AUTHOR=Liu Xiaowen , Xu Ting , Peng Yongjia , Yuan Jialin , Wang Shuxing , Xu Wuyan , Gong Jingshan 

TITLE=Non-contrast cine cardiovascular magnetic resonance-based radiomics nomogram for predicting microvascular obstruction after reperfusion in ST-segment elevation myocardial infarction

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2023.1274267

DOI=10.3389/fcvm.2023.1274267

ISSN=2297-055X

ABSTRACT=Purpose: We aimed to develop and validate a cine cardiovascular magnetic resonance (CMR)-based radiomics nomogram model for predicting the microvascular obstruction (MVO) following reperfusion in patients with ST-segment elevation myocardial infarction (STEMI).
Methods: In total, 167 consecutive patients with STEMI were retrospectively enrolled. The patients were randomly divided into training and validation cohorts in a ratio of 7:3. All patients were diagnosed as having myocardial infarction with or without MVO based on late gadolinium enhancement (LGE) imaging. Radiomics features were extracted from the cine-CMR end-diastolic volume phase of the entire left ventricular myocardium (3D volume). The least absolute shrinkage and selection operator (LASSO) regression was employed to select the features that were most relevant to the MVO; these features were then used to calculate the radiomics score (Rad-score). A combined model was developed based on independent risk factors screened using multivariate regression analysis and visualized using a nomogram. Performance was assessed using receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).
Results: Univariate analysis of clinical features demonstrated that only cardiac troponin I (cTNI) was significantly associated with MVO. LASSO regression revealed that 12 radiomics features were strongly associated with MVO. Multivariate regression analysis indicated that cTNI and Rad-score were independent risk factors for MVO. The nomogram based on these two features achieved an area under the curve (AUC) of 0.86 and 0.78 in the training and validation cohorts, respectively. Calibration curves and DCA indicated the clinical feasibility and utility of the nomogram.
Conclusions: A radiomics nomogram based on cine CMR images offers an effective means of predicting MVO without contrast agents and radiation, which could facilitate risk stratification of patients with STEMI after PCI for reperfusion.