Costa Andre Luiz Ferreira, Fardim Karolina Aparecida Castilho, Ribeiro Isabela Teixeira, Jardini Maria Aparecida Neves, Braz-Silva Paulo Henrique, Orhan Kaan, de Castro Lopes Sérgio Lúcio Pereira
Postgraduate Program in Dentistry, Cruzeiro do Sul University, São Paulo, SP, Brazil.
Department of Diagnosis and Surgery, São José dos Campos School of Dentistry of the São Paulo State University, São José dos Campos, SP, Brazil.
Imaging Sci Dent. 2023 Mar;53(1):43-51. doi: 10.5624/isd.20220166. Epub 2023 Jan 11.
This study aimed to assess texture analysis (TA) of cone-beam computed tomography (CBCT) images as a quantitative tool for the differential diagnosis of odontogenic and non-odontogenic maxillary sinusitis (OS and NOS, respectively).
CBCT images of 40 patients diagnosed with OS (N=20) and NOS (N=20) were evaluated. The gray level co-occurrence (GLCM) matrix parameters, and gray level run length matrix texture (GLRLM) parameters were extracted using manually placed regions of interest on lesion images. Seven texture parameters were calculated using GLCM and 4 parameters using GLRLM. The Mann-Whitney test was used for comparisons between the groups, and the Levene test was performed to confirm the homogeneity of variance (α=5%).
The results showed statistically significant differences (<0.05) between the OS and NOS patients regarding 3 TA parameters. NOS patients presented higher values for contrast, while OS patients presented higher values for correlation and inverse difference moment. Greater textural homogeneity was observed in the OS patients than in the NOS patients, with statistically significant differences in standard deviations between the groups for correlation, sum of squares, sum of entropy, and entropy.
TA enabled quantitative differentiation between OS and NOS on CBCT images by using the parameters of contrast, correlation, and inverse difference moment.
本研究旨在评估锥形束计算机断层扫描（CBCT）图像的纹理分析（TA），作为一种用于鉴别牙源性上颌窦炎和非牙源性上颌窦炎（分别为OS和NOS）的定量工具。
对40例诊断为OS（n = 20）和NOS（n = 20）的患者的CBCT图像进行评估。使用在病变图像上手动放置的感兴趣区域提取灰度共生矩阵（GLCM）参数和灰度游程长度矩阵纹理（GLRLM）参数。使用GLCM计算7个纹理参数，使用GLRLM计算4个参数。采用Mann-Whitney检验进行组间比较，并进行Levene检验以确认方差齐性（α = 5%）。
结果显示，OS组和NOS组患者在3个TA参数方面存在统计学显著差异（<0.05）。NOS组患者的对比度值较高，而OS组患者的相关性和逆差矩值较高。与NOS组患者相比，OS组患者的纹理均匀性更高，两组在相关性、平方和、熵和熵和的标准差方面存在统计学显著差异。
通过使用对比度、相关性和逆差矩参数，TA能够在CBCT图像上对OS和NOS进行定量鉴别。
