Review of Visual Standardized Quantification for LGE in Cardiomyopathy

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medRxiv Published April 22, 2026 Medically reviewed April 25, 2026 Study authors: Aquaro, G. D.; Licordari, R.; De Gori, C.; Todiere, G.; Ianni, U.; Barison, A.; De Luca, A.; Folgher… DOI ↗ By Dr. Amelia Tan, PhD · Internal Medicine & Chronic Disease

Key Takeaway

Consider VISTAQ for more reproducible LGE quantification in cardiomyopathy, but note the evidence is from a retrospective study.

This narrative review summarizes a retrospective study comparing Visual Standardized Quantification of LGE (VISTAQ) with conventional techniques for assessing myocardial fibrosis. The study included 400 patients (100 with prior myocardial infarction, 250 with hypertrophic cardiomyopathy, and 50 with other non-ischemic heart diseases) across multicenter, multivendor settings.

Key findings indicate that VISTAQ demonstrated high intra- and inter-observer reproducibility, with ICC up to 0.98 and 0.97, respectively. Inter-observer differences were significantly lower, with a median absolute difference of 1.3%. Analysis time was substantially shorter with VISTAQ (median 105 vs. 375 seconds, p<0.0001). For prognostic performance, LGE threshold >10% predicted events with higher accuracy using VISTAQ (AUC 0.90; sensitivity 85%; specificity 94%) compared with mean+6SD (AUC 0.75; sensitivity 57%; specificity 93%), with 21 hard cardiac events occurring in the HCM population over a median 5-year follow-up.

The authors note limitations, including the retrospective design and lack of reported safety data. Practice relevance was not reported, and the findings should be interpreted as associative, not causal.

Study Details

Sample sizen = 400

EvidenceLevel 5

PublishedApr 2026

View Original Abstract ↓

Background: Late gadolinium enhancement (LGE) quantification by cardiovascular magnetic resonance is central to risk stratification in hypertrophic cardiomyopathy (HCM), yet conventional techniques require contour tracing and region-of-interest (ROI) placement, which may reduce reproducibility and increase analysis time. We developed a novel visual standardized approach, the Visual Standardized Quantification of LGE (VISTAQ), that does not require myocardial contouring, arbitrary ROI positioning, or dedicated post-processing software. Methods: In this multicenter, multivendor retrospective study, LGE images from 400 patients (100 prior myocardial infarction, 250 HCM, 50 other non-ischemic heart diseases) were analyzed. VISTAQ subdivides each myocardial segment into transmural mini-segments and classifies LGE visually using predefined criteria, expressing global LGE burden as the percentage of positive mini-segments. Reproducibility was assessed in 250 patients across different observer expertise levels using intraclass correlation coefficients (ICC) and Bland?Altman analysis. In 100 HCM patients, VISTAQ was compared with conventional methods (mean+2SD, +5SD, +6SD, FWHM, visual thresholding). Prognostic performance was evaluated in 250 HCM patients over a median 5-year follow-up. Results: VISTAQ demonstrated excellent intra- and inter-observer reproducibility (ICC up to 0.98 and 0.97, respectively), consistent across disease subtypes. Compared with conventional techniques, VISTAQ showed similar ICC to FWHM but significantly lower net and absolute inter-observer differences (median absolute difference 1.3%). Mean+2SD markedly overestimated LGE, whereas mean+6SD slightly underestimated LGE compared with VISTAQ, mean+5SD, FWHM, and visual thresholding. Analysis time was substantially shorter with VISTAQ (median 105 vs. 375 seconds, p<0.0001). During follow-up, 21 hard cardiac events occurred in HCM population. An LGE threshold >10% predicted events with higher accuracy using VISTAQ (AUC 0.90; sensitivity 85%; specificity 94%) compared with mean+6SD (AUC 0.75; sensitivity 57%; specificity 93%). Conclusions: VISTAQ provides highly reproducible, time-efficient LGE quantification without dedicated software and demonstrates non-inferior prognostic discrimination in HCM compared with conventional threshold-based techniques.