Abstract
Pulmonary invasive fungal infections pose a serious risk for immunocompromised patients. Although diagnostic imaging plays an important role in the early detection of pulmonary invasive fungal infections, radiological differentiation between invasive fungal infection and other pulmonary infections is challenging. The aim of this study was to assess the accuracy of chest high-resolution computed tomography (HRCT) in the differentiation between pulmonary invasive fungal infections and other pulmonary infections in paediatric cancer patients. In this retrospective study, baseline HRCTs of patients with probable or proven invasive fungal infections and other pulmonary infections were blindly assessed by two radiologists, followed by a consensus reading. The scoring form included imaging characteristics and radiological invasive fungal infection probability assessment. Inter-rater reliability was determined with Cohen's kappa. Chest HRCTs (n = 77) of paediatric cancer patients with pulmonary invasive fungal infections (n = 45) and with other pulmonary infections (n = 32) were evaluated. In the consensus reading, nodules with halo sign and wedge-shaped consolidations were observed significantly more in pulmonary invasive fungal infections than in other pulmonary infections (86.7% vs. 34.4% and 28.9% vs. 9.4%), and ground-glass opacities were observed less frequently (61.4% vs. 87.5%). The kappa values for the individual imaging characteristics ranged from 0.121 to 0.408. Sensitivity of the HRCT to diagnose a pulmonary invasive fungal infection ranged from 0.78 to 0.80, and specificity from 0.66 to 0.88. The accuracy of chest HRCTs in differentiating between invasive fungal infections and other pulmonary infections is poor. There are two main reasons for this: no individual imaging characteristic was found to be able to fully distinguish between invasive fungal infections and other pulmonary infections, and the agreement between radiologists was only moderate.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Similar Papers
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.