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Acute Invasive Fungal Rhinosinusitis: A Comprehensive Update of CT Findings and Design of an Effective Diagnostic
E H Middlebrooks1, C J Frost2, R O De Jesus3
1From 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.
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.
Insights
A new 7-variable CT model accurately identifies acute invasive fungal rhinosinusitis. This tool helps screen at-risk patients, improving early diagnosis and management of this serious condition.
Area of Science:
- Radiology
- Medical Imaging
- Otolaryngology
Background:
- Acute invasive fungal rhinosinusitis (AIFR) has a high mortality rate.
- Accurate and user-friendly predictive imaging models for AIFR are lacking.
- CT imaging is crucial for diagnosing craniofacial pathologies.
Purpose of the Study:
- To assess the diagnostic performance of various CT findings for AIFR.
- To develop a simple and robust CT-based diagnostic model for AIFR screening.
- To provide an easily applicable tool for identifying at-risk patients.
Main Methods:
- Retrospective analysis of craniofacial CT scans from 42 AIFR patients and 42 controls.
- Two blinded neuroradiologists graded 23 prespecified CT abnormalities.
- Stepwise linear regression was used to develop a predictive model.
Main Results:
- A 7-variable CT model (including periantral fat, bone dehiscence, orbital invasion) was developed.
- The model demonstrated high diagnostic accuracy: 95% sensitivity, 86% specificity.
- Two or more positive findings predicted AIFR with 100% specificity and positive predictive value.
Conclusions:
- The 7-variable CT model is an effective screening tool for AIFR.
- The model allows for confident triage of at-risk patients.
- This CT-based approach aids in early detection and management of AIFR.
