Large-scale Radiomic Profiling of Recurrent Glioblastoma Identifies an Imaging Predictor for Stratifying Anti-Angiogenic Treatment Response.
Large-scale Radiomic Profiling of Recurrent Glioblastoma Identifies an Imaging Predictor for Stratifying Anti-Angiogenic Treatment Response.
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DOI：
10.1158/1078-0432.ccr-16-0702
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发表时间：
2016-12-01
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影响因子：
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通讯作者：
Bonekamp D
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作者：
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
使用贝伐珠单抗（一种针对血管内皮生长因子的单克隆抗体）进行抗血管生成治疗，是治疗复发性胶质母细胞瘤 (GB) 患者最广泛使用的单一治疗药物。一个主要挑战是目前没有经过验证的生物标志物可以预测治疗结果。在这里，我们分析了放射组学的潜力，这是一个新兴的研究领域，旨在充分利用医学成像的潜力。从 172 名贝伐珠单抗治疗前复发性 GB 患者的多参数肿瘤（以 2:1 的比例分配到发现和验证组）中自动提取和分析了总共 4842 个定量 MRI 特征。利用高通量方法，对发现组中患者的放射组学特征进行监督主成分 (superpc) 分析，以生成预测模型，通过无进展生存期和总生存期（PFS 和 OS）对抗血管生成治疗的治疗结果进行分层。 superpc 预测因子将发现组中的患者按 PFS（风险比 (HR)=1.60，p=0.017）和 OS（HR=2.14，p<0.001）分为低风险组或高风险组，并在验证组中成功对患者进行了验证（PFS 为 HR=1.85，p=0.030；OS 为 HR=2.60，p=0.001）。我们基于放射组学的 superpc 特征成为一种假定的成像生物标志物，用于识别可能从抗血管生成治疗中获得最大益处的患者，提高了脑肿瘤非侵入性表征的知识，并强调了放射组学作为一种以低成本改善癌症治疗决策支持的新工具的作用。
Antiangiogenic treatment with bevacizumab, a monoclonal antibody to the vascular endothelial growth factor, is the single most widely used therapeutic agent for patients with recurrent glioblastoma (GB). 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. A total of 4842 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 GB prior to bevacizumab treatment. Leveraging a high throughput 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). The superpc predictor stratified patients in the discovery set into a low or high risk group for PFS (hazard ratio (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). 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 non-invasive characterization of brain tumors, and stresses the role of radiomics as a novel tool for improving decision-support in cancer treatment at low cost.