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.
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.
Authors: Jill S Barnholtz-Sloan; Changhong Yu; Andrew E Sloan; Jaime Vengoechea; Meihua Wang; James J Dignam; Michael A Vogelbaum; Paul W Sperduto; Minesh P Mehta; Mitchell Machtay; Michael W Kattan Journal: Neuro Oncol Date: 2012-04-27 Impact factor: 12.300
Authors: Mark R Gilbert; James J Dignam; Terri S Armstrong; Jeffrey S Wefel; Deborah T Blumenthal; Michael A Vogelbaum; Howard Colman; Arnab Chakravarti; Stephanie Pugh; Minhee Won; Robert Jeraj; Paul D Brown; Kurt A Jaeckle; David Schiff; Volker W Stieber; David G Brachman; Maria Werner-Wasik; Ivo W Tremont-Lukats; Erik P Sulman; Kenneth D Aldape; Walter J Curran; Minesh P Mehta Journal: N Engl J Med Date: 2014-02-20 Impact factor: 91.245
Authors: Jigisha P Thakkar; Therese A Dolecek; Craig Horbinski; Quinn T Ostrom; Donita D Lightner; Jill S Barnholtz-Sloan; John L Villano Journal: Cancer Epidemiol Biomarkers Prev Date: 2014-07-22 Impact factor: 4.254
Authors: W J Curran; C B Scott; J Horton; J S Nelson; A S Weinstein; A J Fischbach; C H Chang; M Rotman; S O Asbell; R E Krisch Journal: J Natl Cancer Inst Date: 1993-05-05 Impact factor: 13.506
Authors: Roger Stupp; Monika E Hegi; Warren P Mason; Martin J van den Bent; Martin J B Taphoorn; Robert C Janzer; Samuel K Ludwin; Anouk Allgeier; Barbara Fisher; Karl Belanger; Peter Hau; Alba A Brandes; Johanna Gijtenbeek; Christine Marosi; Charles J Vecht; Karima Mokhtari; Pieter Wesseling; Salvador Villa; Elizabeth Eisenhauer; Thierry Gorlia; Michael Weller; Denis Lacombe; J Gregory Cairncross; René-Olivier Mirimanoff Journal: Lancet Oncol Date: 2009-03-09 Impact factor: 41.316
Authors: Monika E Hegi; Lili Liu; James G Herman; Roger Stupp; Wolfgang Wick; Michael Weller; Minesh P Mehta; Mark R Gilbert Journal: J Clin Oncol Date: 2008-09-01 Impact factor: 44.544
Authors: Debra A Goldman; Koos Hovinga; Anne S Reiner; Yoshua Esquenazi; Viviane Tabar; Katherine S Panageas Journal: J Neurosurg Date: 2018-11-01 Impact factor: 5.115
Authors: Tathiane M Malta; Camila F de Souza; Thais S Sabedot; Tiago C Silva; Maritza S Mosella; Steven N Kalkanis; James Snyder; Ana Valeria B Castro; Houtan Noushmehr Journal: Neuro Oncol Date: 2018-04-09 Impact factor: 12.300
Authors: Meijing Wu; Jason Miska; Ting Xiao; Peng Zhang; J Robert Kane; Irina V Balyasnikova; James P Chandler; Craig M Horbinski; Maciej S Lesniak Journal: J Neurooncol Date: 2019-01-31 Impact factor: 4.130