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

Nijiati Kudulaiti,Zhirui Zhou,Jie Zhang,Fengping Zhu,Chen Luo,Jinsong Wu

doi:10.1186/s12893-021-01233-z

Abstract

BackgroundThis 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.MethodsWe 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.ResultsThe 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.ConclusionsOur 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.

Highlights

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
The following variables were obtained for each patient: gender, age at surgery, Karnofsky performance status (KPS) score before surgery, number of days in hospital, tumor location, extent of resection (EOR), number of operations, tumor laterality, IDH1 status, methylguanine-DNA methyltransferase (MGMT) status, telomerase reverse transcriptase (TERT) status, Ki67 index, radiotherapy, chemotherapy, adjuvant therapy, and recurrence and survival status
Using least absolute shrinkage and selection operator (LASSO)-Cox analysis, we found the six most valuable variables, namely gender, age at surgery, EOR, radiotherapy, chemotherapy, and IDH1 mutation status

Summary

Introduction

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. Another nomogram, which was both internally and externally validated, was developed from data from two independent, nonoverlapping NRG Oncology Radiation Therapy Oncology Group (RTOG) clinical trials (0525 and 0825) [11] The analysis for this nomogram included only patients who completed concurrent chemoradiation from both trials and several important treatment-related prognostic factors, such as IDH mutation status and use of concurrent chemoradiation therapy were not considered. Another recent study developed a nomogram to estimate individualized survival probabilities for newly diagnosed IDH-wild-type GBM patients using data from the Ohio Brain Tumor Study (OBTS) that was externally validated using data from the University of California San Francisco [12]

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