Prognostic value of cardiac fibroblast activation heterogeneity assessed by FAPI PET radiomics in hypertrophic cardiomyopathy: a proof-of-concept study

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is associated with an increased risk of sudden cardiac death (SCD), and myocardial fibrosis plays a central role in the pathophysiology process. Radiolabelled fibroblast activation protein inhibitor (FAPI) positron emission tomography computed tomography (PET) imaging enables visualisation of activated fibroblasts and may detect earlier stages of myocardial fibrosis than conventional structural imaging. However, most previous studies have relied on simple uptake-based metrics, potentially underusing the quantitative information contained in PET images. OBJECTIVE: To develop and internally validate a radiomics model based on FAPI PET imaging for stratifying predicted 5-year SCD risk in patients with HCM. METHODS: A total of 92 patients with HCM from two centres who underwent FAPI PET/CT or PET/cardiac magnetic resonance (CMR) were retrospectively included. Radiomics features were extracted from myocardial regions of interest and selected using elastic net regression. A radiomics model was constructed and evaluated using bootstrap internal validation. Model performance was compared with a conventional imaging metric (FAPI amount). Sensitivity analysis was performed to assess robustness across imaging modalities. RESULTS: Two radiomics features were selected to construct the model. The apparent area under the receiver operating curve of the radiomics model was 0.839, which decreased to 0.772 after bootstrap correction and became comparable to the conventional metric. Sensitivity analysis demonstrated stable model performance after excluding PET/CMR data. CONCLUSION: FAPI PET-based radiomics is feasible for quantifying myocardial fibroblast activation heterogeneity and shows potential for predicted SCD risk stratification in HCM. These findings should be interpreted as proof-of-concept and require validation in larger prospective cohorts with clinical endpoints.