Abstract:
Introduction: Invasive fungal infections (IFIs) occur mostly in immunosuppressed patients and can be life-threatening. Inadequate treatment is associated with high morbidity and mortality. We examined the role of 2-fluorodeoxyglucose positron emission tomography integrated with CT (FDG-PET/CT) in monitoring IFIs and therapy decision-making, and evaluated the role of baseline metabolic parameters in predicting the metabolic response. Methods: All patients between October 2009 and March 2018, diagnosed with IFIs, treated with antifungal drugs, and who underwent FDG-PET/CT at baseline and at one or more timepoints during treatment were retrospectively included. The electronic patient files were reviewed for pathology, microbiology, and laboratory findings. All FDG-PET/CT scans were performed according to standardized European Association of Nuclear Medicine/EANM Research Limited (EANM/EARL) protocols. For each scan, the global total lesion glycolysis (TLG) and metabolic volume (MV), highest maximum standardized uptake value (SUVmax), and peak standardized uptake value (SUVpeak) were determined. The role of FDG-PET/CT on monitoring antifungal therapy was assessed by looking at the clinical decision made as result of the scan. Furthermore, the added value of the baseline metabolic parameters in predicting metabolic response to the antifungal treatment was evaluated. Results: Twenty-eight patients with in total 98 FDG-PET/CT scans were included with a mean age of 43 ± 22 years. FDG-PET/CT altered management in 14 out of the 28 patients (50%). At the final FDG-PET/CT scan, 19 (68%) had a complete metabolic response (CMR), seven a partial response and two patients were defined as having progressive disease. Using receiver operative analysis, the cut-off value, sensitivity, specificity, and significance for the baseline TLG and MV to discriminate patients with CMR were 160, 94%, 100%, p < 0.001 and 60, 84%, 75%, p = 0.001 respectively. Conclusion: FDG-PET/CT is useful in the monitoring of IFIs resulting in management therapy change in half of the patients. Baseline TLG and MV were found to be able to predict the metabolic response to antifungal treatment. © 2018, The Author(s).
