Key result
Epicardial adipose tissue radiomics predict 30-day mortality in acute PE with an AUC of ~0.78.
Why the study?
Accurate prediction of short-term mortality in acute pulmonary embolism is very important, prompting analysis of the prognostic role of radiomics values of epicardial adipose tissue.
Do radiomics parameters of epicardial adipose tissue predict 7-day and 30-day mortality in patients with acute pulmonary embolism?
Cohort (n=694)
Single-blind
Yes
Do radiomics parameters of epicardial adipose tissue predict 7-day and 30-day mortality in patients with acute pulmonary embolism?
Effect estimate: AUC 0.776 (95% CI 0.671-0.881)
Radiomics features of epicardial adipose tissue on CT pulmonary angiography can predict short-term mortality in patients with acute pulmonary embolism.
May support radiomics for mortality risk stratification in acute PE; hypothesis-generating and requires prospective validation before practice change.
BACKGROUND: Accurate prediction of short-term mortality in acute pulmonary embolism (APE) is very important. The aim of the present study was to analyze the prognostic role of radiomics values of epicardial adipose tissue (EAT) in APE. METHODS: Overall, 508 patients were included into the study, 209 female (42.1%), mean age, 64.7 ± 14.8 years. 4.6%and 12.4% died (7- and 30-day mortality, respectively). For external validation, a cohort of 186 patients was further analysed. 20.2% and 27.7% died (7- and 30-day mortality, respectively). CTPA was performed at admission for every patient before any previous treatment on multi-slice CT scanners. A trained radiologist, blinded to patient outcomes, semiautomatically segmented the EAT on a dedicated workstation using ImageJ software. Extraction of radiomic features was applied using the pyradiomics library. After correction for correlation among features and feature cleansing by random forest and feature ranking, we implemented feature signatures using 247 features of each patient. In total, 26 feature combinations with different feature class combinations were identified. Patients were randomly assigned to a training and a validation cohort with a ratio of 7:3. We characterized two models (30-day and 7-day mortality). The models incorporate a combination of 13 features of seven different image feature classes. FINDINGS: We fitted the characterized models to a validation cohort (n = 169) in order to test accuracy of our models. We observed an AUC of 0.776 (CI 0.671-0.881) and an AUC of 0.724 (CI 0.628-0.820) for the prediction of 30-day mortality and 7-day mortality, respectively. The overall percentage of correct prediction in this regard was 88% and 79% in the validation cohorts. Lastly, the AUC in an independent external validation cohort was 0.721 (CI 0.633-0.808) and 0.750 (CI 0.657-0.842), respectively. INTERPRETATION: Radiomics parameters of EAT are strongly associated with mortality in patients with APE. CLINICAL TRIAL NUMBER: Not applicable.
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Surov et al. (2024) conducted a cohort in Acute pulmonary embolism (n=694). Radiomics parameters of epicardial adipose tissue (EAT) was evaluated on Prediction of 30-day mortality (validation cohort) (AUC 0.776, 95% CI 0.671-0.881). Radiomics parameters of epicardial adipose tissue predicted 30-day mortality in patients with acute pulmonary embolism with an AUC of 0.776 in the validation cohort and 0.721 in an independent external cohort.
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