Prediction of Fontan failure and correlates of Fontan-associated liver disease severity using machine learning and...

Fontan-associated liver disease (FALD) is associated with morbidity and mortality in patients with palliated single ventricle congenital heart disease.

To develop machine learning models using radiomic features from T1-weighted, T2-weighted, and diffusion-weighted MRI with pertinent clinical variables to predict Fontan failure and correlates of FALD severity in patients who underwent the Fontan operation.

In this retrospective study of abdominal MRI examinations and clinical record data from 131 Fontan palliation patients (age range 9.1 - 53.3 years old), radiomic features from the liver and spleen were extracted using axial T1-weighted, T2-weighted fat-suppressed, and diffusion-weighted sequences. Patients were categorized by a composite clinical outcome (i.e., Fontan failure) and by correlates of FALD severity, including liver shear stiffness and portal hypertension. Support vector machine (SVM) and multivariable logistic regression models were used to perform two-class classification using radiomic features and/or clinical data. All models were trained and evaluated using five-fold cross-validation (CV).

The best radiomic-only model utilized T2-weighted imaging of both organs with logistic regression to predict the presence of portal hypertension, achieving an AUROC of 0.85±0.01. Clinical-only models showed inferior diagnostic accuracy with the highest AUROC of 0.70±0.08. Combining radiomic and clinical features also did not enhance performance compared to radiomic-only models, with the highest AUROC of 0.77±0.05. Ensemble modeling, which incorporated radiomics from all three MRI sequences, yielded AUROCs ranging from 0.33 to 0.72.

Models incorporating radiomic features from abdominal MRI in Fontan circulation patients demonstrate moderate diagnostic performance for predicting Fontan failure as well as correlates of FALD severity. These models outperformed models containing only clinical electronic health record data and did not improve with ensembled radiomic and clinical data.