Key result
AI-automated LV mass estimation from non-contrast CT correlates excellently with contrast CTA.
Why the study?
Non-contrast CT scans are not typically used to evaluate left ventricular mass, which is usually measured with contrast CT or cardiac MRI.
Does an AI-based automated model accurately quantify left ventricular mass from non-contrast cardiac CT scans compared to contrast CTA and cardiac MRI?
Observational (n=316)
No
Does an AI-based automated model accurately quantify left ventricular mass from non-contrast cardiac CT scans compared to contrast CTA and cardiac MRI?
Absolute Event Rate: 136.5% vs 139.6%
p-value: p=0.133
Fully automated AI-based quantification of left ventricular mass from standard non-contrast CT scans is feasible and highly correlates with gold-standard contrast CTA and cardiac MRI.
May support LV mass assessment on routine non-contrast CT; hypothesis-generating pending MRI validation and outcome studies.
Background: Non-contrast CT scans are not used for evaluating left ventricle myocardial mass (LV mass), which is typically evaluated with contrast CT or cardiovascular magnetic resonance imaging (MRI). We assessed the feasibility of LV mass estimation from standard, ECG-gated, non-contrast CT using an artificial intelligence (AI) approach and compare it with coronary CT angiography (CTA) and cardiac MRI. Methods: We enrolled consecutive patients who underwent coronary CTA, which included non-contrast CT calcium scanning and contrast CTA, and cardiac MRI. The median interval between coronary CTA and MRI was 22 days (IQR: 3-76). We utilized an nn-Unet AI model that automatically segmented non-contrast CT structures. AI measurement of LV mass was compared to contrast CTA and MRI. Results: A total of 316 patients (Age: 57.1±16.7, 56% male) were included. The AI segmentation took on average 22 seconds per case. An excellent correlation was observed between AI and contrast CTA LV mass measures (r=0.84, p<0.001), with no significant differences (136.5±55.3 vs. 139.6±56.9 g, p=0.133). Bland-Altman analysis showed minimal bias of 2.9. When compared to MRI, measured LV mass was higher with AI (136.5±55.3 vs. 127.1±53.1 g, p<0.001). There was an excellent correlation between AI and MRI (r=0.85, p<0.001), with a small bias (-9.4). There were no statistical differences between the correlations of LV mass between contrast CTA and MRI, or AI and MRI. Conclusions: The AI-based automated estimation of LV mass from non-contrast CT demonstrated excellent correlations and minimal biases when compared to contrast CTA and MRI.
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Han et al. (2024) conducted an observational in Patients undergoing coronary CTA and cardiac MRI (n=316). AI-based automated left ventricular mass quantification from non-contrast CT vs. Contrast CTA and cardiac MRI was evaluated on Left ventricular mass (g) compared to contrast CTA (p=0.133). AI-based automated estimation of left ventricular mass from non-contrast CT showed excellent correlation and no significant difference compared to contrast CTA (136.5 vs 139.6 g, p=0.133).
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