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Updated: Sep 11, 2025

Intraoperative Assessment of Resection Margins in Oral Cavity Cancer: This is the Way
Published on: May 10, 2021
Clinicopathologic Predictors of Survival Following Oral Cancer Surgery: A Retrospective Cohort Study
Katarzyna Stawarz1, Karolina Bieńkowska-Pluta1, Adam Galazka1
1Head and Neck Cancer Department, Maria Sklodowska-Curie National Research Institute of Oncology, 02-781 Warsaw, Poland.
Abstract:
Background: Despite advances in treatment, oral squamous cell carcinoma (OSCC) remains associated with high recurrence and mortality rates. Traditional TNM staging, while foundational, may not fully capture tumor aggressiveness. This study aimed to identify clinical and histopathological predictors of survival to enhance risk stratification and guide treatment planning in OSCC patients. Methods: A retrospective study of 100 patients with confirmed OSCC treated surgically with curative intent between January 2019 and January 2024 was analyzed. Clinicopathologic variables-including tumor volume, angioinvasion, perineural invasion, lymphatic invasion, and nodal status-were evaluated. Disease-specific survival (DSS) was assessed using Kaplan-Meier estimates, Cox regression, and logistic regression models. Results: The cohort had a mean age of 62.1 years, with a 46% OS rate and 43% DSS at study end. Perineural invasion (44%) and lymphatic invasion (42%) were the most common invasive features. Kaplan-Meier analysis revealed significantly reduced DSS in patients with angioinvasion, perineural invasion, and pN+ status. Multivariate logistic regression identified perineural invasion (OR = 3.93, p = 0.0023) and pN+ status (OR = 2.74, p = 0.0284) as independent predictors of cancer-specific mortality. Tumor volume was significantly associated with lymphatic invasion but not directly with DSS. Conclusions: Perineural invasion, angioinvasion, lymph node involvement, and tumor volume are important prognostic markers in OSCC, offering critical information beyond TNM staging. Incorporating these features into risk assessment models could improve prognostic accuracy and inform more individualized treatment strategies for high-risk OSCC patients.
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