61.0 (54.0, 64.1)	0.842
T1/ms	1 224 (1 173, 1 268)	1 228 (1 209, 1 275)	0.260
T2/ms	42 (40, 45)	41 (39, 45)	0.180
ECV/%	32±5	32±4	0.894

Feature	Feature class	Filter	Definition
Autocorrelation	GLCM	Wavelet-HHL	Measures the correlation between the intensities of neighboring voxels
JointAverage	GLCM	Wavelet-HHL	Measures the mean of the joint gray-level distribution of neighboring voxel pairs
Strength	NGTDM	Log.sigma.2.0mm	Measures of the strength/distinctiveness of texture primitives
SumAverage	GLCM	Wavelet-HHL	Measures the mean of the sum of gray levels for neighboring intensity pairs
LargeDependenceLowGrayLevelEmphasis	GLDM	Log.sigma.1.0mm	Measures the extent of large, contiguous low-gray-level regions