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Updated: Feb 9, 2026

Intraoperative Assessment of Resection Margins in Oral Cavity Cancer: This is the Way
Published on: May 10, 2021
Development of an electrosurgery-compatible simulation task for quantitatively assessing oral cancer resection
Kayo Sakamoto1, Sohei Mitani2, Naoki Nishio3
1Department of Otolaryngology-Head and Neck Surgery, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, 791-0295, Japan.
Background:
The quality of oral cancer resection is extremely important for patient outcomes, such as local control and survival. However, most current simulators either provide only rater-dependent feedback or are not compatible with electrosurgery. Therefore, we developed an electrosurgical simulation task for oral cancer resection that provides objective quantitative metrics and collected initial validity evidence.
Methods:
We developed a soft tissue simulation task using a plant-derived model that supports electrosurgery. As quantitative measures demonstrating "ensuring appropriate margins" in oral cancer resection and "maintaining safety" during electrosurgery, we employed nine-directional margin error distance and tumor bed carbonization degree measured using a spectral colorimeter. As validity evidence of the task, 10 expert surgeons completed a questionnaire about the task. In addition, five experts and 12 novices performed the task, and quantitative data obtained from their performance was used for evaluation.
Results:
The replication of oral cancer resection was highly evaluated (4.4 out of 5 points), and quantitative measures for evaluating the skills of surgeons (4.8 out of 5 points) were agreed upon by experts. The internal consistency of the measures was good (Cronbach's alpha: 0.803). Compared to novices, experts had smaller margin errors (0.79 mm vs 2.45 mm), lower carbonization (ΔE: 2.33 vs 8.70), faster resection times, and fewer grasping attempts (all P < 0.001).
Conclusion:
This user-friendly plant-derived simulation task is compatible with electrosurgery and provides objective quantitative performance metrics. These findings support its use as a practical assessment tool for formative feedback in simulation-based training.
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