Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease

TL;DR

This study develops an unsupervised machine learning approach to identify image-based tissue signatures in liver MRI scans, which can track treatment response and differentiate between treatment groups in liver disease.

Contribution

The paper introduces a novel deep clustering method to create a tissue vocabulary from MRI images, enabling non-invasive monitoring of liver disease progression and treatment effects.

Findings

Identifies specific liver tissue change pathways linked to treatment response.

Improves separation between treatment and placebo groups compared to traditional measures.

Predicts biopsy features from non-invasive MRI data.

Abstract

Quantifiable image patterns associated with disease progression and treatment response are critical tools for guiding individual treatment, and for developing novel therapies. Here, we show that unsupervised machine learning can identify a pattern vocabulary of liver tissue in magnetic resonance images that quantifies treatment response in diffuse liver disease. Deep clustering networks simultaneously encode and cluster patches of medical images into a low-dimensional latent space to establish a tissue vocabulary. The resulting tissue types capture differential tissue change and its location in the liver associated with treatment response. We demonstrate the utility of the vocabulary on a randomized controlled trial cohort of non-alcoholic steatohepatitis patients. First, we use the vocabulary to compare longitudinal liver change in a placebo and a treatment cohort. Results show that the method identifies specific liver tissue change pathways associated with treatment, and enables a better separation between treatment groups than established non-imaging measures. Moreover, we show that the vocabulary can predict biopsy derived features from non-invasive imaging data. We validate the method on a separate replication cohort to demonstrate the applicability of the proposed method.