Computed Tomography and Magnetic Resonance Imaging Characteristics of Acute Invasive Fungal Sinusitis

Groppo, Eli R.; El‐Sayed, Ivan H.; Aiken, Ashley H.; Glastonbury, Christine M.

doi:10.1001/archoto.2011.170

Cited by 143 publications

(151 citation statements)

References 23 publications

(40 reference statements)

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“…This finding led to the recommendation that CT be considered a second-line technique. 9 While our study populations were different, our CT-based predictive model nevertheless demonstrated higher sensitivity (95% versus 86%), specificity (86% versus 75%), and NPV (95% versus 60%) than these previously published MR imaging data. 9 Our PPV estimates were similar (87% versus 92%).…”

contrasting

confidence: 46%

“…9 While our study populations were different, our CT-based predictive model nevertheless demonstrated higher sensitivity (95% versus 86%), specificity (86% versus 75%), and NPV (95% versus 60%) than these previously published MR imaging data. 9 Our PPV estimates were similar (87% versus 92%). Moreover, our predictive model even produces higher sensitivity, specificity, and NPV than previously reported with CT. 8,9 The NPV is of particular importance in screening studies because low NPV results in erroneously excluding a patient positive for AIFR.…”

contrasting

confidence: 46%

“…9 Our PPV estimates were similar (87% versus 92%). Moreover, our predictive model even produces higher sensitivity, specificity, and NPV than previously reported with CT. 8,9 The NPV is of particular importance in screening studies because low NPV results in erroneously excluding a patient positive for AIFR. Thus, on the basis of our results, CT with application of our predictive model should be considered a primary technique for evaluating AIFR.…”

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confidence: 50%

“…8,9 A key attribute of this model is that within our clinical dataset, the involvement of any 2 of the 7 variables predicted AIFR with 100% specificity. The predictive power is also enhanced because 88% of patients with AIFR presented with findings captured by Ն2 variables.…”

Section: Discussionmentioning

confidence: 99%

“…9 Application of this proposed 7-variable model may improve evaluation of potential AIFR in an at-risk population and serve as the basis for a subsequent prospective study.…”

Section: Discussionmentioning

confidence: 99%

See 4 more Smart Citations

Acute Invasive Fungal Rhinosinusitis: A Comprehensive Update of CT Findings and Design of an Effective Diagnostic Imaging Model

Middlebrooks

Frost²,

Jesus

et al. 2015

AJNR Am J Neuroradiol

112

110

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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.

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contrasting

confidence: 46%

contrasting

confidence: 46%

mentioning

confidence: 50%

Section: Discussionmentioning

confidence: 99%

“…9 Application of this proposed 7-variable model may improve evaluation of potential AIFR in an at-risk population and serve as the basis for a subsequent prospective study.…”

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

Acute Invasive Fungal Rhinosinusitis: A Comprehensive Update of CT Findings and Design of an Effective Diagnostic Imaging Model

Middlebrooks

Frost²,

Jesus

et al. 2015

AJNR Am J Neuroradiol

112

110

View full text Add to dashboard Cite

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An internally validated diagnostic tool for acute invasive fungal sinusitis

Yin

Spillinger

Lees

et al. 2020

Int Forum Allergy Rhinol

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BackgroundAcute invasive fungal sinusitis (AIFS) is a potentially life‐threatening diagnosis in immunocompromised patients. Identifying patients who could benefit from evaluation and intervention can be challenging for referring providers and otolaryngologists alike. We aimed to develop and validate an accessible diagnostic tool to estimate the probability of AIFS.MethodsRetrospective chart review from 1999 to 2017 identified all patients evaluated for possible AIFS at a tertiary care center. AIFS was diagnosed by pathologic confirmation of fungal tissue angioinvasion. Stepwise selection and univariate logistic regression were used to screen risk factors for a multivariable predictive model. Model performance was assessed using Tukey's goodness‐of‐fit test and the area under the receiver operator characteristic curve (AUC). Model coefficients were internally validated using bootstrapping with 1000 iterations.ResultsA total of 283 patients (244 negative controls, 39 with AIFS) were included. Risk factors in our final diagnostic model included: fever ≥38°C (log‐odds ratio [LOR] 1.72; 95% CI, 0.53 to 2.90), unilateral facial swelling, pain, or erythema (LOR 2.84; 95% CI, 1.46 to 4.23), involvement of the orbit or pterygopalatine fossa on imaging (LOR 3.02; 95% CI, 1.78 to 4.26), and mucosal necrosis seen on endoscopy (LOR 5.52; 95% CI, 3.81 to 7.24), with p < 0.01 for all factors. The model had adequate goodness of fit (p > 0.05) and discrimination (AUC = 0.96).ConclusionWe present an internally validated diagnostic tool to stratify the risk for AIFS. The estimated risk may help determine which patients can be observed with serial nasal endoscopy, which ones could be biopsied, and which ones would benefit from immediate surgical intervention.

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Diagnosis, Prognosticators, and Management of Acute Invasive Fungal Rhinosinusitis: Multidisciplinary Consensus Statement and Evidence‐Based Review with Recommendations

Roland

Humphreys

et al. 2023

Int Forum Allergy Rhinol

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BackgroundAcute invasive fungal sinusitis (AIFS) is an aggressive disease that requires prompt diagnosis and multidisciplinary treatment given its rapid progression. However, there is currently no consensus on diagnosis, prognosis, and management strategies for AIFS, with multiple modalities routinely employed. The purpose of this multi‐institutional and multidisciplinary evidence‐based review with recommendations (EBRR) is to thoroughly review the literature on AIFS, summarize the existing evidence, and provide recommendations on the management of AIFS.MethodsThe PubMed, EMBASE, and Cochrane databases were systematically reviewed from inception through January 2022. Studies evaluating management for orbital, non‐sinonasal head and neck, and intracranial manifestations of AIFS were included. An iterative review process was utilized in accordance with EBRR guidelines. Levels of evidence and recommendations on management principles for AIFS were generated.ResultsA review and evaluation of published literature was performed on 12 topics surrounding AIFS (signs and symptoms, laboratory and microbiology diagnostics, endoscopy, imaging, pathology, surgery, medical therapy, management of extrasinus extension, reversing immunosuppression, and outcomes and survival). The aggregate quality of evidence was varied across reviewed domains.ConclusionBased on the currently available evidence, judicious utilization of a combination of history and physical examination, laboratory and histopathologic techniques, and endoscopy provide the cornerstone for accurate diagnosis of AIFS. In addition, AIFS is optimally managed by a multidisciplinary team via a combination of surgery (including resection whenever possible), antifungal therapy, and correcting sources of immunosuppression. Higher quality (i.e., prospective) studies are needed to better define the roles of each modality and determine diagnosis and treatment algorithms.

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Computed Tomography and Magnetic Resonance Imaging Characteristics of Acute Invasive Fungal Sinusitis

Abstract: Magnetic resonance imaging is more sensitive for detecting early changes of AFIFS than CT. Both imaging modalities have similar specificities. Perisinus invasion with MRI was the most sensitive and specific single parameter evaluated.

Cited by 143 publications

References 23 publications

Acute Invasive Fungal Rhinosinusitis: A Comprehensive Update of CT Findings and Design of an Effective Diagnostic Imaging Model

Acute Invasive Fungal Rhinosinusitis: A Comprehensive Update of CT Findings and Design of an Effective Diagnostic Imaging Model

An internally validated diagnostic tool for acute invasive fungal sinusitis

Diagnosis, Prognosticators, and Management of Acute Invasive Fungal Rhinosinusitis: Multidisciplinary Consensus Statement and Evidence‐Based Review with Recommendations

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