Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer in the world, with 890 000 new cases and 450 000 deaths in 2018 worldwide. 1 Despite improvements in research and therapy made in the last decades, survival has not significantly improved and the 5 years overall survival (OS) rate is still less than 50%. 2 Classic prognostic factors are not sufficient to predict patients' prognosis, due to the heterogeneity of molecular mechanisms and tumour behaviours related to HNSCC. For these reasons, there has been an intensified interest in biomarkers' discovery for early diagnosis, prognosis and personalized treatment.In this scenario, inflammatory biomarkers became a reliable and accessible source of information to investigate and correlate to
(1) Background: An accurate prediction of cancer survival is very important for counseling, treatment planning, follow-up, and postoperative risk assessment in patients with Oral Squamous Cell Carcinoma (OSCC). There has been an increased interest in the development of clinical prognostic models and nomograms which are their graphic representation. The study aimed to revise the prognostic performance of clinical-pathological prognostic models with internal validation for OSCC. (2) Methods: This systematic review was performed according to the Cochrane Handbook for Diagnostic Test Accuracy Reviews chapter on searching, the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines, and the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). (3) Results: Six studies evaluating overall survival in patients with OSCC were identified. All studies performed internal validation, while only four models were externally validated. (4) Conclusions: Based on the results of this systematic review, it is possible to state that it is necessary to carry out internal validation and shrinkage to correct overfitting and provide an adequate performance for optimism. Moreover, calibration, discrimination and nonlinearity of continuous predictors should always be examined. To reduce the risk of bias the study design used should be prospective and imputation techniques should always be applied to handle missing data. In addition, the complete equation of the prognostic model must be reported to allow updating, external validation in a new context and the subsequent evaluation of the impact on health outcomes and on the cost-effectiveness of care.
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