Synergistic application of pulmonary 18 F-FDG PET/HRCT and computer-based CT analysis with conventional severity measures to refine current risk stratification in idiopathic pulmonary fibrosis (IPF)

Introduction To investigate the combined performance of quantitative CT (qCT) following a computer algorithm analysis (IMBIO) and 18 F-FDG PET/CT to assess survival in patients with idiopathic pulmonary fibrosis (IPF). Methods A total of 113 IPF patients (age 70 ± 9 years) prospectively and consecutively underwent 18 F-FDG PET/CT and high-resolution CT (HRCT) at our institution. During a mean follow-up of 29.6 ± 26 months, 44 (48%) patients died. As part of the qCT analysis, pattern evaluation of HRCT (using IMBIO software) included the total extent (percentage) of the following features: normal-appearing lung, hyperlucent lung, parenchymal damage (comprising ground-glass opacification, reticular pattern and honeycombing), and the pulmonary vessels. The maximum (SUV max ) and minimum (SUV min ) standardized uptake value (SUV) for 18 F-FDG uptake in the lungs, and the target-to-background (SUV max /SUV min ) ratio (TBR) were quantified using routine region-of-interest (ROI) analysis. Pulmonary functional tests (PFTs) were acquired within 14 days of the PET/CT/HRCT scan. Kaplan–Meier (KM) survival analysis was used to identify associations with mortality. Results Data from 91 patients were available for comparative analysis. The average ± SD GAP [gender, age, physiology] score was 4.2 ± 1.7 (range 0–8). The average ± SD SUV max , SUV min , and TBR were 3.4 ± 1.4, 0.7 ± 0.2, and 5.6 ± 2.8, respectively. In all patients, qCT analysis demonstrated a predominantly reticular lung pattern (14.9 ± 12.4%). KM analysis showed that TBR ( p = 0.018) and parenchymal damage assessed by qCT ( p = 0.0002) were the best predictors of survival. Adding TBR and qCT to the GAP score significantly increased the ability to differentiate between high and low risk ( p < 0.0001). Conclusion 18 F-FDG PET and qCT are independent and synergistic in predicting mortality in patients with IPF.