Development and Evaluation of Prognostic Nomogram Model for Adult Ventricle Glioma Patients

  Objective   To explore the prognostic factors of adult ventricle glioma (AVG) and to construct and evaluate a survival-related prognostic nomogram model, which could provide further reference for the clinical management of AVG patients.

  Methods  The patients covered in the study were selected from the Surveillance Epidemiology and End Results (SEER) database (1973–2016). They all had definite histological diagnosis of AVG. They were assigned randomly to the training cohort and the validation cohort by random number table at a 2/1 ratio. Survival analysis was performed by Kaplan-Meier analysis. Cox regression analysis was employed to determine the independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS). Then, integrating the basic characteristics of patients, the survival-related nomogram predictive model for OS and CSS in the training cohort was constructed, respectively. After that, internal cross validation and external validation of the model were carried out with the training cohort and the validation cohort in succession. The authenticity and reliability of the nomogram model were evaluated by calculating the concordance index (C-index). Calibration plots were constructed to assess the agreement between the predicted values and the observed values in the training cohort and the validation cohort.

  Results  A total of 369 AVG patients, including 218 males and 151 females, were included. The median age of the patients was 53. According to the WHO classification of gliomas, 66 (17.9%) patients had grade Ⅱ gliomas, 73 (19.8%) had grade Ⅲ gliomas, and 230 (62.3%) had grade Ⅳ gliomas. Regarding the extent of resection (EOR), 59 (16.0%) had gross total resection (GTR) and 145 (39.3%) had subtotal resection (STR) or partial resection (PR). Of all the patients, 167 (45.3%) received postoperative radiotherapy and 143 (38.8%) received postoperative chemotherapy. Patients were randomized into the training cohort (n=246) and the validation cohort (n=123), and there was no significant difference (P>0.05) in the basic clinical characteristics between the training cohort and the validation cohort. In the training cohort, Cox regression analysis revealed that the independent prognostic factors for OS and CSS included age≥65, grades Ⅲ and Ⅳ according to the WHO classification of gliomas, and not receiving radiotherapy. Furthermore, 5 variables, including age, gender, WHO grades, surgery, and radiotherapy, were used to construct the nomogram model for predicting 6-month, 1-year, and 2-year OS and CSS. The results of internal cross validation in the training cohort showed that the C-indexes of OS and CSS were 0.758 and 0.765, respectively. The external validation results of the validation cohort showed that the C-indexes of OS and CSS were 0.733 and 0.719, respectively. Calibration plots for 6-month, 1-year, and 2-year OS in the training cohort showed relatively good agreement, while in the validation cohort the agreement was relatively low. The 6-month, 1-year, and 2-year CSS calibration plots had results similar to the calibration plots of OS.

  Conclusion  This nomogram predictive model of OS and CSS showed moderately reliable predictive performance, providing helpful reference information for clinicians to make quick and simple assessment of the survival probability of AVG patients.