Literature DB >> 28643151

Multi-institutional external validation of a novel glioblastoma prognostic nomogram incorporating MGMT methylation.

Jason K Molitoris1, Yuan James Rao2, Rajal A Patel3, Liam T Kane3, Shahed N Badiyan4, Haley Gittleman5, Jill S Barnholtz-Sloan5, Soren M Bentzen4, Tim J Kruser3, Jiayi Huang2, Minesh P Mehta6,7.   

Abstract

A recent nomogram for glioblastoma (GBM) was designed to incorporate methylguanine-DNA methyltransferase (MGMT) methylation status in trial patients receiving temozolomide. Since clinical trial patients are strictly selected, compared to the general population, we performed a multi-institutional, external, independent assessment of the nomogram. Consecutive adult patients with supratentorial GBM diagnosed between June 2007 and December 2014 who initiated TMZ-based concurrent chemoradiotherapy (CRT) and were not enrolled on RTOG 0525 or 0825 were eligible. We collected age, gender, MGMT status, performance status, resection extent, race, and tumor site and Cox regression analysis of overall survival (OS) was conducted with the 1-year nomogram-predicted survival (NPS). The predictive accuracy was quantified by the concordance index (c-index) as well as by separating patients into quintile-groups of the population distribution of NPS and comparing mean NPS and observed OS. Of 514 patients with GBM, 309 had all nomogram factors. Median OS was 18.7 months. NPS and observed OS demonstrated a c-index of 0.695. On univariate analysis, the NPS and all included factors except gender were significant. On multivariable analysis (MVA) the only significant factor for worse survival was lower NPS. When separated into quintile-groups of NPS, the observed survival was slightly better than the predicted survival for all but the worst prognostic group. Our multi-institutional cohort provides independent external validation of a novel GBM nomogram incorporating MGMT methylation status. No individual factor included in the nomogram retained significance on MVA after adjusting for NPS.

Entities:  

Keywords:  Glioblastoma; Nomogram; O6-Methylguanine-DNA methyltransferase; Survival

Mesh:

Substances:

Year:  2017        PMID: 28643151     DOI: 10.1007/s11060-017-2529-2

Source DB:  PubMed          Journal:  J Neurooncol        ISSN: 0167-594X            Impact factor:   4.130


  28 in total

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Review 7.  Epidemiologic and molecular prognostic review of glioblastoma.

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Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2014-07-22       Impact factor: 4.254

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9.  Dose-dense temozolomide for newly diagnosed glioblastoma: a randomized phase III clinical trial.

Authors:  Mark R Gilbert; Meihua Wang; Kenneth D Aldape; Roger Stupp; Monika E Hegi; Kurt A Jaeckle; Terri S Armstrong; Jeffrey S Wefel; Minhee Won; Deborah T Blumenthal; Anita Mahajan; Christopher J Schultz; Sara Erridge; Brigitta Baumert; Kristen I Hopkins; Tzahala Tzuk-Shina; Paul D Brown; Arnab Chakravarti; Walter J Curran; Minesh P Mehta
Journal:  J Clin Oncol       Date:  2013-10-07       Impact factor: 44.544

10.  IDH1 mutations are present in the majority of common adult gliomas but rare in primary glioblastomas.

Authors:  Koichi Ichimura; Danita M Pearson; Sylvia Kocialkowski; L Magnus Bäcklund; Raymond Chan; David T W Jones; V Peter Collins
Journal:  Neuro Oncol       Date:  2009-05-12       Impact factor: 12.300

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4.  Sexual Dimorphism in Energy Metabolism of Wistar Rats Using Data Analysis.

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

Authors:  Zhe Zhang; Zeping Jin; Dayuan Liu; Yang Zhang; Chunzhao Li; Yazhou Miao; Xiaohan Chi; Jie Feng; Yaming Wang; Shuyu Hao; Nan Ji
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6.  A validated integrated clinical and molecular glioblastoma long-term survival-predictive nomogram.

Authors:  Sherise D Ferguson; Tiffany R Hodges; Nazanin K Majd; Kristin Alfaro-Munoz; Wajd N Al-Holou; Dima Suki; John F de Groot; Gregory N Fuller; Lee Xue; Miao Li; Carmen Jacobs; Ganesh Rao; Rivka R Colen; Joanne Xiu; Roel Verhaak; David Spetzler; Mustafa Khasraw; Raymond Sawaya; James P Long; Amy B Heimberger
Journal:  Neurooncol Adv       Date:  2020-10-31

7.  A Comparison Study of Machine Learning (Random Survival Forest) and Classic Statistic (Cox Proportional Hazards) for Predicting Progression in High-Grade Glioma after Proton and Carbon Ion Radiotherapy.

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Journal:  Front Oncol       Date:  2020-10-30       Impact factor: 6.244

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