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Differentiation of radicular cysts and radicular granulomas via texture analysis of multi-slice computed tomography images.
Yomtako, Supasith; Watanabe, Hiroshi; Kuribayashi, Ami; Sakamoto, Junichiro; Miura, Masahiko.
Afiliación
  • Yomtako S; Department of Dental Radiology and Radiation Oncology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 113-8549, Japan.
  • Watanabe H; School of Dentistry, Mae Fah Luang University, 333 Mool, Thasud, Muang, Chiang Rai, Thailand.
  • Kuribayashi A; Department of Dental Radiology and Radiation Oncology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 113-8549, Japan.
  • Sakamoto J; Department of Dental Radiology and Radiation Oncology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 113-8549, Japan.
  • Miura M; Department of Dental Radiology and Radiation Oncology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 113-8549, Japan.
Dentomaxillofac Radiol ; 53(5): 281-288, 2024 Jun 28.
Article en En | MEDLINE | ID: mdl-38565278
ABSTRACT

OBJECTIVES:

This study aimed to establish a method for differentiating radicular cysts from granulomas via texture analysis (TA) of multi-slice computed tomography (CT) images.

METHODS:

A 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.

RESULTS:

A 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.

CONCLUSION:

Discriminatory models were established for the TA of radicular cysts and granulomas using CT images.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Quiste Radicular / Tomografía Computarizada Multidetector Límite: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Dentomaxillofac Radiol Año: 2024 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Quiste Radicular / Tomografía Computarizada Multidetector Límite: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Dentomaxillofac Radiol Año: 2024 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Reino Unido