Variance in 3D anatomic localization of surgical margins based on conventional margin labeling in head and neck squamous cell carcinoma.

Objective: In head and neck cancer (HNC), positive margins are strongly predictive of treatment failure. We sought to measure the accuracy of localization of margin sampling sites based on conventional anatomic labels using a digital 3D-model.
Methods: Preoperative CT scans for 9 patients with HNC treated operatively at our institution were imported into a multiplanar radiology software, which was used to render a digital 3D model of each tumor intended to represent the resection specimen. Surgical margin labels recorded during the operative case were collected from pathology records. Margin labels (N = 64) were presented to participating physicians.Participants were asked to mark the anatomic location of each surgical margin using the 3D-model and corresponding radiographic planes for reference.For each individual margin, the 3D coordinates of each participant's marker were used to calculate a mean localization point called the geometric centroid. Mean distance from individual markers to the centroid was compared between participantsand margin types.
Results: Amongst 7 surgeons, markers were placed a mean distance of 12.6 mm ([SD] = 7.5) from the centroid.Deep margins were marked with a greater mean distance than mucosal/skin margins (19.6 [24.8] mm vs. 15.3 [14.9] mm, p = 0.034). When asked to relocate a margin following re-resection, surgeons marked a point an average of 20.6 [12.4] mm from their first marker with a range of 3.9- 45.1 mm.
Conclusions: Retrospective localization of conventionally labeled margins is an imprecise process with variability across the care team. Future interventions targeting margin documentation and communication may improve sampling precision.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023. Published by Elsevier Ltd.)