AI-based CT Body Composition Identifies Myosteatosis as Key Mortality Predictor in Asymptomatic Adults

Abstract

Background Body composition data have been limited to adults with disease or older age. The prognostic impact in otherwise asymptomatic adults is unclear. Purpose To use artificial intelligence-based body composition metrics from routine abdominal CT scans in asymptomatic adults to clarify the association between obesity, liver steatosis, myopenia, and myosteatosis and the risk of mortality. Materials and Methods In this retrospective single-center study, consecutive adult outpatients undergoing routine colorectal cancer screening from April 2004 to December 2016 were included. Using a U-Net algorithm, the following body composition metrics were extracted from low-dose, noncontrast, supine multidetector abdominal CT scans: total muscle area, muscle density, subcutaneous and visceral fat area, and volumetric liver density. Abnormal body composition was defined by the presence of liver steatosis, obesity, muscle fatty infiltration (myosteatosis), and/or low muscle mass (myopenia). The incidence of death and major adverse cardiovascular events were recorded during a median follow-up of 8.8 years. Multivariable analyses were performed accounting for age, sex, smoking status, myosteatosis, liver steatosis, myopenia, type 2 diabetes, obesity, visceral fat, and history of cardiovascular events. Results Overall, 8982 consecutive outpatients (mean age, 57 years ± 8 [SD]; 5008 female, 3974 male) were included. Abnormal body composition was found in 86% (434 of 507) of patients who died during follow-up. Myosteatosis was found in 278 of 507 patients (55%) who died (15.5% absolute risk at 10 years). Myosteatosis, obesity, liver steatosis, and myopenia were associated with increased mortality risk (hazard ratio [HR]: 4.33 [95% CI: 3.63, 5.16], 1.27 [95% CI: 1.06, 1.53], 1.86 [95% CI: 1.56, 2.21], and 1.75 [95% CI: 1.43, 2.14], respectively). In 8303 patients (excluding 679 patients without complete data), after multivariable adjustment, myosteatosis remained associated with increased mortality risk (HR, 1.89 [95% CI: 1.52, 2.35]; P < .001). Conclusion Artificial intelligence-based profiling of body composition from routine abdominal CT scans identified myosteatosis as a key predictor of mortality risk in asymptomatic adults. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Tong and Magudia in this issue.

Graphical abstract

Figure 1:

Flowchart shows patient inclusion and exclusion. Patients with missing body composition data (body mass index [BMI], liver density, skeletal muscle index [SMI], or muscle density) or with evident muscle artifacts at CT were excluded before the univariable analysis. Multivariable analysis was performed on the cohort with complete data available, including smoking status, medical history of cardiovascular events, and visceral fat measurements. (* = data were missing in 679 patients, when accounting for overlaps.)

Figure 2:

Venn diagrams show the number of patients according to abnormal body composition feature (myosteatosis, liver steatosis, myopenia, obesity) at baseline and their intersection with the number of patients (A) who experienced at least one adverse event (ie, cardiovascular event or death; n = 1836) and (B) who died during follow-up (n = 507). Adverse events mostly occurred in patients with abnormal body composition.

Figure 3:

(A) Unenhanced axial abdominal CT image with a Hounsfield unit–based color scale of skeletal muscles in a 51-year-old man with obesity, smoking history, no type 2 diabetes, and no history of cardiovascular events at inclusion shows mild fatty infiltration in the muscles (myosteatosis, yellow), with most voxels in the positive range of Hounsfield units (red). The patient was lost to follow-up after 13.2 years. (B) Unenhanced axial abdominal CT image with a Hounsfield unit–based color scale of skeletal muscles in a 53-year-old man with obesity, smoking history, no type 2 diabetes, and no history of cardiovascular events at inclusion shows severe fatty infiltration in the muscles (myosteatosis, yellow), mostly distributed in the paravertebral (ie, erector spinae and multifidus) and oblique muscle groups. The patient died after 9.4 years of follow-up. BMI = body mass index.

Figure 4:

Kaplan-Meier curves of survival probability according to body composition parameter, including (A) obesity, (B) liver steatosis, (C) myopenia, and (D) myosteatosis, show an association between myosteatosis and high mortality risk. Statistics were computed with the Mantel-Cox log-rank test. HR = hazard ratio.

Figure 5:

Schematic shows possible transition states of the multistate Markov model, starting at inclusion (ie, CT scan acquisition) and leading to intermediate states (ie, major adverse cardiovascular events [MACEs], indicated by gray circles) and/or to an absorbing state (ie, death or loss to follow-up, indicated by red circles). Transitions numbered 2, 4, 6, 8, or 9 lead to an absorbing state and transitions numbered 1, 3, 5, or 7 lead to an intermediate state. CV = cardiovascular.

Figure 6:

Partial dependence curves extracted from the random forest survival model show the survival function value of each body composition parameter according to (A) sex as a covariate and (B) male and (C) female sex. These results highlight the importance of muscle density in modulating mortality risk (see Appendix S1), independent from the quartile-based stratification; thus, myosteatosis is the most impactful body composition predictor of mortality. BMI = body mass index, HU = Hounsfield unit.