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Original Investigation
July 25, 2019

Categorization and Analysis of Nasal Base Shapes Using a Parametric Model

Author Affiliations
  • 1Department of Otolaryngology–Head and Neck Surgery, School of Medicine, University of California, Irvine
  • 2Washington University in St Louis, St Louis, Missouri
  • 3Department of Biostatistics, University of California, Los Angeles Fielding School of Public Health, Los Angeles
  • 4Beckman Laser Institute & Medical Clinic, University of California, Irvine
  • 5Department of Biomedical Engineering, University of California, Irvine
JAMA Facial Plast Surg. Published online July 25, 2019. doi:10.1001/jamafacial.2019.0483
Key Points

Question  Can nasal base shape be objectively characterized and classified?

Findings  This cohort of 420 patients who were evaluated for possible facial plastic surgery analyzes images of the nasal base of each patient and uses a parametric model to characterize nasal base shapes. Shape-defining numeric data from the model showed adequate concordance with the predictions of facial plastic surgeons.

Meaning  Numerical description of the nasal base can be derived and potentially used in objective preoperative and postoperative assessment of nasal appearance and in clinical communication.

Abstract

Importance  Nasal base view is important for rhinoplasty analysis. Although some descriptors of nasal base shape exist, they are largely subjective and qualitative.

Objective  To evaluate a parametric model of nasal base shape and compare it with categorization by surgeons to create an objective classification system for clinical evaluation and communication.

Design, Setting, and Participants  Retrospective cohort review of deidentified photographs of 420 patients evaluated for possible facial plastic surgery at a tertiary care academic medical center between January 2013 and June 2017. The nasal bases were classified into 6 shape categories (equilateral, boxy, cloverleaf, flat, round, and narrow) via visual inspection. The contour of each nasal base was traced using MATLAB software (MathWorks Inc). The software then performed a curve fit to the parametric model with output of values for 5 parameters: projection-to-width ratio, the anterior-posterior positioning of the tip bulk, symmetry, degree of lateral recurvature of the nasal base, and size. The differences among shape categories for each parameter were analyzed using 1-way analysis of variance. Pairwise comparisons were then performed to ascertain how the various shapes differed. Finally, a multinomial logistic regression model was used to predict nasal base shape using parameter values. Data were analyzed between April 2017 and January 2018.

Main Outcomes and Measures  An algorithm that categorized nasal base shapes into 6 categories.

Results  The 420 nasal base photographs of patients evaluated for possible plastic surgery were categorized into 1 of 6 categories; 305 photographs were readily classified, and the remaining 115 were termed unclassified and were categorized. For both the classified and unclassified nasal base groups, there were statistically significant differences between projection-to-width ratio (classified, F5,299 = 21.51; unclassified, F4,100 = 10.59; P < .001), the anterior-posterior positioning of the tip bulk (classified, F5,299 = 3.76; P = .003; unclassified, F4,110 = 4.54; P = .002), and degree of lateral recurvature of the nasal base (classified, F5,299 = 24.14; unclassified, F4,100 = 7.21; P < .001). A multinomial logistic regression model categorization was concordant with surgeon categorization in 201 of 305 (65.9%) cases of classified nasal bases and 38 of 115 (33.0%) unclassified nasal bases.

Conclusions and Relevance  The parametric model may provide an objective and numerical approach to analyzing nasal base shape.

Level of Evidence  NA.

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