Department of Molecular Pharmacology, Physiology, and Biotechnology, Brown University.
Division of Plastic & Reconstructive Surgery, the Warren Alpert Medical School of Brown University, Providence, RI.
J Craniofac Surg. 2020 Sep;31(6):1659-1663. doi: 10.1097/SCS.0000000000006597.
Despite extensive literature on the classification and management of nasal septal deviation (NSD) for preoperative planning, standardized objective measures to evaluate the NSD severity remains challenging. In this study, we quantitatively analyzed NSD to determine the most predictive two-dimensional (2D) computed tomography (CT)-landmark for overall three-dimensional (3D) septal morphology derived from nasal airway segmentation.
A retrospective study was conducted at a large academic center. One hundred four patients who underwent CT scans of the face were selected from a computer imaging database. Demographic variables were screened to ensure an equal number of men and women in different age groups. Digital Imaging and Communications in Medicine files were imported for 3D nasal cavity segmentation using 3D Slicer software. A volumetric analysis was performed to determine 3D NSD ratios. These values were compared to previously reported methods of obtaining objective 2D NSD measures using OsiriX and MATLAB software. Maximum deviation values were calculated using OsiriX, while the root mean square values were retrieved using MATLAB. Deviation area and curve to line ratios were both quantified using OsiriX and MATLAB.
The data set consisted of 52 men and 52 women patients aged 20 to 100 years (mean = 58 years, standard deviation = 23 years). There was a strong correlation between 3D NSD ratio and maximum deviation (r = 0.789, P < 0.001) and deviation area (r = 0.775, P < 0.001). Deviation area (r = 0.563, P < 0.001), root mean square (r = 0.594, P < 0.001), and curve to line ratio (r = 0.470, P < 0.001) had a positive correlation of moderate strength. The curve to line ratio was not significant (r = 0.019, P = 0.85).
The 2D CT-based NSD landmarks maximum deviation and deviation area were the most predictive of the severity of NSD from 3D nasal cavity segmentation. We present a robust open-source method that may be useful in predicting the severity of NSD in CT images.
尽管有大量关于鼻中隔偏曲（NSD）分类和管理的文献用于术前规划，但评估 NSD 严重程度的标准化客观测量仍具有挑战性。在这项研究中，我们对 NSD 进行了定量分析，以确定从鼻腔气道分割得出的整体三维（3D）鼻中隔形态的最具预测性的二维（2D）计算机断层扫描（CT）标志。
在一个大型学术中心进行了回顾性研究。从计算机成像数据库中选择了 104 名接受面部 CT 扫描的患者。筛选了人口统计学变量，以确保不同年龄组的男性和女性人数相等。使用 3D Slicer 软件导入数字成像和通信医学文件进行 3D 鼻腔分割。进行了体积分析以确定 3D NSD 比值。将这些值与以前使用 OsiriX 和 MATLAB 软件获得客观 2D NSD 测量值的方法进行了比较。使用 OsiriX 计算最大偏差值，而使用 MATLAB 检索均方根值。使用 OsiriX 和 MATLAB 同时量化了偏差面积和曲线到线的比值。
数据集由 52 名 20 至 100 岁（平均年龄为 58 岁，标准差为 23 岁）的男性和 52 名女性患者组成。3D NSD 比值与最大偏差（r = 0.789，P < 0.001）和偏差面积（r = 0.775，P < 0.001）之间存在很强的相关性。偏差面积（r = 0.563，P < 0.001）、均方根（r = 0.594，P < 0.001）和曲线到线的比值（r = 0.470，P < 0.001）具有中等强度的正相关性。曲线到线的比值不显著（r = 0.019，P = 0.85）。
基于 2D CT 的 NSD 标志最大偏差和偏差面积是从 3D 鼻腔分割预测 NSD 严重程度的最具预测性的指标。我们提出了一种强大的开源方法，可能有助于预测 CT 图像中 NSD 的严重程度。
