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Detection of Invasive Pulmonary Aspergillosis in Haematological Malignancy Patients by using Lateral-flow Technology

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Clinical and Radiological Predictors of Invasive Fungal Sinusitis.

Denise Chazan¹, Adi Brenner^1,2, Lilian Atlan^1,2

¹Gray Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel.

Open Forum Infectious Diseases

|March 30, 2026

Summary

This summary is machine-generated.

Early detection of invasive fungal sinusitis (IFS) is crucial. Ocular pain, specific CT findings, and mucosal necrosis are key indicators for timely diagnosis and improved patient outcomes.

Keywords:

computed tomography endoscopy leukemia mucormycosis neutropenia

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Area of Science:

Medical Mycology
Radiology
Otolaryngology

Background:

Invasive fungal sinusitis (IFS) carries a high mortality rate due to late diagnosis.
Delayed diagnosis allows for spread to critical areas like the orbit, skull base, and brain.
Early identification is vital for effective treatment and improved prognosis.

Purpose of the Study:

To identify early clinical and radiologic indicators of invasive fungal sinusitis (IFS).
To develop a prediction model and clinical management algorithms for IFS.
To improve the diagnostic accuracy and timeliness of IFS detection.

Main Methods:

Prospective monitoring of patients with proven or probable IFS in two tertiary hospitals.
Comparison of computed tomography (CT) and nasal endoscopy findings between IFS cases and controls.
Multivariate regression analysis to identify independent predictors of IFS.

Main Results:

Ocular pain, fat infiltration on sinus CT, and mucosal necrosis on nasal endoscopy were significantly associated with IFS.
The developed prediction model demonstrated high sensitivity (0.77) and specificity (0.96).
Area under the receiver-operating curve was 0.88, indicating strong predictive performance.

Conclusions:

Combining radiologic and clinical features enables early prediction of invasive fungal sinusitis (IFS).
These identified criteria can be integrated into clinical diagnostic algorithms for IFS.
Early prediction facilitates timely intervention, potentially reducing mortality associated with IFS.