Identifying signatures of image phenotypes to track treatment response in liver disease.
Authors
Affiliations (6)
Affiliations (6)
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Institute of Clinical Epidemiology, Public Health, Health Economics, Medical Statistics and Informatics, Medical University of Innsbruck, Innsbruck, Austria. Electronic address: [email protected].
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
- Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA.
- Novartis Pharma AG, Basel, Switzerland.
- Division of Gastroenterology and Hepatology, Department of Medicine III, Medical University of Vienna, Vienna, Austria.
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Comprehensive Center for Artificial Intelligence in Medicine, Medical University of Vienna, Vienna, Austria. Electronic address: [email protected].
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 in a randomized controlled trial cohort of patients with nonalcoholic steatohepatitis. 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 in a separate replication cohort to demonstrate the applicability of the proposed method.