The Role of Blood Inflammatory Biomarkers and Perineural and Lympho-Vascular Invasion to Detect Occult Neck Lymph Node Metastases in Early-Stage (T1-T2/N0) Oral Cavity Carcinomas
Year: 2025
Type: article
Study design: Observational Study
Abstract: BACKGROUND/OBJECTIVES: Oral cavity carcinomas (OCCs) represent roughly 50% of all head and neck cancers. The risk of occult neck metastases for early-stage OCCs ranges from 15 to 35%, thus the need to develop tools that can support the diagnosis detecting these neck metastases. Inflammatory biomarkers and perineural and lympho-vascular invasion are emerging as effective in this field. The aim of this study is to demonstrate the effectiveness of these parameters to detect occult neck metastases in early-stage (T1-T2/N0) OCCs. METHODS: A retrospective analysis was conducted on 81 patients surgically treated for early-stage OCC. For all patients, data regarding TNM, pN status after the histopathological examination, inflammatory biomarkers, and perineural and lympho-vascular invasion have been obtained. A statistical analysis was performed using the receiver operating characteristic (ROC) curve to calculate the optimal cutoff values for SII, SIRI, PLR, and NLR. RESULTS: Fifty-eight patients confirmed N0 status after surgery, while twenty-three resulted pN+. The best cut-off to detect occult neck metastases were PLR 249.30, NLR 13.10, MLR 0.439, SII 1043.12, and SIRI 1.85. The accuracy to detect occult neck metastases was PLR 75%, NLR 81%, MLR 74%, SII 73%, SIRI 70%, perineural invasion 70%, and lympho-vascular invasion 83%. CONCLUSIONS: Our results confirm that inflammatory biomarkers and perineural and lympho-vascular invasion are effective in detecting occult neck metastases in early-stage OCCs. The clinical relevance of this study is that these parameters could be used routinely as preoperative tools to support diagnosis and to help surgeons in the decision-making process, particularly regarding surgical indications for neck lymph nodes treatment.
Source: Cancers
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,499
OpenAlex ID: W4409437934
