Predicting Prognosis of Light-Chain Cardiac Amyloidosis by Magnetic Resonance Imaging and Deep Learning.
Authors
Affiliations (6)
Affiliations (6)
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China.
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
- Department of Radiology, First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China.
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
- Department of Hematology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Abstract
Light-chain cardiac amyloidosis (AL-CA) is a progressive heart disease with high mortality rate and variable prognosis. 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 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 one year of follow-up. The approach involved automated segmentation of 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 prognosis 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 C-Index of 0.91 and an 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 prognosis. 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.