ArticleDento maxillo facial radiology2024
Differentiation of radicular cysts and radicular granulomas via texture analysis of multi-slice computed tomography images.
Article in Dento maxillo facial radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Radiomics-Based Differential Diagnosis of Radicular Cysts and Apical Granulomas on CBCT Images Using RadC-CNN Architecture.Diagnostics (Basel, Switzerland) · 2026Article
- Ensemble learning for predicting periapical lesions from dental radiographs.Journal of dental sciences · 2026Article
- Radiographic signature in apical periodontitis improves prediction of apical lesion healing through survival prediction model.PloS one · 2025Article
- Texture Analysis in Volumetric Imaging for Dentomaxillofacial Radiology: Transforming Diagnostic Approaches and Future Directions.Journal of imaging · 2024Review
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5 authors.
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Abstract
objectivesThis study aimed to establish a method for differentiating radicular cysts from granulomas via texture analysis (TA) of multi-slice computed tomography (CT) images.
methodsA total of 222 lesions with multi-slice computed tomography images acquired at our hospital between 2013 and 2022 that were pathologically diagnosed were included in this study. Cases of contrast-enhanced images, severe metallic artefacts, and lesions that were not sufficiently large to be analysed were excluded. The images were chronologically divided into a training group and a validation group. The radiological characteristics were determined. Subsequently, a TA was performed. Pyradiomics software was used for the TA of three-dimensionally segmented volumes extracted from 2 mm slice thickness images with a soft-tissue algorithm. Features that differed significantly between the two lesions in the training group were extracted and used to create machine-learning models. The discriminative ability of these models was evaluated in the validation group using receiver operating characteristic curve analysis.
resultsA total of 131 lesions, comprising 28 radicular cysts and 103 granulomas, were analysed. Forty-three texture features that exhibited significant variations were extracted. A support vector machine and decision tree model, with areas under the curves of 0.829 and 0.803, respectively, were created. These models showed high discriminative abilities, even for the validation group, with areas under the curve of 0.727 and 0.701, respectively. Both models showed superior performance compared with that of the models based on radiographic findings.
conclusionDiscriminatory models were established for the TA of radicular cysts and granulomas using CT images.
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