Computed Tomography Data to Generate a Reproducible, Anatomically Accurate Hemilaryngeal Model

Justin M. Hintze, Cheryl E. Myers, Michael J. McPhail, Yourka D. Tchoukalova, David G. Lott

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Objective: The study aims to demonstrate the reproducibility and feasibility of creating a hemilaryngeal model with a medialized vocal fold (VF) using 3-dimensional (3D) modeling techniques in both healthy larynges and those affected by cancer. Study Design: Three-dimensional modeling of human larynges. Setting: Tertiary academic referral center and regenerative medicine laboratory. Subjects and Methods: Computed tomography (CT) scans from 10 healthy control and 10 patients with laryngeal cancer were segmented and imported into 3D modeling software. The larynx was cut sagittally to create a hemilaryngeal model and the vocal fold medialized. Measurements were taken from the CT and 3D model data and compared. Results: All control modeling data closely matched the CT data and were not statistically different from each other. There was a significant correlation between subglottic anteroposterior diameter and VF length (r2 = 0.78, P =.0008), and it may be a valuable tool to infer true VF dimension in cases where disruption has occurred. The modeling data from patients with cancer did not show statistical difference to the control data, showing that accurate modeling can also be achieved in patients with laryngeal cancer. Conclusion: CT scan-based 3D modeling of the larynx and VF is possible and reproducible. The results closely match those previously reported in the literature and can also be replicated in cases with laryngeal cancer. This study paves the way for future de novo fabricated laryngeal scaffolds that can be synthesized using 3D printers and tailored to meet surgical demands.

Original languageEnglish (US)
Pages (from-to)472-477
Number of pages6
JournalOtolaryngology - Head and Neck Surgery (United States)
Issue number3
StatePublished - Sep 1 2019

All Science Journal Classification (ASJC) codes

  • Surgery
  • Otorhinolaryngology


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