Document Type
Abstract
Publication Date
2-2021
Academic Year
2020-2021
Abstract
Introduction: Surgical resection is a primary treatment for head and neck cancers that improves prognosis and quality of life for patients. Margin assessment is a critical component in this process as positive margins are associated with poor clinical outcomes. However, there is a lack of consensus on how surgical margins should be labeled for accurate origin identification. The objective of this project is to determine the difference in interpretation of surgical margin labels between and within Thomas Jefferson otolaryngologists and pathologists.
Methods: Adults with head and neck cancer who underwent surgical resection were identified. Pre-operative head and neck CT DICOM files were obtained, and a 3D segmentation of the tumor was generated and validated by radiology. For each surgical specimen, the pathology report designating the text-based label for each surgical margin was obtained. Study subjects include Thomas Jefferson otolaryngologists and pathologists. Each subject will identify and mark surgical margins on each segmented tumor based on the text-based label. The mean difference for each surgical margin coordinate dimension (x, y, z) will be calculated and compared between and within each group using a paired t-test.
Results: Anticipated results include variation in surgical margin origin between and within Thomas Jefferson otolaryngologists and pathologists. Preliminary data indicates lack of significant inter-surgeon reliability in the x dimension (p > 0.02).
Discussion: This study demonstrates inconsistent surgical margin labeling interpretation, suggesting a need for optimization and standardization. An optimized protocol has the potential to improve clinical outcomes for patients with head and neck cancers.
Recommended Citation
Ross, Heather; Banoub, MD, Raphael; Swendseid, MD, Brian; and Curry, MD, Joseph, "Reliability of Surgical Margin Labels Using 3D Radiographic Software" (2021). Phase 1. Paper 67.
https://jdc.jefferson.edu/si_ctr_2023_phase1/67
Language
English