E H Middlebrooks1, C J Frost2, R O De Jesus3, T C Massini4, I M Schmalfuss4, A A Mancuso3. 1. From the Department of Radiology (E.H.M., R.O.D.J., T.C.M., I.M.S., A.A.M.), University of Florida College of Medicine, Gainesville, Florida ehmiddlebrooks@gmail.com. 2. Medical Imaging Consultants, Inc (C.J.F.), Gainesville, Florida. 3. From the Department of Radiology (E.H.M., R.O.D.J., T.C.M., I.M.S., A.A.M.), University of Florida College of Medicine, Gainesville, Florida. 4. From the Department of Radiology (E.H.M., R.O.D.J., T.C.M., I.M.S., A.A.M.), University of Florida College of Medicine, Gainesville, Florida North Florida/South Georgia Veterans Administration (T.C.M., I.M.S.), Gainesville, Florida.
Abstract
BACKGROUND AND PURPOSE: Acute invasive fungal rhinosinusitis carries a high mortality rate. An easy-to-use and accurate predictive imaging model is currently lacking. We assessed the performance of various CT findings for the identification of acute invasive fungal rhinosinusitis and synthesized a simple and robust diagnostic model to serve as an easily applicable screening tool for at-risk patients. MATERIALS AND METHODS: Two blinded neuroradiologists retrospectively graded 23 prespecified imaging abnormalities in the craniofacial region on craniofacial CT examinations from 42 patients with pathology-proven acute invasive fungal rhinosinusitis and 42 control patients proved negative for acute invasive fungal rhinosinusitis from the same high-risk population. A third blinded neuroradiologist decided discrepancies. Specificity, sensitivity, positive predictive value, and negative predictive value were determined for all individual variables. The 23 variables were evaluated for intercorrelations and univariate correlations and were interrogated by using stepwise linear regression. RESULTS: Given the low predictive value of any individual variable, a 7-variable model (periantral fat, bone dehiscence, orbital invasion, septal ulceration, pterygopalatine fossa, nasolacrimal duct, and lacrimal sac) was synthesized on the basis of multivariate analysis. The presence of abnormality involving a single variable in the model has an 87% positive predictive value, 95% negative predictive value, 95% sensitivity, and 86% specificity (R(2) = 0.661). A positive outcome in any 2 of the model variables predicted acute invasive fungal rhinosinusitis with 100% specificity and 100% positive predictive value. CONCLUSIONS: Our 7-variable CT-based model provides an easily applicable and robust screening tool to triage patients at risk for acute invasive fungal rhinosinusitis into a disease-positive or -negative category with a high degree of confidence.
BACKGROUND AND PURPOSE: Acute invasive fungal rhinosinusitis carries a high mortality rate. An easy-to-use and accurate predictive imaging model is currently lacking. We assessed the performance of various CT findings for the identification of acute invasive fungal rhinosinusitis and synthesized a simple and robust diagnostic model to serve as an easily applicable screening tool for at-risk patients. MATERIALS AND METHODS: Two blinded neuroradiologists retrospectively graded 23 prespecified imaging abnormalities in the craniofacial region on craniofacial CT examinations from 42 patients with pathology-proven acute invasive fungal rhinosinusitis and 42 control patients proved negative for acute invasive fungal rhinosinusitis from the same high-risk population. A third blinded neuroradiologist decided discrepancies. Specificity, sensitivity, positive predictive value, and negative predictive value were determined for all individual variables. The 23 variables were evaluated for intercorrelations and univariate correlations and were interrogated by using stepwise linear regression. RESULTS: Given the low predictive value of any individual variable, a 7-variable model (periantral fat, bone dehiscence, orbital invasion, septal ulceration, pterygopalatine fossa, nasolacrimal duct, and lacrimal sac) was synthesized on the basis of multivariate analysis. The presence of abnormality involving a single variable in the model has an 87% positive predictive value, 95% negative predictive value, 95% sensitivity, and 86% specificity (R(2) = 0.661). A positive outcome in any 2 of the model variables predicted acute invasive fungal rhinosinusitis with 100% specificity and 100% positive predictive value. CONCLUSIONS: Our 7-variable CT-based model provides an easily applicable and robust screening tool to triage patients at risk for acute invasive fungal rhinosinusitis into a disease-positive or -negative category with a high degree of confidence.
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