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.

Background: The aim of this study has been to investigate if inflammatory biomarkers such as the systemic immune-inflammation index (SII), neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) have any correlation with the recurrence of pleomorphic adenomas (PA) in patients afflicted by this disease. Methods: A retrospective multicentre study involving two Italian centres, the Maxillofacial Sur-gery at the Policlinico Federico II in Naples and the ENT and Maxillofacial Surgery at the IRCCS G. Pascale National Cancer Institute in Naples, has been performed. The study, involving 608 pa-tients who had received a diagnosis of PA, was conducted between January 2000 and January 2020. A Receiver Operating Characteristic (ROC) curve was used to calculate the optimal cut-off values for the SII, PLR and NLR. A linear regression model (LRM) and a non-linear logistic re-gression model (NLRM) were realized with respect to the NLR, PLR and SII. Results: The highest performance was achieved by the NLR (cut-off 2.960) with the AUC = 0.91, with an accuracy of 96.1%, sensitivity of 78.4% and specificity of 97.2%, PPV = 64.4% and NPV = 98.6%. The LRM and NLRM including all the haematological parameters did not increase the per-formance in terms of recurrence detection. Conclusion: The NLR is an excellent support tool in predicting the recurrence of PA with a cut-off of 2.960. It can be a good reference point for surgical decision-making and follow-up in clinical practice.