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Updated: Aug 2, 2026

A Model for Perineural Invasion in Head and Neck Squamous Cell Carcinoma
Published on: January 5, 2017
Development of a Prognostic Model for Oral Cancer by Incorporating Novel Nodal Parameters Beyond Conventional TNM
Ping-Chia Cheng1,2,3, Chih-Ming Chang1,3, Li-Jen Liao1,2,4
1Department of Otolaryngology Head and Neck Surgery, Far Eastern Memorial Hospital, New Taipei City 220216, Taiwan.
None:
Background: Oral cancer is a major global health burden with heterogeneous survival outcomes. This study aimed to identify clinicopathological factors, particularly lymph node-related parameters, associated with prognosis in patients with oral cancer and to construct a survival model for predicting overall survival (OS). Methods: A total of 174 patients with oral cancer who underwent surgery between January 2018 and November 2021 were retrospectively analyzed. Clinicopathological variables, including age, gender, body mass index (BMI), pathological T, N and overall stage, tumor subsite, perineural invasion (PNI), lymphovascular invasion (LVI), surgical margin status, lymph node yield (LNY), lymph node metastases (LNM), and lymph node ratio (LNR), were evaluated. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors for OS and disease-specific survival (DSS). Results: Univariate analysis showed that older age, lower BMI, advanced pathological stage, presence of PNI or LVI, positive/close margins, LNY < 15, LNM ≥ 3, and LNR ≥ 0.0454 were significantly associated with poorer OS. Multivariate analysis identified age ≥ 63 years, pathological stage 3-4, LNY < 15, LNM ≥ 3, and LNR ≥ 0.0454 as independent predictors of OS. LNR ≥ 0.0454 was the only independent predictor of DSS. A survival model incorporating age, pathological stage, LNY, LNM, and LNR demonstrated good discriminatory ability for OS. Conclusions: Multiple independent prognostic factors for oral cancer survival were identified. The proposed survival model provides a practical tool for risk stratification and may assist personalized treatment planning, with particular emphasis on lymph node-related parameters.
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