ReviewFrontiers in oncology2019
Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation.
Review in Frontiers in oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06492486 (Glioma Adaptive Radiotherapy With Development of an Artificial Intelligence Workflow), which is not on this map. Cited by 107 papers, 7 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Glioma Adaptive Radiotherapy With Development of an Artificial Intelligence Workflow
Who cites it
107 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Application advances of habitat imaging based on CT or PET/CT: a systematic review.La Radiologia medica · 2026Pooled it
- Application of artificial intelligence and radiomics in the prediction of lymph node metastasis and tumour grading of oral cancer - a systematic review and meta analysis.BMC oral health · 2026Pooled it
- MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer: a systematic review.Abdominal radiology (New York) · 2023Pooled it
- Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis.European radiology · 2021Pooled it
- Survival prediction of glioblastoma patients-are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential.Neurosurgical review · 2021Pooled it
- Diagnostic Accuracy of Machine Learning-Based Radiomics in Grading Gliomas: Systematic Review and Meta-Analysis.Contrast media & molecular imaging · 2020Pooled it
- Pooled it
- Spatial and quantitative mapping of glycolysis and hypoxia in glioblastoma as a predictor of radiotherapy response and sites of relapse.European journal of nuclear medicine and molecular imaging · 2020Trial
- Radiomics in glioblastoma recurrence: advances in prediction, localization, and differentiation from treatment-related effects.Journal of translational medicine · 2026Review
- Article
- Clinical value of habitat radiomics within the 2-cm edema zone surrounding the postoperative residual cavity in predicting glioma recurrence.Frontiers in oncology · 2026Article
- AI-Guided Inference of Morphodynamic Attractor-like States in Glioblastoma.Diagnostics (Basel, Switzerland) · 2026Article
- Repeatability of diffusion and perfusion MRI derived radiomic features in glioblastoma: a test-retest study.Physical and engineering sciences in medicine · 2025Article
- Correlation of contrast-enhancing tumor region on pre-radiotherapy MRI with overall survival in glioblastoma.European journal of medical research · 2025Article
- Review
- Advanced MRI, Radiomics and Radiogenomics in Unravelling Incidental Glioma Grading and Genetic Status: Where Are We?Medicina (Kaunas, Lithuania) · 2025Review
- Computed tomography radiomics-based cross-sectional detection of mandibular osteoradionecrosis in head and neck cancer survivors.Oral oncology · 2025Article
- From pixels to prognosis: leveraging radiomics and machine learning to predict IDH1 genotype in gliomas.Neurosurgical review · 2025Article
- A Radiomic Model for Gliomas Grade and Patient Survival Prediction.Bioengineering (Basel, Switzerland) · 2025Article
- Radiomics-clinical nomogram for preoperative tumor-node-metastasis staging prediction in breast cancer patients using dynamic enhanced magnetic resonance imaging.Translational cancer research · 2025Article
47 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomics analysis has had remarkable progress along with advances in medical imaging, most notability in central nervous system malignancies. Radiomics refers to the extraction of a large number of quantitative features that describe the intensity, texture and geometrical characteristics attributed to the tumor radiographic data. These features have been used to build predictive models for diagnosis, prognosis, and therapeutic response. Such models are being combined with clinical, biological, genetics and proteomic features to enhance reproducibility. Broadly, the four steps necessary for radiomic analysis are: (1) image acquisition, (2) segmentation or labeling, (3) feature extraction, and (4) statistical analysis. Major methodological challenges remain prior to clinical implementation. Essential steps include: adoption of an optimized standard imaging process, establishing a common criterion for performing segmentation, fully automated extraction of radiomic features without redundancy, and robust statistical modeling validated in the prospective setting. This review walks through these steps in detail, as it pertains to high grade gliomas. The impact on precision medicine will be discussed, as well as the challenges facing clinical implementation of radiomic in the current management of glioblastoma.
Indexed as
Identifiers
What OpenQuestion holds
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.