Differentiation of radicular cysts and radicular granulomas via texture analysis of multi-slice computed tomography images

Supasith Yomtako; Hiroshi Watanabe; Ami Kuribayashi; Junichiro Sakamoto; Masahiko Miura

doi:10.1093/dmfr/twae011

Differentiation of radicular cysts and radicular granulomas via texture analysis of multi-slice computed tomography images

Dentomaxillofac Radiol. 2024 Apr 2:twae011. doi: 10.1093/dmfr/twae011. Online ahead of print.

Authors

Supasith Yomtako^{1

2}, Hiroshi Watanabe¹, Ami Kuribayashi¹, Junichiro Sakamoto¹, Masahiko Miura¹

Affiliations

¹ Dental Radiology and Radiation Oncology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, Japan.
² School of Dentistry, Mae Fah Luang University, 333 Moo1, Thasud, Muang, Chiang Rai, Thailand.

PMID: 38565278
DOI: 10.1093/dmfr/twae011

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 artifacts, and lesions that were not sufficiently large to be analyzed 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 analyzed. 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.

Keywords: Granuloma; Multi-slice Computed Tomography; Radicular cyst; Radiomics.

© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology and the International Association of Dentomaxillofacial Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.