Development and Validation of Nomograms Predicting Overall and Cancer-specific Survival of Spinal and Pelvic Tumor Patients with Distant Metastasis
Background: Primary spinal bone tumors with distant metastasis are a sign of advanced stage and are usually accompanied by poor prognosis. This study is to identify the risk factors and establish prognostic nomograms to predict 1- and 3-year overall survival (OS) and cancer-specific survival (CSS) rates for spinal and pelvic bone tumor patients with distant metastasis.Patients and methods: Spinal and pelvic bone tumor patients with distant metastasis between 1998 and 2016 were selected for this
... e selected for this study from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms to predict 1- and 3-year OS and CCS rates were constructed based on independent risk factors identified by univariate and multivariate Cox analyses. Concordance indexes (C-indexes), receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA) were used to assess the nomograms.Results: All patients (n=343) were randomly divided into a training cohort (n=243) and validation cohort (n=100). No significant differences were found in thedemographic data of all patients in the training and validation cohorts. Ultimately, only four independent risk factors (patient age, histology, grade and surgery) were identified as significantly associated with OS and CCS. The C-indices were 0.722 (95% CI, 0.685 to 0.759) and 0.686 (95% CI, 0.61 to 0.760) for the internal validation and external validation of the OS nomogram, respectively. Similarly, the C-indices based on the CCS nomogram were 0.717 (95% CI, 0.678 to 0.757) and 0.695 (95% CI, 0.619 to 0.771) for the internal validation and external validation, respectively. The calibration curves revealed that the predicted survival and actual survival were in concordance. DCA showed the clinical utility and benefits of the nomograms.Conclusion: The nomograms we constructed based on the SEER database can accurately predict individual patient survival.