Individualized Predictive Nomograms for Overall Survival and Cancer-specific Survival in Glioblastoma Patients: A Surveillance, Epidemiology and End Results (SEER) Database Analysis

Introduction: Glioblastoma is a fatal primary brain tumor with standard treatments conferring a median overall survival of 14-months. We aim to construct and validate a nomogram that predicts overall survival (OS) and cancer-specific survival (CSS) in glioblastoma patients to guide individualized treatment.

Methods: Data of patients diagnosed with glioblastoma between 1975 and 2019 were collated using SEER database. We identified and randomized 3801 glioblastoma patients using a 7:3 ratio into a training (n=2660) and validation group (n=1140). Independent OS and CSS prognostic factors used to build both nomograms were determined using multivariate Cox regression performed on the training set. The predictive value of both nomograms at 6-, 12-, 24-, and 36-months was assessed using C-indices, area under the curve (AUC), and calibration curves.

Results: Overall, patients had a median age of 65 years, and 59.4% were male. The median OS and CSS were 10.0 months (95% CI: 9.483-10.517) and 11.0 months (95% CI: 10.5-11.5), respectively. Multivariate Cox regression identified age, primary site, marital status, chemotherapy, radiotherapy, surgery, histological subtype independent prognostic factors affecting OS and CSS. In the training cohort, the C-indices for the OS nomogram and the CSS nomogram were 0.759 (95% CI: 0.747-0.771) and 0.758 (95% CI: 0.744-0.772), respectively. In the validation cohort, the C-indices for the OS nomogram and CSS nomogram were 0.748 (95% CI: 0.710-0.786) and 0.751 (95% CI: 0.712-0.79), respectively. The AUC values at 6-, 12-, 24, and 36-months were 0.835, 0.780, 0.732, 0.723 for OS and 0.837, 0.780, 0.726, 0.713 for CSS, respectively. The calibration curves showed excellent consistency between the predicted and actual survival probabilities for both OS and CSS.

Conclusion : Great consistency has been exhibited in our nomogram in predicting patients’ survival based on a comprehensive list of demographic and clinical features. This nomogram may help in guiding individualized treatment in GBM patients.