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

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; Lasso-Cox regression; Nomogram; Prognosis.

Fig. 1

Prognostic factor selection using the least absolute shrinkage and selection operator (LASSO) Cox regression model. a Tuning parameter (λ) selection in the LASSO model used tenfold cross-validation via minimum criteria. The partial likelihood deviance curve was plotted versus log(λ). Dotted vertical lines were drawn at the optimal values by using the minimum criteria and the one standard error of the minimum criteria (the 1-SE criteria). A λ value of 0.1201, with log (λ), − 2.1193 was chosen (1-SE criteria) according to tenfold cross-validation. b LASSO coefficient profiles of the 12 prognostic factors. A coefficient profile plot was produced against the log (λ) sequence. A vertical line was drawn at the value selected using tenfold cross-validation, where optimal λ resulted in six nonzero coefficients

Fig. 2

Nomogram for predicted 6-, 12-, and 24-month survival probabilities in glioblastoma patients. Gender (1 = male, 0 = female); age_at_surgery: age at the time of surgery; surgical_resection: status of surgical excision (0 = gross total resection, 1 = subtotal resection, 2 = partial resection); IDH1_status: IDH1 gene mutation status (0 = wild-type, 1 = mutant); radiotherapy: receipt of radiation therapy (1 = yes, 0 = no); chemotherapy: receipt of chemotherapy (1 = yes, 0 = no)

Fig. 3

Kaplan–Meier survival curves for glioblastoma patients. a Training dataset and b validation dataset

Fig. 4

Concordance indices of the Cox proportional hazard model. Concordance indices of the Cox proportional hazard model at 6, 12, 18, and 24 months in the training dataset (a) and validation dataset (b)

Fig. 5

Calibration curves for survival probability. Calibration curves for survival probability at 6, 12, and 24 months in the training (a–c) and validation (d–f) datasets. The black line shows the observed survival probabilities versus the predicted probabilities and the grey line shows the ideal prediction