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Cardiac Magnetic Resonance Cine Images derived-Radiomics for the Prediction of Event Free Survival in Patients with Acute Myocardial Infarction
Xin A1, Ying Zhang2, and Yundai Chen3
1Department of Cardiology, Chinese PLA General Hospital, Beijing, China, 2Chinese PLA General Hospital, Beijing, China, 3Chinese PLA General Hospital, beijing, China

Synopsis

Keywords: Myocardium, Cardiovascular

Motivation: Prognostic value of radiomic features extracted from CMR cine image remains to be investigated.

Goal(s): To evaluate the prognostic value of radiomic features derived from cine images in patients with ST-segment elevation myocardial infarction (STEMI).

Approach: Radiomic features were extracted from CMR cine images on STEMI patients, and LASSO -Cox regression used to select predictive features for MACE. Cox regression was applied to build models.

Results: RAD score provided an incremental prognostic value above baseline clinical factors and LVEF (C-index 0.78 vs 0.69; p=0.002) and outperformed the addition of CMR markers of infarct injury (C-index: 0.78 vs 0.69, p<0.001).

Impact: Radiomic features provide incremental prognostic value to clinical and infarct size in the prediction of MACE, which would promote the development of the prognostic assessment with non-contrast enhanced CMR.

Background

Radiomic analysis of cardiac magnetic resonance (CMR) non-contrast cine images allows quantitative analysis of myocardial tissue alterations. However, the prognostic value of radiomic features extracted from cine image remains to be investigated. The aim of this study was to evaluate the prognostic value of radiomic features derived from cine images in patients with ST-segment elevation myocardial infarction (STEMI).

Methods

This prospective, multicenter observational study enrolled 303 patients with acute STEMI, who underwent CMR examination one week after percutaneous coronary intervention. The patients were randomly divided into two groups: training cohort (n=211) and validation cohort (n=92). Radiomic features were extracted from CMR cine images, and least absolute shrinkage and selection operator regression (LASSO)-Cox regression used to select predictive features for major adverse cardiac events (MACE). After univariate Cox analysis, multivariate Cox regression was applied to build combined models incorporating radiomic (RAD) score with clinical risk factors. Calibration was graphically investigated.

Results

During a median follow-up of 4 years, 56 patients experienced MACE. Eight features were included in the RAD score, as selected by LASSO-Cox regression. In the multivariate analysis, RAD score remained the only significant CMR predictors in addition to clinical factors. RAD score provided an incremental prognostic value above baseline clinical factors and LVEF in both the training (C-index: 0.81vs 0.65; p<0.001) and validation cohort (C-index 0.78 vs 0.69; p=0.002) and outperformed the addition of CMR markers of infarct injury in both the training (C-index: 0.81 vs 0.61, p<0.001) and validation cohort (C-index: 0.78 vs 0.69, p<0.001). The calibration curves demonstrated a good consistency for survival prediction of the combined model.

Conclusions

The radiomic signature provides important prognostic information for the development of adverse event in patients with STEMI. The prognostic value of RAD score is incremental to clinical parameters and markers of myocardial damage by contrast enhanced CMR.

Acknowledgements

None

References

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Figures

The Study Workflow

RAD score and survival in subgroup analyses.

Discrimination and Calibration of Combined Models in Predicting Four-year MACE.

Decision Curve Analysis for Combined Model in Predicting MACE

Proc. Intl. Soc. Mag. Reson. Med. 32 (2024)
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DOI: https://doi.org/10.58530/2024/1171