A Nomogram Predicts Individual Prognosis in Patients With Newly Diagnosed Glioblastoma by Integrating the Extent of Resection of Non-Enhancing Tumors

Abstract

BackgroundThe extent of resection of non-contrast enhancing tumors (EOR-NCEs) has been shown to be associated with prognosis in patients with newly diagnosed glioblastoma (nGBM). This study aimed to develop and independently validate a nomogram integrated with EOR-NCE to assess individual prognosis.MethodsData for this nomogram were based on 301 patients hospitalized for nGBM from October 2011 to April 2019 at the Beijing Tiantan Hospital, Capital Medical University. These patients were randomly divided into derivation (n=181) and validation (n=120) cohorts at a ratio of 6:4. To evaluate predictive accuracy, discriminative ability, and clinical net benefit, concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were calculated for the extent of resection of contrast enhancing tumor (EOR-CE) and EOR-NCE nomograms. Comparison between these two models was performed as well.ResultsThe Cox proportional hazards model was used to establish nomograms for this study. Older age at diagnosis, Karnofsky performance status (KPS)<70, unmethylated O6-methylguanine-DNA methyltransferase (MGMT) status, wild-type isocitrate dehydrogenase enzyme (IDH), and lower EOR-CE and EOR-NCE were independent factors associated with shorter survival. The EOR-NCE nomogram had a higher C-index than the EOR-CE nomogram. Its calibration curve for the probability of survival exhibited good agreement between the identical and actual probabilities. The EOR-NCE nomogram showed superior net benefits and improved performance over the EOR-CE nomogram with respect to DCA and ROC for survival probability. These results were also confirmed in the validation cohort.ConclusionsAn EOR-NCE nomogram assessing individualized survival probabilities (12-, 18-, and 24-month) for patients with nGBM could be useful to provide patients and their relatives with health care consultations on optimizing therapeutic approaches and prognosis.

Nomograms for prognostic risk assessment in glioblastoma multiforme: Applications and limitations

“…This conclusion was further confirmed by the results of multivariate Cox regression analysis of KPS in seven studies (Figure 4). 8,9,[16][17][18][19][20] Fourth, radiomics signatures refer to high-throughput quantitative features extracted from radiological images that are potentially related to genetic features. By extracting a large number of features that cannot be identified with the naked eye, the heterogeneity of tumor lesions is quantitatively evaluated by image feature parameters, so as to provide an objective basis for disease diagnosis, treatment and prognosis judgment.…”

“…10,16,17,20 Maximum safe surgical resection of contrastenhancing tumor areas is generally considered the main surgical treatment to prolong GBM survival, but some studies have shown that reducing non-contrast-enhancing tumor volume would be conducive to increased survival and should not be ignored in clinical practice. 29,30 Through a retrospective study of 301 GBM patients hospitalized in Beijing Tiantan Hospital of Capital Medical University, Zhang et al 8 found that the resection range of EOR-NCE (extent of resection of NCE) was related to the prognosis of patients with newly diagnosed glioblastoma (nGBM), and the EOR-NCE nomogram had better prediction ability compared with EOR-CE (extent of resection of CE) model. IDH enzymes consist of three isoforms, and are essential enzymes participating in glutamine metabolism, redox regulation and other major metabolic processes.…”

“…Of the 35 GBM nomogram studies, only three studies had their own in‐house training cohorts, 8–10 26 studies used The Cancer Genome Atlas (TCGA) cohort as their training set, 2 studies employed Chinese Glioma Genome Atlas (CGGA) cohort, 2 studies' training cohorts were from NRG Oncology RTOG 0525, and the other two studies collected data from Surveillance, Epidemiology, and End Results (SEER) and the Ohio Brain Tumor Study (OBTS). Most nomograms were independently validated by external data, and these validation cohorts were extracted from CGGA, Gene Expression Omnibus (GEO) and other databases.…”

“…This conclusion was further confirmed by the results of multivariate Cox regression analysis of KPS in seven studies (Figure 4). 8,9,16–20 …”