Evidence map›Paper›PMID 40190916›Full record

ArticleCureus2025

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

Seham Alsalem, Mohammed Alsalem, Ibrahim Al Harithi, Read Al Shehri, Anas Al Zahrani, Muteb Al Khedaidi

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Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Seham AlsalemRadiology, Prince Sultan Military Medical City, Riyadh, SAU.
Mohammed AlsalemDiagnostic Radiology, King Saud University Medical City, Riyadh, SAU.
Ibrahim Al HarithiRadiology, Najran Armed Forces Hospital, Najran, SAU.
Read Al ShehriRadiology, Prince Sultan Medical City, Riyadh, SAU.
Anas Al ZahraniRadiology, Prince Sultan Medical City, Riyadh, SAU.
Muteb Al KhedaidiRadiology, Prince Sultan Medical City, Riyadh, SAU.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

allergic fungal mucinallergic fungal sinusitiscomputed tomography scandiagnosishounsfield unitsnonfungal sinus opacitiessinus fungal balls

Identifiers

PMID40190916
PMCPMC11972099

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