Literature DB >> 31539792

Using CT texture analysis to differentiate cystic and cystic-appearing odontogenic lesions.

Masafumi Oda1, Pedro V Staziaki2, Muhammad M Qureshi3, V Carlota Andreu-Arasa2, Baojun Li2, Koji Takumi4, Margaret N Chapman2, Albert Wang2, Andrew R Salama5, Osamu Sakai6.   

Abstract

PURPOSE: Cystic and cystic-appearing odontogenic lesions of the jaw may appear similar on CT imaging. Accurate diagnosis is often difficult although the relationship of the lesion to the tooth root or crown may offer a clue to the etiology. The purpose of this study was to evaluate CT texture analysis as an aid in differentiating cystic and cystic-appearing odontogenic lesions of the jaw.
METHODS: This was an IRB-approved retrospective study including 42 pathology-proven dentigerous cysts, 37 odontogenic keratocysts, and 19 ameloblastomas. Each lesion was manually segmented on axial CT images, and textural features were analyzed using an in-house-developed Matlab-based texture analysis program that extracted 47 texture features from each segmented volume. Statistical analysis was performed comparing all pairs of the three types of lesions.
RESULTS: Pairwise analysis revealed that nine histogram features, one GLCM feature, three GLRL features, two Laws features, four GLGM features and two Chi-square features showed significant differences between dentigerous cysts and odontogenic keratocysts. Four histogram features and one Chi-square feature showed significant differences between odontogenic keratocysts and ameloblastomas. Two histogram features showed significant differences between dentigerous cysts and ameloblastomas.
CONCLUSIONS: CT texture analysis may be useful as a noninvasive method to obtain additional quantitative information to differentiate cystic and cystic-appearing odontogenic lesions of the jaw.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Ameloblastomas; CT; Dentigerous cysts; Jaw lesions; Odontogenic keratocysts; Texture analysis

Year:  2019        PMID: 31539792     DOI: 10.1016/j.ejrad.2019.108654

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  4 in total

1.  Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis.

Authors:  Catharina Simioni De Rosa; Mariana Lobo Bergamini; Michelle Palmieri; Dmitry José de Santana Sarmento; Marcia Oliveira de Carvalho; Ana Lúcia Franco Ricardo; Bengt Hasseus; Peter Jonasson; Paulo Henrique Braz-Silva; Andre Luiz Ferreira Costa
Journal:  Heliyon       Date:  2020-10-09

2.  Deep Learning-Based Image Segmentation of Cone-Beam Computed Tomography Images for Oral Lesion Detection.

Authors:  Xueling Wang; Xianmin Meng; Shu Yan
Journal:  J Healthc Eng       Date:  2021-09-21       Impact factor: 2.682

3.  Emulating Clinical Diagnostic Reasoning for Jaw Cysts with Machine Learning.

Authors:  Balazs Feher; Ulrike Kuchler; Falk Schwendicke; Lisa Schneider; Jose Eduardo Cejudo Grano de Oro; Tong Xi; Shankeeth Vinayahalingam; Tzu-Ming Harry Hsu; Janet Brinz; Akhilanand Chaurasia; Kunaal Dhingra; Robert Andre Gaudin; Hossein Mohammad-Rahimi; Nielsen Pereira; Francesc Perez-Pastor; Olga Tryfonos; Sergio E Uribe; Marcel Hanisch; Joachim Krois
Journal:  Diagnostics (Basel)       Date:  2022-08-14

4.  Contrast-enhanced multidetector computed tomography features and histogram analysis can differentiate ameloblastomas from central giant cell granulomas.

Authors:  Adarsh Ghosh; Meyyappan Lakshmanan; Smita Manchanda; Ashu Seith Bhalla; Prem Kumar; Ongkila Bhutia; Asit Ranjan Mridha
Journal:  World J Radiol       Date:  2022-09-28
  4 in total

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