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Intraoperative Assessment of Resection Margins in Oral Cavity Cancer: This is the Way
Published on: May 10, 2021
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Pathology protocol increases lymph node yield in neck dissection for oral cavity squamous cell carcinoma
Andrew J Holcomb1, Mollie Perryman1, Sara Goodwin1
1Department of Otolaryngology, Head and Neck Surgery, The University of Kansas Medical Center, Kansas City, Kansas, USA.
Head & Neck
|June 25, 2020
Summary
Optimizing lymph node yield (LNY) in oral cavity squamous cell carcinoma (OCSCC) neck dissections is crucial. A new pathology protocol assessing adipose tissue significantly improves LNY accuracy, influenced by factors like dissected levels and prior treatments.
Area of Science:
- Oncology
- Pathology
- Surgical Quality Indicators
Background:
- Lymph node yield (LNY) is a proposed quality indicator for neck dissections in oral cavity squamous cell carcinoma (OCSCC).
- Accurate LNY is essential for staging and treatment planning in OCSCC patients.
Purpose of the Study:
- To evaluate the impact of a modified pathologic grossing protocol on lymph node yield (LNY) in OCSCC neck dissections.
- To identify factors influencing LNY in patients undergoing neck dissection for OCSCC.
Main Methods:
- Retrospective analysis of 190 OCSCC patients undergoing neck dissection (2016-2018).
- Implementation of a new protocol to assess residual adipose tissue in pathology specimens.
- Generalized linear model used to assess variables impacting LNY.
Main Results:
- Mean LNY was 28.59 (SD = 17.65); the protocol identified a mean of 10.32 lymph nodes per case.
- Multivariable analysis revealed significant associations between LNY and the pathology protocol (P=.02), number of dissected lymph node levels (P<.001), positive lymph nodes (P=.002), BMI (P=.02), prior neck surgery (P=.001), and prior neck radiation (P=.001).
Conclusions:
- Assessing residual adipose tissue in neck dissection specimens enhances LNY accuracy.
- Lymph node yield is multifactorial, significantly influenced by pathologic assessment methods and patient-specific variables.

