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

Oda, Masafumi; Staziaki, Pedro V.; Qureshi, Muhammad M.; Arasa, Vanessa Carlota Andreu; Li, Baojun; Takumi, Koji; Chapman, Margaret N.; Wang, Albert; Salama, Andrew; Sakai, Osamu

doi:10.1016/j.ejrad.2019.108654

Cited by 26 publications

(19 citation statements)

References 36 publications

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Section: Discussionmentioning

confidence: 99%

“…In another study, computed tomography texture analysis was used as an aid in differentiating cystic and cystic-appearing odontogenic lesions of the jaw, showing that texture parameters provided additional diagnostic performance value [35]. In a recent study, the authors used gray level co-occurrence matrixbased texture analysis in CBCT images of patients with aggressive periodontitis.…”

Section: Discussionmentioning

confidence: 99%

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

Rosa

Bergamini

Palmieri

et al. 2020

Heliyon

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Objective This study aimed to investigate the use of texture analysis for characterization of radicular cysts and periapical granulomas and to assess its efficacy to differentiate between both lesions with histological diagnosis. Methods Cone beam computed tomography (CBCT) images were obtained from 19 patients with 25 periapical lesions (14 radicular cysts and 11 periapical granulomas) confirmed by biopsy. Regions of interest were created in the lesions from which 11 texture parameters were calculated. Spearman's correlation analysis was performed and adjusted with Benjamini-Hochberg false discovery rate procedure (FDR <0.005). Results The texture parameters used to differentiate the lesions were assessed by using a receiver operating characteristic analysis. Five texture parameters were predictive of lesion differentiation for eight positions: angular second moment; sum of squares; sum of average; contrast; correlation. Conclusion Texture analysis of CBCT scans distinguishes radicular cysts from periapical granulomas and can be a promising diagnostic tool for periapical lesions. Clinical significance Texture analysis can be used in diagnostic and treatment monitoring to provide supplementary information.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Rosa

Bergamini

Palmieri

et al. 2020

Heliyon

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“…In addition, most of the research is on the human brain, chest, and abdominal organs. Therefore, it is necessary to conduct in-depth research on image analysis and algorithms and apply them to oral diseases [ 15 , 16 ]. According to statistics, the rate of oral diseases in China is only 9∼10%, which also has a great impact on oral health.…”

Section: Discussionmentioning

confidence: 99%

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

Wang

Meng

Yan³

2021

Journal of Healthcare Engineering

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This paper aimed to study the adoption of deep learning (DL) algorithm of oral lesions for segmentation of cone-beam computed tomography (CBCT) images. 90 patients with oral lesions were taken as research subjects, and they were grouped into blank, control, and experimental groups, whose images were treated by the manual segmentation method, threshold segmentation algorithm, and full convolutional neural network (FCNN) DL algorithm, respectively. Then, effects of different methods on oral lesion CBCT image recognition and segmentation were analyzed. The results showed that there was no substantial difference in the number of patients with different types of oral lesions among three groups ( P > 0.05 ). The accuracy of lesion segmentation in the experimental group was as high as 98.3%, while those of the blank group and control group were 78.4% and 62.1%, respectively. The accuracy of segmentation of CBCT images in the blank group and control group was considerably inferior to the experimental group ( P < 0.05 ). The segmentation effect on the lesion and the lesion model in the experimental group and control group was evidently superior to the blank group ( P < 0.05 ). In short, the image segmentation accuracy of the FCNN DL method was better than the traditional manual segmentation and threshold segmentation algorithms. Applying the DL segmentation algorithm to CBCT images of oral lesions can accurately identify and segment the lesions.

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“…Apajalahti et al (2015) concluded that the use of FBCT and contrast is better for analysis of mixed cystic content and solid content in cases of ameloblastomas. On the other hand, the use of contrast could be related to higher doses of radiation and possible allergic reactions and renal dysfunction (Meng et al;Oda et al, 2019). Another disadvantage is the finantial costs and the size of the equipment that need a bigger area than CBCT requirements, usually non compatible with dental clinics (Guttenberg; Gamba et al).…”

Section: Discussionmentioning

confidence: 99%

2D and 3D Imaging for Morphological Assessment of Maxillofacial Cyst and Tumors: A Case Series

de-Moraes¹,

de-Moraes²,

Olate

et al. 2021

Int. J. Morphol.

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The use and importance of cone beam computed tomography (CBCT) in the diagnostic, treatment and long term follow up of odontogenic tumours, as well as one reconstruction and dental implants rehabilitation is reported. This clinical series shows diagnosis of odontogenic tumors using selected and used CBCT for initial diagnosis, morphological characterization, and follow up for 5 to 10 years. The CBCTs showed the size and form of the tumor and the follow up showed a satisfactory remodelling of bone and the success in the rehabilition with dental implants. No signs of recurrence were observed. The conventional radiographies, Fan Beam CT, Cone Beam CT and Magnetic Resonance Imaging (MRI) are discussed in the use for follow up of odontogenic tumors. It is concluded that CBCT is an efficient tool for diagnosis, follow up and assessment of the morphology and size of the tumor in order to achieve the best treatment plan, returning the functional conditions to the patients.

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

Cited by 26 publications

References 36 publications

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

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

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

2D and 3D Imaging for Morphological Assessment of Maxillofacial Cyst and Tumors: A Case Series

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