Pathological classification of non-ischaemic dilated cardiomyopathy based on deep learning.

Aims: Non-ischaemic dilated cardiomyopathy (NIDCM) is a major cause of heart failure (HF) and heart transplantation (HTx), characterized by heterogeneity in aetiology, clinical phenotype, and disease progression. Nevertheless, precision medicine-based diagnostics and treatment strategies for NIDCM remain lacking. This proof-of-concept study aimed to stratify NIDCM patients by pathological features and identify those at high-risk for malignant arrhythmia (MA) and rapid progression to end-stage HF. Methods and results: 293 NIDCM-HTx patients were included in this study. A total of 3516 heart tissue slides from six representative sites of each patient were analyzed using deep learning-based computational pathology (DL-CPath) and unsupervised clustering to identify pathological subgroups (PGs): PGA, PGB, and PGC. PGA was characterized by interstitial fibrosis, cardiomyocyte vacuolization, microvascular intimal hyperplasia, and myocyte disarray, and had the highest rates of MA (P = 0.03) and the shortest interval from diagnosis to HTx (P = 0.03). PGB showed focal fibrosis, whereas PGC demonstrated the mildest histopathological alterations. For clinical features, PGA showed elevated levels of blood biomarkers indicative of myocardial and secondary organ injury. PGB was associated with extensive fibrosis and significant impairment of ejection fraction. PGC presented with the mildest clinical abnormalities. Although LMNA mutation was a significant non-DL-CPath high-risk factor for MA and rapid NIDCM progression, its distribution did not differ significantly across PGs (P = 0.786). Conclusion: DL-based pathological classification effectively extracted clinically-meaningful imaging features and enabled the identification of high-risk NIDCM subgroup. Each PG exhibited unique histopathological and clinical characteristics, highlighting distinct phenotypes and risk profiles.