MACE Risk Prediction in ARVC Patients via CMR: A Three-Tier Spatiotemporal Transformer with Pericardial Adipose Tissue Embedding.

Abstract

Major adverse cardiac events (MACE) pose a high life-threatening risk to patients with arrhythmogenic right ventricular cardiomyopathy (ARVC). Cardiac magnetic resonance (CMR) has been proven to reflect the risk of MACE, but two challenges remain: limited dataset size due to the rarity of ARVC and overlapping image distributions between non-MACE and MACE patients. To address these challenges by fully leveraging the dynamic and spatial information in the limited CMR dataset, a deep learning-based risk prediction model named Three-Tier Spatiotemporal Transformer (TTST) is proposed in this paper, which utilizes three transformer-based tiers to sequentially extract and fuse features from three domains: the 2D spatial domain of each slice, the temporal dimension of slice sequence and the inter-slice depth dimension. In TTST, a pericardial adipose tissue (PAT) embedding unit is proposed to incorporate the dynamic and positional information of PAT, a key biomarker for distinguishing MACE from non-MACE based on its thickening and reduced motion, as prior knowledge to reduce reliance on large-scale datasets. Additionally, a patch voting unit is introduced to pick out local features that highlight more indicative regions in the heart, guided by the PAT embedding information. Experimental results demonstrate that TTST outperforms existing classification methods in MACE prediction (internal: AUC = 0.89, ACC = 84.02%; external: AUC = 0.87, ACC = 86.21%). Clinically, TTST achieves effective risk prediction performance either independently (C-index = 0.744) or in combination with the existing 5-year risk score model (increasing C-index from 0.686 to 0.777). Code and dataset are accessible at https://github.com/DFLAG-NEU.