DIFFERENTIAL DIAGNOSIS OF MAXILLARY SINUSITIS USING CT IMAGING: A SCOPING REVIEW

Background: Maxillary sinusitis is a common clinical condition with multiple etiologies, including infectious, inflammatory, and neoplastic causes. Accurate diagnosis is essential for appropriate management, and computed tomography (CT) is widely used for detailed assessment of the maxillary sinus anatomy and pathology. 

Objective: This scoping review aims to systematically map the literature on the differential diagnosis of maxillary sinusitis using CT, highlighting imaging features that distinguish between common and uncommon causes. 

Materials and Methods: A systematic literature search was conducted in PubMed, Scopus, Web of Science, and Embase for studies published from 2000 to 2026. Keywords included “maxillary sinusitis,” “CT,” “computed tomography,” “differential diagnosis,” and “imaging features.” Studies reporting CT findings in acute, chronic, and recurrent sinusitis, as well as non-infectious pathologies mimicking sinusitis, were included. Data were extracted on patient demographics, CT findings, pathology type, and diagnostic accuracy. 

Results: Out of 312 identified articles, 50 studies met inclusion criteria. CT features differentiating infectious from non-infectious causes included mucosal thickening patterns, presence of air-fluid levels, bony erosions, hyperattenuating material, and opacification symmetry. Chronic sinusitis commonly showed mucosal thickening and sclerosis, while fungal sinusitis exhibited hyperdense material or calcifications. Neoplastic lesions mimicking sinusitis often presented as unilateral opacification with bone destruction or mass effect. Incidental anatomical variants and odontogenic causes were also significant contributors. 

Conclusion: CT imaging provides critical information for the differential diagnosis of maxillary sinusitis, allowing distinction between infectious, inflammatory, and neoplastic etiologies. Recognizing specific CT patterns improves diagnostic accuracy, guides management, and reduces unnecessary interventions. Future research should focus on standardized reporting, quantitative imaging biomarkers, and integration of AI-assisted diagnostic tools.