ArticleJournal of medical imaging (Bellingham, Wash.)2018
Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning.
Article in Journal of medical imaging (Bellingham, Wash.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 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
77 citing papers in PubMed, 1 synthesis or guideline pooled it, 130 citations in OpenAlex.
- The use of artificial intelligence and machine learning to predict tumor recurrence in high-grade gliomas: a systematic review.Neurosurgical review · 2026Pooled it
- Personalized machine learning-guided radiation dose escalation in newly diagnosed glioblastoma: prospective pilot study.Nature communications · 2026Trial
- Radiomics in glioblastoma recurrence: advances in prediction, localization, and differentiation from treatment-related effects.Journal of translational medicine · 2026Review
- Deep learning-based cross-attention fusion of multimodal MRI for survival prediction and risk stratification in IDH-wildtype glioblastoma: a multicenter study.Frontiers in oncology · 2026Article
- Multiparametric MRI radiomics integrating Arterial Spin Labeling, Quantitative Susceptibility Mapping, and Amide Proton Transfer imaging for differentiating glioma recurrence from post-treatment response.Frontiers in oncology · 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
- Using Delta MRI-Based Radiomics for Monitoring Early Peri-Tumoral Changes in a Mouse Model of Glioblastoma: Primary Study.Cancers · 2025Article
- Artificial intelligence in neuro-oncology: methodological bases, practical applications and ethical and regulatory issues.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
- Fusion of habitat analysis and deep learning on contrast-enhanced T1-weighted imaging for predicting Ki-67 status in pediatric brain tumors.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2025Article
- A deep learning model to predict glioma recurrence using integrated genomic and clinical data.Communications medicine · 2025Article
- Multimodal radiomics in glioma: predicting recurrence in the peritumoural brain zone using integrated MRI.BMC medical imaging · 2025Article
- The Prognostic Value of the Systemic Immune-Inflammation Index in Glioblastoma Patients and the Establishment of a Nomogram.Current medical science · 2025Article
- Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Implication of tumor morphology and MRI characteristics on the accuracy of automated versus human segmentation of GBM areas.Scientific reports · 2025Article
- Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- Individual Brain Tumor Invasion Mapping Based on Diffusion Kurtosis Imaging.Sovremennye tekhnologii v meditsine · 2025Article
- Investigating the potential of diffusion tensor atlases to generate anisotropic clinical tumor volumes in glioblastoma patients.Physics and imaging in radiation oncology · 2025Article
- Towards consistency in pediatric brain tumor measurements: Challenges, solutions, and the role of artificial intelligence-based segmentation.Neuro-oncology · 2024Review
- Deep Learning Segmentation of Infiltrative and Enhancing Cellular Tumor at Pre- and Posttreatment Multishell Diffusion MRI of Glioblastoma.Radiology. Artificial intelligence · 2024Article
- Predicting peritumoral glioblastoma infiltration and subsequent recurrence using deep-learning-based analysis of multi-parametric magnetic resonance imaging.Journal of medical imaging (Bellingham, Wash.) · 2024Article
17 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
8 authors at 2 institutions in 1 country.
Funding
Abstract
Standard surgical resection of glioblastoma, mainly guided by the enhancement on postcontrast T1-weighted magnetic resonance imaging (MRI), disregards infiltrating tumor within the peritumoral edema region (ED). Subsequent radiotherapy typically delivers uniform radiation to peritumoral FLAIR-hyperintense regions, without attempting to target areas likely to be infiltrated more heavily. Noninvasive
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.