Key result
CMR ECV ShMOLLI detects severe fibrosis with 0.96 AUC and outperforms LGE in a mini-swine model.
Why the study?
Cardiovascular magnetic resonance non-invasively detects focal and diffuse myocardial fibrosis, but comprehensive validation against histology remains scarce.
Does cardiovascular magnetic resonance accurately detect and quantify myocardial fibrosis compared to histology in a mini-swine model?
Does cardiovascular magnetic resonance accurately detect and quantify myocardial fibrosis compared to histology in a mini-swine model?
Effect estimate: AUC 0.96 (ECV ShMOLLI)
p-value: p=<0.05
CMR, particularly T1 and ECV mapping using the ShMOLLI sequence, accurately detects both severe and mild myocardial fibrosis when validated against histology in a mini-swine model.
Supports ECV ShMOLLI validation in swine models; leaves open human translation before clinical adoption.
Background Myocardial fibrosis predicts adverse outcomes in myocardial infarction (MI) and other cardiovascular conditions. Cardiovascular magnetic resonance (CMR) can non-invasively detect focal and diffuse myocardial fibrosis, but comprehensive validation against histology remains scarce. Aims To assess the comparative diagnostic performance of CMR methods in detecting myocardial fibrosis, using a mini-swine model and histology as gold standard. Methods Eighteen mini-swine (16 MI; 2 healthy) underwent CMR cine, LGE, T1- and ECV-mapping. Two commonly-used T1-mapping methods – MOLLI 5(3)3 and ShMOLLI 5(1)1(1)1 – were included. Pathological sections were categorized as infarcted, peri-infarct, remote and healthy myocardium based on triphenyl tetrazolium chloride staining. Fibrotic burden was quantified by collagen volume fraction (CVF) into severe (CVF≥30%), moderate (CVF=10-25%), and mild (CVF=3-14%). The relationships between LGE, T1, ECV and CVF, and diagnostic performance using area-under-the-curve (AUC), were analyzed. Results For detecting severe fibrosis, LGE, T1 and ECV all had excellent diagnostic performance (AUC: LGE=0.93, ECV ShMOLLI =0.96, T1 ShMOLLI =0.91, T1 MOLLI =0.93, ECV MOLLI =0.88). ECV ShMOLLI showed significantly better discriminatory accuracy than ECV MOLLI in detecting severe fibrosis and MI (both p<0.05), with the highest correlation to CVF (ECV ShMOLLI r=0.86, ECV MOLLI r=0.82, T1 ShMOLLI r=0.77, T1 MOLLI r=0.77, semi-quantitative LGE r=0.75). Only T1 ShMOLLI and ECV ShMOLLI , but not LGE or T1 MOLLI /ECV MOLLI , differentiated remote myocardium with mild fibrosis (CVF=8.23%) from healthy myocardium (CVF=2.01%). Conclusions CMR can detect severe to mild myocardial fibrosis as validated against histology. For low-grade fibrosis, T1-mapping significantly outperformed LGE. Choice of CMR methodologies matters for myocardial fibrosis detection, which has importance for clinical trial design.
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Zhang et al. (2025) studied Myocardial fibrosis (n=18). Cardiovascular magnetic resonance (LGE, T1-mapping, ECV-mapping) vs. Histology was evaluated on Diagnostic performance for detecting severe fibrosis (AUC 0.96 (ECV ShMOLLI), p=<0.05). Cardiovascular magnetic resonance, specifically ECV ShMOLLI, accurately detected severe myocardial fibrosis (AUC 0.96) and outperformed LGE for low-grade fibrosis in a mini-swine model.
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