A survival nomogram model constructed with common clinical characteristics to assist clinical decisions for diffuse low-grade gliomas: A population analysis based on SEER database

BACKGROUND: The prognosis of diffuse low-grade gliomas (DLGGs, WHO grade 2) is highly variable, making it difficult to evaluate individual patient outcomes. In this study, we used common clinical characteristics to construct a predictive model with multiple indicators. METHODS: We identified 2459 patients diagnosed with astrocytoma and oligodendroglioma from 2000 to 2018 in the SEER database. After removing invalid information, we randomly divided the cleaned patient data into training and validation groups. We performed univariate and multivariate Cox regression analyses and constructed a nomogram. Receiver operating characteristic (ROC) curve, c-index, calibration curve, and subgroup analyses were used to assess the accuracy of the nomogram by internal and external validation. RESULTS: After univariate and multivariate Cox regression analyses, we identified seven independent prognostic factors, namely, age (P<0.001), sex (P<0.05), histological type (P<0.001), surgery (P<0.01), radiotherapy (P<0.001), chemotherapy (P<0.05) and tumor size (P<0.001). The ROC curve, c-index, calibration curve, and subgroup analyses of the training group and the validation group showed that the model had good predictive value. The nomogram for DLGGs predicted patients’ 3-, 5- and 10-year survival rates based on these seven variables. CONCLUSIONS: The nomogram constructed with common clinical characteristics has good prognostic value for patients with DLGGs and can help physicians make clinical decisions.