Is age an additional factor in the treatment of elderly patients with glioblastoma? A new stratification model: an Italian Multicenter Study

Tamara Ius*, Teresa Somma, Roberto Altieri, Filippo Flavio Angileri, Giuseppe Maria Barbagallo, Paolo Cappabianca, Francesco Certo, Fabio Cofano, Alessandro D'Elia, Giuseppe Maria Della Pepa, Vincenzo Esposito, Marco Maria Fontanella, Antonino Germanò, Diego Garbossa, Miriam Isola, Giuseppe La Rocca, Francesco Maiuri, Alessandro Olivi, Pier Paolo Panciani, Fabrizio PignottiMiran Skrap, Giannantonio Spena, Giovanni Sabatino

*Corresponding author

Research output: Contribution to journalArticle

3 Citations (Scopus)


OBJECTIVE: Approximately half of glioblastoma (GBM) cases develop in geriatric patients, and this trend is destined to increase with the aging of the population. The optimal strategy for management of GBM in elderly patients remains controversial. The aim of this study was to assess the role of surgery in the elderly (≥ 65 years old) based on clinical, molecular, and imaging data routinely available in neurosurgical departments and to assess a prognostic survival score that could be helpful in stratifying the prognosis for elderly GBM patients. METHODS: Clinical, radiological, surgical, and molecular data were retrospectively analyzed in 322 patients with GBM from 9 neurosurgical centers. Univariate and multivariate analyses were performed to identify predictors of survival. A random forest approach (classification and regression tree [CART] analysis) was utilized to create the prognostic survival score. RESULTS: Survival analysis showed that overall survival (OS) was influenced by age as a continuous variable (p = 0.018), MGMT (p = 0.012), extent of resection (EOR; p = 0.002), and preoperative tumor growth pattern (evaluated with the preoperative T1/T2 MRI index; p = 0.002). CART analysis was used to create the prognostic survival score, forming six different survival groups on the basis of tumor volumetric, surgical, and molecular features. Terminal nodes with similar hazard ratios were grouped together to form a final diagram composed of five classes with different OSs (p < 0.0001). EOR was the most robust influencing factor in the algorithm hierarchy, while age appeared at the third node of the CART algorithm. The ability of the prognostic survival score to predict death was determined by a Harrell's c-index of 0.75 (95% CI 0.76–0.81). CONCLUSIONS: The CART algorithm provided a promising, thorough, and new clinical prognostic survival score for elderly surgical patients with GBM. The prognostic survival score can be useful to stratify survival risk in elderly GBM patients with different surgical, radiological, and molecular profiles, thus assisting physicians in daily clinical management. The preliminary model, however, requires validation with future prospective investigations. Practical recommendations for clinicians/surgeons would strengthen the quality of the study; e.g., surgery can be considered as a first therapeutic option in the workflow of elderly patients with GBM, especially when the preoperative estimated EOR is greater than 80%.
Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalNeurosurgical Focus
Publication statusPublished - 2020


  • CART model
  • classification and regression tree
  • decision tree diagram
  • elderly
  • extent of resection
  • glioblastoma surgery
  • prognostic score


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