ArticleMedical & biological engineering & computing2020
Radiogenomics model for overall survival prediction of glioblastoma.
Article in Medical & biological engineering & computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review.BMC cancer · 2024Pooled it
- Radiogenomics for Glioblastoma Survival Prediction: Integrating Radiomics, Clinical, and Genomic Features Using Artificial Intelligence.Journal of imaging informatics in medicine · 2026Article
- Decoding glioblastoma evolution and heterogeneity through mechanistic modeling: implications for clinical translation.Journal of experimental & clinical cancer research : CR · 2026Review
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- The prognostic relevance of a gene expression signature in MRI-defined highly vascularized glioblastoma.Heliyon · 2024Article
- Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology.Laboratory investigation; a journal of technical methods and pathology · 2024Review
- A novel automatic segmentation method directly based on magnetic resonance imaging K-space data for auxiliary diagnosis of glioma.Quantitative imaging in medicine and surgery · 2024Article
- A prognostic gene signature for gastric cancer and the immune infiltration-associated mechanism underlying the signature gene, PLG.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2023Article
- Multimodal Deep Learning-Based Prognostication in Glioma Patients: A Systematic Review.Cancers · 2023Review
- Multimodal machine learning in precision health: A scoping review.NPJ digital medicine · 2022Article
- Radiomic and Volumetric Measurements as Clinical Trial Endpoints-A Comprehensive Review.Cancers · 2022Review
- Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI.Diagnostics (Basel, Switzerland) · 2022Review
- Radiogenomic Predictors of Recurrence in Glioblastoma-A Systematic Review.Journal of personalized medicine · 2022Review
- Review
- Article
- Efficacy of Location-Based Features for Survival Prediction of Patients With Glioblastoma Depending on Resection Status.Frontiers in oncology · 2021Article
- A Ten-NFrontiers in oncology · 2020Article
- Spherical radiomics for radiogenomic assessment of glioblastoma heterogeneity.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Glioblastoma multiforme (GBM) is a very aggressive and infiltrative brain tumor with a high mortality rate. There are radiomic models with handcrafted features to estimate glioblastoma prognosis. In this work, we evaluate to what extent of combining genomic with radiomic features makes an impact on the prognosis of overall survival (OS) in patients with GBM. We apply a hypercolumn-based convolutional network to segment tumor regions from magnetic resonance images (MRI), extract radiomic features (geometric, shape, histogram), and fuse with gene expression profiling data to predict survival rate for each patient. Several state-of-the-art regression models such as linear regression, support vector machine, and neural network are exploited to conduct prognosis analysis. The Cancer Genome Atlas (TCGA) dataset of MRI and gene expression profiling is used in the study to observe the model performance in radiomic, genomic, and radiogenomic features. The results demonstrate that genomic data are correlated with the GBM OS prediction, and the radiogenomic model outperforms both radiomic and genomic models. We further illustrate the most significant genes, such as IL1B, KLHL4, ATP1A2, IQGAP2, and TMSL8, which contribute highly to prognosis analysis. Graphical Abstract Our Proposed fully automated "Radiogenomic"" approach for survival prediction overview. It fuses geometric, intensity, volumetric, genomic and clinical information to predict OS.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.