Brain Magnetic Resonance Imaging Radiomic Signature and Machine Learning Model Prediction of Hepatic Encephalopathy in Adult Cirrhotic Patients.

BACKGROUND

Hepatic encephalopathy (HE) may arise as a possible consequence of cirrhosis. Magnetic resonance imaging (MRI) may reveal a T1-weighted hyperintensity in the globi pallidi, indicating the deposition of paramagnetic substances. The objective of this research was to implement a machine learning-based radiomic model to predict the diagnosis and severity of chronic hepatic encephalopathy in adult patients with cirrhosis.

METHODS

Between October 2018 and February 2020, brain magnetic resonance imaging (MRI) was conducted on adult patients, both with and without cirrhosis. The control population consisted of individuals who did not have a previous medical record of chronic liver disease. The grade of hepatic encephalopathy (HE) was determined by considering factors such as the presence of underlying liver disease, the severity of clinical symptoms, and the frequency of encephalopathic episodes. Radiomic texture analysis based on five machine learning algorithms was applied to axial T1-weighted MR images of bilateral lentiform nuclei. Using the area under the receiver operating characteristics curve, we determined the accuracy of the five machine learning-based algorithms in predicting the presence of HE and the HE grading.

RESULTS

The ultimate research cohort included 124 individuals, with 70 being cirrhotic patients and 54 being non-cirrhotic controls. Of the total number of patients, 38 had a previous occurrence of HE and, among them, 22 had a grade of HE greater than 1. The multilayer perceptron algorithm classified patients versus controls with an accuracy of 100%. The k-nearest neighbor (KNN) algorithm classified patients with or without HE with an accuracy of 76.5%. The multilayer perceptron algorithm classified HE grade (HE grade 1, HE grade ≥ 2) with an accuracy of 94.1%.

CONCLUSIONS

The machine learning algorithms implemented provide a robust modeling technique for deriving valuable insights from brain MR images in cirrhotic patients and this can serve as an imaging tool valuable for the assessment of the burden of hepatic encephalopathy.