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

PURPOSE: This study aimed to develop and validate a cine cardiovascular magnetic resonance (CMR)-based radiomics nomogram model for predicting microvascular obstruction (MVO) following reperfusion in patients with ST-segment elevation myocardial infarction (STEMI). METHODS: In total, 167 consecutive...

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Autores principales: Liu, Xiaowen, Xu, Ting, Peng, Yongjia, Yuan, Jialin, Wang, Shuxing, Xu, Wuyan, Gong, Jingshan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10655024/
https://www.ncbi.nlm.nih.gov/pubmed/38028453
http://dx.doi.org/10.3389/fcvm.2023.1274267
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author Liu, Xiaowen
Xu, Ting
Peng, Yongjia
Yuan, Jialin
Wang, Shuxing
Xu, Wuyan
Gong, Jingshan
author_facet Liu, Xiaowen
Xu, Ting
Peng, Yongjia
Yuan, Jialin
Wang, Shuxing
Xu, Wuyan
Gong, Jingshan
author_sort Liu, Xiaowen
collection PubMed
description PURPOSE: This study aimed to develop and validate a cine cardiovascular magnetic resonance (CMR)-based radiomics nomogram model for predicting microvascular obstruction (MVO) following reperfusion in patients with ST-segment elevation myocardial infarction (STEMI). METHODS: In total, 167 consecutive STEMI patients were retrospectively enrolled. The patients were randomly divided into training and validation cohorts with a ratio of 7:3. All patients were diagnosed with myocardial infarction with or without MVO based on late gadolinium enhancement 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, calibration curve, and decision curve analysis (DCA). RESULTS: The 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 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 CMR-based radiomics nomogram 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.
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spelling pubmed-106550242023-01-01 Non-contrast cine cardiovascular magnetic resonance-based radiomics nomogram for predicting microvascular obstruction after reperfusion in ST-segment elevation myocardial infarction Liu, Xiaowen Xu, Ting Peng, Yongjia Yuan, Jialin Wang, Shuxing Xu, Wuyan Gong, Jingshan Front Cardiovasc Med Cardiovascular Medicine PURPOSE: This study aimed to develop and validate a cine cardiovascular magnetic resonance (CMR)-based radiomics nomogram model for predicting microvascular obstruction (MVO) following reperfusion in patients with ST-segment elevation myocardial infarction (STEMI). METHODS: In total, 167 consecutive STEMI patients were retrospectively enrolled. The patients were randomly divided into training and validation cohorts with a ratio of 7:3. All patients were diagnosed with myocardial infarction with or without MVO based on late gadolinium enhancement 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, calibration curve, and decision curve analysis (DCA). RESULTS: The 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 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 CMR-based radiomics nomogram 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. Frontiers Media S.A. 2023-11-03 /pmc/articles/PMC10655024/ /pubmed/38028453 http://dx.doi.org/10.3389/fcvm.2023.1274267 Text en © 2023 Liu, Xu, Peng, Yuan, Wang, Xu and Gong. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cardiovascular Medicine
Liu, Xiaowen
Xu, Ting
Peng, Yongjia
Yuan, Jialin
Wang, Shuxing
Xu, Wuyan
Gong, Jingshan
Non-contrast cine cardiovascular magnetic resonance-based radiomics nomogram for predicting microvascular obstruction after reperfusion in ST-segment elevation myocardial infarction
title Non-contrast cine cardiovascular magnetic resonance-based radiomics nomogram for predicting microvascular obstruction after reperfusion in ST-segment elevation myocardial infarction
title_full Non-contrast cine cardiovascular magnetic resonance-based radiomics nomogram for predicting microvascular obstruction after reperfusion in ST-segment elevation myocardial infarction
title_fullStr Non-contrast cine cardiovascular magnetic resonance-based radiomics nomogram for predicting microvascular obstruction after reperfusion in ST-segment elevation myocardial infarction
title_full_unstemmed Non-contrast cine cardiovascular magnetic resonance-based radiomics nomogram for predicting microvascular obstruction after reperfusion in ST-segment elevation myocardial infarction
title_short Non-contrast cine cardiovascular magnetic resonance-based radiomics nomogram for predicting microvascular obstruction after reperfusion in ST-segment elevation myocardial infarction
title_sort non-contrast cine cardiovascular magnetic resonance-based radiomics nomogram for predicting microvascular obstruction after reperfusion in st-segment elevation myocardial infarction
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10655024/
https://www.ncbi.nlm.nih.gov/pubmed/38028453
http://dx.doi.org/10.3389/fcvm.2023.1274267
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