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

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.

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 techniques revealed that the model captures complex, high-dimensional prognostic features across multiple cardiac regions, extending beyond the amyloid-affected areas.

This fully automated DL model can predict individualized prognosis of AL-CA through LGE images, which complements the presently used Mayo staging method.