Cardiac magnetic resonance radiomics for prediction of incident heart failure: a feasibility study in the UK Biobank Imaging cohort

Cardiac magnetic resonance (CMR) radiomics is a novel image quantification technique with the potential to improve image-based disease diagnosis and prediction.

In this proof-of-concept study, we aimed to evaluate the utility of CMR radiomics in the prediction of incident heart failure (HF).

We studied 32,121 UK Biobank participants with CMR. Incident HF was defined from linked Hospital Episode Statistics. To create a balanced cohort, we identified as comparators an equal number of randomly selected subjects who did not develop the outcome of interest during this period. Radiomics shape, first-order and texture features were extracted from short-axis cine images (left and right ventricle, left ventricular myocardium) using the Pyradiomics toolbox. Vascular risk factors (VRFs) were considered as additional predictors. Feature selection was conducted using the sequential forward selection technique and modelling was performed using Support Vector Machine (SVM) methods with 5-fold cross-validation. Models were developed using 1) VRFs alone, 2) radiomics alone, and 3) VRFs and radiomics. We determined model performance using receiver operating characteristic (ROC) curve and area under the curve (AUC) scores.

Over average follow-up time of 3.7 (±1.3) years, 209 participants experienced incident HF. Among vascular risk factors, age, body size, hypertension, diabetes, high cholesterol were chosen for the incident HF predictive model (Accuracy: 0.66, AUC: 0.73) by the SVM methods. The model based on radiomics features reached a marginal improvement compared to vascular risk factors alone (Accuracy: 0.71, AUC: 0.75). The combination of VRFs and radiomics features significantly improved the performance of the model to predict incident HF compared to VRFs alone (Accuracy: 0.77; AUC: 0.83; p<0.05)

We demonstrate the feasibility of CMR radomics features to predict incident HF and illustrate their added value over vascular risk factors.

Type of funding sources: Public grant(s) – EU funding.