Comparison of Sarcopenia Assessment in Liver Transplant Recipients by Computed Tomography Freehand Region-of-Interest versus an Automated Deep Learning System.
Authors
Affiliations (6)
Affiliations (6)
- Division of Solid Organ Transplantation, Department of Surgery, University of Minnesota, Minneapolis, Minnesota, USA.
- University of Minnesota Medical School, Minneapolis, Minnesota, USA.
- University of Minnesota Medical Center, MHealth Fairview, Minneapolis, Minnesota, USA.
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
- Department of Food Science and Nutrition, University of Minnesota, St Paul, Minnesota, USA.
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Medicine, University of Minnesota, Minneapolis, Minnesota, USA.
Abstract
Sarcopenia, or the loss of muscle quality and quantity, has been associated with poor clinical outcomes in liver transplantation such as infection, increased length of stay, and increased patient mortality. Abdominal computed tomography (CT) scans are utilized to measure patient core musculature as a measurement of sarcopenia. Methods to extract information on core body musculature can be through either freehand region-of-interest (ROI) or machine learning algorithms to quantitate total body muscle within a given area. This study directly compares these two collection methods leveraging length of stay (LOS) outcomes previously found to be associated with freehand ROI measurements. A total of 50 individuals were included who underwent liver transplantation from our single center between January 1, 2016, and May 30, 2021, and had a non-contrast abdominal CT scan within 6-months of surgery. CT-derived skeletal muscle measures at the third lumbar vertebrae were obtained using freehand ROI and an automated deep learning system. Correlation analysis of freehand psoas muscle measures, psoas area index (PAI) and mean Hounsfield units (mHU), were significantly correlated to the automated deep learning system's total skeletal muscle measures at the level of the L3, skeletal muscle index (SMI) and skeletal muscle density (SMD), respectively (R<sup>2</sup> = 0.4221; p value < 0.0001; R<sup>2</sup> = 0.6297; p value < 0.0001). The automated deep learning model's SMI predicted ∼20% of the variability (R<sup>2</sup> = 0.2013; hospital length of stay) while the PAI variable only predicted about 10% of the variability (R<sup>2</sup> = 0.0919; total healthcare length of stay) of the length of stay variables. In contrast, both the freehand ROI mHU and the automated deep learning model's muscle density variables were associated with ∼20% of the variability in the inpatient length of stay (R<sup>2</sup> = 0.2383 and 0.1810, respectively) and total healthcare length of stay variables (R<sup>2</sup> = 0.2190 and 0.1947, respectively). Sarcopenia measurements represent an important risk stratification tool for liver transplantation outcomes. For muscle sarcopenia assessment association with LOS, freehand measures of sarcopenia perform similarly to automated deep learning system measurements.