ArticleClinical cancer research : an official journal of the American Association for Cancer Research2016
Large-scale Radiomic Profiling of Recurrent Glioblastoma Identifies an Imaging Predictor for Stratifying Anti-Angiogenic Treatment Response.
Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 159 papers, 2 of them syntheses that pooled it.
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Who cites it
159 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma.AJNR. American journal of neuroradiology · 2022Trial
- A MRI-based radiomics nomogram for evaluation of renal function in ADPKD.Abdominal radiology (New York) · 2022Trial
- AI-Radiomics Can Improve Inclusion Criteria and Clinical Trial Performance.Tomography (Ann Arbor, Mich.) · 2022Trial
- Molecular subtyping of diffuse gliomas using magnetic resonance imaging: comparison and correlation between radiomics and deep learning.European radiology · 2022Trial
- Radiomic signature of the FOWARC trial predicts pathological response to neoadjuvant treatment in rectal cancer.Journal of translational medicine · 2021Trial
- Validation of diffusion MRI phenotypes for predicting response to bevacizumab in recurrent glioblastoma: post-hoc analysis of the EORTC-26101 trial.Neuro-oncology · 2020Trial
- Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab.Neuro-oncology · 2017Trial
- Imaging the hallmarks of cancer.Nature reviews. Cancer · 2026Review
- Radiomics for risk stratification in brain metastases: potential in melanoma brain metastases.Clinical & experimental metastasis · 2026Article
- End-to-end 2.5D multisequence-multichannel fusion model for preoperative survival prediction in glioma: a retrospective study.BMC medical imaging · 2026Article
- Glioblastoma radiomics can delineate systemic immune activity states like blood abundance of T cell populations or transcription factors.Journal of neuroinflammation · 2026Article
- Opportunities and challenges in application of VASARI features in neuro-oncology.Neuro-oncology practice · 2025Review
- Integrated multi-omics analysis and machine learning refine molecular subtypes and prognosis in hepatocellular carcinoma through O-linked glycosylation genes.Functional & integrative genomics · 2025Article
- Article
- Radiomics in glioma: emerging trends and challenges.Annals of clinical and translational neurology · 2025Review
- Advances in Glioblastoma Diagnosis: Integrating Genetics, Noninvasive Sampling, and Advanced Imaging.Cancers · 2025Review
- Predicting podoplanin expression and prognostic significance in high-grade glioma based on TCGA TCIA radiomics.PloS one · 2025Article
- A Weighted Survival Regression Framework for Incorporating External Prediction Information.Journal of statistical theory and practice · 2025Article
99 more citing papers are in PubMed but not listed here.
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14 authors.
Funding
Abstract
purposeAntiangiogenic 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
designA 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 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).
resultsThe 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).
conclusionsOur 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. ©2016 AACR.
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