# Using the Hounsfield Units Derived From Computed Tomography (CT) Scans to Differentiate Between the Subtypes of Allergic Fungal Rhinosinusitis: A Retrospective Study

## TL;DR

This study shows that CT scan Hounsfield units can help distinguish between different types of allergic fungal sinusitis and other sinus conditions.

## Contribution

The study introduces specific Hounsfield unit thresholds for differentiating subtypes of allergic fungal rhinosinusitis using CT scans.

## Key findings

- Allergic fungal mucin showed lower HU heterogeneity and density compared to other pathologies.
- HU maximum values effectively identified fungal balls with high sensitivity and specificity.
- HU average and SD values were highly accurate in detecting allergic fungal mucin.

## Abstract

Background

Allergic fungal sinusitis (AFS) is an inflammatory condition, often diagnosed using computed tomography (CT) scans, where Hounsfield units (HU) serve as a critical metric. However, the diagnostic process can be challenging due to the ambiguous patterns of sinus secretions. This study evaluates whether the HU measurements from preoperative CT scans can reliably differentiate between the subtypes of AFS and other chronic rhinosinusitis (CRS) entities by correlating these values with histopathological findings.

Patients and methods

A retrospective analysis was conducted on 120 patients with suspected AFS. All patients had undergone surgical endoscopy at the King Saud Medical City, Riyadh, Saudi Arabia, between 2012 and 2022. Radiographic data, including average, maximum, minimum, and standard deviation (SD) of HU values from unenhanced CT scans, were collected and analyzed. We assessed the diagnostic utility of HU metrics using one-way analysis of variance (ANOVA) and receiver operating characteristic (ROC) curve analysis to determine optimal HU thresholds for differentiating sinus opacities.

Results

Histopathological analysis revealed that 29 (24.2%) cases exhibited non-fungal sinus opacities, 50 (41.7%) displayed sinus fungal balls, and 41 (34.2%) showed allergic fungal mucin. Notably, allergic fungal mucin demonstrated lower heterogeneity and density compared to the other pathologies. Post hoc analysis indicated significant differences in HU maximum values for fungal balls, along with HU average and HU SD values for allergic fungal mucin. ROC curve analysis for fungal balls yielded a high area under the curve (AUC) for HU maximum (AUC=0.868; 95% CI: 0.794-0.923). The optimal HU maximum threshold of 299 provided a sensitivity of 100% and specificity of 71.43% for detecting fungal balls. Allergic fungal mucin showed high AUC values for HU average (AUC=0.979; 95% CI: 0.934-0.996) and HU SD (AUC=0.973; 95% CI: 0.926-0.994). The optimal HU average and HU SD thresholds of 44.0 and 55.6 yielded sensitivities of 90.2% and 100%, and specificities of 100% and 77.1%, respectively.

Conclusion

This study identifies significant correlations between the HU parameters from paranasal CT scans and the pathological features in AFS. Notably, the HU SD and average values correlate with allergic fungal mucin, while HU maximum value indicates the presence of fungal balls. These results suggest that quantitative CT density assessment can aid in differentiating the pathologies of rhinosinusitis. However, external validation is required, and future studies should focus on diverse populations and establish cut-off points for tailored treatment strategies in suspected fungal sinus disease.