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. 2017 May 1;19(5):669-677.
doi: 10.1093/neuonc/now208.

An independently validated nomogram for individualized estimation of survival among patients with newly diagnosed glioblastoma: NRG Oncology RTOG 0525 and 0825

Haley Gittleman  1 , Daniel Lim  1 , Michael W Kattan  2 , Arnab Chakravarti  3 , Mark R Gilbert  4 , Andrew B Lassman  5 , Simon S Lo  1 , Mitchell Machtay  1 , Andrew E Sloan  1 , Erik P Sulman  6 , Devin Tian  1 , Michael A Vogelbaum  2 , Tony J C Wang  5 , Marta Penas-Prado  6 , Emad Youssef  7 , Deborah T Blumenthal  8 , Peixin Zhang  9 , Minesh P Mehta  10 , Jill S Barnholtz-Sloan  1
Affiliations

An independently validated nomogram for individualized estimation of survival among patients with newly diagnosed glioblastoma: NRG Oncology RTOG 0525 and 0825

Haley Gittleman et al. Neuro Oncol. .

Abstract

Background: Glioblastoma (GBM) is the most common primary malignant brain tumor. Nomograms are often used for individualized estimation of prognosis. This study aimed to build and independently validate a nomogram to estimate individualized survival probabilities for patients with newly diagnosed GBM, using data from 2 independent NRG Oncology Radiation Therapy Oncology Group (RTOG) clinical trials.

Methods: This analysis included information on 799 (RTOG 0525) and 555 (RTOG 0825) eligible and randomized patients with newly diagnosed GBM and contained the following variables: age at diagnosis, race, gender, Karnofsky performance status (KPS), extent of resection, O6-methylguanine-DNA methyltransferase (MGMT) methylation status, and survival (in months). Survival was assessed using Cox proportional hazards regression, random survival forests, and recursive partitioning analysis, with adjustment for known prognostic factors. The models were developed using the 0525 data and were independently validated using the 0825 data. Models were internally validated using 10-fold cross-validation, and individually predicted 6-, 12-, and 24-month survival probabilities were generated to measure the predictive accuracy and calibration against the actual survival status.

Results: A final nomogram was built using the Cox proportional hazards model. Factors that increased the probability of shorter survival included greater age at diagnosis, male gender, lower KPS, not having total resection, and unmethylated MGMT status.

Conclusions: A nomogram that assesses individualized survival probabilities (6-, 12-, and 24-mo) for patients with newly diagnosed GBM could be useful to health care providers for counseling patients regarding treatment decisions and optimizing therapeutic approaches. Free software for implementing this nomogram is provided: http://cancer4.case.edu/rCalculator/rCalculator.html.

Keywords: NRG Oncology; RTOG; glioblastoma; nomogram; survival.

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Figures

Fig. 1.
Fig. 1.
Kaplan–Meier survival results with 95% CI curves for median survival in months and 95% CI for 0525: 16.0 (14.7, 17.0) and 0825: 16.2 (15.4, 17.6); log-rank test by clinical trial P = .996).
Fig. 2.
Fig. 2.
Final nomogram built on training data 0525 and validated on 0825 (http://cancer4.case.edu/rCalculator/rCalculator.html).
Fig. 3.
Fig. 3.
(A-F) Calibration curves for the nomogram training data (0525) at 6 months (A), 12 months (B), and 24 months (C) and for independent validation data (0825) at 6 months (D), 12 months (E), and 24 months (F).
Fig. 4.
Fig. 4.
12-month predicted survival by RPA class, for training data (0525) and validation data (0825).
See this image and copyright information in PMC

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