TY - JOUR
T1 - Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response
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
N1 - Funding Information:
R.T. Shinohara is funded partially by the NIH under award numbers R01NS085211 and U24CA189523. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Publisher Copyright:
©2016 American Association for Cancer Research.
PY - 2016/12/1
Y1 - 2016/12/1
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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U2 - 10.1158/1078-0432.CCR-16-0702
DO - 10.1158/1078-0432.CCR-16-0702
M3 - Article
C2 - 27803067
AN - SCOPUS:85006320610
SN - 1078-0432
VL - 22
SP - 5765
EP - 5771
JO - Clinical Cancer Research
JF - Clinical Cancer Research
IS - 23
ER -