ABSTRACT
BACKGROUND: This aim of study was to develop and validate clinical nomograms to predict the survival of patients with medullary thyroid cancer. PATIENTS AND METHODS: Patient data were collected from the Surveillance, Epidemiology, and End Results database between 2004 and 2013. All included patients were randomly assigned into the training and validation sets. Multivariate analysis using Cox proportional hazards regression was performed, and nomograms were constructed. Model performance was evaluated by discrimination and calibration plots. RESULTS: A total of 1,657 patients were retrospectively analyzed. The multivariate Cox model identified age, tumor size, extrathyroidal extension, N stage, and M stage as independent covariates associated with overall survival (OS) and cancer-specific survival (CSS). Nomograms predicting OS and CSS were constructed based on these covariates. The nomograms predicting both OS and CSS exhibited superior discrimination power to that of TNM staging system in the training and validation sets. Calibration plots indicated that both the nomograms in OS and CSS exhibited high correlation to actual observed results. CONCLUSION: The nomograms established in this study provided an alternative tool for prognostic prediction, which may thereby improve individualized assessment of survival risks and lead to the creation of additional clinical therapies.
ABSTRACT
BACKGROUND: The aim of this study was to develop and validate nomograms for individual risk prediction in patients with liver-only colorectal metastases (CRLM). METHODS: Histologically confirmed CRLM diagnosed between 2010 and 2015 were analysed from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate analyses were used to obtain independent prognostic factors to build nomograms for predicting 1- and 3-year overall survival (OS) and cancer-specific survival (CSS). The predictive accuracy of the nomogram was determined by concordance index (C-index) and calibration plots. RESULTS: A total of 9615 patients with CRLM were included in the study. A nomogram predicting OS was constructed according to 9 independent clinicopathological factors. A nomogram predicting CSS was constructed based on the same 9 factors. The C-indexes of the nomograms were significantly better than the TNM staging system (7th edition) in both sets for predicting both OS and CSS. The calibration plots displayed an optimal agreement between the predictive results and the actual observed outcomes. CONCLUSIONS: The proposed nomograms can help clinicians calculate the probability in patients with CRLM.
