Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma

Philipp Kickingereder, Ulf Neuberger, David Bonekamp, Paula L. Piechotta, Michael Götz, Antje Wick, Martin Sill, Annekathrin Kratz, Russell T. Shinohara, David T.W. Jones, Alexander Radbruch, John Muschelli, Andreas Unterberg, Jürgen Debus, Heinz Peter Schlemmer, Christel Herold-Mende, Stefan Pfister, Andreas Von Deimling, Wolfgang Wick, David CapperKlaus H. Maier-Hein, Martin Bendszus

Research output: Contribution to journalArticlepeer-review

Abstract

Background The purpose of this study was to analyze the potential of radiomics for disease stratification beyond key molecular, clinical, and standard imaging features in patients with glioblastoma. Methods Quantitative imaging features (n = 1043) were extracted from the multiparametric MRI of 181 patients with newly diagnosed glioblastoma prior to standard-of-care treatment (allocated to a discovery and a validation set, 2:1 ratio). A subset of 386/1043 features were identified as reproducible (in an independent MRI test-retest cohort) and selected for analysis. A penalized Cox model with 10-fold cross-validation (Coxnet) was fitted on the discovery set to construct a radiomic signature for predicting progression-free and overall survival (PFS and OS). The incremental value of a radiomic signature beyond molecular (O 6 -methylguanine-DNA methyltransferase [MGMT] promoter methylation, DNA methylation subgroups), clinical (patient's age, KPS, extent of resection, adjuvant treatment), and standard imaging parameters (tumor volumes) for stratifying PFS and OS was assessed with multivariate Cox models (performance quantified with prediction error curves). Results The radiomic signature (constructed from 8/386 features identified through Coxnet) increased the prediction accuracy for PFS and OS (in both discovery and validation sets) beyond the assessed molecular, clinical, and standard imaging parameters (P ≤ 0.01). Prediction errors decreased by 36% for PFS and 37% for OS when adding the radiomic signature (compared with 29% and 27%, respectively, with molecular + clinical features alone). The radiomic signature was - along with MGMT status - the only parameter with independent significance on multivariate analysis (P ≤ 0.01). Conclusions Our study stresses the role of integrating radiomics into a multilayer decision framework with key molecular and clinical features to improve disease stratification and to potentially advance personalized treatment of patients with glioblastoma.

Original languageEnglish (US)
Pages (from-to)848-857
Number of pages10
JournalNeuro-oncology
Volume20
Issue number6
DOIs
StatePublished - May 18 2018

Keywords

  • MGMT
  • glioblastoma
  • radiomics

ASJC Scopus subject areas

  • Oncology
  • Clinical Neurology
  • Cancer Research

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