Predicting prognosis of light-chain cardiac amyloidosis by magnetic resonance imaging and deep learning

作者：Shuo Wang, Chengcai Liu, Yubo Guo, Haolin Sang, Xiao Li, Lu Lin, Xiaohu Li, Yi Wu, Long Jiang Zhang, Jie Tian, Li J, Yining Wang · 发表于：European Heart Journal - Cardiovascular Imaging · 年份：2025 · DOI：10.1093/ehjci/jeaf248 · 被引用次数：7 · 研究领域：Amyloidosis: Diagnosis, Treatment, Outcomes、Cardiac Imaging and Diagnostics、Lung Cancer Research Studies

AIMS: Light-chain cardiac amyloidosis (AL-CA) is a progressive heart disease with high mortality rate and variable prognosis. The presently used Mayo staging method can only stratify patients into four stages, highlighting the necessity for a more individualized prognosis prediction method. We aim to develop a novel deep learning (DL) model for the whole-heart analysis of cardiovascular magnetic resonance-derived late gadolinium enhancement (LGE) images to predict individualized prognosis in AL-CA. METHODS AND RESULTS: This study included 394 patients with AL-CA who underwent standardized chemotherapy and had at least 1 year of follow-up. The approach involved automated segmentation of the heart in LGE images and feature extraction using a Transformer-based DL model. To enhance feature differentiation and mitigate overfitting, a contrastive pretraining strategy was employed to accentuate distinct features between patients with different prognoses while clustering similar cases. Finally, an ensemble learning strategy was used to integrate predictions from 15 models at 15 survival time points into a comprehensive prognostic model. In the testing set of 79 patients, the DL model achieved a concordance index (C-index) of 0.91 and an area under the curve (AUC) of 0.95 in predicting 2.6-year survival (HR: 2.67), outperforming the Mayo model (C-index = 0.65; AUC = 0.71). The DL model effectively distinguished patients with the same Mayo stage but different prognoses. Visualization t...