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

ABSTRACT:

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 enhanc

SUBMITTER: Wang S 

PROVIDER: S-EPMC12571507 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature