ArticleKorean journal of radiology2021
Prediction of Prognosis in Glioblastoma Using Radiomics Features of Dynamic Contrast-Enhanced MRI.
Article in Korean journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review and epistemic meta-analysis to advance binomial AI-radiomics integration for predicting high-grade glioma progression and enhancing patient management.Scientific reports · 2025Pooled it
- PET|MR Open Source Radiomics Models for Treatment Outcome Prediction in Recurrent GBM.Cancers · 2026Article
- Drawing the Line: From U-Net-Based Glioblastoma Segmentation to Machine Learning-Driven Survival Prediction.Medical sciences (Basel, Switzerland) · 2026Article
- Pre-treatment subventricular zone perfusion asymmetry predicts vertical recurrence in glioblastoma.BMC medical imaging · 2026Article
- ASL perfusion radiomic heterogeneity is associated with early mortality in IDH-wildtype glioblastoma, beyond tumor volume: a time-varying effect with tumor-aware DTI-ALPS.Frontiers in oncology · 2026Article
- Prediction of Survival in Surgically Treated Glioblastoma Multiforme Utilizing DTI and Contrast-Enhanced MRI.The Indian journal of radiology & imaging · 2026Article
- Radiomics-based quantification of tumor infiltration in the non-enhancing peritumoral region on postoperative MRI is associated with survival in glioblastoma.Scientific reports · 2025Article
- Novel predictors of tumor growth by exploratory quantitative analysis of radiomics features from MRI data for incidentally discovered meningioma.Journal of neuro-oncology · 2025Article
- Multimodal radiomics in glioma: predicting recurrence in the peritumoural brain zone using integrated MRI.BMC medical imaging · 2025Article
- Combined peritumoral radiomics and clinical features predict 12-month progression free survival in glioblastoma.Journal of neuro-oncology · 2025Article
- Role of dynamic contrast-enhanced and dynamic susceptibility contrast imaging in evaluating the biological features of glioma.Quantitative imaging in medicine and surgery · 2025Article
- Noninvasive prediction of failure of the conservative treatment in lateral epicondylitis by clinicoradiological features and elbow MRI radiomics based on interpretable machine learning: a multicenter cohort study.Journal of orthopaedic surgery and research · 2025Article
- Interstitial fluid transport dynamics predict glioblastoma invasion and progression.npj biomedical innovations · 2025Article
- Radiomics-Based Machine Learning with Natural Gradient Boosting for Continuous Survival Prediction in Glioblastoma.Cancers · 2024Article
- Radiomic Features as Artificial Intelligence Prognostic Models in Glioblastoma: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2024Review
- Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN.Physical and engineering sciences in medicine · 2024Article
- Multiparametric MRI-based radiomic nomogram for predicting HER-2 2+ status of breast cancer.Heliyon · 2024Article
- Diffusion- and Perfusion-Weighted MRI Radiomics for Survival Prediction in Patients with Lower-Grade Gliomas.Yonsei medical journal · 2024Article
- Added prognostic value of 3D deep learning-derived features from preoperative MRI for adult-type diffuse gliomas.Neuro-oncology · 2024Article
- Added value of dynamic contrast-enhanced MR imaging in deep learning-based prediction of local recurrence in grade 4 adult-type diffuse gliomas patients.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop a radiomics risk score based on dynamic contrast-enhanced (DCE) MRI for prognosis prediction in patients with glioblastoma. MATERIALS AND
methodsOne hundred and fifty patients (92 male [61.3%]; mean age ± standard deviation, 60.5 ± 13.5 years) with glioblastoma who underwent preoperative MRI were enrolled in the study. Six hundred and forty-two radiomic features were extracted from volume transfer constant (K
results16 radiomic features obtained from non-enhancing T2 hyperintense areas were selected among the 642 features identified. The radiomics risk score was used to stratify high- and low-risk groups in both the discovery and validation sets (both
conclusionWe developed and validated the "radiomics risk score" from the features of DCE MRI based on non-enhancing T2 hyperintense areas for risk stratification of patients with glioblastoma. It was associated with progression-free survival independently of IDH mutation status.
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.