P10.03 Establishment and validation of clinical prediction model in WHO grade II glioma

OBJECTIVE WHO grade II glioma has the characteristics of heterogeneity, and this disease progresses rapidly in some patients, in whom the malignant degree is equivalent to that of high-grade glioma. In order to accurately predict the prognosis of patients, an effective clinical prediction model based on relevant risk factors is needed which could provide a theoretical basis for optimization of clinical individualized treatment. METHODS According to the inclusion and exclusion criteria, eligible patients from January 2010 to December 2018 in our hospital were selected, and those who met the criteria were randomly assigned 4:1 to the training group and the validation group, respectively. The predictors were screened by univariate and multivariate Cox regression analysis, the prediction model was established, and the model was verified and evaluated. RESULTS A total of 258 patients with WHO grade II glioma were recruited, including 208 patients as the training group and 50 patients as the validation group. Six independent risk factors, including patient age, preoperative Karnofsky performance status (KPS) score, preoperative seizure symptoms, surgical resection range, tumor size and IDH status, were selected and included into the prediction model by univariate and multivariate Cox regression analysis, and were visualized in the form of Nomogram. The concordance index (C index) was used to evaluate the predictive ability of the model. Results showed that the C-index was 0.832 in the training group and 0.853 in the validation group, respectively, indicating good performance for the prediction model. The calibration charts were drawn in both groups respectively, which showed that the calibration lines were in good agreement with the standard lines, indicating good consistency between the two groups. CONCLUSIONS In this study, a clinical prediction model for WHO grade II glioma was established, and it was verified that the model has good predictive ability, which may be beneficial for clinical work.