Predictors of tunneling time in transoral robotic thyroidectomy: impact of anatomical measurements

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Authors: Minwoo Kim, Seo Young Kim, Pilkeun Jang, Jeong-Yeon Ji, Wonjae Cha, Kyung Tae, Woo‐Jin Jeong
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References: 14

Abstract

PURPOSE: To identify anatomical and clinical predictors of tunneling time during transoral robotic thyroidectomy via the vestibular approach (TORTVA) and to clarify the potential utility of body habitus-related predictors for case selection. METHODS: We retrospectively reviewed 33 consecutive patients who underwent TORTVA between October 2023 and December 2024. Tunneling time was defined as the interval from vestibular incision to completion of the subplatysmal tunnel. Preoperative sagittal neck computed tomography (CT) images were used to measure linear distances and angles between the mentum, hyoid bone, thyroid cartilage, thyroid isthmus, and sternal notch, as well as anterior neck soft tissue thickness. Clinicopathologic data and CT measurements were analyzed using univariate and multivariate linear regression. RESULTS: The mean tunneling time was 25.3 min, with no significant difference observed between males (25.6 min) and females (25.2 min). In multivariate linear regression analysis, the skin-to-thyroid cartilage notch thickness (tST) (B=0.550, p=.024) and the mentum-to-thyroid cartilage notch distance (dMT) (B=0.347, p=.028) were significantly associated with tunneling time (p=.006). CONCLUSION: Tunneling time was associated with skin thickness over the thyroid cartilage notch and the mentum-to-thyroid cartilage notch distance. These exploratory findings may inform future studies on patient selection and technical difficulty in TORTVA, particularly for surgeons early in the learning curve.

Fields of Study & Subjects

Thyroid and Parathyroid Surgery Thyroid Cancer Diagnosis and Treatment Oropharyngeal Anatomy and Pathologies

Publication details

Journal / source
European Archives of Oto-Rhino-Laryngology
Publisher
Springer Science and Business Media LLC
Publication date
19 August 2026
Language
EN