Pericardial Fat Radiomics to Predict Left Ventricular Involvement and Provide Incremental Prognostic Value in ARVC
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background/Objectives: To explore the predictive value of pericardial fat tissue (PFT) radiomics for left ventricular (LV) involvement and major adverse cardiac events (MACE) in arrhythmogenic right ventricular cardiomyopathy (ARVC). Methods: In this retrospective multicenter study, LV involvement was assessed using cardiac magnetic resonance (CMR). A radiomic score (RS) derived from PFT was developed to predict LV involvement. The predictive accuracy of the RS was evaluated through receiver operating characteristic (ROC) analysis. Additionally, multivariable Cox regression analysis was employed to assess the prognosis across the entire dataset. Kaplan–Meier survival curves were used to evaluate the association between RS and MACE. Results: A total of 122 patients (mean age, 44 years ± 17; 76 male) were included, 90 for a development set and 32 for an external test set. The RS demonstrated good predictive performance for LV involvement in both the development and external test sets, with area under the curve (AUC) values of 0.771 and 0.785, respectively. Moreover, a high RS (≥−0.38) was independently associated with MACE during a median follow-up of 5 years (hazard ratio, 3.452; p < 0.001). Based on the right ventricular ejection fraction (RVEF) and RS, a simplified risk score was developed to categorize patients into three groups: high-risk (RVEF ≤ 40%, RS ≥ −0.38), intermediate-risk (RVEF ≤ 40%, RS < −0.38 or RVEF > 40%, RS ≥ −0.38), and low-risk (RVEF > 40%, RS < −0.38). Conclusions: The PFT radiomics can predict LV involvement and be associated with MACE in ARVC patients.
Source: Diagnostics
Authors: Mengqi Guo, Jinyu Zheng, Weihui Xie, Binghua Chen, Dongaolei An, Ruoyang Shi, Jinyi Xiang, Lianming Wu
Institutions: Shanghai Jiao Tong University, Renji Hospital
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4417455934
