ArticleJCO clinical cancer informatics2018
Distinct Phenotypic Clusters of Glioblastoma Growth and Response Kinetics Predict Survival.
Article in JCO clinical cancer informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
16 citing papers in PubMed.
- The logarithmic phase as a therapeutic direction: evaluating pharmacological strategies against cancer progression.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- DBSCAN applied to EHRs data from patients with glioblastoma clusters patients based on cytosolic Hsp70 protein, sex, and brain subventricular zone.BioData mining · 2026Article
- Do the benefits of IDH mutations in high-grade glioma persist beyond the first recurrence? A multi-institutional retrospective analysis.Journal of neuro-oncology · 2025Article
- Clustering Functional Magnetic Resonance Imaging Time Series in Glioblastoma Characterization: A Review of the Evolution, Applications, and Potentials.Brain sciences · 2024Review
- Exploring Monocytes-Macrophages in Immune Microenvironment of Glioblastoma for the Design of Novel Therapeutic Strategies.Brain sciences · 2023Review
- Novel Clinical Trial Designs in Neuro-Oncology.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2022Review
- Radiographic Response Assessment Strategies for Early-Phase Brain Trials in Complex Tumor Types and Drug Combinations: from Digital "Flipbooks" to Control Systems Theory.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2022Review
- Hypothetical generalized framework for a new imaging endpoint of therapeutic activity in early phase clinical trials in brain tumors.Neuro-oncology · 2022Article
- Enhanced Detection of Treatment Effects on Metastatic Colorectal Cancer with Volumetric CT Measurements for Tumor Burden Growth Rate Evaluation.Clinical cancer research : an official journal of the American Association for Cancer Research · 2020Article
- A Deep Convolutional Neural Network for Annotation of Magnetic Resonance Imaging Sequence Type.Journal of digital imaging · 2020Article
- Mathematical deconvolution of CAR T-cell proliferation and exhaustion from real-time killing assay data.Journal of the Royal Society, Interface · 2020Article
- Novel approaches for glioblastoma treatment: Focus on tumor heterogeneity, treatment resistance, and computational tools.Cancer reports (Hoboken, N.J.) · 2019Article
- Quantifying Uncertainty and Robustness in a Biomathematical Model-Based Patient-Specific Response Metric for Glioblastoma.JCO clinical cancer informatics · 2019Article
- The Novel Roles of Connexin Channels and Tunneling Nanotubes in Cancer Pathogenesis.International journal of molecular sciences · 2018Review
- Change in volumetric tumor growth rate after cytotoxic therapy is predictive of overall survival in recurrent glioblastoma.Neuro-oncology advancesArticle
- Therapeutic Response Assessment of High-Grade Gliomas During Early-Phase Drug Development in the Era of Molecular and Immunotherapies.Cancer journal (Sudbury, Mass.)Review
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
purposeDespite the intra- and intertumoral heterogeneity seen in glioblastoma multiforme (GBM), there is little definitive data on the underlying cause of the differences in patient survivals. Serial imaging assessment of tumor growth allows quantification of tumor growth kinetics (TGK) measured in terms of changes in the velocity of radial expansion seen on imaging. Because a systematic study of this entire TGK phenotype-growth before treatment and during each treatment to recurrence -has never been coordinately studied in GBMs, we sought to identify whether patients cluster into discrete groups on the basis of their TGK. PATIENTS AND
methodsFrom our multi-institutional database, we identified 48 patients who underwent maximally safe resection followed by radiotherapy with imaging follow-up through the time of recurrence. The patients were then clustered into two groups through a k-means algorithm taking as input only the TGK before and during treatment.
resultsThere was a significant survival difference between the clusters ( P = .003). Paradoxically, patients among the long-lived cluster had significantly larger tumors at diagnosis ( P = .027) and faster growth before treatment ( P = .003) but demonstrated a better response to adjuvant chemotherapy ( P = .048). A predictive model was built to identify which cluster patients would likely fall into on the basis of information that would be available to clinicians immediately after radiotherapy (accuracy, 90.3%).
conclusionDichotomizing the heterogeneity of GBMs into two populations-one faster growing yet more responsive with increased survival and one slower growing yet less responsive with shorter survival-suggests that many patients who receive standard-of-care treatments may get better benefit from select alternative treatments.
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