Quantification Of Nasal Septal Deviation With Computed Tomography Data

Background: 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.Methods: 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.Results: 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).Conclusions: 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.