Automated 3D-Body Composition Analysis as a Predictor of Survival in Patients With Idiopathic Pulmonary Fibrosis

Purpose: Idiopathic pulmonary fibrosis (IPF) is the most common interstitial lung disease, with a median survival time of 2 to 5 years. The focus of this study is to establish a novel imaging biomarker. Materials and Methods: In this study, 79 patients (19% female) with a median age of 70 years were studied retrospectively. Fully automated body composition analysis (BCA) features (bone, muscle, total adipose tissue, intermuscular, and intramuscular adipose tissue) were combined into Sarcopenia, Fat, and Myosteatosis indices and compared between patients with a survival of more or less than 2 years. In addition, we divided the cohort at the median (high=≥ median, low=<median) of the respective BCA index and tested the impact on the overall survival using the Kaplan-Meier methodology, a log-rank test, and adjusted multivariate Cox-regression analysis. Results: A high Sarcopenia and Fat index and low Myosteatosis index were associated with longer median survival (35 vs. 16 mo for high vs. low Sarcopenia index, P =0.066; 44 vs. 14 mo for high vs. low Fat index, P <0.001; and 33 vs. 14 mo for low vs. high Myosteatosis index, P =0.0056) and better 5-year survival rates (34.0% vs. 23.6% for high vs. low Sarcopenia index; 47.3% vs. 9.2% for high vs. low Fat index; and 11.2% vs. 42.7% for high vs. low Myosteatosis index). Adjusted multivariate Cox regression showed a significant impact of the Fat (HR=0.71, P =0.01) and Myosteatosis (HR=1.12, P =0.005) on overall survival. Conclusion: The fully automated BCA provides biomarkers with a predictive value for the overall survival in patients with IPF.