Codari Marina, Zago Matteo, Guidugli Giulia A, Pucciarelli Valentina, Tartaglia Gianluca M, Ottaviani Francesco, Righini Stefano, Sforza Chiarella
1 Functional Anatomy Research Center (FARC), Laboratorio di Anatomia Funzionale, dell'Apparato Stomatognatico, Dipartimento di Scienze Biomediche per la Salute, Facoltà di Medicina e Chirurgia, Università degli Studi di Milano, Milano, Italy.
2 Dipartimento di Scienze Cliniche e di Comunità, Facoltà di Medicina e Chirurgia, Università degli Studi di Milano, Milano, Italy.
Dentomaxillofac Radiol. 2016;45(2):20150327. doi: 10.1259/dmfr.20150327. Epub 2015 Dec 21.
To assess whether three-dimensional morphometric parameters could be useful in nasal septal deviation (NSD) diagnosis and, secondarily, whether CBCT could be considered an adequate imaging technique for the proposed task.
We analysed images of 46 subjects who underwent CBCT for reasons not related to this study. Two experienced operators divided all the images into healthy and NSD subjects. Subsequently, the images were segmented using ITK Snap in order to obtain the three-dimensional model of the nasal airways and compute four morphological parameters: septal deviation angle (SDA), percentage of volume difference between right and left side of the nasal airways, nasal airway total volume and a new synthetic septal deviation index (SDI). Principal component analysis (PCA) was used to unveil relationships between each variable and the global nasal airway variability.
Differences between the groups were found in SDA (p < 0.001), in volume percentage difference (p < 0.05) and in SDI (p < 0.001). PCA showed high correlation between the SDI and the first principal component (0.97, p < 0.001).
Among the analysed parameters, SDI seemed to be the most suitable for the quantitative assessment of NSD, and CBCT allowed accurate assessment of airway morphology.
评估三维形态学参数是否有助于鼻中隔偏曲(NSD)的诊断,其次,CBCT是否可被视为用于该任务的合适成像技术。
我们分析了46名因与本研究无关的原因接受CBCT检查的受试者的图像。两名经验丰富的操作人员将所有图像分为健康受试者和NSD受试者。随后,使用ITK Snap对图像进行分割,以获得鼻气道的三维模型,并计算四个形态学参数:鼻中隔偏曲角度(SDA)、鼻气道左右两侧体积差异百分比、鼻气道总体积和一个新的综合鼻中隔偏曲指数(SDI)。主成分分析(PCA)用于揭示每个变量与全球鼻气道变异性之间的关系。
两组之间在SDA(p < 0.001)、体积百分比差异(p < 0.05)和SDI(p < 0.001)方面存在差异。PCA显示SDI与第一主成分之间高度相关(0.97,p < 0.001)。
在所分析的参数中,SDI似乎最适合用于NSD的定量评估,并且CBCT能够准确评估气道形态。