ArticleImaging science in dentistry2023
Cone-beam computed tomography texture analysis can help differentiate odontogenic and non-odontogenic maxillary sinusitis.
Article in Imaging science in dentistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics of the Paranasal Sinuses: A Systematic Review of Computer-Assisted Techniques to Assess Computed Tomography Radiological Data.American journal of rhinology & allergy · 2025Pooled it
- Ectopic maxillary third molar associated with an odontogenic keratocyst in the maxillary Sinus presenting as refractory unilateral sinonasal symptoms: a case report with implications for early dental diagnosis.Frontiers in oral health · 2026Article
- CT Texture Patterns Reflect HPV Status but Not Histological Differentiation in Oropharyngeal Squamous Cell Carcinoma.Cancers · 2025Article
- Radiographic biomarkers on grayscale gradient transition zone improve differentiation of deep caries/reversible pulpitis and chronic pulpitis through diagnostic model analysis.Oral radiology · 2025Article
- Radiomics-Driven CBCT Texture Analysis as a Novel Biosensor for Quantifying Periapical Bone Healing: A Comparative Study of Intracanal Medications.Biosensors · 2025Article
- Unveiling degenerative bone changes in the condyle: a texture analysis approach using cone-beam computed tomography.Acta cirurgica brasileira · 2025Article
- Enhanced Diagnostic Precision: Assessing Tumor Differentiation in Head and Neck Squamous Cell Carcinoma Using Multi-Slice Spiral CT Texture Analysis.Journal of clinical medicine · 2024Article
- Expert consensus on odontogenic maxillary sinusitis multi-disciplinary treatment.International journal of oral science · 2024Review
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7 authors.
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Abstract
Purpose: This study aimed to assess texture analysis (TA) of cone-beam computed tomography (CBCT) images as a quantitative tool for the differential diagnosis of odontogenic and non-odontogenic maxillary sinusitis (OS and NOS, respectively). Materials and Methods: CBCT images of 40 patients diagnosed with OS (N=20) and NOS (N=20) were evaluated. The gray level co-occurrence (GLCM) matrix parameters, and gray level run length matrix texture (GLRLM) parameters were extracted using manually placed regions of interest on lesion images. Seven texture parameters were calculated using GLCM and 4 parameters using GLRLM. The Mann-Whitney test was used for comparisons between the groups, and the Levene test was performed to confirm the homogeneity of variance (α=5%). Results: The results showed statistically significant differences ( Conclusion: TA enabled quantitative differentiation between OS and NOS on CBCT images by using the parameters of contrast, correlation, and inverse difference moment.
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