A novel comprehensive clinical stratification model to refine prognosis of glioblastoma patients undergoing surgical resection

Simona Gaudino, Alessandro Olivi, Giovanni Sabatino, Fabrizio Pignotti, Giuseppe La Rocca, Silvia Chiesa, Benjamin Skrap, Tamara Ius, Teresa Somma, Miriam Isola, Claudio Battistella, Maurizio Polano, Michele Dal Bo, Daniele Bagatto, Enrico Pegolo, Mauro Arcicasa, Miran Skrap

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Despite recent discoveries in genetics and molecular fields, glioblastoma (GBM) prognosis still remains unfavorable with less than 10% of patients alive 5 years after diagnosis. Numerous studies have focused on the research of biological biomarkers to stratify GBM patients. We addressed this issue in our study by using clinical/molecular and image data, which is generally available to Neurosurgical Departments in order to create a prognostic score that can be useful to stratify GBM patients undergoing surgical resection. By using the random forest approach [CART analysis (classification and regression tree)] on Survival time data of 465 cases, we developed a new prediction score resulting in 10 groups based on extent of resection (EOR), age, tumor volumetric features, intraoperative protocols and tumor molecular classes. The resulting tree was trimmed according to similarities in the relative hazard ratios amongst groups, giving rise to a 5-group classification tree. These 5 groups were different in terms of overall survival (OS) (p < 0.000). The score performance in predicting death was defined by a Harrell’s c-index of 0.79 (95% confidence interval [0.76–0.81]). The proposed score could be useful in a clinical setting to refine the prognosis of GBM patients after surgery and prior to postoperative treatment.
Original languageEnglish
Pages (from-to)386-N/A
Number of pages19
JournalCancers
Volume12
DOIs
Publication statusPublished - 2020

Keywords

  • Decision tree
  • Extent of resection
  • Glioblastoma prognosis
  • Overall survival
  • Personalized precision oncology
  • Random forest

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