A novel nomogram and risk classification system for predicting overall survival in head and neck squamous cell cancer with distant metastasis at initial diagnosis.

INTRODUCTION: Head and neck squamous cell carcinoma (HNSCC) is one of the most invasive cancer types globally, and distant metastasis (DM) is associated with a poor prognosis. The objective of this study was designed to construct a novel nomogram and risk classification system to predict overall survival (OS) in HNSCC patients presenting with DM at initial diagnosis. METHODS: HNSCC patients with initially diagnosed DM between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Firstly, all patients were randomly assigned to a training cohort and validation cohort (8:2), respectively. The Cox proportional hazards regression model was used to analyze the prognostic factors associated with OS. Then, the nomogram based on the prognostic factors and the predictive ability of the nomogram were assessed by the calibration curves, receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Finally, a risk classification system was established according to the nomogram scores. RESULTS: A total of 1240 patients initially diagnosed with HNSCC with DM were included, and the 6-, 12- and 18-month OS of HNSCC with DM were 62.7%, 40.8% and 30%, respectively. The independent prognostic factors for HNSCC patients with DM included age, marital status, primary site, T stage, N stage, bone metastasis, brain metastasis, liver metastasis, lung metastasis, surgery, radiotherapy and chemotherapy. Based on the independent prognostic factors, a nomogram was constructed to predict OS in HNSCC patients with DM. The C-index values of the nomogram were 0.713 in the training cohort and 0.674 in the validation cohort, respectively. The calibration curves and DCA also indicated the good predictability of the nomogram. Finally, a risk classification system was built and it revealed a statistically significant difference among the three groups of patients according to the nomogram scores. CONCLUSIONS: Factors associated with the overall survival of HNSCC patients with DM were found. According to the identified factors, we generated a nomogram and risk classification system to predict the OS of patients with initially diagnosed HNSCC with DM. The prognostic nomogram and risk classification system can help to assess survival time and provide guidance when making treatment decisions for HNSCC patients with DM.

Development and validation of novel nomograms to predict survival of patients with tongue squamous cell carcinoma.

BACKGROUND: There is no unified standard to predict postoperative survival in patients with tongue squamous cell carcinoma (TSCC), hence the urgency to develop a model to accurately predict the prognosis of these patients. AIM: To develop and validate nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) of patients with TSCC. METHODS: A cohort of 3454 patients with TSCC from the Surveillance, Epidemiology, and End Results (SEER) database was used to develop nomograms; another independent cohort of 203 patients with TSCC from the Department of Oral and Maxillofacial Surgery, First Affiliated Hospital of Zhejiang University School of Medicine, was used for external validation. Univariate and multivariate analyses were performed to identify useful variables for the development of nomograms. The calibration curve, area under the receiver operating characteristic curve (AUC) analysis, concordance index (C-index), net reclassification index (NRI), and decision curve analysis (DCA) were used to assess the calibration, discrimination ability, and clinical utility of the nomograms. RESULTS: Eight variables were selected and used to develop nomograms for patients with TSCC. The C-index (0.741 and 0.757 for OS and CSS in the training cohort and 0.800 and 0.830 in the validation cohort, respectively) and AUC indicated that the discrimination abilities of these nomograms were acceptable. The calibration curves of OS and CSS indicated that the predicted and actual values were consistent in both the training and validation cohorts. The NRI values (training cohort: 0.493 and 0.482 for 3- and 5-year OS and 0.424 and 0.402 for 3- and 5-year CSS; validation cohort: 0.635 and 0.750 for 3- and 5-year OS and 0.354 and 0.608 for 3- and 5-year CSS, respectively) and DCA results indicated that the nomograms were significantly better than the tumor-node-metastasis staging system in predicting the prognosis of patients with TSCC. CONCLUSION: Our nomograms can accurately predict patient prognoses and assist clinicians in improving decision-making concerning patients with TSCC in clinical practice.