A nomogram for individualized prediction of overall survival in patients with newly diagnosed glioblastoma: a real-world retrospective cohort study

Abstract

Abstract Background This study aimed to identify the most valuable predictors of prognosis in glioblastoma (GBM) patients and develop and validate a nomogram to estimate individualized survival probability. Methods We conducted a real-world retrospective cohort study of 987 GBM patients diagnosed between September 2010 and December 2018. Computer generated random numbers were used to assign patients into a training cohort (694 patients) and internal validation cohort (293 patients). A least absolute shrinkage and selection operator (LASSO)-Cox model was used to select candidate variables for the prediction model. Cox proportional hazards regression was used to estimate overall survival. Models were internally validated using the bootstrap method and generated individualized predicted survival probabilities at 6, 12, and 24 months, which were compared with actual survival. Results The final nomogram was developed using the Cox proportional hazards model, which was the model with best fit and calibration. Gender, age at surgery, extent of tumor resection, radiotherapy, chemotherapy, and IDH1 mutation status were used as variables. The concordance indices for 6-, 12-, 18-, and 24-month survival probabilities were 0.776, 0.677, 0.643, and 0.629 in the training set, and 0.725, 0.695, 0.652, and 0.634 in the validation set, respectively. Conclusions Our nomogram that assesses individualized survival probabilities (6-, 12-, and 24-month) in newly diagnosed GBM patients can assist healthcare providers in optimizing treatment and counseling patients. Trial registration: retrospectively registered.

Keywords

• Glioblastoma
• Nomogram
• Lasso-Cox regression
• Prognosis