New validated prognostic models and prognostic calculators in patients with low-grade gliomas diagnosed by central pathology review: a pooled analysis of EORTC/RTOG/NCCTG phase III clinical trials

Thierry Gorlia*, Wenting Wu, Meihua Wang, Brigitta G. Baumert, Minesh Mehta, Jan C. Buckner, Edward Shaw, Paul Brown, Roger Stupp, Evanthia Galanis, Denis Lacombe, Martin J. van den Bent

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

63 Citations (Web of Science)


In a previous study, the European Organisation for Research and Treatment of Cancer (EORTC) reported a scoring system to predict survival of patients with low-grade gliomas (LGGs). A major issue in the diagnosis of brain tumors is the lack of agreement among pathologists. New models in patients with LGGs diagnosed by central pathology review are needed. Data from 339 EORTC patients with LGGs diagnosed by central pathology review were used to develop new prognostic models for progression-free survival (PFS) and overall survival (OS). Data from 450 patients with centrally diagnosed LGGs recruited into 2 large studies conducted by North American cooperative groups were used to validate the models. Both PFS and OS were negatively influenced by the presence of baseline neurological deficits, a shorter time since first symptoms (30 wk), an astrocytic tumor type, and tumors larger than 5 cm in diameter. Early irradiation improved PFS but not OS. Three risk groups have been identified (low, intermediate, and high) and validated. We have developed new prognostic models in a more homogeneous LGG population diagnosed by central pathology review. This population better fits with modern practice, where patients are enrolled in clinical trials based on central or panel pathology review. We could validate the models in a large, external, and independent dataset. The models can divide LGG patients into 3 risk groups and provide reliable individual survival predictions. Inclusion of other clinical and molecular factors might still improve models predictions.
Original languageEnglish
Pages (from-to)1568-1579
Issue number11
Publication statusPublished - Nov 2013


  • low-grade glioma
  • predictive accuracy
  • prognostic factors

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