Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response

Philipp Kickingereder, Michael Götz, John Muschelli, Antje Wick, Ulf Neuberger, Russell T. Shinohara, Martin Sill, Martha Nowosielski, Heinz Peter Schlemmer, Alexander Radbruch, Wolfgang Wick, Martin Bendszus, Klaus H. Maier-Hein, David Bonekamp

Research output: Contribution to journalArticle

Abstract

Purpose: Antiangiogenic treatment with bevacizumab, a mAb to the VEGF, is the single most widely used therapeutic agent for patients with recurrent glioblastoma. A major challenge is that there are currently no validated biomarkers that can predict treatment outcome. Here we analyze the potential of radiomics, an emerging field of research that aims to utilize the full potential of medical imaging. Experimental Design: A total of 4,842 quantitative MRI features were automatically extracted and analyzed from the multiparametric tumor of 172 patients (allocated to a discovery and validation set with a 2:1 ratio) with recurrent glioblastoma prior to bevacizumab treatment. Leveraging a highthroughput approach, radiomic features of patients in the discovery set were subjected to a supervised principal component (superpc) analysis to generate a prediction model for stratifying treatment outcome to antiangiogenic therapy by means of both progression-free and overall survival (PFS and OS). Results: The superpc predictor stratified patients in the discovery set into a low or high risk group for PFS (HR=1.60; P=0.017) and OS (HR = 2.14; P < 0.001) and was successfully validated for patients in the validation set (HR = 1.85, P = 0.030 for PFS; HR = 2.60, P = 0.001 for OS). Conclusions: Our radiomic-based superpc signature emerges as a putative imaging biomarker for the identification of patients who may derive the most benefit from antiangiogenic therapy, advances the knowledge in the noninvasive characterization of brain tumors, and stresses the role of radiomics as a novel tool for improving decision support in cancer treatment at low cost. Clin Cancer Res; 22(23); 5765-71.

Original languageEnglish (US)
Pages (from-to)5765-5771
Number of pages7
JournalClinical Cancer Research
Volume22
Issue number23
DOIs
StatePublished - Dec 1 2016

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Glioblastoma
Therapeutics
Biomarkers
Neoplasms
Proxy
Diagnostic Imaging
Principal Component Analysis
Brain Neoplasms
Vascular Endothelial Growth Factor A
Disease-Free Survival
Research Design
Costs and Cost Analysis
Research

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response. / Kickingereder, Philipp; Götz, Michael; Muschelli, John; Wick, Antje; Neuberger, Ulf; Shinohara, Russell T.; Sill, Martin; Nowosielski, Martha; Schlemmer, Heinz Peter; Radbruch, Alexander; Wick, Wolfgang; Bendszus, Martin; Maier-Hein, Klaus H.; Bonekamp, David.

In: Clinical Cancer Research, Vol. 22, No. 23, 01.12.2016, p. 5765-5771.

Research output: Contribution to journalArticle

Kickingereder, P, Götz, M, Muschelli, J, Wick, A, Neuberger, U, Shinohara, RT, Sill, M, Nowosielski, M, Schlemmer, HP, Radbruch, A, Wick, W, Bendszus, M, Maier-Hein, KH & Bonekamp, D 2016, 'Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response', Clinical Cancer Research, vol. 22, no. 23, pp. 5765-5771. https://doi.org/10.1158/1078-0432.CCR-16-0702
Kickingereder, Philipp ; Götz, Michael ; Muschelli, John ; Wick, Antje ; Neuberger, Ulf ; Shinohara, Russell T. ; Sill, Martin ; Nowosielski, Martha ; Schlemmer, Heinz Peter ; Radbruch, Alexander ; Wick, Wolfgang ; Bendszus, Martin ; Maier-Hein, Klaus H. ; Bonekamp, David. / Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response. In: Clinical Cancer Research. 2016 ; Vol. 22, No. 23. pp. 5765-5771.
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AU - Kickingereder, Philipp

AU - Götz, Michael

AU - Muschelli, John

AU - Wick, Antje

AU - Neuberger, Ulf

AU - Shinohara, Russell T.

AU - Sill, Martin

AU - Nowosielski, Martha

AU - Schlemmer, Heinz Peter

AU - Radbruch, Alexander

AU - Wick, Wolfgang

AU - Bendszus, Martin

AU - Maier-Hein, Klaus H.

AU - Bonekamp, David

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N2 - Purpose: Antiangiogenic treatment with bevacizumab, a mAb to the VEGF, is the single most widely used therapeutic agent for patients with recurrent glioblastoma. A major challenge is that there are currently no validated biomarkers that can predict treatment outcome. Here we analyze the potential of radiomics, an emerging field of research that aims to utilize the full potential of medical imaging. Experimental Design: A total of 4,842 quantitative MRI features were automatically extracted and analyzed from the multiparametric tumor of 172 patients (allocated to a discovery and validation set with a 2:1 ratio) with recurrent glioblastoma prior to bevacizumab treatment. Leveraging a highthroughput approach, radiomic features of patients in the discovery set were subjected to a supervised principal component (superpc) analysis to generate a prediction model for stratifying treatment outcome to antiangiogenic therapy by means of both progression-free and overall survival (PFS and OS). Results: The superpc predictor stratified patients in the discovery set into a low or high risk group for PFS (HR=1.60; P=0.017) and OS (HR = 2.14; P < 0.001) and was successfully validated for patients in the validation set (HR = 1.85, P = 0.030 for PFS; HR = 2.60, P = 0.001 for OS). Conclusions: Our radiomic-based superpc signature emerges as a putative imaging biomarker for the identification of patients who may derive the most benefit from antiangiogenic therapy, advances the knowledge in the noninvasive characterization of brain tumors, and stresses the role of radiomics as a novel tool for improving decision support in cancer treatment at low cost. Clin Cancer Res; 22(23); 5765-71.

AB - Purpose: Antiangiogenic treatment with bevacizumab, a mAb to the VEGF, is the single most widely used therapeutic agent for patients with recurrent glioblastoma. A major challenge is that there are currently no validated biomarkers that can predict treatment outcome. Here we analyze the potential of radiomics, an emerging field of research that aims to utilize the full potential of medical imaging. Experimental Design: A total of 4,842 quantitative MRI features were automatically extracted and analyzed from the multiparametric tumor of 172 patients (allocated to a discovery and validation set with a 2:1 ratio) with recurrent glioblastoma prior to bevacizumab treatment. Leveraging a highthroughput approach, radiomic features of patients in the discovery set were subjected to a supervised principal component (superpc) analysis to generate a prediction model for stratifying treatment outcome to antiangiogenic therapy by means of both progression-free and overall survival (PFS and OS). Results: The superpc predictor stratified patients in the discovery set into a low or high risk group for PFS (HR=1.60; P=0.017) and OS (HR = 2.14; P < 0.001) and was successfully validated for patients in the validation set (HR = 1.85, P = 0.030 for PFS; HR = 2.60, P = 0.001 for OS). Conclusions: Our radiomic-based superpc signature emerges as a putative imaging biomarker for the identification of patients who may derive the most benefit from antiangiogenic therapy, advances the knowledge in the noninvasive characterization of brain tumors, and stresses the role of radiomics as a novel tool for improving decision support in cancer treatment at low cost. Clin Cancer Res; 22(23); 5765-71.

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