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

Rosa, C. A. Santa; Bergamini, M; Palmieri, Michelle; Sarmento, Dmitry José de Santana; Carvalho, Marcia Oliveira de; Ricardo, Ana Lucia Franco; Hasséus, Bengt; Jonasson, Peter; Braz‐Silva, Paulo Henrique; Costa, André Luiz Ferreira

doi:10.1016/j.heliyon.2020.e05194

Cited by 29 publications

(19 citation statements)

References 37 publications

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“…To our knowledge, this cohort study was the first to include texture feature analysis for automatic discrimination between cysts and granulomas based on intraoral images, although texture feature map correlation has been used for cyst and granuloma detection in magnetic resonance [37] and computed tomography [38].…”

Section: Discussionmentioning

confidence: 99%

Differential Diagnosis of Cysts and Granulomas Supported by Texture Analysis of Intraoral Radiographs

Pociask

Nurzynska

Obuchowicz

et al. 2021

Sensors

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The aim of this study was to evaluate whether textural analysis could differentiate between the two common types of lytic lesions imaged with use of radiography. Sixty-two patients were enrolled in the study with intraoral radiograph images and a histological reference study. Full textural analysis was performed using MaZda software. For over 10,000 features, logistic regression models were applied. Fragments containing lesion edges were characterized by significant correlation of structural information. Although the input images were stored using lossy compression and their scale was not preserved, the obtained results confirmed the possibility of distinguishing between cysts and granulomas with use of textural analysis of intraoral radiographs. It was shown that the important information distinguishing the aforementioned types of lesions is located at the edges and not within the lesion.

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Section: Discussionmentioning

confidence: 99%

Differential Diagnosis of Cysts and Granulomas Supported by Texture Analysis of Intraoral Radiographs

Pociask

Nurzynska

Obuchowicz

et al. 2021

Sensors

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“…Thus CBCT provide accurate pre-operative diagnosis and there by dictate the surgical planning. If the lesion is granuloma no need for surgical intervention but if it is cyst then immediate enucleation of the cyst 3 . Researchers have evaluated the accuracy of CBCT imaging to detect apical periodontitis (AP) compared with periapical (PA) radiographs [4][5][6] .…”

Section: Introductionmentioning

confidence: 99%

“…In 13 of 17 lesions, the diagnoses of CBCT images were consistent with pathological reports. The authors indicated that CBCT images may provide more accurate diagnosis than pathological reports 3 . CBCT is a good diagnostic modality for these lesions8.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Cone-beam Computed Tomography to differentiate Odontogenic Periapical Cysts and Granulomas

Soma

Akhtar

Begum

et al. 2023

J. Natl Inst. Neurosci Bangladesh

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Background: Cone-beam Computed Tomography is a useful diagnostic tools for the detection of odontogenic peripheral cysts and granulomas. Objective: The purpose of the present study was to assess the accuracy of CBCT for diagnosing periapical cyst and granuloma. Methodology: This cross-sectional study was conducted from February 2017 to November 2017. Following inclusion and exclusion criteria consecutive patients who were consult in the department of oral and maxillofacial surgery of Dhaka Dental College Hospital and Bangabandhu Seikh Mujib Medical University for periapical infection was selected. Only those patients showing a periapical lesion of minimum 5 mm size was included in the study subject. Intra-oral periapical radiograph showing criteria was presence of apical radiolucency of 5 mm, corticated borders and displacements of roots. The CBCT images were done for those patients. The following features were documented on CBCT like location at the apex of involved tooth, well defined corticated border, shape is curved or circular, internal structure of lesion is radiolucent, displacement and resorption of the roots of adjacent teeth with a curved outline and perforation of cortical plate. Histopathological specimens were obtained for microscopic examination. The findings of histopathological examinations were correlated with the findings of CBCT. Results: Out of 50 subjects, 41(82.0%) subjects were diagnosed as cyst and 9(18.0%) subjects were as granuloma. Postoperative histopathology revealed 37 out of 41 were cysts and 4 were granulomas. Out of 9 granulomas 3 were cysts and 6 were granulomas. In this study we found the PPV 90.2439 and NPV 66.66. Diagnostic Accuracy was measured by calculating TP, TN and total population. The accuracy was 86%. Conclusion: In conclusion Cone-beam Computed Tomography has high accuracy of CBCT for diagnosing periapical cyst and granuloma. Journal of National Institute of Neurosciences Bangladesh, July 2022;8(2):171-174

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“…Texture analysis (TA) is a statistical imaging technique using radiomics to examine the distribution of the pixel signal intensity and the association between neighboring pixel values, thus allowing quantifying subtle differences in the image 11 , 12 . This enables one to perform a quantitative investigation of the image without the subjectivity of the human vision 13 , 14 .…”

Section: Introductionmentioning

confidence: 99%

Magnetic resonance imaging texture analysis to differentiate ameloblastoma from odontogenic keratocyst

Gomes

Ogawa

Silveira

et al. 2022

Sci Rep

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The differentiation between ameloblastoma (AB) and odontogenic keratocyst (OKC) is essential for the formulation of the surgical plan, especially considering the biological behavior of these two pathological entities. Therefore, developing means to increase the accuracy of the diagnostic process is extremely important for a safe treatment. The aim of this study was to use magnetic resonance imaging (MRI) based on texture analysis (TA) as an aid in differentiating AB from OKC. This study comprised 18 patients; eight patients with AB and ten with OKC. All diagnoses were determined through incisional biopsy and later through histological examination of the surgical specimen. MRI was performed using a 3 T scanner with a neurovascular coil according to a specific protocol. All images were exported to segmentation software in which the volume of interest (VOI) was determined by a radiologist, who was blind to the histopathological results. Next, the textural parameters were computed by using the MATLAB software. Spearman's correlation coefficient was used to assess the correlation between texture parameters and the selected variables. Differences in TA parameters were compared between AB and OKC by using the Mann–Whitney test. Mann–Whitney test showed a statistically significant difference between AB and OKC for the parameters entropy (P = 0.033) and sum average (P = 0.033). MRI texture analysis has the potential to discriminate between AB and OKC as a noninvasive method. MRI texture analysis can be an additional tool to differentiate ameloblastoma from odontogenic keratocyst.

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

Cited by 29 publications

References 37 publications

Differential Diagnosis of Cysts and Granulomas Supported by Texture Analysis of Intraoral Radiographs

Differential Diagnosis of Cysts and Granulomas Supported by Texture Analysis of Intraoral Radiographs

Evaluation of Cone-beam Computed Tomography to differentiate Odontogenic Periapical Cysts and Granulomas

Magnetic resonance imaging texture analysis to differentiate ameloblastoma from odontogenic keratocyst

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