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Research Article
Jingshan Gong
Jinan University
Corresponding Author
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Background: Although myocardial infarction (MI) can be assessed quantitatively and qualitatively by using late gadolinium-enhanced (LGE) cardiovascular magnetic resonance (CMR) imaging, intravenous administration of gadolinium can expose patients to high risk of nephrogenic systemic fibrosis, especially in those with cardiovascular diseases. The purpose of this study is to harness cine CMR-based radiomics for predicting MI without introducing gadolinium.
Methods: In this retrospective study, we included 48 patients with acute myocardial infarction (AMI) confirmed by later gadolinium enhancement (LGE) at CMR. CMR examinations were performed within 2 to 6 days after PCI. According to the LGE, each myocardial segment was dichotomized into with and without MI. Radiomic features of myocardial segments were extracted from cine CMR images and the myocardial segments were divided into training and validation sets randomly at a ratio of 0.7:0.3. Pearson correlation and Mann-Whitney U rank test were used to eliminate redundant and irrelevant features. A least absolute shrinkage and selection operator (LASSO) algorithm was used for features selection in the training set. Radiomic signatures were constructed in both the training and validation sets and its predictive performance was assessed using area under the cure of receiver operating characteristic (AUC-ROC).
Results：Of 768 myocardial segments in the 48 patients, there were 291 (38%) segments with MI and 477 (62%) segments without MI. After univariate analysis, there were 22 RFs related to MI with statistical significance. LASSO regression selected 18 RFs for radiomics signature builting. AUC-ROC of radiomic signatures in prediction of segments with MI was 0.74(95% CI:0.69-0.78)and 0.68 (95%CI: 0.60-0.75) in the training and validation sets, respectively. The difference was not statistically significant (p=0.14).
Conclusion: Cine MR-based radiomics signature can achieve a good prediction performance for MI, which showed the potential to be a promising imaging biomarker for MI without the administration of contrast agent.
Keywords
Myocardial Infarction, cine Magnetic Resonance, Radiomics
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No competing interests reported.
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A new preprint design is coming!
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research Article
Jingshan Gong
Jinan University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 11 Jun, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Nuclear Medicine & Medical Imaging
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Although myocardial infarction (MI) can be assessed quantitatively and qualitatively by using late gadolinium-enhanced (LGE) cardiovascular magnetic resonance (CMR) imaging, intravenous administration of gadolinium can expose patients to high risk of nephrogenic systemic fibrosis, especially in those with cardiovascular diseases. The purpose of this study is to harness cine CMR-based radiomics for predicting MI without introducing gadolinium.
Methods: In this retrospective study, we included 48 patients with acute myocardial infarction (AMI) confirmed by later gadolinium enhancement (LGE) at CMR. CMR examinations were performed within 2 to 6 days after PCI. According to the LGE, each myocardial segment was dichotomized into with and without MI. Radiomic features of myocardial segments were extracted from cine CMR images and the myocardial segments were divided into training and validation sets randomly at a ratio of 0.7:0.3. Pearson correlation and Mann-Whitney U rank test were used to eliminate redundant and irrelevant features. A least absolute shrinkage and selection operator (LASSO) algorithm was used for features selection in the training set. Radiomic signatures were constructed in both the training and validation sets and its predictive performance was assessed using area under the cure of receiver operating characteristic (AUC-ROC).
Results：Of 768 myocardial segments in the 48 patients, there were 291 (38%) segments with MI and 477 (62%) segments without MI. After univariate analysis, there were 22 RFs related to MI with statistical significance. LASSO regression selected 18 RFs for radiomics signature builting. AUC-ROC of radiomic signatures in prediction of segments with MI was 0.74(95% CI:0.69-0.78)and 0.68 (95%CI: 0.60-0.75) in the training and validation sets, respectively. The difference was not statistically significant (p=0.14).
Conclusion: Cine MR-based radiomics signature can achieve a good prediction performance for MI, which showed the potential to be a promising imaging biomarker for MI without the administration of contrast agent.
Figure 1
Figure 2
Figure 3
No competing interests reported.
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