Prediction of survival in patients with head and neck cancer

In patients with head and neck squamous cell carcinoma (HNSCC) the estimated prognosis is usually based on the TNM classification. The relative weight of the three contributing parameters is often not completely clear. Moreover, the impact of other important clinical variables such as age, gender, prior malignancies, etc is very difficult to substantiate in daily clinical practice. The Cox-regression model allows us to estimate the effect of different variables simultaneously. The purpose of this study was to design a model for application in new HNSCC patients. In our historical data-base of patients with HNSCC, patient, treatment, and follow-up data are stored by trained oncological data managers. With these hospital-based data, we developed a statistical model for risk assessment and prediction of overall survival. This model serves in clinical decision making and appropriate counseling of patients with HNSCC.

All patients with HNSCC of the oral cavity, the pharynx, and the larynx diagnosed in our hospital between 1981 and 1998 were included. In these 1396 patients, the prognostic value of site of the primary tumor, age at diagnosis, gender, T-, N-, and M-stage, and prior malignancies were studied univariately by Kaplan-Meier curves and the log-rank test. The Cox-regression model was used to investigate the effect of these variables simultaneously on overall survival and to develop a prediction model for individual patients.

In the univariate analyses, all variables except gender contributed significantly to overall survival. Their contribution remained significant in the multivariate Cox model. Based on the relative risks and the baseline survival curve, the expected survival for a new HNSCC patient can be calculated.

It is possible to predict survival probabilities in a new patient with HNSCC based on historical results from a data-set analyzed with the Cox-regression model. The model is supplied with hospital-based data. Our model can be extended by other prognostic factors such as co-morbidity, histological data, molecular biology markers, etc. The results of the Cox-regression may be used in patient counseling, clinical decision making, and quality maintenance.